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web/templates/flux_schnell.jpg create mode 100644 web/templates/flux_schnell.json create mode 100644 web/templates/image2image.jpg create mode 100644 web/templates/image2image.json create mode 100644 web/templates/upscale.jpg create mode 100644 web/templates/upscale.json create mode 100644 web/user.css diff --git a/CODEOWNERS b/CODEOWNERS new file mode 100644 index 0000000..7c7c3e1 --- /dev/null +++ b/CODEOWNERS @@ -0,0 +1 @@ +* @comfyanonymous diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..048f127 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,41 @@ +# Contributing to ComfyUI + +Welcome, and thank you for your interest in contributing to ComfyUI! + +There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved. + +## Asking Questions + +Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you. + +## Providing Feedback + +Your comments and feedback are welcome, and the development team is available via a handful of different channels. + +See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord. + +## Reporting Issues + +Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible. + + +### Look For an Existing Issue + +Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed. + +If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment: + +* 👍 - upvote +* 👎 - downvote + +If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues. + + +### Creating Pull Requests + +* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project. + + +## Thank You + +Your contributions to open source, large or small, make great projects like this possible. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..7476b57 --- /dev/null +++ b/README.md @@ -0,0 +1,300 @@ +
+ +# ComfyUI +**The most powerful and modular diffusion model GUI and backend.** + + +[![Website][website-shield]][website-url] +[![Dynamic JSON Badge][discord-shield]][discord-url] +[![Matrix][matrix-shield]][matrix-url] +
+[![][github-release-shield]][github-release-link] +[![][github-release-date-shield]][github-release-link] +[![][github-downloads-shield]][github-downloads-link] +[![][github-downloads-latest-shield]][github-downloads-link] + +[matrix-shield]: https://img.shields.io/badge/Matrix-000000?style=flat&logo=matrix&logoColor=white +[matrix-url]: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org +[website-shield]: https://img.shields.io/badge/ComfyOrg-4285F4?style=flat +[website-url]: https://www.comfy.org/ + +[discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total +[discord-url]: https://www.comfy.org/discord + +[github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver +[github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases +[github-release-date-shield]: https://img.shields.io/github/release-date/comfyanonymous/ComfyUI?style=flat +[github-downloads-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/total?style=flat +[github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest +[github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases + +![ComfyUI Screenshot](comfyui_screenshot.png) +
+ +This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out: +### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/) + +### [Installing ComfyUI](#installing) + +## Features +- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. +- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/) +- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/) +- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/) +- Asynchronous Queue system +- Many optimizations: Only re-executes the parts of the workflow that changes between executions. +- Smart memory management: can automatically run models on GPUs with as low as 1GB vram. +- Works even if you don't have a GPU with: ```--cpu``` (slow) +- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models. +- Embeddings/Textual inversion +- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/) +- [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/) +- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files. +- Saving/Loading workflows as Json files. +- Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones. +- [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/) +- [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models. +- [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/) +- [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/) +- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/) +- [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/) +- [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/) +- [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/) +- [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/) +- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/) +- [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/) +- Latent previews with [TAESD](#how-to-show-high-quality-previews) +- Starts up very fast. +- Works fully offline: will never download anything. +- [Config file](extra_model_paths.yaml.example) to set the search paths for models. + +Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/) + +## Shortcuts + +| Keybind | Explanation | +|------------------------------------|--------------------------------------------------------------------------------------------------------------------| +| Ctrl + Enter | Queue up current graph for generation | +| Ctrl + Shift + Enter | Queue up current graph as first for generation | +| Ctrl + Alt + Enter | Cancel current generation | +| Ctrl + Z/Ctrl + Y | Undo/Redo | +| Ctrl + S | Save workflow | +| Ctrl + O | Load workflow | +| Ctrl + A | Select all nodes | +| Alt + C | Collapse/uncollapse selected nodes | +| Ctrl + M | Mute/unmute selected nodes | +| Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) | +| Delete/Backspace | Delete selected nodes | +| Ctrl + Backspace | Delete the current graph | +| Space | Move the canvas around when held and moving the cursor | +| Ctrl/Shift + Click | Add clicked node to selection | +| Ctrl + C/Ctrl + V | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) | +| Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) | +| Shift + Drag | Move multiple selected nodes at the same time | +| Ctrl + D | Load default graph | +| Alt + `+` | Canvas Zoom in | +| Alt + `-` | Canvas Zoom out | +| Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out | +| P | Pin/Unpin selected nodes | +| Ctrl + G | Group selected nodes | +| Q | Toggle visibility of the queue | +| H | Toggle visibility of history | +| R | Refresh graph | +| Double-Click LMB | Open node quick search palette | +| Shift + Drag | Move multiple wires at once | +| Ctrl + Alt + LMB | Disconnect all wires from clicked slot | + +Ctrl can also be replaced with Cmd instead for macOS users + +# Installing + +## Windows + +There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases). + +### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z) + +Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints + +If you have trouble extracting it, right click the file -> properties -> unblock + +#### How do I share models between another UI and ComfyUI? + +See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor. + +## Jupyter Notebook + +To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb) + +## Manual Install (Windows, Linux) + +Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended. + +Git clone this repo. + +Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints + +Put your VAE in: models/vae + + +### AMD GPUs (Linux only) +AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version: + +```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1``` + +This is the command to install the nightly with ROCm 6.2 which might have some performance improvements: + +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2``` + +### NVIDIA + +Nvidia users should install stable pytorch using this command: + +```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124``` + +This is the command to install pytorch nightly instead which might have performance improvements: + +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124``` + +#### Troubleshooting + +If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with: + +```pip uninstall torch``` + +And install it again with the command above. + +### Dependencies + +Install the dependencies by opening your terminal inside the ComfyUI folder and: + +```pip install -r requirements.txt``` + +After this you should have everything installed and can proceed to running ComfyUI. + +### Others: + +#### Intel GPUs + +Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) page. Choose your platform and method of install and follow the instructions. The steps are as follows: + +1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed. +1. Follow the instructions to install [Intel's oneAPI Basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for your platform. +1. Install the packages for IPEX using the instructions provided in the Installation page for your platform. +1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed. + +Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476). + +#### Apple Mac silicon + +You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version. + +1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly). +1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux. +1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies). +1. Launch ComfyUI by running `python main.py` + +> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux). + +#### DirectML (AMD Cards on Windows) + +```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml``` + +# Running + +```python main.py``` + +### For AMD cards not officially supported by ROCm + +Try running it with this command if you have issues: + +For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py``` + +For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py``` + +# Notes + +Only parts of the graph that have an output with all the correct inputs will be executed. + +Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed. + +Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it. + +You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\). + +You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}. + +Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`. + +To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension): + +```embedding:embedding_filename.pt``` + + +## How to show high-quality previews? + +Use ```--preview-method auto``` to enable previews. + +The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews. + +## How to use TLS/SSL? +Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"` + +Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`. + +> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above. +

If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal. + +## Support and dev channel + +[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source). + +See also: [https://www.comfy.org/](https://www.comfy.org/) + +## Frontend Development + +As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory. + +### Reporting Issues and Requesting Features + +For any bugs, issues, or feature requests related to the frontend, please use the [ComfyUI Frontend repository](https://github.com/Comfy-Org/ComfyUI_frontend). This will help us manage and address frontend-specific concerns more efficiently. + +### Using the Latest Frontend + +The new frontend is now the default for ComfyUI. However, please note: + +1. The frontend in the main ComfyUI repository is updated weekly. +2. Daily releases are available in the separate frontend repository. + +To use the most up-to-date frontend version: + +1. For the latest daily release, launch ComfyUI with this command line argument: + + ``` + --front-end-version Comfy-Org/ComfyUI_frontend@latest + ``` + +2. For a specific version, replace `latest` with the desired version number: + + ``` + --front-end-version Comfy-Org/ComfyUI_frontend@1.2.2 + ``` + +This approach allows you to easily switch between the stable weekly release and the cutting-edge daily updates, or even specific versions for testing purposes. + +### Accessing the Legacy Frontend + +If you need to use the legacy frontend for any reason, you can access it using the following command line argument: + +``` +--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest +``` + +This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend). + +# QA + +### Which GPU should I buy for this? + +[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI) + diff --git a/api_server/__init__.py b/api_server/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api_server/routes/__init__.py b/api_server/routes/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api_server/routes/internal/README.md b/api_server/routes/internal/README.md new file mode 100644 index 0000000..35330c3 --- /dev/null +++ b/api_server/routes/internal/README.md @@ -0,0 +1,3 @@ +# ComfyUI Internal Routes + +All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice. diff --git a/api_server/routes/internal/__init__.py b/api_server/routes/internal/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api_server/routes/internal/internal_routes.py b/api_server/routes/internal/internal_routes.py new file mode 100644 index 0000000..8893500 --- /dev/null +++ b/api_server/routes/internal/internal_routes.py @@ -0,0 +1,76 @@ +from aiohttp import web +from typing import Optional +from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths +from api_server.services.file_service import FileService +from api_server.services.terminal_service import TerminalService +import app.logger + +class InternalRoutes: + ''' + The top level web router for internal routes: /internal/* + The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only. + Check README.md for more information. + + ''' + + def __init__(self, prompt_server): + self.routes: web.RouteTableDef = web.RouteTableDef() + self._app: Optional[web.Application] = None + self.file_service = FileService({ + "models": models_dir, + "user": user_directory, + "output": output_directory + }) + self.prompt_server = prompt_server + self.terminal_service = TerminalService(prompt_server) + + def setup_routes(self): + @self.routes.get('/files') + async def list_files(request): + directory_key = request.query.get('directory', '') + try: + file_list = self.file_service.list_files(directory_key) + return web.json_response({"files": file_list}) + except ValueError as e: + return web.json_response({"error": str(e)}, status=400) + except Exception as e: + return web.json_response({"error": str(e)}, status=500) + + @self.routes.get('/logs') + async def get_logs(request): + return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()])) + + @self.routes.get('/logs/raw') + async def get_logs(request): + self.terminal_service.update_size() + return web.json_response({ + "entries": list(app.logger.get_logs()), + "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows} + }) + + @self.routes.patch('/logs/subscribe') + async def subscribe_logs(request): + json_data = await request.json() + client_id = json_data["clientId"] + enabled = json_data["enabled"] + if enabled: + self.terminal_service.subscribe(client_id) + else: + self.terminal_service.unsubscribe(client_id) + + return web.Response(status=200) + + + @self.routes.get('/folder_paths') + async def get_folder_paths(request): + response = {} + for key in folder_names_and_paths: + response[key] = folder_names_and_paths[key][0] + return web.json_response(response) + + def get_app(self): + if self._app is None: + self._app = web.Application() + self.setup_routes() + self._app.add_routes(self.routes) + return self._app diff --git a/api_server/services/__init__.py b/api_server/services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api_server/services/file_service.py b/api_server/services/file_service.py new file mode 100644 index 0000000..3945710 --- /dev/null +++ b/api_server/services/file_service.py @@ -0,0 +1,13 @@ +from typing import Dict, List, Optional +from api_server.utils.file_operations import FileSystemOperations, FileSystemItem + +class FileService: + def __init__(self, allowed_directories: Dict[str, str], file_system_ops: Optional[FileSystemOperations] = None): + self.allowed_directories: Dict[str, str] = allowed_directories + self.file_system_ops: FileSystemOperations = file_system_ops or FileSystemOperations() + + def list_files(self, directory_key: str) -> List[FileSystemItem]: + if directory_key not in self.allowed_directories: + raise ValueError("Invalid directory key") + directory_path: str = self.allowed_directories[directory_key] + return self.file_system_ops.walk_directory(directory_path) \ No newline at end of file diff --git a/api_server/services/terminal_service.py b/api_server/services/terminal_service.py new file mode 100644 index 0000000..284afab --- /dev/null +++ b/api_server/services/terminal_service.py @@ -0,0 +1,47 @@ +from app.logger import on_flush +import os + + +class TerminalService: + def __init__(self, server): + self.server = server + self.cols = None + self.rows = None + self.subscriptions = set() + on_flush(self.send_messages) + + def update_size(self): + sz = os.get_terminal_size() + changed = False + if sz.columns != self.cols: + self.cols = sz.columns + changed = True + + if sz.lines != self.rows: + self.rows = sz.lines + changed = True + + if changed: + return {"cols": self.cols, "rows": self.rows} + + return None + + def subscribe(self, client_id): + self.subscriptions.add(client_id) + + def unsubscribe(self, client_id): + self.subscriptions.discard(client_id) + + def send_messages(self, entries): + if not len(entries) or not len(self.subscriptions): + return + + new_size = self.update_size() + + for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration + if client_id not in self.server.sockets: + # Automatically unsub if the socket has disconnected + self.unsubscribe(client_id) + continue + + self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id) diff --git a/api_server/utils/file_operations.py b/api_server/utils/file_operations.py new file mode 100644 index 0000000..ef1bf99 --- /dev/null +++ b/api_server/utils/file_operations.py @@ -0,0 +1,42 @@ +import os +from typing import List, Union, TypedDict, Literal +from typing_extensions import TypeGuard +class FileInfo(TypedDict): + name: str + path: str + type: Literal["file"] + size: int + +class DirectoryInfo(TypedDict): + name: str + path: str + type: Literal["directory"] + +FileSystemItem = Union[FileInfo, DirectoryInfo] + +def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]: + return item["type"] == "file" + +class FileSystemOperations: + @staticmethod + def walk_directory(directory: str) -> List[FileSystemItem]: + file_list: List[FileSystemItem] = [] + for root, dirs, files in os.walk(directory): + for name in files: + file_path = os.path.join(root, name) + relative_path = os.path.relpath(file_path, directory) + file_list.append({ + "name": name, + "path": relative_path, + "type": "file", + "size": os.path.getsize(file_path) + }) + for name in dirs: + dir_path = os.path.join(root, name) + relative_path = os.path.relpath(dir_path, directory) + file_list.append({ + "name": name, + "path": relative_path, + "type": "directory" + }) + return file_list \ No newline at end of file diff --git a/app/__init__.py b/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/app_settings.py b/app/app_settings.py new file mode 100644 index 0000000..8c6edc5 --- /dev/null +++ b/app/app_settings.py @@ -0,0 +1,54 @@ +import os +import json +from aiohttp import web + + +class AppSettings(): + def __init__(self, user_manager): + self.user_manager = user_manager + + def get_settings(self, request): + file = self.user_manager.get_request_user_filepath( + request, "comfy.settings.json") + if os.path.isfile(file): + with open(file) as f: + return json.load(f) + else: + return {} + + def save_settings(self, request, settings): + file = self.user_manager.get_request_user_filepath( + request, "comfy.settings.json") + with open(file, "w") as f: + f.write(json.dumps(settings, indent=4)) + + def add_routes(self, routes): + @routes.get("/settings") + async def get_settings(request): + return web.json_response(self.get_settings(request)) + + @routes.get("/settings/{id}") + async def get_setting(request): + value = None + settings = self.get_settings(request) + setting_id = request.match_info.get("id", None) + if setting_id and setting_id in settings: + value = settings[setting_id] + return web.json_response(value) + + @routes.post("/settings") + async def post_settings(request): + settings = self.get_settings(request) + new_settings = await request.json() + self.save_settings(request, {**settings, **new_settings}) + return web.Response(status=200) + + @routes.post("/settings/{id}") + async def post_setting(request): + setting_id = request.match_info.get("id", None) + if not setting_id: + return web.Response(status=400) + settings = self.get_settings(request) + settings[setting_id] = await request.json() + self.save_settings(request, settings) + return web.Response(status=200) \ No newline at end of file diff --git a/app/frontend_management.py b/app/frontend_management.py new file mode 100644 index 0000000..6f20e43 --- /dev/null +++ b/app/frontend_management.py @@ -0,0 +1,204 @@ +from __future__ import annotations +import argparse +import logging +import os +import re +import tempfile +import zipfile +from dataclasses import dataclass +from functools import cached_property +from pathlib import Path +from typing import TypedDict, Optional + +import requests +from typing_extensions import NotRequired +from comfy.cli_args import DEFAULT_VERSION_STRING + + +REQUEST_TIMEOUT = 10 # seconds + + +class Asset(TypedDict): + url: str + + +class Release(TypedDict): + id: int + tag_name: str + name: str + prerelease: bool + created_at: str + published_at: str + body: str + assets: NotRequired[list[Asset]] + + +@dataclass +class FrontEndProvider: + owner: str + repo: str + + @property + def folder_name(self) -> str: + return f"{self.owner}_{self.repo}" + + @property + def release_url(self) -> str: + return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases" + + @cached_property + def all_releases(self) -> list[Release]: + releases = [] + api_url = self.release_url + while api_url: + response = requests.get(api_url, timeout=REQUEST_TIMEOUT) + response.raise_for_status() # Raises an HTTPError if the response was an error + releases.extend(response.json()) + # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly. + if "next" in response.links: + api_url = response.links["next"]["url"] + else: + api_url = None + return releases + + @cached_property + def latest_release(self) -> Release: + latest_release_url = f"{self.release_url}/latest" + response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT) + response.raise_for_status() # Raises an HTTPError if the response was an error + return response.json() + + def get_release(self, version: str) -> Release: + if version == "latest": + return self.latest_release + else: + for release in self.all_releases: + if release["tag_name"] in [version, f"v{version}"]: + return release + raise ValueError(f"Version {version} not found in releases") + + +def download_release_asset_zip(release: Release, destination_path: str) -> None: + """Download dist.zip from github release.""" + asset_url = None + for asset in release.get("assets", []): + if asset["name"] == "dist.zip": + asset_url = asset["url"] + break + + if not asset_url: + raise ValueError("dist.zip not found in the release assets") + + # Use a temporary file to download the zip content + with tempfile.TemporaryFile() as tmp_file: + headers = {"Accept": "application/octet-stream"} + response = requests.get( + asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT + ) + response.raise_for_status() # Ensure we got a successful response + + # Write the content to the temporary file + tmp_file.write(response.content) + + # Go back to the beginning of the temporary file + tmp_file.seek(0) + + # Extract the zip file content to the destination path + with zipfile.ZipFile(tmp_file, "r") as zip_ref: + zip_ref.extractall(destination_path) + + +class FrontendManager: + DEFAULT_FRONTEND_PATH = str(Path(__file__).parents[1] / "web") + CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions") + + @classmethod + def parse_version_string(cls, value: str) -> tuple[str, str, str]: + """ + Args: + value (str): The version string to parse. + + Returns: + tuple[str, str]: A tuple containing provider name and version. + + Raises: + argparse.ArgumentTypeError: If the version string is invalid. + """ + VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+|latest)$" + match_result = re.match(VERSION_PATTERN, value) + if match_result is None: + raise argparse.ArgumentTypeError(f"Invalid version string: {value}") + + return match_result.group(1), match_result.group(2), match_result.group(3) + + @classmethod + def init_frontend_unsafe(cls, version_string: str, provider: Optional[FrontEndProvider] = None) -> str: + """ + Initializes the frontend for the specified version. + + Args: + version_string (str): The version string. + provider (FrontEndProvider, optional): The provider to use. Defaults to None. + + Returns: + str: The path to the initialized frontend. + + Raises: + Exception: If there is an error during the initialization process. + main error source might be request timeout or invalid URL. + """ + if version_string == DEFAULT_VERSION_STRING: + return cls.DEFAULT_FRONTEND_PATH + + repo_owner, repo_name, version = cls.parse_version_string(version_string) + + if version.startswith("v"): + expected_path = str(Path(cls.CUSTOM_FRONTENDS_ROOT) / f"{repo_owner}_{repo_name}" / version.lstrip("v")) + if os.path.exists(expected_path): + logging.info(f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}") + return expected_path + + logging.info(f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub...") + + provider = provider or FrontEndProvider(repo_owner, repo_name) + release = provider.get_release(version) + + semantic_version = release["tag_name"].lstrip("v") + web_root = str( + Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version + ) + if not os.path.exists(web_root): + try: + os.makedirs(web_root, exist_ok=True) + logging.info( + "Downloading frontend(%s) version(%s) to (%s)", + provider.folder_name, + semantic_version, + web_root, + ) + logging.debug(release) + download_release_asset_zip(release, destination_path=web_root) + finally: + # Clean up the directory if it is empty, i.e. the download failed + if not os.listdir(web_root): + os.rmdir(web_root) + + return web_root + + @classmethod + def init_frontend(cls, version_string: str) -> str: + """ + Initializes the frontend with the specified version string. + + Args: + version_string (str): The version string to initialize the frontend with. + + Returns: + str: The path of the initialized frontend. + """ + try: + return cls.init_frontend_unsafe(version_string) + except Exception as e: + logging.error("Failed to initialize frontend: %s", e) + logging.info("Falling back to the default frontend.") + return cls.DEFAULT_FRONTEND_PATH diff --git a/app/logger.py b/app/logger.py new file mode 100644 index 0000000..527be9f --- /dev/null +++ b/app/logger.py @@ -0,0 +1,73 @@ +from collections import deque +from datetime import datetime +import io +import logging +import sys +import threading + +logs = None +stdout_interceptor = None +stderr_interceptor = None + + +class LogInterceptor(io.TextIOWrapper): + def __init__(self, stream, *args, **kwargs): + buffer = stream.buffer + encoding = stream.encoding + super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering) + self._lock = threading.Lock() + self._flush_callbacks = [] + self._logs_since_flush = [] + + def write(self, data): + entry = {"t": datetime.now().isoformat(), "m": data} + with self._lock: + self._logs_since_flush.append(entry) + + # Simple handling for cr to overwrite the last output if it isnt a full line + # else logs just get full of progress messages + if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"): + logs.pop() + logs.append(entry) + super().write(data) + + def flush(self): + super().flush() + for cb in self._flush_callbacks: + cb(self._logs_since_flush) + self._logs_since_flush = [] + + def on_flush(self, callback): + self._flush_callbacks.append(callback) + + +def get_logs(): + return logs + + +def on_flush(callback): + if stdout_interceptor is not None: + stdout_interceptor.on_flush(callback) + if stderr_interceptor is not None: + stderr_interceptor.on_flush(callback) + +def setup_logger(log_level: str = 'INFO', capacity: int = 300): + global logs + if logs: + return + + # Override output streams and log to buffer + logs = deque(maxlen=capacity) + + global stdout_interceptor + global stderr_interceptor + stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout) + stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr) + + # Setup default global logger + logger = logging.getLogger() + logger.setLevel(log_level) + + stream_handler = logging.StreamHandler() + stream_handler.setFormatter(logging.Formatter("%(message)s")) + logger.addHandler(stream_handler) diff --git a/app/user_manager.py b/app/user_manager.py new file mode 100644 index 0000000..4e545c0 --- /dev/null +++ b/app/user_manager.py @@ -0,0 +1,329 @@ +import json +import os +import re +import uuid +import glob +import shutil +import logging +from aiohttp import web +from urllib import parse +from comfy.cli_args import args +import folder_paths +from .app_settings import AppSettings +from typing import TypedDict + +default_user = "default" + + +class FileInfo(TypedDict): + path: str + size: int + modified: int + + +def get_file_info(path: str, relative_to: str) -> FileInfo: + return { + "path": os.path.relpath(path, relative_to).replace(os.sep, '/'), + "size": os.path.getsize(path), + "modified": os.path.getmtime(path) + } + + +class UserManager(): + def __init__(self): + user_directory = folder_paths.get_user_directory() + + self.settings = AppSettings(self) + if not os.path.exists(user_directory): + os.mkdir(user_directory) + if not args.multi_user: + print("****** User settings have been changed to be stored on the server instead of browser storage. ******") + print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******") + + if args.multi_user: + if os.path.isfile(self.get_users_file()): + with open(self.get_users_file()) as f: + self.users = json.load(f) + else: + self.users = {} + else: + self.users = {"default": "default"} + + def get_users_file(self): + return os.path.join(folder_paths.get_user_directory(), "users.json") + + def get_request_user_id(self, request): + user = "default" + if args.multi_user and "comfy-user" in request.headers: + user = request.headers["comfy-user"] + + if user not in self.users: + raise KeyError("Unknown user: " + user) + + return user + + def get_request_user_filepath(self, request, file, type="userdata", create_dir=True): + user_directory = folder_paths.get_user_directory() + + if type == "userdata": + root_dir = user_directory + else: + raise KeyError("Unknown filepath type:" + type) + + user = self.get_request_user_id(request) + path = user_root = os.path.abspath(os.path.join(root_dir, user)) + + # prevent leaving /{type} + if os.path.commonpath((root_dir, user_root)) != root_dir: + return None + + if file is not None: + # Check if filename is url encoded + if "%" in file: + file = parse.unquote(file) + + # prevent leaving /{type}/{user} + path = os.path.abspath(os.path.join(user_root, file)) + if os.path.commonpath((user_root, path)) != user_root: + return None + + parent = os.path.split(path)[0] + + if create_dir and not os.path.exists(parent): + os.makedirs(parent, exist_ok=True) + + return path + + def add_user(self, name): + name = name.strip() + if not name: + raise ValueError("username not provided") + user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name) + user_id = user_id + "_" + str(uuid.uuid4()) + + self.users[user_id] = name + + with open(self.get_users_file(), "w") as f: + json.dump(self.users, f) + + return user_id + + def add_routes(self, routes): + self.settings.add_routes(routes) + + @routes.get("/users") + async def get_users(request): + if args.multi_user: + return web.json_response({"storage": "server", "users": self.users}) + else: + user_dir = self.get_request_user_filepath(request, None, create_dir=False) + return web.json_response({ + "storage": "server", + "migrated": os.path.exists(user_dir) + }) + + @routes.post("/users") + async def post_users(request): + body = await request.json() + username = body["username"] + if username in self.users.values(): + return web.json_response({"error": "Duplicate username."}, status=400) + + user_id = self.add_user(username) + return web.json_response(user_id) + + @routes.get("/userdata") + async def listuserdata(request): + """ + List user data files in a specified directory. + + This endpoint allows listing files in a user's data directory, with options for recursion, + full file information, and path splitting. + + Query Parameters: + - dir (required): The directory to list files from. + - recurse (optional): If "true", recursively list files in subdirectories. + - full_info (optional): If "true", return detailed file information (path, size, modified time). + - split (optional): If "true", split file paths into components (only applies when full_info is false). + + Returns: + - 400: If 'dir' parameter is missing. + - 403: If the requested path is not allowed. + - 404: If the requested directory does not exist. + - 200: JSON response with the list of files or file information. + + The response format depends on the query parameters: + - Default: List of relative file paths. + - full_info=true: List of dictionaries with file details. + - split=true (and full_info=false): List of lists, each containing path components. + """ + directory = request.rel_url.query.get('dir', '') + if not directory: + return web.Response(status=400, text="Directory not provided") + + path = self.get_request_user_filepath(request, directory) + if not path: + return web.Response(status=403, text="Invalid directory") + + if not os.path.exists(path): + return web.Response(status=404, text="Directory not found") + + recurse = request.rel_url.query.get('recurse', '').lower() == "true" + full_info = request.rel_url.query.get('full_info', '').lower() == "true" + split_path = request.rel_url.query.get('split', '').lower() == "true" + + # Use different patterns based on whether we're recursing or not + if recurse: + pattern = os.path.join(glob.escape(path), '**', '*') + else: + pattern = os.path.join(glob.escape(path), '*') + + def process_full_path(full_path: str) -> FileInfo | str | list[str]: + if full_info: + return get_file_info(full_path, path) + + rel_path = os.path.relpath(full_path, path).replace(os.sep, '/') + if split_path: + return [rel_path] + rel_path.split('/') + + return rel_path + + results = [ + process_full_path(full_path) + for full_path in glob.glob(pattern, recursive=recurse) + if os.path.isfile(full_path) + ] + + return web.json_response(results) + + def get_user_data_path(request, check_exists = False, param = "file"): + file = request.match_info.get(param, None) + if not file: + return web.Response(status=400) + + path = self.get_request_user_filepath(request, file) + if not path: + return web.Response(status=403) + + if check_exists and not os.path.exists(path): + return web.Response(status=404) + + return path + + @routes.get("/userdata/{file}") + async def getuserdata(request): + path = get_user_data_path(request, check_exists=True) + if not isinstance(path, str): + return path + + return web.FileResponse(path) + + @routes.post("/userdata/{file}") + async def post_userdata(request): + """ + Upload or update a user data file. + + This endpoint handles file uploads to a user's data directory, with options for + controlling overwrite behavior and response format. + + Query Parameters: + - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". + - full_info (optional): If "true", returns detailed file information (path, size, modified time). + If "false", returns only the relative file path. + + Path Parameters: + - file: The target file path (URL encoded if necessary). + + Returns: + - 400: If 'file' parameter is missing. + - 403: If the requested path is not allowed. + - 409: If overwrite=false and the file already exists. + - 200: JSON response with either: + - Full file information (if full_info=true) + - Relative file path (if full_info=false) + + The request body should contain the raw file content to be written. + """ + path = get_user_data_path(request) + if not isinstance(path, str): + return path + + overwrite = request.query.get("overwrite", 'true') != "false" + full_info = request.query.get('full_info', 'false').lower() == "true" + + if not overwrite and os.path.exists(path): + return web.Response(status=409, text="File already exists") + + body = await request.read() + + with open(path, "wb") as f: + f.write(body) + + user_path = self.get_request_user_filepath(request, None) + if full_info: + resp = get_file_info(path, user_path) + else: + resp = os.path.relpath(path, user_path) + + return web.json_response(resp) + + @routes.delete("/userdata/{file}") + async def delete_userdata(request): + path = get_user_data_path(request, check_exists=True) + if not isinstance(path, str): + return path + + os.remove(path) + + return web.Response(status=204) + + @routes.post("/userdata/{file}/move/{dest}") + async def move_userdata(request): + """ + Move or rename a user data file. + + This endpoint handles moving or renaming files within a user's data directory, with options for + controlling overwrite behavior and response format. + + Path Parameters: + - file: The source file path (URL encoded if necessary) + - dest: The destination file path (URL encoded if necessary) + + Query Parameters: + - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". + - full_info (optional): If "true", returns detailed file information (path, size, modified time). + If "false", returns only the relative file path. + + Returns: + - 400: If either 'file' or 'dest' parameter is missing + - 403: If either requested path is not allowed + - 404: If the source file does not exist + - 409: If overwrite=false and the destination file already exists + - 200: JSON response with either: + - Full file information (if full_info=true) + - Relative file path (if full_info=false) + """ + source = get_user_data_path(request, check_exists=True) + if not isinstance(source, str): + return source + + dest = get_user_data_path(request, check_exists=False, param="dest") + if not isinstance(source, str): + return dest + + overwrite = request.query.get("overwrite", 'true') != "false" + full_info = request.query.get('full_info', 'false').lower() == "true" + + if not overwrite and os.path.exists(dest): + return web.Response(status=409, text="File already exists") + + logging.info(f"moving '{source}' -> '{dest}'") + shutil.move(source, dest) + + user_path = self.get_request_user_filepath(request, None) + if full_info: + resp = get_file_info(dest, user_path) + else: + resp = os.path.relpath(dest, user_path) + + return web.json_response(resp) diff --git a/comfy/checkpoint_pickle.py b/comfy/checkpoint_pickle.py new file mode 100644 index 0000000..206551d --- /dev/null +++ b/comfy/checkpoint_pickle.py @@ -0,0 +1,13 @@ +import pickle + +load = pickle.load + +class Empty: + pass + +class Unpickler(pickle.Unpickler): + def find_class(self, module, name): + #TODO: safe unpickle + if module.startswith("pytorch_lightning"): + return Empty + return super().find_class(module, name) diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py new file mode 100644 index 0000000..9ec64a2 --- /dev/null +++ b/comfy/cldm/cldm.py @@ -0,0 +1,437 @@ +#taken from: https://github.com/lllyasviel/ControlNet +#and modified + +import torch +import torch as th +import torch.nn as nn + +from ..ldm.modules.diffusionmodules.util import ( + zero_module, + timestep_embedding, +) + +from ..ldm.modules.attention import SpatialTransformer +from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample +from ..ldm.util import exists +from .control_types import UNION_CONTROLNET_TYPES +from collections import OrderedDict +import comfy.ops +from comfy.ldm.modules.attention import optimized_attention + +class OptimizedAttention(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.heads = nhead + self.c = c + + self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device) + self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + def forward(self, x): + x = self.in_proj(x) + q, k, v = x.split(self.c, dim=2) + out = optimized_attention(q, k, v, self.heads) + return self.out_proj(out) + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + +class ResBlockUnionControlnet(nn.Module): + def __init__(self, dim, nhead, dtype=None, device=None, operations=None): + super().__init__() + self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations) + self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device) + self.mlp = nn.Sequential( + OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()), + ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))])) + self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device) + + def attention(self, x: torch.Tensor): + return self.attn(x) + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + +class ControlledUnetModel(UNetModel): + #implemented in the ldm unet + pass + +class ControlNet(nn.Module): + def __init__( + self, + image_size, + in_channels, + model_channels, + hint_channels, + num_res_blocks, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + dtype=torch.float32, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + transformer_depth_middle=None, + transformer_depth_output=None, + attn_precision=None, + union_controlnet_num_control_type=None, + device=None, + operations=comfy.ops.disable_weight_init, + **kwargs, + ): + super().__init__() + assert use_spatial_transformer == True, "use_spatial_transformer has to be true" + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + # from omegaconf.listconfig import ListConfig + # if type(context_dim) == ListConfig: + # context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.dims = dims + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) + + transformer_depth = transformer_depth[:] + + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + elif self.num_classes == "continuous": + print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + ] + ) + self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) + + self.input_hint_block = TimestepEmbedSequential( + operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = mult * model_channels + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append( + SpatialTransformer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + mid_block = [ + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + if transformer_depth_middle >= 0: + mid_block += [SpatialTransformer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + self.middle_block = TimestepEmbedSequential(*mid_block) + self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) + self._feature_size += ch + + if union_controlnet_num_control_type is not None: + self.num_control_type = union_controlnet_num_control_type + num_trans_channel = 320 + num_trans_head = 8 + num_trans_layer = 1 + num_proj_channel = 320 + # task_scale_factor = num_trans_channel ** 0.5 + self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device)) + + self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)]) + self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device) + #----------------------------------------------------------------------------------------------------- + + control_add_embed_dim = 256 + class ControlAddEmbedding(nn.Module): + def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None): + super().__init__() + self.num_control_type = num_control_type + self.in_dim = in_dim + self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device) + self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device) + def forward(self, control_type, dtype, device): + c_type = torch.zeros((self.num_control_type,), device=device) + c_type[control_type] = 1.0 + c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim)) + return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type))) + + self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations) + else: + self.task_embedding = None + self.control_add_embedding = None + + def union_controlnet_merge(self, hint, control_type, emb, context): + # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main + inputs = [] + condition_list = [] + + for idx in range(min(1, len(control_type))): + controlnet_cond = self.input_hint_block(hint[idx], emb, context) + feat_seq = torch.mean(controlnet_cond, dim=(2, 3)) + if idx < len(control_type): + feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device) + + inputs.append(feat_seq.unsqueeze(1)) + condition_list.append(controlnet_cond) + + x = torch.cat(inputs, dim=1) + x = self.transformer_layes(x) + controlnet_cond_fuser = None + for idx in range(len(control_type)): + alpha = self.spatial_ch_projs(x[:, idx]) + alpha = alpha.unsqueeze(-1).unsqueeze(-1) + o = condition_list[idx] + alpha + if controlnet_cond_fuser is None: + controlnet_cond_fuser = o + else: + controlnet_cond_fuser += o + return controlnet_cond_fuser + + def make_zero_conv(self, channels, operations=None, dtype=None, device=None): + return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) + + def forward(self, x, hint, timesteps, context, y=None, **kwargs): + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + guided_hint = None + if self.control_add_embedding is not None: #Union Controlnet + control_type = kwargs.get("control_type", []) + + if any([c >= self.num_control_type for c in control_type]): + max_type = max(control_type) + max_type_name = { + v: k for k, v in UNION_CONTROLNET_TYPES.items() + }[max_type] + raise ValueError( + f"Control type {max_type_name}({max_type}) is out of range for the number of control types" + + f"({self.num_control_type}) supported.\n" + + "Please consider using the ProMax ControlNet Union model.\n" + + "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main" + ) + + emb += self.control_add_embedding(control_type, emb.dtype, emb.device) + if len(control_type) > 0: + if len(hint.shape) < 5: + hint = hint.unsqueeze(dim=0) + guided_hint = self.union_controlnet_merge(hint, control_type, emb, context) + + if guided_hint is None: + guided_hint = self.input_hint_block(hint, emb, context) + + out_output = [] + out_middle = [] + + hs = [] + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for module, zero_conv in zip(self.input_blocks, self.zero_convs): + if guided_hint is not None: + h = module(h, emb, context) + h += guided_hint + guided_hint = None + else: + h = module(h, emb, context) + out_output.append(zero_conv(h, emb, context)) + + h = self.middle_block(h, emb, context) + out_middle.append(self.middle_block_out(h, emb, context)) + + return {"middle": out_middle, "output": out_output} + diff --git a/comfy/cldm/control_types.py b/comfy/cldm/control_types.py new file mode 100644 index 0000000..4128631 --- /dev/null +++ b/comfy/cldm/control_types.py @@ -0,0 +1,10 @@ +UNION_CONTROLNET_TYPES = { + "openpose": 0, + "depth": 1, + "hed/pidi/scribble/ted": 2, + "canny/lineart/anime_lineart/mlsd": 3, + "normal": 4, + "segment": 5, + "tile": 6, + "repaint": 7, +} diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py new file mode 100644 index 0000000..54a58ab --- /dev/null +++ b/comfy/cldm/mmdit.py @@ -0,0 +1,81 @@ +import torch +from typing import Dict, Optional +import comfy.ldm.modules.diffusionmodules.mmdit + +class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): + def __init__( + self, + num_blocks = None, + control_latent_channels = None, + dtype = None, + device = None, + operations = None, + **kwargs, + ): + super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs) + # controlnet_blocks + self.controlnet_blocks = torch.nn.ModuleList([]) + for _ in range(len(self.joint_blocks)): + self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype)) + + if control_latent_channels is None: + control_latent_channels = self.in_channels + + self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed( + None, + self.patch_size, + control_latent_channels, + self.hidden_size, + bias=True, + strict_img_size=False, + dtype=dtype, + device=device, + operations=operations + ) + + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + hint = None, + ) -> torch.Tensor: + + #weird sd3 controlnet specific stuff + y = torch.zeros_like(y) + + if self.context_processor is not None: + context = self.context_processor(context) + + hw = x.shape[-2:] + x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device) + x += self.pos_embed_input(hint) + + c = self.t_embedder(timesteps, dtype=x.dtype) + if y is not None and self.y_embedder is not None: + y = self.y_embedder(y) + c = c + y + + if context is not None: + context = self.context_embedder(context) + + output = [] + + blocks = len(self.joint_blocks) + for i in range(blocks): + context, x = self.joint_blocks[i]( + context, + x, + c=c, + use_checkpoint=self.use_checkpoint, + ) + + out = self.controlnet_blocks[i](x) + count = self.depth // blocks + if i == blocks - 1: + count -= 1 + for j in range(count): + output.append(out) + + return {"output": output} diff --git a/comfy/cli_args.py b/comfy/cli_args.py new file mode 100644 index 0000000..20b9f47 --- /dev/null +++ b/comfy/cli_args.py @@ -0,0 +1,185 @@ +import argparse +import enum +import os +from typing import Optional +import comfy.options + + +class EnumAction(argparse.Action): + """ + Argparse action for handling Enums + """ + def __init__(self, **kwargs): + # Pop off the type value + enum_type = kwargs.pop("type", None) + + # Ensure an Enum subclass is provided + if enum_type is None: + raise ValueError("type must be assigned an Enum when using EnumAction") + if not issubclass(enum_type, enum.Enum): + raise TypeError("type must be an Enum when using EnumAction") + + # Generate choices from the Enum + choices = tuple(e.value for e in enum_type) + kwargs.setdefault("choices", choices) + kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") + + super(EnumAction, self).__init__(**kwargs) + + self._enum = enum_type + + def __call__(self, parser, namespace, values, option_string=None): + # Convert value back into an Enum + value = self._enum(values) + setattr(namespace, self.dest, value) + + +parser = argparse.ArgumentParser() + +parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)") +parser.add_argument("--port", type=int, default=8188, help="Set the listen port.") +parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function") +parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function") +parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") +parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.") + +parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.") +parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.") +parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).") +parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.") +parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.") +parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.") +parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.") +cm_group = parser.add_mutually_exclusive_group() +cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") +cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") + + +fp_group = parser.add_mutually_exclusive_group() +fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).") +fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.") + +fpunet_group = parser.add_mutually_exclusive_group() +fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.") +fpunet_group.add_argument("--fp16-unet", action="store_true", help="Store unet weights in fp16.") +fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.") +fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.") + +fpvae_group = parser.add_mutually_exclusive_group() +fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") +fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.") +fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.") + +parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU.") + +fpte_group = parser.add_mutually_exclusive_group() +fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).") +fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).") +fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.") +fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") + +parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.") + +parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") + +parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.") + +class LatentPreviewMethod(enum.Enum): + NoPreviews = "none" + Auto = "auto" + Latent2RGB = "latent2rgb" + TAESD = "taesd" + +parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction) + +parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.") + +cache_group = parser.add_mutually_exclusive_group() +cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.") +cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.") + +attn_group = parser.add_mutually_exclusive_group() +attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") +attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.") +attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.") + +parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") + +upcast = parser.add_mutually_exclusive_group() +upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.") +upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.") + + +vram_group = parser.add_mutually_exclusive_group() +vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") +vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") +vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.") +vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.") +vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.") +vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") + +parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reverved depending on your OS.") + + +parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.") + +parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.") +parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.") +parser.add_argument("--fast", action="store_true", help="Enable some untested and potentially quality deteriorating optimizations.") + +parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") +parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") +parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") + +parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") +parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.") + +parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") + +parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level') + +# The default built-in provider hosted under web/ +DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest" + +parser.add_argument( + "--front-end-version", + type=str, + default=DEFAULT_VERSION_STRING, + help=""" + Specifies the version of the frontend to be used. This command needs internet connectivity to query and + download available frontend implementations from GitHub releases. + + The version string should be in the format of: + [repoOwner]/[repoName]@[version] + where version is one of: "latest" or a valid version number (e.g. "1.0.0") + """, +) + +def is_valid_directory(path: Optional[str]) -> Optional[str]: + """Validate if the given path is a directory.""" + if path is None: + return None + + if not os.path.isdir(path): + raise argparse.ArgumentTypeError(f"{path} is not a valid directory.") + return path + +parser.add_argument( + "--front-end-root", + type=is_valid_directory, + default=None, + help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.", +) + +parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path.") + +if comfy.options.args_parsing: + args = parser.parse_args() +else: + args = parser.parse_args([]) + +if args.windows_standalone_build: + args.auto_launch = True + +if args.disable_auto_launch: + args.auto_launch = False diff --git a/comfy/clip_config_bigg.json b/comfy/clip_config_bigg.json new file mode 100644 index 0000000..35261de --- /dev/null +++ b/comfy/clip_config_bigg.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "gelu", + "hidden_size": 1280, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 20, + "num_hidden_layers": 32, + "pad_token_id": 1, + "projection_dim": 1280, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/comfy/clip_model.py b/comfy/clip_model.py new file mode 100644 index 0000000..42cdc4f --- /dev/null +++ b/comfy/clip_model.py @@ -0,0 +1,196 @@ +import torch +from comfy.ldm.modules.attention import optimized_attention_for_device +import comfy.ops + +class CLIPAttention(torch.nn.Module): + def __init__(self, embed_dim, heads, dtype, device, operations): + super().__init__() + + self.heads = heads + self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x, mask=None, optimized_attention=None): + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + out = optimized_attention(q, k, v, self.heads, mask) + return self.out_proj(out) + +ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), + "gelu": torch.nn.functional.gelu, +} + +class CLIPMLP(torch.nn.Module): + def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): + super().__init__() + self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) + self.activation = ACTIVATIONS[activation] + self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x): + x = self.fc1(x) + x = self.activation(x) + x = self.fc2(x) + return x + +class CLIPLayer(torch.nn.Module): + def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): + super().__init__() + self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) + self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) + + def forward(self, x, mask=None, optimized_attention=None): + x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) + x += self.mlp(self.layer_norm2(x)) + return x + + +class CLIPEncoder(torch.nn.Module): + def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): + super().__init__() + self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) + + def forward(self, x, mask=None, intermediate_output=None): + optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layers) + intermediate_output + + intermediate = None + for i, l in enumerate(self.layers): + x = l(x, mask, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + +class CLIPEmbeddings(torch.nn.Module): + def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None): + super().__init__() + self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) + self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device) + + def forward(self, input_tokens, dtype=torch.float32): + return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device) + + +class CLIPTextModel_(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + num_positions = config_dict["max_position_embeddings"] + self.eos_token_id = config_dict["eos_token_id"] + + super().__init__() + self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations) + self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) + self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + + def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32): + x = self.embeddings(input_tokens, dtype=dtype) + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) + if mask is not None: + mask += causal_mask + else: + mask = causal_mask + + x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) + x = self.final_layer_norm(x) + if i is not None and final_layer_norm_intermediate: + i = self.final_layer_norm(i) + + pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),] + return x, i, pooled_output + +class CLIPTextModel(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.num_layers = config_dict["num_hidden_layers"] + self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) + embed_dim = config_dict["hidden_size"] + self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) + self.dtype = dtype + + def get_input_embeddings(self): + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, embeddings): + self.text_model.embeddings.token_embedding = embeddings + + def forward(self, *args, **kwargs): + x = self.text_model(*args, **kwargs) + out = self.text_projection(x[2]) + return (x[0], x[1], out, x[2]) + + +class CLIPVisionEmbeddings(torch.nn.Module): + def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None): + super().__init__() + self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) + + self.patch_embedding = operations.Conv2d( + in_channels=num_channels, + out_channels=embed_dim, + kernel_size=patch_size, + stride=patch_size, + bias=False, + dtype=dtype, + device=device + ) + + num_patches = (image_size // patch_size) ** 2 + num_positions = num_patches + 1 + self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device) + + def forward(self, pixel_values): + embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) + return torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + comfy.ops.cast_to_input(self.position_embedding.weight, embeds) + + +class CLIPVision(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + + self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=dtype, device=device, operations=operations) + self.pre_layrnorm = operations.LayerNorm(embed_dim) + self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) + self.post_layernorm = operations.LayerNorm(embed_dim) + + def forward(self, pixel_values, attention_mask=None, intermediate_output=None): + x = self.embeddings(pixel_values) + x = self.pre_layrnorm(x) + #TODO: attention_mask? + x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) + pooled_output = self.post_layernorm(x[:, 0, :]) + return x, i, pooled_output + +class CLIPVisionModelProjection(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.vision_model = CLIPVision(config_dict, dtype, device, operations) + self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) + + def forward(self, *args, **kwargs): + x = self.vision_model(*args, **kwargs) + out = self.visual_projection(x[2]) + return (x[0], x[1], out) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py new file mode 100644 index 0000000..64392e2 --- /dev/null +++ b/comfy/clip_vision.py @@ -0,0 +1,120 @@ +from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace +import os +import torch +import json +import logging + +import comfy.ops +import comfy.model_patcher +import comfy.model_management +import comfy.utils +import comfy.clip_model + +class Output: + def __getitem__(self, key): + return getattr(self, key) + def __setitem__(self, key, item): + setattr(self, key, item) + +def clip_preprocess(image, size=224): + mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype) + std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype) + image = image.movedim(-1, 1) + if not (image.shape[2] == size and image.shape[3] == size): + scale = (size / min(image.shape[2], image.shape[3])) + image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True) + h = (image.shape[2] - size)//2 + w = (image.shape[3] - size)//2 + image = image[:,:,h:h+size,w:w+size] + image = torch.clip((255. * image), 0, 255).round() / 255.0 + return (image - mean.view([3,1,1])) / std.view([3,1,1]) + +class ClipVisionModel(): + def __init__(self, json_config): + with open(json_config) as f: + config = json.load(f) + + self.image_size = config.get("image_size", 224) + self.load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) + self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast) + self.model.eval() + + self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) + + def load_sd(self, sd): + return self.model.load_state_dict(sd, strict=False) + + def get_sd(self): + return self.model.state_dict() + + def encode_image(self, image): + comfy.model_management.load_model_gpu(self.patcher) + pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size).float() + out = self.model(pixel_values=pixel_values, intermediate_output=-2) + + outputs = Output() + outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device()) + outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device()) + outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) + return outputs + +def convert_to_transformers(sd, prefix): + sd_k = sd.keys() + if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: + keys_to_replace = { + "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", + "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", + "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", + "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", + "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", + "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", + "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", + } + + for x in keys_to_replace: + if x in sd_k: + sd[keys_to_replace[x]] = sd.pop(x) + + if "{}proj".format(prefix) in sd_k: + sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) + + sd = transformers_convert(sd, prefix, "vision_model.", 48) + else: + replace_prefix = {prefix: ""} + sd = state_dict_prefix_replace(sd, replace_prefix) + return sd + +def load_clipvision_from_sd(sd, prefix="", convert_keys=False): + if convert_keys: + sd = convert_to_transformers(sd, prefix) + if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") + elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") + elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: + if sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") + else: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") + else: + return None + + clip = ClipVisionModel(json_config) + m, u = clip.load_sd(sd) + if len(m) > 0: + logging.warning("missing clip vision: {}".format(m)) + u = set(u) + keys = list(sd.keys()) + for k in keys: + if k not in u: + sd.pop(k) + return clip + +def load(ckpt_path): + sd = load_torch_file(ckpt_path) + if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: + return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) + else: + return load_clipvision_from_sd(sd) diff --git a/comfy/clip_vision_config_g.json b/comfy/clip_vision_config_g.json new file mode 100644 index 0000000..708e7e2 --- /dev/null +++ b/comfy/clip_vision_config_g.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1664, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 8192, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 48, + "patch_size": 14, + "projection_dim": 1280, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_h.json b/comfy/clip_vision_config_h.json new file mode 100644 index 0000000..bb71be4 --- /dev/null +++ b/comfy/clip_vision_config_h.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1280, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 32, + "patch_size": 14, + "projection_dim": 1024, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_vitl.json b/comfy/clip_vision_config_vitl.json new file mode 100644 index 0000000..c59b8ed --- /dev/null +++ b/comfy/clip_vision_config_vitl.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_vitl_336.json b/comfy/clip_vision_config_vitl_336.json new file mode 100644 index 0000000..f269452 --- /dev/null +++ b/comfy/clip_vision_config_vitl_336.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 336, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-5, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float32" +} diff --git a/comfy/comfy_types.py b/comfy/comfy_types.py new file mode 100644 index 0000000..70cf4b1 --- /dev/null +++ b/comfy/comfy_types.py @@ -0,0 +1,32 @@ +import torch +from typing import Callable, Protocol, TypedDict, Optional, List + + +class UnetApplyFunction(Protocol): + """Function signature protocol on comfy.model_base.BaseModel.apply_model""" + + def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor: + pass + + +class UnetApplyConds(TypedDict): + """Optional conditions for unet apply function.""" + + c_concat: Optional[torch.Tensor] + c_crossattn: Optional[torch.Tensor] + control: Optional[torch.Tensor] + transformer_options: Optional[dict] + + +class UnetParams(TypedDict): + # Tensor of shape [B, C, H, W] + input: torch.Tensor + # Tensor of shape [B] + timestep: torch.Tensor + c: UnetApplyConds + # List of [0, 1], [0], [1], ... + # 0 means conditional, 1 means conditional unconditional + cond_or_uncond: List[int] + + +UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor] diff --git a/comfy/conds.py b/comfy/conds.py new file mode 100644 index 0000000..660690a --- /dev/null +++ b/comfy/conds.py @@ -0,0 +1,83 @@ +import torch +import math +import comfy.utils + + +def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9) + return abs(a*b) // math.gcd(a, b) + +class CONDRegular: + def __init__(self, cond): + self.cond = cond + + def _copy_with(self, cond): + return self.__class__(cond) + + def process_cond(self, batch_size, device, **kwargs): + return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) + + def can_concat(self, other): + if self.cond.shape != other.cond.shape: + return False + return True + + def concat(self, others): + conds = [self.cond] + for x in others: + conds.append(x.cond) + return torch.cat(conds) + +class CONDNoiseShape(CONDRegular): + def process_cond(self, batch_size, device, area, **kwargs): + data = self.cond + if area is not None: + dims = len(area) // 2 + for i in range(dims): + data = data.narrow(i + 2, area[i + dims], area[i]) + + return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device)) + + +class CONDCrossAttn(CONDRegular): + def can_concat(self, other): + s1 = self.cond.shape + s2 = other.cond.shape + if s1 != s2: + if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen + return False + + mult_min = lcm(s1[1], s2[1]) + diff = mult_min // min(s1[1], s2[1]) + if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much + return False + return True + + def concat(self, others): + conds = [self.cond] + crossattn_max_len = self.cond.shape[1] + for x in others: + c = x.cond + crossattn_max_len = lcm(crossattn_max_len, c.shape[1]) + conds.append(c) + + out = [] + for c in conds: + if c.shape[1] < crossattn_max_len: + c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result + out.append(c) + return torch.cat(out) + +class CONDConstant(CONDRegular): + def __init__(self, cond): + self.cond = cond + + def process_cond(self, batch_size, device, **kwargs): + return self._copy_with(self.cond) + + def can_concat(self, other): + if self.cond != other.cond: + return False + return True + + def concat(self, others): + return self.cond diff --git a/comfy/controlnet.py b/comfy/controlnet.py new file mode 100644 index 0000000..d2744e4 --- /dev/null +++ b/comfy/controlnet.py @@ -0,0 +1,766 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + + +import torch +from enum import Enum +import math +import os +import logging +import comfy.utils +import comfy.model_management +import comfy.model_detection +import comfy.model_patcher +import comfy.ops +import comfy.latent_formats + +import comfy.cldm.cldm +import comfy.t2i_adapter.adapter +import comfy.ldm.cascade.controlnet +import comfy.cldm.mmdit +import comfy.ldm.hydit.controlnet +import comfy.ldm.flux.controlnet + + +def broadcast_image_to(tensor, target_batch_size, batched_number): + current_batch_size = tensor.shape[0] + #print(current_batch_size, target_batch_size) + if current_batch_size == 1: + return tensor + + per_batch = target_batch_size // batched_number + tensor = tensor[:per_batch] + + if per_batch > tensor.shape[0]: + tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) + + current_batch_size = tensor.shape[0] + if current_batch_size == target_batch_size: + return tensor + else: + return torch.cat([tensor] * batched_number, dim=0) + +class StrengthType(Enum): + CONSTANT = 1 + LINEAR_UP = 2 + +class ControlBase: + def __init__(self): + self.cond_hint_original = None + self.cond_hint = None + self.strength = 1.0 + self.timestep_percent_range = (0.0, 1.0) + self.latent_format = None + self.vae = None + self.global_average_pooling = False + self.timestep_range = None + self.compression_ratio = 8 + self.upscale_algorithm = 'nearest-exact' + self.extra_args = {} + self.previous_controlnet = None + self.extra_conds = [] + self.strength_type = StrengthType.CONSTANT + self.concat_mask = False + self.extra_concat_orig = [] + self.extra_concat = None + + def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]): + self.cond_hint_original = cond_hint + self.strength = strength + self.timestep_percent_range = timestep_percent_range + if self.latent_format is not None: + if vae is None: + logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.") + self.vae = vae + self.extra_concat_orig = extra_concat.copy() + if self.concat_mask and len(self.extra_concat_orig) == 0: + self.extra_concat_orig.append(torch.tensor([[[[1.0]]]])) + return self + + def pre_run(self, model, percent_to_timestep_function): + self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) + if self.previous_controlnet is not None: + self.previous_controlnet.pre_run(model, percent_to_timestep_function) + + def set_previous_controlnet(self, controlnet): + self.previous_controlnet = controlnet + return self + + def cleanup(self): + if self.previous_controlnet is not None: + self.previous_controlnet.cleanup() + + self.cond_hint = None + self.extra_concat = None + self.timestep_range = None + + def get_models(self): + out = [] + if self.previous_controlnet is not None: + out += self.previous_controlnet.get_models() + return out + + def copy_to(self, c): + c.cond_hint_original = self.cond_hint_original + c.strength = self.strength + c.timestep_percent_range = self.timestep_percent_range + c.global_average_pooling = self.global_average_pooling + c.compression_ratio = self.compression_ratio + c.upscale_algorithm = self.upscale_algorithm + c.latent_format = self.latent_format + c.extra_args = self.extra_args.copy() + c.vae = self.vae + c.extra_conds = self.extra_conds.copy() + c.strength_type = self.strength_type + c.concat_mask = self.concat_mask + c.extra_concat_orig = self.extra_concat_orig.copy() + + def inference_memory_requirements(self, dtype): + if self.previous_controlnet is not None: + return self.previous_controlnet.inference_memory_requirements(dtype) + return 0 + + def control_merge(self, control, control_prev, output_dtype): + out = {'input':[], 'middle':[], 'output': []} + + for key in control: + control_output = control[key] + applied_to = set() + for i in range(len(control_output)): + x = control_output[i] + if x is not None: + if self.global_average_pooling: + x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) + + if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once + applied_to.add(x) + if self.strength_type == StrengthType.CONSTANT: + x *= self.strength + elif self.strength_type == StrengthType.LINEAR_UP: + x *= (self.strength ** float(len(control_output) - i)) + + if output_dtype is not None and x.dtype != output_dtype: + x = x.to(output_dtype) + + out[key].append(x) + + if control_prev is not None: + for x in ['input', 'middle', 'output']: + o = out[x] + for i in range(len(control_prev[x])): + prev_val = control_prev[x][i] + if i >= len(o): + o.append(prev_val) + elif prev_val is not None: + if o[i] is None: + o[i] = prev_val + else: + if o[i].shape[0] < prev_val.shape[0]: + o[i] = prev_val + o[i] + else: + o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue + return out + + def set_extra_arg(self, argument, value=None): + self.extra_args[argument] = value + + +class ControlNet(ControlBase): + def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False): + super().__init__() + self.control_model = control_model + self.load_device = load_device + if control_model is not None: + self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) + + self.compression_ratio = compression_ratio + self.global_average_pooling = global_average_pooling + self.model_sampling_current = None + self.manual_cast_dtype = manual_cast_dtype + self.latent_format = latent_format + self.extra_conds += extra_conds + self.strength_type = strength_type + self.concat_mask = concat_mask + + def get_control(self, x_noisy, t, cond, batched_number): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return None + + dtype = self.control_model.dtype + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + + if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.cond_hint = None + compression_ratio = self.compression_ratio + if self.vae is not None: + compression_ratio *= self.vae.downscale_ratio + else: + if self.latent_format is not None: + raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.") + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center") + if self.vae is not None: + loaded_models = comfy.model_management.loaded_models(only_currently_used=True) + self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1)) + comfy.model_management.load_models_gpu(loaded_models) + if self.latent_format is not None: + self.cond_hint = self.latent_format.process_in(self.cond_hint) + if len(self.extra_concat_orig) > 0: + to_concat = [] + for c in self.extra_concat_orig: + c = c.to(self.cond_hint.device) + c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center") + to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0])) + self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1) + + self.cond_hint = self.cond_hint.to(device=x_noisy.device, dtype=dtype) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + + context = cond.get('crossattn_controlnet', cond['c_crossattn']) + extra = self.extra_args.copy() + for c in self.extra_conds: + temp = cond.get(c, None) + if temp is not None: + extra[c] = temp.to(dtype) + + timestep = self.model_sampling_current.timestep(t) + x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) + + control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra) + return self.control_merge(control, control_prev, output_dtype=None) + + def copy(self): + c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) + c.control_model = self.control_model + c.control_model_wrapped = self.control_model_wrapped + self.copy_to(c) + return c + + def get_models(self): + out = super().get_models() + out.append(self.control_model_wrapped) + return out + + def pre_run(self, model, percent_to_timestep_function): + super().pre_run(model, percent_to_timestep_function) + self.model_sampling_current = model.model_sampling + + def cleanup(self): + self.model_sampling_current = None + super().cleanup() + +class ControlLoraOps: + class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp): + def __init__(self, in_features: int, out_features: int, bias: bool = True, + device=None, dtype=None) -> None: + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = None + self.up = None + self.down = None + self.bias = None + + def forward(self, input): + weight, bias = comfy.ops.cast_bias_weight(self, input) + if self.up is not None: + return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) + else: + return torch.nn.functional.linear(input, weight, bias) + + class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros', + device=None, + dtype=None + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.transposed = False + self.output_padding = 0 + self.groups = groups + self.padding_mode = padding_mode + + self.weight = None + self.bias = None + self.up = None + self.down = None + + + def forward(self, input): + weight, bias = comfy.ops.cast_bias_weight(self, input) + if self.up is not None: + return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) + else: + return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) + + +class ControlLora(ControlNet): + def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options + ControlBase.__init__(self) + self.control_weights = control_weights + self.global_average_pooling = global_average_pooling + self.extra_conds += ["y"] + + def pre_run(self, model, percent_to_timestep_function): + super().pre_run(model, percent_to_timestep_function) + controlnet_config = model.model_config.unet_config.copy() + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] + self.manual_cast_dtype = model.manual_cast_dtype + dtype = model.get_dtype() + if self.manual_cast_dtype is None: + class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init): + pass + else: + class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast): + pass + dtype = self.manual_cast_dtype + + controlnet_config["operations"] = control_lora_ops + controlnet_config["dtype"] = dtype + self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) + self.control_model.to(comfy.model_management.get_torch_device()) + diffusion_model = model.diffusion_model + sd = diffusion_model.state_dict() + cm = self.control_model.state_dict() + + for k in sd: + weight = sd[k] + try: + comfy.utils.set_attr_param(self.control_model, k, weight) + except: + pass + + for k in self.control_weights: + if k not in {"lora_controlnet"}: + comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) + + def copy(self): + c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) + self.copy_to(c) + return c + + def cleanup(self): + del self.control_model + self.control_model = None + super().cleanup() + + def get_models(self): + out = ControlBase.get_models(self) + return out + + def inference_memory_requirements(self, dtype): + return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) + +def controlnet_config(sd, model_options={}): + model_config = comfy.model_detection.model_config_from_unet(sd, "", True) + + unet_dtype = model_options.get("dtype", None) + if unet_dtype is None: + weight_dtype = comfy.utils.weight_dtype(sd) + + supported_inference_dtypes = list(model_config.supported_inference_dtypes) + if weight_dtype is not None: + supported_inference_dtypes.append(weight_dtype) + + unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes) + + load_device = comfy.model_management.get_torch_device() + manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) + + operations = model_options.get("custom_operations", None) + if operations is None: + operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True) + + offload_device = comfy.model_management.unet_offload_device() + return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device + +def controlnet_load_state_dict(control_model, sd): + missing, unexpected = control_model.load_state_dict(sd, strict=False) + + if len(missing) > 0: + logging.warning("missing controlnet keys: {}".format(missing)) + + if len(unexpected) > 0: + logging.debug("unexpected controlnet keys: {}".format(unexpected)) + return control_model + +def load_controlnet_mmdit(sd, model_options={}): + new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options) + num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.') + for k in sd: + new_sd[k] = sd[k] + + concat_mask = False + control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1] + if control_latent_channels == 17: #inpaint controlnet + concat_mask = True + + control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) + control_model = controlnet_load_state_dict(control_model, new_sd) + + latent_format = comfy.latent_formats.SD3() + latent_format.shift_factor = 0 #SD3 controlnet weirdness + control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype) + return control + + +def load_controlnet_hunyuandit(controlnet_data, model_options={}): + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options) + + control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype) + control_model = controlnet_load_state_dict(control_model, controlnet_data) + + latent_format = comfy.latent_formats.SDXL() + extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img'] + control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT) + return control + +def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}): + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options) + control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) + control_model = controlnet_load_state_dict(control_model, sd) + extra_conds = ['y', 'guidance'] + control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) + return control + +def load_controlnet_flux_instantx(sd, model_options={}): + new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options) + for k in sd: + new_sd[k] = sd[k] + + num_union_modes = 0 + union_cnet = "controlnet_mode_embedder.weight" + if union_cnet in new_sd: + num_union_modes = new_sd[union_cnet].shape[0] + + control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4 + concat_mask = False + if control_latent_channels == 17: + concat_mask = True + + control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) + control_model = controlnet_load_state_dict(control_model, new_sd) + + latent_format = comfy.latent_formats.Flux() + extra_conds = ['y', 'guidance'] + control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) + return control + +def convert_mistoline(sd): + return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."}) + + +def load_controlnet_state_dict(state_dict, model=None, model_options={}): + controlnet_data = state_dict + if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT + return load_controlnet_hunyuandit(controlnet_data, model_options=model_options) + + if "lora_controlnet" in controlnet_data: + return ControlLora(controlnet_data, model_options=model_options) + + controlnet_config = None + supported_inference_dtypes = None + + if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format + controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data) + diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config) + diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" + diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + k_in = "controlnet_down_blocks.{}{}".format(count, s) + k_out = "zero_convs.{}.0{}".format(count, s) + if k_in not in controlnet_data: + loop = False + break + diffusers_keys[k_in] = k_out + count += 1 + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + if count == 0: + k_in = "controlnet_cond_embedding.conv_in{}".format(s) + else: + k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) + k_out = "input_hint_block.{}{}".format(count * 2, s) + if k_in not in controlnet_data: + k_in = "controlnet_cond_embedding.conv_out{}".format(s) + loop = False + diffusers_keys[k_in] = k_out + count += 1 + + new_sd = {} + for k in diffusers_keys: + if k in controlnet_data: + new_sd[diffusers_keys[k]] = controlnet_data.pop(k) + + if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet + controlnet_config["union_controlnet_num_control_type"] = controlnet_data["task_embedding"].shape[0] + for k in list(controlnet_data.keys()): + new_k = k.replace('.attn.in_proj_', '.attn.in_proj.') + new_sd[new_k] = controlnet_data.pop(k) + + leftover_keys = controlnet_data.keys() + if len(leftover_keys) > 0: + logging.warning("leftover keys: {}".format(leftover_keys)) + controlnet_data = new_sd + elif "controlnet_blocks.0.weight" in controlnet_data: + if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data: + return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options) + elif "pos_embed_input.proj.weight" in controlnet_data: + return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet + elif "controlnet_x_embedder.weight" in controlnet_data: + return load_controlnet_flux_instantx(controlnet_data, model_options=model_options) + elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux + return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options) + + pth_key = 'control_model.zero_convs.0.0.weight' + pth = False + key = 'zero_convs.0.0.weight' + if pth_key in controlnet_data: + pth = True + key = pth_key + prefix = "control_model." + elif key in controlnet_data: + prefix = "" + else: + net = load_t2i_adapter(controlnet_data, model_options=model_options) + if net is None: + logging.error("error could not detect control model type.") + return net + + if controlnet_config is None: + model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True) + supported_inference_dtypes = list(model_config.supported_inference_dtypes) + controlnet_config = model_config.unet_config + + unet_dtype = model_options.get("dtype", None) + if unet_dtype is None: + weight_dtype = comfy.utils.weight_dtype(controlnet_data) + + if supported_inference_dtypes is None: + supported_inference_dtypes = [comfy.model_management.unet_dtype()] + + if weight_dtype is not None: + supported_inference_dtypes.append(weight_dtype) + + unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes) + + load_device = comfy.model_management.get_torch_device() + + manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) + operations = model_options.get("custom_operations", None) + if operations is None: + operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype) + + controlnet_config["operations"] = operations + controlnet_config["dtype"] = unet_dtype + controlnet_config["device"] = comfy.model_management.unet_offload_device() + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] + control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) + + if pth: + if 'difference' in controlnet_data: + if model is not None: + comfy.model_management.load_models_gpu([model]) + model_sd = model.model_state_dict() + for x in controlnet_data: + c_m = "control_model." + if x.startswith(c_m): + sd_key = "diffusion_model.{}".format(x[len(c_m):]) + if sd_key in model_sd: + cd = controlnet_data[x] + cd += model_sd[sd_key].type(cd.dtype).to(cd.device) + else: + logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.control_model = control_model + missing, unexpected = w.load_state_dict(controlnet_data, strict=False) + else: + missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) + + if len(missing) > 0: + logging.warning("missing controlnet keys: {}".format(missing)) + + if len(unexpected) > 0: + logging.debug("unexpected controlnet keys: {}".format(unexpected)) + + global_average_pooling = model_options.get("global_average_pooling", False) + control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) + return control + +def load_controlnet(ckpt_path, model=None, model_options={}): + if "global_average_pooling" not in model_options: + filename = os.path.splitext(ckpt_path)[0] + if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling + model_options["global_average_pooling"] = True + + cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options) + if cnet is None: + logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path)) + return cnet + +class T2IAdapter(ControlBase): + def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None): + super().__init__() + self.t2i_model = t2i_model + self.channels_in = channels_in + self.control_input = None + self.compression_ratio = compression_ratio + self.upscale_algorithm = upscale_algorithm + if device is None: + device = comfy.model_management.get_torch_device() + self.device = device + + def scale_image_to(self, width, height): + unshuffle_amount = self.t2i_model.unshuffle_amount + width = math.ceil(width / unshuffle_amount) * unshuffle_amount + height = math.ceil(height / unshuffle_amount) * unshuffle_amount + return width, height + + def get_control(self, x_noisy, t, cond, batched_number): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return None + + if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.control_input = None + self.cond_hint = None + width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio) + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device) + if self.channels_in == 1 and self.cond_hint.shape[1] > 1: + self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + if self.control_input is None: + self.t2i_model.to(x_noisy.dtype) + self.t2i_model.to(self.device) + self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) + self.t2i_model.cpu() + + control_input = {} + for k in self.control_input: + control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k])) + + return self.control_merge(control_input, control_prev, x_noisy.dtype) + + def copy(self): + c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm) + self.copy_to(c) + return c + +def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options + compression_ratio = 8 + upscale_algorithm = 'nearest-exact' + + if 'adapter' in t2i_data: + t2i_data = t2i_data['adapter'] + if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format + prefix_replace = {} + for i in range(4): + for j in range(2): + prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) + prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2) + prefix_replace["adapter."] = "" + t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace) + keys = t2i_data.keys() + + if "body.0.in_conv.weight" in keys: + cin = t2i_data['body.0.in_conv.weight'].shape[1] + model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) + elif 'conv_in.weight' in keys: + cin = t2i_data['conv_in.weight'].shape[1] + channel = t2i_data['conv_in.weight'].shape[0] + ksize = t2i_data['body.0.block2.weight'].shape[2] + use_conv = False + down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) + if len(down_opts) > 0: + use_conv = True + xl = False + if cin == 256 or cin == 768: + xl = True + model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) + elif "backbone.0.0.weight" in keys: + model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) + compression_ratio = 32 + upscale_algorithm = 'bilinear' + elif "backbone.10.blocks.0.weight" in keys: + model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) + compression_ratio = 1 + upscale_algorithm = 'nearest-exact' + else: + return None + + missing, unexpected = model_ad.load_state_dict(t2i_data) + if len(missing) > 0: + logging.warning("t2i missing {}".format(missing)) + + if len(unexpected) > 0: + logging.debug("t2i unexpected {}".format(unexpected)) + + return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py new file mode 100644 index 0000000..ed2a45f --- /dev/null +++ b/comfy/diffusers_convert.py @@ -0,0 +1,281 @@ +import re +import torch +import logging + +# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py + +# =================# +# UNet Conversion # +# =================# + +unet_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), +] + +unet_conversion_map_resnet = [ + # (stable-diffusion, HF Diffusers) + ("in_layers.0", "norm1"), + ("in_layers.2", "conv1"), + ("out_layers.0", "norm2"), + ("out_layers.3", "conv2"), + ("emb_layers.1", "time_emb_proj"), + ("skip_connection", "conv_shortcut"), +] + +unet_conversion_map_layer = [] +# hardcoded number of downblocks and resnets/attentions... +# would need smarter logic for other networks. +for i in range(4): + # loop over downblocks/upblocks + + for j in range(2): + # loop over resnets/attentions for downblocks + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(3): + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + if i > 0: + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) + +hf_mid_atn_prefix = "mid_block.attentions.0." +sd_mid_atn_prefix = "middle_block.1." +unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + +for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2 * j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + +def convert_unet_state_dict(unet_state_dict): + # buyer beware: this is a *brittle* function, + # and correct output requires that all of these pieces interact in + # the exact order in which I have arranged them. + mapping = {k: k for k in unet_state_dict.keys()} + for sd_name, hf_name in unet_conversion_map: + mapping[hf_name] = sd_name + for k, v in mapping.items(): + if "resnets" in k: + for sd_part, hf_part in unet_conversion_map_resnet: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + for sd_part, hf_part in unet_conversion_map_layer: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} + return new_state_dict + + +# ================# +# VAE Conversion # +# ================# + +vae_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("nin_shortcut", "conv_shortcut"), + ("norm_out", "conv_norm_out"), + ("mid.attn_1.", "mid_block.attentions.0."), +] + +for i in range(4): + # down_blocks have two resnets + for j in range(2): + hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." + sd_down_prefix = f"encoder.down.{i}.block.{j}." + vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) + + if i < 3: + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." + sd_downsample_prefix = f"down.{i}.downsample." + vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) + + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"up.{3 - i}.upsample." + vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) + + # up_blocks have three resnets + # also, up blocks in hf are numbered in reverse from sd + for j in range(3): + hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." + sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." + vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) + +# this part accounts for mid blocks in both the encoder and the decoder +for i in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{i}." + sd_mid_res_prefix = f"mid.block_{i + 1}." + vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) + +vae_conversion_map_attn = [ + # (stable-diffusion, HF Diffusers) + ("norm.", "group_norm."), + ("q.", "query."), + ("k.", "key."), + ("v.", "value."), + ("q.", "to_q."), + ("k.", "to_k."), + ("v.", "to_v."), + ("proj_out.", "to_out.0."), + ("proj_out.", "proj_attn."), +] + + +def reshape_weight_for_sd(w): + # convert HF linear weights to SD conv2d weights + return w.reshape(*w.shape, 1, 1) + + +def convert_vae_state_dict(vae_state_dict): + mapping = {k: k for k in vae_state_dict.keys()} + for k, v in mapping.items(): + for sd_part, hf_part in vae_conversion_map: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + if "attentions" in k: + for sd_part, hf_part in vae_conversion_map_attn: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} + weights_to_convert = ["q", "k", "v", "proj_out"] + for k, v in new_state_dict.items(): + for weight_name in weights_to_convert: + if f"mid.attn_1.{weight_name}.weight" in k: + logging.debug(f"Reshaping {k} for SD format") + new_state_dict[k] = reshape_weight_for_sd(v) + return new_state_dict + + +# =========================# +# Text Encoder Conversion # +# =========================# + + +textenc_conversion_lst = [ + # (stable-diffusion, HF Diffusers) + ("resblocks.", "text_model.encoder.layers."), + ("ln_1", "layer_norm1"), + ("ln_2", "layer_norm2"), + (".c_fc.", ".fc1."), + (".c_proj.", ".fc2."), + (".attn", ".self_attn"), + ("ln_final.", "transformer.text_model.final_layer_norm."), + ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), +] +protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + +# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp +code2idx = {"q": 0, "k": 1, "v": 2} + +# This function exists because at the time of writing torch.cat can't do fp8 with cuda +def cat_tensors(tensors): + x = 0 + for t in tensors: + x += t.shape[0] + + shape = [x] + list(tensors[0].shape)[1:] + out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype) + + x = 0 + for t in tensors: + out[x:x + t.shape[0]] = t + x += t.shape[0] + + return out + +def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): + new_state_dict = {} + capture_qkv_weight = {} + capture_qkv_bias = {} + for k, v in text_enc_dict.items(): + if not k.startswith(prefix): + continue + if ( + k.endswith(".self_attn.q_proj.weight") + or k.endswith(".self_attn.k_proj.weight") + or k.endswith(".self_attn.v_proj.weight") + ): + k_pre = k[: -len(".q_proj.weight")] + k_code = k[-len("q_proj.weight")] + if k_pre not in capture_qkv_weight: + capture_qkv_weight[k_pre] = [None, None, None] + capture_qkv_weight[k_pre][code2idx[k_code]] = v + continue + + if ( + k.endswith(".self_attn.q_proj.bias") + or k.endswith(".self_attn.k_proj.bias") + or k.endswith(".self_attn.v_proj.bias") + ): + k_pre = k[: -len(".q_proj.bias")] + k_code = k[-len("q_proj.bias")] + if k_pre not in capture_qkv_bias: + capture_qkv_bias[k_pre] = [None, None, None] + capture_qkv_bias[k_pre][code2idx[k_code]] = v + continue + + text_proj = "transformer.text_projection.weight" + if k.endswith(text_proj): + new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous() + else: + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v + + for k_pre, tensors in capture_qkv_weight.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors) + + for k_pre, tensors in capture_qkv_bias.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors) + + return new_state_dict + + +def convert_text_enc_state_dict(text_enc_dict): + return text_enc_dict + + diff --git a/comfy/diffusers_load.py b/comfy/diffusers_load.py new file mode 100644 index 0000000..56e63a7 --- /dev/null +++ b/comfy/diffusers_load.py @@ -0,0 +1,36 @@ +import os + +import comfy.sd + +def first_file(path, filenames): + for f in filenames: + p = os.path.join(path, f) + if os.path.exists(p): + return p + return None + +def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): + diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] + unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) + vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) + + text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] + text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) + text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) + + text_encoder_paths = [text_encoder1_path] + if text_encoder2_path is not None: + text_encoder_paths.append(text_encoder2_path) + + unet = comfy.sd.load_diffusion_model(unet_path) + + clip = None + if output_clip: + clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) + + vae = None + if output_vae: + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + + return (unet, clip, vae) diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py new file mode 100644 index 0000000..3ab42c6 --- /dev/null +++ b/comfy/extra_samplers/uni_pc.py @@ -0,0 +1,875 @@ +#code taken from: https://github.com/wl-zhao/UniPC and modified + +import torch +import torch.nn.functional as F +import math + +from tqdm.auto import trange, tqdm + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + r"""Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + output = model(x, t_input, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class UniPC: + def __init__( + self, + model_fn, + noise_schedule, + predict_x0=True, + thresholding=False, + max_val=1., + variant='bh1', + ): + """Construct a UniPC. + + We support both data_prediction and noise_prediction. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.variant = variant + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + + def dynamic_thresholding_fn(self, x0, t=None): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = steps + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): + if len(t.shape) == 0: + t = t.view(-1) + if 'bh' in self.variant: + return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + else: + assert self.variant == 'vary_coeff' + return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + + def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): + print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_t = ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + K = len(rks) + # build C matrix + C = [] + + col = torch.ones_like(rks) + for k in range(1, K + 1): + C.append(col) + col = col * rks / (k + 1) + C = torch.stack(C, dim=1) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + C_inv_p = torch.linalg.inv(C[:-1, :-1]) + A_p = C_inv_p + + if use_corrector: + print('using corrector') + C_inv = torch.linalg.inv(C) + A_c = C_inv + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) + h_phi_ks = [] + factorial_k = 1 + h_phi_k = h_phi_1 + for k in range(1, K + 2): + h_phi_ks.append(h_phi_k) + h_phi_k = h_phi_k / hh - 1 / factorial_k + factorial_k *= (k + 1) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + else: + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + x_t_ = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * h_phi_1) * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + return x_t, model_t + + def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): + # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') + ns = self.noise_schedule + assert order <= len(model_prev_list) + dims = x.dim() + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = ((lambda_prev_i - lambda_prev_0) / h)[0] + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + R = [] + b = [] + + hh = -h[0] if self.predict_x0 else h[0] + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.variant == 'bh1': + B_h = hh + elif self.variant == 'bh2': + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= (i + 1) + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=x.device) + + # now predictor + use_predictor = len(D1s) > 0 and x_t is None + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + if x_t is None: + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], device=b.device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) + else: + D1s = None + + if use_corrector: + # print('using corrector') + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], device=b.device) + else: + rhos_c = torch.linalg.solve(R, b) + + model_t = None + if self.predict_x0: + x_t_ = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 + ) + + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 + ) + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + return x_t, model_t + + + def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False + ): + # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + # t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + steps = len(timesteps) - 1 + if method == 'multistep': + assert steps >= order + # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + # with torch.no_grad(): + for step_index in trange(steps, disable=disable_pbar): + if step_index == 0: + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + elif step_index < order: + init_order = step_index + # Init the first `order` values by lower order multistep DPM-Solver. + # for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list.append(model_x) + t_prev_list.append(vec_t) + else: + extra_final_step = 0 + if step_index == (steps - 1): + extra_final_step = 1 + for step in range(step_index, step_index + 1 + extra_final_step): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + # print('this step order:', step_order) + if step == steps: + # print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list[-1] = model_x + if callback is not None: + callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]}) + else: + raise NotImplementedError() + # if denoise_to_zero: + # x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] + + +class SigmaConvert: + schedule = "" + def marginal_log_mean_coeff(self, sigma): + return 0.5 * torch.log(1 / ((sigma * sigma) + 1)) + + def marginal_alpha(self, t): + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + +def predict_eps_sigma(model, input, sigma_in, **kwargs): + sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1)) + input = input * ((sigma ** 2 + 1.0) ** 0.5) + return (input - model(input, sigma_in, **kwargs)) / sigma + + +def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'): + timesteps = sigmas.clone() + if sigmas[-1] == 0: + timesteps = sigmas[:] + timesteps[-1] = 0.001 + else: + timesteps = sigmas.clone() + ns = SigmaConvert() + + noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0) + model_type = "noise" + + model_fn = model_wrapper( + lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs), + ns, + model_type=model_type, + guidance_type="uncond", + model_kwargs=extra_args, + ) + + order = min(3, len(timesteps) - 2) + uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant) + x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) + x /= ns.marginal_alpha(timesteps[-1]) + return x + +def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False): + return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2') \ No newline at end of file diff --git a/comfy/float.py b/comfy/float.py new file mode 100644 index 0000000..521316f --- /dev/null +++ b/comfy/float.py @@ -0,0 +1,67 @@ +import torch + +def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None): + mantissa_scaled = torch.where( + normal_mask, + (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS), + (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS))) + ) + + mantissa_scaled += torch.rand(mantissa_scaled.size(), dtype=mantissa_scaled.dtype, layout=mantissa_scaled.layout, device=mantissa_scaled.device, generator=generator) + return mantissa_scaled.floor() / (2**MANTISSA_BITS) + +#Not 100% sure about this +def manual_stochastic_round_to_float8(x, dtype, generator=None): + if dtype == torch.float8_e4m3fn: + EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 4, 3, 7 + elif dtype == torch.float8_e5m2: + EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 5, 2, 15 + else: + raise ValueError("Unsupported dtype") + + x = x.half() + sign = torch.sign(x) + abs_x = x.abs() + sign = torch.where(abs_x == 0, 0, sign) + + # Combine exponent calculation and clamping + exponent = torch.clamp( + torch.floor(torch.log2(abs_x)) + EXPONENT_BIAS, + 0, 2**EXPONENT_BITS - 1 + ) + + # Combine mantissa calculation and rounding + normal_mask = ~(exponent == 0) + + abs_x[:] = calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=generator) + + sign *= torch.where( + normal_mask, + (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x), + (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x + ) + + inf = torch.finfo(dtype) + torch.clamp(sign, min=inf.min, max=inf.max, out=sign) + return sign + + + +def stochastic_rounding(value, dtype, seed=0): + if dtype == torch.float32: + return value.to(dtype=torch.float32) + if dtype == torch.float16: + return value.to(dtype=torch.float16) + if dtype == torch.bfloat16: + return value.to(dtype=torch.bfloat16) + if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2: + generator = torch.Generator(device=value.device) + generator.manual_seed(seed) + output = torch.empty_like(value, dtype=dtype) + num_slices = max(1, (value.numel() / (4096 * 4096))) + slice_size = max(1, round(value.shape[0] / num_slices)) + for i in range(0, value.shape[0], slice_size): + output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype, generator=generator)) + return output + + return value.to(dtype=dtype) diff --git a/comfy/gligen.py b/comfy/gligen.py new file mode 100644 index 0000000..5925227 --- /dev/null +++ b/comfy/gligen.py @@ -0,0 +1,343 @@ +import torch +from torch import nn +from .ldm.modules.attention import CrossAttention +from inspect import isfunction +import comfy.ops +ops = comfy.ops.manual_cast + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = ops.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * torch.nn.functional.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + ops.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + ops.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class GatedCrossAttentionDense(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + self.attn = CrossAttention( + query_dim=query_dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + operations=ops) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + x = x + self.scale * \ + torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs) + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class GatedSelfAttentionDense(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj + # feature + self.linear = ops.Linear(context_dim, query_dim) + + self.attn = CrossAttention( + query_dim=query_dim, + context_dim=query_dim, + heads=n_heads, + dim_head=d_head, + operations=ops) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + N_visual = x.shape[1] + objs = self.linear(objs) + + x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn( + self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :] + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class GatedSelfAttentionDense2(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj + # feature + self.linear = ops.Linear(context_dim, query_dim) + + self.attn = CrossAttention( + query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + B, N_visual, _ = x.shape + B, N_ground, _ = objs.shape + + objs = self.linear(objs) + + # sanity check + size_v = math.sqrt(N_visual) + size_g = math.sqrt(N_ground) + assert int(size_v) == size_v, "Visual tokens must be square rootable" + assert int(size_g) == size_g, "Grounding tokens must be square rootable" + size_v = int(size_v) + size_g = int(size_g) + + # select grounding token and resize it to visual token size as residual + out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[ + :, N_visual:, :] + out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g) + out = torch.nn.functional.interpolate( + out, (size_v, size_v), mode='bicubic') + residual = out.reshape(B, -1, N_visual).permute(0, 2, 1) + + # add residual to visual feature + x = x + self.scale * torch.tanh(self.alpha_attn) * residual + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class FourierEmbedder(): + def __init__(self, num_freqs=64, temperature=100): + + self.num_freqs = num_freqs + self.temperature = temperature + self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs) + + @torch.no_grad() + def __call__(self, x, cat_dim=-1): + "x: arbitrary shape of tensor. dim: cat dim" + out = [] + for freq in self.freq_bands: + out.append(torch.sin(freq * x)) + out.append(torch.cos(freq * x)) + return torch.cat(out, cat_dim) + + +class PositionNet(nn.Module): + def __init__(self, in_dim, out_dim, fourier_freqs=8): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) + self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy + + self.linears = nn.Sequential( + ops.Linear(self.in_dim + self.position_dim, 512), + nn.SiLU(), + ops.Linear(512, 512), + nn.SiLU(), + ops.Linear(512, out_dim), + ) + + self.null_positive_feature = torch.nn.Parameter( + torch.zeros([self.in_dim])) + self.null_position_feature = torch.nn.Parameter( + torch.zeros([self.position_dim])) + + def forward(self, boxes, masks, positive_embeddings): + B, N, _ = boxes.shape + masks = masks.unsqueeze(-1) + positive_embeddings = positive_embeddings + + # embedding position (it may includes padding as placeholder) + xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C + + # learnable null embedding + positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) + xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) + + # replace padding with learnable null embedding + positive_embeddings = positive_embeddings * \ + masks + (1 - masks) * positive_null + xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null + + objs = self.linears( + torch.cat([positive_embeddings, xyxy_embedding], dim=-1)) + assert objs.shape == torch.Size([B, N, self.out_dim]) + return objs + + +class Gligen(nn.Module): + def __init__(self, modules, position_net, key_dim): + super().__init__() + self.module_list = nn.ModuleList(modules) + self.position_net = position_net + self.key_dim = key_dim + self.max_objs = 30 + self.current_device = torch.device("cpu") + + def _set_position(self, boxes, masks, positive_embeddings): + objs = self.position_net(boxes, masks, positive_embeddings) + def func(x, extra_options): + key = extra_options["transformer_index"] + module = self.module_list[key] + return module(x, objs.to(device=x.device, dtype=x.dtype)) + return func + + def set_position(self, latent_image_shape, position_params, device): + batch, c, h, w = latent_image_shape + masks = torch.zeros([self.max_objs], device="cpu") + boxes = [] + positive_embeddings = [] + for p in position_params: + x1 = (p[4]) / w + y1 = (p[3]) / h + x2 = (p[4] + p[2]) / w + y2 = (p[3] + p[1]) / h + masks[len(boxes)] = 1.0 + boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)] + positive_embeddings += [p[0]] + append_boxes = [] + append_conds = [] + if len(boxes) < self.max_objs: + append_boxes = [torch.zeros( + [self.max_objs - len(boxes), 4], device="cpu")] + append_conds = [torch.zeros( + [self.max_objs - len(boxes), self.key_dim], device="cpu")] + + box_out = torch.cat( + boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1) + masks = masks.unsqueeze(0).repeat(batch, 1) + conds = torch.cat(positive_embeddings + + append_conds).unsqueeze(0).repeat(batch, 1, 1) + return self._set_position( + box_out.to(device), + masks.to(device), + conds.to(device)) + + def set_empty(self, latent_image_shape, device): + batch, c, h, w = latent_image_shape + masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1) + box_out = torch.zeros([self.max_objs, 4], + device="cpu").repeat(batch, 1, 1) + conds = torch.zeros([self.max_objs, self.key_dim], + device="cpu").repeat(batch, 1, 1) + return self._set_position( + box_out.to(device), + masks.to(device), + conds.to(device)) + + +def load_gligen(sd): + sd_k = sd.keys() + output_list = [] + key_dim = 768 + for a in ["input_blocks", "middle_block", "output_blocks"]: + for b in range(20): + k_temp = filter(lambda k: "{}.{}.".format(a, b) + in k and ".fuser." in k, sd_k) + k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp) + + n_sd = {} + for k in k_temp: + n_sd[k[1]] = sd[k[0]] + if len(n_sd) > 0: + query_dim = n_sd["linear.weight"].shape[0] + key_dim = n_sd["linear.weight"].shape[1] + + if key_dim == 768: # SD1.x + n_heads = 8 + d_head = query_dim // n_heads + else: + d_head = 64 + n_heads = query_dim // d_head + + gated = GatedSelfAttentionDense( + query_dim, key_dim, n_heads, d_head) + gated.load_state_dict(n_sd, strict=False) + output_list.append(gated) + + if "position_net.null_positive_feature" in sd_k: + in_dim = sd["position_net.null_positive_feature"].shape[0] + out_dim = sd["position_net.linears.4.weight"].shape[0] + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.position_net = PositionNet(in_dim, out_dim) + w.load_state_dict(sd, strict=False) + + gligen = Gligen(output_list, w.position_net, key_dim) + return gligen diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py new file mode 100644 index 0000000..6074106 --- /dev/null +++ b/comfy/k_diffusion/deis.py @@ -0,0 +1,121 @@ +#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py +#under Apache 2 license +import torch +import numpy as np + +# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis). +############################# +### Utils for DEIS solver ### +############################# +#---------------------------------------------------------------------------- +# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS. + +def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80): + vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t ** 2) + beta_min * t) - 1) ** 0.5 + vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d + vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1) + vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d + t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu()) + return t_steps, vp_beta_min, vp_beta_d + vp_beta_min + +#---------------------------------------------------------------------------- + +def cal_poly(prev_t, j, taus): + poly = 1 + for k in range(prev_t.shape[0]): + if k == j: + continue + poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k]) + return poly + +#---------------------------------------------------------------------------- +# Transfer from t to alpha_t. + +def t2alpha_fn(beta_0, beta_1, t): + return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0) + +#---------------------------------------------------------------------------- + +def cal_intergrand(beta_0, beta_1, taus): + with torch.inference_mode(mode=False): + taus = taus.clone() + beta_0 = beta_0.clone() + beta_1 = beta_1.clone() + with torch.enable_grad(): + taus.requires_grad_(True) + alpha = t2alpha_fn(beta_0, beta_1, taus) + log_alpha = alpha.log() + log_alpha.sum().backward() + d_log_alpha_dtau = taus.grad + integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha)) + return integrand + +#---------------------------------------------------------------------------- + +def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'): + """ + Get the coefficient list for DEIS sampling. + + Args: + t_steps: A pytorch tensor. The time steps for sampling. + max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4 + N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'. + deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS. + Returns: + A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True. + """ + if deis_mode == 'tab': + t_steps, beta_0, beta_1 = edm2t(t_steps) + C = [] + for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): + order = min(i+1, max_order) + if order == 1: + C.append([]) + else: + taus = torch.linspace(t_cur, t_next, N) # split the interval for integral appximation + dtau = (t_next - t_cur) / N + prev_t = t_steps[[i - k for k in range(order)]] + coeff_temp = [] + integrand = cal_intergrand(beta_0, beta_1, taus) + for j in range(order): + poly = cal_poly(prev_t, j, taus) + coeff_temp.append(torch.sum(integrand * poly) * dtau) + C.append(coeff_temp) + + elif deis_mode == 'rhoab': + # Analytical solution, second order + def get_def_intergral_2(a, b, start, end, c): + coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b + return coeff / ((c - a) * (c - b)) + + # Analytical solution, third order + def get_def_intergral_3(a, b, c, start, end, d): + coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \ + + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c + return coeff / ((d - a) * (d - b) * (d - c)) + + C = [] + for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): + order = min(i, max_order) + if order == 0: + C.append([]) + else: + prev_t = t_steps[[i - k for k in range(order+1)]] + if order == 1: + coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1])) + coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur)) + coeff_temp = [coeff_cur, coeff_prev1] + elif order == 2: + coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur) + coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1]) + coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2]) + coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2] + elif order == 3: + coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur) + coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1]) + coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2]) + coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3]) + coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3] + C.append(coeff_temp) + return C + diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py new file mode 100644 index 0000000..ffdd888 --- /dev/null +++ b/comfy/k_diffusion/sampling.py @@ -0,0 +1,1211 @@ +import math + +from scipy import integrate +import torch +from torch import nn +import torchsde +from tqdm.auto import trange, tqdm + +from . import utils +from . import deis +import comfy.model_patcher +import comfy.model_sampling + +def append_zero(x): + return torch.cat([x, x.new_zeros([1])]) + + +def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): + """Constructs the noise schedule of Karras et al. (2022).""" + ramp = torch.linspace(0, 1, n, device=device) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return append_zero(sigmas).to(device) + + +def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): + """Constructs an exponential noise schedule.""" + sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() + return append_zero(sigmas) + + +def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): + """Constructs an polynomial in log sigma noise schedule.""" + ramp = torch.linspace(1, 0, n, device=device) ** rho + sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) + return append_zero(sigmas) + + +def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): + """Constructs a continuous VP noise schedule.""" + t = torch.linspace(1, eps_s, n, device=device) + sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) + return append_zero(sigmas) + + +def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'): + """Constructs the noise schedule proposed by Tiankai et al. (2024). """ + epsilon = 1e-5 # avoid log(0) + x = torch.linspace(0, 1, n, device=device) + clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max) + lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon) + sigmas = clamp(torch.exp(lmb)) + return sigmas + + + +def to_d(x, sigma, denoised): + """Converts a denoiser output to a Karras ODE derivative.""" + return (x - denoised) / utils.append_dims(sigma, x.ndim) + + +def get_ancestral_step(sigma_from, sigma_to, eta=1.): + """Calculates the noise level (sigma_down) to step down to and the amount + of noise to add (sigma_up) when doing an ancestral sampling step.""" + if not eta: + return sigma_to, 0. + sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) + sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 + return sigma_down, sigma_up + + +def default_noise_sampler(x): + return lambda sigma, sigma_next: torch.randn_like(x) + + +class BatchedBrownianTree: + """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" + + def __init__(self, x, t0, t1, seed=None, **kwargs): + self.cpu_tree = True + if "cpu" in kwargs: + self.cpu_tree = kwargs.pop("cpu") + t0, t1, self.sign = self.sort(t0, t1) + w0 = kwargs.get('w0', torch.zeros_like(x)) + if seed is None: + seed = torch.randint(0, 2 ** 63 - 1, []).item() + self.batched = True + try: + assert len(seed) == x.shape[0] + w0 = w0[0] + except TypeError: + seed = [seed] + self.batched = False + if self.cpu_tree: + self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed] + else: + self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] + + @staticmethod + def sort(a, b): + return (a, b, 1) if a < b else (b, a, -1) + + def __call__(self, t0, t1): + t0, t1, sign = self.sort(t0, t1) + if self.cpu_tree: + w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign) + else: + w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) + + return w if self.batched else w[0] + + +class BrownianTreeNoiseSampler: + """A noise sampler backed by a torchsde.BrownianTree. + + Args: + x (Tensor): The tensor whose shape, device and dtype to use to generate + random samples. + sigma_min (float): The low end of the valid interval. + sigma_max (float): The high end of the valid interval. + seed (int or List[int]): The random seed. If a list of seeds is + supplied instead of a single integer, then the noise sampler will + use one BrownianTree per batch item, each with its own seed. + transform (callable): A function that maps sigma to the sampler's + internal timestep. + """ + + def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): + self.transform = transform + t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) + self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) + + def __call__(self, sigma, sigma_next): + t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) + return self.tree(t0, t1) / (t1 - t0).abs().sqrt() + + +@torch.no_grad() +def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + # Euler method + x = x + d * dt + return x + + +@torch.no_grad() +def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): + return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + +@torch.no_grad() +def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta + sigma_down = sigmas[i+1] * downstep_ratio + alpha_ip1 = 1 - sigmas[i+1] + alpha_down = 1 - sigma_down + renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + # Euler method + sigma_down_i_ratio = sigma_down / sigmas[i] + x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised + if sigmas[i + 1] > 0 and eta > 0: + x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff + return x + +@torch.no_grad() +def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == 0: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + return x + + +@torch.no_grad() +def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + dt = sigmas[i + 1] - sigma_hat + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() + dt_1 = sigma_mid - sigma_hat + dt_2 = sigmas[i + 1] - sigma_hat + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + return x + + +@torch.no_grad() +def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + if sigma_down == 0: + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() + dt_1 = sigma_mid - sigmas[i] + dt_2 = sigma_down - sigmas[i] + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +def linear_multistep_coeff(order, t, i, j): + if order - 1 > i: + raise ValueError(f'Order {order} too high for step {i}') + def fn(tau): + prod = 1. + for k in range(order): + if j == k: + continue + prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) + return prod + return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] + + +@torch.no_grad() +def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigmas_cpu = sigmas.detach().cpu().numpy() + ds = [] + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + d = to_d(x, sigmas[i], denoised) + ds.append(d) + if len(ds) > order: + ds.pop(0) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + cur_order = min(i + 1, order) + coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] + x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) + return x + + +class PIDStepSizeController: + """A PID controller for ODE adaptive step size control.""" + def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): + self.h = h + self.b1 = (pcoeff + icoeff + dcoeff) / order + self.b2 = -(pcoeff + 2 * dcoeff) / order + self.b3 = dcoeff / order + self.accept_safety = accept_safety + self.eps = eps + self.errs = [] + + def limiter(self, x): + return 1 + math.atan(x - 1) + + def propose_step(self, error): + inv_error = 1 / (float(error) + self.eps) + if not self.errs: + self.errs = [inv_error, inv_error, inv_error] + self.errs[0] = inv_error + factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 + factor = self.limiter(factor) + accept = factor >= self.accept_safety + if accept: + self.errs[2] = self.errs[1] + self.errs[1] = self.errs[0] + self.h *= factor + return accept + + +class DPMSolver(nn.Module): + """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" + + def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): + super().__init__() + self.model = model + self.extra_args = {} if extra_args is None else extra_args + self.eps_callback = eps_callback + self.info_callback = info_callback + + def t(self, sigma): + return -sigma.log() + + def sigma(self, t): + return t.neg().exp() + + def eps(self, eps_cache, key, x, t, *args, **kwargs): + if key in eps_cache: + return eps_cache[key], eps_cache + sigma = self.sigma(t) * x.new_ones([x.shape[0]]) + eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) + if self.eps_callback is not None: + self.eps_callback() + return eps, {key: eps, **eps_cache} + + def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + x_1 = x - self.sigma(t_next) * h.expm1() * eps + return x_1, eps_cache + + def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) + return x_2, eps_cache + + def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + s2 = t + r2 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) + eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) + x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) + return x_3, eps_cache + + def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if not t_end > t_start and eta: + raise ValueError('eta must be 0 for reverse sampling') + + m = math.floor(nfe / 3) + 1 + ts = torch.linspace(t_start, t_end, m + 1, device=x.device) + + if nfe % 3 == 0: + orders = [3] * (m - 2) + [2, 1] + else: + orders = [3] * (m - 1) + [nfe % 3] + + for i in range(len(orders)): + eps_cache = {} + t, t_next = ts[i], ts[i + 1] + if eta: + sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) + t_next_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 + else: + t_next_, su = t_next, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + denoised = x - self.sigma(t) * eps + if self.info_callback is not None: + self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) + + if orders[i] == 1: + x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) + elif orders[i] == 2: + x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) + else: + x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) + + x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) + + return x + + def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if order not in {2, 3}: + raise ValueError('order should be 2 or 3') + forward = t_end > t_start + if not forward and eta: + raise ValueError('eta must be 0 for reverse sampling') + h_init = abs(h_init) * (1 if forward else -1) + atol = torch.tensor(atol) + rtol = torch.tensor(rtol) + s = t_start + x_prev = x + accept = True + pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) + info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} + + while s < t_end - 1e-5 if forward else s > t_end + 1e-5: + eps_cache = {} + t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) + if eta: + sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) + t_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 + else: + t_, su = t, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, s) + denoised = x - self.sigma(s) * eps + + if order == 2: + x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) + else: + x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) + delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) + error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 + accept = pid.propose_step(error) + if accept: + x_prev = x_low + x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) + s = t + info['n_accept'] += 1 + else: + info['n_reject'] += 1 + info['nfe'] += order + info['steps'] += 1 + + if self.info_callback is not None: + self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) + + return x, info + + +@torch.no_grad() +def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): + """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(total=n, disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) + + +@torch.no_grad() +def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): + """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) + if return_info: + return x, info + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): + return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) + + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1 + lambda_fn = lambda sigma: ((1-sigma)/sigma).log() + + # logged_x = x.unsqueeze(0) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta + sigma_down = sigmas[i+1] * downstep_ratio + alpha_ip1 = 1 - sigmas[i+1] + alpha_down = 1 - sigma_down + renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 + # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + if sigmas[i] == 1.0: + sigma_s = 0.9999 + else: + t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down) + r = 1 / 2 + h = t_down - t_i + s = t_i + r * h + sigma_s = sigma_fn(s) + # sigma_s = sigmas[i+1] + sigma_s_i_ratio = sigma_s / sigmas[i] + u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised + D_i = model(u, sigma_s * s_in, **extra_args) + sigma_down_i_ratio = sigma_down / sigmas[i] + x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i + # print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff) + # Noise addition + if sigmas[i + 1] > 0 and eta > 0: + x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff + # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0) + return x + +@torch.no_grad() +def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + """DPM-Solver++ (stochastic).""" + if len(sigmas) <= 1: + return x + + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + seed = extra_args.get("seed", None) + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigmas[i + 1] - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++ + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + s = t + h * r + fac = 1 / (2 * r) + + # Step 1 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) + s_ = t_fn(sd) + x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised + x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + + # Step 2 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) + t_next_ = t_fn(sd) + denoised_d = (1 - fac) * denoised + fac * denoised_2 + x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d + x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su + return x + + +@torch.no_grad() +def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + old_denoised = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + if old_denoised is None or sigmas[i + 1] == 0: + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + r = h_last / h + denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d + old_denoised = denoised + return x + +@torch.no_grad() +def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + """DPM-Solver++(2M) SDE.""" + if len(sigmas) <= 1: + return x + + if solver_type not in {'heun', 'midpoint'}: + raise ValueError('solver_type must be \'heun\' or \'midpoint\'') + + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + old_denoised = None + h_last = None + h = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + # DPM-Solver++(2M) SDE + t, s = -sigmas[i].log(), -sigmas[i + 1].log() + h = s - t + eta_h = eta * h + + x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised + + if old_denoised is not None: + r = h_last / h + if solver_type == 'heun': + x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised) + elif solver_type == 'midpoint': + x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised) + + if eta: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise + + old_denoised = denoised + h_last = h + return x + +@torch.no_grad() +def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """DPM-Solver++(3M) SDE.""" + + if len(sigmas) <= 1: + return x + + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + denoised_1, denoised_2 = None, None + h, h_1, h_2 = None, None, None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + t, s = -sigmas[i].log(), -sigmas[i + 1].log() + h = s - t + h_eta = h * (eta + 1) + + x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised + + if h_2 is not None: + r0 = h_1 / h + r1 = h_2 / h + d1_0 = (denoised - denoised_1) / r0 + d1_1 = (denoised_1 - denoised_2) / r1 + d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) + d2 = (d1_0 - d1_1) / (r0 + r1) + phi_2 = h_eta.neg().expm1() / h_eta + 1 + phi_3 = phi_2 / h_eta - 0.5 + x = x + phi_2 * d1 - phi_3 * d2 + elif h_1 is not None: + r = h_1 / h + d = (denoised - denoised_1) / r + phi_2 = h_eta.neg().expm1() / h_eta + 1 + x = x + phi_2 * d + + if eta: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise + + denoised_1, denoised_2 = denoised, denoised_1 + h_1, h_2 = h, h_1 + return x + +@torch.no_grad() +def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + if len(sigmas) <= 1: + return x + + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) + +@torch.no_grad() +def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + if len(sigmas) <= 1: + return x + + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) + +@torch.no_grad() +def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + if len(sigmas) <= 1: + return x + + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) + + +def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): + alpha_cumprod = 1 / ((sigma * sigma) + 1) + alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) + alpha = (alpha_cumprod / alpha_cumprod_prev) + + mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) + if sigma_prev > 0: + mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) + return mu + +def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) + if sigmas[i + 1] != 0: + x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) + return x + + +@torch.no_grad() +def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): + return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) + +@torch.no_grad() +def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + x = denoised + if sigmas[i + 1] > 0: + x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x) + return x + + + +@torch.no_grad() +def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/ + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + s_end = sigmas[-1] + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == s_end: + # Euler method + x = x + d * dt + elif sigmas[i + 2] == s_end: + + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + + w = 2 * sigmas[0] + w2 = sigmas[i+1]/w + w1 = 1 - w2 + + d_prime = d * w1 + d_2 * w2 + + + x = x + d_prime * dt + + else: + # Heun++ + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + dt_2 = sigmas[i + 2] - sigmas[i + 1] + + x_3 = x_2 + d_2 * dt_2 + denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args) + d_3 = to_d(x_3, sigmas[i + 2], denoised_3) + + w = 3 * sigmas[0] + w2 = sigmas[i + 1] / w + w3 = sigmas[i + 2] / w + w1 = 1 - w2 - w3 + + d_prime = w1 * d + w2 * d_2 + w3 * d_3 + x = x + d_prime * dt + return x + + +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2 + elif order == 3: # Use two history points. + x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12 + elif order == 4: # Use three history points. + x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24 + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur + else: + buffer_model.append(d_cur) + + return x_next + +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + t_steps = sigmas + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + coeff1 = (2 + (h_n / h_n_1)) / 2 + coeff2 = -(h_n / h_n_1) / 2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1]) + elif order == 3: # Use two history points. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + h_n_2 = (t_steps[i-1] - t_steps[i-2]) + temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 + coeff1 = (2 + (h_n / h_n_1)) / 2 + temp + coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp + coeff3 = temp * h_n_1 / h_n_2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2]) + elif order == 4: # Use three history points. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + h_n_2 = (t_steps[i-1] - t_steps[i-2]) + h_n_3 = (t_steps[i-2] - t_steps[i-3]) + temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 + temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \ + * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3)) + coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2 + coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2 + coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2 + coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3]) + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur.detach() + else: + buffer_model.append(d_cur.detach()) + + return x_next + +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +@torch.no_grad() +def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + t_steps = sigmas + + coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode) + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if t_next <= 0: + order = 1 + + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + coeff_cur, coeff_prev1 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + elif order == 3: # Use two history points. + coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + elif order == 4: # Use three history points. + coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3] + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur.detach() + else: + buffer_model.append(d_cur.detach()) + + return x_next + +@torch.no_grad() +def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): + extra_args = {} if extra_args is None else extra_args + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + sigma_hat = sigmas[i] + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, temp[0]) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + # Euler method + x = denoised + d * sigmas[i + 1] + return x + +@torch.no_grad() +def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], temp[0]) + # Euler method + x = denoised + d * sigma_down + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x +@torch.no_grad() +def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], temp[0]) + x = denoised + d * sigma_down + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + +@torch.no_grad() +def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + t_fn = lambda sigma: sigma.log().neg() + + old_uncond_denoised = None + uncond_denoised = None + def post_cfg_function(args): + nonlocal uncond_denoised + uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + if old_uncond_denoised is None or sigmas[i + 1] == 0: + denoised_mix = -torch.exp(-h) * uncond_denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + r = h_last / h + denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised) + x = denoised + denoised_mix + torch.exp(-h) * x + old_uncond_denoised = uncond_denoised + return x diff --git a/comfy/k_diffusion/utils.py b/comfy/k_diffusion/utils.py new file mode 100644 index 0000000..a644df2 --- /dev/null +++ b/comfy/k_diffusion/utils.py @@ -0,0 +1,313 @@ +from contextlib import contextmanager +import hashlib +import math +from pathlib import Path +import shutil +import urllib +import warnings + +from PIL import Image +import torch +from torch import nn, optim +from torch.utils import data + + +def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): + """Apply passed in transforms for HuggingFace Datasets.""" + images = [transform(image.convert(mode)) for image in examples[image_key]] + return {image_key: images} + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') + expanded = x[(...,) + (None,) * dims_to_append] + # MPS will get inf values if it tries to index into the new axes, but detaching fixes this. + # https://github.com/pytorch/pytorch/issues/84364 + return expanded.detach().clone() if expanded.device.type == 'mps' else expanded + + +def n_params(module): + """Returns the number of trainable parameters in a module.""" + return sum(p.numel() for p in module.parameters()) + + +def download_file(path, url, digest=None): + """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + if not path.exists(): + with urllib.request.urlopen(url) as response, open(path, 'wb') as f: + shutil.copyfileobj(response, f) + if digest is not None: + file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() + if digest != file_digest: + raise OSError(f'hash of {path} (url: {url}) failed to validate') + return path + + +@contextmanager +def train_mode(model, mode=True): + """A context manager that places a model into training mode and restores + the previous mode on exit.""" + modes = [module.training for module in model.modules()] + try: + yield model.train(mode) + finally: + for i, module in enumerate(model.modules()): + module.training = modes[i] + + +def eval_mode(model): + """A context manager that places a model into evaluation mode and restores + the previous mode on exit.""" + return train_mode(model, False) + + +@torch.no_grad() +def ema_update(model, averaged_model, decay): + """Incorporates updated model parameters into an exponential moving averaged + version of a model. It should be called after each optimizer step.""" + model_params = dict(model.named_parameters()) + averaged_params = dict(averaged_model.named_parameters()) + assert model_params.keys() == averaged_params.keys() + + for name, param in model_params.items(): + averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) + + model_buffers = dict(model.named_buffers()) + averaged_buffers = dict(averaged_model.named_buffers()) + assert model_buffers.keys() == averaged_buffers.keys() + + for name, buf in model_buffers.items(): + averaged_buffers[name].copy_(buf) + + +class EMAWarmup: + """Implements an EMA warmup using an inverse decay schedule. + If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are + good values for models you plan to train for a million or more steps (reaches decay + factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models + you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at + 215.4k steps). + Args: + inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. + power (float): Exponential factor of EMA warmup. Default: 1. + min_value (float): The minimum EMA decay rate. Default: 0. + max_value (float): The maximum EMA decay rate. Default: 1. + start_at (int): The epoch to start averaging at. Default: 0. + last_epoch (int): The index of last epoch. Default: 0. + """ + + def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, + last_epoch=0): + self.inv_gamma = inv_gamma + self.power = power + self.min_value = min_value + self.max_value = max_value + self.start_at = start_at + self.last_epoch = last_epoch + + def state_dict(self): + """Returns the state of the class as a :class:`dict`.""" + return dict(self.__dict__.items()) + + def load_state_dict(self, state_dict): + """Loads the class's state. + Args: + state_dict (dict): scaler state. Should be an object returned + from a call to :meth:`state_dict`. + """ + self.__dict__.update(state_dict) + + def get_value(self): + """Gets the current EMA decay rate.""" + epoch = max(0, self.last_epoch - self.start_at) + value = 1 - (1 + epoch / self.inv_gamma) ** -self.power + return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) + + def step(self): + """Updates the step count.""" + self.last_epoch += 1 + + +class InverseLR(optim.lr_scheduler._LRScheduler): + """Implements an inverse decay learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. + inv_gamma is the number of steps/epochs required for the learning rate to decay to + (1 / 2)**power of its original value. + Args: + optimizer (Optimizer): Wrapped optimizer. + inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. + power (float): Exponential factor of learning rate decay. Default: 1. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.inv_gamma = inv_gamma + self.power = power + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +class ExponentialLR(optim.lr_scheduler._LRScheduler): + """Implements an exponential learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate + continuously by decay (default 0.5) every num_steps steps. + Args: + optimizer (Optimizer): Wrapped optimizer. + num_steps (float): The number of steps to decay the learning rate by decay in. + decay (float): The factor by which to decay the learning rate every num_steps + steps. Default: 0.5. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.num_steps = num_steps + self.decay = decay + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): + """Draws samples from an lognormal distribution.""" + return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() + + +def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from an optionally truncated log-logistic distribution.""" + min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) + max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) + min_cdf = min_value.log().sub(loc).div(scale).sigmoid() + max_cdf = max_value.log().sub(loc).div(scale).sigmoid() + u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf + return u.logit().mul(scale).add(loc).exp().to(dtype) + + +def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): + """Draws samples from an log-uniform distribution.""" + min_value = math.log(min_value) + max_value = math.log(max_value) + return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() + + +def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from a truncated v-diffusion training timestep distribution.""" + min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi + max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi + u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf + return torch.tan(u * math.pi / 2) * sigma_data + + +def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): + """Draws samples from a split lognormal distribution.""" + n = torch.randn(shape, device=device, dtype=dtype).abs() + u = torch.rand(shape, device=device, dtype=dtype) + n_left = n * -scale_1 + loc + n_right = n * scale_2 + loc + ratio = scale_1 / (scale_1 + scale_2) + return torch.where(u < ratio, n_left, n_right).exp() + + +class FolderOfImages(data.Dataset): + """Recursively finds all images in a directory. It does not support + classes/targets.""" + + IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} + + def __init__(self, root, transform=None): + super().__init__() + self.root = Path(root) + self.transform = nn.Identity() if transform is None else transform + self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) + + def __repr__(self): + return f'FolderOfImages(root="{self.root}", len: {len(self)})' + + def __len__(self): + return len(self.paths) + + def __getitem__(self, key): + path = self.paths[key] + with open(path, 'rb') as f: + image = Image.open(f).convert('RGB') + image = self.transform(image) + return image, + + +class CSVLogger: + def __init__(self, filename, columns): + self.filename = Path(filename) + self.columns = columns + if self.filename.exists(): + self.file = open(self.filename, 'a') + else: + self.file = open(self.filename, 'w') + self.write(*self.columns) + + def write(self, *args): + print(*args, sep=',', file=self.file, flush=True) + + +@contextmanager +def tf32_mode(cudnn=None, matmul=None): + """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" + cudnn_old = torch.backends.cudnn.allow_tf32 + matmul_old = torch.backends.cuda.matmul.allow_tf32 + try: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul + yield + finally: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn_old + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul_old diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py new file mode 100644 index 0000000..a48f60c --- /dev/null +++ b/comfy/latent_formats.py @@ -0,0 +1,204 @@ +import torch + +class LatentFormat: + scale_factor = 1.0 + latent_channels = 4 + latent_rgb_factors = None + latent_rgb_factors_bias = None + taesd_decoder_name = None + + def process_in(self, latent): + return latent * self.scale_factor + + def process_out(self, latent): + return latent / self.scale_factor + +class SD15(LatentFormat): + def __init__(self, scale_factor=0.18215): + self.scale_factor = scale_factor + self.latent_rgb_factors = [ + # R G B + [ 0.3512, 0.2297, 0.3227], + [ 0.3250, 0.4974, 0.2350], + [-0.2829, 0.1762, 0.2721], + [-0.2120, -0.2616, -0.7177] + ] + self.taesd_decoder_name = "taesd_decoder" + +class SDXL(LatentFormat): + scale_factor = 0.13025 + + def __init__(self): + self.latent_rgb_factors = [ + # R G B + [ 0.3651, 0.4232, 0.4341], + [-0.2533, -0.0042, 0.1068], + [ 0.1076, 0.1111, -0.0362], + [-0.3165, -0.2492, -0.2188] + ] + self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011] + + self.taesd_decoder_name = "taesdxl_decoder" + +class SDXL_Playground_2_5(LatentFormat): + def __init__(self): + self.scale_factor = 0.5 + self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1) + self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1) + + self.latent_rgb_factors = [ + # R G B + [ 0.3920, 0.4054, 0.4549], + [-0.2634, -0.0196, 0.0653], + [ 0.0568, 0.1687, -0.0755], + [-0.3112, -0.2359, -0.2076] + ] + self.taesd_decoder_name = "taesdxl_decoder" + + def process_in(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return (latent - latents_mean) * self.scale_factor / latents_std + + def process_out(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return latent * latents_std / self.scale_factor + latents_mean + + +class SD_X4(LatentFormat): + def __init__(self): + self.scale_factor = 0.08333 + self.latent_rgb_factors = [ + [-0.2340, -0.3863, -0.3257], + [ 0.0994, 0.0885, -0.0908], + [-0.2833, -0.2349, -0.3741], + [ 0.2523, -0.0055, -0.1651] + ] + +class SC_Prior(LatentFormat): + latent_channels = 16 + def __init__(self): + self.scale_factor = 1.0 + self.latent_rgb_factors = [ + [-0.0326, -0.0204, -0.0127], + [-0.1592, -0.0427, 0.0216], + [ 0.0873, 0.0638, -0.0020], + [-0.0602, 0.0442, 0.1304], + [ 0.0800, -0.0313, -0.1796], + [-0.0810, -0.0638, -0.1581], + [ 0.1791, 0.1180, 0.0967], + [ 0.0740, 0.1416, 0.0432], + [-0.1745, -0.1888, -0.1373], + [ 0.2412, 0.1577, 0.0928], + [ 0.1908, 0.0998, 0.0682], + [ 0.0209, 0.0365, -0.0092], + [ 0.0448, -0.0650, -0.1728], + [-0.1658, -0.1045, -0.1308], + [ 0.0542, 0.1545, 0.1325], + [-0.0352, -0.1672, -0.2541] + ] + +class SC_B(LatentFormat): + def __init__(self): + self.scale_factor = 1.0 / 0.43 + self.latent_rgb_factors = [ + [ 0.1121, 0.2006, 0.1023], + [-0.2093, -0.0222, -0.0195], + [-0.3087, -0.1535, 0.0366], + [ 0.0290, -0.1574, -0.4078] + ] + +class SD3(LatentFormat): + latent_channels = 16 + def __init__(self): + self.scale_factor = 1.5305 + self.shift_factor = 0.0609 + self.latent_rgb_factors = [ + [-0.0922, -0.0175, 0.0749], + [ 0.0311, 0.0633, 0.0954], + [ 0.1994, 0.0927, 0.0458], + [ 0.0856, 0.0339, 0.0902], + [ 0.0587, 0.0272, -0.0496], + [-0.0006, 0.1104, 0.0309], + [ 0.0978, 0.0306, 0.0427], + [-0.0042, 0.1038, 0.1358], + [-0.0194, 0.0020, 0.0669], + [-0.0488, 0.0130, -0.0268], + [ 0.0922, 0.0988, 0.0951], + [-0.0278, 0.0524, -0.0542], + [ 0.0332, 0.0456, 0.0895], + [-0.0069, -0.0030, -0.0810], + [-0.0596, -0.0465, -0.0293], + [-0.1448, -0.1463, -0.1189] + ] + self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925] + self.taesd_decoder_name = "taesd3_decoder" + + def process_in(self, latent): + return (latent - self.shift_factor) * self.scale_factor + + def process_out(self, latent): + return (latent / self.scale_factor) + self.shift_factor + +class StableAudio1(LatentFormat): + latent_channels = 64 + +class Flux(SD3): + latent_channels = 16 + def __init__(self): + self.scale_factor = 0.3611 + self.shift_factor = 0.1159 + self.latent_rgb_factors =[ + [-0.0346, 0.0244, 0.0681], + [ 0.0034, 0.0210, 0.0687], + [ 0.0275, -0.0668, -0.0433], + [-0.0174, 0.0160, 0.0617], + [ 0.0859, 0.0721, 0.0329], + [ 0.0004, 0.0383, 0.0115], + [ 0.0405, 0.0861, 0.0915], + [-0.0236, -0.0185, -0.0259], + [-0.0245, 0.0250, 0.1180], + [ 0.1008, 0.0755, -0.0421], + [-0.0515, 0.0201, 0.0011], + [ 0.0428, -0.0012, -0.0036], + [ 0.0817, 0.0765, 0.0749], + [-0.1264, -0.0522, -0.1103], + [-0.0280, -0.0881, -0.0499], + [-0.1262, -0.0982, -0.0778] + ] + self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851] + self.taesd_decoder_name = "taef1_decoder" + + def process_in(self, latent): + return (latent - self.shift_factor) * self.scale_factor + + def process_out(self, latent): + return (latent / self.scale_factor) + self.shift_factor + +class Mochi(LatentFormat): + latent_channels = 12 + + def __init__(self): + self.scale_factor = 1.0 + self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141, + -0.05565264470995561, 0.012767231469026969, -0.04703542746246419, + 0.043896967884726704, -0.09346305707025976, -0.09918314763016893, + -0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1) + self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617, + 0.959253732819592, 0.8244560132752793, 0.917259975397747, + 0.9294154431013696, 1.3720942357788521, 0.881393668867029, + 0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1) + + self.latent_rgb_factors = None #TODO + self.taesd_decoder_name = None #TODO + + def process_in(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return (latent - latents_mean) * self.scale_factor / latents_std + + def process_out(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return latent * latents_std / self.scale_factor + latents_mean diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py new file mode 100644 index 0000000..8123e66 --- /dev/null +++ b/comfy/ldm/audio/autoencoder.py @@ -0,0 +1,282 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +import torch +from torch import nn +from typing import Literal, Dict, Any +import math +import comfy.ops +ops = comfy.ops.disable_weight_init + +def vae_sample(mean, scale): + stdev = nn.functional.softplus(scale) + 1e-4 + var = stdev * stdev + logvar = torch.log(var) + latents = torch.randn_like(mean) * stdev + mean + + kl = (mean * mean + var - logvar - 1).sum(1).mean() + + return latents, kl + +class VAEBottleneck(nn.Module): + def __init__(self): + super().__init__() + self.is_discrete = False + + def encode(self, x, return_info=False, **kwargs): + info = {} + + mean, scale = x.chunk(2, dim=1) + + x, kl = vae_sample(mean, scale) + + info["kl"] = kl + + if return_info: + return x, info + else: + return x + + def decode(self, x): + return x + + +def snake_beta(x, alpha, beta): + return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2) + +# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license +class SnakeBeta(nn.Module): + + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): + super(SnakeBeta, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = nn.Parameter(torch.zeros(in_features) * alpha) + self.beta = nn.Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = nn.Parameter(torch.ones(in_features) * alpha) + self.beta = nn.Parameter(torch.ones(in_features) * alpha) + + # self.alpha.requires_grad = alpha_trainable + # self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T] + beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device) + if self.alpha_logscale: + alpha = torch.exp(alpha) + beta = torch.exp(beta) + x = snake_beta(x, alpha, beta) + + return x + +def WNConv1d(*args, **kwargs): + try: + return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) + except: + return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older + +def WNConvTranspose1d(*args, **kwargs): + try: + return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) + except: + return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older + +def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: + if activation == "elu": + act = torch.nn.ELU() + elif activation == "snake": + act = SnakeBeta(channels) + elif activation == "none": + act = torch.nn.Identity() + else: + raise ValueError(f"Unknown activation {activation}") + + if antialias: + act = Activation1d(act) + + return act + + +class ResidualUnit(nn.Module): + def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False): + super().__init__() + + self.dilation = dilation + + padding = (dilation * (7-1)) // 2 + + self.layers = nn.Sequential( + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), + WNConv1d(in_channels=in_channels, out_channels=out_channels, + kernel_size=7, dilation=dilation, padding=padding), + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), + WNConv1d(in_channels=out_channels, out_channels=out_channels, + kernel_size=1) + ) + + def forward(self, x): + res = x + + #x = checkpoint(self.layers, x) + x = self.layers(x) + + return x + res + +class EncoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False): + super().__init__() + + self.layers = nn.Sequential( + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=1, use_snake=use_snake), + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=3, use_snake=use_snake), + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=9, use_snake=use_snake), + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), + WNConv1d(in_channels=in_channels, out_channels=out_channels, + kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)), + ) + + def forward(self, x): + return self.layers(x) + +class DecoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False): + super().__init__() + + if use_nearest_upsample: + upsample_layer = nn.Sequential( + nn.Upsample(scale_factor=stride, mode="nearest"), + WNConv1d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=2*stride, + stride=1, + bias=False, + padding='same') + ) + else: + upsample_layer = WNConvTranspose1d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)) + + self.layers = nn.Sequential( + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), + upsample_layer, + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=1, use_snake=use_snake), + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=3, use_snake=use_snake), + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=9, use_snake=use_snake), + ) + + def forward(self, x): + return self.layers(x) + +class OobleckEncoder(nn.Module): + def __init__(self, + in_channels=2, + channels=128, + latent_dim=32, + c_mults = [1, 2, 4, 8], + strides = [2, 4, 8, 8], + use_snake=False, + antialias_activation=False + ): + super().__init__() + + c_mults = [1] + c_mults + + self.depth = len(c_mults) + + layers = [ + WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3) + ] + + for i in range(self.depth-1): + layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)] + + layers += [ + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels), + WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1) + ] + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class OobleckDecoder(nn.Module): + def __init__(self, + out_channels=2, + channels=128, + latent_dim=32, + c_mults = [1, 2, 4, 8], + strides = [2, 4, 8, 8], + use_snake=False, + antialias_activation=False, + use_nearest_upsample=False, + final_tanh=True): + super().__init__() + + c_mults = [1] + c_mults + + self.depth = len(c_mults) + + layers = [ + WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3), + ] + + for i in range(self.depth-1, 0, -1): + layers += [DecoderBlock( + in_channels=c_mults[i]*channels, + out_channels=c_mults[i-1]*channels, + stride=strides[i-1], + use_snake=use_snake, + antialias_activation=antialias_activation, + use_nearest_upsample=use_nearest_upsample + ) + ] + + layers += [ + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels), + WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False), + nn.Tanh() if final_tanh else nn.Identity() + ] + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class AudioOobleckVAE(nn.Module): + def __init__(self, + in_channels=2, + channels=128, + latent_dim=64, + c_mults = [1, 2, 4, 8, 16], + strides = [2, 4, 4, 8, 8], + use_snake=True, + antialias_activation=False, + use_nearest_upsample=False, + final_tanh=False): + super().__init__() + self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation) + self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation, + use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh) + self.bottleneck = VAEBottleneck() + + def encode(self, x): + return self.bottleneck.encode(self.encoder(x)) + + def decode(self, x): + return self.decoder(self.bottleneck.decode(x)) + diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py new file mode 100644 index 0000000..4d2185b --- /dev/null +++ b/comfy/ldm/audio/dit.py @@ -0,0 +1,891 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +from comfy.ldm.modules.attention import optimized_attention +import typing as tp + +import torch + +from einops import rearrange +from torch import nn +from torch.nn import functional as F +import math +import comfy.ops + +class FourierFeatures(nn.Module): + def __init__(self, in_features, out_features, std=1., dtype=None, device=None): + super().__init__() + assert out_features % 2 == 0 + self.weight = nn.Parameter(torch.empty( + [out_features // 2, in_features], dtype=dtype, device=device)) + + def forward(self, input): + f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input) + return torch.cat([f.cos(), f.sin()], dim=-1) + +# norms +class LayerNorm(nn.Module): + def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None): + """ + bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less + """ + super().__init__() + + self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) + + if bias: + self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) + else: + self.beta = None + + def forward(self, x): + beta = self.beta + if beta is not None: + beta = comfy.ops.cast_to_input(beta, x) + return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta) + +class GLU(nn.Module): + def __init__( + self, + dim_in, + dim_out, + activation, + use_conv = False, + conv_kernel_size = 3, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.act = activation + self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device) + self.use_conv = use_conv + + def forward(self, x): + if self.use_conv: + x = rearrange(x, 'b n d -> b d n') + x = self.proj(x) + x = rearrange(x, 'b d n -> b n d') + else: + x = self.proj(x) + + x, gate = x.chunk(2, dim = -1) + return x * self.act(gate) + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.scale = dim ** -0.5 + self.max_seq_len = max_seq_len + self.emb = nn.Embedding(max_seq_len, dim) + + def forward(self, x, pos = None, seq_start_pos = None): + seq_len, device = x.shape[1], x.device + assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}' + + if pos is None: + pos = torch.arange(seq_len, device = device) + + if seq_start_pos is not None: + pos = (pos - seq_start_pos[..., None]).clamp(min = 0) + + pos_emb = self.emb(pos) + pos_emb = pos_emb * self.scale + return pos_emb + +class ScaledSinusoidalEmbedding(nn.Module): + def __init__(self, dim, theta = 10000): + super().__init__() + assert (dim % 2) == 0, 'dimension must be divisible by 2' + self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5) + + half_dim = dim // 2 + freq_seq = torch.arange(half_dim).float() / half_dim + inv_freq = theta ** -freq_seq + self.register_buffer('inv_freq', inv_freq, persistent = False) + + def forward(self, x, pos = None, seq_start_pos = None): + seq_len, device = x.shape[1], x.device + + if pos is None: + pos = torch.arange(seq_len, device = device) + + if seq_start_pos is not None: + pos = pos - seq_start_pos[..., None] + + emb = torch.einsum('i, j -> i j', pos, self.inv_freq) + emb = torch.cat((emb.sin(), emb.cos()), dim = -1) + return emb * self.scale + +class RotaryEmbedding(nn.Module): + def __init__( + self, + dim, + use_xpos = False, + scale_base = 512, + interpolation_factor = 1., + base = 10000, + base_rescale_factor = 1., + dtype=None, + device=None, + ): + super().__init__() + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + base *= base_rescale_factor ** (dim / (dim - 2)) + + # inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', torch.empty((dim // 2,), device=device, dtype=dtype)) + + assert interpolation_factor >= 1. + self.interpolation_factor = interpolation_factor + + if not use_xpos: + self.register_buffer('scale', None) + return + + scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) + + self.scale_base = scale_base + self.register_buffer('scale', scale) + + def forward_from_seq_len(self, seq_len, device, dtype): + # device = self.inv_freq.device + + t = torch.arange(seq_len, device=device, dtype=dtype) + return self.forward(t) + + def forward(self, t): + # device = self.inv_freq.device + device = t.device + dtype = t.dtype + + # t = t.to(torch.float32) + + t = t / self.interpolation_factor + + freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t)) + freqs = torch.cat((freqs, freqs), dim = -1) + + if self.scale is None: + return freqs, 1. + + power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base + scale = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1') + scale = torch.cat((scale, scale), dim = -1) + + return freqs, scale + +def rotate_half(x): + x = rearrange(x, '... (j d) -> ... j d', j = 2) + x1, x2 = x.unbind(dim = -2) + return torch.cat((-x2, x1), dim = -1) + +def apply_rotary_pos_emb(t, freqs, scale = 1): + out_dtype = t.dtype + + # cast to float32 if necessary for numerical stability + dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32)) + rot_dim, seq_len = freqs.shape[-1], t.shape[-2] + freqs, t = freqs.to(dtype), t.to(dtype) + freqs = freqs[-seq_len:, :] + + if t.ndim == 4 and freqs.ndim == 3: + freqs = rearrange(freqs, 'b n d -> b 1 n d') + + # partial rotary embeddings, Wang et al. GPT-J + t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:] + t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale) + + t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype) + + return torch.cat((t, t_unrotated), dim = -1) + +class FeedForward(nn.Module): + def __init__( + self, + dim, + dim_out = None, + mult = 4, + no_bias = False, + glu = True, + use_conv = False, + conv_kernel_size = 3, + zero_init_output = True, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + inner_dim = int(dim * mult) + + # Default to SwiGLU + + activation = nn.SiLU() + + dim_out = dim if dim_out is None else dim_out + + if glu: + linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations) + else: + linear_in = nn.Sequential( + Rearrange('b n d -> b d n') if use_conv else nn.Identity(), + operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device), + Rearrange('b n d -> b d n') if use_conv else nn.Identity(), + activation + ) + + linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device) + + # # init last linear layer to 0 + # if zero_init_output: + # nn.init.zeros_(linear_out.weight) + # if not no_bias: + # nn.init.zeros_(linear_out.bias) + + + self.ff = nn.Sequential( + linear_in, + Rearrange('b d n -> b n d') if use_conv else nn.Identity(), + linear_out, + Rearrange('b n d -> b d n') if use_conv else nn.Identity(), + ) + + def forward(self, x): + return self.ff(x) + +class Attention(nn.Module): + def __init__( + self, + dim, + dim_heads = 64, + dim_context = None, + causal = False, + zero_init_output=True, + qk_norm = False, + natten_kernel_size = None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.dim = dim + self.dim_heads = dim_heads + self.causal = causal + + dim_kv = dim_context if dim_context is not None else dim + + self.num_heads = dim // dim_heads + self.kv_heads = dim_kv // dim_heads + + if dim_context is not None: + self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device) + else: + self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device) + + self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + # if zero_init_output: + # nn.init.zeros_(self.to_out.weight) + + self.qk_norm = qk_norm + + + def forward( + self, + x, + context = None, + mask = None, + context_mask = None, + rotary_pos_emb = None, + causal = None + ): + h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None + + kv_input = context if has_context else x + + if hasattr(self, 'to_q'): + # Use separate linear projections for q and k/v + q = self.to_q(x) + q = rearrange(q, 'b n (h d) -> b h n d', h = h) + + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v)) + else: + # Use fused linear projection + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) + + # Normalize q and k for cosine sim attention + if self.qk_norm: + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + if rotary_pos_emb is not None and not has_context: + freqs, _ = rotary_pos_emb + + q_dtype = q.dtype + k_dtype = k.dtype + + q = q.to(torch.float32) + k = k.to(torch.float32) + freqs = freqs.to(torch.float32) + + q = apply_rotary_pos_emb(q, freqs) + k = apply_rotary_pos_emb(k, freqs) + + q = q.to(q_dtype) + k = k.to(k_dtype) + + input_mask = context_mask + + if input_mask is None and not has_context: + input_mask = mask + + # determine masking + masks = [] + final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account + + if input_mask is not None: + input_mask = rearrange(input_mask, 'b j -> b 1 1 j') + masks.append(~input_mask) + + # Other masks will be added here later + + if len(masks) > 0: + final_attn_mask = ~or_reduce(masks) + + n, device = q.shape[-2], q.device + + causal = self.causal if causal is None else causal + + if n == 1 and causal: + causal = False + + if h != kv_h: + # Repeat interleave kv_heads to match q_heads + heads_per_kv_head = h // kv_h + k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v)) + + out = optimized_attention(q, k, v, h, skip_reshape=True) + out = self.to_out(out) + + if mask is not None: + mask = rearrange(mask, 'b n -> b n 1') + out = out.masked_fill(~mask, 0.) + + return out + +class ConformerModule(nn.Module): + def __init__( + self, + dim, + norm_kwargs = {}, + ): + + super().__init__() + + self.dim = dim + + self.in_norm = LayerNorm(dim, **norm_kwargs) + self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False) + self.glu = GLU(dim, dim, nn.SiLU()) + self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False) + self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm + self.swish = nn.SiLU() + self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False) + + def forward(self, x): + x = self.in_norm(x) + x = rearrange(x, 'b n d -> b d n') + x = self.pointwise_conv(x) + x = rearrange(x, 'b d n -> b n d') + x = self.glu(x) + x = rearrange(x, 'b n d -> b d n') + x = self.depthwise_conv(x) + x = rearrange(x, 'b d n -> b n d') + x = self.mid_norm(x) + x = self.swish(x) + x = rearrange(x, 'b n d -> b d n') + x = self.pointwise_conv_2(x) + x = rearrange(x, 'b d n -> b n d') + + return x + +class TransformerBlock(nn.Module): + def __init__( + self, + dim, + dim_heads = 64, + cross_attend = False, + dim_context = None, + global_cond_dim = None, + causal = False, + zero_init_branch_outputs = True, + conformer = False, + layer_ix = -1, + remove_norms = False, + attn_kwargs = {}, + ff_kwargs = {}, + norm_kwargs = {}, + dtype=None, + device=None, + operations=None, + ): + + super().__init__() + self.dim = dim + self.dim_heads = dim_heads + self.cross_attend = cross_attend + self.dim_context = dim_context + self.causal = causal + + self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + + self.self_attn = Attention( + dim, + dim_heads = dim_heads, + causal = causal, + zero_init_output=zero_init_branch_outputs, + dtype=dtype, + device=device, + operations=operations, + **attn_kwargs + ) + + if cross_attend: + self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + self.cross_attn = Attention( + dim, + dim_heads = dim_heads, + dim_context=dim_context, + causal = causal, + zero_init_output=zero_init_branch_outputs, + dtype=dtype, + device=device, + operations=operations, + **attn_kwargs + ) + + self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs) + + self.layer_ix = layer_ix + + self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None + + self.global_cond_dim = global_cond_dim + + if global_cond_dim is not None: + self.to_scale_shift_gate = nn.Sequential( + nn.SiLU(), + nn.Linear(global_cond_dim, dim * 6, bias=False) + ) + + nn.init.zeros_(self.to_scale_shift_gate[1].weight) + #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias) + + def forward( + self, + x, + context = None, + global_cond=None, + mask = None, + context_mask = None, + rotary_pos_emb = None + ): + if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: + + scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1) + + # self-attention with adaLN + residual = x + x = self.pre_norm(x) + x = x * (1 + scale_self) + shift_self + x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb) + x = x * torch.sigmoid(1 - gate_self) + x = x + residual + + if context is not None: + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + + if self.conformer is not None: + x = x + self.conformer(x) + + # feedforward with adaLN + residual = x + x = self.ff_norm(x) + x = x * (1 + scale_ff) + shift_ff + x = self.ff(x) + x = x * torch.sigmoid(1 - gate_ff) + x = x + residual + + else: + x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb) + + if context is not None: + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + + if self.conformer is not None: + x = x + self.conformer(x) + + x = x + self.ff(self.ff_norm(x)) + + return x + +class ContinuousTransformer(nn.Module): + def __init__( + self, + dim, + depth, + *, + dim_in = None, + dim_out = None, + dim_heads = 64, + cross_attend=False, + cond_token_dim=None, + global_cond_dim=None, + causal=False, + rotary_pos_emb=True, + zero_init_branch_outputs=True, + conformer=False, + use_sinusoidal_emb=False, + use_abs_pos_emb=False, + abs_pos_emb_max_length=10000, + dtype=None, + device=None, + operations=None, + **kwargs + ): + + super().__init__() + + self.dim = dim + self.depth = depth + self.causal = causal + self.layers = nn.ModuleList([]) + + self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity() + self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity() + + if rotary_pos_emb: + self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), device=device, dtype=dtype) + else: + self.rotary_pos_emb = None + + self.use_sinusoidal_emb = use_sinusoidal_emb + if use_sinusoidal_emb: + self.pos_emb = ScaledSinusoidalEmbedding(dim) + + self.use_abs_pos_emb = use_abs_pos_emb + if use_abs_pos_emb: + self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length) + + for i in range(depth): + self.layers.append( + TransformerBlock( + dim, + dim_heads = dim_heads, + cross_attend = cross_attend, + dim_context = cond_token_dim, + global_cond_dim = global_cond_dim, + causal = causal, + zero_init_branch_outputs = zero_init_branch_outputs, + conformer=conformer, + layer_ix=i, + dtype=dtype, + device=device, + operations=operations, + **kwargs + ) + ) + + def forward( + self, + x, + mask = None, + prepend_embeds = None, + prepend_mask = None, + global_cond = None, + return_info = False, + **kwargs + ): + batch, seq, device = *x.shape[:2], x.device + + info = { + "hidden_states": [], + } + + x = self.project_in(x) + + if prepend_embeds is not None: + prepend_length, prepend_dim = prepend_embeds.shape[1:] + + assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension' + + x = torch.cat((prepend_embeds, x), dim = -2) + + if prepend_mask is not None or mask is not None: + mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool) + prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool) + + mask = torch.cat((prepend_mask, mask), dim = -1) + + # Attention layers + + if self.rotary_pos_emb is not None: + rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device) + else: + rotary_pos_emb = None + + if self.use_sinusoidal_emb or self.use_abs_pos_emb: + x = x + self.pos_emb(x) + + # Iterate over the transformer layers + for layer in self.layers: + x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) + # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) + + if return_info: + info["hidden_states"].append(x) + + x = self.project_out(x) + + if return_info: + return x, info + + return x + +class AudioDiffusionTransformer(nn.Module): + def __init__(self, + io_channels=64, + patch_size=1, + embed_dim=1536, + cond_token_dim=768, + project_cond_tokens=False, + global_cond_dim=1536, + project_global_cond=True, + input_concat_dim=0, + prepend_cond_dim=0, + depth=24, + num_heads=24, + transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer", + global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend", + audio_model="", + dtype=None, + device=None, + operations=None, + **kwargs): + + super().__init__() + + self.dtype = dtype + self.cond_token_dim = cond_token_dim + + # Timestep embeddings + timestep_features_dim = 256 + + self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device) + + self.to_timestep_embed = nn.Sequential( + operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device), + ) + + if cond_token_dim > 0: + # Conditioning tokens + + cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim + self.to_cond_embed = nn.Sequential( + operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device) + ) + else: + cond_embed_dim = 0 + + if global_cond_dim > 0: + # Global conditioning + global_embed_dim = global_cond_dim if not project_global_cond else embed_dim + self.to_global_embed = nn.Sequential( + operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device) + ) + + if prepend_cond_dim > 0: + # Prepend conditioning + self.to_prepend_embed = nn.Sequential( + operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) + ) + + self.input_concat_dim = input_concat_dim + + dim_in = io_channels + self.input_concat_dim + + self.patch_size = patch_size + + # Transformer + + self.transformer_type = transformer_type + + self.global_cond_type = global_cond_type + + if self.transformer_type == "continuous_transformer": + + global_dim = None + + if self.global_cond_type == "adaLN": + # The global conditioning is projected to the embed_dim already at this point + global_dim = embed_dim + + self.transformer = ContinuousTransformer( + dim=embed_dim, + depth=depth, + dim_heads=embed_dim // num_heads, + dim_in=dim_in * patch_size, + dim_out=io_channels * patch_size, + cross_attend = cond_token_dim > 0, + cond_token_dim = cond_embed_dim, + global_cond_dim=global_dim, + dtype=dtype, + device=device, + operations=operations, + **kwargs + ) + else: + raise ValueError(f"Unknown transformer type: {self.transformer_type}") + + self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device) + self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device) + + def _forward( + self, + x, + t, + mask=None, + cross_attn_cond=None, + cross_attn_cond_mask=None, + input_concat_cond=None, + global_embed=None, + prepend_cond=None, + prepend_cond_mask=None, + return_info=False, + **kwargs): + + if cross_attn_cond is not None: + cross_attn_cond = self.to_cond_embed(cross_attn_cond) + + if global_embed is not None: + # Project the global conditioning to the embedding dimension + global_embed = self.to_global_embed(global_embed) + + prepend_inputs = None + prepend_mask = None + prepend_length = 0 + if prepend_cond is not None: + # Project the prepend conditioning to the embedding dimension + prepend_cond = self.to_prepend_embed(prepend_cond) + + prepend_inputs = prepend_cond + if prepend_cond_mask is not None: + prepend_mask = prepend_cond_mask + + if input_concat_cond is not None: + + # Interpolate input_concat_cond to the same length as x + if input_concat_cond.shape[2] != x.shape[2]: + input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest') + + x = torch.cat([x, input_concat_cond], dim=1) + + # Get the batch of timestep embeddings + timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim) + + # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists + if global_embed is not None: + global_embed = global_embed + timestep_embed + else: + global_embed = timestep_embed + + # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer + if self.global_cond_type == "prepend": + if prepend_inputs is None: + # Prepend inputs are just the global embed, and the mask is all ones + prepend_inputs = global_embed.unsqueeze(1) + prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool) + else: + # Prepend inputs are the prepend conditioning + the global embed + prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1) + prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1) + + prepend_length = prepend_inputs.shape[1] + + x = self.preprocess_conv(x) + x + + x = rearrange(x, "b c t -> b t c") + + extra_args = {} + + if self.global_cond_type == "adaLN": + extra_args["global_cond"] = global_embed + + if self.patch_size > 1: + x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size) + + if self.transformer_type == "x-transformers": + output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs) + elif self.transformer_type == "continuous_transformer": + output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs) + + if return_info: + output, info = output + elif self.transformer_type == "mm_transformer": + output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs) + + output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:] + + if self.patch_size > 1: + output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size) + + output = self.postprocess_conv(output) + output + + if return_info: + return output, info + + return output + + def forward( + self, + x, + timestep, + context=None, + context_mask=None, + input_concat_cond=None, + global_embed=None, + negative_global_embed=None, + prepend_cond=None, + prepend_cond_mask=None, + mask=None, + return_info=False, + control=None, + transformer_options={}, + **kwargs): + return self._forward( + x, + timestep, + cross_attn_cond=context, + cross_attn_cond_mask=context_mask, + input_concat_cond=input_concat_cond, + global_embed=global_embed, + prepend_cond=prepend_cond, + prepend_cond_mask=prepend_cond_mask, + mask=mask, + return_info=return_info, + **kwargs + ) diff --git a/comfy/ldm/audio/embedders.py b/comfy/ldm/audio/embedders.py new file mode 100644 index 0000000..82a3210 --- /dev/null +++ b/comfy/ldm/audio/embedders.py @@ -0,0 +1,108 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +import torch +import torch.nn as nn +from torch import Tensor, einsum +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union +from einops import rearrange +import math +import comfy.ops + +class LearnedPositionalEmbedding(nn.Module): + """Used for continuous time""" + + def __init__(self, dim: int): + super().__init__() + assert (dim % 2) == 0 + half_dim = dim // 2 + self.weights = nn.Parameter(torch.empty(half_dim)) + + def forward(self, x: Tensor) -> Tensor: + x = rearrange(x, "b -> b 1") + freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi + fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1) + fouriered = torch.cat((x, fouriered), dim=-1) + return fouriered + +def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module: + return nn.Sequential( + LearnedPositionalEmbedding(dim), + comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features), + ) + + +class NumberEmbedder(nn.Module): + def __init__( + self, + features: int, + dim: int = 256, + ): + super().__init__() + self.features = features + self.embedding = TimePositionalEmbedding(dim=dim, out_features=features) + + def forward(self, x: Union[List[float], Tensor]) -> Tensor: + if not torch.is_tensor(x): + device = next(self.embedding.parameters()).device + x = torch.tensor(x, device=device) + assert isinstance(x, Tensor) + shape = x.shape + x = rearrange(x, "... -> (...)") + embedding = self.embedding(x) + x = embedding.view(*shape, self.features) + return x # type: ignore + + +class Conditioner(nn.Module): + def __init__( + self, + dim: int, + output_dim: int, + project_out: bool = False + ): + + super().__init__() + + self.dim = dim + self.output_dim = output_dim + self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity() + + def forward(self, x): + raise NotImplementedError() + +class NumberConditioner(Conditioner): + ''' + Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings + ''' + def __init__(self, + output_dim: int, + min_val: float=0, + max_val: float=1 + ): + super().__init__(output_dim, output_dim) + + self.min_val = min_val + self.max_val = max_val + + self.embedder = NumberEmbedder(features=output_dim) + + def forward(self, floats, device=None): + # Cast the inputs to floats + floats = [float(x) for x in floats] + + if device is None: + device = next(self.embedder.parameters()).device + + floats = torch.tensor(floats).to(device) + + floats = floats.clamp(self.min_val, self.max_val) + + normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val) + + # Cast floats to same type as embedder + embedder_dtype = next(self.embedder.parameters()).dtype + normalized_floats = normalized_floats.to(embedder_dtype) + + float_embeds = self.embedder(normalized_floats).unsqueeze(1) + + return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)] diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py new file mode 100644 index 0000000..cd9a421 --- /dev/null +++ b/comfy/ldm/aura/mmdit.py @@ -0,0 +1,478 @@ +#AuraFlow MMDiT +#Originally written by the AuraFlow Authors + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from comfy.ldm.modules.attention import optimized_attention +import comfy.ops +import comfy.ldm.common_dit + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def find_multiple(n: int, k: int) -> int: + if n % k == 0: + return n + return n + k - (n % k) + + +class MLP(nn.Module): + def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None: + super().__init__() + if hidden_dim is None: + hidden_dim = 4 * dim + + n_hidden = int(2 * hidden_dim / 3) + n_hidden = find_multiple(n_hidden, 256) + + self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device) + self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device) + self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = F.silu(self.c_fc1(x)) * self.c_fc2(x) + x = self.c_proj(x) + return x + + +class MultiHeadLayerNorm(nn.Module): + def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None): + # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78 + + super().__init__() + self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + mean = hidden_states.mean(-1, keepdim=True) + variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True) + hidden_states = (hidden_states - mean) * torch.rsqrt( + variance + self.variance_epsilon + ) + hidden_states = self.weight.to(torch.float32) * hidden_states + return hidden_states.to(input_dtype) + +class SingleAttention(nn.Module): + def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None): + super().__init__() + + self.n_heads = n_heads + self.head_dim = dim // n_heads + + # this is for cond + self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + self.q_norm1 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + self.k_norm1 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + + #@torch.compile() + def forward(self, c): + + bsz, seqlen1, _ = c.shape + + q, k, v = self.w1q(c), self.w1k(c), self.w1v(c) + q = q.view(bsz, seqlen1, self.n_heads, self.head_dim) + k = k.view(bsz, seqlen1, self.n_heads, self.head_dim) + v = v.view(bsz, seqlen1, self.n_heads, self.head_dim) + q, k = self.q_norm1(q), self.k_norm1(k) + + output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) + c = self.w1o(output) + return c + + + +class DoubleAttention(nn.Module): + def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None): + super().__init__() + + self.n_heads = n_heads + self.head_dim = dim // n_heads + + # this is for cond + self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + # this is for x + self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + self.q_norm1 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + self.k_norm1 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + + self.q_norm2 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + self.k_norm2 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + + + #@torch.compile() + def forward(self, c, x): + + bsz, seqlen1, _ = c.shape + bsz, seqlen2, _ = x.shape + seqlen = seqlen1 + seqlen2 + + cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c) + cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim) + ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim) + cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim) + cq, ck = self.q_norm1(cq), self.k_norm1(ck) + + xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x) + xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim) + xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim) + xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim) + xq, xk = self.q_norm2(xq), self.k_norm2(xk) + + # concat all + q, k, v = ( + torch.cat([cq, xq], dim=1), + torch.cat([ck, xk], dim=1), + torch.cat([cv, xv], dim=1), + ) + + output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) + + c, x = output.split([seqlen1, seqlen2], dim=1) + c = self.w1o(c) + x = self.w2o(x) + + return c, x + + +class MMDiTBlock(nn.Module): + def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None): + super().__init__() + + self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + if not is_last: + self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) + self.modC = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), + ) + else: + self.modC = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device), + ) + + self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) + self.modX = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), + ) + + self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations) + self.is_last = is_last + + #@torch.compile() + def forward(self, c, x, global_cond, **kwargs): + + cres, xres = c, x + + cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = ( + self.modC(global_cond).chunk(6, dim=1) + ) + + c = modulate(self.normC1(c), cshift_msa, cscale_msa) + + # xpath + xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = ( + self.modX(global_cond).chunk(6, dim=1) + ) + + x = modulate(self.normX1(x), xshift_msa, xscale_msa) + + # attention + c, x = self.attn(c, x) + + + c = self.normC2(cres + cgate_msa.unsqueeze(1) * c) + c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp)) + c = cres + c + + x = self.normX2(xres + xgate_msa.unsqueeze(1) * x) + x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp)) + x = xres + x + + return c, x + +class DiTBlock(nn.Module): + # like MMDiTBlock, but it only has X + def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None): + super().__init__() + + self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + + self.modCX = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), + ) + + self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations) + self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) + + #@torch.compile() + def forward(self, cx, global_cond, **kwargs): + cxres = cx + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX( + global_cond + ).chunk(6, dim=1) + cx = modulate(self.norm1(cx), shift_msa, scale_msa) + cx = self.attn(cx) + cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx) + mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp)) + cx = gate_mlp.unsqueeze(1) * mlpout + + cx = cxres + cx + + return cx + + + +class TimestepEmbedder(nn.Module): + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + half = dim // 2 + freqs = 1000 * torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half) / half + ).to(t.device) + args = t[:, None] * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + #@torch.compile() + def forward(self, t, dtype): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class MMDiT(nn.Module): + def __init__( + self, + in_channels=4, + out_channels=4, + patch_size=2, + dim=3072, + n_layers=36, + n_double_layers=4, + n_heads=12, + global_conddim=3072, + cond_seq_dim=2048, + max_seq=32 * 32, + device=None, + dtype=None, + operations=None, + ): + super().__init__() + self.dtype = dtype + + self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations) + + self.cond_seq_linear = operations.Linear( + cond_seq_dim, dim, bias=False, dtype=dtype, device=device + ) # linear for something like text sequence. + self.init_x_linear = operations.Linear( + patch_size * patch_size * in_channels, dim, dtype=dtype, device=device + ) # init linear for patchified image. + + self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device)) + self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device)) + + self.double_layers = nn.ModuleList([]) + self.single_layers = nn.ModuleList([]) + + + for idx in range(n_double_layers): + self.double_layers.append( + MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations) + ) + + for idx in range(n_double_layers, n_layers): + self.single_layers.append( + DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations) + ) + + + self.final_linear = operations.Linear( + dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device + ) + + self.modF = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device), + ) + + self.out_channels = out_channels + self.patch_size = patch_size + self.n_double_layers = n_double_layers + self.n_layers = n_layers + + self.h_max = round(max_seq**0.5) + self.w_max = round(max_seq**0.5) + + @torch.no_grad() + def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)): + # extend pe + pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]] + + pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1) + + # now we need to extend this to target_dim. for this we will use interpolation. + # we will use torch.nn.functional.interpolate + pe_as_2d = F.interpolate( + pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear" + ) + pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1) + self.positional_encoding.data = pe_new.unsqueeze(0).contiguous() + self.h_max, self.w_max = target_dim + print("PE extended to", target_dim) + + def pe_selection_index_based_on_dim(self, h, w): + h_p, w_p = h // self.patch_size, w // self.patch_size + original_pe_indexes = torch.arange(self.positional_encoding.shape[1]) + original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max) + starth = self.h_max // 2 - h_p // 2 + endh =starth + h_p + startw = self.w_max // 2 - w_p // 2 + endw = startw + w_p + original_pe_indexes = original_pe_indexes[ + starth:endh, startw:endw + ] + return original_pe_indexes.flatten() + + def unpatchify(self, x, h, w): + c = self.out_channels + p = self.patch_size + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum("nhwpqc->nchpwq", x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs + + def patchify(self, x): + B, C, H, W = x.size() + x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + x = x.view( + B, + C, + (H + 1) // self.patch_size, + self.patch_size, + (W + 1) // self.patch_size, + self.patch_size, + ) + x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2) + return x + + def apply_pos_embeds(self, x, h, w): + h = (h + 1) // self.patch_size + w = (w + 1) // self.patch_size + max_dim = max(h, w) + + cur_dim = self.h_max + pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x) + + if max_dim > cur_dim: + pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1) + cur_dim = max_dim + + from_h = (cur_dim - h) // 2 + from_w = (cur_dim - w) // 2 + pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w] + return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1]) + + def forward(self, x, timestep, context, **kwargs): + # patchify x, add PE + b, c, h, w = x.shape + + # pe_indexes = self.pe_selection_index_based_on_dim(h, w) + # print(pe_indexes, pe_indexes.shape) + + x = self.init_x_linear(self.patchify(x)) # B, T_x, D + x = self.apply_pos_embeds(x, h, w) + # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype) + # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype) + + # process conditions for MMDiT Blocks + c_seq = context # B, T_c, D_c + t = timestep + + c = self.cond_seq_linear(c_seq) # B, T_c, D + c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1) + + global_cond = self.t_embedder(t, x.dtype) # B, D + + if len(self.double_layers) > 0: + for layer in self.double_layers: + c, x = layer(c, x, global_cond, **kwargs) + + if len(self.single_layers) > 0: + c_len = c.size(1) + cx = torch.cat([c, x], dim=1) + for layer in self.single_layers: + cx = layer(cx, global_cond, **kwargs) + + x = cx[:, c_len:] + + fshift, fscale = self.modF(global_cond).chunk(2, dim=1) + + x = modulate(x, fshift, fscale) + x = self.final_linear(x) + x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w] + return x diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py new file mode 100644 index 0000000..3eaa0c8 --- /dev/null +++ b/comfy/ldm/cascade/common.py @@ -0,0 +1,154 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import torch.nn as nn +from comfy.ldm.modules.attention import optimized_attention +import comfy.ops + +class OptimizedAttention(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.heads = nhead + + self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + def forward(self, q, k, v): + q = self.to_q(q) + k = self.to_k(k) + v = self.to_v(v) + + out = optimized_attention(q, k, v, self.heads) + + return self.out_proj(out) + +class Attention2D(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations) + # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device) + + def forward(self, x, kv, self_attn=False): + orig_shape = x.shape + x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4 + if self_attn: + kv = torch.cat([x, kv], dim=1) + # x = self.attn(x, kv, kv, need_weights=False)[0] + x = self.attn(x, kv, kv) + x = x.permute(0, 2, 1).view(*orig_shape) + return x + + +def LayerNorm2d_op(operations): + class LayerNorm2d(operations.LayerNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x): + return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return LayerNorm2d + +class GlobalResponseNorm(nn.Module): + "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105" + def __init__(self, dim, dtype=None, device=None): + super().__init__() + self.gamma = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device)) + self.beta = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) + Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) + return comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x + + +class ResBlock(nn.Module): + def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): # , num_heads=4, expansion=2): + super().__init__() + self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device) + # self.depthwise = SAMBlock(c, num_heads, expansion) + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.channelwise = nn.Sequential( + operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device), + nn.GELU(), + GlobalResponseNorm(c * 4, dtype=dtype, device=device), + nn.Dropout(dropout), + operations.Linear(c * 4, c, dtype=dtype, device=device) + ) + + def forward(self, x, x_skip=None): + x_res = x + x = self.norm(self.depthwise(x)) + if x_skip is not None: + x = torch.cat([x, x_skip], dim=1) + x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return x + x_res + + +class AttnBlock(nn.Module): + def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.self_attn = self_attn + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations) + self.kv_mapper = nn.Sequential( + nn.SiLU(), + operations.Linear(c_cond, c, dtype=dtype, device=device) + ) + + def forward(self, x, kv): + kv = self.kv_mapper(kv) + x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn) + return x + + +class FeedForwardBlock(nn.Module): + def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.channelwise = nn.Sequential( + operations.Linear(c, c * 4, dtype=dtype, device=device), + nn.GELU(), + GlobalResponseNorm(c * 4, dtype=dtype, device=device), + nn.Dropout(dropout), + operations.Linear(c * 4, c, dtype=dtype, device=device) + ) + + def forward(self, x): + x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return x + + +class TimestepBlock(nn.Module): + def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None): + super().__init__() + self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device) + self.conds = conds + for cname in conds: + setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)) + + def forward(self, x, t): + t = t.chunk(len(self.conds) + 1, dim=1) + a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1) + for i, c in enumerate(self.conds): + ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1) + a, b = a + ac, b + bc + return x * (1 + a) + b diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py new file mode 100644 index 0000000..7a52c3c --- /dev/null +++ b/comfy/ldm/cascade/controlnet.py @@ -0,0 +1,93 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import torchvision +from torch import nn +from .common import LayerNorm2d_op + + +class CNetResBlock(nn.Module): + def __init__(self, c, dtype=None, device=None, operations=None): + super().__init__() + self.blocks = nn.Sequential( + LayerNorm2d_op(operations)(c, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c, c, kernel_size=3, padding=1), + LayerNorm2d_op(operations)(c, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c, c, kernel_size=3, padding=1), + ) + + def forward(self, x): + return x + self.blocks(x) + + +class ControlNet(nn.Module): + def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn): + super().__init__() + if bottleneck_mode is None: + bottleneck_mode = 'effnet' + self.proj_blocks = proj_blocks + if bottleneck_mode == 'effnet': + embd_channels = 1280 + self.backbone = torchvision.models.efficientnet_v2_s().features.eval() + if c_in != 3: + in_weights = self.backbone[0][0].weight.data + self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device) + if c_in > 3: + # nn.init.constant_(self.backbone[0][0].weight, 0) + self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone() + else: + self.backbone[0][0].weight.data = in_weights[:, :c_in].clone() + elif bottleneck_mode == 'simple': + embd_channels = c_in + self.backbone = nn.Sequential( + operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device), + ) + elif bottleneck_mode == 'large': + self.backbone = nn.Sequential( + operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device), + *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)], + operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device), + ) + embd_channels = 1280 + else: + raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}') + self.projections = nn.ModuleList() + for _ in range(len(proj_blocks)): + self.projections.append(nn.Sequential( + operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device), + )) + # nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection + self.xl = False + self.input_channels = c_in + self.unshuffle_amount = 8 + + def forward(self, x): + x = self.backbone(x) + proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] + for i, idx in enumerate(self.proj_blocks): + proj_outputs[idx] = self.projections[i](x) + return {"input": proj_outputs[::-1]} diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py new file mode 100644 index 0000000..ca8867e --- /dev/null +++ b/comfy/ldm/cascade/stage_a.py @@ -0,0 +1,255 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +from torch import nn +from torch.autograd import Function + +class vector_quantize(Function): + @staticmethod + def forward(ctx, x, codebook): + with torch.no_grad(): + codebook_sqr = torch.sum(codebook ** 2, dim=1) + x_sqr = torch.sum(x ** 2, dim=1, keepdim=True) + + dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0) + _, indices = dist.min(dim=1) + + ctx.save_for_backward(indices, codebook) + ctx.mark_non_differentiable(indices) + + nn = torch.index_select(codebook, 0, indices) + return nn, indices + + @staticmethod + def backward(ctx, grad_output, grad_indices): + grad_inputs, grad_codebook = None, None + + if ctx.needs_input_grad[0]: + grad_inputs = grad_output.clone() + if ctx.needs_input_grad[1]: + # Gradient wrt. the codebook + indices, codebook = ctx.saved_tensors + + grad_codebook = torch.zeros_like(codebook) + grad_codebook.index_add_(0, indices, grad_output) + + return (grad_inputs, grad_codebook) + + +class VectorQuantize(nn.Module): + def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False): + """ + Takes an input of variable size (as long as the last dimension matches the embedding size). + Returns one tensor containing the nearest neigbour embeddings to each of the inputs, + with the same size as the input, vq and commitment components for the loss as a touple + in the second output and the indices of the quantized vectors in the third: + quantized, (vq_loss, commit_loss), indices + """ + super(VectorQuantize, self).__init__() + + self.codebook = nn.Embedding(k, embedding_size) + self.codebook.weight.data.uniform_(-1./k, 1./k) + self.vq = vector_quantize.apply + + self.ema_decay = ema_decay + self.ema_loss = ema_loss + if ema_loss: + self.register_buffer('ema_element_count', torch.ones(k)) + self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight)) + + def _laplace_smoothing(self, x, epsilon): + n = torch.sum(x) + return ((x + epsilon) / (n + x.size(0) * epsilon) * n) + + def _updateEMA(self, z_e_x, indices): + mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float() + elem_count = mask.sum(dim=0) + weight_sum = torch.mm(mask.t(), z_e_x) + + self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count) + self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5) + self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum) + + self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1) + + def idx2vq(self, idx, dim=-1): + q_idx = self.codebook(idx) + if dim != -1: + q_idx = q_idx.movedim(-1, dim) + return q_idx + + def forward(self, x, get_losses=True, dim=-1): + if dim != -1: + x = x.movedim(dim, -1) + z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x + z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach()) + vq_loss, commit_loss = None, None + if self.ema_loss and self.training: + self._updateEMA(z_e_x.detach(), indices.detach()) + # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss + z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices) + if get_losses: + vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean() + commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean() + + z_q_x = z_q_x.view(x.shape) + if dim != -1: + z_q_x = z_q_x.movedim(-1, dim) + return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1]) + + +class ResBlock(nn.Module): + def __init__(self, c, c_hidden): + super().__init__() + # depthwise/attention + self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) + self.depthwise = nn.Sequential( + nn.ReplicationPad2d(1), + nn.Conv2d(c, c, kernel_size=3, groups=c) + ) + + # channelwise + self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) + self.channelwise = nn.Sequential( + nn.Linear(c, c_hidden), + nn.GELU(), + nn.Linear(c_hidden, c), + ) + + self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True) + + # Init weights + def _basic_init(module): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + + self.apply(_basic_init) + + def _norm(self, x, norm): + return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + + def forward(self, x): + mods = self.gammas + + x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1] + try: + x = x + self.depthwise(x_temp) * mods[2] + except: #operation not implemented for bf16 + x_temp = self.depthwise[0](x_temp.float()).to(x.dtype) + x = x + self.depthwise[1](x_temp) * mods[2] + + x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4] + x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5] + + return x + + +class StageA(nn.Module): + def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192): + super().__init__() + self.c_latent = c_latent + c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))] + + # Encoder blocks + self.in_block = nn.Sequential( + nn.PixelUnshuffle(2), + nn.Conv2d(3 * 4, c_levels[0], kernel_size=1) + ) + down_blocks = [] + for i in range(levels): + if i > 0: + down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1)) + block = ResBlock(c_levels[i], c_levels[i] * 4) + down_blocks.append(block) + down_blocks.append(nn.Sequential( + nn.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False), + nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1 + )) + self.down_blocks = nn.Sequential(*down_blocks) + self.down_blocks[0] + + self.codebook_size = codebook_size + self.vquantizer = VectorQuantize(c_latent, k=codebook_size) + + # Decoder blocks + up_blocks = [nn.Sequential( + nn.Conv2d(c_latent, c_levels[-1], kernel_size=1) + )] + for i in range(levels): + for j in range(bottleneck_blocks if i == 0 else 1): + block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4) + up_blocks.append(block) + if i < levels - 1: + up_blocks.append( + nn.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, + padding=1)) + self.up_blocks = nn.Sequential(*up_blocks) + self.out_block = nn.Sequential( + nn.Conv2d(c_levels[0], 3 * 4, kernel_size=1), + nn.PixelShuffle(2), + ) + + def encode(self, x, quantize=False): + x = self.in_block(x) + x = self.down_blocks(x) + if quantize: + qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1) + return qe, x, indices, vq_loss + commit_loss * 0.25 + else: + return x + + def decode(self, x): + x = self.up_blocks(x) + x = self.out_block(x) + return x + + def forward(self, x, quantize=False): + qe, x, _, vq_loss = self.encode(x, quantize) + x = self.decode(qe) + return x, vq_loss + + +class Discriminator(nn.Module): + def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6): + super().__init__() + d = max(depth - 3, 3) + layers = [ + nn.utils.spectral_norm(nn.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)), + nn.LeakyReLU(0.2), + ] + for i in range(depth - 1): + c_in = c_hidden // (2 ** max((d - i), 0)) + c_out = c_hidden // (2 ** max((d - 1 - i), 0)) + layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) + layers.append(nn.InstanceNorm2d(c_out)) + layers.append(nn.LeakyReLU(0.2)) + self.encoder = nn.Sequential(*layers) + self.shuffle = nn.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1) + self.logits = nn.Sigmoid() + + def forward(self, x, cond=None): + x = self.encoder(x) + if cond is not None: + cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1)) + x = torch.cat([x, cond], dim=1) + x = self.shuffle(x) + x = self.logits(x) + return x diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py new file mode 100644 index 0000000..7c3d8fe --- /dev/null +++ b/comfy/ldm/cascade/stage_b.py @@ -0,0 +1,256 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import math +import torch +from torch import nn +from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock + +class StageB(nn.Module): + def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280], + nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]], + block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280, + c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True, + t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None): + super().__init__() + self.dtype = dtype + self.c_r = c_r + self.t_conds = t_conds + self.c_clip_seq = c_clip_seq + if not isinstance(dropout, list): + dropout = [dropout] * len(c_hidden) + if not isinstance(self_attn, list): + self_attn = [self_attn] * len(c_hidden) + + # CONDITIONING + self.effnet_mapper = nn.Sequential( + operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + self.pixels_mapper = nn.Sequential( + operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.embedding = nn.Sequential( + nn.PixelUnshuffle(patch_size), + operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + + def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): + if block_type == 'C': + return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'A': + return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'F': + return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'T': + return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) + else: + raise Exception(f'Block type {block_type} not supported') + + # BLOCKS + # -- down blocks + self.down_blocks = nn.ModuleList() + self.down_downscalers = nn.ModuleList() + self.down_repeat_mappers = nn.ModuleList() + for i in range(len(c_hidden)): + if i > 0: + self.down_downscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device), + )) + else: + self.down_downscalers.append(nn.Identity()) + down_block = nn.ModuleList() + for _ in range(blocks[0][i]): + for block_type in level_config[i]: + block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) + down_block.append(block) + self.down_blocks.append(down_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[0][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.down_repeat_mappers.append(block_repeat_mappers) + + # -- up blocks + self.up_blocks = nn.ModuleList() + self.up_upscalers = nn.ModuleList() + self.up_repeat_mappers = nn.ModuleList() + for i in reversed(range(len(c_hidden))): + if i > 0: + self.up_upscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device), + )) + else: + self.up_upscalers.append(nn.Identity()) + up_block = nn.ModuleList() + for j in range(blocks[1][::-1][i]): + for k, block_type in enumerate(level_config[i]): + c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 + block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], + self_attn=self_attn[i]) + up_block.append(block) + self.up_blocks.append(up_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[1][::-1][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.up_repeat_mappers.append(block_repeat_mappers) + + # OUTPUT + self.clf = nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), + nn.PixelShuffle(patch_size), + ) + + # --- WEIGHT INIT --- + # self.apply(self._init_weights) # General init + # nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings + # nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings + # nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings + # nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings + # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs + # nn.init.constant_(self.clf[1].weight, 0) # outputs + # + # # blocks + # for level_block in self.down_blocks + self.up_blocks: + # for block in level_block: + # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): + # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) + # elif isinstance(block, TimestepBlock): + # for layer in block.modules(): + # if isinstance(layer, nn.Linear): + # nn.init.constant_(layer.weight, 0) + # + # def _init_weights(self, m): + # if isinstance(m, (nn.Conv2d, nn.Linear)): + # torch.nn.init.xavier_uniform_(m.weight) + # if m.bias is not None: + # nn.init.constant_(m.bias, 0) + + def gen_r_embedding(self, r, max_positions=10000): + r = r * max_positions + half_dim = self.c_r // 2 + emb = math.log(max_positions) / (half_dim - 1) + emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() + emb = r[:, None] * emb[None, :] + emb = torch.cat([emb.sin(), emb.cos()], dim=1) + if self.c_r % 2 == 1: # zero pad + emb = nn.functional.pad(emb, (0, 1), mode='constant') + return emb + + def gen_c_embeddings(self, clip): + if len(clip.shape) == 2: + clip = clip.unsqueeze(1) + clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1) + clip = self.clip_norm(clip) + return clip + + def _down_encode(self, x, r_embed, clip): + level_outputs = [] + block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) + for down_block, downscaler, repmap in block_group: + x = downscaler(x) + for i in range(len(repmap) + 1): + for block in down_block: + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + x = block(x) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if i < len(repmap): + x = repmap[i](x) + level_outputs.insert(0, x) + return level_outputs + + def _up_decode(self, level_outputs, r_embed, clip): + x = level_outputs[0] + block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) + for i, (up_block, upscaler, repmap) in enumerate(block_group): + for j in range(len(repmap) + 1): + for k, block in enumerate(up_block): + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + skip = level_outputs[i] if k == 0 and i > 0 else None + if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): + x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', + align_corners=True) + x = block(x, skip) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if j < len(repmap): + x = repmap[j](x) + x = upscaler(x) + return x + + def forward(self, x, r, effnet, clip, pixels=None, **kwargs): + if pixels is None: + pixels = x.new_zeros(x.size(0), 3, 8, 8) + + # Process the conditioning embeddings + r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) + for c in self.t_conds: + t_cond = kwargs.get(c, torch.zeros_like(r)) + r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) + clip = self.gen_c_embeddings(clip) + + # Model Blocks + x = self.embedding(x) + x = x + self.effnet_mapper( + nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True)) + x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear', + align_corners=True) + level_outputs = self._down_encode(x, r_embed, clip) + x = self._up_decode(level_outputs, r_embed, clip) + return self.clf(x) + + def update_weights_ema(self, src_model, beta=0.999): + for self_params, src_params in zip(self.parameters(), src_model.parameters()): + self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) + for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): + self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py new file mode 100644 index 0000000..c85da1f --- /dev/null +++ b/comfy/ldm/cascade/stage_c.py @@ -0,0 +1,273 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +from torch import nn +import math +from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock +# from .controlnet import ControlNetDeliverer + +class UpDownBlock2d(nn.Module): + def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None): + super().__init__() + assert mode in ['up', 'down'] + interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear', + align_corners=True) if enabled else nn.Identity() + mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device) + self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation]) + + def forward(self, x): + for block in self.blocks: + x = block(x) + return x + + +class StageC(nn.Module): + def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32], + blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'], + c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3, + dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None, + dtype=None, device=None, operations=None): + super().__init__() + self.dtype = dtype + self.c_r = c_r + self.t_conds = t_conds + self.c_clip_seq = c_clip_seq + if not isinstance(dropout, list): + dropout = [dropout] * len(c_hidden) + if not isinstance(self_attn, list): + self_attn = [self_attn] * len(c_hidden) + + # CONDITIONING + self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device) + self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.embedding = nn.Sequential( + nn.PixelUnshuffle(patch_size), + operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6) + ) + + def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): + if block_type == 'C': + return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'A': + return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'F': + return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'T': + return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) + else: + raise Exception(f'Block type {block_type} not supported') + + # BLOCKS + # -- down blocks + self.down_blocks = nn.ModuleList() + self.down_downscalers = nn.ModuleList() + self.down_repeat_mappers = nn.ModuleList() + for i in range(len(c_hidden)): + if i > 0: + self.down_downscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6), + UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) + )) + else: + self.down_downscalers.append(nn.Identity()) + down_block = nn.ModuleList() + for _ in range(blocks[0][i]): + for block_type in level_config[i]: + block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) + down_block.append(block) + self.down_blocks.append(down_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[0][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.down_repeat_mappers.append(block_repeat_mappers) + + # -- up blocks + self.up_blocks = nn.ModuleList() + self.up_upscalers = nn.ModuleList() + self.up_repeat_mappers = nn.ModuleList() + for i in reversed(range(len(c_hidden))): + if i > 0: + self.up_upscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6), + UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) + )) + else: + self.up_upscalers.append(nn.Identity()) + up_block = nn.ModuleList() + for j in range(blocks[1][::-1][i]): + for k, block_type in enumerate(level_config[i]): + c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 + block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], + self_attn=self_attn[i]) + up_block.append(block) + self.up_blocks.append(up_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[1][::-1][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.up_repeat_mappers.append(block_repeat_mappers) + + # OUTPUT + self.clf = nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), + nn.PixelShuffle(patch_size), + ) + + # --- WEIGHT INIT --- + # self.apply(self._init_weights) # General init + # nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.clip_img_mapper.weight, std=0.02) # conditionings + # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs + # nn.init.constant_(self.clf[1].weight, 0) # outputs + # + # # blocks + # for level_block in self.down_blocks + self.up_blocks: + # for block in level_block: + # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): + # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) + # elif isinstance(block, TimestepBlock): + # for layer in block.modules(): + # if isinstance(layer, nn.Linear): + # nn.init.constant_(layer.weight, 0) + # + # def _init_weights(self, m): + # if isinstance(m, (nn.Conv2d, nn.Linear)): + # torch.nn.init.xavier_uniform_(m.weight) + # if m.bias is not None: + # nn.init.constant_(m.bias, 0) + + def gen_r_embedding(self, r, max_positions=10000): + r = r * max_positions + half_dim = self.c_r // 2 + emb = math.log(max_positions) / (half_dim - 1) + emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() + emb = r[:, None] * emb[None, :] + emb = torch.cat([emb.sin(), emb.cos()], dim=1) + if self.c_r % 2 == 1: # zero pad + emb = nn.functional.pad(emb, (0, 1), mode='constant') + return emb + + def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img): + clip_txt = self.clip_txt_mapper(clip_txt) + if len(clip_txt_pooled.shape) == 2: + clip_txt_pooled = clip_txt_pooled.unsqueeze(1) + if len(clip_img.shape) == 2: + clip_img = clip_img.unsqueeze(1) + clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1) + clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1) + clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1) + clip = self.clip_norm(clip) + return clip + + def _down_encode(self, x, r_embed, clip, cnet=None): + level_outputs = [] + block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) + for down_block, downscaler, repmap in block_group: + x = downscaler(x) + for i in range(len(repmap) + 1): + for block in down_block: + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + if cnet is not None: + next_cnet = cnet.pop() + if next_cnet is not None: + x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', + align_corners=True).to(x.dtype) + x = block(x) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if i < len(repmap): + x = repmap[i](x) + level_outputs.insert(0, x) + return level_outputs + + def _up_decode(self, level_outputs, r_embed, clip, cnet=None): + x = level_outputs[0] + block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) + for i, (up_block, upscaler, repmap) in enumerate(block_group): + for j in range(len(repmap) + 1): + for k, block in enumerate(up_block): + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + skip = level_outputs[i] if k == 0 and i > 0 else None + if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): + x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', + align_corners=True) + if cnet is not None: + next_cnet = cnet.pop() + if next_cnet is not None: + x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', + align_corners=True).to(x.dtype) + x = block(x, skip) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if j < len(repmap): + x = repmap[j](x) + x = upscaler(x) + return x + + def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs): + # Process the conditioning embeddings + r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) + for c in self.t_conds: + t_cond = kwargs.get(c, torch.zeros_like(r)) + r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) + clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img) + + if control is not None: + cnet = control.get("input") + else: + cnet = None + + # Model Blocks + x = self.embedding(x) + level_outputs = self._down_encode(x, r_embed, clip, cnet) + x = self._up_decode(level_outputs, r_embed, clip, cnet) + return self.clf(x) + + def update_weights_ema(self, src_model, beta=0.999): + for self_params, src_params in zip(self.parameters(), src_model.parameters()): + self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) + for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): + self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) diff --git a/comfy/ldm/cascade/stage_c_coder.py b/comfy/ldm/cascade/stage_c_coder.py new file mode 100644 index 0000000..0cb7c49 --- /dev/null +++ b/comfy/ldm/cascade/stage_c_coder.py @@ -0,0 +1,95 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" +import torch +import torchvision +from torch import nn + + +# EfficientNet +class EfficientNetEncoder(nn.Module): + def __init__(self, c_latent=16): + super().__init__() + self.backbone = torchvision.models.efficientnet_v2_s().features.eval() + self.mapper = nn.Sequential( + nn.Conv2d(1280, c_latent, kernel_size=1, bias=False), + nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1 + ) + self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406])) + self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225])) + + def forward(self, x): + x = x * 0.5 + 0.5 + x = (x - self.mean.view([3,1,1])) / self.std.view([3,1,1]) + o = self.mapper(self.backbone(x)) + return o + + +# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192 +class Previewer(nn.Module): + def __init__(self, c_in=16, c_hidden=512, c_out=3): + super().__init__() + self.blocks = nn.Sequential( + nn.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels + nn.GELU(), + nn.BatchNorm2d(c_hidden), + + nn.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden), + + nn.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 2), + + nn.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 2), + + nn.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + nn.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + nn.Conv2d(c_hidden // 4, c_out, kernel_size=1), + ) + + def forward(self, x): + return (self.blocks(x) - 0.5) * 2.0 + +class StageC_coder(nn.Module): + def __init__(self): + super().__init__() + self.previewer = Previewer() + self.encoder = EfficientNetEncoder() + + def encode(self, x): + return self.encoder(x) + + def decode(self, x): + return self.previewer(x) diff --git a/comfy/ldm/common_dit.py b/comfy/ldm/common_dit.py new file mode 100644 index 0000000..cb6b741 --- /dev/null +++ b/comfy/ldm/common_dit.py @@ -0,0 +1,27 @@ +import torch +import comfy.ops + +def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"): + if padding_mode == "circular" and torch.jit.is_tracing() or torch.jit.is_scripting(): + padding_mode = "reflect" + pad_h = (patch_size[0] - img.shape[-2] % patch_size[0]) % patch_size[0] + pad_w = (patch_size[1] - img.shape[-1] % patch_size[1]) % patch_size[1] + return torch.nn.functional.pad(img, (0, pad_w, 0, pad_h), mode=padding_mode) + +try: + rms_norm_torch = torch.nn.functional.rms_norm +except: + rms_norm_torch = None + +def rms_norm(x, weight=None, eps=1e-6): + if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()): + if weight is None: + return rms_norm_torch(x, (x.shape[-1],), eps=eps) + else: + return rms_norm_torch(x, weight.shape, weight=comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps) + else: + r = x * torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps) + if weight is None: + return r + else: + return r * comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device) diff --git a/comfy/ldm/flux/controlnet.py b/comfy/ldm/flux/controlnet.py new file mode 100644 index 0000000..c033dea --- /dev/null +++ b/comfy/ldm/flux/controlnet.py @@ -0,0 +1,205 @@ +#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py +#modified to support different types of flux controlnets + +import torch +import math +from torch import Tensor, nn +from einops import rearrange, repeat + +from .layers import (DoubleStreamBlock, EmbedND, LastLayer, + MLPEmbedder, SingleStreamBlock, + timestep_embedding) + +from .model import Flux +import comfy.ldm.common_dit + +class MistolineCondDownsamplBlock(nn.Module): + def __init__(self, dtype=None, device=None, operations=None): + super().__init__() + self.encoder = nn.Sequential( + operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) + ) + + def forward(self, x): + return self.encoder(x) + +class MistolineControlnetBlock(nn.Module): + def __init__(self, hidden_size, dtype=None, device=None, operations=None): + super().__init__() + self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device) + self.act = nn.SiLU() + + def forward(self, x): + return self.act(self.linear(x)) + + +class ControlNetFlux(Flux): + def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs): + super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs) + + self.main_model_double = 19 + self.main_model_single = 38 + + self.mistoline = mistoline + # add ControlNet blocks + if self.mistoline: + control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations) + else: + control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) + + self.controlnet_blocks = nn.ModuleList([]) + for _ in range(self.params.depth): + self.controlnet_blocks.append(control_block()) + + self.controlnet_single_blocks = nn.ModuleList([]) + for _ in range(self.params.depth_single_blocks): + self.controlnet_single_blocks.append(control_block()) + + self.num_union_modes = num_union_modes + self.controlnet_mode_embedder = None + if self.num_union_modes > 0: + self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device) + + self.gradient_checkpointing = False + self.latent_input = latent_input + if control_latent_channels is None: + control_latent_channels = self.in_channels + else: + control_latent_channels *= 2 * 2 #patch size + + self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device) + if not self.latent_input: + if self.mistoline: + self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations) + else: + self.input_hint_block = nn.Sequential( + operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) + ) + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + controlnet_cond: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control_type: Tensor = None, + ) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + + controlnet_cond = self.pos_embed_input(controlnet_cond) + img = img + controlnet_cond + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + if self.controlnet_mode_embedder is not None and len(control_type) > 0: + control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1)) + txt = torch.cat([control_cond, txt], dim=1) + txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + controlnet_double = () + + for i in range(len(self.double_blocks)): + img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe) + controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),) + + img = torch.cat((txt, img), 1) + + controlnet_single = () + + for i in range(len(self.single_blocks)): + img = self.single_blocks[i](img, vec=vec, pe=pe) + controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),) + + repeat = math.ceil(self.main_model_double / len(controlnet_double)) + if self.latent_input: + out_input = () + for x in controlnet_double: + out_input += (x,) * repeat + else: + out_input = (controlnet_double * repeat) + + out = {"input": out_input[:self.main_model_double]} + if len(controlnet_single) > 0: + repeat = math.ceil(self.main_model_single / len(controlnet_single)) + out_output = () + if self.latent_input: + for x in controlnet_single: + out_output += (x,) * repeat + else: + out_output = (controlnet_single * repeat) + out["output"] = out_output[:self.main_model_single] + return out + + def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs): + patch_size = 2 + if self.latent_input: + hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size)) + elif self.mistoline: + hint = hint * 2.0 - 1.0 + hint = self.input_cond_block(hint) + else: + hint = hint * 2.0 - 1.0 + hint = self.input_hint_block(hint) + + hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + + bs, c, h, w = x.shape + x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) + + img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + + h_len = ((h + (patch_size // 2)) // patch_size) + w_len = ((w + (patch_size // 2)) // patch_size) + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None] + img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :] + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", [])) diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py new file mode 100644 index 0000000..dabab3e --- /dev/null +++ b/comfy/ldm/flux/layers.py @@ -0,0 +1,249 @@ +import math +from dataclasses import dataclass + +import torch +from torch import Tensor, nn + +from .math import attention, rope +import comfy.ops +import comfy.ldm.common_dit + + +class EmbedND(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: list): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: Tensor) -> Tensor: + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + + return emb.unsqueeze(1) + + +def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + t = time_factor * t + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half) + + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(t) + return embedding + +class MLPEmbedder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None): + super().__init__() + self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) + self.silu = nn.SiLU() + self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x: Tensor) -> Tensor: + return self.out_layer(self.silu(self.in_layer(x))) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, dtype=None, device=None, operations=None): + super().__init__() + self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device)) + + def forward(self, x: Tensor): + return comfy.ldm.common_dit.rms_norm(x, self.scale, 1e-6) + + +class QKNorm(torch.nn.Module): + def __init__(self, dim: int, dtype=None, device=None, operations=None): + super().__init__() + self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) + self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple: + q = self.query_norm(q) + k = self.key_norm(k) + return q.to(v), k.to(v) + + +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + + +@dataclass +class ModulationOut: + shift: Tensor + scale: Tensor + gate: Tensor + + +class Modulation(nn.Module): + def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None): + super().__init__() + self.is_double = double + self.multiplier = 6 if double else 3 + self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device) + + def forward(self, vec: Tensor) -> tuple: + out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) + + return ( + ModulationOut(*out[:3]), + ModulationOut(*out[3:]) if self.is_double else None, + ) + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor): + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + attn = attention(torch.cat((txt_q, img_q), dim=2), + torch.cat((txt_k, img_k), dim=2), + torch.cat((txt_v, img_v), dim=2), pe=pe) + + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + + return img, txt + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float = None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) + # proj and mlp_out + self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) + + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + + self.hidden_size = hidden_size + self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.mlp_act = nn.GELU(approximate="tanh") + self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor: + mod, _ = self.modulation(vec) + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + x += mod.gate * output + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) + return x + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py new file mode 100644 index 0000000..136ce2a --- /dev/null +++ b/comfy/ldm/flux/math.py @@ -0,0 +1,35 @@ +import torch +from einops import rearrange +from torch import Tensor +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor: + q, k = apply_rope(q, k, pe) + + heads = q.shape[1] + x = optimized_attention(q, k, v, heads, skip_reshape=True) + return x + + +def rope(pos: Tensor, dim: int, theta: int) -> Tensor: + assert dim % 2 == 0 + if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu(): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.to(dtype=torch.float32, device=pos.device) + + +def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py new file mode 100644 index 0000000..233d783 --- /dev/null +++ b/comfy/ldm/flux/model.py @@ -0,0 +1,160 @@ +#Original code can be found on: https://github.com/black-forest-labs/flux + +from dataclasses import dataclass + +import torch +from torch import Tensor, nn + +from .layers import ( + DoubleStreamBlock, + EmbedND, + LastLayer, + MLPEmbedder, + SingleStreamBlock, + timestep_embedding, +) + +from einops import rearrange, repeat +import comfy.ldm.common_dit + +@dataclass +class FluxParams: + in_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list + theta: int + qkv_bias: bool + guidance_embed: bool + + +class Flux(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + params = FluxParams(**kwargs) + self.params = params + self.in_channels = params.in_channels * 2 * 2 + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() + ) + self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) + for _ in range(params.depth_single_blocks) + ] + ) + + if final_layer: + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control=None, + ) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + + vec = vec + self.vector_in(y[:,:self.params.vec_in_dim]) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + for i, block in enumerate(self.double_blocks): + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + img = block(img, vec=vec, pe=pe) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img + + def forward(self, x, timestep, context, y, guidance, control=None, **kwargs): + bs, c, h, w = x.shape + patch_size = 2 + x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) + + img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + + h_len = ((h + (patch_size // 2)) // patch_size) + w_len = ((w + (patch_size // 2)) // patch_size) + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control) + return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w] diff --git a/comfy/ldm/genmo/joint_model/asymm_models_joint.py b/comfy/ldm/genmo/joint_model/asymm_models_joint.py new file mode 100644 index 0000000..c36a000 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/asymm_models_joint.py @@ -0,0 +1,541 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license +#adapted to ComfyUI + +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +# from flash_attn import flash_attn_varlen_qkvpacked_func +from comfy.ldm.modules.attention import optimized_attention + +from .layers import ( + FeedForward, + PatchEmbed, + RMSNorm, + TimestepEmbedder, +) + +from .rope_mixed import ( + compute_mixed_rotation, + create_position_matrix, +) +from .temporal_rope import apply_rotary_emb_qk_real +from .utils import ( + AttentionPool, + modulate, +) + +import comfy.ldm.common_dit +import comfy.ops + + +def modulated_rmsnorm(x, scale, eps=1e-6): + # Normalize and modulate + x_normed = comfy.ldm.common_dit.rms_norm(x, eps=eps) + x_modulated = x_normed * (1 + scale.unsqueeze(1)) + + return x_modulated + + +def residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6): + # Apply tanh to gate + tanh_gate = torch.tanh(gate).unsqueeze(1) + + # Normalize and apply gated scaling + x_normed = comfy.ldm.common_dit.rms_norm(x_res, eps=eps) * tanh_gate + + # Apply residual connection + output = x + x_normed + + return output + +class AsymmetricAttention(nn.Module): + def __init__( + self, + dim_x: int, + dim_y: int, + num_heads: int = 8, + qkv_bias: bool = True, + qk_norm: bool = False, + attn_drop: float = 0.0, + update_y: bool = True, + out_bias: bool = True, + attend_to_padding: bool = False, + softmax_scale: Optional[float] = None, + device: Optional[torch.device] = None, + dtype=None, + operations=None, + ): + super().__init__() + self.dim_x = dim_x + self.dim_y = dim_y + self.num_heads = num_heads + self.head_dim = dim_x // num_heads + self.attn_drop = attn_drop + self.update_y = update_y + self.attend_to_padding = attend_to_padding + self.softmax_scale = softmax_scale + if dim_x % num_heads != 0: + raise ValueError( + f"dim_x={dim_x} should be divisible by num_heads={num_heads}" + ) + + # Input layers. + self.qkv_bias = qkv_bias + self.qkv_x = operations.Linear(dim_x, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype) + # Project text features to match visual features (dim_y -> dim_x) + self.qkv_y = operations.Linear(dim_y, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype) + + # Query and key normalization for stability. + assert qk_norm + self.q_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype) + self.k_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype) + self.q_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype) + self.k_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype) + + # Output layers. y features go back down from dim_x -> dim_y. + self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype) + self.proj_y = ( + operations.Linear(dim_x, dim_y, bias=out_bias, device=device, dtype=dtype) + if update_y + else nn.Identity() + ) + + def forward( + self, + x: torch.Tensor, # (B, N, dim_x) + y: torch.Tensor, # (B, L, dim_y) + scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm. + scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm. + crop_y, + **rope_rotation, + ) -> Tuple[torch.Tensor, torch.Tensor]: + rope_cos = rope_rotation.get("rope_cos") + rope_sin = rope_rotation.get("rope_sin") + # Pre-norm for visual features + x = modulated_rmsnorm(x, scale_x) # (B, M, dim_x) where M = N / cp_group_size + + # Process visual features + # qkv_x = self.qkv_x(x) # (B, M, 3 * dim_x) + # assert qkv_x.dtype == torch.bfloat16 + # qkv_x = all_to_all_collect_tokens( + # qkv_x, self.num_heads + # ) # (3, B, N, local_h, head_dim) + + # Process text features + y = modulated_rmsnorm(y, scale_y) # (B, L, dim_y) + q_y, k_y, v_y = self.qkv_y(y).view(y.shape[0], y.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim) + + q_y = self.q_norm_y(q_y) + k_y = self.k_norm_y(k_y) + + # Split qkv_x into q, k, v + q_x, k_x, v_x = self.qkv_x(x).view(x.shape[0], x.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim) + q_x = self.q_norm_x(q_x) + q_x = apply_rotary_emb_qk_real(q_x, rope_cos, rope_sin) + k_x = self.k_norm_x(k_x) + k_x = apply_rotary_emb_qk_real(k_x, rope_cos, rope_sin) + + q = torch.cat([q_x, q_y[:, :crop_y]], dim=1).transpose(1, 2) + k = torch.cat([k_x, k_y[:, :crop_y]], dim=1).transpose(1, 2) + v = torch.cat([v_x, v_y[:, :crop_y]], dim=1).transpose(1, 2) + + xy = optimized_attention(q, + k, + v, self.num_heads, skip_reshape=True) + + x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1) + x = self.proj_x(x) + o = torch.zeros(y.shape[0], q_y.shape[1], y.shape[-1], device=y.device, dtype=y.dtype) + o[:, :y.shape[1]] = y + + y = self.proj_y(o) + # print("ox", x) + # print("oy", y) + return x, y + + +class AsymmetricJointBlock(nn.Module): + def __init__( + self, + hidden_size_x: int, + hidden_size_y: int, + num_heads: int, + *, + mlp_ratio_x: float = 8.0, # Ratio of hidden size to d_model for MLP for visual tokens. + mlp_ratio_y: float = 4.0, # Ratio of hidden size to d_model for MLP for text tokens. + update_y: bool = True, # Whether to update text tokens in this block. + device: Optional[torch.device] = None, + dtype=None, + operations=None, + **block_kwargs, + ): + super().__init__() + self.update_y = update_y + self.hidden_size_x = hidden_size_x + self.hidden_size_y = hidden_size_y + self.mod_x = operations.Linear(hidden_size_x, 4 * hidden_size_x, device=device, dtype=dtype) + if self.update_y: + self.mod_y = operations.Linear(hidden_size_x, 4 * hidden_size_y, device=device, dtype=dtype) + else: + self.mod_y = operations.Linear(hidden_size_x, hidden_size_y, device=device, dtype=dtype) + + # Self-attention: + self.attn = AsymmetricAttention( + hidden_size_x, + hidden_size_y, + num_heads=num_heads, + update_y=update_y, + device=device, + dtype=dtype, + operations=operations, + **block_kwargs, + ) + + # MLP. + mlp_hidden_dim_x = int(hidden_size_x * mlp_ratio_x) + assert mlp_hidden_dim_x == int(1536 * 8) + self.mlp_x = FeedForward( + in_features=hidden_size_x, + hidden_size=mlp_hidden_dim_x, + multiple_of=256, + ffn_dim_multiplier=None, + device=device, + dtype=dtype, + operations=operations, + ) + + # MLP for text not needed in last block. + if self.update_y: + mlp_hidden_dim_y = int(hidden_size_y * mlp_ratio_y) + self.mlp_y = FeedForward( + in_features=hidden_size_y, + hidden_size=mlp_hidden_dim_y, + multiple_of=256, + ffn_dim_multiplier=None, + device=device, + dtype=dtype, + operations=operations, + ) + + def forward( + self, + x: torch.Tensor, + c: torch.Tensor, + y: torch.Tensor, + **attn_kwargs, + ): + """Forward pass of a block. + + Args: + x: (B, N, dim) tensor of visual tokens + c: (B, dim) tensor of conditioned features + y: (B, L, dim) tensor of text tokens + num_frames: Number of frames in the video. N = num_frames * num_spatial_tokens + + Returns: + x: (B, N, dim) tensor of visual tokens after block + y: (B, L, dim) tensor of text tokens after block + """ + N = x.size(1) + + c = F.silu(c) + mod_x = self.mod_x(c) + scale_msa_x, gate_msa_x, scale_mlp_x, gate_mlp_x = mod_x.chunk(4, dim=1) + + mod_y = self.mod_y(c) + if self.update_y: + scale_msa_y, gate_msa_y, scale_mlp_y, gate_mlp_y = mod_y.chunk(4, dim=1) + else: + scale_msa_y = mod_y + + # Self-attention block. + x_attn, y_attn = self.attn( + x, + y, + scale_x=scale_msa_x, + scale_y=scale_msa_y, + **attn_kwargs, + ) + + assert x_attn.size(1) == N + x = residual_tanh_gated_rmsnorm(x, x_attn, gate_msa_x) + if self.update_y: + y = residual_tanh_gated_rmsnorm(y, y_attn, gate_msa_y) + + # MLP block. + x = self.ff_block_x(x, scale_mlp_x, gate_mlp_x) + if self.update_y: + y = self.ff_block_y(y, scale_mlp_y, gate_mlp_y) + + return x, y + + def ff_block_x(self, x, scale_x, gate_x): + x_mod = modulated_rmsnorm(x, scale_x) + x_res = self.mlp_x(x_mod) + x = residual_tanh_gated_rmsnorm(x, x_res, gate_x) # Sandwich norm + return x + + def ff_block_y(self, y, scale_y, gate_y): + y_mod = modulated_rmsnorm(y, scale_y) + y_res = self.mlp_y(y_mod) + y = residual_tanh_gated_rmsnorm(y, y_res, gate_y) # Sandwich norm + return y + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + + def __init__( + self, + hidden_size, + patch_size, + out_channels, + device: Optional[torch.device] = None, + dtype=None, + operations=None, + ): + super().__init__() + self.norm_final = operations.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype + ) + self.mod = operations.Linear(hidden_size, 2 * hidden_size, device=device, dtype=dtype) + self.linear = operations.Linear( + hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype + ) + + def forward(self, x, c): + c = F.silu(c) + shift, scale = self.mod(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class AsymmDiTJoint(nn.Module): + """ + Diffusion model with a Transformer backbone. + + Ingests text embeddings instead of a label. + """ + + def __init__( + self, + *, + patch_size=2, + in_channels=4, + hidden_size_x=1152, + hidden_size_y=1152, + depth=48, + num_heads=16, + mlp_ratio_x=8.0, + mlp_ratio_y=4.0, + use_t5: bool = False, + t5_feat_dim: int = 4096, + t5_token_length: int = 256, + learn_sigma=True, + patch_embed_bias: bool = True, + timestep_mlp_bias: bool = True, + attend_to_padding: bool = False, + timestep_scale: Optional[float] = None, + use_extended_posenc: bool = False, + posenc_preserve_area: bool = False, + rope_theta: float = 10000.0, + image_model=None, + device: Optional[torch.device] = None, + dtype=None, + operations=None, + **block_kwargs, + ): + super().__init__() + + self.dtype = dtype + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size_x = hidden_size_x + self.hidden_size_y = hidden_size_y + self.head_dim = ( + hidden_size_x // num_heads + ) # Head dimension and count is determined by visual. + self.attend_to_padding = attend_to_padding + self.use_extended_posenc = use_extended_posenc + self.posenc_preserve_area = posenc_preserve_area + self.use_t5 = use_t5 + self.t5_token_length = t5_token_length + self.t5_feat_dim = t5_feat_dim + self.rope_theta = ( + rope_theta # Scaling factor for frequency computation for temporal RoPE. + ) + + self.x_embedder = PatchEmbed( + patch_size=patch_size, + in_chans=in_channels, + embed_dim=hidden_size_x, + bias=patch_embed_bias, + dtype=dtype, + device=device, + operations=operations + ) + # Conditionings + # Timestep + self.t_embedder = TimestepEmbedder( + hidden_size_x, bias=timestep_mlp_bias, timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations + ) + + if self.use_t5: + # Caption Pooling (T5) + self.t5_y_embedder = AttentionPool( + t5_feat_dim, num_heads=8, output_dim=hidden_size_x, dtype=dtype, device=device, operations=operations + ) + + # Dense Embedding Projection (T5) + self.t5_yproj = operations.Linear( + t5_feat_dim, hidden_size_y, bias=True, dtype=dtype, device=device + ) + + # Initialize pos_frequencies as an empty parameter. + self.pos_frequencies = nn.Parameter( + torch.empty(3, self.num_heads, self.head_dim // 2, dtype=dtype, device=device) + ) + + assert not self.attend_to_padding + + # for depth 48: + # b = 0: AsymmetricJointBlock, update_y=True + # b = 1: AsymmetricJointBlock, update_y=True + # ... + # b = 46: AsymmetricJointBlock, update_y=True + # b = 47: AsymmetricJointBlock, update_y=False. No need to update text features. + blocks = [] + for b in range(depth): + # Joint multi-modal block + update_y = b < depth - 1 + block = AsymmetricJointBlock( + hidden_size_x, + hidden_size_y, + num_heads, + mlp_ratio_x=mlp_ratio_x, + mlp_ratio_y=mlp_ratio_y, + update_y=update_y, + attend_to_padding=attend_to_padding, + device=device, + dtype=dtype, + operations=operations, + **block_kwargs, + ) + + blocks.append(block) + self.blocks = nn.ModuleList(blocks) + + self.final_layer = FinalLayer( + hidden_size_x, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations + ) + + def embed_x(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: (B, C=12, T, H, W) tensor of visual tokens + + Returns: + x: (B, C=3072, N) tensor of visual tokens with positional embedding. + """ + return self.x_embedder(x) # Convert BcTHW to BCN + + def prepare( + self, + x: torch.Tensor, + sigma: torch.Tensor, + t5_feat: torch.Tensor, + t5_mask: torch.Tensor, + ): + """Prepare input and conditioning embeddings.""" + # Visual patch embeddings with positional encoding. + T, H, W = x.shape[-3:] + pH, pW = H // self.patch_size, W // self.patch_size + x = self.embed_x(x) # (B, N, D), where N = T * H * W / patch_size ** 2 + assert x.ndim == 3 + B = x.size(0) + + + pH, pW = H // self.patch_size, W // self.patch_size + N = T * pH * pW + assert x.size(1) == N + pos = create_position_matrix( + T, pH=pH, pW=pW, device=x.device, dtype=torch.float32 + ) # (N, 3) + rope_cos, rope_sin = compute_mixed_rotation( + freqs=comfy.ops.cast_to(self.pos_frequencies, dtype=x.dtype, device=x.device), pos=pos + ) # Each are (N, num_heads, dim // 2) + + c_t = self.t_embedder(1 - sigma, out_dtype=x.dtype) # (B, D) + + t5_y_pool = self.t5_y_embedder(t5_feat, t5_mask) # (B, D) + + c = c_t + t5_y_pool + + y_feat = self.t5_yproj(t5_feat) # (B, L, t5_feat_dim) --> (B, L, D) + + return x, c, y_feat, rope_cos, rope_sin + + def forward( + self, + x: torch.Tensor, + timestep: torch.Tensor, + context: List[torch.Tensor], + attention_mask: List[torch.Tensor], + num_tokens=256, + packed_indices: Dict[str, torch.Tensor] = None, + rope_cos: torch.Tensor = None, + rope_sin: torch.Tensor = None, + control=None, **kwargs + ): + y_feat = context + y_mask = attention_mask + sigma = timestep + """Forward pass of DiT. + + Args: + x: (B, C, T, H, W) tensor of spatial inputs (images or latent representations of images) + sigma: (B,) tensor of noise standard deviations + y_feat: List((B, L, y_feat_dim) tensor of caption token features. For SDXL text encoders: L=77, y_feat_dim=2048) + y_mask: List((B, L) boolean tensor indicating which tokens are not padding) + packed_indices: Dict with keys for Flash Attention. Result of compute_packed_indices. + """ + B, _, T, H, W = x.shape + + x, c, y_feat, rope_cos, rope_sin = self.prepare( + x, sigma, y_feat, y_mask + ) + del y_mask + + for i, block in enumerate(self.blocks): + x, y_feat = block( + x, + c, + y_feat, + rope_cos=rope_cos, + rope_sin=rope_sin, + crop_y=num_tokens, + ) # (B, M, D), (B, L, D) + del y_feat # Final layers don't use dense text features. + + x = self.final_layer(x, c) # (B, M, patch_size ** 2 * out_channels) + x = rearrange( + x, + "B (T hp wp) (p1 p2 c) -> B c T (hp p1) (wp p2)", + T=T, + hp=H // self.patch_size, + wp=W // self.patch_size, + p1=self.patch_size, + p2=self.patch_size, + c=self.out_channels, + ) + + return -x diff --git a/comfy/ldm/genmo/joint_model/layers.py b/comfy/ldm/genmo/joint_model/layers.py new file mode 100644 index 0000000..51d9795 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/layers.py @@ -0,0 +1,164 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license +#adapted to ComfyUI + +import collections.abc +import math +from itertools import repeat +from typing import Callable, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +import comfy.ldm.common_dit + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return tuple(x) + return tuple(repeat(x, n)) + + return parse + + +to_2tuple = _ntuple(2) + + +class TimestepEmbedder(nn.Module): + def __init__( + self, + hidden_size: int, + frequency_embedding_size: int = 256, + *, + bias: bool = True, + timestep_scale: Optional[float] = None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, bias=bias, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=bias, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + self.timestep_scale = timestep_scale + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + half = dim // 2 + freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) + freqs.mul_(-math.log(max_period) / half).exp_() + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + def forward(self, t, out_dtype): + if self.timestep_scale is not None: + t = t * self.timestep_scale + t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype=out_dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class FeedForward(nn.Module): + def __init__( + self, + in_features: int, + hidden_size: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + device: Optional[torch.device] = None, + dtype=None, + operations=None, + ): + super().__init__() + # keep parameter count and computation constant compared to standard FFN + hidden_size = int(2 * hidden_size / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_size = int(ffn_dim_multiplier * hidden_size) + hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of) + + self.hidden_dim = hidden_size + self.w1 = operations.Linear(in_features, 2 * hidden_size, bias=False, device=device, dtype=dtype) + self.w2 = operations.Linear(hidden_size, in_features, bias=False, device=device, dtype=dtype) + + def forward(self, x): + x, gate = self.w1(x).chunk(2, dim=-1) + x = self.w2(F.silu(x) * gate) + return x + + +class PatchEmbed(nn.Module): + def __init__( + self, + patch_size: int = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer: Optional[Callable] = None, + flatten: bool = True, + bias: bool = True, + dynamic_img_pad: bool = False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.patch_size = to_2tuple(patch_size) + self.flatten = flatten + self.dynamic_img_pad = dynamic_img_pad + + self.proj = operations.Conv2d( + in_chans, + embed_dim, + kernel_size=patch_size, + stride=patch_size, + bias=bias, + device=device, + dtype=dtype, + ) + assert norm_layer is None + self.norm = ( + norm_layer(embed_dim, device=device) if norm_layer else nn.Identity() + ) + + def forward(self, x): + B, _C, T, H, W = x.shape + if not self.dynamic_img_pad: + assert H % self.patch_size[0] == 0, f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})." + assert W % self.patch_size[1] == 0, f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})." + else: + pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0] + pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1] + x = F.pad(x, (0, pad_w, 0, pad_h)) + + x = rearrange(x, "B C T H W -> (B T) C H W", B=B, T=T) + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode='circular') + x = self.proj(x) + + # Flatten temporal and spatial dimensions. + if not self.flatten: + raise NotImplementedError("Must flatten output.") + x = rearrange(x, "(B T) C H W -> B (T H W) C", B=B, T=T) + + x = self.norm(x) + return x + + +class RMSNorm(torch.nn.Module): + def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None): + super().__init__() + self.eps = eps + self.weight = torch.nn.Parameter(torch.empty(hidden_size, device=device, dtype=dtype)) + self.register_parameter("bias", None) + + def forward(self, x): + return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps) diff --git a/comfy/ldm/genmo/joint_model/rope_mixed.py b/comfy/ldm/genmo/joint_model/rope_mixed.py new file mode 100644 index 0000000..dee3fa2 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/rope_mixed.py @@ -0,0 +1,88 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license + +# import functools +import math + +import torch + + +def centers(start: float, stop, num, dtype=None, device=None): + """linspace through bin centers. + + Args: + start (float): Start of the range. + stop (float): End of the range. + num (int): Number of points. + dtype (torch.dtype): Data type of the points. + device (torch.device): Device of the points. + + Returns: + centers (Tensor): Centers of the bins. Shape: (num,). + """ + edges = torch.linspace(start, stop, num + 1, dtype=dtype, device=device) + return (edges[:-1] + edges[1:]) / 2 + + +# @functools.lru_cache(maxsize=1) +def create_position_matrix( + T: int, + pH: int, + pW: int, + device: torch.device, + dtype: torch.dtype, + *, + target_area: float = 36864, +): + """ + Args: + T: int - Temporal dimension + pH: int - Height dimension after patchify + pW: int - Width dimension after patchify + + Returns: + pos: [T * pH * pW, 3] - position matrix + """ + # Create 1D tensors for each dimension + t = torch.arange(T, dtype=dtype) + + # Positionally interpolate to area 36864. + # (3072x3072 frame with 16x16 patches = 192x192 latents). + # This automatically scales rope positions when the resolution changes. + # We use a large target area so the model is more sensitive + # to changes in the learned pos_frequencies matrix. + scale = math.sqrt(target_area / (pW * pH)) + w = centers(-pW * scale / 2, pW * scale / 2, pW) + h = centers(-pH * scale / 2, pH * scale / 2, pH) + + # Use meshgrid to create 3D grids + grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij") + + # Stack and reshape the grids. + pos = torch.stack([grid_t, grid_h, grid_w], dim=-1) # [T, pH, pW, 3] + pos = pos.view(-1, 3) # [T * pH * pW, 3] + pos = pos.to(dtype=dtype, device=device) + + return pos + + +def compute_mixed_rotation( + freqs: torch.Tensor, + pos: torch.Tensor, +): + """ + Project each 3-dim position into per-head, per-head-dim 1D frequencies. + + Args: + freqs: [3, num_heads, num_freqs] - learned rotation frequency (for t, row, col) for each head position + pos: [N, 3] - position of each token + num_heads: int + + Returns: + freqs_cos: [N, num_heads, num_freqs] - cosine components + freqs_sin: [N, num_heads, num_freqs] - sine components + """ + assert freqs.ndim == 3 + freqs_sum = torch.einsum("Nd,dhf->Nhf", pos.to(freqs), freqs) + freqs_cos = torch.cos(freqs_sum) + freqs_sin = torch.sin(freqs_sum) + return freqs_cos, freqs_sin diff --git a/comfy/ldm/genmo/joint_model/temporal_rope.py b/comfy/ldm/genmo/joint_model/temporal_rope.py new file mode 100644 index 0000000..88f5d6d --- /dev/null +++ b/comfy/ldm/genmo/joint_model/temporal_rope.py @@ -0,0 +1,34 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license + +# Based on Llama3 Implementation. +import torch + + +def apply_rotary_emb_qk_real( + xqk: torch.Tensor, + freqs_cos: torch.Tensor, + freqs_sin: torch.Tensor, +) -> torch.Tensor: + """ + Apply rotary embeddings to input tensors using the given frequency tensor without complex numbers. + + Args: + xqk (torch.Tensor): Query and/or Key tensors to apply rotary embeddings. Shape: (B, S, *, num_heads, D) + Can be either just query or just key, or both stacked along some batch or * dim. + freqs_cos (torch.Tensor): Precomputed cosine frequency tensor. + freqs_sin (torch.Tensor): Precomputed sine frequency tensor. + + Returns: + torch.Tensor: The input tensor with rotary embeddings applied. + """ + # Split the last dimension into even and odd parts + xqk_even = xqk[..., 0::2] + xqk_odd = xqk[..., 1::2] + + # Apply rotation + cos_part = (xqk_even * freqs_cos - xqk_odd * freqs_sin).type_as(xqk) + sin_part = (xqk_even * freqs_sin + xqk_odd * freqs_cos).type_as(xqk) + + # Interleave the results back into the original shape + out = torch.stack([cos_part, sin_part], dim=-1).flatten(-2) + return out diff --git a/comfy/ldm/genmo/joint_model/utils.py b/comfy/ldm/genmo/joint_model/utils.py new file mode 100644 index 0000000..4119024 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/utils.py @@ -0,0 +1,102 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license +#adapted to ComfyUI + +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor: + """ + Pool tokens in x using mask. + + NOTE: We assume x does not require gradients. + + Args: + x: (B, L, D) tensor of tokens. + mask: (B, L) boolean tensor indicating which tokens are not padding. + + Returns: + pooled: (B, D) tensor of pooled tokens. + """ + assert x.size(1) == mask.size(1) # Expected mask to have same length as tokens. + assert x.size(0) == mask.size(0) # Expected mask to have same batch size as tokens. + mask = mask[:, :, None].to(dtype=x.dtype) + mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1) + pooled = (x * mask).sum(dim=1, keepdim=keepdim) + return pooled + + +class AttentionPool(nn.Module): + def __init__( + self, + embed_dim: int, + num_heads: int, + output_dim: int = None, + device: Optional[torch.device] = None, + dtype=None, + operations=None, + ): + """ + Args: + spatial_dim (int): Number of tokens in sequence length. + embed_dim (int): Dimensionality of input tokens. + num_heads (int): Number of attention heads. + output_dim (int): Dimensionality of output tokens. Defaults to embed_dim. + """ + super().__init__() + self.num_heads = num_heads + self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype) + self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype) + self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype) + + def forward(self, x, mask): + """ + Args: + x (torch.Tensor): (B, L, D) tensor of input tokens. + mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding. + + NOTE: We assume x does not require gradients. + + Returns: + x (torch.Tensor): (B, D) tensor of pooled tokens. + """ + D = x.size(2) + + # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L). + attn_mask = mask[:, None, None, :].bool() # (B, 1, 1, L). + attn_mask = F.pad(attn_mask, (1, 0), value=True) # (B, 1, 1, 1+L). + + # Average non-padding token features. These will be used as the query. + x_pool = pool_tokens(x, mask, keepdim=True) # (B, 1, D) + + # Concat pooled features to input sequence. + x = torch.cat([x_pool, x], dim=1) # (B, L+1, D) + + # Compute queries, keys, values. Only the mean token is used to create a query. + kv = self.to_kv(x) # (B, L+1, 2 * D) + q = self.to_q(x[:, 0]) # (B, D) + + # Extract heads. + head_dim = D // self.num_heads + kv = kv.unflatten(2, (2, self.num_heads, head_dim)) # (B, 1+L, 2, H, head_dim) + kv = kv.transpose(1, 3) # (B, H, 2, 1+L, head_dim) + k, v = kv.unbind(2) # (B, H, 1+L, head_dim) + q = q.unflatten(1, (self.num_heads, head_dim)) # (B, H, head_dim) + q = q.unsqueeze(2) # (B, H, 1, head_dim) + + # Compute attention. + x = F.scaled_dot_product_attention( + q, k, v, attn_mask=attn_mask, dropout_p=0.0 + ) # (B, H, 1, head_dim) + + # Concatenate heads and run output. + x = x.squeeze(2).flatten(1, 2) # (B, D = H * head_dim) + x = self.to_out(x) + return x diff --git a/comfy/ldm/genmo/vae/model.py b/comfy/ldm/genmo/vae/model.py new file mode 100644 index 0000000..b68d48a --- /dev/null +++ b/comfy/ldm/genmo/vae/model.py @@ -0,0 +1,711 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license +#adapted to ComfyUI + +from typing import Callable, List, Optional, Tuple, Union +from functools import partial +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from comfy.ldm.modules.attention import optimized_attention + +import comfy.ops +ops = comfy.ops.disable_weight_init + +# import mochi_preview.dit.joint_model.context_parallel as cp +# from mochi_preview.vae.cp_conv import cp_pass_frames, gather_all_frames + + +def cast_tuple(t, length=1): + return t if isinstance(t, tuple) else ((t,) * length) + + +class GroupNormSpatial(ops.GroupNorm): + """ + GroupNorm applied per-frame. + """ + + def forward(self, x: torch.Tensor, *, chunk_size: int = 8): + B, C, T, H, W = x.shape + x = rearrange(x, "B C T H W -> (B T) C H W") + # Run group norm in chunks. + output = torch.empty_like(x) + for b in range(0, B * T, chunk_size): + output[b : b + chunk_size] = super().forward(x[b : b + chunk_size]) + return rearrange(output, "(B T) C H W -> B C T H W", B=B, T=T) + +class PConv3d(ops.Conv3d): + def __init__( + self, + in_channels, + out_channels, + kernel_size: Union[int, Tuple[int, int, int]], + stride: Union[int, Tuple[int, int, int]], + causal: bool = True, + context_parallel: bool = True, + **kwargs, + ): + self.causal = causal + self.context_parallel = context_parallel + kernel_size = cast_tuple(kernel_size, 3) + stride = cast_tuple(stride, 3) + height_pad = (kernel_size[1] - 1) // 2 + width_pad = (kernel_size[2] - 1) // 2 + + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + dilation=(1, 1, 1), + padding=(0, height_pad, width_pad), + **kwargs, + ) + + def forward(self, x: torch.Tensor): + # Compute padding amounts. + context_size = self.kernel_size[0] - 1 + if self.causal: + pad_front = context_size + pad_back = 0 + else: + pad_front = context_size // 2 + pad_back = context_size - pad_front + + # Apply padding. + assert self.padding_mode == "replicate" # DEBUG + mode = "constant" if self.padding_mode == "zeros" else self.padding_mode + x = F.pad(x, (0, 0, 0, 0, pad_front, pad_back), mode=mode) + return super().forward(x) + + +class Conv1x1(ops.Linear): + """*1x1 Conv implemented with a linear layer.""" + + def __init__(self, in_features: int, out_features: int, *args, **kwargs): + super().__init__(in_features, out_features, *args, **kwargs) + + def forward(self, x: torch.Tensor): + """Forward pass. + + Args: + x: Input tensor. Shape: [B, C, *] or [B, *, C]. + + Returns: + x: Output tensor. Shape: [B, C', *] or [B, *, C']. + """ + x = x.movedim(1, -1) + x = super().forward(x) + x = x.movedim(-1, 1) + return x + + +class DepthToSpaceTime(nn.Module): + def __init__( + self, + temporal_expansion: int, + spatial_expansion: int, + ): + super().__init__() + self.temporal_expansion = temporal_expansion + self.spatial_expansion = spatial_expansion + + # When printed, this module should show the temporal and spatial expansion factors. + def extra_repr(self): + return f"texp={self.temporal_expansion}, sexp={self.spatial_expansion}" + + def forward(self, x: torch.Tensor): + """Forward pass. + + Args: + x: Input tensor. Shape: [B, C, T, H, W]. + + Returns: + x: Rearranged tensor. Shape: [B, C/(st*s*s), T*st, H*s, W*s]. + """ + x = rearrange( + x, + "B (C st sh sw) T H W -> B C (T st) (H sh) (W sw)", + st=self.temporal_expansion, + sh=self.spatial_expansion, + sw=self.spatial_expansion, + ) + + # cp_rank, _ = cp.get_cp_rank_size() + if self.temporal_expansion > 1: # and cp_rank == 0: + # Drop the first self.temporal_expansion - 1 frames. + # This is because we always want the 3x3x3 conv filter to only apply + # to the first frame, and the first frame doesn't need to be repeated. + assert all(x.shape) + x = x[:, :, self.temporal_expansion - 1 :] + assert all(x.shape) + + return x + + +def norm_fn( + in_channels: int, + affine: bool = True, +): + return GroupNormSpatial(affine=affine, num_groups=32, num_channels=in_channels) + + +class ResBlock(nn.Module): + """Residual block that preserves the spatial dimensions.""" + + def __init__( + self, + channels: int, + *, + affine: bool = True, + attn_block: Optional[nn.Module] = None, + causal: bool = True, + prune_bottleneck: bool = False, + padding_mode: str, + bias: bool = True, + ): + super().__init__() + self.channels = channels + + assert causal + self.stack = nn.Sequential( + norm_fn(channels, affine=affine), + nn.SiLU(inplace=True), + PConv3d( + in_channels=channels, + out_channels=channels // 2 if prune_bottleneck else channels, + kernel_size=(3, 3, 3), + stride=(1, 1, 1), + padding_mode=padding_mode, + bias=bias, + causal=causal, + ), + norm_fn(channels, affine=affine), + nn.SiLU(inplace=True), + PConv3d( + in_channels=channels // 2 if prune_bottleneck else channels, + out_channels=channels, + kernel_size=(3, 3, 3), + stride=(1, 1, 1), + padding_mode=padding_mode, + bias=bias, + causal=causal, + ), + ) + + self.attn_block = attn_block if attn_block else nn.Identity() + + def forward(self, x: torch.Tensor): + """Forward pass. + + Args: + x: Input tensor. Shape: [B, C, T, H, W]. + """ + residual = x + x = self.stack(x) + x = x + residual + del residual + + return self.attn_block(x) + + +class Attention(nn.Module): + def __init__( + self, + dim: int, + head_dim: int = 32, + qkv_bias: bool = False, + out_bias: bool = True, + qk_norm: bool = True, + ) -> None: + super().__init__() + self.head_dim = head_dim + self.num_heads = dim // head_dim + self.qk_norm = qk_norm + + self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) + self.out = nn.Linear(dim, dim, bias=out_bias) + + def forward( + self, + x: torch.Tensor, + ) -> torch.Tensor: + """Compute temporal self-attention. + + Args: + x: Input tensor. Shape: [B, C, T, H, W]. + chunk_size: Chunk size for large tensors. + + Returns: + x: Output tensor. Shape: [B, C, T, H, W]. + """ + B, _, T, H, W = x.shape + + if T == 1: + # No attention for single frame. + x = x.movedim(1, -1) # [B, C, T, H, W] -> [B, T, H, W, C] + qkv = self.qkv(x) + _, _, x = qkv.chunk(3, dim=-1) # Throw away queries and keys. + x = self.out(x) + return x.movedim(-1, 1) # [B, T, H, W, C] -> [B, C, T, H, W] + + # 1D temporal attention. + x = rearrange(x, "B C t h w -> (B h w) t C") + qkv = self.qkv(x) + + # Input: qkv with shape [B, t, 3 * num_heads * head_dim] + # Output: x with shape [B, num_heads, t, head_dim] + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2) + + if self.qk_norm: + q = F.normalize(q, p=2, dim=-1) + k = F.normalize(k, p=2, dim=-1) + + x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True) + + assert x.size(0) == q.size(0) + + x = self.out(x) + x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W) + return x + + +class AttentionBlock(nn.Module): + def __init__( + self, + dim: int, + **attn_kwargs, + ) -> None: + super().__init__() + self.norm = norm_fn(dim) + self.attn = Attention(dim, **attn_kwargs) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return x + self.attn(self.norm(x)) + + +class CausalUpsampleBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_res_blocks: int, + *, + temporal_expansion: int = 2, + spatial_expansion: int = 2, + **block_kwargs, + ): + super().__init__() + + blocks = [] + for _ in range(num_res_blocks): + blocks.append(block_fn(in_channels, **block_kwargs)) + self.blocks = nn.Sequential(*blocks) + + self.temporal_expansion = temporal_expansion + self.spatial_expansion = spatial_expansion + + # Change channels in the final convolution layer. + self.proj = Conv1x1( + in_channels, + out_channels * temporal_expansion * (spatial_expansion**2), + ) + + self.d2st = DepthToSpaceTime( + temporal_expansion=temporal_expansion, spatial_expansion=spatial_expansion + ) + + def forward(self, x): + x = self.blocks(x) + x = self.proj(x) + x = self.d2st(x) + return x + + +def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs): + attn_block = AttentionBlock(channels) if has_attention else None + return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs) + + +class DownsampleBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_res_blocks, + *, + temporal_reduction=2, + spatial_reduction=2, + **block_kwargs, + ): + """ + Downsample block for the VAE encoder. + + Args: + in_channels: Number of input channels. + out_channels: Number of output channels. + num_res_blocks: Number of residual blocks. + temporal_reduction: Temporal reduction factor. + spatial_reduction: Spatial reduction factor. + """ + super().__init__() + layers = [] + + # Change the channel count in the strided convolution. + # This lets the ResBlock have uniform channel count, + # as in ConvNeXt. + assert in_channels != out_channels + layers.append( + PConv3d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction), + stride=(temporal_reduction, spatial_reduction, spatial_reduction), + # First layer in each block always uses replicate padding + padding_mode="replicate", + bias=block_kwargs["bias"], + ) + ) + + for _ in range(num_res_blocks): + layers.append(block_fn(out_channels, **block_kwargs)) + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1): + num_freqs = (stop - start) // step + assert inputs.ndim == 5 + C = inputs.size(1) + + # Create Base 2 Fourier features. + freqs = torch.arange(start, stop, step, dtype=inputs.dtype, device=inputs.device) + assert num_freqs == len(freqs) + w = torch.pow(2.0, freqs) * (2 * torch.pi) # [num_freqs] + C = inputs.shape[1] + w = w.repeat(C)[None, :, None, None, None] # [1, C * num_freqs, 1, 1, 1] + + # Interleaved repeat of input channels to match w. + h = inputs.repeat_interleave(num_freqs, dim=1) # [B, C * num_freqs, T, H, W] + # Scale channels by frequency. + h = w * h + + return torch.cat( + [ + inputs, + torch.sin(h), + torch.cos(h), + ], + dim=1, + ) + + +class FourierFeatures(nn.Module): + def __init__(self, start: int = 6, stop: int = 8, step: int = 1): + super().__init__() + self.start = start + self.stop = stop + self.step = step + + def forward(self, inputs): + """Add Fourier features to inputs. + + Args: + inputs: Input tensor. Shape: [B, C, T, H, W] + + Returns: + h: Output tensor. Shape: [B, (1 + 2 * num_freqs) * C, T, H, W] + """ + return add_fourier_features(inputs, self.start, self.stop, self.step) + + +class Decoder(nn.Module): + def __init__( + self, + *, + out_channels: int = 3, + latent_dim: int, + base_channels: int, + channel_multipliers: List[int], + num_res_blocks: List[int], + temporal_expansions: Optional[List[int]] = None, + spatial_expansions: Optional[List[int]] = None, + has_attention: List[bool], + output_norm: bool = True, + nonlinearity: str = "silu", + output_nonlinearity: str = "silu", + causal: bool = True, + **block_kwargs, + ): + super().__init__() + self.input_channels = latent_dim + self.base_channels = base_channels + self.channel_multipliers = channel_multipliers + self.num_res_blocks = num_res_blocks + self.output_nonlinearity = output_nonlinearity + assert nonlinearity == "silu" + assert causal + + ch = [mult * base_channels for mult in channel_multipliers] + self.num_up_blocks = len(ch) - 1 + assert len(num_res_blocks) == self.num_up_blocks + 2 + + blocks = [] + + first_block = [ + ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1)) + ] # Input layer. + # First set of blocks preserve channel count. + for _ in range(num_res_blocks[-1]): + first_block.append( + block_fn( + ch[-1], + has_attention=has_attention[-1], + causal=causal, + **block_kwargs, + ) + ) + blocks.append(nn.Sequential(*first_block)) + + assert len(temporal_expansions) == len(spatial_expansions) == self.num_up_blocks + assert len(num_res_blocks) == len(has_attention) == self.num_up_blocks + 2 + + upsample_block_fn = CausalUpsampleBlock + + for i in range(self.num_up_blocks): + block = upsample_block_fn( + ch[-i - 1], + ch[-i - 2], + num_res_blocks=num_res_blocks[-i - 2], + has_attention=has_attention[-i - 2], + temporal_expansion=temporal_expansions[-i - 1], + spatial_expansion=spatial_expansions[-i - 1], + causal=causal, + **block_kwargs, + ) + blocks.append(block) + + assert not output_norm + + # Last block. Preserve channel count. + last_block = [] + for _ in range(num_res_blocks[0]): + last_block.append( + block_fn( + ch[0], has_attention=has_attention[0], causal=causal, **block_kwargs + ) + ) + blocks.append(nn.Sequential(*last_block)) + + self.blocks = nn.ModuleList(blocks) + self.output_proj = Conv1x1(ch[0], out_channels) + + def forward(self, x): + """Forward pass. + + Args: + x: Latent tensor. Shape: [B, input_channels, t, h, w]. Scaled [-1, 1]. + + Returns: + x: Reconstructed video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]. + T + 1 = (t - 1) * 4. + H = h * 16, W = w * 16. + """ + for block in self.blocks: + x = block(x) + + if self.output_nonlinearity == "silu": + x = F.silu(x, inplace=not self.training) + else: + assert ( + not self.output_nonlinearity + ) # StyleGAN3 omits the to-RGB nonlinearity. + + return self.output_proj(x).contiguous() + +class LatentDistribution: + def __init__(self, mean: torch.Tensor, logvar: torch.Tensor): + """Initialize latent distribution. + + Args: + mean: Mean of the distribution. Shape: [B, C, T, H, W]. + logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W]. + """ + assert mean.shape == logvar.shape + self.mean = mean + self.logvar = logvar + + def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None): + if temperature == 0.0: + return self.mean + + if noise is None: + noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator) + else: + assert noise.device == self.mean.device + noise = noise.to(self.mean.dtype) + + if temperature != 1.0: + raise NotImplementedError(f"Temperature {temperature} is not supported.") + + # Just Gaussian sample with no scaling of variance. + return noise * torch.exp(self.logvar * 0.5) + self.mean + + def mode(self): + return self.mean + +class Encoder(nn.Module): + def __init__( + self, + *, + in_channels: int, + base_channels: int, + channel_multipliers: List[int], + num_res_blocks: List[int], + latent_dim: int, + temporal_reductions: List[int], + spatial_reductions: List[int], + prune_bottlenecks: List[bool], + has_attentions: List[bool], + affine: bool = True, + bias: bool = True, + input_is_conv_1x1: bool = False, + padding_mode: str, + ): + super().__init__() + self.temporal_reductions = temporal_reductions + self.spatial_reductions = spatial_reductions + self.base_channels = base_channels + self.channel_multipliers = channel_multipliers + self.num_res_blocks = num_res_blocks + self.latent_dim = latent_dim + + self.fourier_features = FourierFeatures() + ch = [mult * base_channels for mult in channel_multipliers] + num_down_blocks = len(ch) - 1 + assert len(num_res_blocks) == num_down_blocks + 2 + + layers = ( + [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)] + if not input_is_conv_1x1 + else [Conv1x1(in_channels, ch[0])] + ) + + assert len(prune_bottlenecks) == num_down_blocks + 2 + assert len(has_attentions) == num_down_blocks + 2 + block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias) + + for _ in range(num_res_blocks[0]): + layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0])) + prune_bottlenecks = prune_bottlenecks[1:] + has_attentions = has_attentions[1:] + + assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1 + for i in range(num_down_blocks): + layer = DownsampleBlock( + ch[i], + ch[i + 1], + num_res_blocks=num_res_blocks[i + 1], + temporal_reduction=temporal_reductions[i], + spatial_reduction=spatial_reductions[i], + prune_bottleneck=prune_bottlenecks[i], + has_attention=has_attentions[i], + affine=affine, + bias=bias, + padding_mode=padding_mode, + ) + + layers.append(layer) + + # Additional blocks. + for _ in range(num_res_blocks[-1]): + layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1])) + + self.layers = nn.Sequential(*layers) + + # Output layers. + self.output_norm = norm_fn(ch[-1]) + self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False) + + @property + def temporal_downsample(self): + return math.prod(self.temporal_reductions) + + @property + def spatial_downsample(self): + return math.prod(self.spatial_reductions) + + def forward(self, x) -> LatentDistribution: + """Forward pass. + + Args: + x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1] + + Returns: + means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1]. + h = H // 8, w = W // 8, t - 1 = (T - 1) // 6 + logvar: Shape: [B, latent_dim, t, h, w]. + """ + assert x.ndim == 5, f"Expected 5D input, got {x.shape}" + x = self.fourier_features(x) + + x = self.layers(x) + + x = self.output_norm(x) + x = F.silu(x, inplace=True) + x = self.output_proj(x) + + means, logvar = torch.chunk(x, 2, dim=1) + + assert means.ndim == 5 + assert logvar.shape == means.shape + assert means.size(1) == self.latent_dim + + return LatentDistribution(means, logvar) + + +class VideoVAE(nn.Module): + def __init__(self): + super().__init__() + self.encoder = Encoder( + in_channels=15, + base_channels=64, + channel_multipliers=[1, 2, 4, 6], + num_res_blocks=[3, 3, 4, 6, 3], + latent_dim=12, + temporal_reductions=[1, 2, 3], + spatial_reductions=[2, 2, 2], + prune_bottlenecks=[False, False, False, False, False], + has_attentions=[False, True, True, True, True], + affine=True, + bias=True, + input_is_conv_1x1=True, + padding_mode="replicate" + ) + self.decoder = Decoder( + out_channels=3, + base_channels=128, + channel_multipliers=[1, 2, 4, 6], + temporal_expansions=[1, 2, 3], + spatial_expansions=[2, 2, 2], + num_res_blocks=[3, 3, 4, 6, 3], + latent_dim=12, + has_attention=[False, False, False, False, False], + padding_mode="replicate", + output_norm=False, + nonlinearity="silu", + output_nonlinearity="silu", + causal=True, + ) + + def encode(self, x): + return self.encoder(x).mode() + + def decode(self, x): + return self.decoder(x) diff --git a/comfy/ldm/hydit/attn_layers.py b/comfy/ldm/hydit/attn_layers.py new file mode 100644 index 0000000..e2801f7 --- /dev/null +++ b/comfy/ldm/hydit/attn_layers.py @@ -0,0 +1,218 @@ +import torch +import torch.nn as nn +from typing import Tuple, Union, Optional +from comfy.ldm.modules.attention import optimized_attention + + +def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as the target tensor 'x' + for the purpose of broadcasting the frequency tensor during element-wise operations. + + Args: + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected shape. + AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + + if isinstance(freqs_cis, tuple): + # freqs_cis: (cos, sin) in real space + if head_first: + assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + else: + assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + else: + # freqs_cis: values in complex space + if head_first: + assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + else: + assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + +def rotate_half(x): + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + +def apply_rotary_emb( + xq: torch.Tensor, + xk: Optional[torch.Tensor], + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + head_first: bool = False, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. + + This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided + frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are + returned as real tensors. + + Args: + xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D] + xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D] + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + + """ + xk_out = None + if isinstance(freqs_cis, tuple): + cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] + xq_out = (xq * cos + rotate_half(xq) * sin) + if xk is not None: + xk_out = (xk * cos + rotate_half(xk) * sin) + else: + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2] + freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2] + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) + if xk is not None: + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2] + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) + + return xq_out, xk_out + + + +class CrossAttention(nn.Module): + """ + Use QK Normalization. + """ + def __init__(self, + qdim, + kdim, + num_heads, + qkv_bias=True, + qk_norm=False, + attn_drop=0.0, + proj_drop=0.0, + attn_precision=None, + device=None, + dtype=None, + operations=None, + ): + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.attn_precision = attn_precision + self.qdim = qdim + self.kdim = kdim + self.num_heads = num_heads + assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" + self.head_dim = self.qdim // num_heads + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + self.scale = self.head_dim ** -0.5 + + self.q_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.kv_proj = operations.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) + + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() + self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() + self.attn_drop = nn.Dropout(attn_drop) + self.out_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y, freqs_cis_img=None): + """ + Parameters + ---------- + x: torch.Tensor + (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim) + y: torch.Tensor + (batch, seqlen2, hidden_dim2) + freqs_cis_img: torch.Tensor + (batch, hidden_dim // 2), RoPE for image + """ + b, s1, c = x.shape # [b, s1, D] + _, s2, c = y.shape # [b, s2, 1024] + + q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] + kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] + k, v = kv.unbind(dim=2) # [b, s, h, d] + q = self.q_norm(q) + k = self.k_norm(k) + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, _ = apply_rotary_emb(q, None, freqs_cis_img) + assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' + q = qq + + q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C + k = k.transpose(-2, -3).contiguous() # k -> B, L2, H, C - B, H, C, L2 + v = v.transpose(-2, -3).contiguous() + + context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision) + + out = self.out_proj(context) # context.reshape - B, L1, -1 + out = self.proj_drop(out) + + out_tuple = (out,) + + return out_tuple + + +class Attention(nn.Module): + """ + We rename some layer names to align with flash attention + """ + def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., attn_precision=None, dtype=None, device=None, operations=None): + super().__init__() + self.attn_precision = attn_precision + self.dim = dim + self.num_heads = num_heads + assert self.dim % num_heads == 0, 'dim should be divisible by num_heads' + self.head_dim = self.dim // num_heads + # This assertion is aligned with flash attention + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + self.scale = self.head_dim ** -0.5 + + # qkv --> Wqkv + self.Wqkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() + self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() + self.attn_drop = nn.Dropout(attn_drop) + self.out_proj = operations.Linear(dim, dim, dtype=dtype, device=device) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, freqs_cis_img=None): + B, N, C = x.shape + qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d] + q, k, v = qkv.unbind(0) # [b, h, s, d] + q = self.q_norm(q) # [b, h, s, d] + k = self.k_norm(k) # [b, h, s, d] + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True) + assert qq.shape == q.shape and kk.shape == k.shape, \ + f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' + q, k = qq, kk + + x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision) + x = self.out_proj(x) + x = self.proj_drop(x) + + out_tuple = (x,) + + return out_tuple diff --git a/comfy/ldm/hydit/controlnet.py b/comfy/ldm/hydit/controlnet.py new file mode 100644 index 0000000..cd71fca --- /dev/null +++ b/comfy/ldm/hydit/controlnet.py @@ -0,0 +1,321 @@ +from typing import Any, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torch.utils import checkpoint + +from comfy.ldm.modules.diffusionmodules.mmdit import ( + Mlp, + TimestepEmbedder, + PatchEmbed, + RMSNorm, +) +from comfy.ldm.modules.diffusionmodules.util import timestep_embedding +from .poolers import AttentionPool + +import comfy.latent_formats +from .models import HunYuanDiTBlock, calc_rope + +from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop + + +class HunYuanControlNet(nn.Module): + """ + HunYuanDiT: Diffusion model with a Transformer backbone. + + Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. + + Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. + + Parameters + ---------- + args: argparse.Namespace + The arguments parsed by argparse. + input_size: tuple + The size of the input image. + patch_size: int + The size of the patch. + in_channels: int + The number of input channels. + hidden_size: int + The hidden size of the transformer backbone. + depth: int + The number of transformer blocks. + num_heads: int + The number of attention heads. + mlp_ratio: float + The ratio of the hidden size of the MLP in the transformer block. + log_fn: callable + The logging function. + """ + + def __init__( + self, + input_size: tuple = 128, + patch_size: int = 2, + in_channels: int = 4, + hidden_size: int = 1408, + depth: int = 40, + num_heads: int = 16, + mlp_ratio: float = 4.3637, + text_states_dim=1024, + text_states_dim_t5=2048, + text_len=77, + text_len_t5=256, + qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details. + size_cond=False, + use_style_cond=False, + learn_sigma=True, + norm="layer", + log_fn: callable = print, + attn_precision=None, + dtype=None, + device=None, + operations=None, + **kwargs, + ): + super().__init__() + self.log_fn = log_fn + self.depth = depth + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.text_states_dim = text_states_dim + self.text_states_dim_t5 = text_states_dim_t5 + self.text_len = text_len + self.text_len_t5 = text_len_t5 + self.size_cond = size_cond + self.use_style_cond = use_style_cond + self.norm = norm + self.dtype = dtype + self.latent_format = comfy.latent_formats.SDXL + + self.mlp_t5 = nn.Sequential( + nn.Linear( + self.text_states_dim_t5, + self.text_states_dim_t5 * 4, + bias=True, + dtype=dtype, + device=device, + ), + nn.SiLU(), + nn.Linear( + self.text_states_dim_t5 * 4, + self.text_states_dim, + bias=True, + dtype=dtype, + device=device, + ), + ) + # learnable replace + self.text_embedding_padding = nn.Parameter( + torch.randn( + self.text_len + self.text_len_t5, + self.text_states_dim, + dtype=dtype, + device=device, + ) + ) + + # Attention pooling + pooler_out_dim = 1024 + self.pooler = AttentionPool( + self.text_len_t5, + self.text_states_dim_t5, + num_heads=8, + output_dim=pooler_out_dim, + dtype=dtype, + device=device, + operations=operations, + ) + + # Dimension of the extra input vectors + self.extra_in_dim = pooler_out_dim + + if self.size_cond: + # Image size and crop size conditions + self.extra_in_dim += 6 * 256 + + if self.use_style_cond: + # Here we use a default learned embedder layer for future extension. + self.style_embedder = nn.Embedding( + 1, hidden_size, dtype=dtype, device=device + ) + self.extra_in_dim += hidden_size + + # Text embedding for `add` + self.x_embedder = PatchEmbed( + input_size, + patch_size, + in_channels, + hidden_size, + dtype=dtype, + device=device, + operations=operations, + ) + self.t_embedder = TimestepEmbedder( + hidden_size, dtype=dtype, device=device, operations=operations + ) + self.extra_embedder = nn.Sequential( + operations.Linear( + self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device + ), + nn.SiLU(), + operations.Linear( + hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device + ), + ) + + # Image embedding + num_patches = self.x_embedder.num_patches + + # HUnYuanDiT Blocks + self.blocks = nn.ModuleList( + [ + HunYuanDiTBlock( + hidden_size=hidden_size, + c_emb_size=hidden_size, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + text_states_dim=self.text_states_dim, + qk_norm=qk_norm, + norm_type=self.norm, + skip=False, + attn_precision=attn_precision, + dtype=dtype, + device=device, + operations=operations, + ) + for _ in range(19) + ] + ) + + # Input zero linear for the first block + self.before_proj = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) + + + # Output zero linear for the every block + self.after_proj_list = nn.ModuleList( + [ + + operations.Linear( + self.hidden_size, self.hidden_size, dtype=dtype, device=device + ) + for _ in range(len(self.blocks)) + ] + ) + + def forward( + self, + x, + hint, + timesteps, + context,#encoder_hidden_states=None, + text_embedding_mask=None, + encoder_hidden_states_t5=None, + text_embedding_mask_t5=None, + image_meta_size=None, + style=None, + return_dict=False, + **kwarg, + ): + """ + Forward pass of the encoder. + + Parameters + ---------- + x: torch.Tensor + (B, D, H, W) + t: torch.Tensor + (B) + encoder_hidden_states: torch.Tensor + CLIP text embedding, (B, L_clip, D) + text_embedding_mask: torch.Tensor + CLIP text embedding mask, (B, L_clip) + encoder_hidden_states_t5: torch.Tensor + T5 text embedding, (B, L_t5, D) + text_embedding_mask_t5: torch.Tensor + T5 text embedding mask, (B, L_t5) + image_meta_size: torch.Tensor + (B, 6) + style: torch.Tensor + (B) + cos_cis_img: torch.Tensor + sin_cis_img: torch.Tensor + return_dict: bool + Whether to return a dictionary. + """ + condition = hint + if condition.shape[0] == 1: + condition = torch.repeat_interleave(condition, x.shape[0], dim=0) + + text_states = context # 2,77,1024 + text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 + text_states_mask = text_embedding_mask.bool() # 2,77 + text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 + b_t5, l_t5, c_t5 = text_states_t5.shape + text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1) + + padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states) + + text_states[:, -self.text_len :] = torch.where( + text_states_mask[:, -self.text_len :].unsqueeze(2), + text_states[:, -self.text_len :], + padding[: self.text_len], + ) + text_states_t5[:, -self.text_len_t5 :] = torch.where( + text_states_t5_mask[:, -self.text_len_t5 :].unsqueeze(2), + text_states_t5[:, -self.text_len_t5 :], + padding[self.text_len :], + ) + + text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024 + + # _, _, oh, ow = x.shape + # th, tw = oh // self.patch_size, ow // self.patch_size + + # Get image RoPE embedding according to `reso`lution. + freqs_cis_img = calc_rope( + x, self.patch_size, self.hidden_size // self.num_heads + ) # (cos_cis_img, sin_cis_img) + + # ========================= Build time and image embedding ========================= + t = self.t_embedder(timesteps, dtype=self.dtype) + x = self.x_embedder(x) + + # ========================= Concatenate all extra vectors ========================= + # Build text tokens with pooling + extra_vec = self.pooler(encoder_hidden_states_t5) + + # Build image meta size tokens if applicable + # if image_meta_size is not None: + # image_meta_size = timestep_embedding(image_meta_size.view(-1), 256) # [B * 6, 256] + # if image_meta_size.dtype != self.dtype: + # image_meta_size = image_meta_size.half() + # image_meta_size = image_meta_size.view(-1, 6 * 256) + # extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] + + # Build style tokens + if style is not None: + style_embedding = self.style_embedder(style) + extra_vec = torch.cat([extra_vec, style_embedding], dim=1) + + # Concatenate all extra vectors + c = t + self.extra_embedder(extra_vec) # [B, D] + + # ========================= Deal with Condition ========================= + condition = self.x_embedder(condition) + + # ========================= Forward pass through HunYuanDiT blocks ========================= + controls = [] + x = x + self.before_proj(condition) # add condition + for layer, block in enumerate(self.blocks): + x = block(x, c, text_states, freqs_cis_img) + controls.append(self.after_proj_list[layer](x)) # zero linear for output + + return {"output": controls} diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py new file mode 100644 index 0000000..44e806c --- /dev/null +++ b/comfy/ldm/hydit/models.py @@ -0,0 +1,410 @@ +from typing import Any + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.ops +from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm +from comfy.ldm.modules.diffusionmodules.util import timestep_embedding +from torch.utils import checkpoint + +from .attn_layers import Attention, CrossAttention +from .poolers import AttentionPool +from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop + +def calc_rope(x, patch_size, head_size): + th = (x.shape[2] + (patch_size // 2)) // patch_size + tw = (x.shape[3] + (patch_size // 2)) // patch_size + base_size = 512 // 8 // patch_size + start, stop = get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads'] + rope = get_2d_rotary_pos_embed(head_size, *sub_args) + rope = (rope[0].to(x), rope[1].to(x)) + return rope + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class HunYuanDiTBlock(nn.Module): + """ + A HunYuanDiT block with `add` conditioning. + """ + def __init__(self, + hidden_size, + c_emb_size, + num_heads, + mlp_ratio=4.0, + text_states_dim=1024, + qk_norm=False, + norm_type="layer", + skip=False, + attn_precision=None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + use_ele_affine = True + + if norm_type == "layer": + norm_layer = operations.LayerNorm + elif norm_type == "rms": + norm_layer = RMSNorm + else: + raise ValueError(f"Unknown norm_type: {norm_type}") + + # ========================= Self-Attention ========================= + self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device) + self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) + + # ========================= FFN ========================= + self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations) + + # ========================= Add ========================= + # Simply use add like SDXL. + self.default_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device) + ) + + # ========================= Cross-Attention ========================= + self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True, + qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) + self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) + + # ========================= Skip Connection ========================= + if skip: + self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) + self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device) + else: + self.skip_linear = None + + self.gradient_checkpointing = False + + def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): + # Long Skip Connection + if self.skip_linear is not None: + cat = torch.cat([x, skip], dim=-1) + if cat.dtype != x.dtype: + cat = cat.to(x.dtype) + cat = self.skip_norm(cat) + x = self.skip_linear(cat) + + # Self-Attention + shift_msa = self.default_modulation(c).unsqueeze(dim=1) + attn_inputs = ( + self.norm1(x) + shift_msa, freq_cis_img, + ) + x = x + self.attn1(*attn_inputs)[0] + + # Cross-Attention + cross_inputs = ( + self.norm3(x), text_states, freq_cis_img + ) + x = x + self.attn2(*cross_inputs)[0] + + # FFN Layer + mlp_inputs = self.norm2(x) + x = x + self.mlp(mlp_inputs) + + return x + + def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): + if self.gradient_checkpointing and self.training: + return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip) + return self._forward(x, c, text_states, freq_cis_img, skip) + + +class FinalLayer(nn.Module): + """ + The final layer of HunYuanDiT. + """ + def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class HunYuanDiT(nn.Module): + """ + HunYuanDiT: Diffusion model with a Transformer backbone. + + Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. + + Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. + + Parameters + ---------- + args: argparse.Namespace + The arguments parsed by argparse. + input_size: tuple + The size of the input image. + patch_size: int + The size of the patch. + in_channels: int + The number of input channels. + hidden_size: int + The hidden size of the transformer backbone. + depth: int + The number of transformer blocks. + num_heads: int + The number of attention heads. + mlp_ratio: float + The ratio of the hidden size of the MLP in the transformer block. + log_fn: callable + The logging function. + """ + #@register_to_config + def __init__(self, + input_size: tuple = 32, + patch_size: int = 2, + in_channels: int = 4, + hidden_size: int = 1152, + depth: int = 28, + num_heads: int = 16, + mlp_ratio: float = 4.0, + text_states_dim = 1024, + text_states_dim_t5 = 2048, + text_len = 77, + text_len_t5 = 256, + qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details. + size_cond = False, + use_style_cond = False, + learn_sigma = True, + norm = "layer", + log_fn: callable = print, + attn_precision=None, + dtype=None, + device=None, + operations=None, + **kwargs, + ): + super().__init__() + self.log_fn = log_fn + self.depth = depth + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.text_states_dim = text_states_dim + self.text_states_dim_t5 = text_states_dim_t5 + self.text_len = text_len + self.text_len_t5 = text_len_t5 + self.size_cond = size_cond + self.use_style_cond = use_style_cond + self.norm = norm + self.dtype = dtype + #import pdb + #pdb.set_trace() + + self.mlp_t5 = nn.Sequential( + operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device), + ) + # learnable replace + self.text_embedding_padding = nn.Parameter( + torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device)) + + # Attention pooling + pooler_out_dim = 1024 + self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations) + + # Dimension of the extra input vectors + self.extra_in_dim = pooler_out_dim + + if self.size_cond: + # Image size and crop size conditions + self.extra_in_dim += 6 * 256 + + if self.use_style_cond: + # Here we use a default learned embedder layer for future extension. + self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device) + self.extra_in_dim += hidden_size + + # Text embedding for `add` + self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations) + self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations) + self.extra_embedder = nn.Sequential( + operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device), + ) + + # Image embedding + num_patches = self.x_embedder.num_patches + + # HUnYuanDiT Blocks + self.blocks = nn.ModuleList([ + HunYuanDiTBlock(hidden_size=hidden_size, + c_emb_size=hidden_size, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + text_states_dim=self.text_states_dim, + qk_norm=qk_norm, + norm_type=self.norm, + skip=layer > depth // 2, + attn_precision=attn_precision, + dtype=dtype, + device=device, + operations=operations, + ) + for layer in range(depth) + ]) + + self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) + self.unpatchify_channels = self.out_channels + + + + def forward(self, + x, + t, + context,#encoder_hidden_states=None, + text_embedding_mask=None, + encoder_hidden_states_t5=None, + text_embedding_mask_t5=None, + image_meta_size=None, + style=None, + return_dict=False, + control=None, + transformer_options=None, + ): + """ + Forward pass of the encoder. + + Parameters + ---------- + x: torch.Tensor + (B, D, H, W) + t: torch.Tensor + (B) + encoder_hidden_states: torch.Tensor + CLIP text embedding, (B, L_clip, D) + text_embedding_mask: torch.Tensor + CLIP text embedding mask, (B, L_clip) + encoder_hidden_states_t5: torch.Tensor + T5 text embedding, (B, L_t5, D) + text_embedding_mask_t5: torch.Tensor + T5 text embedding mask, (B, L_t5) + image_meta_size: torch.Tensor + (B, 6) + style: torch.Tensor + (B) + cos_cis_img: torch.Tensor + sin_cis_img: torch.Tensor + return_dict: bool + Whether to return a dictionary. + """ + #import pdb + #pdb.set_trace() + encoder_hidden_states = context + text_states = encoder_hidden_states # 2,77,1024 + text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 + text_states_mask = text_embedding_mask.bool() # 2,77 + text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 + b_t5, l_t5, c_t5 = text_states_t5.shape + text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1) + + padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states) + + text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len]) + text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:]) + + text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024 + # clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1) + + _, _, oh, ow = x.shape + th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size + + + # Get image RoPE embedding according to `reso`lution. + freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img) + + # ========================= Build time and image embedding ========================= + t = self.t_embedder(t, dtype=x.dtype) + x = self.x_embedder(x) + + # ========================= Concatenate all extra vectors ========================= + # Build text tokens with pooling + extra_vec = self.pooler(encoder_hidden_states_t5) + + # Build image meta size tokens if applicable + if self.size_cond: + image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype) # [B * 6, 256] + image_meta_size = image_meta_size.view(-1, 6 * 256) + extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] + + # Build style tokens + if self.use_style_cond: + if style is None: + style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int) + style_embedding = self.style_embedder(style, out_dtype=x.dtype) + extra_vec = torch.cat([extra_vec, style_embedding], dim=1) + + # Concatenate all extra vectors + c = t + self.extra_embedder(extra_vec) # [B, D] + + controls = None + if control: + controls = control.get("output", None) + # ========================= Forward pass through HunYuanDiT blocks ========================= + skips = [] + for layer, block in enumerate(self.blocks): + if layer > self.depth // 2: + if controls is not None: + skip = skips.pop() + controls.pop().to(dtype=x.dtype) + else: + skip = skips.pop() + x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D) + else: + x = block(x, c, text_states, freqs_cis_img) # (N, L, D) + + if layer < (self.depth // 2 - 1): + skips.append(x) + if controls is not None and len(controls) != 0: + raise ValueError("The number of controls is not equal to the number of skip connections.") + + # ========================= Final layer ========================= + x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels) + x = self.unpatchify(x, th, tw) # (N, out_channels, H, W) + + if return_dict: + return {'x': x} + if self.learn_sigma: + return x[:,:self.out_channels // 2,:oh,:ow] + return x[:,:,:oh,:ow] + + def unpatchify(self, x, h, w): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.unpatchify_channels + p = self.x_embedder.patch_size[0] + # h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs diff --git a/comfy/ldm/hydit/poolers.py b/comfy/ldm/hydit/poolers.py new file mode 100644 index 0000000..f5e5b40 --- /dev/null +++ b/comfy/ldm/hydit/poolers.py @@ -0,0 +1,37 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from comfy.ldm.modules.attention import optimized_attention +import comfy.ops + +class AttentionPool(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.empty(spacial_dim + 1, embed_dim, dtype=dtype, device=device)) + self.k_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) + self.q_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) + self.v_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) + self.c_proj = operations.Linear(embed_dim, output_dim or embed_dim, dtype=dtype, device=device) + self.num_heads = num_heads + self.embed_dim = embed_dim + + def forward(self, x): + x = x[:,:self.positional_embedding.shape[0] - 1] + x = x.permute(1, 0, 2) # NLC -> LNC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC + x = x + comfy.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC + + q = self.q_proj(x[:1]) + k = self.k_proj(x) + v = self.v_proj(x) + + batch_size = q.shape[1] + head_dim = self.embed_dim // self.num_heads + q = q.view(1, batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) + k = k.view(k.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) + v = v.view(v.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) + + attn_output = optimized_attention(q, k, v, self.num_heads, skip_reshape=True).transpose(0, 1) + + attn_output = self.c_proj(attn_output) + return attn_output.squeeze(0) diff --git a/comfy/ldm/hydit/posemb_layers.py b/comfy/ldm/hydit/posemb_layers.py new file mode 100644 index 0000000..dcb41a7 --- /dev/null +++ b/comfy/ldm/hydit/posemb_layers.py @@ -0,0 +1,224 @@ +import torch +import numpy as np +from typing import Union + + +def _to_tuple(x): + if isinstance(x, int): + return x, x + else: + return x + + +def get_fill_resize_and_crop(src, tgt): + th, tw = _to_tuple(tgt) + h, w = _to_tuple(src) + + tr = th / tw # base resolution + r = h / w # target resolution + + # resize + if r > tr: + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) # resize the target resolution down based on the base resolution + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + +def get_meshgrid(start, *args): + if len(args) == 0: + # start is grid_size + num = _to_tuple(start) + start = (0, 0) + stop = num + elif len(args) == 1: + # start is start, args[0] is stop, step is 1 + start = _to_tuple(start) + stop = _to_tuple(args[0]) + num = (stop[0] - start[0], stop[1] - start[1]) + elif len(args) == 2: + # start is start, args[0] is stop, args[1] is num + start = _to_tuple(start) + stop = _to_tuple(args[0]) + num = _to_tuple(args[1]) + else: + raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") + + grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) + grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) # [2, W, H] + return grid + +################################################################################# +# Sine/Cosine Positional Embedding Functions # +################################################################################# +# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py + +def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid = get_meshgrid(start, *args) # [2, H, w] + # grid_h = np.arange(grid_size, dtype=np.float32) + # grid_w = np.arange(grid_size, dtype=np.float32) + # grid = np.meshgrid(grid_w, grid_h) # here w goes first + # grid = np.stack(grid, axis=0) # [2, W, H] + + grid = grid.reshape([2, 1, *grid.shape[1:]]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (W,H) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2. + omega = 1. / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +################################################################################# +# Rotary Positional Embedding Functions # +################################################################################# +# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443 + +def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True): + """ + This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure. + + Parameters + ---------- + embed_dim: int + embedding dimension size + start: int or tuple of int + If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1; + If len(args) == 2, start is start, args[0] is stop, args[1] is num. + use_real: bool + If True, return real part and imaginary part separately. Otherwise, return complex numbers. + + Returns + ------- + pos_embed: torch.Tensor + [HW, D/2] + """ + grid = get_meshgrid(start, *args) # [2, H, w] + grid = grid.reshape([2, 1, *grid.shape[1:]]) # Returns a sampling matrix with the same resolution as the target resolution + pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) + return pos_embed + + +def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False): + assert embed_dim % 4 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) + emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) + + if use_real: + cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) + sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) + return cos, sin + else: + emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) + return emb + + +def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False): + """ + Precompute the frequency tensor for complex exponentials (cis) with given dimensions. + + This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' + and the end index 'end'. The 'theta' parameter scales the frequencies. + The returned tensor contains complex values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar + theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. + use_real (bool, optional): If True, return real part and imaginary part separately. + Otherwise, return complex numbers. + + Returns: + torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2] + + """ + if isinstance(pos, int): + pos = np.arange(pos) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] + t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] + freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] + if use_real: + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + else: + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] + return freqs_cis + + + +def calc_sizes(rope_img, patch_size, th, tw): + if rope_img == 'extend': + # Expansion mode + sub_args = [(th, tw)] + elif rope_img.startswith('base'): + # Based on the specified dimensions, other dimensions are obtained through interpolation. + base_size = int(rope_img[4:]) // 8 // patch_size + start, stop = get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + else: + raise ValueError(f"Unknown rope_img: {rope_img}") + return sub_args + + +def init_image_posemb(rope_img, + resolutions, + patch_size, + hidden_size, + num_heads, + log_fn, + rope_real=True, + ): + freqs_cis_img = {} + for reso in resolutions: + th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size + sub_args = calc_sizes(rope_img, patch_size, th, tw) + freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real) + log_fn(f" Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) " + f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}") + return freqs_cis_img diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py new file mode 100644 index 0000000..f5f4de2 --- /dev/null +++ b/comfy/ldm/models/autoencoder.py @@ -0,0 +1,226 @@ +import torch +from contextlib import contextmanager +from typing import Any, Dict, List, Optional, Tuple, Union + +from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution + +from comfy.ldm.util import instantiate_from_config +from comfy.ldm.modules.ema import LitEma +import comfy.ops + +class DiagonalGaussianRegularizer(torch.nn.Module): + def __init__(self, sample: bool = True): + super().__init__() + self.sample = sample + + def get_trainable_parameters(self) -> Any: + yield from () + + def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: + log = dict() + posterior = DiagonalGaussianDistribution(z) + if self.sample: + z = posterior.sample() + else: + z = posterior.mode() + kl_loss = posterior.kl() + kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] + log["kl_loss"] = kl_loss + return z, log + + +class AbstractAutoencoder(torch.nn.Module): + """ + This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators, + unCLIP models, etc. Hence, it is fairly general, and specific features + (e.g. discriminator training, encoding, decoding) must be implemented in subclasses. + """ + + def __init__( + self, + ema_decay: Union[None, float] = None, + monitor: Union[None, str] = None, + input_key: str = "jpg", + **kwargs, + ): + super().__init__() + + self.input_key = input_key + self.use_ema = ema_decay is not None + if monitor is not None: + self.monitor = monitor + + if self.use_ema: + self.model_ema = LitEma(self, decay=ema_decay) + logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + def get_input(self, batch) -> Any: + raise NotImplementedError() + + def on_train_batch_end(self, *args, **kwargs): + # for EMA computation + if self.use_ema: + self.model_ema(self) + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + logpy.info(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + logpy.info(f"{context}: Restored training weights") + + def encode(self, *args, **kwargs) -> torch.Tensor: + raise NotImplementedError("encode()-method of abstract base class called") + + def decode(self, *args, **kwargs) -> torch.Tensor: + raise NotImplementedError("decode()-method of abstract base class called") + + def instantiate_optimizer_from_config(self, params, lr, cfg): + logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config") + return get_obj_from_str(cfg["target"])( + params, lr=lr, **cfg.get("params", dict()) + ) + + def configure_optimizers(self) -> Any: + raise NotImplementedError() + + +class AutoencodingEngine(AbstractAutoencoder): + """ + Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL + (we also restore them explicitly as special cases for legacy reasons). + Regularizations such as KL or VQ are moved to the regularizer class. + """ + + def __init__( + self, + *args, + encoder_config: Dict, + decoder_config: Dict, + regularizer_config: Dict, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.encoder: torch.nn.Module = instantiate_from_config(encoder_config) + self.decoder: torch.nn.Module = instantiate_from_config(decoder_config) + self.regularization: AbstractRegularizer = instantiate_from_config( + regularizer_config + ) + + def get_last_layer(self): + return self.decoder.get_last_layer() + + def encode( + self, + x: torch.Tensor, + return_reg_log: bool = False, + unregularized: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: + z = self.encoder(x) + if unregularized: + return z, dict() + z, reg_log = self.regularization(z) + if return_reg_log: + return z, reg_log + return z + + def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor: + x = self.decoder(z, **kwargs) + return x + + def forward( + self, x: torch.Tensor, **additional_decode_kwargs + ) -> Tuple[torch.Tensor, torch.Tensor, dict]: + z, reg_log = self.encode(x, return_reg_log=True) + dec = self.decode(z, **additional_decode_kwargs) + return z, dec, reg_log + + +class AutoencodingEngineLegacy(AutoencodingEngine): + def __init__(self, embed_dim: int, **kwargs): + self.max_batch_size = kwargs.pop("max_batch_size", None) + ddconfig = kwargs.pop("ddconfig") + super().__init__( + encoder_config={ + "target": "comfy.ldm.modules.diffusionmodules.model.Encoder", + "params": ddconfig, + }, + decoder_config={ + "target": "comfy.ldm.modules.diffusionmodules.model.Decoder", + "params": ddconfig, + }, + **kwargs, + ) + self.quant_conv = comfy.ops.disable_weight_init.Conv2d( + (1 + ddconfig["double_z"]) * ddconfig["z_channels"], + (1 + ddconfig["double_z"]) * embed_dim, + 1, + ) + self.post_quant_conv = comfy.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + + def get_autoencoder_params(self) -> list: + params = super().get_autoencoder_params() + return params + + def encode( + self, x: torch.Tensor, return_reg_log: bool = False + ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: + if self.max_batch_size is None: + z = self.encoder(x) + z = self.quant_conv(z) + else: + N = x.shape[0] + bs = self.max_batch_size + n_batches = int(math.ceil(N / bs)) + z = list() + for i_batch in range(n_batches): + z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs]) + z_batch = self.quant_conv(z_batch) + z.append(z_batch) + z = torch.cat(z, 0) + + z, reg_log = self.regularization(z) + if return_reg_log: + return z, reg_log + return z + + def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: + if self.max_batch_size is None: + dec = self.post_quant_conv(z) + dec = self.decoder(dec, **decoder_kwargs) + else: + N = z.shape[0] + bs = self.max_batch_size + n_batches = int(math.ceil(N / bs)) + dec = list() + for i_batch in range(n_batches): + dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs]) + dec_batch = self.decoder(dec_batch, **decoder_kwargs) + dec.append(dec_batch) + dec = torch.cat(dec, 0) + + return dec + + +class AutoencoderKL(AutoencodingEngineLegacy): + def __init__(self, **kwargs): + if "lossconfig" in kwargs: + kwargs["loss_config"] = kwargs.pop("lossconfig") + super().__init__( + regularizer_config={ + "target": ( + "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer" + ) + }, + **kwargs, + ) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py new file mode 100644 index 0000000..3f543ab --- /dev/null +++ b/comfy/ldm/modules/attention.py @@ -0,0 +1,877 @@ +import math +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat +from typing import Optional +import logging + +from .diffusionmodules.util import AlphaBlender, timestep_embedding +from .sub_quadratic_attention import efficient_dot_product_attention + +from comfy import model_management + +if model_management.xformers_enabled(): + import xformers + import xformers.ops + +from comfy.cli_args import args +import comfy.ops +ops = comfy.ops.disable_weight_init + +FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype() + +def get_attn_precision(attn_precision): + if args.dont_upcast_attention: + return None + if FORCE_UPCAST_ATTENTION_DTYPE is not None: + return FORCE_UPCAST_ATTENTION_DTYPE + return attn_precision + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): + super().__init__() + self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + operations.Linear(dim, inner_dim, dtype=dtype, device=device), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) + ) + + def forward(self, x): + return self.net(x) + +def Normalize(in_channels, dtype=None, device=None): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) + +def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): + attn_precision = get_attn_precision(attn_precision) + + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + + scale = dim_head ** -0.5 + + h = heads + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + # force cast to fp32 to avoid overflowing + if attn_precision == torch.float32: + sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * scale + + del q, k + + if exists(mask): + if mask.dtype == torch.bool: + mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + else: + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + sim.add_(mask) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return out + + +def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False): + attn_precision = get_attn_precision(attn_precision) + + if skip_reshape: + b, _, _, dim_head = query.shape + else: + b, _, dim_head = query.shape + dim_head //= heads + + scale = dim_head ** -0.5 + + if skip_reshape: + query = query.reshape(b * heads, -1, dim_head) + value = value.reshape(b * heads, -1, dim_head) + key = key.reshape(b * heads, -1, dim_head).movedim(1, 2) + else: + query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) + value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) + key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) + + + dtype = query.dtype + upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32 + if upcast_attention: + bytes_per_token = torch.finfo(torch.float32).bits//8 + else: + bytes_per_token = torch.finfo(query.dtype).bits//8 + batch_x_heads, q_tokens, _ = query.shape + _, _, k_tokens = key.shape + qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens + + mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True) + + kv_chunk_size_min = None + kv_chunk_size = None + query_chunk_size = None + + for x in [4096, 2048, 1024, 512, 256]: + count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0) + if count >= k_tokens: + kv_chunk_size = k_tokens + query_chunk_size = x + break + + if query_chunk_size is None: + query_chunk_size = 512 + + if mask is not None: + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + + hidden_states = efficient_dot_product_attention( + query, + key, + value, + query_chunk_size=query_chunk_size, + kv_chunk_size=kv_chunk_size, + kv_chunk_size_min=kv_chunk_size_min, + use_checkpoint=False, + upcast_attention=upcast_attention, + mask=mask, + ) + + hidden_states = hidden_states.to(dtype) + + hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) + return hidden_states + +def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): + attn_precision = get_attn_precision(attn_precision) + + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + + scale = dim_head ** -0.5 + + h = heads + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + + mem_free_total = model_management.get_free_memory(q.device) + + if attn_precision == torch.float32: + element_size = 4 + upcast = True + else: + element_size = q.element_size() + upcast = False + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size + modifier = 3 + mem_required = tensor_size * modifier + steps = 1 + + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " + # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") + + if steps > 64: + max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 + raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' + f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') + + if mask is not None: + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + + # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) + first_op_done = False + cleared_cache = False + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + if upcast: + with torch.autocast(enabled=False, device_type = 'cuda'): + s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale + else: + s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale + + if mask is not None: + if len(mask.shape) == 2: + s1 += mask[i:end] + else: + s1 += mask[:, i:end] + + s2 = s1.softmax(dim=-1).to(v.dtype) + del s1 + first_op_done = True + + r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) + del s2 + break + except model_management.OOM_EXCEPTION as e: + if first_op_done == False: + model_management.soft_empty_cache(True) + if cleared_cache == False: + cleared_cache = True + logging.warning("out of memory error, emptying cache and trying again") + continue + steps *= 2 + if steps > 64: + raise e + logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) + else: + raise e + + del q, k, v + + r1 = ( + r1.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return r1 + +BROKEN_XFORMERS = False +try: + x_vers = xformers.__version__ + # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error) + BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20") +except: + pass + +def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + + disabled_xformers = False + + if BROKEN_XFORMERS: + if b * heads > 65535: + disabled_xformers = True + + if not disabled_xformers: + if torch.jit.is_tracing() or torch.jit.is_scripting(): + disabled_xformers = True + + if disabled_xformers: + return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape) + + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.reshape(b, -1, heads, dim_head), + (q, k, v), + ) + + if mask is not None: + pad = 8 - q.shape[1] % 8 + mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device) + mask_out[:, :, :mask.shape[-1]] = mask + mask = mask_out[:, :, :mask.shape[-1]] + + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) + + if skip_reshape: + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + else: + out = ( + out.reshape(b, -1, heads * dim_head) + ) + + return out + +if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue. + SDP_BATCH_LIMIT = 2**15 +else: + #TODO: other GPUs ? + SDP_BATCH_LIMIT = 2**31 + + +def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), + (q, k, v), + ) + + if SDP_BATCH_LIMIT >= q.shape[0]: + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + out = ( + out.transpose(1, 2).reshape(b, -1, heads * dim_head) + ) + else: + out = torch.empty((q.shape[0], q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device) + for i in range(0, q.shape[0], SDP_BATCH_LIMIT): + out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(q[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], attn_mask=mask, dropout_p=0.0, is_causal=False).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head) + return out + + +optimized_attention = attention_basic + +if model_management.xformers_enabled(): + logging.info("Using xformers cross attention") + optimized_attention = attention_xformers +elif model_management.pytorch_attention_enabled(): + logging.info("Using pytorch cross attention") + optimized_attention = attention_pytorch +else: + if args.use_split_cross_attention: + logging.info("Using split optimization for cross attention") + optimized_attention = attention_split + else: + logging.info("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention") + optimized_attention = attention_sub_quad + +optimized_attention_masked = optimized_attention + +def optimized_attention_for_device(device, mask=False, small_input=False): + if small_input: + if model_management.pytorch_attention_enabled(): + return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases + else: + return attention_basic + + if device == torch.device("cpu"): + return attention_sub_quad + + if mask: + return optimized_attention_masked + + return optimized_attention + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + self.attn_precision = attn_precision + + self.heads = heads + self.dim_head = dim_head + + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + + self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) + + def forward(self, x, context=None, value=None, mask=None): + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + if value is not None: + v = self.to_v(value) + del value + else: + v = self.to_v(context) + + if mask is None: + out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) + else: + out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, + disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops): + super().__init__() + + self.ff_in = ff_in or inner_dim is not None + if inner_dim is None: + inner_dim = dim + + self.is_res = inner_dim == dim + self.attn_precision = attn_precision + + if self.ff_in: + self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) + self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + + self.disable_self_attn = disable_self_attn + self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + + if disable_temporal_crossattention: + if switch_temporal_ca_to_sa: + raise ValueError + else: + self.attn2 = None + else: + context_dim_attn2 = None + if not switch_temporal_ca_to_sa: + context_dim_attn2 = context_dim + + self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, + heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is self-attn if context is none + self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + + self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.n_heads = n_heads + self.d_head = d_head + self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa + + def forward(self, x, context=None, transformer_options={}): + extra_options = {} + block = transformer_options.get("block", None) + block_index = transformer_options.get("block_index", 0) + transformer_patches = {} + transformer_patches_replace = {} + + for k in transformer_options: + if k == "patches": + transformer_patches = transformer_options[k] + elif k == "patches_replace": + transformer_patches_replace = transformer_options[k] + else: + extra_options[k] = transformer_options[k] + + extra_options["n_heads"] = self.n_heads + extra_options["dim_head"] = self.d_head + extra_options["attn_precision"] = self.attn_precision + + if self.ff_in: + x_skip = x + x = self.ff_in(self.norm_in(x)) + if self.is_res: + x += x_skip + + n = self.norm1(x) + if self.disable_self_attn: + context_attn1 = context + else: + context_attn1 = None + value_attn1 = None + + if "attn1_patch" in transformer_patches: + patch = transformer_patches["attn1_patch"] + if context_attn1 is None: + context_attn1 = n + value_attn1 = context_attn1 + for p in patch: + n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options) + + if block is not None: + transformer_block = (block[0], block[1], block_index) + else: + transformer_block = None + attn1_replace_patch = transformer_patches_replace.get("attn1", {}) + block_attn1 = transformer_block + if block_attn1 not in attn1_replace_patch: + block_attn1 = block + + if block_attn1 in attn1_replace_patch: + if context_attn1 is None: + context_attn1 = n + value_attn1 = n + n = self.attn1.to_q(n) + context_attn1 = self.attn1.to_k(context_attn1) + value_attn1 = self.attn1.to_v(value_attn1) + n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) + n = self.attn1.to_out(n) + else: + n = self.attn1(n, context=context_attn1, value=value_attn1) + + if "attn1_output_patch" in transformer_patches: + patch = transformer_patches["attn1_output_patch"] + for p in patch: + n = p(n, extra_options) + + x += n + if "middle_patch" in transformer_patches: + patch = transformer_patches["middle_patch"] + for p in patch: + x = p(x, extra_options) + + if self.attn2 is not None: + n = self.norm2(x) + if self.switch_temporal_ca_to_sa: + context_attn2 = n + else: + context_attn2 = context + value_attn2 = None + if "attn2_patch" in transformer_patches: + patch = transformer_patches["attn2_patch"] + value_attn2 = context_attn2 + for p in patch: + n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) + + attn2_replace_patch = transformer_patches_replace.get("attn2", {}) + block_attn2 = transformer_block + if block_attn2 not in attn2_replace_patch: + block_attn2 = block + + if block_attn2 in attn2_replace_patch: + if value_attn2 is None: + value_attn2 = context_attn2 + n = self.attn2.to_q(n) + context_attn2 = self.attn2.to_k(context_attn2) + value_attn2 = self.attn2.to_v(value_attn2) + n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) + n = self.attn2.to_out(n) + else: + n = self.attn2(n, context=context_attn2, value=value_attn2) + + if "attn2_output_patch" in transformer_patches: + patch = transformer_patches["attn2_output_patch"] + for p in patch: + n = p(n, extra_options) + + x += n + if self.is_res: + x_skip = x + x = self.ff(self.norm3(x)) + if self.is_res: + x += x_skip + + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] * depth + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) + if not use_linear: + self.proj_in = operations.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0, dtype=dtype, device=device) + else: + self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = operations.Conv2d(inner_dim,in_channels, + kernel_size=1, + stride=1, + padding=0, dtype=dtype, device=device) + else: + self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) + self.use_linear = use_linear + + def forward(self, x, context=None, transformer_options={}): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] * len(self.transformer_blocks) + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = x.movedim(1, 3).flatten(1, 2).contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + transformer_options["block_index"] = i + x = block(x, context=context[i], transformer_options=transformer_options) + if self.use_linear: + x = self.proj_out(x) + x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class SpatialVideoTransformer(SpatialTransformer): + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + use_linear=False, + context_dim=None, + use_spatial_context=False, + timesteps=None, + merge_strategy: str = "fixed", + merge_factor: float = 0.5, + time_context_dim=None, + ff_in=False, + checkpoint=False, + time_depth=1, + disable_self_attn=False, + disable_temporal_crossattention=False, + max_time_embed_period: int = 10000, + attn_precision=None, + dtype=None, device=None, operations=ops + ): + super().__init__( + in_channels, + n_heads, + d_head, + depth=depth, + dropout=dropout, + use_checkpoint=checkpoint, + context_dim=context_dim, + use_linear=use_linear, + disable_self_attn=disable_self_attn, + attn_precision=attn_precision, + dtype=dtype, device=device, operations=operations + ) + self.time_depth = time_depth + self.depth = depth + self.max_time_embed_period = max_time_embed_period + + time_mix_d_head = d_head + n_time_mix_heads = n_heads + + time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads) + + inner_dim = n_heads * d_head + if use_spatial_context: + time_context_dim = context_dim + + self.time_stack = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_time_mix_heads, + time_mix_d_head, + dropout=dropout, + context_dim=time_context_dim, + # timesteps=timesteps, + checkpoint=checkpoint, + ff_in=ff_in, + inner_dim=time_mix_inner_dim, + disable_self_attn=disable_self_attn, + disable_temporal_crossattention=disable_temporal_crossattention, + attn_precision=attn_precision, + dtype=dtype, device=device, operations=operations + ) + for _ in range(self.depth) + ] + ) + + assert len(self.time_stack) == len(self.transformer_blocks) + + self.use_spatial_context = use_spatial_context + self.in_channels = in_channels + + time_embed_dim = self.in_channels * 4 + self.time_pos_embed = nn.Sequential( + operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device), + ) + + self.time_mixer = AlphaBlender( + alpha=merge_factor, merge_strategy=merge_strategy + ) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + time_context: Optional[torch.Tensor] = None, + timesteps: Optional[int] = None, + image_only_indicator: Optional[torch.Tensor] = None, + transformer_options={} + ) -> torch.Tensor: + _, _, h, w = x.shape + x_in = x + spatial_context = None + if exists(context): + spatial_context = context + + if self.use_spatial_context: + assert ( + context.ndim == 3 + ), f"n dims of spatial context should be 3 but are {context.ndim}" + + if time_context is None: + time_context = context + time_context_first_timestep = time_context[::timesteps] + time_context = repeat( + time_context_first_timestep, "b ... -> (b n) ...", n=h * w + ) + elif time_context is not None and not self.use_spatial_context: + time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w) + if time_context.ndim == 2: + time_context = rearrange(time_context, "b c -> b 1 c") + + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c") + if self.use_linear: + x = self.proj_in(x) + + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype) + emb = self.time_pos_embed(t_emb) + emb = emb[:, None, :] + + for it_, (block, mix_block) in enumerate( + zip(self.transformer_blocks, self.time_stack) + ): + transformer_options["block_index"] = it_ + x = block( + x, + context=spatial_context, + transformer_options=transformer_options, + ) + + x_mix = x + x_mix = x_mix + emb + + B, S, C = x_mix.shape + x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) + x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options + x_mix = rearrange( + x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps + ) + + x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + if not self.use_linear: + x = self.proj_out(x) + out = x + x_in + return out + + diff --git a/comfy/ldm/modules/diffusionmodules/__init__.py b/comfy/ldm/modules/diffusionmodules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py new file mode 100644 index 0000000..6f8f506 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -0,0 +1,1042 @@ +import logging +import math +from typing import Dict, Optional, List + +import numpy as np +import torch +import torch.nn as nn +from ..attention import optimized_attention +from einops import rearrange, repeat +from .util import timestep_embedding +import comfy.ops +import comfy.ldm.common_dit + +def default(x, y): + if x is not None: + return x + return y + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0., + use_conv=False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + drop_probs = drop + linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs) + self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device) + self.drop2 = nn.Dropout(drop_probs) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + +class PatchEmbed(nn.Module): + """ 2D Image to Patch Embedding + """ + dynamic_img_pad: torch.jit.Final[bool] + + def __init__( + self, + img_size: Optional[int] = 224, + patch_size: int = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer = None, + flatten: bool = True, + bias: bool = True, + strict_img_size: bool = True, + dynamic_img_pad: bool = True, + padding_mode='circular', + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.patch_size = (patch_size, patch_size) + self.padding_mode = padding_mode + if img_size is not None: + self.img_size = (img_size, img_size) + self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)]) + self.num_patches = self.grid_size[0] * self.grid_size[1] + else: + self.img_size = None + self.grid_size = None + self.num_patches = None + + # flatten spatial dim and transpose to channels last, kept for bwd compat + self.flatten = flatten + self.strict_img_size = strict_img_size + self.dynamic_img_pad = dynamic_img_pad + + self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + # B, C, H, W = x.shape + # if self.img_size is not None: + # if self.strict_img_size: + # _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).") + # _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).") + # elif not self.dynamic_img_pad: + # _assert( + # H % self.patch_size[0] == 0, + # f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})." + # ) + # _assert( + # W % self.patch_size[1] == 0, + # f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})." + # ) + if self.dynamic_img_pad: + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode) + x = self.proj(x) + if self.flatten: + x = x.flatten(2).transpose(1, 2) # NCHW -> NLC + x = self.norm(x) + return x + +def modulate(x, shift, scale): + if shift is None: + shift = torch.zeros_like(scale) + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +################################################################################# +# Sine/Cosine Positional Embedding Functions # +################################################################################# + + +def get_2d_sincos_pos_embed( + embed_dim, + grid_size, + cls_token=False, + extra_tokens=0, + scaling_factor=None, + offset=None, +): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + if scaling_factor is not None: + grid = grid / scaling_factor + if offset is not None: + grid = grid - offset + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate( + [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0 + ) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + +def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32): + omega = torch.arange(embed_dim // 2, device=device, dtype=dtype) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + pos = pos.reshape(-1) # (M,) + out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product + emb_sin = torch.sin(out) # (M, D/2) + emb_cos = torch.cos(out) # (M, D/2) + emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D) + return emb + +def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32): + small = min(h, w) + val_h = (h / small) * val_magnitude + val_w = (w / small) * val_magnitude + grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij') + emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) + emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) + emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) + return emb + + +################################################################################# +# Embedding Layers for Timesteps and Class Labels # +################################################################################# + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + + def forward(self, t, dtype, **kwargs): + t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class VectorEmbedder(nn.Module): + """ + Embeds a flat vector of dimension input_dim + """ + + def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + emb = self.mlp(x) + return emb + + +################################################################################# +# Core DiT Model # +################################################################################# + + +def split_qkv(qkv, head_dim): + qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0) + return qkv[0], qkv[1], qkv[2] + + +class SelfAttention(nn.Module): + ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") + + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + qk_scale: Optional[float] = None, + proj_drop: float = 0.0, + attn_mode: str = "xformers", + pre_only: bool = False, + qk_norm: Optional[str] = None, + rmsnorm: bool = False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.num_heads = num_heads + self.head_dim = dim // num_heads + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + if not pre_only: + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + self.proj_drop = nn.Dropout(proj_drop) + assert attn_mode in self.ATTENTION_MODES + self.attn_mode = attn_mode + self.pre_only = pre_only + + if qk_norm == "rms": + self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + elif qk_norm == "ln": + self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + elif qk_norm is None: + self.ln_q = nn.Identity() + self.ln_k = nn.Identity() + else: + raise ValueError(qk_norm) + + def pre_attention(self, x: torch.Tensor) -> torch.Tensor: + B, L, C = x.shape + qkv = self.qkv(x) + q, k, v = split_qkv(qkv, self.head_dim) + q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1) + k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1) + return (q, k, v) + + def post_attention(self, x: torch.Tensor) -> torch.Tensor: + assert not self.pre_only + x = self.proj(x) + x = self.proj_drop(x) + return x + + def forward(self, x: torch.Tensor) -> torch.Tensor: + q, k, v = self.pre_attention(x) + x = optimized_attention( + q, k, v, heads=self.num_heads + ) + x = self.post_attention(x) + return x + + +class RMSNorm(torch.nn.Module): + def __init__( + self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None + ): + """ + Initialize the RMSNorm normalization layer. + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + """ + super().__init__() + self.eps = eps + self.learnable_scale = elementwise_affine + if self.learnable_scale: + self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + else: + self.register_parameter("weight", None) + + def forward(self, x): + return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps) + + + +class SwiGLUFeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float] = None, + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. + + Attributes: + w1 (ColumnParallelLinear): Linear transformation for the first layer. + w2 (RowParallelLinear): Linear transformation for the second layer. + w3 (ColumnParallelLinear): Linear transformation for the third layer. + + """ + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + def forward(self, x): + return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) + + +class DismantledBlock(nn.Module): + """ + A DiT block with gated adaptive layer norm (adaLN) conditioning. + """ + + ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: str = "xformers", + qkv_bias: bool = False, + pre_only: bool = False, + rmsnorm: bool = False, + scale_mod_only: bool = False, + swiglu: bool = False, + qk_norm: Optional[str] = None, + x_block_self_attn: bool = False, + dtype=None, + device=None, + operations=None, + **block_kwargs, + ): + super().__init__() + assert attn_mode in self.ATTENTION_MODES + if not rmsnorm: + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + else: + self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = SelfAttention( + dim=hidden_size, + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_mode=attn_mode, + pre_only=pre_only, + qk_norm=qk_norm, + rmsnorm=rmsnorm, + dtype=dtype, + device=device, + operations=operations + ) + if x_block_self_attn: + assert not pre_only + assert not scale_mod_only + self.x_block_self_attn = True + self.attn2 = SelfAttention( + dim=hidden_size, + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_mode=attn_mode, + pre_only=False, + qk_norm=qk_norm, + rmsnorm=rmsnorm, + dtype=dtype, + device=device, + operations=operations + ) + else: + self.x_block_self_attn = False + if not pre_only: + if not rmsnorm: + self.norm2 = operations.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device + ) + else: + self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + if not pre_only: + if not swiglu: + self.mlp = Mlp( + in_features=hidden_size, + hidden_features=mlp_hidden_dim, + act_layer=lambda: nn.GELU(approximate="tanh"), + drop=0, + dtype=dtype, + device=device, + operations=operations + ) + else: + self.mlp = SwiGLUFeedForward( + dim=hidden_size, + hidden_dim=mlp_hidden_dim, + multiple_of=256, + ) + self.scale_mod_only = scale_mod_only + if x_block_self_attn: + assert not pre_only + assert not scale_mod_only + n_mods = 9 + elif not scale_mod_only: + n_mods = 6 if not pre_only else 2 + else: + n_mods = 4 if not pre_only else 1 + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device) + ) + self.pre_only = pre_only + + def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + if not self.pre_only: + if not self.scale_mod_only: + ( + shift_msa, + scale_msa, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) = self.adaLN_modulation(c).chunk(6, dim=1) + else: + shift_msa = None + shift_mlp = None + ( + scale_msa, + gate_msa, + scale_mlp, + gate_mlp, + ) = self.adaLN_modulation( + c + ).chunk(4, dim=1) + qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) + return qkv, ( + x, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) + else: + if not self.scale_mod_only: + ( + shift_msa, + scale_msa, + ) = self.adaLN_modulation( + c + ).chunk(2, dim=1) + else: + shift_msa = None + scale_msa = self.adaLN_modulation(c) + qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) + return qkv, None + + def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp): + assert not self.pre_only + x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn) + x = x + gate_mlp.unsqueeze(1) * self.mlp( + modulate(self.norm2(x), shift_mlp, scale_mlp) + ) + return x + + def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + assert self.x_block_self_attn + ( + shift_msa, + scale_msa, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + shift_msa2, + scale_msa2, + gate_msa2, + ) = self.adaLN_modulation(c).chunk(9, dim=1) + x_norm = self.norm1(x) + qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa)) + qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2)) + return qkv, qkv2, ( + x, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + gate_msa2, + ) + + def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2): + assert not self.pre_only + attn1 = self.attn.post_attention(attn) + attn2 = self.attn2.post_attention(attn2) + out1 = gate_msa.unsqueeze(1) * attn1 + out2 = gate_msa2.unsqueeze(1) * attn2 + x = x + out1 + x = x + out2 + x = x + gate_mlp.unsqueeze(1) * self.mlp( + modulate(self.norm2(x), shift_mlp, scale_mlp) + ) + return x + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + assert not self.pre_only + if self.x_block_self_attn: + qkv, qkv2, intermediates = self.pre_attention_x(x, c) + attn, _ = optimized_attention( + qkv[0], qkv[1], qkv[2], + num_heads=self.attn.num_heads, + ) + attn2, _ = optimized_attention( + qkv2[0], qkv2[1], qkv2[2], + num_heads=self.attn2.num_heads, + ) + return self.post_attention_x(attn, attn2, *intermediates) + else: + qkv, intermediates = self.pre_attention(x, c) + attn = optimized_attention( + qkv[0], qkv[1], qkv[2], + heads=self.attn.num_heads, + ) + return self.post_attention(attn, *intermediates) + + +def block_mixing(*args, use_checkpoint=True, **kwargs): + if use_checkpoint: + return torch.utils.checkpoint.checkpoint( + _block_mixing, *args, use_reentrant=False, **kwargs + ) + else: + return _block_mixing(*args, **kwargs) + + +def _block_mixing(context, x, context_block, x_block, c): + context_qkv, context_intermediates = context_block.pre_attention(context, c) + + if x_block.x_block_self_attn: + x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c) + else: + x_qkv, x_intermediates = x_block.pre_attention(x, c) + + o = [] + for t in range(3): + o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1)) + qkv = tuple(o) + + attn = optimized_attention( + qkv[0], qkv[1], qkv[2], + heads=x_block.attn.num_heads, + ) + context_attn, x_attn = ( + attn[:, : context_qkv[0].shape[1]], + attn[:, context_qkv[0].shape[1] :], + ) + + if not context_block.pre_only: + context = context_block.post_attention(context_attn, *context_intermediates) + + else: + context = None + if x_block.x_block_self_attn: + attn2 = optimized_attention( + x_qkv2[0], x_qkv2[1], x_qkv2[2], + heads=x_block.attn2.num_heads, + ) + x = x_block.post_attention_x(x_attn, attn2, *x_intermediates) + else: + x = x_block.post_attention(x_attn, *x_intermediates) + return context, x + + +class JointBlock(nn.Module): + """just a small wrapper to serve as a fsdp unit""" + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__() + pre_only = kwargs.pop("pre_only") + qk_norm = kwargs.pop("qk_norm", None) + x_block_self_attn = kwargs.pop("x_block_self_attn", False) + self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs) + self.x_block = DismantledBlock(*args, + pre_only=False, + qk_norm=qk_norm, + x_block_self_attn=x_block_self_attn, + **kwargs) + + def forward(self, *args, **kwargs): + return block_mixing( + *args, context_block=self.context_block, x_block=self.x_block, **kwargs + ) + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + + def __init__( + self, + hidden_size: int, + patch_size: int, + out_channels: int, + total_out_channels: Optional[int] = None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = ( + operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + if (total_out_channels is None) + else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device) + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) + ) + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + +class SelfAttentionContext(nn.Module): + def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None): + super().__init__() + dim_head = dim // heads + inner_dim = dim + + self.heads = heads + self.dim_head = dim_head + + self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device) + + self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device) + + def forward(self, x): + qkv = self.qkv(x) + q, k, v = split_qkv(qkv, self.dim_head) + x = optimized_attention(q.reshape(q.shape[0], q.shape[1], -1), k, v, heads=self.heads) + return self.proj(x) + +class ContextProcessorBlock(nn.Module): + def __init__(self, context_size, dtype=None, device=None, operations=None): + super().__init__() + self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations) + self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations) + + def forward(self, x): + x += self.attn(self.norm1(x)) + x += self.mlp(self.norm2(x)) + return x + +class ContextProcessor(nn.Module): + def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None): + super().__init__() + self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)]) + self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + def forward(self, x): + for i, l in enumerate(self.layers): + x = l(x) + return self.norm(x) + +class MMDiT(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + + def __init__( + self, + input_size: int = 32, + patch_size: int = 2, + in_channels: int = 4, + depth: int = 28, + # hidden_size: Optional[int] = None, + # num_heads: Optional[int] = None, + mlp_ratio: float = 4.0, + learn_sigma: bool = False, + adm_in_channels: Optional[int] = None, + context_embedder_config: Optional[Dict] = None, + compile_core: bool = False, + use_checkpoint: bool = False, + register_length: int = 0, + attn_mode: str = "torch", + rmsnorm: bool = False, + scale_mod_only: bool = False, + swiglu: bool = False, + out_channels: Optional[int] = None, + pos_embed_scaling_factor: Optional[float] = None, + pos_embed_offset: Optional[float] = None, + pos_embed_max_size: Optional[int] = None, + num_patches = None, + qk_norm: Optional[str] = None, + qkv_bias: bool = True, + context_processor_layers = None, + x_block_self_attn: bool = False, + x_block_self_attn_layers: Optional[List[int]] = [], + context_size = 4096, + num_blocks = None, + final_layer = True, + skip_blocks = False, + dtype = None, #TODO + device = None, + operations = None, + ): + super().__init__() + self.dtype = dtype + self.learn_sigma = learn_sigma + self.in_channels = in_channels + default_out_channels = in_channels * 2 if learn_sigma else in_channels + self.out_channels = default(out_channels, default_out_channels) + self.patch_size = patch_size + self.pos_embed_scaling_factor = pos_embed_scaling_factor + self.pos_embed_offset = pos_embed_offset + self.pos_embed_max_size = pos_embed_max_size + self.x_block_self_attn_layers = x_block_self_attn_layers + + # hidden_size = default(hidden_size, 64 * depth) + # num_heads = default(num_heads, hidden_size // 64) + + # apply magic --> this defines a head_size of 64 + self.hidden_size = 64 * depth + num_heads = depth + if num_blocks is None: + num_blocks = depth + + self.depth = depth + self.num_heads = num_heads + + self.x_embedder = PatchEmbed( + input_size, + patch_size, + in_channels, + self.hidden_size, + bias=True, + strict_img_size=self.pos_embed_max_size is None, + dtype=dtype, + device=device, + operations=operations + ) + self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) + + self.y_embedder = None + if adm_in_channels is not None: + assert isinstance(adm_in_channels, int) + self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations) + + if context_processor_layers is not None: + self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations) + else: + self.context_processor = None + + self.context_embedder = nn.Identity() + if context_embedder_config is not None: + if context_embedder_config["target"] == "torch.nn.Linear": + self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device) + + self.register_length = register_length + if self.register_length > 0: + self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device)) + + # num_patches = self.x_embedder.num_patches + # Will use fixed sin-cos embedding: + # just use a buffer already + if num_patches is not None: + self.register_buffer( + "pos_embed", + torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device), + ) + else: + self.pos_embed = None + + self.use_checkpoint = use_checkpoint + if not skip_blocks: + self.joint_blocks = nn.ModuleList( + [ + JointBlock( + self.hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + attn_mode=attn_mode, + pre_only=(i == num_blocks - 1) and final_layer, + rmsnorm=rmsnorm, + scale_mod_only=scale_mod_only, + swiglu=swiglu, + qk_norm=qk_norm, + x_block_self_attn=(i in self.x_block_self_attn_layers) or x_block_self_attn, + dtype=dtype, + device=device, + operations=operations, + ) + for i in range(num_blocks) + ] + ) + + if final_layer: + self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) + + if compile_core: + assert False + self.forward_core_with_concat = torch.compile(self.forward_core_with_concat) + + def cropped_pos_embed(self, hw, device=None): + p = self.x_embedder.patch_size[0] + h, w = hw + # patched size + h = (h + 1) // p + w = (w + 1) // p + if self.pos_embed is None: + return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device) + assert self.pos_embed_max_size is not None + assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size) + assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size) + top = (self.pos_embed_max_size - h) // 2 + left = (self.pos_embed_max_size - w) // 2 + spatial_pos_embed = rearrange( + self.pos_embed, + "1 (h w) c -> 1 h w c", + h=self.pos_embed_max_size, + w=self.pos_embed_max_size, + ) + spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :] + spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c") + # print(spatial_pos_embed, top, left, h, w) + # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res + # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better + # # print(t) + # return t + return spatial_pos_embed + + def unpatchify(self, x, hw=None): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + p = self.x_embedder.patch_size[0] + if hw is None: + h = w = int(x.shape[1] ** 0.5) + else: + h, w = hw + h = (h + 1) // p + w = (w + 1) // p + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum("nhwpqc->nchpwq", x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs + + def forward_core_with_concat( + self, + x: torch.Tensor, + c_mod: torch.Tensor, + context: Optional[torch.Tensor] = None, + control = None, + transformer_options = {}, + ) -> torch.Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + if self.register_length > 0: + context = torch.cat( + ( + repeat(self.register, "1 ... -> b ...", b=x.shape[0]), + default(context, torch.Tensor([]).type_as(x)), + ), + 1, + ) + + # context is B, L', D + # x is B, L, D + blocks_replace = patches_replace.get("dit", {}) + blocks = len(self.joint_blocks) + for i in range(blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"]) + return out + + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap}) + context = out["txt"] + x = out["img"] + else: + context, x = self.joint_blocks[i]( + context, + x, + c=c_mod, + use_checkpoint=self.use_checkpoint, + ) + if control is not None: + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + x += add + + x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels) + return x + + def forward( + self, + x: torch.Tensor, + t: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + control = None, + transformer_options = {}, + ) -> torch.Tensor: + """ + Forward pass of DiT. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of class labels + """ + + if self.context_processor is not None: + context = self.context_processor(context) + + hw = x.shape[-2:] + x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x) + c = self.t_embedder(t, dtype=x.dtype) # (N, D) + if y is not None and self.y_embedder is not None: + y = self.y_embedder(y) # (N, D) + c = c + y # (N, D) + + if context is not None: + context = self.context_embedder(context) + + x = self.forward_core_with_concat(x, c, context, control, transformer_options) + + x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W) + return x[:,:,:hw[-2],:hw[-1]] + + +class OpenAISignatureMMDITWrapper(MMDiT): + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + control = None, + transformer_options = {}, + **kwargs, + ) -> torch.Tensor: + return super().forward(x, timesteps, context=context, y=y, control=control, transformer_options=transformer_options) + diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py new file mode 100644 index 0000000..04eb83b --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -0,0 +1,650 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from typing import Optional, Any +import logging + +from comfy import model_management +import comfy.ops +ops = comfy.ops.disable_weight_init + +if model_management.xformers_enabled_vae(): + import xformers + import xformers.ops + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = ops.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + try: + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + except: #operation not implemented for bf16 + b, c, h, w = x.shape + out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device) + split = 8 + l = out.shape[1] // split + for i in range(0, out.shape[1], l): + out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype) + del x + x = out + + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = ops.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.swish = torch.nn.SiLU(inplace=True) + self.norm1 = Normalize(in_channels) + self.conv1 = ops.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = ops.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout, inplace=True) + self.conv2 = ops.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = ops.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = ops.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = self.swish(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(self.swish(temb))[:,:,None,None] + + h = self.norm2(h) + h = self.swish(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + +def slice_attention(q, k, v): + r1 = torch.zeros_like(k, device=q.device) + scale = (int(q.shape[-1])**(-0.5)) + + mem_free_total = model_management.get_free_memory(q.device) + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() + modifier = 3 if q.element_size() == 2 else 2.5 + mem_required = tensor_size * modifier + steps = 1 + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + s1 = torch.bmm(q[:, i:end], k) * scale + + s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1) + del s1 + + r1[:, :, i:end] = torch.bmm(v, s2) + del s2 + break + except model_management.OOM_EXCEPTION as e: + model_management.soft_empty_cache(True) + steps *= 2 + if steps > 128: + raise e + logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) + + return r1 + +def normal_attention(q, k, v): + # compute attention + b,c,h,w = q.shape + + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + v = v.reshape(b,c,h*w) + + r1 = slice_attention(q, k, v) + h_ = r1.reshape(b,c,h,w) + del r1 + return h_ + +def xformers_attention(q, k, v): + # compute attention + B, C, H, W = q.shape + q, k, v = map( + lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), + (q, k, v), + ) + + try: + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) + out = out.transpose(1, 2).reshape(B, C, H, W) + except NotImplementedError as e: + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) + return out + +def pytorch_attention(q, k, v): + # compute attention + B, C, H, W = q.shape + q, k, v = map( + lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), + (q, k, v), + ) + + try: + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) + out = out.transpose(2, 3).reshape(B, C, H, W) + except model_management.OOM_EXCEPTION as e: + logging.warning("scaled_dot_product_attention OOMed: switched to slice attention") + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) + return out + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + if model_management.xformers_enabled_vae(): + logging.info("Using xformers attention in VAE") + self.optimized_attention = xformers_attention + elif model_management.pytorch_attention_enabled(): + logging.info("Using pytorch attention in VAE") + self.optimized_attention = pytorch_attention + else: + logging.info("Using split attention in VAE") + self.optimized_attention = normal_attention + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + h_ = self.optimized_attention(q, k, v) + + h_ = self.proj_out(h_) + + return x+h_ + + +def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): + return AttnBlock(in_channels) + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + ops.Linear(self.ch, + self.temb_ch), + ops.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = ops.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = ops.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = ops.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = ops.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + # downsampling + h = self.conv_in(x) + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + if i_level != self.num_resolutions-1: + h = self.down[i_level].downsample(h) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + conv_out_op=ops.Conv2d, + resnet_op=ResnetBlock, + attn_op=AttnBlock, + **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + logging.debug("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = ops.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = resnet_op(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = attn_op(block_in) + self.mid.block_2 = resnet_op(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(resnet_op(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(attn_op(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = conv_out_op(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z, **kwargs): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb, **kwargs) + h = self.mid.attn_1(h, **kwargs) + h = self.mid.block_2(h, temb, **kwargs) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb, **kwargs) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h, **kwargs) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h, **kwargs) + if self.tanh_out: + h = torch.tanh(h) + return h diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py new file mode 100644 index 0000000..2902073 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,897 @@ +from abc import abstractmethod + +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +import logging + +from .util import ( + checkpoint, + avg_pool_nd, + zero_module, + timestep_embedding, + AlphaBlender, +) +from ..attention import SpatialTransformer, SpatialVideoTransformer, default +from comfy.ldm.util import exists +import comfy.ops +ops = comfy.ops.disable_weight_init + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + +#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index" +def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): + for layer in ts: + if isinstance(layer, VideoResBlock): + x = layer(x, emb, num_video_frames, image_only_indicator) + elif isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialVideoTransformer): + x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options) + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 + elif isinstance(layer, SpatialTransformer): + x = layer(x, context, transformer_options) + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 + elif isinstance(layer, Upsample): + x = layer(x, output_shape=output_shape) + else: + x = layer(x) + return x + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, *args, **kwargs): + return forward_timestep_embed(self, *args, **kwargs) + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) + + def forward(self, x, output_shape=None): + assert x.shape[1] == self.channels + if self.dims == 3: + shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] + if output_shape is not None: + shape[1] = output_shape[3] + shape[2] = output_shape[4] + else: + shape = [x.shape[2] * 2, x.shape[3] * 2] + if output_shape is not None: + shape[0] = output_shape[2] + shape[1] = output_shape[3] + + x = F.interpolate(x, size=shape, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = operations.conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + kernel_size=3, + exchange_temb_dims=False, + skip_t_emb=False, + dtype=None, + device=None, + operations=ops + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.exchange_temb_dims = exchange_temb_dims + + if isinstance(kernel_size, list): + padding = [k // 2 for k in kernel_size] + else: + padding = kernel_size // 2 + + self.in_layers = nn.Sequential( + operations.GroupNorm(32, channels, dtype=dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + elif down: + self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.skip_t_emb = skip_t_emb + if self.skip_t_emb: + self.emb_layers = None + self.exchange_temb_dims = False + else: + self.emb_layers = nn.Sequential( + nn.SiLU(), + operations.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device + ), + ) + self.out_layers = nn.Sequential( + operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), + nn.SiLU(), + nn.Dropout(p=dropout), + operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device) + , + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = operations.conv_nd( + dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device + ) + else: + self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + + emb_out = None + if not self.skip_t_emb: + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + h = out_norm(h) + if emb_out is not None: + scale, shift = th.chunk(emb_out, 2, dim=1) + h *= (1 + scale) + h += shift + h = out_rest(h) + else: + if emb_out is not None: + if self.exchange_temb_dims: + emb_out = emb_out.movedim(1, 2) + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class VideoResBlock(ResBlock): + def __init__( + self, + channels: int, + emb_channels: int, + dropout: float, + video_kernel_size=3, + merge_strategy: str = "fixed", + merge_factor: float = 0.5, + out_channels=None, + use_conv: bool = False, + use_scale_shift_norm: bool = False, + dims: int = 2, + use_checkpoint: bool = False, + up: bool = False, + down: bool = False, + dtype=None, + device=None, + operations=ops + ): + super().__init__( + channels, + emb_channels, + dropout, + out_channels=out_channels, + use_conv=use_conv, + use_scale_shift_norm=use_scale_shift_norm, + dims=dims, + use_checkpoint=use_checkpoint, + up=up, + down=down, + dtype=dtype, + device=device, + operations=operations + ) + + self.time_stack = ResBlock( + default(out_channels, channels), + emb_channels, + dropout=dropout, + dims=3, + out_channels=default(out_channels, channels), + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=use_checkpoint, + exchange_temb_dims=True, + dtype=dtype, + device=device, + operations=operations + ) + self.time_mixer = AlphaBlender( + alpha=merge_factor, + merge_strategy=merge_strategy, + rearrange_pattern="b t -> b 1 t 1 1", + ) + + def forward( + self, + x: th.Tensor, + emb: th.Tensor, + num_video_frames: int, + image_only_indicator = None, + ) -> th.Tensor: + x = super().forward(x, emb) + + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) + x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) + + x = self.time_stack( + x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) + ) + x = self.time_mixer( + x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator + ) + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class Timestep(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, t): + return timestep_embedding(t, self.dim) + +def apply_control(h, control, name): + if control is not None and name in control and len(control[name]) > 0: + ctrl = control[name].pop() + if ctrl is not None: + try: + h += ctrl + except: + logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape)) + return h + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + dtype=th.float32, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + transformer_depth_middle=None, + transformer_depth_output=None, + use_temporal_resblock=False, + use_temporal_attention=False, + time_context_dim=None, + extra_ff_mix_layer=False, + use_spatial_context=False, + merge_strategy=None, + merge_factor=0.0, + video_kernel_size=None, + disable_temporal_crossattention=False, + max_ddpm_temb_period=10000, + attn_precision=None, + device=None, + operations=ops, + ): + super().__init__() + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + # from omegaconf.listconfig import ListConfig + # if type(context_dim) == ListConfig: + # context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + + transformer_depth = transformer_depth[:] + transformer_depth_output = transformer_depth_output[:] + + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.use_temporal_resblocks = use_temporal_resblock + self.predict_codebook_ids = n_embed is not None + + self.default_num_video_frames = None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device) + elif self.num_classes == "continuous": + logging.debug("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + + def get_attention_layer( + ch, + num_heads, + dim_head, + depth=1, + context_dim=None, + use_checkpoint=False, + disable_self_attn=False, + ): + if use_temporal_attention: + return SpatialVideoTransformer( + ch, + num_heads, + dim_head, + depth=depth, + context_dim=context_dim, + time_context_dim=time_context_dim, + dropout=dropout, + ff_in=extra_ff_mix_layer, + use_spatial_context=use_spatial_context, + merge_strategy=merge_strategy, + merge_factor=merge_factor, + checkpoint=use_checkpoint, + use_linear=use_linear_in_transformer, + disable_self_attn=disable_self_attn, + disable_temporal_crossattention=disable_temporal_crossattention, + max_time_embed_period=max_ddpm_temb_period, + attn_precision=attn_precision, + dtype=self.dtype, device=device, operations=operations + ) + else: + return SpatialTransformer( + ch, num_heads, dim_head, depth=depth, context_dim=context_dim, + disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations + ) + + def get_resblock( + merge_factor, + merge_strategy, + video_kernel_size, + ch, + time_embed_dim, + dropout, + out_channels, + dims, + use_checkpoint, + use_scale_shift_norm, + down=False, + up=False, + dtype=None, + device=None, + operations=ops + ): + if self.use_temporal_resblocks: + return VideoResBlock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations + ) + else: + return ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + use_checkpoint=use_checkpoint, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations + ) + + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = mult * model_channels + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append(get_attention_layer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + mid_block = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + + self.middle_block = None + if transformer_depth_middle >= -1: + if transformer_depth_middle >= 0: + mid_block += [get_attention_layer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint + ), + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + self.middle_block = TimestepEmbedSequential(*mid_block) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch + ich, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + ) + ] + ch = model_channels * mult + num_transformers = transformer_depth_output.pop() + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or i < num_attention_blocks[level]: + layers.append( + get_attention_layer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), + operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), + #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + transformer_options["original_shape"] = list(x.shape) + transformer_options["transformer_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", None) + time_context = kwargs.get("time_context", None) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + if "emb_patch" in transformer_patches: + patch = transformer_patches["emb_patch"] + for p in patch: + emb = p(emb, self.model_channels, transformer_options) + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for id, module in enumerate(self.input_blocks): + transformer_options["block"] = ("input", id) + h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'input') + if "input_block_patch" in transformer_patches: + patch = transformer_patches["input_block_patch"] + for p in patch: + h = p(h, transformer_options) + + hs.append(h) + if "input_block_patch_after_skip" in transformer_patches: + patch = transformer_patches["input_block_patch_after_skip"] + for p in patch: + h = p(h, transformer_options) + + transformer_options["block"] = ("middle", 0) + if self.middle_block is not None: + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'middle') + + + for id, module in enumerate(self.output_blocks): + transformer_options["block"] = ("output", id) + hsp = hs.pop() + hsp = apply_control(hsp, control, 'output') + + if "output_block_patch" in transformer_patches: + patch = transformer_patches["output_block_patch"] + for p in patch: + h, hsp = p(h, hsp, transformer_options) + + h = th.cat([h, hsp], dim=1) + del hsp + if len(hs) > 0: + output_shape = hs[-1].shape + else: + output_shape = None + h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) diff --git a/comfy/ldm/modules/diffusionmodules/upscaling.py b/comfy/ldm/modules/diffusionmodules/upscaling.py new file mode 100644 index 0000000..f5ac7c2 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/upscaling.py @@ -0,0 +1,85 @@ +import torch +import torch.nn as nn +import numpy as np +from functools import partial + +from .util import extract_into_tensor, make_beta_schedule +from comfy.ldm.util import default + + +class AbstractLowScaleModel(nn.Module): + # for concatenating a downsampled image to the latent representation + def __init__(self, noise_schedule_config=None): + super(AbstractLowScaleModel, self).__init__() + if noise_schedule_config is not None: + self.register_schedule(**noise_schedule_config) + + def register_schedule(self, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + def q_sample(self, x_start, t, noise=None, seed=None): + if noise is None: + if seed is None: + noise = torch.randn_like(x_start) + else: + noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device) + return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise) + + def forward(self, x): + return x, None + + def decode(self, x): + return x + + +class SimpleImageConcat(AbstractLowScaleModel): + # no noise level conditioning + def __init__(self): + super(SimpleImageConcat, self).__init__(noise_schedule_config=None) + self.max_noise_level = 0 + + def forward(self, x): + # fix to constant noise level + return x, torch.zeros(x.shape[0], device=x.device).long() + + +class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): + def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): + super().__init__(noise_schedule_config=noise_schedule_config) + self.max_noise_level = max_noise_level + + def forward(self, x, noise_level=None, seed=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + z = self.q_sample(x, noise_level, seed=seed) + return z, noise_level + + + diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py new file mode 100644 index 0000000..ce14ad5 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/util.py @@ -0,0 +1,306 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import os +import math +import torch +import torch.nn as nn +import numpy as np +from einops import repeat, rearrange + +from comfy.ldm.util import instantiate_from_config + +class AlphaBlender(nn.Module): + strategies = ["learned", "fixed", "learned_with_images"] + + def __init__( + self, + alpha: float, + merge_strategy: str = "learned_with_images", + rearrange_pattern: str = "b t -> (b t) 1 1", + ): + super().__init__() + self.merge_strategy = merge_strategy + self.rearrange_pattern = rearrange_pattern + + assert ( + merge_strategy in self.strategies + ), f"merge_strategy needs to be in {self.strategies}" + + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif ( + self.merge_strategy == "learned" + or self.merge_strategy == "learned_with_images" + ): + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor: + # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t) + if self.merge_strategy == "fixed": + # make shape compatible + # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) + alpha = self.mix_factor.to(device) + elif self.merge_strategy == "learned": + alpha = torch.sigmoid(self.mix_factor.to(device)) + # make shape compatible + # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) + elif self.merge_strategy == "learned_with_images": + if image_only_indicator is None: + alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1") + else: + alpha = torch.where( + image_only_indicator.bool(), + torch.ones(1, 1, device=image_only_indicator.device), + rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), + ) + alpha = rearrange(alpha, self.rearrange_pattern) + # make shape compatible + # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) + else: + raise NotImplementedError() + return alpha + + def forward( + self, + x_spatial, + x_temporal, + image_only_indicator=None, + ) -> torch.Tensor: + alpha = self.get_alpha(image_only_indicator, x_spatial.device) + x = ( + alpha.to(x_spatial.dtype) * x_spatial + + (1.0 - alpha).to(x_spatial.dtype) * x_temporal + ) + return x + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = torch.clamp(betas, min=0, max=0.999) + + elif schedule == "squaredcos_cap_v2": # used for karlo prior + # return early + return betas_for_alpha_bar( + n_timestep, + lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, + ) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), + "dtype": torch.get_autocast_gpu_dtype(), + "cache_enabled": torch.is_autocast_cache_enabled()} + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(), \ + torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half + ) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() diff --git a/comfy/ldm/modules/distributions/__init__.py b/comfy/ldm/modules/distributions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/comfy/ldm/modules/distributions/distributions.py b/comfy/ldm/modules/distributions/distributions.py new file mode 100644 index 0000000..f2b8ef9 --- /dev/null +++ b/comfy/ldm/modules/distributions/distributions.py @@ -0,0 +1,92 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/comfy/ldm/modules/ema.py b/comfy/ldm/modules/ema.py new file mode 100644 index 0000000..bded250 --- /dev/null +++ b/comfy/ldm/modules/ema.py @@ -0,0 +1,80 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates + else torch.tensor(-1, dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + # remove as '.'-character is not allowed in buffers + s_name = name.replace('.', '') + self.m_name2s_name.update({name: s_name}) + self.register_buffer(s_name, p.clone().detach().data) + + self.collected_params = [] + + def reset_num_updates(self): + del self.num_updates + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) + + def forward(self, model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/comfy/ldm/modules/encoders/__init__.py b/comfy/ldm/modules/encoders/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/comfy/ldm/modules/encoders/noise_aug_modules.py b/comfy/ldm/modules/encoders/noise_aug_modules.py new file mode 100644 index 0000000..a5d8660 --- /dev/null +++ b/comfy/ldm/modules/encoders/noise_aug_modules.py @@ -0,0 +1,35 @@ +from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation +from ..diffusionmodules.openaimodel import Timestep +import torch + +class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): + def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): + super().__init__(*args, **kwargs) + if clip_stats_path is None: + clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) + else: + clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") + self.register_buffer("data_mean", clip_mean[None, :], persistent=False) + self.register_buffer("data_std", clip_std[None, :], persistent=False) + self.time_embed = Timestep(timestep_dim) + + def scale(self, x): + # re-normalize to centered mean and unit variance + x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device) + return x + + def unscale(self, x): + # back to original data stats + x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) + return x + + def forward(self, x, noise_level=None, seed=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + x = self.scale(x) + z = self.q_sample(x, noise_level, seed=seed) + z = self.unscale(z) + noise_level = self.time_embed(noise_level) + return z, noise_level diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py new file mode 100644 index 0000000..1bc4138 --- /dev/null +++ b/comfy/ldm/modules/sub_quadratic_attention.py @@ -0,0 +1,274 @@ +# original source: +# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py +# license: +# MIT +# credit: +# Amin Rezaei (original author) +# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) +# implementation of: +# Self-attention Does Not Need O(n2) Memory": +# https://arxiv.org/abs/2112.05682v2 + +from functools import partial +import torch +from torch import Tensor +from torch.utils.checkpoint import checkpoint +import math +import logging + +try: + from typing import Optional, NamedTuple, List, Protocol +except ImportError: + from typing import Optional, NamedTuple, List + from typing_extensions import Protocol + +from torch import Tensor +from typing import List + +from comfy import model_management + +def dynamic_slice( + x: Tensor, + starts: List[int], + sizes: List[int], +) -> Tensor: + slicing = [slice(start, start + size) for start, size in zip(starts, sizes)] + return x[slicing] + +class AttnChunk(NamedTuple): + exp_values: Tensor + exp_weights_sum: Tensor + max_score: Tensor + +class SummarizeChunk(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> AttnChunk: ... + +class ComputeQueryChunkAttn(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> Tensor: ... + +def _summarize_chunk( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, + upcast_attention: bool, + mask, +) -> AttnChunk: + if upcast_attention: + with torch.autocast(enabled=False, device_type = 'cuda'): + query = query.float() + key_t = key_t.float() + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + else: + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + max_score, _ = torch.max(attn_weights, -1, keepdim=True) + max_score = max_score.detach() + attn_weights -= max_score + if mask is not None: + attn_weights += mask + torch.exp(attn_weights, out=attn_weights) + exp_weights = attn_weights.to(value.dtype) + exp_values = torch.bmm(exp_weights, value) + max_score = max_score.squeeze(-1) + return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) + +def _query_chunk_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + summarize_chunk: SummarizeChunk, + kv_chunk_size: int, + mask, +) -> Tensor: + batch_x_heads, k_channels_per_head, k_tokens = key_t.shape + _, _, v_channels_per_head = value.shape + + def chunk_scanner(chunk_idx: int, mask) -> AttnChunk: + key_chunk = dynamic_slice( + key_t, + (0, 0, chunk_idx), + (batch_x_heads, k_channels_per_head, kv_chunk_size) + ) + value_chunk = dynamic_slice( + value, + (0, chunk_idx, 0), + (batch_x_heads, kv_chunk_size, v_channels_per_head) + ) + if mask is not None: + mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size] + + return summarize_chunk(query, key_chunk, value_chunk, mask=mask) + + chunks: List[AttnChunk] = [ + chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size) + ] + acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) + chunk_values, chunk_weights, chunk_max = acc_chunk + + global_max, _ = torch.max(chunk_max, 0, keepdim=True) + max_diffs = torch.exp(chunk_max - global_max) + chunk_values *= torch.unsqueeze(max_diffs, -1) + chunk_weights *= max_diffs + + all_values = chunk_values.sum(dim=0) + all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) + return all_values / all_weights + +# TODO: refactor CrossAttention#get_attention_scores to share code with this +def _get_attention_scores_no_kv_chunking( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, + upcast_attention: bool, + mask, +) -> Tensor: + if upcast_attention: + with torch.autocast(enabled=False, device_type = 'cuda'): + query = query.float() + key_t = key_t.float() + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + else: + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + + if mask is not None: + attn_scores += mask + try: + attn_probs = attn_scores.softmax(dim=-1) + del attn_scores + except model_management.OOM_EXCEPTION: + logging.warning("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") + attn_scores -= attn_scores.max(dim=-1, keepdim=True).values + torch.exp(attn_scores, out=attn_scores) + summed = torch.sum(attn_scores, dim=-1, keepdim=True) + attn_scores /= summed + attn_probs = attn_scores + + hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value) + return hidden_states_slice + +class ScannedChunk(NamedTuple): + chunk_idx: int + attn_chunk: AttnChunk + +def efficient_dot_product_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + query_chunk_size=1024, + kv_chunk_size: Optional[int] = None, + kv_chunk_size_min: Optional[int] = None, + use_checkpoint=True, + upcast_attention=False, + mask = None, +): + """Computes efficient dot-product attention given query, transposed key, and value. + This is efficient version of attention presented in + https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. + Args: + query: queries for calculating attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + key_t: keys for calculating attention with shape of + `[batch * num_heads, channels_per_head, tokens]`. + value: values to be used in attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + query_chunk_size: int: query chunks size + kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) + kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). + use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) + Returns: + Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. + """ + batch_x_heads, q_tokens, q_channels_per_head = query.shape + _, _, k_tokens = key_t.shape + scale = q_channels_per_head ** -0.5 + + kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) + if kv_chunk_size_min is not None: + kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) + + if mask is not None and len(mask.shape) == 2: + mask = mask.unsqueeze(0) + + def get_query_chunk(chunk_idx: int) -> Tensor: + return dynamic_slice( + query, + (0, chunk_idx, 0), + (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) + ) + + def get_mask_chunk(chunk_idx: int) -> Tensor: + if mask is None: + return None + chunk = min(query_chunk_size, q_tokens) + return mask[:,chunk_idx:chunk_idx + chunk] + + summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention) + summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk + compute_query_chunk_attn: ComputeQueryChunkAttn = partial( + _get_attention_scores_no_kv_chunking, + scale=scale, + upcast_attention=upcast_attention + ) if k_tokens <= kv_chunk_size else ( + # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) + partial( + _query_chunk_attention, + kv_chunk_size=kv_chunk_size, + summarize_chunk=summarize_chunk, + ) + ) + + if q_tokens <= query_chunk_size: + # fast-path for when there's just 1 query chunk + return compute_query_chunk_attn( + query=query, + key_t=key_t, + value=value, + mask=mask, + ) + + # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, + # and pass slices to be mutated, instead of torch.cat()ing the returned slices + res = torch.cat([ + compute_query_chunk_attn( + query=get_query_chunk(i * query_chunk_size), + key_t=key_t, + value=value, + mask=get_mask_chunk(i * query_chunk_size) + ) for i in range(math.ceil(q_tokens / query_chunk_size)) + ], dim=1) + return res diff --git a/comfy/ldm/modules/temporal_ae.py b/comfy/ldm/modules/temporal_ae.py new file mode 100644 index 0000000..2992aea --- /dev/null +++ b/comfy/ldm/modules/temporal_ae.py @@ -0,0 +1,245 @@ +import functools +from typing import Callable, Iterable, Union + +import torch +from einops import rearrange, repeat + +import comfy.ops +ops = comfy.ops.disable_weight_init + +from .diffusionmodules.model import ( + AttnBlock, + Decoder, + ResnetBlock, +) +from .diffusionmodules.openaimodel import ResBlock, timestep_embedding +from .attention import BasicTransformerBlock + +def partialclass(cls, *args, **kwargs): + class NewCls(cls): + __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) + + return NewCls + + +class VideoResBlock(ResnetBlock): + def __init__( + self, + out_channels, + *args, + dropout=0.0, + video_kernel_size=3, + alpha=0.0, + merge_strategy="learned", + **kwargs, + ): + super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) + if video_kernel_size is None: + video_kernel_size = [3, 1, 1] + self.time_stack = ResBlock( + channels=out_channels, + emb_channels=0, + dropout=dropout, + dims=3, + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=False, + skip_t_emb=True, + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, bs): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError() + + def forward(self, x, temb, skip_video=False, timesteps=None): + b, c, h, w = x.shape + if timesteps is None: + timesteps = b + + x = super().forward(x, temb) + + if not skip_video: + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = self.time_stack(x, temb) + + alpha = self.get_alpha(bs=b // timesteps).to(x.device) + x = alpha * x + (1.0 - alpha) * x_mix + + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class AE3DConv(ops.Conv2d): + def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): + super().__init__(in_channels, out_channels, *args, **kwargs) + if isinstance(video_kernel_size, Iterable): + padding = [int(k // 2) for k in video_kernel_size] + else: + padding = int(video_kernel_size // 2) + + self.time_mix_conv = ops.Conv3d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=video_kernel_size, + padding=padding, + ) + + def forward(self, input, timesteps=None, skip_video=False): + if timesteps is None: + timesteps = input.shape[0] + x = super().forward(input) + if skip_video: + return x + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + x = self.time_mix_conv(x) + return rearrange(x, "b c t h w -> (b t) c h w") + + +class AttnVideoBlock(AttnBlock): + def __init__( + self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" + ): + super().__init__(in_channels) + # no context, single headed, as in base class + self.time_mix_block = BasicTransformerBlock( + dim=in_channels, + n_heads=1, + d_head=in_channels, + checkpoint=False, + ff_in=True, + ) + + time_embed_dim = self.in_channels * 4 + self.video_time_embed = torch.nn.Sequential( + ops.Linear(self.in_channels, time_embed_dim), + torch.nn.SiLU(), + ops.Linear(time_embed_dim, self.in_channels), + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def forward(self, x, timesteps=None, skip_time_block=False): + if skip_time_block: + return super().forward(x) + + if timesteps is None: + timesteps = x.shape[0] + + x_in = x + x = self.attention(x) + h, w = x.shape[2:] + x = rearrange(x, "b c h w -> b (h w) c") + + x_mix = x + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) + emb = self.video_time_embed(t_emb) # b, n_channels + emb = emb[:, None, :] + x_mix = x_mix + emb + + alpha = self.get_alpha().to(x.device) + x_mix = self.time_mix_block(x_mix, timesteps=timesteps) + x = alpha * x + (1.0 - alpha) * x_mix # alpha merge + + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + x = self.proj_out(x) + + return x_in + x + + def get_alpha( + self, + ): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") + + + +def make_time_attn( + in_channels, + attn_type="vanilla", + attn_kwargs=None, + alpha: float = 0, + merge_strategy: str = "learned", +): + return partialclass( + AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy + ) + + +class Conv2DWrapper(torch.nn.Conv2d): + def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: + return super().forward(input) + + +class VideoDecoder(Decoder): + available_time_modes = ["all", "conv-only", "attn-only"] + + def __init__( + self, + *args, + video_kernel_size: Union[int, list] = 3, + alpha: float = 0.0, + merge_strategy: str = "learned", + time_mode: str = "conv-only", + **kwargs, + ): + self.video_kernel_size = video_kernel_size + self.alpha = alpha + self.merge_strategy = merge_strategy + self.time_mode = time_mode + assert ( + self.time_mode in self.available_time_modes + ), f"time_mode parameter has to be in {self.available_time_modes}" + + if self.time_mode != "attn-only": + kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) + if self.time_mode not in ["conv-only", "only-last-conv"]: + kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) + if self.time_mode not in ["attn-only", "only-last-conv"]: + kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) + + super().__init__(*args, **kwargs) + + def get_last_layer(self, skip_time_mix=False, **kwargs): + if self.time_mode == "attn-only": + raise NotImplementedError("TODO") + else: + return ( + self.conv_out.time_mix_conv.weight + if not skip_time_mix + else self.conv_out.weight + ) diff --git a/comfy/ldm/util.py b/comfy/ldm/util.py new file mode 100644 index 0000000..8c09ca1 --- /dev/null +++ b/comfy/ldm/util.py @@ -0,0 +1,197 @@ +import importlib + +import torch +from torch import optim +import numpy as np + +from inspect import isfunction +from PIL import Image, ImageDraw, ImageFont + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +class AdamWwithEMAandWings(optim.Optimizer): + # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 + def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using + weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code + ema_power=1., param_names=()): + """AdamW that saves EMA versions of the parameters.""" + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0.0 <= weight_decay: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + if not 0.0 <= ema_decay <= 1.0: + raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) + defaults = dict(lr=lr, betas=betas, eps=eps, + weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, + ema_power=ema_power, param_names=param_names) + super().__init__(params, defaults) + + def __setstate__(self, state): + super().__setstate__(state) + for group in self.param_groups: + group.setdefault('amsgrad', False) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + params_with_grad = [] + grads = [] + exp_avgs = [] + exp_avg_sqs = [] + ema_params_with_grad = [] + state_sums = [] + max_exp_avg_sqs = [] + state_steps = [] + amsgrad = group['amsgrad'] + beta1, beta2 = group['betas'] + ema_decay = group['ema_decay'] + ema_power = group['ema_power'] + + for p in group['params']: + if p.grad is None: + continue + params_with_grad.append(p) + if p.grad.is_sparse: + raise RuntimeError('AdamW does not support sparse gradients') + grads.append(p.grad) + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of parameter values + state['param_exp_avg'] = p.detach().float().clone() + + exp_avgs.append(state['exp_avg']) + exp_avg_sqs.append(state['exp_avg_sq']) + ema_params_with_grad.append(state['param_exp_avg']) + + if amsgrad: + max_exp_avg_sqs.append(state['max_exp_avg_sq']) + + # update the steps for each param group update + state['step'] += 1 + # record the step after step update + state_steps.append(state['step']) + + optim._functional.adamw(params_with_grad, + grads, + exp_avgs, + exp_avg_sqs, + max_exp_avg_sqs, + state_steps, + amsgrad=amsgrad, + beta1=beta1, + beta2=beta2, + lr=group['lr'], + weight_decay=group['weight_decay'], + eps=group['eps'], + maximize=False) + + cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) + for param, ema_param in zip(params_with_grad, ema_params_with_grad): + ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) + + return loss \ No newline at end of file diff --git a/comfy/lora.py b/comfy/lora.py new file mode 100644 index 0000000..b745ca4 --- /dev/null +++ b/comfy/lora.py @@ -0,0 +1,595 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +from __future__ import annotations +import comfy.utils +import comfy.model_management +import comfy.model_base +import logging +import torch + +LORA_CLIP_MAP = { + "mlp.fc1": "mlp_fc1", + "mlp.fc2": "mlp_fc2", + "self_attn.k_proj": "self_attn_k_proj", + "self_attn.q_proj": "self_attn_q_proj", + "self_attn.v_proj": "self_attn_v_proj", + "self_attn.out_proj": "self_attn_out_proj", +} + + +def load_lora(lora, to_load): + patch_dict = {} + loaded_keys = set() + for x in to_load: + alpha_name = "{}.alpha".format(x) + alpha = None + if alpha_name in lora.keys(): + alpha = lora[alpha_name].item() + loaded_keys.add(alpha_name) + + dora_scale_name = "{}.dora_scale".format(x) + dora_scale = None + if dora_scale_name in lora.keys(): + dora_scale = lora[dora_scale_name] + loaded_keys.add(dora_scale_name) + + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + diffusers2_lora = "{}.lora_B.weight".format(x) + diffusers3_lora = "{}.lora.up.weight".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif diffusers2_lora in lora.keys(): + A_name = diffusers2_lora + B_name = "{}.lora_A.weight".format(x) + mid_name = None + elif diffusers3_lora in lora.keys(): + A_name = diffusers3_lora + B_name = "{}.lora.down.weight".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name ="{}.lora_linear_layer.down.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale)) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + + + ######## loha + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale)) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + + + ######## lokr + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale)) + + #glora + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale)) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) + + w_norm_name = "{}.w_norm".format(x) + b_norm_name = "{}.b_norm".format(x) + w_norm = lora.get(w_norm_name, None) + b_norm = lora.get(b_norm_name, None) + + if w_norm is not None: + loaded_keys.add(w_norm_name) + patch_dict[to_load[x]] = ("diff", (w_norm,)) + if b_norm is not None: + loaded_keys.add(b_norm_name) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) + + diff_name = "{}.diff".format(x) + diff_weight = lora.get(diff_name, None) + if diff_weight is not None: + patch_dict[to_load[x]] = ("diff", (diff_weight,)) + loaded_keys.add(diff_name) + + diff_bias_name = "{}.diff_b".format(x) + diff_bias = lora.get(diff_bias_name, None) + if diff_bias is not None: + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) + loaded_keys.add(diff_bias_name) + + for x in lora.keys(): + if x not in loaded_keys: + logging.warning("lora key not loaded: {}".format(x)) + + return patch_dict + +def model_lora_keys_clip(model, key_map={}): + sdk = model.state_dict().keys() + for k in sdk: + if k.endswith(".weight"): + key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names + + text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" + clip_l_present = False + clip_g_present = False + for b in range(32): #TODO: clean up + for c in LORA_CLIP_MAP: + k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + clip_l_present = True + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + clip_g_present = True + if clip_l_present: + lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + else: + lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config + key_map[lora_key] = k + + for k in sdk: + if k.endswith(".weight"): + if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora + l_key = k[len("t5xxl.transformer."):-len(".weight")] + t5_index = 1 + if clip_g_present: + t5_index += 1 + if clip_l_present: + t5_index += 1 + if t5_index == 2: + key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux + t5_index += 1 + + key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k + elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora + l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")] + lora_key = "lora_te1_{}".format(l_key.replace(".", "_")) + key_map[lora_key] = k + + + k = "clip_g.transformer.text_projection.weight" + if k in sdk: + key_map["lora_prior_te_text_projection"] = k #cascade lora? + # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too + key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora + + k = "clip_l.transformer.text_projection.weight" + if k in sdk: + key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning + + return key_map + +def model_lora_keys_unet(model, key_map={}): + sd = model.state_dict() + sdk = sd.keys() + + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = k + key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config + key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names + + diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) + for k in diffusers_keys: + if k.endswith(".weight"): + unet_key = "diffusion_model.{}".format(diffusers_keys[k]) + key_lora = k[:-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = unet_key + key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format + + diffusers_lora_prefix = ["", "unet."] + for p in diffusers_lora_prefix: + diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) + if diffusers_lora_key.endswith(".to_out.0"): + diffusers_lora_key = diffusers_lora_key[:-2] + key_map[diffusers_lora_key] = unet_key + + if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 + diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") + for k in diffusers_keys: + if k.endswith(".weight"): + to = diffusers_keys[k] + key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format + key_map[key_lora] = to + + key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others? + key_map[key_lora] = to + + key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora + key_map[key_lora] = to + + key_lora = "lycoris_{}".format(k[:-len(".weight")].replace(".", "_")) #simpletuner lycoris format + key_map[key_lora] = to + + + if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow + diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") + for k in diffusers_keys: + if k.endswith(".weight"): + to = diffusers_keys[k] + key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format + key_map[key_lora] = to + + if isinstance(model, comfy.model_base.HunyuanDiT): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format + + if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux + diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") + for k in diffusers_keys: + if k.endswith(".weight"): + to = diffusers_keys[k] + key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format + key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris + key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer + + return key_map + + +def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function): + dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype) + lora_diff *= alpha + weight_calc = weight + function(lora_diff).type(weight.dtype) + weight_norm = ( + weight_calc.transpose(0, 1) + .reshape(weight_calc.shape[1], -1) + .norm(dim=1, keepdim=True) + .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) + .transpose(0, 1) + ) + + weight_calc *= (dora_scale / weight_norm).type(weight.dtype) + if strength != 1.0: + weight_calc -= weight + weight += strength * (weight_calc) + else: + weight[:] = weight_calc + return weight + +def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: + """ + Pad a tensor to a new shape with zeros. + + Args: + tensor (torch.Tensor): The original tensor to be padded. + new_shape (List[int]): The desired shape of the padded tensor. + + Returns: + torch.Tensor: A new tensor padded with zeros to the specified shape. + + Note: + If the new shape is smaller than the original tensor in any dimension, + the original tensor will be truncated in that dimension. + """ + if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]): + raise ValueError("The new shape must be larger than the original tensor in all dimensions") + + if len(new_shape) != len(tensor.shape): + raise ValueError("The new shape must have the same number of dimensions as the original tensor") + + # Create a new tensor filled with zeros + padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) + + # Create slicing tuples for both tensors + orig_slices = tuple(slice(0, dim) for dim in tensor.shape) + new_slices = tuple(slice(0, dim) for dim in tensor.shape) + + # Copy the original tensor into the new tensor + padded_tensor[new_slices] = tensor[orig_slices] + + return padded_tensor + +def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32): + for p in patches: + strength = p[0] + v = p[1] + strength_model = p[2] + offset = p[3] + function = p[4] + if function is None: + function = lambda a: a + + old_weight = None + if offset is not None: + old_weight = weight + weight = weight.narrow(offset[0], offset[1], offset[2]) + + if strength_model != 1.0: + weight *= strength_model + + if isinstance(v, list): + v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), ) + + if len(v) == 1: + patch_type = "diff" + elif len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "diff": + diff: torch.Tensor = v[0] + # An extra flag to pad the weight if the diff's shape is larger than the weight + do_pad_weight = len(v) > 1 and v[1]['pad_weight'] + if do_pad_weight and diff.shape != weight.shape: + logging.info("Pad weight {} from {} to shape: {}".format(key, weight.shape, diff.shape)) + weight = pad_tensor_to_shape(weight, diff.shape) + + if strength != 0.0: + if diff.shape != weight.shape: + logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, diff.shape, weight.shape)) + else: + weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype)) + elif patch_type == "lora": #lora/locon + mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype) + mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype) + dora_scale = v[4] + if v[2] is not None: + alpha = v[2] / mat2.shape[0] + else: + alpha = 1.0 + + if v[3] is not None: + #locon mid weights, hopefully the math is fine because I didn't properly test it + mat3 = comfy.model_management.cast_to_device(v[3], weight.device, intermediate_dtype) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) + try: + lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(patch_type, key, e)) + elif patch_type == "lokr": + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dora_scale = v[8] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype)) + else: + w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype)) + else: + w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha = v[2] / dim + else: + alpha = 1.0 + + try: + lora_diff = torch.kron(w1, w2).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(patch_type, key, e)) + elif patch_type == "loha": + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha = v[2] / w1b.shape[0] + else: + alpha = 1.0 + + w2a = v[3] + w2b = v[4] + dora_scale = v[7] + if v[5] is not None: #cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype)) + + m2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype)) + else: + m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype)) + m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype)) + + try: + lora_diff = (m1 * m2).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(patch_type, key, e)) + elif patch_type == "glora": + dora_scale = v[5] + + old_glora = False + if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: + rank = v[0].shape[0] + old_glora = True + + if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: + if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]: + pass + else: + old_glora = False + rank = v[1].shape[0] + + a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype) + a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype) + b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype) + b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype) + + if v[4] is not None: + alpha = v[4] / rank + else: + alpha = 1.0 + + try: + if old_glora: + lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora + else: + if weight.dim() > 2: + lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) + else: + lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) + lora_diff += torch.mm(b1, b2).reshape(weight.shape) + + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(patch_type, key, e)) + else: + logging.warning("patch type not recognized {} {}".format(patch_type, key)) + + if old_weight is not None: + weight = old_weight + + return weight diff --git a/comfy/model_base.py b/comfy/model_base.py new file mode 100644 index 0000000..f283316 --- /dev/null +++ b/comfy/model_base.py @@ -0,0 +1,736 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import logging +from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep +from comfy.ldm.cascade.stage_c import StageC +from comfy.ldm.cascade.stage_b import StageB +from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation +from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation +from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper +import comfy.ldm.genmo.joint_model.asymm_models_joint +import comfy.ldm.aura.mmdit +import comfy.ldm.hydit.models +import comfy.ldm.audio.dit +import comfy.ldm.audio.embedders +import comfy.ldm.flux.model + +import comfy.model_management +import comfy.conds +import comfy.ops +from enum import Enum +from . import utils +import comfy.latent_formats +import math + +class ModelType(Enum): + EPS = 1 + V_PREDICTION = 2 + V_PREDICTION_EDM = 3 + STABLE_CASCADE = 4 + EDM = 5 + FLOW = 6 + V_PREDICTION_CONTINUOUS = 7 + FLUX = 8 + + +from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV + + +def model_sampling(model_config, model_type): + s = ModelSamplingDiscrete + + if model_type == ModelType.EPS: + c = EPS + elif model_type == ModelType.V_PREDICTION: + c = V_PREDICTION + elif model_type == ModelType.V_PREDICTION_EDM: + c = V_PREDICTION + s = ModelSamplingContinuousEDM + elif model_type == ModelType.FLOW: + c = comfy.model_sampling.CONST + s = comfy.model_sampling.ModelSamplingDiscreteFlow + elif model_type == ModelType.STABLE_CASCADE: + c = EPS + s = StableCascadeSampling + elif model_type == ModelType.EDM: + c = EDM + s = ModelSamplingContinuousEDM + elif model_type == ModelType.V_PREDICTION_CONTINUOUS: + c = V_PREDICTION + s = ModelSamplingContinuousV + elif model_type == ModelType.FLUX: + c = comfy.model_sampling.CONST + s = comfy.model_sampling.ModelSamplingFlux + + class ModelSampling(s, c): + pass + + return ModelSampling(model_config) + + +class BaseModel(torch.nn.Module): + def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel): + super().__init__() + + unet_config = model_config.unet_config + self.latent_format = model_config.latent_format + self.model_config = model_config + self.manual_cast_dtype = model_config.manual_cast_dtype + self.device = device + + if not unet_config.get("disable_unet_model_creation", False): + if model_config.custom_operations is None: + fp8 = model_config.optimizations.get("fp8", model_config.scaled_fp8 is not None) + operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8) + else: + operations = model_config.custom_operations + self.diffusion_model = unet_model(**unet_config, device=device, operations=operations) + if comfy.model_management.force_channels_last(): + self.diffusion_model.to(memory_format=torch.channels_last) + logging.debug("using channels last mode for diffusion model") + logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype)) + self.model_type = model_type + self.model_sampling = model_sampling(model_config, model_type) + + self.adm_channels = unet_config.get("adm_in_channels", None) + if self.adm_channels is None: + self.adm_channels = 0 + + self.concat_keys = () + logging.info("model_type {}".format(model_type.name)) + logging.debug("adm {}".format(self.adm_channels)) + self.memory_usage_factor = model_config.memory_usage_factor + + def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): + sigma = t + xc = self.model_sampling.calculate_input(sigma, x) + if c_concat is not None: + xc = torch.cat([xc] + [c_concat], dim=1) + + context = c_crossattn + dtype = self.get_dtype() + + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + + xc = xc.to(dtype) + t = self.model_sampling.timestep(t).float() + context = context.to(dtype) + extra_conds = {} + for o in kwargs: + extra = kwargs[o] + if hasattr(extra, "dtype"): + if extra.dtype != torch.int and extra.dtype != torch.long: + extra = extra.to(dtype) + extra_conds[o] = extra + + model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() + return self.model_sampling.calculate_denoised(sigma, model_output, x) + + def get_dtype(self): + return self.diffusion_model.dtype + + def is_adm(self): + return self.adm_channels > 0 + + def encode_adm(self, **kwargs): + return None + + def extra_conds(self, **kwargs): + out = {} + if len(self.concat_keys) > 0: + cond_concat = [] + denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + concat_latent_image = kwargs.get("concat_latent_image", None) + if concat_latent_image is None: + concat_latent_image = kwargs.get("latent_image", None) + else: + concat_latent_image = self.process_latent_in(concat_latent_image) + + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if concat_latent_image.shape[1:] != noise.shape[1:]: + concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) + + if denoise_mask is not None: + if len(denoise_mask.shape) == len(noise.shape): + denoise_mask = denoise_mask[:,:1] + + denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1])) + if denoise_mask.shape[-2:] != noise.shape[-2:]: + denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") + denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) + + for ck in self.concat_keys: + if denoise_mask is not None: + if ck == "mask": + cond_concat.append(denoise_mask.to(device)) + elif ck == "masked_image": + cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space + else: + if ck == "mask": + cond_concat.append(torch.ones_like(noise)[:,:1]) + elif ck == "masked_image": + cond_concat.append(self.blank_inpaint_image_like(noise)) + data = torch.cat(cond_concat, dim=1) + out['c_concat'] = comfy.conds.CONDNoiseShape(data) + + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = comfy.conds.CONDRegular(adm) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + + cross_attn_cnet = kwargs.get("cross_attn_controlnet", None) + if cross_attn_cnet is not None: + out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet) + + c_concat = kwargs.get("noise_concat", None) + if c_concat is not None: + out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat) + + return out + + def load_model_weights(self, sd, unet_prefix=""): + to_load = {} + keys = list(sd.keys()) + for k in keys: + if k.startswith(unet_prefix): + to_load[k[len(unet_prefix):]] = sd.pop(k) + + to_load = self.model_config.process_unet_state_dict(to_load) + m, u = self.diffusion_model.load_state_dict(to_load, strict=False) + if len(m) > 0: + logging.warning("unet missing: {}".format(m)) + + if len(u) > 0: + logging.warning("unet unexpected: {}".format(u)) + del to_load + return self + + def process_latent_in(self, latent): + return self.latent_format.process_in(latent) + + def process_latent_out(self, latent): + return self.latent_format.process_out(latent) + + def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): + extra_sds = [] + if clip_state_dict is not None: + extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict)) + if vae_state_dict is not None: + extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict)) + if clip_vision_state_dict is not None: + extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict)) + + unet_state_dict = self.diffusion_model.state_dict() + + if self.model_config.scaled_fp8 is not None: + unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8) + + unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) + + if self.model_type == ModelType.V_PREDICTION: + unet_state_dict["v_pred"] = torch.tensor([]) + + for sd in extra_sds: + unet_state_dict.update(sd) + + return unet_state_dict + + def set_inpaint(self): + self.concat_keys = ("mask", "masked_image") + def blank_inpaint_image_like(latent_image): + blank_image = torch.ones_like(latent_image) + # these are the values for "zero" in pixel space translated to latent space + blank_image[:,0] *= 0.8223 + blank_image[:,1] *= -0.6876 + blank_image[:,2] *= 0.6364 + blank_image[:,3] *= 0.1380 + return blank_image + self.blank_inpaint_image_like = blank_inpaint_image_like + + def memory_required(self, input_shape): + if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): + dtype = self.get_dtype() + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + #TODO: this needs to be tweaked + area = input_shape[0] * math.prod(input_shape[2:]) + return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024) + else: + #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. + area = input_shape[0] * math.prod(input_shape[2:]) + return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024) + + +def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): + adm_inputs = [] + weights = [] + noise_aug = [] + for unclip_cond in unclip_conditioning: + for adm_cond in unclip_cond["clip_vision_output"].image_embeds: + weight = unclip_cond["strength"] + noise_augment = unclip_cond["noise_augmentation"] + noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) + c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed) + adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight + weights.append(weight) + noise_aug.append(noise_augment) + adm_inputs.append(adm_out) + + if len(noise_aug) > 1: + adm_out = torch.stack(adm_inputs).sum(0) + noise_augment = noise_augment_merge + noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) + c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) + adm_out = torch.cat((c_adm, noise_level_emb), 1) + + return adm_out + +class SD21UNCLIP(BaseModel): + def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) + + def encode_adm(self, **kwargs): + unclip_conditioning = kwargs.get("unclip_conditioning", None) + device = kwargs["device"] + if unclip_conditioning is None: + return torch.zeros((1, self.adm_channels)) + else: + return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) + +def sdxl_pooled(args, noise_augmentor): + if "unclip_conditioning" in args: + return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280] + else: + return args["pooled_output"] + +class SDXLRefiner(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + + if kwargs.get("prompt_type", "") == "negative": + aesthetic_score = kwargs.get("aesthetic_score", 2.5) + else: + aesthetic_score = kwargs.get("aesthetic_score", 6) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([aesthetic_score]))) + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + +class SDXL(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + target_width = kwargs.get("target_width", width) + target_height = kwargs.get("target_height", height) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([target_height]))) + out.append(self.embedder(torch.Tensor([target_width]))) + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + + +class SVD_img2vid(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + + def encode_adm(self, **kwargs): + fps_id = kwargs.get("fps", 6) - 1 + motion_bucket_id = kwargs.get("motion_bucket_id", 127) + augmentation = kwargs.get("augmentation_level", 0) + + out = [] + out.append(self.embedder(torch.Tensor([fps_id]))) + out.append(self.embedder(torch.Tensor([motion_bucket_id]))) + out.append(self.embedder(torch.Tensor([augmentation]))) + + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) + return flat + + def extra_conds(self, **kwargs): + out = {} + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = comfy.conds.CONDRegular(adm) + + latent_image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if latent_image is None: + latent_image = torch.zeros_like(noise) + + if latent_image.shape[1:] != noise.shape[1:]: + latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) + + out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + + if "time_conditioning" in kwargs: + out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"]) + + out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0]) + return out + +class SV3D_u(SVD_img2vid): + def encode_adm(self, **kwargs): + augmentation = kwargs.get("augmentation_level", 0) + + out = [] + out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) + + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) + return flat + +class SV3D_p(SVD_img2vid): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder_512 = Timestep(512) + + def encode_adm(self, **kwargs): + augmentation = kwargs.get("augmentation_level", 0) + elevation = kwargs.get("elevation", 0) #elevation and azimuth are in degrees here + azimuth = kwargs.get("azimuth", 0) + noise = kwargs.get("noise", None) + + out = [] + out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) + out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0)))) + out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0)))) + + out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out)) + return torch.cat(out, dim=1) + + +class Stable_Zero123(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): + super().__init__(model_config, model_type, device=device) + self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) + self.cc_projection.weight.copy_(cc_projection_weight) + self.cc_projection.bias.copy_(cc_projection_bias) + + def extra_conds(self, **kwargs): + out = {} + + latent_image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + + if latent_image is None: + latent_image = torch.zeros_like(noise) + + if latent_image.shape[1:] != noise.shape[1:]: + latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) + + out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + if cross_attn.shape[-1] != 768: + cross_attn = self.cc_projection(cross_attn) + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + return out + +class SD_X4Upscaler(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device) + self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350) + + def extra_conds(self, **kwargs): + out = {} + + image = kwargs.get("concat_image", None) + noise = kwargs.get("noise", None) + noise_augment = kwargs.get("noise_augmentation", 0.0) + device = kwargs["device"] + seed = kwargs["seed"] - 10 + + noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment) + + if image is None: + image = torch.zeros_like(noise)[:,:3] + + if image.shape[1:] != noise.shape[1:]: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + + noise_level = torch.tensor([noise_level], device=device) + if noise_augment > 0: + image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed) + + image = utils.resize_to_batch_size(image, noise.shape[0]) + + out['c_concat'] = comfy.conds.CONDNoiseShape(image) + out['y'] = comfy.conds.CONDRegular(noise_level) + return out + +class IP2P: + def extra_conds(self, **kwargs): + out = {} + + image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if image is None: + image = torch.zeros_like(noise) + + if image.shape[1:] != noise.shape[1:]: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + + image = utils.resize_to_batch_size(image, noise.shape[0]) + + out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_ip2p_image_in(image)) + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = comfy.conds.CONDRegular(adm) + return out + +class SD15_instructpix2pix(IP2P, BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.process_ip2p_image_in = lambda image: image + +class SDXL_instructpix2pix(IP2P, SDXL): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + if model_type == ModelType.V_PREDICTION_EDM: + self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p + else: + self.process_ip2p_image_in = lambda image: image #diffusers ip2p + + +class StableCascade_C(BaseModel): + def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): + super().__init__(model_config, model_type, device=device, unet_model=StageC) + self.diffusion_model.eval().requires_grad_(False) + + def extra_conds(self, **kwargs): + out = {} + clip_text_pooled = kwargs["pooled_output"] + if clip_text_pooled is not None: + out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) + + if "unclip_conditioning" in kwargs: + embeds = [] + for unclip_cond in kwargs["unclip_conditioning"]: + weight = unclip_cond["strength"] + embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight) + clip_img = torch.cat(embeds, dim=1) + else: + clip_img = torch.zeros((1, 1, 768)) + out["clip_img"] = comfy.conds.CONDRegular(clip_img) + out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) + out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,))) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn) + return out + + +class StableCascade_B(BaseModel): + def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): + super().__init__(model_config, model_type, device=device, unet_model=StageB) + self.diffusion_model.eval().requires_grad_(False) + + def extra_conds(self, **kwargs): + out = {} + noise = kwargs.get("noise", None) + + clip_text_pooled = kwargs["pooled_output"] + if clip_text_pooled is not None: + out['clip'] = comfy.conds.CONDRegular(clip_text_pooled) + + #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched + prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device)) + + out["effnet"] = comfy.conds.CONDRegular(prior) + out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) + return out + + +class SD3(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper) + + def encode_adm(self, **kwargs): + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + + +class AuraFlow(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + + +class StableAudio1(BaseModel): + def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer) + self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) + self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) + self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights) + self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights) + + def extra_conds(self, **kwargs): + out = {} + + noise = kwargs.get("noise", None) + device = kwargs["device"] + + seconds_start = kwargs.get("seconds_start", 0) + seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53)) + + seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device) + seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device) + + global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1)) + out['global_embed'] = comfy.conds.CONDRegular(global_embed) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1) + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + + def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): + sd = super().state_dict_for_saving(clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict) + d = {"conditioner.conditioners.seconds_start.": self.seconds_start_embedder.state_dict(), "conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()} + for k in d: + s = d[k] + for l in s: + sd["{}{}".format(k, l)] = s[l] + return sd + +class HunyuanDiT(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + out['text_embedding_mask'] = comfy.conds.CONDRegular(attention_mask) + + conditioning_mt5xl = kwargs.get("conditioning_mt5xl", None) + if conditioning_mt5xl is not None: + out['encoder_hidden_states_t5'] = comfy.conds.CONDRegular(conditioning_mt5xl) + + attention_mask_mt5xl = kwargs.get("attention_mask_mt5xl", None) + if attention_mask_mt5xl is not None: + out['text_embedding_mask_t5'] = comfy.conds.CONDRegular(attention_mask_mt5xl) + + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + target_width = kwargs.get("target_width", width) + target_height = kwargs.get("target_height", height) + + out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]])) + return out + +class Flux(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLUX, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux) + + def encode_adm(self, **kwargs): + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)])) + return out + +class GenmoMochi(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py new file mode 100644 index 0000000..b98820d --- /dev/null +++ b/comfy/model_detection.py @@ -0,0 +1,593 @@ +import comfy.supported_models +import comfy.supported_models_base +import comfy.utils +import math +import logging +import torch + +def count_blocks(state_dict_keys, prefix_string): + count = 0 + while True: + c = False + for k in state_dict_keys: + if k.startswith(prefix_string.format(count)): + c = True + break + if c == False: + break + count += 1 + return count + +def calculate_transformer_depth(prefix, state_dict_keys, state_dict): + context_dim = None + use_linear_in_transformer = False + + transformer_prefix = prefix + "1.transformer_blocks." + transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys))) + if len(transformer_keys) > 0: + last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') + context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] + use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 + time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict + time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict + return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross + return None + +def detect_unet_config(state_dict, key_prefix): + state_dict_keys = list(state_dict.keys()) + + if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model + unet_config = {} + unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1] + patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2] + unet_config["patch_size"] = patch_size + final_layer = '{}final_layer.linear.weight'.format(key_prefix) + if final_layer in state_dict: + unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size) + + unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64 + unet_config["input_size"] = None + y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix) + if y_key in state_dict_keys: + unet_config["adm_in_channels"] = state_dict[y_key].shape[1] + + context_key = '{}context_embedder.weight'.format(key_prefix) + if context_key in state_dict_keys: + in_features = state_dict[context_key].shape[1] + out_features = state_dict[context_key].shape[0] + unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}} + num_patches_key = '{}pos_embed'.format(key_prefix) + if num_patches_key in state_dict_keys: + num_patches = state_dict[num_patches_key].shape[1] + unet_config["num_patches"] = num_patches + unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches)) + + rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix) + if rms_qk in state_dict_keys: + unet_config["qk_norm"] = "rms" + + unet_config["pos_embed_scaling_factor"] = None #unused for inference + context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix) + if context_processor in state_dict_keys: + unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.') + unet_config["x_block_self_attn_layers"] = [] + for key in state_dict_keys: + if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'): + layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')] + unet_config["x_block_self_attn_layers"].append(int(layer)) + return unet_config + + if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade + unet_config = {} + text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix) + if text_mapper_name in state_dict_keys: + unet_config['stable_cascade_stage'] = 'c' + w = state_dict[text_mapper_name] + if w.shape[0] == 1536: #stage c lite + unet_config['c_cond'] = 1536 + unet_config['c_hidden'] = [1536, 1536] + unet_config['nhead'] = [24, 24] + unet_config['blocks'] = [[4, 12], [12, 4]] + elif w.shape[0] == 2048: #stage c full + unet_config['c_cond'] = 2048 + elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys: + unet_config['stable_cascade_stage'] = 'b' + w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)] + if w.shape[-1] == 640: + unet_config['c_hidden'] = [320, 640, 1280, 1280] + unet_config['nhead'] = [-1, -1, 20, 20] + unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]] + unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]] + elif w.shape[-1] == 576: #stage b lite + unet_config['c_hidden'] = [320, 576, 1152, 1152] + unet_config['nhead'] = [-1, 9, 18, 18] + unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]] + unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] + return unet_config + + if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit + unet_config = {} + unet_config["audio_model"] = "dit1.0" + return unet_config + + if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit + unet_config = {} + unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1] + unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1] + double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.') + single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.') + unet_config["n_double_layers"] = double_layers + unet_config["n_layers"] = double_layers + single_layers + return unet_config + + if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT + unet_config = {} + unet_config["image_model"] = "hydit" + unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') + unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] + if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2 + unet_config["mlp_ratio"] = 4.3637 + if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968: + unet_config["size_cond"] = True + unet_config["use_style_cond"] = True + unet_config["image_model"] = "hydit1" + return unet_config + + if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux + dit_config = {} + dit_config["image_model"] = "flux" + dit_config["in_channels"] = 16 + dit_config["vec_in_dim"] = 768 + dit_config["context_in_dim"] = 4096 + dit_config["hidden_size"] = 3072 + dit_config["mlp_ratio"] = 4.0 + dit_config["num_heads"] = 24 + dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') + dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') + dit_config["axes_dim"] = [16, 56, 56] + dit_config["theta"] = 10000 + dit_config["qkv_bias"] = True + dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys + return dit_config + + if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview + dit_config = {} + dit_config["image_model"] = "mochi_preview" + dit_config["depth"] = 48 + dit_config["patch_size"] = 2 + dit_config["num_heads"] = 24 + dit_config["hidden_size_x"] = 3072 + dit_config["hidden_size_y"] = 1536 + dit_config["mlp_ratio_x"] = 4.0 + dit_config["mlp_ratio_y"] = 4.0 + dit_config["learn_sigma"] = False + dit_config["in_channels"] = 12 + dit_config["qk_norm"] = True + dit_config["qkv_bias"] = False + dit_config["out_bias"] = True + dit_config["attn_drop"] = 0.0 + dit_config["patch_embed_bias"] = True + dit_config["posenc_preserve_area"] = True + dit_config["timestep_mlp_bias"] = True + dit_config["attend_to_padding"] = False + dit_config["timestep_scale"] = 1000.0 + dit_config["use_t5"] = True + dit_config["t5_feat_dim"] = 4096 + dit_config["t5_token_length"] = 256 + dit_config["rope_theta"] = 10000.0 + return dit_config + + + if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys: + return None + + unet_config = { + "use_checkpoint": False, + "image_size": 32, + "use_spatial_transformer": True, + "legacy": False + } + + y_input = '{}label_emb.0.0.weight'.format(key_prefix) + if y_input in state_dict_keys: + unet_config["num_classes"] = "sequential" + unet_config["adm_in_channels"] = state_dict[y_input].shape[1] + else: + unet_config["adm_in_channels"] = None + + model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] + in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] + + out_key = '{}out.2.weight'.format(key_prefix) + if out_key in state_dict: + out_channels = state_dict[out_key].shape[0] + else: + out_channels = 4 + + num_res_blocks = [] + channel_mult = [] + attention_resolutions = [] + transformer_depth = [] + transformer_depth_output = [] + context_dim = None + use_linear_in_transformer = False + + video_model = False + video_model_cross = False + + current_res = 1 + count = 0 + + last_res_blocks = 0 + last_channel_mult = 0 + + input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.') + for count in range(input_block_count): + prefix = '{}input_blocks.{}.'.format(key_prefix, count) + prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1) + + block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys))) + if len(block_keys) == 0: + break + + block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys))) + + if "{}0.op.weight".format(prefix) in block_keys: #new layer + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + + current_res *= 2 + last_res_blocks = 0 + last_channel_mult = 0 + out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) + if out is not None: + transformer_depth_output.append(out[0]) + else: + transformer_depth_output.append(0) + else: + res_block_prefix = "{}0.in_layers.0.weight".format(prefix) + if res_block_prefix in block_keys: + last_res_blocks += 1 + last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels + + out = calculate_transformer_depth(prefix, state_dict_keys, state_dict) + if out is not None: + transformer_depth.append(out[0]) + if context_dim is None: + context_dim = out[1] + use_linear_in_transformer = out[2] + video_model = out[3] + video_model_cross = out[4] + else: + transformer_depth.append(0) + + res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) + if res_block_prefix in block_keys_output: + out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) + if out is not None: + transformer_depth_output.append(out[0]) + else: + transformer_depth_output.append(0) + + + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: + transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') + elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: + transformer_depth_middle = -1 + else: + transformer_depth_middle = -2 + + unet_config["in_channels"] = in_channels + unet_config["out_channels"] = out_channels + unet_config["model_channels"] = model_channels + unet_config["num_res_blocks"] = num_res_blocks + unet_config["transformer_depth"] = transformer_depth + unet_config["transformer_depth_output"] = transformer_depth_output + unet_config["channel_mult"] = channel_mult + unet_config["transformer_depth_middle"] = transformer_depth_middle + unet_config['use_linear_in_transformer'] = use_linear_in_transformer + unet_config["context_dim"] = context_dim + + if video_model: + unet_config["extra_ff_mix_layer"] = True + unet_config["use_spatial_context"] = True + unet_config["merge_strategy"] = "learned_with_images" + unet_config["merge_factor"] = 0.0 + unet_config["video_kernel_size"] = [3, 1, 1] + unet_config["use_temporal_resblock"] = True + unet_config["use_temporal_attention"] = True + unet_config["disable_temporal_crossattention"] = not video_model_cross + else: + unet_config["use_temporal_resblock"] = False + unet_config["use_temporal_attention"] = False + + return unet_config + +def model_config_from_unet_config(unet_config, state_dict=None): + for model_config in comfy.supported_models.models: + if model_config.matches(unet_config, state_dict): + return model_config(unet_config) + + logging.error("no match {}".format(unet_config)) + return None + +def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False): + unet_config = detect_unet_config(state_dict, unet_key_prefix) + if unet_config is None: + return None + model_config = model_config_from_unet_config(unet_config, state_dict) + if model_config is None and use_base_if_no_match: + model_config = comfy.supported_models_base.BASE(unet_config) + + scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix) + if scaled_fp8_key in state_dict: + scaled_fp8_weight = state_dict.pop(scaled_fp8_key) + model_config.scaled_fp8 = scaled_fp8_weight.dtype + if model_config.scaled_fp8 == torch.float32: + model_config.scaled_fp8 = torch.float8_e4m3fn + + return model_config + +def unet_prefix_from_state_dict(state_dict): + candidates = ["model.diffusion_model.", #ldm/sgm models + "model.model.", #audio models + ] + counts = {k: 0 for k in candidates} + for k in state_dict: + for c in candidates: + if k.startswith(c): + counts[c] += 1 + break + + top = max(counts, key=counts.get) + if counts[top] > 5: + return top + else: + return "model." #aura flow and others + + +def convert_config(unet_config): + new_config = unet_config.copy() + num_res_blocks = new_config.get("num_res_blocks", None) + channel_mult = new_config.get("channel_mult", None) + + if isinstance(num_res_blocks, int): + num_res_blocks = len(channel_mult) * [num_res_blocks] + + if "attention_resolutions" in new_config: + attention_resolutions = new_config.pop("attention_resolutions") + transformer_depth = new_config.get("transformer_depth", None) + transformer_depth_middle = new_config.get("transformer_depth_middle", None) + + if isinstance(transformer_depth, int): + transformer_depth = len(channel_mult) * [transformer_depth] + if transformer_depth_middle is None: + transformer_depth_middle = transformer_depth[-1] + t_in = [] + t_out = [] + s = 1 + for i in range(len(num_res_blocks)): + res = num_res_blocks[i] + d = 0 + if s in attention_resolutions: + d = transformer_depth[i] + + t_in += [d] * res + t_out += [d] * (res + 1) + s *= 2 + transformer_depth = t_in + transformer_depth_output = t_out + new_config["transformer_depth"] = t_in + new_config["transformer_depth_output"] = t_out + new_config["transformer_depth_middle"] = transformer_depth_middle + + new_config["num_res_blocks"] = num_res_blocks + return new_config + + +def unet_config_from_diffusers_unet(state_dict, dtype=None): + match = {} + transformer_depth = [] + + attn_res = 1 + down_blocks = count_blocks(state_dict, "down_blocks.{}") + for i in range(down_blocks): + attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}') + res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}') + for ab in range(attn_blocks): + transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') + transformer_depth.append(transformer_count) + if transformer_count > 0: + match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1] + + attn_res *= 2 + if attn_blocks == 0: + for i in range(res_blocks): + transformer_depth.append(0) + + match["transformer_depth"] = transformer_depth + + match["model_channels"] = state_dict["conv_in.weight"].shape[0] + match["in_channels"] = state_dict["conv_in.weight"].shape[1] + match["adm_in_channels"] = None + if "class_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] + elif "add_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] + + SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4, + 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], + 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, + 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, + 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], + 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, + 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, + 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], + 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, + 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} + + SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], + 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False, + 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, + 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], + 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, + 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint] + + for unet_config in supported_models: + matches = True + for k in match: + if match[k] != unet_config[k]: + matches = False + break + if matches: + return convert_config(unet_config) + return None + +def model_config_from_diffusers_unet(state_dict): + unet_config = unet_config_from_diffusers_unet(state_dict) + if unet_config is not None: + return model_config_from_unet_config(unet_config) + return None + +def convert_diffusers_mmdit(state_dict, output_prefix=""): + out_sd = {} + + if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow + num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.') + num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.') + sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix) + elif 'x_embedder.weight' in state_dict: #Flux + depth = count_blocks(state_dict, 'transformer_blocks.{}.') + depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.') + hidden_size = state_dict["x_embedder.bias"].shape[0] + sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix) + elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3 + num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') + depth = state_dict["pos_embed.proj.weight"].shape[0] // 64 + sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix) + else: + return None + + for k in sd_map: + weight = state_dict.get(k, None) + if weight is not None: + t = sd_map[k] + + if not isinstance(t, str): + if len(t) > 2: + fun = t[2] + else: + fun = lambda a: a + offset = t[1] + if offset is not None: + old_weight = out_sd.get(t[0], None) + if old_weight is None: + old_weight = torch.empty_like(weight) + if old_weight.shape[offset[0]] < offset[1] + offset[2]: + exp = list(weight.shape) + exp[offset[0]] = offset[1] + offset[2] + new = torch.empty(exp, device=weight.device, dtype=weight.dtype) + new[:old_weight.shape[0]] = old_weight + old_weight = new + + w = old_weight.narrow(offset[0], offset[1], offset[2]) + else: + old_weight = weight + w = weight + w[:] = fun(weight) + t = t[0] + out_sd[t] = old_weight + else: + out_sd[t] = weight + state_dict.pop(k) + + return out_sd diff --git a/comfy/model_management.py b/comfy/model_management.py new file mode 100644 index 0000000..fd493af --- /dev/null +++ b/comfy/model_management.py @@ -0,0 +1,1131 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import psutil +import logging +from enum import Enum +from comfy.cli_args import args +import torch +import sys +import platform + +class VRAMState(Enum): + DISABLED = 0 #No vram present: no need to move models to vram + NO_VRAM = 1 #Very low vram: enable all the options to save vram + LOW_VRAM = 2 + NORMAL_VRAM = 3 + HIGH_VRAM = 4 + SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both. + +class CPUState(Enum): + GPU = 0 + CPU = 1 + MPS = 2 + +# Determine VRAM State +vram_state = VRAMState.NORMAL_VRAM +set_vram_to = VRAMState.NORMAL_VRAM +cpu_state = CPUState.GPU + +total_vram = 0 + +xpu_available = False +torch_version = "" +try: + torch_version = torch.version.__version__ + xpu_available = (int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)) and torch.xpu.is_available() +except: + pass + +lowvram_available = True +if args.deterministic: + logging.info("Using deterministic algorithms for pytorch") + torch.use_deterministic_algorithms(True, warn_only=True) + +directml_enabled = False +if args.directml is not None: + import torch_directml + directml_enabled = True + device_index = args.directml + if device_index < 0: + directml_device = torch_directml.device() + else: + directml_device = torch_directml.device(device_index) + logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index))) + # torch_directml.disable_tiled_resources(True) + lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. + +try: + import intel_extension_for_pytorch as ipex + _ = torch.xpu.device_count() + xpu_available = torch.xpu.is_available() +except: + xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available()) + +try: + if torch.backends.mps.is_available(): + cpu_state = CPUState.MPS + import torch.mps +except: + pass + +if args.cpu: + cpu_state = CPUState.CPU + +def is_intel_xpu(): + global cpu_state + global xpu_available + if cpu_state == CPUState.GPU: + if xpu_available: + return True + return False + +def get_torch_device(): + global directml_enabled + global cpu_state + if directml_enabled: + global directml_device + return directml_device + if cpu_state == CPUState.MPS: + return torch.device("mps") + if cpu_state == CPUState.CPU: + return torch.device("cpu") + else: + if is_intel_xpu(): + return torch.device("xpu", torch.xpu.current_device()) + else: + return torch.device(torch.cuda.current_device()) + +def get_total_memory(dev=None, torch_total_too=False): + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): + mem_total = psutil.virtual_memory().total + mem_total_torch = mem_total + else: + if directml_enabled: + mem_total = 1024 * 1024 * 1024 #TODO + mem_total_torch = mem_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + mem_total_torch = mem_reserved + mem_total = torch.xpu.get_device_properties(dev).total_memory + else: + stats = torch.cuda.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + _, mem_total_cuda = torch.cuda.mem_get_info(dev) + mem_total_torch = mem_reserved + mem_total = mem_total_cuda + + if torch_total_too: + return (mem_total, mem_total_torch) + else: + return mem_total + +total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) +total_ram = psutil.virtual_memory().total / (1024 * 1024) +logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) + +try: + logging.info("pytorch version: {}".format(torch_version)) +except: + pass + +try: + OOM_EXCEPTION = torch.cuda.OutOfMemoryError +except: + OOM_EXCEPTION = Exception + +XFORMERS_VERSION = "" +XFORMERS_ENABLED_VAE = True +if args.disable_xformers: + XFORMERS_IS_AVAILABLE = False +else: + try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILABLE = True + try: + XFORMERS_IS_AVAILABLE = xformers._has_cpp_library + except: + pass + try: + XFORMERS_VERSION = xformers.version.__version__ + logging.info("xformers version: {}".format(XFORMERS_VERSION)) + if XFORMERS_VERSION.startswith("0.0.18"): + logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") + logging.warning("Please downgrade or upgrade xformers to a different version.\n") + XFORMERS_ENABLED_VAE = False + except: + pass + except: + XFORMERS_IS_AVAILABLE = False + +def is_nvidia(): + global cpu_state + if cpu_state == CPUState.GPU: + if torch.version.cuda: + return True + return False + +ENABLE_PYTORCH_ATTENTION = False +if args.use_pytorch_cross_attention: + ENABLE_PYTORCH_ATTENTION = True + XFORMERS_IS_AVAILABLE = False + +VAE_DTYPES = [torch.float32] + +try: + if is_nvidia(): + if int(torch_version[0]) >= 2: + if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + ENABLE_PYTORCH_ATTENTION = True + if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8: + VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES + if is_intel_xpu(): + if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + ENABLE_PYTORCH_ATTENTION = True +except: + pass + +if is_intel_xpu(): + VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES + +if args.cpu_vae: + VAE_DTYPES = [torch.float32] + + +if ENABLE_PYTORCH_ATTENTION: + torch.backends.cuda.enable_math_sdp(True) + torch.backends.cuda.enable_flash_sdp(True) + torch.backends.cuda.enable_mem_efficient_sdp(True) + +if args.lowvram: + set_vram_to = VRAMState.LOW_VRAM + lowvram_available = True +elif args.novram: + set_vram_to = VRAMState.NO_VRAM +elif args.highvram or args.gpu_only: + vram_state = VRAMState.HIGH_VRAM + +FORCE_FP32 = False +FORCE_FP16 = False +if args.force_fp32: + logging.info("Forcing FP32, if this improves things please report it.") + FORCE_FP32 = True + +if args.force_fp16: + logging.info("Forcing FP16.") + FORCE_FP16 = True + +if lowvram_available: + if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): + vram_state = set_vram_to + + +if cpu_state != CPUState.GPU: + vram_state = VRAMState.DISABLED + +if cpu_state == CPUState.MPS: + vram_state = VRAMState.SHARED + +logging.info(f"Set vram state to: {vram_state.name}") + +DISABLE_SMART_MEMORY = args.disable_smart_memory + +if DISABLE_SMART_MEMORY: + logging.info("Disabling smart memory management") + +def get_torch_device_name(device): + if hasattr(device, 'type'): + if device.type == "cuda": + try: + allocator_backend = torch.cuda.get_allocator_backend() + except: + allocator_backend = "" + return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) + else: + return "{}".format(device.type) + elif is_intel_xpu(): + return "{} {}".format(device, torch.xpu.get_device_name(device)) + else: + return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) + +try: + logging.info("Device: {}".format(get_torch_device_name(get_torch_device()))) +except: + logging.warning("Could not pick default device.") + + +current_loaded_models = [] + +def module_size(module): + module_mem = 0 + sd = module.state_dict() + for k in sd: + t = sd[k] + module_mem += t.nelement() * t.element_size() + return module_mem + +class LoadedModel: + def __init__(self, model): + self.model = model + self.device = model.load_device + self.weights_loaded = False + self.real_model = None + self.currently_used = True + + def model_memory(self): + return self.model.model_size() + + def model_offloaded_memory(self): + return self.model.model_size() - self.model.loaded_size() + + def model_memory_required(self, device): + if device == self.model.current_loaded_device(): + return self.model_offloaded_memory() + else: + return self.model_memory() + + def model_load(self, lowvram_model_memory=0, force_patch_weights=False): + patch_model_to = self.device + + self.model.model_patches_to(self.device) + self.model.model_patches_to(self.model.model_dtype()) + + load_weights = not self.weights_loaded + + if self.model.loaded_size() > 0: + use_more_vram = lowvram_model_memory + if use_more_vram == 0: + use_more_vram = 1e32 + self.model_use_more_vram(use_more_vram) + else: + try: + self.real_model = self.model.patch_model(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, load_weights=load_weights, force_patch_weights=force_patch_weights) + except Exception as e: + self.model.unpatch_model(self.model.offload_device) + self.model_unload() + raise e + + if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and self.real_model is not None: + with torch.no_grad(): + self.real_model = ipex.optimize(self.real_model.eval(), inplace=True, graph_mode=True, concat_linear=True) + + self.weights_loaded = True + return self.real_model + + def should_reload_model(self, force_patch_weights=False): + if force_patch_weights and self.model.lowvram_patch_counter() > 0: + return True + return False + + def model_unload(self, memory_to_free=None, unpatch_weights=True): + if memory_to_free is not None: + if memory_to_free < self.model.loaded_size(): + freed = self.model.partially_unload(self.model.offload_device, memory_to_free) + if freed >= memory_to_free: + return False + self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights) + self.model.model_patches_to(self.model.offload_device) + self.weights_loaded = self.weights_loaded and not unpatch_weights + self.real_model = None + return True + + def model_use_more_vram(self, extra_memory): + return self.model.partially_load(self.device, extra_memory) + + def __eq__(self, other): + return self.model is other.model + +def use_more_memory(extra_memory, loaded_models, device): + for m in loaded_models: + if m.device == device: + extra_memory -= m.model_use_more_vram(extra_memory) + if extra_memory <= 0: + break + +def offloaded_memory(loaded_models, device): + offloaded_mem = 0 + for m in loaded_models: + if m.device == device: + offloaded_mem += m.model_offloaded_memory() + return offloaded_mem + +WINDOWS = any(platform.win32_ver()) + +EXTRA_RESERVED_VRAM = 400 * 1024 * 1024 +if WINDOWS: + EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue + +if args.reserve_vram is not None: + EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024 + logging.debug("Reserving {}MB vram for other applications.".format(EXTRA_RESERVED_VRAM / (1024 * 1024))) + +def extra_reserved_memory(): + return EXTRA_RESERVED_VRAM + +def minimum_inference_memory(): + return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory() + +def unload_model_clones(model, unload_weights_only=True, force_unload=True): + to_unload = [] + for i in range(len(current_loaded_models)): + if model.is_clone(current_loaded_models[i].model): + to_unload = [i] + to_unload + + if len(to_unload) == 0: + return True + + same_weights = 0 + for i in to_unload: + if model.clone_has_same_weights(current_loaded_models[i].model): + same_weights += 1 + + if same_weights == len(to_unload): + unload_weight = False + else: + unload_weight = True + + if not force_unload: + if unload_weights_only and unload_weight == False: + return None + else: + unload_weight = True + + for i in to_unload: + logging.debug("unload clone {} {}".format(i, unload_weight)) + current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight) + + return unload_weight + +def free_memory(memory_required, device, keep_loaded=[]): + unloaded_model = [] + can_unload = [] + unloaded_models = [] + + for i in range(len(current_loaded_models) -1, -1, -1): + shift_model = current_loaded_models[i] + if shift_model.device == device: + if shift_model not in keep_loaded: + can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i)) + shift_model.currently_used = False + + for x in sorted(can_unload): + i = x[-1] + memory_to_free = None + if not DISABLE_SMART_MEMORY: + free_mem = get_free_memory(device) + if free_mem > memory_required: + break + memory_to_free = memory_required - free_mem + logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}") + if current_loaded_models[i].model_unload(memory_to_free): + unloaded_model.append(i) + + for i in sorted(unloaded_model, reverse=True): + unloaded_models.append(current_loaded_models.pop(i)) + + if len(unloaded_model) > 0: + soft_empty_cache() + else: + if vram_state != VRAMState.HIGH_VRAM: + mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True) + if mem_free_torch > mem_free_total * 0.25: + soft_empty_cache() + return unloaded_models + +def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False): + global vram_state + + inference_memory = minimum_inference_memory() + extra_mem = max(inference_memory, memory_required + extra_reserved_memory()) + if minimum_memory_required is None: + minimum_memory_required = extra_mem + else: + minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory()) + + models = set(models) + + models_to_load = [] + models_already_loaded = [] + for x in models: + loaded_model = LoadedModel(x) + loaded = None + + try: + loaded_model_index = current_loaded_models.index(loaded_model) + except: + loaded_model_index = None + + if loaded_model_index is not None: + loaded = current_loaded_models[loaded_model_index] + if loaded.should_reload_model(force_patch_weights=force_patch_weights): #TODO: cleanup this model reload logic + current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True) + loaded = None + else: + loaded.currently_used = True + models_already_loaded.append(loaded) + + if loaded is None: + if hasattr(x, "model"): + logging.info(f"Requested to load {x.model.__class__.__name__}") + models_to_load.append(loaded_model) + + if len(models_to_load) == 0: + devs = set(map(lambda a: a.device, models_already_loaded)) + for d in devs: + if d != torch.device("cpu"): + free_memory(extra_mem + offloaded_memory(models_already_loaded, d), d, models_already_loaded) + free_mem = get_free_memory(d) + if free_mem < minimum_memory_required: + logging.info("Unloading models for lowram load.") #TODO: partial model unloading when this case happens, also handle the opposite case where models can be unlowvramed. + models_to_load = free_memory(minimum_memory_required, d) + logging.info("{} models unloaded.".format(len(models_to_load))) + else: + use_more_memory(free_mem - minimum_memory_required, models_already_loaded, d) + if len(models_to_load) == 0: + return + + logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") + + total_memory_required = {} + for loaded_model in models_to_load: + unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) #unload clones where the weights are different + total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) + + for loaded_model in models_already_loaded: + total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) + + for loaded_model in models_to_load: + weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded + if weights_unloaded is not None: + loaded_model.weights_loaded = not weights_unloaded + + for device in total_memory_required: + if device != torch.device("cpu"): + free_memory(total_memory_required[device] * 1.1 + extra_mem, device, models_already_loaded) + + for loaded_model in models_to_load: + model = loaded_model.model + torch_dev = model.load_device + if is_device_cpu(torch_dev): + vram_set_state = VRAMState.DISABLED + else: + vram_set_state = vram_state + lowvram_model_memory = 0 + if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load: + model_size = loaded_model.model_memory_required(torch_dev) + current_free_mem = get_free_memory(torch_dev) + lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), min(current_free_mem * 0.4, current_free_mem - minimum_inference_memory())) + if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary + lowvram_model_memory = 0 + + if vram_set_state == VRAMState.NO_VRAM: + lowvram_model_memory = 64 * 1024 * 1024 + + cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights) + current_loaded_models.insert(0, loaded_model) + + + devs = set(map(lambda a: a.device, models_already_loaded)) + for d in devs: + if d != torch.device("cpu"): + free_mem = get_free_memory(d) + if free_mem > minimum_memory_required: + use_more_memory(free_mem - minimum_memory_required, models_already_loaded, d) + return + + +def load_model_gpu(model): + return load_models_gpu([model]) + +def loaded_models(only_currently_used=False): + output = [] + for m in current_loaded_models: + if only_currently_used: + if not m.currently_used: + continue + + output.append(m.model) + return output + +def cleanup_models(keep_clone_weights_loaded=False): + to_delete = [] + for i in range(len(current_loaded_models)): + #TODO: very fragile function needs improvement + num_refs = sys.getrefcount(current_loaded_models[i].model) + if num_refs <= 2: + if not keep_clone_weights_loaded: + to_delete = [i] + to_delete + #TODO: find a less fragile way to do this. + elif sys.getrefcount(current_loaded_models[i].real_model) <= 3: #references from .real_model + the .model + to_delete = [i] + to_delete + + for i in to_delete: + x = current_loaded_models.pop(i) + x.model_unload() + del x + +def dtype_size(dtype): + dtype_size = 4 + if dtype == torch.float16 or dtype == torch.bfloat16: + dtype_size = 2 + elif dtype == torch.float32: + dtype_size = 4 + else: + try: + dtype_size = dtype.itemsize + except: #Old pytorch doesn't have .itemsize + pass + return dtype_size + +def unet_offload_device(): + if vram_state == VRAMState.HIGH_VRAM: + return get_torch_device() + else: + return torch.device("cpu") + +def unet_inital_load_device(parameters, dtype): + torch_dev = get_torch_device() + if vram_state == VRAMState.HIGH_VRAM: + return torch_dev + + cpu_dev = torch.device("cpu") + if DISABLE_SMART_MEMORY: + return cpu_dev + + model_size = dtype_size(dtype) * parameters + + mem_dev = get_free_memory(torch_dev) + mem_cpu = get_free_memory(cpu_dev) + if mem_dev > mem_cpu and model_size < mem_dev: + return torch_dev + else: + return cpu_dev + +def maximum_vram_for_weights(device=None): + return (get_total_memory(device) * 0.88 - minimum_inference_memory()) + +def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): + if model_params < 0: + model_params = 1000000000000000000000 + if args.bf16_unet: + return torch.bfloat16 + if args.fp16_unet: + return torch.float16 + if args.fp8_e4m3fn_unet: + return torch.float8_e4m3fn + if args.fp8_e5m2_unet: + return torch.float8_e5m2 + + fp8_dtype = None + try: + for dtype in [torch.float8_e4m3fn, torch.float8_e5m2]: + if dtype in supported_dtypes: + fp8_dtype = dtype + break + except: + pass + + if fp8_dtype is not None: + if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive + return fp8_dtype + + free_model_memory = maximum_vram_for_weights(device) + if model_params * 2 > free_model_memory: + return fp8_dtype + + for dt in supported_dtypes: + if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params): + if torch.float16 in supported_dtypes: + return torch.float16 + if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params): + if torch.bfloat16 in supported_dtypes: + return torch.bfloat16 + + for dt in supported_dtypes: + if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True): + if torch.float16 in supported_dtypes: + return torch.float16 + if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True): + if torch.bfloat16 in supported_dtypes: + return torch.bfloat16 + + return torch.float32 + +# None means no manual cast +def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): + if weight_dtype == torch.float32: + return None + + fp16_supported = should_use_fp16(inference_device, prioritize_performance=False) + if fp16_supported and weight_dtype == torch.float16: + return None + + bf16_supported = should_use_bf16(inference_device) + if bf16_supported and weight_dtype == torch.bfloat16: + return None + + fp16_supported = should_use_fp16(inference_device, prioritize_performance=True) + for dt in supported_dtypes: + if dt == torch.float16 and fp16_supported: + return torch.float16 + if dt == torch.bfloat16 and bf16_supported: + return torch.bfloat16 + + return torch.float32 + +def text_encoder_offload_device(): + if args.gpu_only: + return get_torch_device() + else: + return torch.device("cpu") + +def text_encoder_device(): + if args.gpu_only: + return get_torch_device() + elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: + if should_use_fp16(prioritize_performance=False): + return get_torch_device() + else: + return torch.device("cpu") + else: + return torch.device("cpu") + +def text_encoder_initial_device(load_device, offload_device, model_size=0): + if load_device == offload_device or model_size <= 1024 * 1024 * 1024: + return offload_device + + if is_device_mps(load_device): + return offload_device + + mem_l = get_free_memory(load_device) + mem_o = get_free_memory(offload_device) + if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l: + return load_device + else: + return offload_device + +def text_encoder_dtype(device=None): + if args.fp8_e4m3fn_text_enc: + return torch.float8_e4m3fn + elif args.fp8_e5m2_text_enc: + return torch.float8_e5m2 + elif args.fp16_text_enc: + return torch.float16 + elif args.fp32_text_enc: + return torch.float32 + + if is_device_cpu(device): + return torch.float16 + + return torch.float16 + + +def intermediate_device(): + if args.gpu_only: + return get_torch_device() + else: + return torch.device("cpu") + +def vae_device(): + if args.cpu_vae: + return torch.device("cpu") + return get_torch_device() + +def vae_offload_device(): + if args.gpu_only: + return get_torch_device() + else: + return torch.device("cpu") + +def vae_dtype(device=None, allowed_dtypes=[]): + global VAE_DTYPES + if args.fp16_vae: + return torch.float16 + elif args.bf16_vae: + return torch.bfloat16 + elif args.fp32_vae: + return torch.float32 + + for d in allowed_dtypes: + if d == torch.float16 and should_use_fp16(device, prioritize_performance=False): + return d + if d in VAE_DTYPES: + return d + + return VAE_DTYPES[0] + +def get_autocast_device(dev): + if hasattr(dev, 'type'): + return dev.type + return "cuda" + +def supports_dtype(device, dtype): #TODO + if dtype == torch.float32: + return True + if is_device_cpu(device): + return False + if dtype == torch.float16: + return True + if dtype == torch.bfloat16: + return True + return False + +def supports_cast(device, dtype): #TODO + if dtype == torch.float32: + return True + if dtype == torch.float16: + return True + if directml_enabled: #TODO: test this + return False + if dtype == torch.bfloat16: + return True + if is_device_mps(device): + return False + if dtype == torch.float8_e4m3fn: + return True + if dtype == torch.float8_e5m2: + return True + return False + +def pick_weight_dtype(dtype, fallback_dtype, device=None): + if dtype is None: + dtype = fallback_dtype + elif dtype_size(dtype) > dtype_size(fallback_dtype): + dtype = fallback_dtype + + if not supports_cast(device, dtype): + dtype = fallback_dtype + + return dtype + +def device_supports_non_blocking(device): + if is_device_mps(device): + return False #pytorch bug? mps doesn't support non blocking + if is_intel_xpu(): + return False + if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews) + return False + if directml_enabled: + return False + return True + +def device_should_use_non_blocking(device): + if not device_supports_non_blocking(device): + return False + return False + # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others + +def force_channels_last(): + if args.force_channels_last: + return True + + #TODO + return False + +def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False): + if device is None or weight.device == device: + if not copy: + if dtype is None or weight.dtype == dtype: + return weight + return weight.to(dtype=dtype, copy=copy) + + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight, non_blocking=non_blocking) + return r + +def cast_to_device(tensor, device, dtype, copy=False): + non_blocking = device_supports_non_blocking(device) + return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy) + + +def xformers_enabled(): + global directml_enabled + global cpu_state + if cpu_state != CPUState.GPU: + return False + if is_intel_xpu(): + return False + if directml_enabled: + return False + return XFORMERS_IS_AVAILABLE + + +def xformers_enabled_vae(): + enabled = xformers_enabled() + if not enabled: + return False + + return XFORMERS_ENABLED_VAE + +def pytorch_attention_enabled(): + global ENABLE_PYTORCH_ATTENTION + return ENABLE_PYTORCH_ATTENTION + +def pytorch_attention_flash_attention(): + global ENABLE_PYTORCH_ATTENTION + if ENABLE_PYTORCH_ATTENTION: + #TODO: more reliable way of checking for flash attention? + if is_nvidia(): #pytorch flash attention only works on Nvidia + return True + if is_intel_xpu(): + return True + return False + +def force_upcast_attention_dtype(): + upcast = args.force_upcast_attention + try: + macos_version = tuple(int(n) for n in platform.mac_ver()[0].split(".")) + if (14, 5) <= macos_version <= (15, 2): # black image bug on recent versions of macOS + upcast = True + except: + pass + if upcast: + return torch.float32 + else: + return None + +def get_free_memory(dev=None, torch_free_too=False): + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): + mem_free_total = psutil.virtual_memory().available + mem_free_torch = mem_free_total + else: + if directml_enabled: + mem_free_total = 1024 * 1024 * 1024 #TODO + mem_free_torch = mem_free_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_torch = mem_reserved - mem_active + mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved + mem_free_total = mem_free_xpu + mem_free_torch + else: + stats = torch.cuda.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(dev) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + + if torch_free_too: + return (mem_free_total, mem_free_torch) + else: + return mem_free_total + +def cpu_mode(): + global cpu_state + return cpu_state == CPUState.CPU + +def mps_mode(): + global cpu_state + return cpu_state == CPUState.MPS + +def is_device_type(device, type): + if hasattr(device, 'type'): + if (device.type == type): + return True + return False + +def is_device_cpu(device): + return is_device_type(device, 'cpu') + +def is_device_mps(device): + return is_device_type(device, 'mps') + +def is_device_cuda(device): + return is_device_type(device, 'cuda') + +def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): + global directml_enabled + + if device is not None: + if is_device_cpu(device): + return False + + if FORCE_FP16: + return True + + if device is not None: + if is_device_mps(device): + return True + + if FORCE_FP32: + return False + + if directml_enabled: + return False + + if mps_mode(): + return True + + if cpu_mode(): + return False + + if is_intel_xpu(): + return True + + if torch.version.hip: + return True + + props = torch.cuda.get_device_properties(device) + if props.major >= 8: + return True + + if props.major < 6: + return False + + #FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32 + nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"] + for x in nvidia_10_series: + if x in props.name.lower(): + if WINDOWS or manual_cast: + return True + else: + return False #weird linux behavior where fp32 is faster + + if manual_cast: + free_model_memory = maximum_vram_for_weights(device) + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + + if props.major < 7: + return False + + #FP16 is just broken on these cards + nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"] + for x in nvidia_16_series: + if x in props.name: + return False + + return True + +def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): + if device is not None: + if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow + return False + + if device is not None: + if is_device_mps(device): + return True + + if FORCE_FP32: + return False + + if directml_enabled: + return False + + if mps_mode(): + return True + + if cpu_mode(): + return False + + if is_intel_xpu(): + return True + + props = torch.cuda.get_device_properties(device) + if props.major >= 8: + return True + + bf16_works = torch.cuda.is_bf16_supported() + + if bf16_works or manual_cast: + free_model_memory = maximum_vram_for_weights(device) + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + + return False + +def supports_fp8_compute(device=None): + if not is_nvidia(): + return False + + props = torch.cuda.get_device_properties(device) + if props.major >= 9: + return True + if props.major < 8: + return False + if props.minor < 9: + return False + + if int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) < 3): + return False + + if WINDOWS: + if (int(torch_version[0]) == 2 and int(torch_version[2]) < 4): + return False + + return True + +def soft_empty_cache(force=False): + global cpu_state + if cpu_state == CPUState.MPS: + torch.mps.empty_cache() + elif is_intel_xpu(): + torch.xpu.empty_cache() + elif torch.cuda.is_available(): + if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + +def unload_all_models(): + free_memory(1e30, get_torch_device()) + + +def resolve_lowvram_weight(weight, model, key): #TODO: remove + print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.") + return weight + +#TODO: might be cleaner to put this somewhere else +import threading + +class InterruptProcessingException(Exception): + pass + +interrupt_processing_mutex = threading.RLock() + +interrupt_processing = False +def interrupt_current_processing(value=True): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + interrupt_processing = value + +def processing_interrupted(): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + return interrupt_processing + +def throw_exception_if_processing_interrupted(): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + if interrupt_processing: + interrupt_processing = False + raise InterruptProcessingException() diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py new file mode 100644 index 0000000..3bba217 --- /dev/null +++ b/comfy/model_patcher.py @@ -0,0 +1,572 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import copy +import inspect +import logging +import uuid +import collections +import math + +import comfy.utils +import comfy.float +import comfy.model_management +import comfy.lora +from comfy.comfy_types import UnetWrapperFunction + +def string_to_seed(data): + crc = 0xFFFFFFFF + for byte in data: + if isinstance(byte, str): + byte = ord(byte) + crc ^= byte + for _ in range(8): + if crc & 1: + crc = (crc >> 1) ^ 0xEDB88320 + else: + crc >>= 1 + return crc ^ 0xFFFFFFFF + +def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): + to = model_options["transformer_options"].copy() + + if "patches_replace" not in to: + to["patches_replace"] = {} + else: + to["patches_replace"] = to["patches_replace"].copy() + + if name not in to["patches_replace"]: + to["patches_replace"][name] = {} + else: + to["patches_replace"][name] = to["patches_replace"][name].copy() + + if transformer_index is not None: + block = (block_name, number, transformer_index) + else: + block = (block_name, number) + to["patches_replace"][name][block] = patch + model_options["transformer_options"] = to + return model_options + +def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False): + model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] + if disable_cfg1_optimization: + model_options["disable_cfg1_optimization"] = True + return model_options + +def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False): + model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function] + if disable_cfg1_optimization: + model_options["disable_cfg1_optimization"] = True + return model_options + +def wipe_lowvram_weight(m): + if hasattr(m, "prev_comfy_cast_weights"): + m.comfy_cast_weights = m.prev_comfy_cast_weights + del m.prev_comfy_cast_weights + m.weight_function = None + m.bias_function = None + +class LowVramPatch: + def __init__(self, key, patches): + self.key = key + self.patches = patches + def __call__(self, weight): + intermediate_dtype = weight.dtype + if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops + intermediate_dtype = torch.float32 + return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key)) + + return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype) + +def get_key_weight(model, key): + set_func = None + convert_func = None + op_keys = key.rsplit('.', 1) + if len(op_keys) < 2: + weight = comfy.utils.get_attr(model, key) + else: + op = comfy.utils.get_attr(model, op_keys[0]) + try: + set_func = getattr(op, "set_{}".format(op_keys[1])) + except AttributeError: + pass + + try: + convert_func = getattr(op, "convert_{}".format(op_keys[1])) + except AttributeError: + pass + + weight = getattr(op, op_keys[1]) + if convert_func is not None: + weight = comfy.utils.get_attr(model, key) + + return weight, set_func, convert_func + +class ModelPatcher: + def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False): + self.size = size + self.model = model + if not hasattr(self.model, 'device'): + logging.debug("Model doesn't have a device attribute.") + self.model.device = offload_device + elif self.model.device is None: + self.model.device = offload_device + + self.patches = {} + self.backup = {} + self.object_patches = {} + self.object_patches_backup = {} + self.model_options = {"transformer_options":{}} + self.model_size() + self.load_device = load_device + self.offload_device = offload_device + self.weight_inplace_update = weight_inplace_update + self.patches_uuid = uuid.uuid4() + + if not hasattr(self.model, 'model_loaded_weight_memory'): + self.model.model_loaded_weight_memory = 0 + + if not hasattr(self.model, 'lowvram_patch_counter'): + self.model.lowvram_patch_counter = 0 + + if not hasattr(self.model, 'model_lowvram'): + self.model.model_lowvram = False + + def model_size(self): + if self.size > 0: + return self.size + self.size = comfy.model_management.module_size(self.model) + return self.size + + def loaded_size(self): + return self.model.model_loaded_weight_memory + + def lowvram_patch_counter(self): + return self.model.lowvram_patch_counter + + def clone(self): + n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update) + n.patches = {} + for k in self.patches: + n.patches[k] = self.patches[k][:] + n.patches_uuid = self.patches_uuid + + n.object_patches = self.object_patches.copy() + n.model_options = copy.deepcopy(self.model_options) + n.backup = self.backup + n.object_patches_backup = self.object_patches_backup + return n + + def is_clone(self, other): + if hasattr(other, 'model') and self.model is other.model: + return True + return False + + def clone_has_same_weights(self, clone): + if not self.is_clone(clone): + return False + + if len(self.patches) == 0 and len(clone.patches) == 0: + return True + + if self.patches_uuid == clone.patches_uuid: + if len(self.patches) != len(clone.patches): + logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.") + else: + return True + + def memory_required(self, input_shape): + return self.model.memory_required(input_shape=input_shape) + + def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False): + if len(inspect.signature(sampler_cfg_function).parameters) == 3: + self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way + else: + self.model_options["sampler_cfg_function"] = sampler_cfg_function + if disable_cfg1_optimization: + self.model_options["disable_cfg1_optimization"] = True + + def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): + self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization) + + def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False): + self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization) + + def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction): + self.model_options["model_function_wrapper"] = unet_wrapper_function + + def set_model_denoise_mask_function(self, denoise_mask_function): + self.model_options["denoise_mask_function"] = denoise_mask_function + + def set_model_patch(self, patch, name): + to = self.model_options["transformer_options"] + if "patches" not in to: + to["patches"] = {} + to["patches"][name] = to["patches"].get(name, []) + [patch] + + def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): + self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index) + + def set_model_attn1_patch(self, patch): + self.set_model_patch(patch, "attn1_patch") + + def set_model_attn2_patch(self, patch): + self.set_model_patch(patch, "attn2_patch") + + def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None): + self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index) + + def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None): + self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index) + + def set_model_attn1_output_patch(self, patch): + self.set_model_patch(patch, "attn1_output_patch") + + def set_model_attn2_output_patch(self, patch): + self.set_model_patch(patch, "attn2_output_patch") + + def set_model_input_block_patch(self, patch): + self.set_model_patch(patch, "input_block_patch") + + def set_model_input_block_patch_after_skip(self, patch): + self.set_model_patch(patch, "input_block_patch_after_skip") + + def set_model_output_block_patch(self, patch): + self.set_model_patch(patch, "output_block_patch") + + def add_object_patch(self, name, obj): + self.object_patches[name] = obj + + def get_model_object(self, name): + if name in self.object_patches: + return self.object_patches[name] + else: + if name in self.object_patches_backup: + return self.object_patches_backup[name] + else: + return comfy.utils.get_attr(self.model, name) + + def model_patches_to(self, device): + to = self.model_options["transformer_options"] + if "patches" in to: + patches = to["patches"] + for name in patches: + patch_list = patches[name] + for i in range(len(patch_list)): + if hasattr(patch_list[i], "to"): + patch_list[i] = patch_list[i].to(device) + if "patches_replace" in to: + patches = to["patches_replace"] + for name in patches: + patch_list = patches[name] + for k in patch_list: + if hasattr(patch_list[k], "to"): + patch_list[k] = patch_list[k].to(device) + if "model_function_wrapper" in self.model_options: + wrap_func = self.model_options["model_function_wrapper"] + if hasattr(wrap_func, "to"): + self.model_options["model_function_wrapper"] = wrap_func.to(device) + + def model_dtype(self): + if hasattr(self.model, "get_dtype"): + return self.model.get_dtype() + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + p = set() + model_sd = self.model.state_dict() + for k in patches: + offset = None + function = None + if isinstance(k, str): + key = k + else: + offset = k[1] + key = k[0] + if len(k) > 2: + function = k[2] + + if key in model_sd: + p.add(k) + current_patches = self.patches.get(key, []) + current_patches.append((strength_patch, patches[k], strength_model, offset, function)) + self.patches[key] = current_patches + + self.patches_uuid = uuid.uuid4() + return list(p) + + def get_key_patches(self, filter_prefix=None): + model_sd = self.model_state_dict() + p = {} + for k in model_sd: + if filter_prefix is not None: + if not k.startswith(filter_prefix): + continue + bk = self.backup.get(k, None) + weight, set_func, convert_func = get_key_weight(self.model, k) + if bk is not None: + weight = bk.weight + if convert_func is None: + convert_func = lambda a, **kwargs: a + + if k in self.patches: + p[k] = [(weight, convert_func)] + self.patches[k] + else: + p[k] = [(weight, convert_func)] + return p + + def model_state_dict(self, filter_prefix=None): + sd = self.model.state_dict() + keys = list(sd.keys()) + if filter_prefix is not None: + for k in keys: + if not k.startswith(filter_prefix): + sd.pop(k) + return sd + + def patch_weight_to_device(self, key, device_to=None, inplace_update=False): + if key not in self.patches: + return + + weight, set_func, convert_func = get_key_weight(self.model, key) + inplace_update = self.weight_inplace_update or inplace_update + + if key not in self.backup: + self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update) + + if device_to is not None: + temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) + else: + temp_weight = weight.to(torch.float32, copy=True) + if convert_func is not None: + temp_weight = convert_func(temp_weight, inplace=True) + + out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) + if set_func is None: + out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key)) + if inplace_update: + comfy.utils.copy_to_param(self.model, key, out_weight) + else: + comfy.utils.set_attr_param(self.model, key, out_weight) + else: + set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key)) + + def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False): + mem_counter = 0 + patch_counter = 0 + lowvram_counter = 0 + loading = [] + for n, m in self.model.named_modules(): + if hasattr(m, "comfy_cast_weights") or hasattr(m, "weight"): + loading.append((comfy.model_management.module_size(m), n, m)) + + load_completely = [] + loading.sort(reverse=True) + for x in loading: + n = x[1] + m = x[2] + module_mem = x[0] + + lowvram_weight = False + + if not full_load and hasattr(m, "comfy_cast_weights"): + if mem_counter + module_mem >= lowvram_model_memory: + lowvram_weight = True + lowvram_counter += 1 + if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed + continue + + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + + if lowvram_weight: + if weight_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(weight_key) + else: + m.weight_function = LowVramPatch(weight_key, self.patches) + patch_counter += 1 + if bias_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(bias_key) + else: + m.bias_function = LowVramPatch(bias_key, self.patches) + patch_counter += 1 + + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + else: + if hasattr(m, "comfy_cast_weights"): + if m.comfy_cast_weights: + wipe_lowvram_weight(m) + + if hasattr(m, "weight"): + mem_counter += module_mem + load_completely.append((module_mem, n, m)) + + load_completely.sort(reverse=True) + for x in load_completely: + n = x[1] + m = x[2] + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + if hasattr(m, "comfy_patched_weights"): + if m.comfy_patched_weights == True: + continue + + self.patch_weight_to_device(weight_key, device_to=device_to) + self.patch_weight_to_device(bias_key, device_to=device_to) + logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) + m.comfy_patched_weights = True + + for x in load_completely: + x[2].to(device_to) + + if lowvram_counter > 0: + logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter)) + self.model.model_lowvram = True + else: + logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load)) + self.model.model_lowvram = False + if full_load: + self.model.to(device_to) + mem_counter = self.model_size() + + self.model.lowvram_patch_counter += patch_counter + self.model.device = device_to + self.model.model_loaded_weight_memory = mem_counter + + def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False): + for k in self.object_patches: + old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) + if k not in self.object_patches_backup: + self.object_patches_backup[k] = old + + if lowvram_model_memory == 0: + full_load = True + else: + full_load = False + + if load_weights: + self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load) + return self.model + + def unpatch_model(self, device_to=None, unpatch_weights=True): + if unpatch_weights: + if self.model.model_lowvram: + for m in self.model.modules(): + wipe_lowvram_weight(m) + + self.model.model_lowvram = False + self.model.lowvram_patch_counter = 0 + + keys = list(self.backup.keys()) + + for k in keys: + bk = self.backup[k] + if bk.inplace_update: + comfy.utils.copy_to_param(self.model, k, bk.weight) + else: + comfy.utils.set_attr_param(self.model, k, bk.weight) + + self.backup.clear() + + if device_to is not None: + self.model.to(device_to) + self.model.device = device_to + self.model.model_loaded_weight_memory = 0 + + for m in self.model.modules(): + if hasattr(m, "comfy_patched_weights"): + del m.comfy_patched_weights + + keys = list(self.object_patches_backup.keys()) + for k in keys: + comfy.utils.set_attr(self.model, k, self.object_patches_backup[k]) + + self.object_patches_backup.clear() + + def partially_unload(self, device_to, memory_to_free=0): + memory_freed = 0 + patch_counter = 0 + unload_list = [] + + for n, m in self.model.named_modules(): + shift_lowvram = False + if hasattr(m, "comfy_cast_weights"): + module_mem = comfy.model_management.module_size(m) + unload_list.append((module_mem, n, m)) + + unload_list.sort() + for unload in unload_list: + if memory_to_free < memory_freed: + break + module_mem = unload[0] + n = unload[1] + m = unload[2] + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + + if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True: + for key in [weight_key, bias_key]: + bk = self.backup.get(key, None) + if bk is not None: + if bk.inplace_update: + comfy.utils.copy_to_param(self.model, key, bk.weight) + else: + comfy.utils.set_attr_param(self.model, key, bk.weight) + self.backup.pop(key) + + m.to(device_to) + if weight_key in self.patches: + m.weight_function = LowVramPatch(weight_key, self.patches) + patch_counter += 1 + if bias_key in self.patches: + m.bias_function = LowVramPatch(bias_key, self.patches) + patch_counter += 1 + + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + m.comfy_patched_weights = False + memory_freed += module_mem + logging.debug("freed {}".format(n)) + + self.model.model_lowvram = True + self.model.lowvram_patch_counter += patch_counter + self.model.model_loaded_weight_memory -= memory_freed + return memory_freed + + def partially_load(self, device_to, extra_memory=0): + self.unpatch_model(unpatch_weights=False) + self.patch_model(load_weights=False) + full_load = False + if self.model.model_lowvram == False: + return 0 + if self.model.model_loaded_weight_memory + extra_memory > self.model_size(): + full_load = True + current_used = self.model.model_loaded_weight_memory + self.load(device_to, lowvram_model_memory=current_used + extra_memory, full_load=full_load) + return self.model.model_loaded_weight_memory - current_used + + def current_loaded_device(self): + return self.model.device + + def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32): + print("WARNING the ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead") + return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype) diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py new file mode 100644 index 0000000..4a0f2db --- /dev/null +++ b/comfy/model_sampling.py @@ -0,0 +1,314 @@ +import torch +from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule +import math + +class EPS: + def calculate_input(self, sigma, noise): + sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + if max_denoise: + noise = noise * torch.sqrt(1.0 + sigma ** 2.0) + else: + noise = noise * sigma + + noise += latent_image + return noise + + def inverse_noise_scaling(self, sigma, latent): + return latent + +class V_PREDICTION(EPS): + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + +class EDM(V_PREDICTION): + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + +class CONST: + def calculate_input(self, sigma, noise): + return noise + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + return sigma * noise + (1.0 - sigma) * latent_image + + def inverse_noise_scaling(self, sigma, latent): + return latent / (1.0 - sigma) + +class ModelSamplingDiscrete(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + beta_schedule = sampling_settings.get("beta_schedule", "linear") + linear_start = sampling_settings.get("linear_start", 0.00085) + linear_end = sampling_settings.get("linear_end", 0.012) + timesteps = sampling_settings.get("timesteps", 1000) + + self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3) + self.sigma_data = 1.0 + + def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if given_betas is not None: + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + + # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) + # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) + # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) + + sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 + self.set_sigmas(sigmas) + + def set_sigmas(self, sigmas): + self.register_buffer('sigmas', sigmas.float()) + self.register_buffer('log_sigmas', sigmas.log().float()) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) + + def sigma(self, timestep): + t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1)) + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp().to(timestep.device) + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + percent = 1.0 - percent + return self.sigma(torch.tensor(percent * 999.0)).item() + +class ModelSamplingDiscreteEDM(ModelSamplingDiscrete): + def timestep(self, sigma): + return 0.25 * sigma.log() + + def sigma(self, timestep): + return (timestep / 0.25).exp() + +class ModelSamplingContinuousEDM(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + sigma_min = sampling_settings.get("sigma_min", 0.002) + sigma_max = sampling_settings.get("sigma_max", 120.0) + sigma_data = sampling_settings.get("sigma_data", 1.0) + self.set_parameters(sigma_min, sigma_max, sigma_data) + + def set_parameters(self, sigma_min, sigma_max, sigma_data): + self.sigma_data = sigma_data + sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() + + self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers + self.register_buffer('log_sigmas', sigmas.log()) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return 0.25 * sigma.log() + + def sigma(self, timestep): + return (timestep / 0.25).exp() + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + percent = 1.0 - percent + + log_sigma_min = math.log(self.sigma_min) + return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) + + +class ModelSamplingContinuousV(ModelSamplingContinuousEDM): + def timestep(self, sigma): + return sigma.atan() / math.pi * 2 + + def sigma(self, timestep): + return (timestep * math.pi / 2).tan() + + +def time_snr_shift(alpha, t): + if alpha == 1.0: + return t + return alpha * t / (1 + (alpha - 1) * t) + +class ModelSamplingDiscreteFlow(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000)) + + def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000): + self.shift = shift + self.multiplier = multiplier + ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier) + self.register_buffer('sigmas', ts) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return sigma * self.multiplier + + def sigma(self, timestep): + return time_snr_shift(self.shift, timestep / self.multiplier) + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 1.0 + if percent >= 1.0: + return 0.0 + return 1.0 - percent + +class StableCascadeSampling(ModelSamplingDiscrete): + def __init__(self, model_config=None): + super().__init__() + + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + self.set_parameters(sampling_settings.get("shift", 1.0)) + + def set_parameters(self, shift=1.0, cosine_s=8e-3): + self.shift = shift + self.cosine_s = torch.tensor(cosine_s) + self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 + + #This part is just for compatibility with some schedulers in the codebase + self.num_timesteps = 10000 + sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) + for x in range(self.num_timesteps): + t = (x + 1) / self.num_timesteps + sigmas[x] = self.sigma(t) + + self.set_sigmas(sigmas) + + def sigma(self, timestep): + alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod) + + if self.shift != 1.0: + var = alpha_cumprod + logSNR = (var/(1-var)).log() + logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift)) + alpha_cumprod = logSNR.sigmoid() + + alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999) + return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5 + + def timestep(self, sigma): + var = 1 / ((sigma * sigma) + 1) + var = var.clamp(0, 1.0) + s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device) + t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s + return t + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + + percent = 1.0 - percent + return self.sigma(torch.tensor(percent)) + + +def flux_time_shift(mu: float, sigma: float, t): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + +class ModelSamplingFlux(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + self.set_parameters(shift=sampling_settings.get("shift", 1.15)) + + def set_parameters(self, shift=1.15, timesteps=10000): + self.shift = shift + ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps)) + self.register_buffer('sigmas', ts) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return sigma + + def sigma(self, timestep): + return flux_time_shift(self.shift, 1.0, timestep) + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 1.0 + if percent >= 1.0: + return 0.0 + return 1.0 - percent diff --git a/comfy/ops.py b/comfy/ops.py new file mode 100644 index 0000000..3c5ba01 --- /dev/null +++ b/comfy/ops.py @@ -0,0 +1,366 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import comfy.model_management +from comfy.cli_args import args +import comfy.float + +cast_to = comfy.model_management.cast_to #TODO: remove once no more references + +def cast_to_input(weight, input, non_blocking=False, copy=True): + return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) + +def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): + if input is not None: + if dtype is None: + dtype = input.dtype + if bias_dtype is None: + bias_dtype = dtype + if device is None: + device = input.device + + bias = None + non_blocking = comfy.model_management.device_supports_non_blocking(device) + if s.bias is not None: + has_function = s.bias_function is not None + bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function) + if has_function: + bias = s.bias_function(bias) + + has_function = s.weight_function is not None + weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function) + if has_function: + weight = s.weight_function(weight) + return weight, bias + +class CastWeightBiasOp: + comfy_cast_weights = False + weight_function = None + bias_function = None + +class disable_weight_init: + class Linear(torch.nn.Linear, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv1d(torch.nn.Conv1d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv3d(torch.nn.Conv3d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + + class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + if self.weight is not None: + weight, bias = cast_bias_weight(self, input) + else: + weight = None + bias = None + return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input, output_size=None): + num_spatial_dims = 2 + output_padding = self._output_padding( + input, output_size, self.stride, self.padding, self.kernel_size, + num_spatial_dims, self.dilation) + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.conv_transpose2d( + input, weight, bias, self.stride, self.padding, + output_padding, self.groups, self.dilation) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input, output_size=None): + num_spatial_dims = 1 + output_padding = self._output_padding( + input, output_size, self.stride, self.padding, self.kernel_size, + num_spatial_dims, self.dilation) + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.conv_transpose1d( + input, weight, bias, self.stride, self.padding, + output_padding, self.groups, self.dilation) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Embedding(torch.nn.Embedding, CastWeightBiasOp): + def reset_parameters(self): + self.bias = None + return None + + def forward_comfy_cast_weights(self, input, out_dtype=None): + output_dtype = out_dtype + if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16: + out_dtype = None + weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype) + return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + if "out_dtype" in kwargs: + kwargs.pop("out_dtype") + return super().forward(*args, **kwargs) + + @classmethod + def conv_nd(s, dims, *args, **kwargs): + if dims == 2: + return s.Conv2d(*args, **kwargs) + elif dims == 3: + return s.Conv3d(*args, **kwargs) + else: + raise ValueError(f"unsupported dimensions: {dims}") + + +class manual_cast(disable_weight_init): + class Linear(disable_weight_init.Linear): + comfy_cast_weights = True + + class Conv1d(disable_weight_init.Conv1d): + comfy_cast_weights = True + + class Conv2d(disable_weight_init.Conv2d): + comfy_cast_weights = True + + class Conv3d(disable_weight_init.Conv3d): + comfy_cast_weights = True + + class GroupNorm(disable_weight_init.GroupNorm): + comfy_cast_weights = True + + class LayerNorm(disable_weight_init.LayerNorm): + comfy_cast_weights = True + + class ConvTranspose2d(disable_weight_init.ConvTranspose2d): + comfy_cast_weights = True + + class ConvTranspose1d(disable_weight_init.ConvTranspose1d): + comfy_cast_weights = True + + class Embedding(disable_weight_init.Embedding): + comfy_cast_weights = True + + +def fp8_linear(self, input): + dtype = self.weight.dtype + if dtype not in [torch.float8_e4m3fn]: + return None + + tensor_2d = False + if len(input.shape) == 2: + tensor_2d = True + input = input.unsqueeze(1) + + + if len(input.shape) == 3: + w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input.dtype) + w = w.t() + + scale_weight = self.scale_weight + scale_input = self.scale_input + if scale_weight is None: + scale_weight = torch.ones((), device=input.device, dtype=torch.float32) + else: + scale_weight = scale_weight.to(input.device) + + if scale_input is None: + scale_input = torch.ones((), device=input.device, dtype=torch.float32) + inn = input.reshape(-1, input.shape[2]).to(dtype) + else: + scale_input = scale_input.to(input.device) + inn = (input * (1.0 / scale_input).to(input.dtype)).reshape(-1, input.shape[2]).to(dtype) + + if bias is not None: + o = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight) + else: + o = torch._scaled_mm(inn, w, out_dtype=input.dtype, scale_a=scale_input, scale_b=scale_weight) + + if isinstance(o, tuple): + o = o[0] + + if tensor_2d: + return o.reshape(input.shape[0], -1) + + return o.reshape((-1, input.shape[1], self.weight.shape[0])) + + return None + +class fp8_ops(manual_cast): + class Linear(manual_cast.Linear): + def reset_parameters(self): + self.scale_weight = None + self.scale_input = None + return None + + def forward_comfy_cast_weights(self, input): + out = fp8_linear(self, input) + if out is not None: + return out + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + +def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None): + class scaled_fp8_op(manual_cast): + class Linear(manual_cast.Linear): + def __init__(self, *args, **kwargs): + if override_dtype is not None: + kwargs['dtype'] = override_dtype + super().__init__(*args, **kwargs) + + def reset_parameters(self): + if not hasattr(self, 'scale_weight'): + self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False) + + if not scale_input: + self.scale_input = None + + if not hasattr(self, 'scale_input'): + self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False) + return None + + def forward_comfy_cast_weights(self, input): + if fp8_matrix_mult: + out = fp8_linear(self, input) + if out is not None: + return out + + weight, bias = cast_bias_weight(self, input) + + if weight.numel() < input.numel(): #TODO: optimize + return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias) + else: + return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias) + + def convert_weight(self, weight, inplace=False, **kwargs): + if inplace: + weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype) + return weight + else: + return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype) + + def set_weight(self, weight, inplace_update=False, seed=None, **kwargs): + weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed) + if inplace_update: + self.weight.data.copy_(weight) + else: + self.weight = torch.nn.Parameter(weight, requires_grad=False) + + return scaled_fp8_op + +def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None): + fp8_compute = comfy.model_management.supports_fp8_compute(load_device) + if scaled_fp8 is not None: + return scaled_fp8_ops(fp8_matrix_mult=fp8_compute, scale_input=True, override_dtype=scaled_fp8) + + if fp8_compute and (fp8_optimizations or args.fast) and not disable_fast_fp8: + return fp8_ops + + if compute_dtype is None or weight_dtype == compute_dtype: + return disable_weight_init + + return manual_cast diff --git a/comfy/options.py b/comfy/options.py new file mode 100644 index 0000000..f7f8af4 --- /dev/null +++ b/comfy/options.py @@ -0,0 +1,6 @@ + +args_parsing = False + +def enable_args_parsing(enable=True): + global args_parsing + args_parsing = enable diff --git a/comfy/sample.py b/comfy/sample.py new file mode 100644 index 0000000..98dcaca --- /dev/null +++ b/comfy/sample.py @@ -0,0 +1,50 @@ +import torch +import comfy.model_management +import comfy.samplers +import comfy.utils +import numpy as np +import logging + +def prepare_noise(latent_image, seed, noise_inds=None): + """ + creates random noise given a latent image and a seed. + optional arg skip can be used to skip and discard x number of noise generations for a given seed + """ + generator = torch.manual_seed(seed) + if noise_inds is None: + return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) + noises = [] + for i in range(unique_inds[-1]+1): + noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + if i in unique_inds: + noises.append(noise) + noises = [noises[i] for i in inverse] + noises = torch.cat(noises, axis=0) + return noises + +def fix_empty_latent_channels(model, latent_image): + latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels + if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0: + latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1) + return latent_image + +def prepare_sampling(model, noise_shape, positive, negative, noise_mask): + logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed") + return model, positive, negative, noise_mask, [] + +def cleanup_additional_models(models): + logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed") + +def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): + sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) + + samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.to(comfy.model_management.intermediate_device()) + return samples + +def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): + samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.to(comfy.model_management.intermediate_device()) + return samples diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py new file mode 100644 index 0000000..4a2ec12 --- /dev/null +++ b/comfy/sampler_helpers.py @@ -0,0 +1,78 @@ +import torch +import comfy.model_management +import comfy.conds + +def prepare_mask(noise_mask, shape, device): + """ensures noise mask is of proper dimensions""" + noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") + noise_mask = torch.cat([noise_mask] * shape[1], dim=1) + noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0]) + noise_mask = noise_mask.to(device) + return noise_mask + +def get_models_from_cond(cond, model_type): + models = [] + for c in cond: + if model_type in c: + models += [c[model_type]] + return models + +def convert_cond(cond): + out = [] + for c in cond: + temp = c[1].copy() + model_conds = temp.get("model_conds", {}) + if c[0] is not None: + model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove + temp["cross_attn"] = c[0] + temp["model_conds"] = model_conds + out.append(temp) + return out + +def get_additional_models(conds, dtype): + """loads additional models in conditioning""" + cnets = [] + gligen = [] + + for k in conds: + cnets += get_models_from_cond(conds[k], "control") + gligen += get_models_from_cond(conds[k], "gligen") + + control_nets = set(cnets) + + inference_memory = 0 + control_models = [] + for m in control_nets: + control_models += m.get_models() + inference_memory += m.inference_memory_requirements(dtype) + + gligen = [x[1] for x in gligen] + models = control_models + gligen + return models, inference_memory + +def cleanup_additional_models(models): + """cleanup additional models that were loaded""" + for m in models: + if hasattr(m, 'cleanup'): + m.cleanup() + + +def prepare_sampling(model, noise_shape, conds): + device = model.load_device + real_model = None + models, inference_memory = get_additional_models(conds, model.model_dtype()) + memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory + minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory + comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required) + real_model = model.model + + return real_model, conds, models + +def cleanup_models(conds, models): + cleanup_additional_models(models) + + control_cleanup = [] + for k in conds: + control_cleanup += get_models_from_cond(conds[k], "control") + + cleanup_additional_models(set(control_cleanup)) diff --git a/comfy/samplers.py b/comfy/samplers.py new file mode 100644 index 0000000..94cba03 --- /dev/null +++ b/comfy/samplers.py @@ -0,0 +1,855 @@ +from .k_diffusion import sampling as k_diffusion_sampling +from .extra_samplers import uni_pc +import torch +import collections +from comfy import model_management +import math +import logging +import comfy.sampler_helpers +import scipy.stats +import numpy + +def get_area_and_mult(conds, x_in, timestep_in): + dims = tuple(x_in.shape[2:]) + area = None + strength = 1.0 + + if 'timestep_start' in conds: + timestep_start = conds['timestep_start'] + if timestep_in[0] > timestep_start: + return None + if 'timestep_end' in conds: + timestep_end = conds['timestep_end'] + if timestep_in[0] < timestep_end: + return None + if 'area' in conds: + area = list(conds['area']) + if 'strength' in conds: + strength = conds['strength'] + + input_x = x_in + if area is not None: + for i in range(len(dims)): + area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i]) + input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i]) + + if 'mask' in conds: + # Scale the mask to the size of the input + # The mask should have been resized as we began the sampling process + mask_strength = 1.0 + if "mask_strength" in conds: + mask_strength = conds["mask_strength"] + mask = conds['mask'] + assert(mask.shape[1:] == x_in.shape[2:]) + + mask = mask[:input_x.shape[0]] + if area is not None: + for i in range(len(dims)): + mask = mask.narrow(i + 1, area[len(dims) + i], area[i]) + + mask = mask * mask_strength + mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) + else: + mask = torch.ones_like(input_x) + mult = mask * strength + + if 'mask' not in conds and area is not None: + rr = 8 + for i in range(len(dims)): + if area[len(dims) + i] != 0: + for t in range(rr): + m = mult.narrow(i + 2, t, 1) + m *= ((1.0/rr) * (t + 1)) + if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]: + for t in range(rr): + m = mult.narrow(i + 2, area[i] - 1 - t, 1) + m *= ((1.0/rr) * (t + 1)) + + conditioning = {} + model_conds = conds["model_conds"] + for c in model_conds: + conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) + + control = conds.get('control', None) + + patches = None + if 'gligen' in conds: + gligen = conds['gligen'] + patches = {} + gligen_type = gligen[0] + gligen_model = gligen[1] + if gligen_type == "position": + gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) + else: + gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) + + patches['middle_patch'] = [gligen_patch] + + cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches']) + return cond_obj(input_x, mult, conditioning, area, control, patches) + +def cond_equal_size(c1, c2): + if c1 is c2: + return True + if c1.keys() != c2.keys(): + return False + for k in c1: + if not c1[k].can_concat(c2[k]): + return False + return True + +def can_concat_cond(c1, c2): + if c1.input_x.shape != c2.input_x.shape: + return False + + def objects_concatable(obj1, obj2): + if (obj1 is None) != (obj2 is None): + return False + if obj1 is not None: + if obj1 is not obj2: + return False + return True + + if not objects_concatable(c1.control, c2.control): + return False + + if not objects_concatable(c1.patches, c2.patches): + return False + + return cond_equal_size(c1.conditioning, c2.conditioning) + +def cond_cat(c_list): + c_crossattn = [] + c_concat = [] + c_adm = [] + crossattn_max_len = 0 + + temp = {} + for x in c_list: + for k in x: + cur = temp.get(k, []) + cur.append(x[k]) + temp[k] = cur + + out = {} + for k in temp: + conds = temp[k] + out[k] = conds[0].concat(conds[1:]) + + return out + +def calc_cond_batch(model, conds, x_in, timestep, model_options): + out_conds = [] + out_counts = [] + to_run = [] + + for i in range(len(conds)): + out_conds.append(torch.zeros_like(x_in)) + out_counts.append(torch.ones_like(x_in) * 1e-37) + + cond = conds[i] + if cond is not None: + for x in cond: + p = get_area_and_mult(x, x_in, timestep) + if p is None: + continue + + to_run += [(p, i)] + + while len(to_run) > 0: + first = to_run[0] + first_shape = first[0][0].shape + to_batch_temp = [] + for x in range(len(to_run)): + if can_concat_cond(to_run[x][0], first[0]): + to_batch_temp += [x] + + to_batch_temp.reverse() + to_batch = to_batch_temp[:1] + + free_memory = model_management.get_free_memory(x_in.device) + for i in range(1, len(to_batch_temp) + 1): + batch_amount = to_batch_temp[:len(to_batch_temp)//i] + input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] + if model.memory_required(input_shape) * 1.5 < free_memory: + to_batch = batch_amount + break + + input_x = [] + mult = [] + c = [] + cond_or_uncond = [] + area = [] + control = None + patches = None + for x in to_batch: + o = to_run.pop(x) + p = o[0] + input_x.append(p.input_x) + mult.append(p.mult) + c.append(p.conditioning) + area.append(p.area) + cond_or_uncond.append(o[1]) + control = p.control + patches = p.patches + + batch_chunks = len(cond_or_uncond) + input_x = torch.cat(input_x) + c = cond_cat(c) + timestep_ = torch.cat([timestep] * batch_chunks) + + if control is not None: + c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) + + transformer_options = {} + if 'transformer_options' in model_options: + transformer_options = model_options['transformer_options'].copy() + + if patches is not None: + if "patches" in transformer_options: + cur_patches = transformer_options["patches"].copy() + for p in patches: + if p in cur_patches: + cur_patches[p] = cur_patches[p] + patches[p] + else: + cur_patches[p] = patches[p] + transformer_options["patches"] = cur_patches + else: + transformer_options["patches"] = patches + + transformer_options["cond_or_uncond"] = cond_or_uncond[:] + transformer_options["sigmas"] = timestep + + c['transformer_options'] = transformer_options + + if 'model_function_wrapper' in model_options: + output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) + else: + output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) + + for o in range(batch_chunks): + cond_index = cond_or_uncond[o] + a = area[o] + if a is None: + out_conds[cond_index] += output[o] * mult[o] + out_counts[cond_index] += mult[o] + else: + out_c = out_conds[cond_index] + out_cts = out_counts[cond_index] + dims = len(a) // 2 + for i in range(dims): + out_c = out_c.narrow(i + 2, a[i + dims], a[i]) + out_cts = out_cts.narrow(i + 2, a[i + dims], a[i]) + out_c += output[o] * mult[o] + out_cts += mult[o] + + for i in range(len(out_conds)): + out_conds[i] /= out_counts[i] + + return out_conds + +def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove + logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.") + return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options)) + +def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None): + if "sampler_cfg_function" in model_options: + args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, + "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} + cfg_result = x - model_options["sampler_cfg_function"](args) + else: + cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale + + for fn in model_options.get("sampler_post_cfg_function", []): + args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, + "sigma": timestep, "model_options": model_options, "input": x} + cfg_result = fn(args) + + return cfg_result + +#The main sampling function shared by all the samplers +#Returns denoised +def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): + if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: + uncond_ = None + else: + uncond_ = uncond + + conds = [cond, uncond_] + out = calc_cond_batch(model, conds, x, timestep, model_options) + + for fn in model_options.get("sampler_pre_cfg_function", []): + args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep, + "input": x, "sigma": timestep, "model": model, "model_options": model_options} + out = fn(args) + + return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_) + + +class KSamplerX0Inpaint: + def __init__(self, model, sigmas): + self.inner_model = model + self.sigmas = sigmas + def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None): + if denoise_mask is not None: + if "denoise_mask_function" in model_options: + denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) + latent_mask = 1. - denoise_mask + x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask + out = self.inner_model(x, sigma, model_options=model_options, seed=seed) + if denoise_mask is not None: + out = out * denoise_mask + self.latent_image * latent_mask + return out + +def simple_scheduler(model_sampling, steps): + s = model_sampling + sigs = [] + ss = len(s.sigmas) / steps + for x in range(steps): + sigs += [float(s.sigmas[-(1 + int(x * ss))])] + sigs += [0.0] + return torch.FloatTensor(sigs) + +def ddim_scheduler(model_sampling, steps): + s = model_sampling + sigs = [] + x = 1 + if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001): + steps += 1 + sigs = [] + else: + sigs = [0.0] + + ss = max(len(s.sigmas) // steps, 1) + while x < len(s.sigmas): + sigs += [float(s.sigmas[x])] + x += ss + sigs = sigs[::-1] + return torch.FloatTensor(sigs) + +def normal_scheduler(model_sampling, steps, sgm=False, floor=False): + s = model_sampling + start = s.timestep(s.sigma_max) + end = s.timestep(s.sigma_min) + + append_zero = True + if sgm: + timesteps = torch.linspace(start, end, steps + 1)[:-1] + else: + if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001): + steps += 1 + append_zero = False + timesteps = torch.linspace(start, end, steps) + + sigs = [] + for x in range(len(timesteps)): + ts = timesteps[x] + sigs.append(float(s.sigma(ts))) + + if append_zero: + sigs += [0.0] + + return torch.FloatTensor(sigs) + +# Implemented based on: https://arxiv.org/abs/2407.12173 +def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6): + total_timesteps = (len(model_sampling.sigmas) - 1) + ts = 1 - numpy.linspace(0, 1, steps, endpoint=False) + ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps) + + sigs = [] + last_t = -1 + for t in ts: + if t != last_t: + sigs += [float(model_sampling.sigmas[int(t)])] + last_t = t + sigs += [0.0] + return torch.FloatTensor(sigs) + +# from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41 +def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None): + if steps == 1: + sigma_schedule = [1.0, 0.0] + else: + if linear_steps is None: + linear_steps = steps // 2 + linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)] + threshold_noise_step_diff = linear_steps - threshold_noise * steps + quadratic_steps = steps - linear_steps + quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps ** 2) + linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps ** 2) + const = quadratic_coef * (linear_steps ** 2) + quadratic_sigma_schedule = [ + quadratic_coef * (i ** 2) + linear_coef * i + const + for i in range(linear_steps, steps) + ] + sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0] + sigma_schedule = [1.0 - x for x in sigma_schedule] + return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu() + +def get_mask_aabb(masks): + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device, dtype=torch.int) + + b = masks.shape[0] + + bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) + is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) + for i in range(b): + mask = masks[i] + if mask.numel() == 0: + continue + if torch.max(mask != 0) == False: + is_empty[i] = True + continue + y, x = torch.where(mask) + bounding_boxes[i, 0] = torch.min(x) + bounding_boxes[i, 1] = torch.min(y) + bounding_boxes[i, 2] = torch.max(x) + bounding_boxes[i, 3] = torch.max(y) + + return bounding_boxes, is_empty + +def resolve_areas_and_cond_masks_multidim(conditions, dims, device): + # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. + # While we're doing this, we can also resolve the mask device and scaling for performance reasons + for i in range(len(conditions)): + c = conditions[i] + if 'area' in c: + area = c['area'] + if area[0] == "percentage": + modified = c.copy() + a = area[1:] + a_len = len(a) // 2 + area = () + for d in range(len(dims)): + area += (max(1, round(a[d] * dims[d])),) + for d in range(len(dims)): + area += (round(a[d + a_len] * dims[d]),) + + modified['area'] = area + c = modified + conditions[i] = c + + if 'mask' in c: + mask = c['mask'] + mask = mask.to(device=device) + modified = c.copy() + if len(mask.shape) == len(dims): + mask = mask.unsqueeze(0) + if mask.shape[1:] != dims: + mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1) + + if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2 + bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) + boxes, is_empty = get_mask_aabb(bounds) + if is_empty[0]: + # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway) + modified['area'] = (8, 8, 0, 0) + else: + box = boxes[0] + H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) + H = max(8, H) + W = max(8, W) + area = (int(H), int(W), int(Y), int(X)) + modified['area'] = area + + modified['mask'] = mask + conditions[i] = modified + +def resolve_areas_and_cond_masks(conditions, h, w, device): + logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.") + return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device) + +def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2 + if 'area' not in c: + return + + c_area = c['area'] + smallest = None + for x in conds: + if 'area' in x: + a = x['area'] + if c_area[2] >= a[2] and c_area[3] >= a[3]: + if a[0] + a[2] >= c_area[0] + c_area[2]: + if a[1] + a[3] >= c_area[1] + c_area[3]: + if smallest is None: + smallest = x + elif 'area' not in smallest: + smallest = x + else: + if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]: + smallest = x + else: + if smallest is None: + smallest = x + if smallest is None: + return + if 'area' in smallest: + if smallest['area'] == c_area: + return + + out = c.copy() + out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied? + conds += [out] + +def calculate_start_end_timesteps(model, conds): + s = model.model_sampling + for t in range(len(conds)): + x = conds[t] + + timestep_start = None + timestep_end = None + if 'start_percent' in x: + timestep_start = s.percent_to_sigma(x['start_percent']) + if 'end_percent' in x: + timestep_end = s.percent_to_sigma(x['end_percent']) + + if (timestep_start is not None) or (timestep_end is not None): + n = x.copy() + if (timestep_start is not None): + n['timestep_start'] = timestep_start + if (timestep_end is not None): + n['timestep_end'] = timestep_end + conds[t] = n + +def pre_run_control(model, conds): + s = model.model_sampling + for t in range(len(conds)): + x = conds[t] + + timestep_start = None + timestep_end = None + percent_to_timestep_function = lambda a: s.percent_to_sigma(a) + if 'control' in x: + x['control'].pre_run(model, percent_to_timestep_function) + +def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): + cond_cnets = [] + cond_other = [] + uncond_cnets = [] + uncond_other = [] + for t in range(len(conds)): + x = conds[t] + if 'area' not in x: + if name in x and x[name] is not None: + cond_cnets.append(x[name]) + else: + cond_other.append((x, t)) + for t in range(len(uncond)): + x = uncond[t] + if 'area' not in x: + if name in x and x[name] is not None: + uncond_cnets.append(x[name]) + else: + uncond_other.append((x, t)) + + if len(uncond_cnets) > 0: + return + + for x in range(len(cond_cnets)): + temp = uncond_other[x % len(uncond_other)] + o = temp[0] + if name in o and o[name] is not None: + n = o.copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond += [n] + else: + n = o.copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond[temp[1]] = n + +def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs): + for t in range(len(conds)): + x = conds[t] + params = x.copy() + params["device"] = device + params["noise"] = noise + default_width = None + if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model + default_width = noise.shape[3] * 8 + params["width"] = params.get("width", default_width) + params["height"] = params.get("height", noise.shape[2] * 8) + params["prompt_type"] = params.get("prompt_type", prompt_type) + for k in kwargs: + if k not in params: + params[k] = kwargs[k] + + out = model_function(**params) + x = x.copy() + model_conds = x['model_conds'].copy() + for k in out: + model_conds[k] = out[k] + x['model_conds'] = model_conds + conds[t] = x + return conds + +class Sampler: + def sample(self): + pass + + def max_denoise(self, model_wrap, sigmas): + max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) + sigma = float(sigmas[0]) + return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma + +KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", + "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu", + "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", + "ipndm", "ipndm_v", "deis"] + +class KSAMPLER(Sampler): + def __init__(self, sampler_function, extra_options={}, inpaint_options={}): + self.sampler_function = sampler_function + self.extra_options = extra_options + self.inpaint_options = inpaint_options + + def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): + extra_args["denoise_mask"] = denoise_mask + model_k = KSamplerX0Inpaint(model_wrap, sigmas) + model_k.latent_image = latent_image + if self.inpaint_options.get("random", False): #TODO: Should this be the default? + generator = torch.manual_seed(extra_args.get("seed", 41) + 1) + model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) + else: + model_k.noise = noise + + noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas)) + + k_callback = None + total_steps = len(sigmas) - 1 + if callback is not None: + k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) + + samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) + samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples) + return samples + + +def ksampler(sampler_name, extra_options={}, inpaint_options={}): + if sampler_name == "dpm_fast": + def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): + if len(sigmas) <= 1: + return noise + + sigma_min = sigmas[-1] + if sigma_min == 0: + sigma_min = sigmas[-2] + total_steps = len(sigmas) - 1 + return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) + sampler_function = dpm_fast_function + elif sampler_name == "dpm_adaptive": + def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): + if len(sigmas) <= 1: + return noise + + sigma_min = sigmas[-1] + if sigma_min == 0: + sigma_min = sigmas[-2] + return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options) + sampler_function = dpm_adaptive_function + else: + sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) + + return KSAMPLER(sampler_function, extra_options, inpaint_options) + + +def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None): + for k in conds: + conds[k] = conds[k][:] + resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) + + for k in conds: + calculate_start_end_timesteps(model, conds[k]) + + if hasattr(model, 'extra_conds'): + for k in conds: + conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) + + #make sure each cond area has an opposite one with the same area + for k in conds: + for c in conds[k]: + for kk in conds: + if k != kk: + create_cond_with_same_area_if_none(conds[kk], c) + + for k in conds: + pre_run_control(model, conds[k]) + + if "positive" in conds: + positive = conds["positive"] + for k in conds: + if k != "positive": + apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x]) + apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x]) + + return conds + +class CFGGuider: + def __init__(self, model_patcher): + self.model_patcher = model_patcher + self.model_options = model_patcher.model_options + self.original_conds = {} + self.cfg = 1.0 + + def set_conds(self, positive, negative): + self.inner_set_conds({"positive": positive, "negative": negative}) + + def set_cfg(self, cfg): + self.cfg = cfg + + def inner_set_conds(self, conds): + for k in conds: + self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) + + def __call__(self, *args, **kwargs): + return self.predict_noise(*args, **kwargs) + + def predict_noise(self, x, timestep, model_options={}, seed=None): + return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed) + + def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed): + if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. + latent_image = self.inner_model.process_latent_in(latent_image) + + self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed) + + extra_args = {"model_options": self.model_options, "seed":seed} + + samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) + return self.inner_model.process_latent_out(samples.to(torch.float32)) + + def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + if sigmas.shape[-1] == 0: + return latent_image + + self.conds = {} + for k in self.original_conds: + self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) + + self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds) + device = self.model_patcher.load_device + + if denoise_mask is not None: + denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device) + + noise = noise.to(device) + latent_image = latent_image.to(device) + sigmas = sigmas.to(device) + + output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + + comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models) + del self.inner_model + del self.conds + del self.loaded_models + return output + + +def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + cfg_guider = CFGGuider(model) + cfg_guider.set_conds(positive, negative) + cfg_guider.set_cfg(cfg) + return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + + +SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "linear_quadratic"] +SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] + +def calculate_sigmas(model_sampling, scheduler_name, steps): + if scheduler_name == "karras": + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) + elif scheduler_name == "exponential": + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) + elif scheduler_name == "normal": + sigmas = normal_scheduler(model_sampling, steps) + elif scheduler_name == "simple": + sigmas = simple_scheduler(model_sampling, steps) + elif scheduler_name == "ddim_uniform": + sigmas = ddim_scheduler(model_sampling, steps) + elif scheduler_name == "sgm_uniform": + sigmas = normal_scheduler(model_sampling, steps, sgm=True) + elif scheduler_name == "beta": + sigmas = beta_scheduler(model_sampling, steps) + elif scheduler_name == "linear_quadratic": + sigmas = linear_quadratic_schedule(model_sampling, steps) + else: + logging.error("error invalid scheduler {}".format(scheduler_name)) + return sigmas + +def sampler_object(name): + if name == "uni_pc": + sampler = KSAMPLER(uni_pc.sample_unipc) + elif name == "uni_pc_bh2": + sampler = KSAMPLER(uni_pc.sample_unipc_bh2) + elif name == "ddim": + sampler = ksampler("euler", inpaint_options={"random": True}) + else: + sampler = ksampler(name) + return sampler + +class KSampler: + SCHEDULERS = SCHEDULER_NAMES + SAMPLERS = SAMPLER_NAMES + DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2')) + + def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): + self.model = model + self.device = device + if scheduler not in self.SCHEDULERS: + scheduler = self.SCHEDULERS[0] + if sampler not in self.SAMPLERS: + sampler = self.SAMPLERS[0] + self.scheduler = scheduler + self.sampler = sampler + self.set_steps(steps, denoise) + self.denoise = denoise + self.model_options = model_options + + def calculate_sigmas(self, steps): + sigmas = None + + discard_penultimate_sigma = False + if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS: + steps += 1 + discard_penultimate_sigma = True + + sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps) + + if discard_penultimate_sigma: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + return sigmas + + def set_steps(self, steps, denoise=None): + self.steps = steps + if denoise is None or denoise > 0.9999: + self.sigmas = self.calculate_sigmas(steps).to(self.device) + else: + if denoise <= 0.0: + self.sigmas = torch.FloatTensor([]) + else: + new_steps = int(steps/denoise) + sigmas = self.calculate_sigmas(new_steps).to(self.device) + self.sigmas = sigmas[-(steps + 1):] + + def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): + if sigmas is None: + sigmas = self.sigmas + + if last_step is not None and last_step < (len(sigmas) - 1): + sigmas = sigmas[:last_step + 1] + if force_full_denoise: + sigmas[-1] = 0 + + if start_step is not None: + if start_step < (len(sigmas) - 1): + sigmas = sigmas[start_step:] + else: + if latent_image is not None: + return latent_image + else: + return torch.zeros_like(noise) + + sampler = sampler_object(self.sampler) + + return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) diff --git a/comfy/sd.py b/comfy/sd.py new file mode 100644 index 0000000..95fc6d2 --- /dev/null +++ b/comfy/sd.py @@ -0,0 +1,791 @@ +import torch +from enum import Enum +import logging + +from comfy import model_management +from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine +from .ldm.cascade.stage_a import StageA +from .ldm.cascade.stage_c_coder import StageC_coder +from .ldm.audio.autoencoder import AudioOobleckVAE +import comfy.ldm.genmo.vae.model +import yaml + +import comfy.utils + +from . import clip_vision +from . import gligen +from . import diffusers_convert +from . import model_detection + +from . import sd1_clip +from . import sdxl_clip +import comfy.text_encoders.sd2_clip +import comfy.text_encoders.sd3_clip +import comfy.text_encoders.sa_t5 +import comfy.text_encoders.aura_t5 +import comfy.text_encoders.hydit +import comfy.text_encoders.flux +import comfy.text_encoders.long_clipl +import comfy.text_encoders.genmo + +import comfy.model_patcher +import comfy.lora +import comfy.t2i_adapter.adapter +import comfy.taesd.taesd + +def load_lora_for_models(model, clip, lora, strength_model, strength_clip): + key_map = {} + if model is not None: + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) + if clip is not None: + key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + + loaded = comfy.lora.load_lora(lora, key_map) + if model is not None: + new_modelpatcher = model.clone() + k = new_modelpatcher.add_patches(loaded, strength_model) + else: + k = () + new_modelpatcher = None + + if clip is not None: + new_clip = clip.clone() + k1 = new_clip.add_patches(loaded, strength_clip) + else: + k1 = () + new_clip = None + k = set(k) + k1 = set(k1) + for x in loaded: + if (x not in k) and (x not in k1): + logging.warning("NOT LOADED {}".format(x)) + + return (new_modelpatcher, new_clip) + + +class CLIP: + def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}): + if no_init: + return + params = target.params.copy() + clip = target.clip + tokenizer = target.tokenizer + + load_device = model_options.get("load_device", model_management.text_encoder_device()) + offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device()) + dtype = model_options.get("dtype", None) + if dtype is None: + dtype = model_management.text_encoder_dtype(load_device) + + params['dtype'] = dtype + params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype))) + params['model_options'] = model_options + + self.cond_stage_model = clip(**(params)) + + for dt in self.cond_stage_model.dtypes: + if not model_management.supports_cast(load_device, dt): + load_device = offload_device + if params['device'] != offload_device: + self.cond_stage_model.to(offload_device) + logging.warning("Had to shift TE back.") + + self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) + if params['device'] == load_device: + model_management.load_models_gpu([self.patcher], force_full_load=True) + self.layer_idx = None + logging.debug("CLIP model load device: {}, offload device: {}, current: {}".format(load_device, offload_device, params['device'])) + + def clone(self): + n = CLIP(no_init=True) + n.patcher = self.patcher.clone() + n.cond_stage_model = self.cond_stage_model + n.tokenizer = self.tokenizer + n.layer_idx = self.layer_idx + return n + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + return self.patcher.add_patches(patches, strength_patch, strength_model) + + def clip_layer(self, layer_idx): + self.layer_idx = layer_idx + + def tokenize(self, text, return_word_ids=False): + return self.tokenizer.tokenize_with_weights(text, return_word_ids) + + def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False): + self.cond_stage_model.reset_clip_options() + + if self.layer_idx is not None: + self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) + + if return_pooled == "unprojected": + self.cond_stage_model.set_clip_options({"projected_pooled": False}) + + self.load_model() + o = self.cond_stage_model.encode_token_weights(tokens) + cond, pooled = o[:2] + if return_dict: + out = {"cond": cond, "pooled_output": pooled} + if len(o) > 2: + for k in o[2]: + out[k] = o[2][k] + return out + + if return_pooled: + return cond, pooled + return cond + + def encode(self, text): + tokens = self.tokenize(text) + return self.encode_from_tokens(tokens) + + def load_sd(self, sd, full_model=False): + if full_model: + return self.cond_stage_model.load_state_dict(sd, strict=False) + else: + return self.cond_stage_model.load_sd(sd) + + def get_sd(self): + sd_clip = self.cond_stage_model.state_dict() + sd_tokenizer = self.tokenizer.state_dict() + for k in sd_tokenizer: + sd_clip[k] = sd_tokenizer[k] + return sd_clip + + def load_model(self): + model_management.load_model_gpu(self.patcher) + return self.patcher + + def get_key_patches(self): + return self.patcher.get_key_patches() + +class VAE: + def __init__(self, sd=None, device=None, config=None, dtype=None): + if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format + sd = diffusers_convert.convert_vae_state_dict(sd) + + self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) + self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) + self.downscale_ratio = 8 + self.upscale_ratio = 8 + self.latent_channels = 4 + self.latent_dim = 2 + self.output_channels = 3 + self.process_input = lambda image: image * 2.0 - 1.0 + self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) + self.working_dtypes = [torch.bfloat16, torch.float32] + + if config is None: + if "decoder.mid.block_1.mix_factor" in sd: + encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + decoder_config = encoder_config.copy() + decoder_config["video_kernel_size"] = [3, 1, 1] + decoder_config["alpha"] = 0.0 + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, + decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) + elif "taesd_decoder.1.weight" in sd: + self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] + self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) + elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade + self.first_stage_model = StageA() + self.downscale_ratio = 4 + self.upscale_ratio = 4 + #TODO + #self.memory_used_encode + #self.memory_used_decode + self.process_input = lambda image: image + self.process_output = lambda image: image + elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade + self.first_stage_model = StageC_coder() + self.downscale_ratio = 32 + self.latent_channels = 16 + new_sd = {} + for k in sd: + new_sd["encoder.{}".format(k)] = sd[k] + sd = new_sd + elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade + self.first_stage_model = StageC_coder() + self.latent_channels = 16 + new_sd = {} + for k in sd: + new_sd["previewer.{}".format(k)] = sd[k] + sd = new_sd + elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade + self.first_stage_model = StageC_coder() + self.downscale_ratio = 32 + self.latent_channels = 16 + elif "decoder.conv_in.weight" in sd: + #default SD1.x/SD2.x VAE parameters + ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + + if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE + ddconfig['ch_mult'] = [1, 2, 4] + self.downscale_ratio = 4 + self.upscale_ratio = 4 + + self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] + if 'quant_conv.weight' in sd: + self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) + else: + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, + decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) + elif "decoder.layers.1.layers.0.beta" in sd: + self.first_stage_model = AudioOobleckVAE() + self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype) + self.latent_channels = 64 + self.output_channels = 2 + self.upscale_ratio = 2048 + self.downscale_ratio = 2048 + self.latent_dim = 1 + self.process_output = lambda audio: audio + self.process_input = lambda audio: audio + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae + if "blocks.2.blocks.3.stack.5.weight" in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."}) + if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."}) + self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE() + self.latent_channels = 12 + self.latent_dim = 3 + self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) + self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8) + self.working_dtypes = [torch.float16, torch.float32] + else: + logging.warning("WARNING: No VAE weights detected, VAE not initalized.") + self.first_stage_model = None + return + else: + self.first_stage_model = AutoencoderKL(**(config['params'])) + self.first_stage_model = self.first_stage_model.eval() + + m, u = self.first_stage_model.load_state_dict(sd, strict=False) + if len(m) > 0: + logging.warning("Missing VAE keys {}".format(m)) + + if len(u) > 0: + logging.debug("Leftover VAE keys {}".format(u)) + + if device is None: + device = model_management.vae_device() + self.device = device + offload_device = model_management.vae_offload_device() + if dtype is None: + dtype = model_management.vae_dtype(self.device, self.working_dtypes) + self.vae_dtype = dtype + self.first_stage_model.to(self.vae_dtype) + self.output_device = model_management.intermediate_device() + + self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) + logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype)) + + def vae_encode_crop_pixels(self, pixels): + dims = pixels.shape[1:-1] + for d in range(len(dims)): + x = (dims[d] // self.downscale_ratio) * self.downscale_ratio + x_offset = (dims[d] % self.downscale_ratio) // 2 + if x != dims[d]: + pixels = pixels.narrow(d + 1, x_offset, x) + return pixels + + def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): + steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) + steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = comfy.utils.ProgressBar(steps) + + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + output = self.process_output( + (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar)) + / 3.0) + return output + + def decode_tiled_1d(self, samples, tile_x=128, overlap=32): + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device) + + def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)): + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)) + + def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): + steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) + steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = comfy.utils.ProgressBar(steps) + + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples /= 3.0 + return samples + + def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048): + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device) + + def decode(self, samples_in): + pixel_samples = None + try: + memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) + model_management.load_models_gpu([self.patcher], memory_required=memory_used) + free_memory = model_management.get_free_memory(self.device) + batch_number = int(free_memory / memory_used) + batch_number = max(1, batch_number) + + for x in range(0, samples_in.shape[0], batch_number): + samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) + out = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) + if pixel_samples is None: + pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device) + pixel_samples[x:x+batch_number] = out + except model_management.OOM_EXCEPTION as e: + logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") + dims = samples_in.ndim - 2 + if dims == 1: + pixel_samples = self.decode_tiled_1d(samples_in) + elif dims == 2: + pixel_samples = self.decode_tiled_(samples_in) + elif dims == 3: + pixel_samples = self.decode_tiled_3d(samples_in) + + pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) + return pixel_samples + + def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None): + memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile + model_management.load_models_gpu([self.patcher], memory_required=memory_used) + dims = samples.ndim - 2 + args = {} + if tile_x is not None: + args["tile_x"] = tile_x + if tile_y is not None: + args["tile_y"] = tile_y + if overlap is not None: + args["overlap"] = overlap + + if dims == 1: + args.pop("tile_y") + output = self.decode_tiled_1d(samples, **args) + elif dims == 2: + output = self.decode_tiled_(samples, **args) + elif dims == 3: + output = self.decode_tiled_3d(samples, **args) + return output.movedim(1, -1) + + def encode(self, pixel_samples): + pixel_samples = self.vae_encode_crop_pixels(pixel_samples) + pixel_samples = pixel_samples.movedim(-1, 1) + if self.latent_dim == 3: + pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) + try: + memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) + model_management.load_models_gpu([self.patcher], memory_required=memory_used) + free_memory = model_management.get_free_memory(self.device) + batch_number = int(free_memory / max(1, memory_used)) + batch_number = max(1, batch_number) + samples = None + for x in range(0, pixel_samples.shape[0], batch_number): + pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device) + out = self.first_stage_model.encode(pixels_in).to(self.output_device).float() + if samples is None: + samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device) + samples[x:x + batch_number] = out + + except model_management.OOM_EXCEPTION as e: + logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") + if len(pixel_samples.shape) == 3: + samples = self.encode_tiled_1d(pixel_samples) + else: + samples = self.encode_tiled_(pixel_samples) + + return samples + + def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): + pixel_samples = self.vae_encode_crop_pixels(pixel_samples) + model_management.load_model_gpu(self.patcher) + pixel_samples = pixel_samples.movedim(-1,1) + samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) + return samples + + def get_sd(self): + return self.first_stage_model.state_dict() + +class StyleModel: + def __init__(self, model, device="cpu"): + self.model = model + + def get_cond(self, input): + return self.model(input.last_hidden_state) + + +def load_style_model(ckpt_path): + model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) + keys = model_data.keys() + if "style_embedding" in keys: + model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) + else: + raise Exception("invalid style model {}".format(ckpt_path)) + model.load_state_dict(model_data) + return StyleModel(model) + +class CLIPType(Enum): + STABLE_DIFFUSION = 1 + STABLE_CASCADE = 2 + SD3 = 3 + STABLE_AUDIO = 4 + HUNYUAN_DIT = 5 + FLUX = 6 + MOCHI = 7 + +def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): + clip_data = [] + for p in ckpt_paths: + clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) + return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options) + + +class TEModel(Enum): + CLIP_L = 1 + CLIP_H = 2 + CLIP_G = 3 + T5_XXL = 4 + T5_XL = 5 + T5_BASE = 6 + +def detect_te_model(sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return TEModel.CLIP_G + if "text_model.encoder.layers.22.mlp.fc1.weight" in sd: + return TEModel.CLIP_H + if "text_model.encoder.layers.0.mlp.fc1.weight" in sd: + return TEModel.CLIP_L + if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: + weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"] + if weight.shape[-1] == 4096: + return TEModel.T5_XXL + elif weight.shape[-1] == 2048: + return TEModel.T5_XL + if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd: + return TEModel.T5_BASE + return None + + +def t5xxl_detect(clip_data): + weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" + + for sd in clip_data: + if weight_name in sd: + return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd) + + return {} + + +def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): + clip_data = state_dicts + + class EmptyClass: + pass + + for i in range(len(clip_data)): + if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: + clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") + else: + if "text_projection" in clip_data[i]: + clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node + + clip_target = EmptyClass() + clip_target.params = {} + if len(clip_data) == 1: + te_model = detect_te_model(clip_data[0]) + if te_model == TEModel.CLIP_G: + if clip_type == CLIPType.STABLE_CASCADE: + clip_target.clip = sdxl_clip.StableCascadeClipModel + clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer + elif clip_type == CLIPType.SD3: + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + else: + clip_target.clip = sdxl_clip.SDXLRefinerClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer + elif te_model == TEModel.CLIP_H: + clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel + clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer + elif te_model == TEModel.T5_XXL: + if clip_type == CLIPType.SD3: + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + else: #CLIPType.MOCHI + clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer + elif te_model == TEModel.T5_XL: + clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model + clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer + elif te_model == TEModel.T5_BASE: + clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model + clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer + else: + if clip_type == CLIPType.SD3: + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + else: + clip_target.clip = sd1_clip.SD1ClipModel + clip_target.tokenizer = sd1_clip.SD1Tokenizer + elif len(clip_data) == 2: + if clip_type == CLIPType.SD3: + te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])] + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif clip_type == CLIPType.HUNYUAN_DIT: + clip_target.clip = comfy.text_encoders.hydit.HyditModel + clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer + elif clip_type == CLIPType.FLUX: + clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer + else: + clip_target.clip = sdxl_clip.SDXLClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer + elif len(clip_data) == 3: + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + + parameters = 0 + tokenizer_data = {} + for c in clip_data: + parameters += comfy.utils.calculate_parameters(c) + tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) + + clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options) + for c in clip_data: + m, u = clip.load_sd(c) + if len(m) > 0: + logging.warning("clip missing: {}".format(m)) + + if len(u) > 0: + logging.debug("clip unexpected: {}".format(u)) + return clip + +def load_gligen(ckpt_path): + data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) + model = gligen.load_gligen(data) + if model_management.should_use_fp16(): + model = model.half() + return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) + +def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): + logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.") + model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True) + #TODO: this function is a mess and should be removed eventually + if config is None: + with open(config_path, 'r') as stream: + config = yaml.safe_load(stream) + model_config_params = config['model']['params'] + clip_config = model_config_params['cond_stage_config'] + scale_factor = model_config_params['scale_factor'] + + if "parameterization" in model_config_params: + if model_config_params["parameterization"] == "v": + m = model.clone() + class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION): + pass + m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config)) + model = m + + layer_idx = clip_config.get("params", {}).get("layer_idx", None) + if layer_idx is not None: + clip.clip_layer(layer_idx) + + return (model, clip, vae) + +def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): + sd = comfy.utils.load_torch_file(ckpt_path) + out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options) + if out is None: + raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) + return out + +def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): + clip = None + clipvision = None + vae = None + model = None + model_patcher = None + + diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) + parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) + weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) + load_device = model_management.get_torch_device() + + model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix) + if model_config is None: + return None + + unet_weight_dtype = list(model_config.supported_inference_dtypes) + if weight_dtype is not None and model_config.scaled_fp8 is None: + unet_weight_dtype.append(weight_dtype) + + model_config.custom_operations = model_options.get("custom_operations", None) + unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) + + if unet_dtype is None: + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype) + + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) + + if model_config.clip_vision_prefix is not None: + if output_clipvision: + clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) + + if output_model: + inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) + model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) + model.load_model_weights(sd, diffusion_model_prefix) + + if output_vae: + vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) + vae_sd = model_config.process_vae_state_dict(vae_sd) + vae = VAE(sd=vae_sd) + + if output_clip: + clip_target = model_config.clip_target(state_dict=sd) + if clip_target is not None: + clip_sd = model_config.process_clip_state_dict(sd) + if len(clip_sd) > 0: + parameters = comfy.utils.calculate_parameters(clip_sd) + clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options) + m, u = clip.load_sd(clip_sd, full_model=True) + if len(m) > 0: + m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) + if len(m_filter) > 0: + logging.warning("clip missing: {}".format(m)) + else: + logging.debug("clip missing: {}".format(m)) + + if len(u) > 0: + logging.debug("clip unexpected {}:".format(u)) + else: + logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") + + left_over = sd.keys() + if len(left_over) > 0: + logging.debug("left over keys: {}".format(left_over)) + + if output_model: + model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device()) + if inital_load_device != torch.device("cpu"): + logging.info("loaded straight to GPU") + model_management.load_models_gpu([model_patcher], force_full_load=True) + + return (model_patcher, clip, vae, clipvision) + + +def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffusers or regular format + dtype = model_options.get("dtype", None) + + #Allow loading unets from checkpoint files + diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) + temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) + if len(temp_sd) > 0: + sd = temp_sd + + parameters = comfy.utils.calculate_parameters(sd) + weight_dtype = comfy.utils.weight_dtype(sd) + + load_device = model_management.get_torch_device() + model_config = model_detection.model_config_from_unet(sd, "") + + if model_config is not None: + new_sd = sd + else: + new_sd = model_detection.convert_diffusers_mmdit(sd, "") + if new_sd is not None: #diffusers mmdit + model_config = model_detection.model_config_from_unet(new_sd, "") + if model_config is None: + return None + else: #diffusers unet + model_config = model_detection.model_config_from_diffusers_unet(sd) + if model_config is None: + return None + + diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config) + + new_sd = {} + for k in diffusers_keys: + if k in sd: + new_sd[diffusers_keys[k]] = sd.pop(k) + else: + logging.warning("{} {}".format(diffusers_keys[k], k)) + + offload_device = model_management.unet_offload_device() + unet_weight_dtype = list(model_config.supported_inference_dtypes) + if weight_dtype is not None and model_config.scaled_fp8 is None: + unet_weight_dtype.append(weight_dtype) + + if dtype is None: + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype) + else: + unet_dtype = dtype + + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) + model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations) + if model_options.get("fp8_optimizations", False): + model_config.optimizations["fp8"] = True + + model = model_config.get_model(new_sd, "") + model = model.to(offload_device) + model.load_model_weights(new_sd, "") + left_over = sd.keys() + if len(left_over) > 0: + logging.info("left over keys in unet: {}".format(left_over)) + return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) + + +def load_diffusion_model(unet_path, model_options={}): + sd = comfy.utils.load_torch_file(unet_path) + model = load_diffusion_model_state_dict(sd, model_options=model_options) + if model is None: + logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path)) + raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) + return model + +def load_unet(unet_path, dtype=None): + print("WARNING: the load_unet function has been deprecated and will be removed please switch to: load_diffusion_model") + return load_diffusion_model(unet_path, model_options={"dtype": dtype}) + +def load_unet_state_dict(sd, dtype=None): + print("WARNING: the load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict") + return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype}) + +def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}): + clip_sd = None + load_models = [model] + if clip is not None: + load_models.append(clip.load_model()) + clip_sd = clip.get_sd() + vae_sd = None + if vae is not None: + vae_sd = vae.get_sd() + + model_management.load_models_gpu(load_models, force_patch_weights=True) + clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None + sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd) + for k in extra_keys: + sd[k] = extra_keys[k] + + for k in sd: + t = sd[k] + if not t.is_contiguous(): + sd[k] = t.contiguous() + + comfy.utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py new file mode 100644 index 0000000..a454f3b --- /dev/null +++ b/comfy/sd1_clip.py @@ -0,0 +1,602 @@ +import os + +from transformers import CLIPTokenizer +import comfy.ops +import torch +import traceback +import zipfile +from . import model_management +import comfy.clip_model +import json +import logging +import numbers + +def gen_empty_tokens(special_tokens, length): + start_token = special_tokens.get("start", None) + end_token = special_tokens.get("end", None) + pad_token = special_tokens.get("pad") + output = [] + if start_token is not None: + output.append(start_token) + if end_token is not None: + output.append(end_token) + output += [pad_token] * (length - len(output)) + return output + +class ClipTokenWeightEncoder: + def encode_token_weights(self, token_weight_pairs): + to_encode = list() + max_token_len = 0 + has_weights = False + for x in token_weight_pairs: + tokens = list(map(lambda a: a[0], x)) + max_token_len = max(len(tokens), max_token_len) + has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) + to_encode.append(tokens) + + sections = len(to_encode) + if has_weights or sections == 0: + to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) + + o = self.encode(to_encode) + out, pooled = o[:2] + + if pooled is not None: + first_pooled = pooled[0:1].to(model_management.intermediate_device()) + else: + first_pooled = pooled + + output = [] + for k in range(0, sections): + z = out[k:k+1] + if has_weights: + z_empty = out[-1] + for i in range(len(z)): + for j in range(len(z[i])): + weight = token_weight_pairs[k][j][1] + if weight != 1.0: + z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] + output.append(z) + + if (len(output) == 0): + r = (out[-1:].to(model_management.intermediate_device()), first_pooled) + else: + r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled) + + if len(o) > 2: + extra = {} + for k in o[2]: + v = o[2][k] + if k == "attention_mask": + v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device()) + extra[k] = v + + r = r + (extra,) + return r + +class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): + LAYERS = [ + "last", + "pooled", + "hidden" + ] + def __init__(self, device="cpu", max_length=77, + freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, + return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + + if textmodel_json_config is None: + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") + + with open(textmodel_json_config) as f: + config = json.load(f) + + operations = model_options.get("custom_operations", None) + scaled_fp8 = None + + if operations is None: + scaled_fp8 = model_options.get("scaled_fp8", None) + if scaled_fp8 is not None: + operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8) + else: + operations = comfy.ops.manual_cast + + self.operations = operations + self.transformer = model_class(config, dtype, device, self.operations) + if scaled_fp8 is not None: + self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8)) + + self.num_layers = self.transformer.num_layers + + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = None + self.special_tokens = special_tokens + + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) + self.enable_attention_masks = enable_attention_masks + self.zero_out_masked = zero_out_masked + + self.layer_norm_hidden_state = layer_norm_hidden_state + self.return_projected_pooled = return_projected_pooled + self.return_attention_masks = return_attention_masks + + if layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) < self.num_layers + self.set_clip_options({"layer": layer_idx}) + self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled) + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def set_clip_options(self, options): + layer_idx = options.get("layer", self.layer_idx) + self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) + if layer_idx is None or abs(layer_idx) > self.num_layers: + self.layer = "last" + else: + self.layer = "hidden" + self.layer_idx = layer_idx + + def reset_clip_options(self): + self.layer = self.options_default[0] + self.layer_idx = self.options_default[1] + self.return_projected_pooled = self.options_default[2] + + def set_up_textual_embeddings(self, tokens, current_embeds): + out_tokens = [] + next_new_token = token_dict_size = current_embeds.weight.shape[0] + embedding_weights = [] + + for x in tokens: + tokens_temp = [] + for y in x: + if isinstance(y, numbers.Integral): + tokens_temp += [int(y)] + else: + if y.shape[0] == current_embeds.weight.shape[1]: + embedding_weights += [y] + tokens_temp += [next_new_token] + next_new_token += 1 + else: + logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1])) + while len(tokens_temp) < len(x): + tokens_temp += [self.special_tokens["pad"]] + out_tokens += [tokens_temp] + + n = token_dict_size + if len(embedding_weights) > 0: + new_embedding = self.operations.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype) + new_embedding.weight[:token_dict_size] = current_embeds.weight + for x in embedding_weights: + new_embedding.weight[n] = x + n += 1 + self.transformer.set_input_embeddings(new_embedding) + + processed_tokens = [] + for x in out_tokens: + processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one + + return processed_tokens + + def forward(self, tokens): + backup_embeds = self.transformer.get_input_embeddings() + device = backup_embeds.weight.device + tokens = self.set_up_textual_embeddings(tokens, backup_embeds) + tokens = torch.LongTensor(tokens).to(device) + + attention_mask = None + if self.enable_attention_masks or self.zero_out_masked or self.return_attention_masks: + attention_mask = torch.zeros_like(tokens) + end_token = self.special_tokens.get("end", -1) + for x in range(attention_mask.shape[0]): + for y in range(attention_mask.shape[1]): + attention_mask[x, y] = 1 + if tokens[x, y] == end_token: + break + + attention_mask_model = None + if self.enable_attention_masks: + attention_mask_model = attention_mask + + outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32) + self.transformer.set_input_embeddings(backup_embeds) + + if self.layer == "last": + z = outputs[0].float() + else: + z = outputs[1].float() + + if self.zero_out_masked: + z *= attention_mask.unsqueeze(-1).float() + + pooled_output = None + if len(outputs) >= 3: + if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None: + pooled_output = outputs[3].float() + elif outputs[2] is not None: + pooled_output = outputs[2].float() + + extra = {} + if self.return_attention_masks: + extra["attention_mask"] = attention_mask + + if len(extra) > 0: + return z, pooled_output, extra + + return z, pooled_output + + def encode(self, tokens): + return self(tokens) + + def load_sd(self, sd): + return self.transformer.load_state_dict(sd, strict=False) + +def parse_parentheses(string): + result = [] + current_item = "" + nesting_level = 0 + for char in string: + if char == "(": + if nesting_level == 0: + if current_item: + result.append(current_item) + current_item = "(" + else: + current_item = "(" + else: + current_item += char + nesting_level += 1 + elif char == ")": + nesting_level -= 1 + if nesting_level == 0: + result.append(current_item + ")") + current_item = "" + else: + current_item += char + else: + current_item += char + if current_item: + result.append(current_item) + return result + +def token_weights(string, current_weight): + a = parse_parentheses(string) + out = [] + for x in a: + weight = current_weight + if len(x) >= 2 and x[-1] == ')' and x[0] == '(': + x = x[1:-1] + xx = x.rfind(":") + weight *= 1.1 + if xx > 0: + try: + weight = float(x[xx+1:]) + x = x[:xx] + except: + pass + out += token_weights(x, weight) + else: + out += [(x, current_weight)] + return out + +def escape_important(text): + text = text.replace("\\)", "\0\1") + text = text.replace("\\(", "\0\2") + return text + +def unescape_important(text): + text = text.replace("\0\1", ")") + text = text.replace("\0\2", "(") + return text + +def safe_load_embed_zip(embed_path): + with zipfile.ZipFile(embed_path) as myzip: + names = list(filter(lambda a: "data/" in a, myzip.namelist())) + names.reverse() + for n in names: + with myzip.open(n) as myfile: + data = myfile.read() + number = len(data) // 4 + length_embed = 1024 #sd2.x + if number < 768: + continue + if number % 768 == 0: + length_embed = 768 #sd1.x + num_embeds = number // length_embed + embed = torch.frombuffer(data, dtype=torch.float) + out = embed.reshape((num_embeds, length_embed)).clone() + del embed + return out + +def expand_directory_list(directories): + dirs = set() + for x in directories: + dirs.add(x) + for root, subdir, file in os.walk(x, followlinks=True): + dirs.add(root) + return list(dirs) + +def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format + i = 0 + out_list = [] + for k in embed: + if k.startswith(prefix) and k.endswith(suffix): + out_list.append(embed[k]) + if len(out_list) == 0: + return None + + return torch.cat(out_list, dim=0) + +def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): + if isinstance(embedding_directory, str): + embedding_directory = [embedding_directory] + + embedding_directory = expand_directory_list(embedding_directory) + + valid_file = None + for embed_dir in embedding_directory: + embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) + embed_dir = os.path.abspath(embed_dir) + try: + if os.path.commonpath((embed_dir, embed_path)) != embed_dir: + continue + except: + continue + if not os.path.isfile(embed_path): + extensions = ['.safetensors', '.pt', '.bin'] + for x in extensions: + t = embed_path + x + if os.path.isfile(t): + valid_file = t + break + else: + valid_file = embed_path + if valid_file is not None: + break + + if valid_file is None: + return None + + embed_path = valid_file + + embed_out = None + + try: + if embed_path.lower().endswith(".safetensors"): + import safetensors.torch + embed = safetensors.torch.load_file(embed_path, device="cpu") + else: + if 'weights_only' in torch.load.__code__.co_varnames: + try: + embed = torch.load(embed_path, weights_only=True, map_location="cpu") + except: + embed_out = safe_load_embed_zip(embed_path) + else: + embed = torch.load(embed_path, map_location="cpu") + except Exception as e: + logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name)) + return None + + if embed_out is None: + if 'string_to_param' in embed: + values = embed['string_to_param'].values() + embed_out = next(iter(values)) + elif isinstance(embed, list): + out_list = [] + for x in range(len(embed)): + for k in embed[x]: + t = embed[x][k] + if t.shape[-1] != embedding_size: + continue + out_list.append(t.reshape(-1, t.shape[-1])) + embed_out = torch.cat(out_list, dim=0) + elif embed_key is not None and embed_key in embed: + embed_out = embed[embed_key] + else: + embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*') + if embed_out is None: + embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key)) + if embed_out is None: + values = embed.values() + embed_out = next(iter(values)) + return embed_out + +class SDTokenizer: + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None, tokenizer_data={}): + if tokenizer_path is None: + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") + self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) + self.max_length = max_length + self.min_length = min_length + + empty = self.tokenizer('')["input_ids"] + if has_start_token: + self.tokens_start = 1 + self.start_token = empty[0] + self.end_token = empty[1] + else: + self.tokens_start = 0 + self.start_token = None + self.end_token = empty[0] + + if pad_token is not None: + self.pad_token = pad_token + elif pad_with_end: + self.pad_token = self.end_token + else: + self.pad_token = 0 + + self.pad_with_end = pad_with_end + self.pad_to_max_length = pad_to_max_length + + vocab = self.tokenizer.get_vocab() + self.inv_vocab = {v: k for k, v in vocab.items()} + self.embedding_directory = embedding_directory + self.max_word_length = 8 + self.embedding_identifier = "embedding:" + self.embedding_size = embedding_size + self.embedding_key = embedding_key + + def _try_get_embedding(self, embedding_name:str): + ''' + Takes a potential embedding name and tries to retrieve it. + Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. + ''' + embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) + if embed is None: + stripped = embedding_name.strip(',') + if len(stripped) < len(embedding_name): + embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) + return (embed, embedding_name[len(stripped):]) + return (embed, "") + + + def tokenize_with_weights(self, text:str, return_word_ids=False): + ''' + Takes a prompt and converts it to a list of (token, weight, word id) elements. + Tokens can both be integer tokens and pre computed CLIP tensors. + Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. + Returned list has the dimensions NxM where M is the input size of CLIP + ''' + + text = escape_important(text) + parsed_weights = token_weights(text, 1.0) + + #tokenize words + tokens = [] + for weighted_segment, weight in parsed_weights: + to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ') + to_tokenize = [x for x in to_tokenize if x != ""] + for word in to_tokenize: + #if we find an embedding, deal with the embedding + if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: + embedding_name = word[len(self.embedding_identifier):].strip('\n') + embed, leftover = self._try_get_embedding(embedding_name) + if embed is None: + logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring") + else: + if len(embed.shape) == 1: + tokens.append([(embed, weight)]) + else: + tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) + #if we accidentally have leftover text, continue parsing using leftover, else move on to next word + if leftover != "": + word = leftover + else: + continue + #parse word + tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]]) + + #reshape token array to CLIP input size + batched_tokens = [] + batch = [] + if self.start_token is not None: + batch.append((self.start_token, 1.0, 0)) + batched_tokens.append(batch) + for i, t_group in enumerate(tokens): + #determine if we're going to try and keep the tokens in a single batch + is_large = len(t_group) >= self.max_word_length + + while len(t_group) > 0: + if len(t_group) + len(batch) > self.max_length - 1: + remaining_length = self.max_length - len(batch) - 1 + #break word in two and add end token + if is_large: + batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) + batch.append((self.end_token, 1.0, 0)) + t_group = t_group[remaining_length:] + #add end token and pad + else: + batch.append((self.end_token, 1.0, 0)) + if self.pad_to_max_length: + batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length)) + #start new batch + batch = [] + if self.start_token is not None: + batch.append((self.start_token, 1.0, 0)) + batched_tokens.append(batch) + else: + batch.extend([(t,w,i+1) for t,w in t_group]) + t_group = [] + + #fill last batch + batch.append((self.end_token, 1.0, 0)) + if self.pad_to_max_length: + batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch))) + if self.min_length is not None and len(batch) < self.min_length: + batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch))) + + if not return_word_ids: + batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] + + return batched_tokens + + + def untokenize(self, token_weight_pair): + return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) + + def state_dict(self): + return {} + +class SD1Tokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer): + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer) + setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return getattr(self, self.clip).untokenize(token_weight_pair) + + def state_dict(self): + return {} + +class SD1CheckpointClipModel(SDClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options) + +class SD1ClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs): + super().__init__() + + if name is not None: + self.clip_name = name + self.clip = "{}".format(self.clip_name) + else: + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + + clip_model = model_options.get("{}_class".format(self.clip), clip_model) + setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs)) + + self.dtypes = set() + if dtype is not None: + self.dtypes.add(dtype) + + def set_clip_options(self, options): + getattr(self, self.clip).set_clip_options(options) + + def reset_clip_options(self): + getattr(self, self.clip).reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs = token_weight_pairs[self.clip_name] + out = getattr(self, self.clip).encode_token_weights(token_weight_pairs) + return out + + def load_sd(self, sd): + return getattr(self, self.clip).load_sd(sd) diff --git a/comfy/sd1_clip_config.json b/comfy/sd1_clip_config.json new file mode 100644 index 0000000..3ba8c6b --- /dev/null +++ b/comfy/sd1_clip_config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/comfy/sd1_tokenizer/merges.txt b/comfy/sd1_tokenizer/merges.txt new file mode 100644 index 0000000..76e821f --- /dev/null +++ b/comfy/sd1_tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l 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def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}): + if layer == "penultimate": + layer="hidden" + layer_idx=-2 + + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, + special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False, return_projected_pooled=True, model_options=model_options) + + def load_sd(self, sd): + return super().load_sd(sd) + +class SDXLClipGTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') + + +class SDXLTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) + self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) + self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + + def state_dict(self): + return {} + +class SDXLClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__() + clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) + self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options) + self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options) + self.dtypes = set([dtype]) + + def set_clip_options(self, options): + self.clip_l.set_clip_options(options) + self.clip_g.set_clip_options(options) + + def reset_clip_options(self): + self.clip_g.reset_clip_options() + self.clip_l.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pairs_l = token_weight_pairs["l"] + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + cut_to = min(l_out.shape[1], g_out.shape[1]) + return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], dim=-1), g_pooled + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + else: + return self.clip_l.load_sd(sd) + +class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG, model_options=model_options) + + +class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): + super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') + +class StableCascadeTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="g", tokenizer=StableCascadeClipGTokenizer) + +class StableCascadeClipG(sd1_clip.SDClipModel): + def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options) + + def load_sd(self, sd): + return super().load_sd(sd) + +class StableCascadeClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG, model_options=model_options) diff --git a/comfy/supported_models.py b/comfy/supported_models.py new file mode 100644 index 0000000..9931f4c --- /dev/null +++ b/comfy/supported_models.py @@ -0,0 +1,706 @@ +import torch +from . import model_base +from . import utils + +from . import sd1_clip +from . import sdxl_clip +import comfy.text_encoders.sd2_clip +import comfy.text_encoders.sd3_clip +import comfy.text_encoders.sa_t5 +import comfy.text_encoders.aura_t5 +import comfy.text_encoders.hydit +import comfy.text_encoders.flux +import comfy.text_encoders.genmo + +from . import supported_models_base +from . import latent_formats + +from . import diffusers_convert + +class SD15(supported_models_base.BASE): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config = { + "num_heads": 8, + "num_head_channels": -1, + } + + latent_format = latent_formats.SD15 + memory_usage_factor = 1.0 + + def process_clip_state_dict(self, state_dict): + k = list(state_dict.keys()) + for x in k: + if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): + y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") + state_dict[y] = state_dict.pop(x) + + if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: + ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] + if ids.dtype == torch.float32: + state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() + + replace_prefix = {} + replace_prefix["cond_stage_model."] = "clip_l." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] + for p in pop_keys: + if p in state_dict: + state_dict.pop(p) + + replace_prefix = {"clip_l.": "cond_stage_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) + +class SD20(supported_models_base.BASE): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + "attn_precision": torch.float32, + } + + latent_format = latent_formats.SD15 + memory_usage_factor = 1.0 + + def model_type(self, state_dict, prefix=""): + if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction + k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) + out = state_dict.get(k, None) + if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. + return model_base.ModelType.V_PREDICTION + return model_base.ModelType.EPS + + def process_clip_state_dict(self, state_dict): + replace_prefix = {} + replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format + replace_prefix["cond_stage_model.model."] = "clip_h." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.") + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + replace_prefix["clip_h"] = "cond_stage_model.model" + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) + return state_dict + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel) + +class SD21UnclipL(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": 1536, + "use_temporal_attention": False, + } + + clip_vision_prefix = "embedder.model.visual." + noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} + + +class SD21UnclipH(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": 2048, + "use_temporal_attention": False, + } + + clip_vision_prefix = "embedder.model.visual." + noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} + +class SDXLRefiner(supported_models_base.BASE): + unet_config = { + "model_channels": 384, + "use_linear_in_transformer": True, + "context_dim": 1280, + "adm_in_channels": 2560, + "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], + "use_temporal_attention": False, + } + + latent_format = latent_formats.SDXL + memory_usage_factor = 1.0 + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXLRefiner(self, device=device) + + def process_clip_state_dict(self, state_dict): + keys_to_replace = {} + replace_prefix = {} + replace_prefix["conditioner.embedders.0.model."] = "clip_g." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") + if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: + state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") + replace_prefix["clip_g"] = "conditioner.embedders.0.model" + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) + return state_dict_g + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) + +class SDXL(supported_models_base.BASE): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 10, 10], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + + latent_format = latent_formats.SDXL + + memory_usage_factor = 0.8 + + def model_type(self, state_dict, prefix=""): + if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5 + self.latent_format = latent_formats.SDXL_Playground_2_5() + self.sampling_settings["sigma_data"] = 0.5 + self.sampling_settings["sigma_max"] = 80.0 + self.sampling_settings["sigma_min"] = 0.002 + return model_base.ModelType.EDM + elif "edm_vpred.sigma_max" in state_dict: + self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item()) + if "edm_vpred.sigma_min" in state_dict: + self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item()) + return model_base.ModelType.V_PREDICTION_EDM + elif "v_pred" in state_dict: + return model_base.ModelType.V_PREDICTION + else: + return model_base.ModelType.EPS + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out + + def process_clip_state_dict(self, state_dict): + keys_to_replace = {} + replace_prefix = {} + + replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model" + replace_prefix["conditioner.embedders.1.model."] = "clip_g." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + keys_to_replace = {} + state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") + for k in state_dict: + if k.startswith("clip_l"): + state_dict_g[k] = state_dict[k] + + state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1)) + pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] + for p in pop_keys: + if p in state_dict_g: + state_dict_g.pop(p) + + replace_prefix["clip_g"] = "conditioner.embedders.1.model" + replace_prefix["clip_l"] = "conditioner.embedders.0" + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) + return state_dict_g + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) + +class SSD1B(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 4, 4], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class Segmind_Vega(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 1, 1, 2, 2], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class KOALA_700M(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 2, 5], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class KOALA_1B(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 2, 6], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class SVD_img2vid(supported_models_base.BASE): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 768, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + "attn_precision": torch.float32, + } + + clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." + + latent_format = latent_formats.SD15 + + sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SVD_img2vid(self, device=device) + return out + + def clip_target(self, state_dict={}): + return None + +class SV3D_u(SVD_img2vid): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 256, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + vae_key_prefix = ["conditioner.embedders.1.encoder."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SV3D_u(self, device=device) + return out + +class SV3D_p(SV3D_u): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 1280, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SV3D_p(self, device=device) + return out + +class Stable_Zero123(supported_models_base.BASE): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + "in_channels": 8, + } + + unet_extra_config = { + "num_heads": 8, + "num_head_channels": -1, + } + + required_keys = { + "cc_projection.weight": None, + "cc_projection.bias": None, + } + + clip_vision_prefix = "cond_stage_model.model.visual." + + latent_format = latent_formats.SD15 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) + return out + + def clip_target(self, state_dict={}): + return None + +class SD_X4Upscaler(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 256, + 'in_channels': 7, + "use_linear_in_transformer": True, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config = { + "disable_self_attentions": [True, True, True, False], + "num_classes": 1000, + "num_heads": 8, + "num_head_channels": -1, + } + + latent_format = latent_formats.SD_X4 + + sampling_settings = { + "linear_start": 0.0001, + "linear_end": 0.02, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SD_X4Upscaler(self, device=device) + return out + +class Stable_Cascade_C(supported_models_base.BASE): + unet_config = { + "stable_cascade_stage": 'c', + } + + unet_extra_config = {} + + latent_format = latent_formats.SC_Prior + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + sampling_settings = { + "shift": 2.0, + } + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoder."] + clip_vision_prefix = "clip_l_vision." + + def process_unet_state_dict(self, state_dict): + key_list = list(state_dict.keys()) + for y in ["weight", "bias"]: + suffix = "in_proj_{}".format(y) + keys = filter(lambda a: a.endswith(suffix), key_list) + for k_from in keys: + weights = state_dict.pop(k_from) + prefix = k_from[:-(len(suffix) + 1)] + shape_from = weights.shape[0] // 3 + for x in range(3): + p = ["to_q", "to_k", "to_v"] + k_to = "{}.{}.{}".format(prefix, p[x], y) + state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)] + return state_dict + + def process_clip_state_dict(self, state_dict): + state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) + if "clip_g.text_projection" in state_dict: + state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1) + return state_dict + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.StableCascade_C(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel) + +class Stable_Cascade_B(Stable_Cascade_C): + unet_config = { + "stable_cascade_stage": 'b', + } + + unet_extra_config = {} + + latent_format = latent_formats.SC_B + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + sampling_settings = { + "shift": 1.0, + } + + clip_vision_prefix = None + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.StableCascade_B(self, device=device) + return out + +class SD15_instructpix2pix(SD15): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + "in_channels": 8, + } + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SD15_instructpix2pix(self, device=device) + +class SDXL_instructpix2pix(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 10, 10], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + "in_channels": 8, + } + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device) + +class SD3(supported_models_base.BASE): + unet_config = { + "in_channels": 16, + "pos_embed_scaling_factor": None, + } + + sampling_settings = { + "shift": 3.0, + } + + unet_extra_config = {} + latent_format = latent_formats.SD3 + + memory_usage_factor = 1.2 + + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SD3(self, device=device) + return out + + def clip_target(self, state_dict={}): + clip_l = False + clip_g = False + t5 = False + dtype_t5 = None + pref = self.text_encoder_key_prefix[0] + if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: + clip_l = True + if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: + clip_g = True + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + if "dtype_t5" in t5_detect: + t5 = True + + return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect)) + +class StableAudio(supported_models_base.BASE): + unet_config = { + "audio_model": "dit1.0", + } + + sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03} + + unet_extra_config = {} + latent_format = latent_formats.StableAudio1 + + text_encoder_key_prefix = ["text_encoders."] + vae_key_prefix = ["pretransform.model."] + + def get_model(self, state_dict, prefix="", device=None): + seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True) + seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True) + return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device) + + def process_unet_state_dict(self, state_dict): + for k in list(state_dict.keys()): + if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero + state_dict.pop(k) + return state_dict + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model.model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model) + +class AuraFlow(supported_models_base.BASE): + unet_config = { + "cond_seq_dim": 2048, + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 1.73, + } + + unet_extra_config = {} + latent_format = latent_formats.SDXL + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.AuraFlow(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model) + +class HunyuanDiT(supported_models_base.BASE): + unet_config = { + "image_model": "hydit", + } + + unet_extra_config = { + "attn_precision": torch.float32, + } + + sampling_settings = { + "linear_start": 0.00085, + "linear_end": 0.018, + } + + latent_format = latent_formats.SDXL + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanDiT(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel) + +class HunyuanDiT1(HunyuanDiT): + unet_config = { + "image_model": "hydit1", + } + + unet_extra_config = {} + + sampling_settings = { + "linear_start" : 0.00085, + "linear_end" : 0.03, + } + +class Flux(supported_models_base.BASE): + unet_config = { + "image_model": "flux", + "guidance_embed": True, + } + + sampling_settings = { + } + + unet_extra_config = {} + latent_format = latent_formats.Flux + + memory_usage_factor = 2.8 + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Flux(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect)) + +class FluxSchnell(Flux): + unet_config = { + "image_model": "flux", + "guidance_embed": False, + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 1.0, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device) + return out + +class GenmoMochi(supported_models_base.BASE): + unet_config = { + "image_model": "mochi_preview", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 6.0, + } + + unet_extra_config = {} + latent_format = latent_formats.Mochi + + memory_usage_factor = 2.0 #TODO + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.GenmoMochi(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect)) + + +models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell, GenmoMochi] + +models += [SVD_img2vid] diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py new file mode 100644 index 0000000..54573ab --- /dev/null +++ b/comfy/supported_models_base.py @@ -0,0 +1,119 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +from . import model_base +from . import utils +from . import latent_formats + +class ClipTarget: + def __init__(self, tokenizer, clip): + self.clip = clip + self.tokenizer = tokenizer + self.params = {} + +class BASE: + unet_config = {} + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + } + + required_keys = {} + + clip_prefix = [] + clip_vision_prefix = None + noise_aug_config = None + sampling_settings = {} + latent_format = latent_formats.LatentFormat + vae_key_prefix = ["first_stage_model."] + text_encoder_key_prefix = ["cond_stage_model."] + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + memory_usage_factor = 2.0 + + manual_cast_dtype = None + custom_operations = None + scaled_fp8 = None + optimizations = {"fp8": False} + + @classmethod + def matches(s, unet_config, state_dict=None): + for k in s.unet_config: + if k not in unet_config or s.unet_config[k] != unet_config[k]: + return False + if state_dict is not None: + for k in s.required_keys: + if k not in state_dict: + return False + return True + + def model_type(self, state_dict, prefix=""): + return model_base.ModelType.EPS + + def inpaint_model(self): + return self.unet_config["in_channels"] > 4 + + def __init__(self, unet_config): + self.unet_config = unet_config.copy() + self.sampling_settings = self.sampling_settings.copy() + self.latent_format = self.latent_format() + self.optimizations = self.optimizations.copy() + for x in self.unet_extra_config: + self.unet_config[x] = self.unet_extra_config[x] + + def get_model(self, state_dict, prefix="", device=None): + if self.noise_aug_config is not None: + out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) + else: + out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out + + def process_clip_state_dict(self, state_dict): + state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) + return state_dict + + def process_unet_state_dict(self, state_dict): + return state_dict + + def process_vae_state_dict(self, state_dict): + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {"": self.text_encoder_key_prefix[0]} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_clip_vision_state_dict_for_saving(self, state_dict): + replace_prefix = {} + if self.clip_vision_prefix is not None: + replace_prefix[""] = self.clip_vision_prefix + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model.diffusion_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_vae_state_dict_for_saving(self, state_dict): + replace_prefix = {"": self.vae_key_prefix[0]} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def set_inference_dtype(self, dtype, manual_cast_dtype): + self.unet_config['dtype'] = dtype + self.manual_cast_dtype = manual_cast_dtype diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py new file mode 100644 index 0000000..10ea18e --- /dev/null +++ b/comfy/t2i_adapter/adapter.py @@ -0,0 +1,299 @@ +#taken from https://github.com/TencentARC/T2I-Adapter +import torch +import torch.nn as nn +from collections import OrderedDict + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + if not self.use_conv: + padding = [x.shape[2] % 2, x.shape[3] % 2] + self.op.padding = padding + + x = self.op(x) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): + super().__init__() + ps = ksize // 2 + if in_c != out_c or sk == False: + self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + # print('n_in') + self.in_conv = None + self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) + if sk == False: + self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + self.skep = None + + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=use_conv) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + if self.in_conv is not None: # edit + x = self.in_conv(x) + + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + if self.skep is not None: + return h + self.skep(x) + else: + return h + x + + +class Adapter(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): + super(Adapter, self).__init__() + self.unshuffle_amount = 8 + resblock_no_downsample = [] + resblock_downsample = [3, 2, 1] + self.xl = xl + if self.xl: + self.unshuffle_amount = 16 + resblock_no_downsample = [1] + resblock_downsample = [2] + + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + for i in range(len(channels)): + for j in range(nums_rb): + if (i in resblock_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) + elif (i in resblock_no_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + else: + self.body.append( + ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + self.body = nn.ModuleList(self.body) + self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + x = self.conv_in(x) + for i in range(len(self.channels)): + for j in range(self.nums_rb): + idx = i * self.nums_rb + j + x = self.body[idx](x) + if self.xl: + features.append(None) + if i == 0: + features.append(None) + features.append(None) + if i == 2: + features.append(None) + else: + features.append(None) + features.append(None) + features.append(x) + + features = features[::-1] + + if self.xl: + return {"input": features[1:], "middle": features[:1]} + else: + return {"input": features} + + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model))])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class StyleAdapter(nn.Module): + + def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): + super().__init__() + + scale = width ** -0.5 + self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) + self.num_token = num_token + self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) + self.ln_post = LayerNorm(width) + self.ln_pre = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) + + def forward(self, x): + # x shape [N, HW+1, C] + style_embedding = self.style_embedding + torch.zeros( + (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) + x = torch.cat([x, style_embedding], dim=1) + x = self.ln_pre(x) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer_layes(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, -self.num_token:, :]) + x = x @ self.proj + + return x + + +class ResnetBlock_light(nn.Module): + def __init__(self, in_c): + super().__init__() + self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) + + def forward(self, x): + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + + return h + x + + +class extractor(nn.Module): + def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): + super().__init__() + self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) + self.body = [] + for _ in range(nums_rb): + self.body.append(ResnetBlock_light(inter_c)) + self.body = nn.Sequential(*self.body) + self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=False) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + x = self.in_conv(x) + x = self.body(x) + x = self.out_conv(x) + + return x + + +class Adapter_light(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): + super(Adapter_light, self).__init__() + self.unshuffle_amount = 8 + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + self.xl = False + + for i in range(len(channels)): + if i == 0: + self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) + else: + self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) + self.body = nn.ModuleList(self.body) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + for i in range(len(self.channels)): + x = self.body[i](x) + features.append(None) + features.append(None) + features.append(x) + + return {"input": features[::-1]} diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py new file mode 100644 index 0000000..ce36f1a --- /dev/null +++ b/comfy/taesd/taesd.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +""" +Tiny AutoEncoder for Stable Diffusion +(DNN for encoding / decoding SD's latent space) +""" +import torch +import torch.nn as nn + +import comfy.utils +import comfy.ops + +def conv(n_in, n_out, **kwargs): + return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + +class Clamp(nn.Module): + def forward(self, x): + return torch.tanh(x / 3) * 3 + +class Block(nn.Module): + def __init__(self, n_in, n_out): + super().__init__() + self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) + self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() + self.fuse = nn.ReLU() + def forward(self, x): + return self.fuse(self.conv(x) + self.skip(x)) + +def Encoder(latent_channels=4): + return nn.Sequential( + conv(3, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, latent_channels), + ) + + +def Decoder(latent_channels=4): + return nn.Sequential( + Clamp(), conv(latent_channels, 64), nn.ReLU(), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), conv(64, 3), + ) + +class TAESD(nn.Module): + latent_magnitude = 3 + latent_shift = 0.5 + + def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4): + """Initialize pretrained TAESD on the given device from the given checkpoints.""" + super().__init__() + self.taesd_encoder = Encoder(latent_channels=latent_channels) + self.taesd_decoder = Decoder(latent_channels=latent_channels) + self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) + self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) + if encoder_path is not None: + self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True)) + if decoder_path is not None: + self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True)) + + @staticmethod + def scale_latents(x): + """raw latents -> [0, 1]""" + return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) + + @staticmethod + def unscale_latents(x): + """[0, 1] -> raw latents""" + return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) + + def decode(self, x): + x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) + x_sample = x_sample.sub(0.5).mul(2) + return x_sample + + def encode(self, x): + return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py new file mode 100644 index 0000000..e9ad45a --- /dev/null +++ b/comfy/text_encoders/aura_t5.py @@ -0,0 +1,22 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.t5 +import os + +class PT5XlModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options) + +class PT5XlTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1) + +class AuraT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer) + +class AuraT5Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, name="pile_t5xl", clip_model=PT5XlModel, **kwargs) diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py new file mode 100644 index 0000000..fc9bac1 --- /dev/null +++ b/comfy/text_encoders/bert.py @@ -0,0 +1,140 @@ +import torch +from comfy.ldm.modules.attention import optimized_attention_for_device +import comfy.ops + +class BertAttention(torch.nn.Module): + def __init__(self, embed_dim, heads, dtype, device, operations): + super().__init__() + + self.heads = heads + self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + + def forward(self, x, mask=None, optimized_attention=None): + q = self.query(x) + k = self.key(x) + v = self.value(x) + + out = optimized_attention(q, k, v, self.heads, mask) + return out + +class BertOutput(torch.nn.Module): + def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations): + super().__init__() + self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device) + self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device) + # self.dropout = nn.Dropout(0.0) + + def forward(self, x, y): + x = self.dense(x) + # hidden_states = self.dropout(hidden_states) + x = self.LayerNorm(x + y) + return x + +class BertAttentionBlock(torch.nn.Module): + def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.self = BertAttention(embed_dim, heads, dtype, device, operations) + self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) + + def forward(self, x, mask, optimized_attention): + y = self.self(x, mask, optimized_attention) + return self.output(y, x) + +class BertIntermediate(torch.nn.Module): + def __init__(self, embed_dim, intermediate_dim, dtype, device, operations): + super().__init__() + self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device) + + def forward(self, x): + x = self.dense(x) + return torch.nn.functional.gelu(x) + + +class BertBlock(torch.nn.Module): + def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations) + self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations) + self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations) + + def forward(self, x, mask, optimized_attention): + x = self.attention(x, mask, optimized_attention) + y = self.intermediate(x) + return self.output(y, x) + +class BertEncoder(torch.nn.Module): + def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)]) + + def forward(self, x, mask=None, intermediate_output=None): + optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layer) + intermediate_output + + intermediate = None + for i, l in enumerate(self.layer): + x = l(x, mask, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + +class BertEmbeddings(torch.nn.Module): + def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations): + super().__init__() + self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device) + self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device) + self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device) + + self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device) + + def forward(self, input_tokens, token_type_ids=None, dtype=None): + x = self.word_embeddings(input_tokens, out_dtype=dtype) + x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x) + if token_type_ids is not None: + x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype) + else: + x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x) + x = self.LayerNorm(x) + return x + + +class BertModel_(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + embed_dim = config_dict["hidden_size"] + layer_norm_eps = config_dict["layer_norm_eps"] + + self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations) + self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations) + + def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): + x = self.embeddings(input_tokens, dtype=dtype) + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + x, i = self.encoder(x, mask, intermediate_output) + return x, i + + +class BertModel(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.bert = BertModel_(config_dict, dtype, device, operations) + self.num_layers = config_dict["num_hidden_layers"] + + def get_input_embeddings(self): + return self.bert.embeddings.word_embeddings + + def set_input_embeddings(self, embeddings): + self.bert.embeddings.word_embeddings = embeddings + + def forward(self, *args, **kwargs): + return self.bert(*args, **kwargs) diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py new file mode 100644 index 0000000..b945b1a --- /dev/null +++ b/comfy/text_encoders/flux.py @@ -0,0 +1,72 @@ +from comfy import sd1_clip +import comfy.text_encoders.t5 +import comfy.text_encoders.sd3_clip +import comfy.model_management +from transformers import T5TokenizerFast +import torch +import os + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256) + + +class FluxTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) + self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) + self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return self.clip_l.untokenize(token_weight_pair) + + def state_dict(self): + return {} + + +class FluxClipModel(torch.nn.Module): + def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}): + super().__init__() + dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) + clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) + self.clip_l = clip_l_class(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) + self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options) + self.dtypes = set([dtype, dtype_t5]) + + def set_clip_options(self, options): + self.clip_l.set_clip_options(options) + self.t5xxl.set_clip_options(options) + + def reset_clip_options(self): + self.clip_l.reset_clip_options() + self.t5xxl.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_t5 = token_weight_pairs["t5xxl"] + + t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + return t5_out, l_pooled + + def load_sd(self, sd): + if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + else: + return self.t5xxl.load_sd(sd) + +def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None): + class FluxClipModel_(FluxClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options) + return FluxClipModel_ diff --git a/comfy/text_encoders/genmo.py b/comfy/text_encoders/genmo.py new file mode 100644 index 0000000..45987a4 --- /dev/null +++ b/comfy/text_encoders/genmo.py @@ -0,0 +1,38 @@ +from comfy import sd1_clip +import comfy.text_encoders.sd3_clip +import os +from transformers import T5TokenizerFast + + +class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): + def __init__(self, **kwargs): + kwargs["attention_mask"] = True + super().__init__(**kwargs) + + +class MochiT5XXL(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) + + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256) + + +class MochiT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + + +def mochi_te(dtype_t5=None, t5xxl_scaled_fp8=None): + class MochiTEModel_(MochiT5XXL): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if dtype is None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return MochiTEModel_ diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py new file mode 100644 index 0000000..7cb790f --- /dev/null +++ b/comfy/text_encoders/hydit.py @@ -0,0 +1,79 @@ +from comfy import sd1_clip +from transformers import BertTokenizer +from .spiece_tokenizer import SPieceTokenizer +from .bert import BertModel +import comfy.text_encoders.t5 +import os +import torch + +class HyditBertModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) + +class HyditBertTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77) + + +class MT5XLModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) + +class MT5XLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_tokenizer"), "spiece.model") + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + +class HyditTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None) + self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory) + self.mt5xl = MT5XLTokenizer(tokenizer_data={"spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids) + out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return self.hydit_clip.untokenize(token_weight_pair) + + def state_dict(self): + return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]} + +class HyditModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__() + self.hydit_clip = HyditBertModel(dtype=dtype, model_options=model_options) + self.mt5xl = MT5XLModel(dtype=dtype, model_options=model_options) + + self.dtypes = set() + if dtype is not None: + self.dtypes.add(dtype) + + def encode_token_weights(self, token_weight_pairs): + hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"]) + mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"]) + return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]} + + def load_sd(self, sd): + if "bert.encoder.layer.0.attention.self.query.weight" in sd: + return self.hydit_clip.load_sd(sd) + else: + return self.mt5xl.load_sd(sd) + + def set_clip_options(self, options): + self.hydit_clip.set_clip_options(options) + self.mt5xl.set_clip_options(options) + + def reset_clip_options(self): + self.hydit_clip.reset_clip_options() + self.mt5xl.reset_clip_options() diff --git a/comfy/text_encoders/hydit_clip.json b/comfy/text_encoders/hydit_clip.json new file mode 100644 index 0000000..c41c7c1 --- /dev/null +++ b/comfy/text_encoders/hydit_clip.json @@ -0,0 +1,35 @@ +{ + "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "bos_token_id": 0, + "classifier_dropout": null, + "directionality": "bidi", + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "output_past": true, + "pad_token_id": 0, + "pooler_fc_size": 768, + "pooler_num_attention_heads": 12, + "pooler_num_fc_layers": 3, + "pooler_size_per_head": 128, + "pooler_type": "first_token_transform", + "position_embedding_type": "absolute", + "torch_dtype": "float32", + "transformers_version": "4.22.1", + "type_vocab_size": 2, + "use_cache": true, + "vocab_size": 47020 +} + diff --git a/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json new file mode 100644 index 0000000..a8b3208 --- /dev/null +++ b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json @@ -0,0 +1,7 @@ +{ + "cls_token": "[CLS]", + "mask_token": "[MASK]", + "pad_token": "[PAD]", + "sep_token": "[SEP]", + "unk_token": "[UNK]" +} diff --git a/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json new file mode 100644 index 0000000..a143560 --- /dev/null +++ b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "cls_token": "[CLS]", + "do_basic_tokenize": true, + "do_lower_case": true, + "mask_token": "[MASK]", + "name_or_path": "hfl/chinese-roberta-wwm-ext", + "never_split": null, + "pad_token": "[PAD]", + "sep_token": "[SEP]", + "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json", + "strip_accents": null, + "tokenize_chinese_chars": true, + "tokenizer_class": "BertTokenizer", + "unk_token": "[UNK]", + "model_max_length": 77 +} diff --git a/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt new file mode 100644 index 0000000..6246906 --- /dev/null +++ b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt @@ -0,0 +1,47020 @@ +[PAD] +[unused1] +[unused2] +[unused3] +[unused4] +[unused5] +[unused6] +[unused7] +[unused8] +[unused9] +[unused10] +[unused11] +[unused12] +[unused13] +[unused14] +[unused15] +[unused16] +[unused17] +[unused18] +[unused19] +[unused20] +[unused21] +[unused22] +[unused23] +[unused24] +[unused25] +[unused26] +[unused27] +[unused28] +[unused29] +[unused30] +[unused31] +[unused32] +[unused33] +[unused34] +[unused35] +[unused36] +[unused37] +[unused38] +[unused39] +[unused40] +[unused41] +[unused42] +[unused43] +[unused44] +[unused45] +[unused46] +[unused47] +[unused48] +[unused49] +[unused50] +[unused51] +[unused52] +[unused53] +[unused54] +[unused55] +[unused56] +[unused57] +[unused58] +[unused59] +[unused60] +[unused61] +[unused62] +[unused63] +[unused64] +[unused65] +[unused66] +[unused67] +[unused68] +[unused69] +[unused70] +[unused71] +[unused72] +[unused73] +[unused74] +[unused75] +[unused76] +[unused77] +[unused78] +[unused79] +[unused80] +[unused81] +[unused82] +[unused83] +[unused84] +[unused85] +[unused86] +[unused87] +[unused88] +[unused89] +[unused90] +[unused91] +[unused92] +[unused93] +[unused94] +[unused95] +[unused96] +[unused97] +[unused98] +[unused99] +[UNK] +[CLS] +[SEP] +[MASK] + + +! +" +# +$ +% +& +' +( +) +* ++ +, +- +. +/ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +: +; +< += +> +? +@ +[ +\ +] +^ +_ +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +{ +| +} +~ +£ +¤ +¥ +§ +© +« +® +° +± +² +³ +µ +· +¹ +º +» +¼ +× +ß +æ +÷ +ø +đ +ŋ +ɔ +ə +ɡ +ʰ +ˇ +ˈ +ˊ +ˋ +ˍ +ː +˙ +˚ +ˢ +α +β +γ +δ +ε +η +θ +ι +κ +λ +μ +ν +ο +π +ρ +ς +σ +τ +υ +φ +χ +ψ +ω +а +б +в +г +д +е +ж +з +и +к +л +м +н +о +п +р +с +т +у +ф +х +ц +ч +ш +ы +ь +я +і +ا +ب +ة +ت +د +ر +س +ع +ل +م +ن +ه +و +ي +۩ +ก +ง +น +ม +ย +ร +อ +า +เ +๑ +་ +ღ +ᄀ +ᄁ +ᄂ +ᄃ +ᄅ +ᄆ +ᄇ +ᄈ +ᄉ +ᄋ +ᄌ +ᄎ 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"intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 248, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/comfy/text_encoders/long_clipl.py b/comfy/text_encoders/long_clipl.py new file mode 100644 index 0000000..b81912c --- /dev/null +++ b/comfy/text_encoders/long_clipl.py @@ -0,0 +1,30 @@ +from comfy import sd1_clip +import os + +class LongClipTokenizer_(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(max_length=248, embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + +class LongClipModel_(sd1_clip.SDClipModel): + def __init__(self, *args, **kwargs): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "long_clipl.json") + super().__init__(*args, textmodel_json_config=textmodel_json_config, **kwargs) + +class LongClipTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, tokenizer=LongClipTokenizer_) + +class LongClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, clip_model=LongClipModel_, **kwargs) + +def model_options_long_clip(sd, tokenizer_data, model_options): + w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None) + if w is None: + w = sd.get("text_model.embeddings.position_embedding.weight", None) + if w is not None and w.shape[0] == 248: + tokenizer_data = tokenizer_data.copy() + model_options = model_options.copy() + tokenizer_data["clip_l_tokenizer_class"] = LongClipTokenizer_ + model_options["clip_l_class"] = LongClipModel_ + return tokenizer_data, model_options diff --git a/comfy/text_encoders/mt5_config_xl.json b/comfy/text_encoders/mt5_config_xl.json new file mode 100644 index 0000000..092fefd --- /dev/null +++ b/comfy/text_encoders/mt5_config_xl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 5120, + "d_kv": 64, + "d_model": 2048, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "mt5", + "num_decoder_layers": 24, + "num_heads": 32, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 250112 +} diff --git a/comfy/text_encoders/sa_t5.py b/comfy/text_encoders/sa_t5.py new file mode 100644 index 0000000..7778ce4 --- /dev/null +++ b/comfy/text_encoders/sa_t5.py @@ -0,0 +1,22 @@ +from comfy import sd1_clip +from transformers import T5TokenizerFast +import comfy.text_encoders.t5 +import os + +class T5BaseModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True) + +class T5BaseTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128) + +class SAT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5base", tokenizer=T5BaseTokenizer) + +class SAT5Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, name="t5base", clip_model=T5BaseModel, **kwargs) diff --git a/comfy/text_encoders/sd2_clip.py b/comfy/text_encoders/sd2_clip.py new file mode 100644 index 0000000..31fc898 --- /dev/null +++ b/comfy/text_encoders/sd2_clip.py @@ -0,0 +1,23 @@ +from comfy import sd1_clip +import os + +class SD2ClipHModel(sd1_clip.SDClipModel): + def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}): + if layer == "penultimate": + layer="hidden" + layer_idx=-2 + + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=True, model_options=model_options) + +class SD2ClipHTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024) + +class SD2Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer) + +class SD2ClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="h", clip_model=SD2ClipHModel, **kwargs) diff --git a/comfy/text_encoders/sd2_clip_config.json b/comfy/text_encoders/sd2_clip_config.json new file mode 100644 index 0000000..00893cf --- /dev/null +++ b/comfy/text_encoders/sd2_clip_config.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "gelu", + "hidden_size": 1024, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "pad_token_id": 1, + "projection_dim": 1024, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py new file mode 100644 index 0000000..00d7e31 --- /dev/null +++ b/comfy/text_encoders/sd3_clip.py @@ -0,0 +1,167 @@ +from comfy import sd1_clip +from comfy import sdxl_clip +from transformers import T5TokenizerFast +import comfy.text_encoders.t5 +import torch +import os +import comfy.model_management +import logging + +class T5XXLModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") + t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) + if t5xxl_scaled_fp8 is not None: + model_options = model_options.copy() + model_options["scaled_fp8"] = t5xxl_scaled_fp8 + + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +def t5_xxl_detect(state_dict, prefix=""): + out = {} + t5_key = "{}encoder.final_layer_norm.weight".format(prefix) + if t5_key in state_dict: + out["dtype_t5"] = state_dict[t5_key].dtype + + scaled_fp8_key = "{}scaled_fp8".format(prefix) + if scaled_fp8_key in state_dict: + out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype + + return out + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77) + + +class SD3Tokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) + self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) + self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory) + self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + + def state_dict(self): + return {} + +class SD3ClipModel(torch.nn.Module): + def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}): + super().__init__() + self.dtypes = set() + if clip_l: + clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) + self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options) + self.dtypes.add(dtype) + else: + self.clip_l = None + + if clip_g: + self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) + self.dtypes.add(dtype) + else: + self.clip_g = None + + if t5: + dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) + self.t5_attention_mask = t5_attention_mask + self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask) + self.dtypes.add(dtype_t5) + else: + self.t5xxl = None + + logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5)) + + def set_clip_options(self, options): + if self.clip_l is not None: + self.clip_l.set_clip_options(options) + if self.clip_g is not None: + self.clip_g.set_clip_options(options) + if self.t5xxl is not None: + self.t5xxl.set_clip_options(options) + + def reset_clip_options(self): + if self.clip_l is not None: + self.clip_l.reset_clip_options() + if self.clip_g is not None: + self.clip_g.reset_clip_options() + if self.t5xxl is not None: + self.t5xxl.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pairs_t5 = token_weight_pairs["t5xxl"] + lg_out = None + pooled = None + out = None + extra = {} + + if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: + if self.clip_l is not None: + lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + else: + l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) + + if self.clip_g is not None: + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + if lg_out is not None: + cut_to = min(lg_out.shape[1], g_out.shape[1]) + lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1) + else: + lg_out = torch.nn.functional.pad(g_out, (768, 0)) + else: + g_out = None + g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) + + if lg_out is not None: + lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) + out = lg_out + pooled = torch.cat((l_pooled, g_pooled), dim=-1) + + if self.t5xxl is not None: + t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) + t5_out, t5_pooled = t5_output[:2] + if self.t5_attention_mask: + extra["attention_mask"] = t5_output[2]["attention_mask"] + + if lg_out is not None: + out = torch.cat([lg_out, t5_out], dim=-2) + else: + out = t5_out + + if out is None: + out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device()) + + if pooled is None: + pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) + + return out, pooled, extra + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + else: + return self.t5xxl.load_sd(sd) + +def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False): + class SD3ClipModel_(SD3ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options) + return SD3ClipModel_ diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py new file mode 100644 index 0000000..7373955 --- /dev/null +++ b/comfy/text_encoders/spiece_tokenizer.py @@ -0,0 +1,32 @@ +import os +import torch + +class SPieceTokenizer: + add_eos = True + + @staticmethod + def from_pretrained(path): + return SPieceTokenizer(path) + + def __init__(self, tokenizer_path): + import sentencepiece + if torch.is_tensor(tokenizer_path): + tokenizer_path = tokenizer_path.numpy().tobytes() + + if isinstance(tokenizer_path, bytes): + self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_eos=self.add_eos) + else: + self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos) + + def get_vocab(self): + out = {} + for i in range(self.tokenizer.get_piece_size()): + out[self.tokenizer.id_to_piece(i)] = i + return out + + def __call__(self, string): + out = self.tokenizer.encode(string) + return {"input_ids": out} + + def serialize_model(self): + return torch.ByteTensor(list(self.tokenizer.serialized_model_proto())) diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py new file mode 100644 index 0000000..a1420c6 --- /dev/null +++ b/comfy/text_encoders/t5.py @@ -0,0 +1,241 @@ +import torch +import math +from comfy.ldm.modules.attention import optimized_attention_for_device +import comfy.ops + +class T5LayerNorm(torch.nn.Module): + def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) + self.variance_epsilon = eps + + def forward(self, x): + variance = x.pow(2).mean(-1, keepdim=True) + x = x * torch.rsqrt(variance + self.variance_epsilon) + return comfy.ops.cast_to_input(self.weight, x) * x + +activations = { + "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), + "relu": torch.nn.functional.relu, +} + +class T5DenseActDense(torch.nn.Module): + def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): + super().__init__() + self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) + # self.dropout = nn.Dropout(config.dropout_rate) + self.act = activations[ff_activation] + + def forward(self, x): + x = self.act(self.wi(x)) + # x = self.dropout(x) + x = self.wo(x) + return x + +class T5DenseGatedActDense(torch.nn.Module): + def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): + super().__init__() + self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) + # self.dropout = nn.Dropout(config.dropout_rate) + self.act = activations[ff_activation] + + def forward(self, x): + hidden_gelu = self.act(self.wi_0(x)) + hidden_linear = self.wi_1(x) + x = hidden_gelu * hidden_linear + # x = self.dropout(x) + x = self.wo(x) + return x + +class T5LayerFF(torch.nn.Module): + def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations): + super().__init__() + if gated_act: + self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) + else: + self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) + + self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x): + forwarded_states = self.layer_norm(x) + forwarded_states = self.DenseReluDense(forwarded_states) + # x = x + self.dropout(forwarded_states) + x += forwarded_states + return x + +class T5Attention(torch.nn.Module): + def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + + # Mesh TensorFlow initialization to avoid scaling before softmax + self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device) + self.num_heads = num_heads + + self.relative_attention_bias = None + if relative_attention_bias: + self.relative_attention_num_buckets = 32 + self.relative_attention_max_distance = 128 + self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype) + + @staticmethod + def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): + """ + Adapted from Mesh Tensorflow: + https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 + + Translate relative position to a bucket number for relative attention. The relative position is defined as + memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to + position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for + small absolute relative_position and larger buckets for larger absolute relative_positions. All relative + positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. + This should allow for more graceful generalization to longer sequences than the model has been trained on + + Args: + relative_position: an int32 Tensor + bidirectional: a boolean - whether the attention is bidirectional + num_buckets: an integer + max_distance: an integer + + Returns: + a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) + """ + relative_buckets = 0 + if bidirectional: + num_buckets //= 2 + relative_buckets += (relative_position > 0).to(torch.long) * num_buckets + relative_position = torch.abs(relative_position) + else: + relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) + # now relative_position is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = relative_position < max_exact + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + relative_position_if_large = max_exact + ( + torch.log(relative_position.float() / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_position_if_large = torch.min( + relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) + ) + + relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) + return relative_buckets + + def compute_bias(self, query_length, key_length, device, dtype): + """Compute binned relative position bias""" + context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] + memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] + relative_position = memory_position - context_position # shape (query_length, key_length) + relative_position_bucket = self._relative_position_bucket( + relative_position, # shape (query_length, key_length) + bidirectional=True, + num_buckets=self.relative_attention_num_buckets, + max_distance=self.relative_attention_max_distance, + ) + values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype) # shape (query_length, key_length, num_heads) + values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) + return values + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + q = self.q(x) + k = self.k(x) + v = self.v(x) + if self.relative_attention_bias is not None: + past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype) + + if past_bias is not None: + if mask is not None: + mask = mask + past_bias + else: + mask = past_bias + + out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask) + return self.o(out), past_bias + +class T5LayerSelfAttention(torch.nn.Module): + def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations) + self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + normed_hidden_states = self.layer_norm(x) + output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention) + # x = x + self.dropout(attention_output) + x += output + return x, past_bias + +class T5Block(torch.nn.Module): + def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + self.layer = torch.nn.ModuleList() + self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations)) + self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations)) + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) + x = self.layer[-1](x) + return x, past_bias + +class T5Stack(torch.nn.Module): + def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations): + super().__init__() + + self.block = torch.nn.ModuleList( + [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] + ) + self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + intermediate = None + optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True) + past_bias = None + for i, l in enumerate(self.block): + x, past_bias = l(x, mask, past_bias, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + x = self.final_layer_norm(x) + if intermediate is not None and final_layer_norm_intermediate: + intermediate = self.final_layer_norm(intermediate) + return x, intermediate + +class T5(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.num_layers = config_dict["num_layers"] + model_dim = config_dict["d_model"] + + self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations) + self.dtype = dtype + self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype) + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, embeddings): + self.shared = embeddings + + def forward(self, input_ids, *args, **kwargs): + x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) + if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]: + x = torch.nan_to_num(x) #Fix for fp8 T5 base + return self.encoder(x, *args, **kwargs) diff --git a/comfy/text_encoders/t5_config_base.json b/comfy/text_encoders/t5_config_base.json new file mode 100644 index 0000000..71f6832 --- /dev/null +++ b/comfy/text_encoders/t5_config_base.json @@ -0,0 +1,22 @@ +{ + "d_ff": 3072, + "d_kv": 64, + "d_model": 768, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "relu", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": false, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 12, + "num_heads": 12, + "num_layers": 12, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/comfy/text_encoders/t5_config_xxl.json b/comfy/text_encoders/t5_config_xxl.json new file mode 100644 index 0000000..28283b5 --- /dev/null +++ b/comfy/text_encoders/t5_config_xxl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 10240, + "d_kv": 64, + "d_model": 4096, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 24, + "num_heads": 64, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/comfy/text_encoders/t5_pile_config_xl.json b/comfy/text_encoders/t5_pile_config_xl.json new file mode 100644 index 0000000..ee4e03f --- /dev/null +++ b/comfy/text_encoders/t5_pile_config_xl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 5120, + "d_kv": 64, + "d_model": 2048, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 2, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "umt5", + "num_decoder_layers": 24, + "num_heads": 32, + "num_layers": 24, + "output_past": true, + "pad_token_id": 1, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + 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MAIBKDACA0R0AgMwEAQDHCgCAcRsAgKelAADTCgCAo40AAMwUAgCAuQAAgbkAAKeFAAAIDACAgmUAAIEbAICMNQAA8wsAgMzsHADN/AMAiRsAgK6tAADZCgCAkRsAgMzABgDN0AYAsL0BAMyQBwDfCgCAgckBAMwYHQDNIAIAhBEAAOsKAIDNuAYAzKwGAKEbAIDlCgCAgSkAALEbAICpGwCAo+0BAMxAHQDNEAIAuRsAgMEbAICBCQAAyRsAgMxAHQDN0AIAqNkBABQMAIDMkAcAzBwBAMxgBgDNZAYA8QoAgBwKAIDRGwCAkSkBAP0KAICBzR8A2RsAgPcKAIDpGwCA4RsAgMzEBgDNwAYAgTEAAIDZAAAfCgCAIgoAgIK5AQCDRQEAgLkBAIG5AQCGXQEA8R0AgIRdAQDpHQCAzcAAAMzwAACIARwAiXkBAAEeAICPVQEAjGEBAPkdAICB3R4AgRUfAJkbAICBXR8AjIEfAIdBHwDMGAMAzWgDAIBNHwCBpR8AJQoAgIOpHwCMFR8AjNEeACgKAICHtR8AgJUfAIGZHwCBEQAAg70fAICFHwCBiR8A8RsAgIQ9AACbDACAiZkfAPkbAICIBQAABgsAgAEcAICADQAAgf0AAAkcAICj2R8Ao3keAKOFAAAMCwCArTUfAKdhHgCnqR8AoQwAgIQNAACnDACAozUfACsKAICtiR8AhHEAAKchHwCxPR4AsYUfAJUMAIDhHQCAEgsAgLcLAIDMtBwAzbAcAFAMAICxQR8AVgwAgJwLAIAZHgCAER4AgCkeAIAhHgCAgLkeAIG5HgCCIQEAgzUBAIRhAQAxHgCAhokBAIe9AQCIkQEAiekBANkdAICL/QEAjOUBAIINAAAJHgCAj90BAIO5AQCRrQEAgb0BAIC9AQCAoQEAgaEBAPkLAID/CwCAhD0AABEcAICJlQEAm4EBAIHNHgCAzR4AzPwCAM3wAgCB5QAAGRwAgIHtAACjpQAAzJABAM1cAgCHHQAAGwsAgKj5AAAhHACAJwsAgFwMAIBiDACAKRwAgIQFAAAxHACAo9UAACELAIA5HACAgVEAAMz0AQDN0AEALQsAgIc9AABRHACAMwsAgEEcAIA/CwCAhwUAAFkcAIBJHACAh/EDAIHZAwCBmQMAgZEAAGEcAIB0DACAjPkDAMwkAQCHuQMAgfkDADkLAIDMZAIAgskDAIyZAwBpHACAh9EDAI+RAwCB3QYAkfUDAMwABADN7AMAh2UAABkdAIBLCwCAcRwAgHoMAIBFCwCAzBgBAIg5AACBHACAeRwAgMxcAwCMJQAALgoAgMwsAQCx/QAAozkDADEKAIA0CgCAoRwAgKdZAwDMdAMAiAkAAKNRAwCpHACAXQsAgINtDQCnnQAApq0AAKOdAACxDQMAzCgBANULAICntQAAprUAAMkLAIDMMAEAgdUHAMMLAIDMKAEAzwsAgEEeAIBjCwCArYkAAGkLAICAzQEAgd0BAMxEAQDNnB4AhPUBAL0LAIDMWAEAzUwBAIDtAQCB/QEAg7UAAGgMAICM3QEAbgwAgMwIHgCM8QYAzDgBAM08AQBRHgCAiREAAIEFBgBJHgCAYR4AgFkeAIBpHgCAgz0AAIAhAACBOQAAgDkAAIEhAAA5HgCAiRwAgMwoAQCB2QYAbwsAgIH9BgDMJAEAmRwAgJEcAICxHACAgCEBAIE1AQCjBQAAuRwAgMEcAIDJHACAzIwFAM1AAgC3HAMAdQsAgIfNBwDZHACA0RwAgB0dAIDNiAAAzJAAAIzdBQCjhQAAFgoAgMzgAgDhHACAiNUHAIFNAACATQAAUQsAgOkcAIBXCwCAkTkHADcKAICIxQcApQsAgIrJBwDxHACAmz0AAIflBwBxHgCAgYUHAICFBwA6CgCAgvkHAILVBgCDRQAAgMkGAIHdBgCG4QYAewsAgIRRAACJHgCAipUGAIuZBgCIeQAAiZ0GAK0MAICPWQcAjG0HAPkcAIDMgAMAzSQCALARBwA9CgCAgR4AgCEdAIB5HgCAhAsAgICNAACBnQAAzOwDAM3oBAABHQCAigsAgKNJBwCQCwCACR0AgKO9BwARHQCAGwAAgOcHAIALAACApKUHAOsEAICKBQCAAwAAgKhhBwDZDQCAZQAAgMgDAIAbCQCArWkHAIAtAQCBPQEAgl0BAINRAQCEYQEAuAQAgKwEAICHYQEAiK0BAIm1AQCKvQEAjykVALwFAIAdDACAzHgCAM3YBQCB3QEAgXEAAOQLAICC/QEAhBkAACMMAICH7QEAIAwAgMw0BADNMAQA5wsAgJ9pFQAmDACAjMkBAM34BADM8AIAsUkBACEHAICB1QAAoxUBAKCZFQBzCACARgcAgIT1AADMKAQAzSwEAMMIAICveQEAqH0BADENAICqaQEAUgkAgLQlAQC1KQEAowkBAAIMAIDqBgCA7gYAgLIFAQCzPQEAvPUAAL39AAC+2QAAOAgAgLgBAQC5AQEAugEBADwHAIBDBwCAhgwAALOdAwCyiQMAswgAgIC9AwBpBwCAbAcAgBIJAIDkBgCA5wYAgDUIAICJhQMAzOQHAL+hAwAFDACA1wwAgIxlAADN5AwAzCQMAIlBAACIVQAAi0UAAIpFAACFtQMAhLUDAIeVAwCGgQMAAQ0AgAQNAIAHDQCAmCwAABMAAICmyAAAzYwGAMyoBgCFaQAAFwAAgDEAAIBpAACAzPADAAcAAIA1AACA0QwAgLGVAAAlDQCAs5UAALKVAAA1DQCAOA0AgEANAIA7DQCALg0AgHUAAICmBgCAJQAAgJgJAIAdIQCAv1UDAEMNAIAZIQCAFSEAgGEgAIC4bAAAlGUNAJIAAgCcrQEAnaUBAJqJAQCbiQEAmJkBAJmJAQDMIAYAzQQGAMxABgDNXAYAzDwHAM04BwDMvAcAhXUAAIABDwCBDQ8AaSAAgLqZAQCFBQAAcSAAgFkgAIC+hQEAgSkPAIAlDwBlIACAgiEPAIUpAAC0pQEAhREAAG0gAICziQ8AsoUPALHJAQCwAQwAt4EPALbtAQC17QEAtO0BAIFlAQCAZQEAg2EBALi1DwDMPAsAhHkBAIDhDwCB3Q8AdSAAgF0gAIDMyAQAzbgEAIWtAACFFQAAISEAgDkhAIDM6BkAzbQZAKRdAQBGDQCAok0CAKPxDwCgVQEAod0PAH8IAIBuCQCAOwkAgO0eAIBsCQCA9R4AgHcJAIDxHgCAsQgAgJMNAACtHgCA+R4AgITVDACF6Q4AlGkAAIfdDgC1HgCAmbQCAL0eAIDFHgCAsR4AgD0hAIC5HgCAn3QBAMEeAICRGA0AgI0OAIGBDgCGhQ4AlYwDAISJDgCXRAIAghEAAKm4AACA0QAAge0AAMkeAIBJDQCA5R4AgIVZDwCDiQAAoTQNAIFFDgCASQ4A6R4AgKU0AQCFYQ8AzPAUAB0fAIC5xAUAzMgDAM3cAwCA3QAAgcEAACUfAIC/kAUAhREAALHsBwCA9QAAgcEAAKEgAIC1jAYALR8AgLdABgCA3Q4AgekOAMwoAgDNtAIAgM0OAIH5DgCFKQAAg4UBAIB1AQCBsQEAgPEBAIHVAQCpIACANR8AgIUFAACxIACAgJkBAIG9AQCCfQAAk9UBAJThAQCFDQAAmSAAgCEfAICACQAAgRkAACkfAICTrQEAlC0AAKUgAICFDQAAMR8AgIUFAACtIACAOR8AgIUpAACCGQAAhTUAAIDxAACB4QAAtSAAgJ0gAIBBIQCAhQUAAGEhAICDdQEAgO0BAIEpAQDM8AEAzbABAEwNAIBdIQCAWSEAgKMNAIBdHwCAZR8AgIA9AACBDQAAbR8AgHUfAICALQAAgR0AAIIVAABhHwCAzSwBAGkfAIBxHwCAeR8AgIjFAwClIQCAzJACAM28AgCE7QMATw0AgIb5AwCdHwCAgIEDAIH9AwCAPQAAgTUAAIFJAACAQQAAzdwBAIJBAAClHwCAoR8AgKkfAIDNMAEAlJ0DAI0hAIDN8AEAzAwBAIG5AwCAxQMAg6EDAJOlAwCArQAAgdUAAICdAACBqQAAiSEAgFINAICBwQAAgMkAAIC1AACBgQAAhSEAgINpBADMcAMAzbQDAIEhAIDNPAEApg0AgJMBBADNjAIAzPQCAIANAACBNQAAlNkGANEfAIDVHwCA2R8AgMwIAQDNHAEAgREAAIApAACpIQCAghkAAICRAQCBkQEAzWgFAMyUAgDMEAkAzSgWAMxYDgDNeA4AzBQNAM3YCgDMKAwAzYwNAMzgFwDM4AoAzDgLAM30CACFEQAAVQ0AgIBRBwCBUQcA4SAAgM2QDgCFBQAA6SAAgMzYDgDN7AEA8SAAgM0ADgCFGQAAzfAPAM08DgDNVA4AzGgBAM1sAQDZIACAYQgAgJSZBwDMwDsAgGEBAIHZAACFKQAAzWQOAMx4AQDNfAEAga0HAICtBwCFZQAAgp0HAIBRAQCBUQEAlOEHAM3AAACEeQEAk8UHAIZhAQDlIACAiCEBAIUNAADtIACAzRgBAMzYAADNtAAAgN0HAIHNBwCZHwCAhQkAAM0fAID1IACA/R8AgN0gAIAFIACADSAAgBUgAIAJIACAASAAgK0hAIARIACAGSAAgMy4AgDNHAMAgGUAAIF1AACCfQAAHSAAgIUJAACFQQAAASEAgKkNAICAmQYAgSEHAIUZAACDfQAACSEAgIVZAAD9IACA+SAAgIDNAACB2QAAjR4AgIURAACE6QAAlR4AgIblAABBIACAgDUAAIENAACdHgCAhR0AAEkgAIClHgCAhQUAAFEgAICAVQAAgW0AAIJ9AACTRQAAlA0AAIUNAAA5IACAkR4AgIAJAACBEQAAmR4AgIUdAABFIACAoR4AgIUFAABNIACAgOkBAIHxAQCCBQAAqR4AgIUJAACFCQAAVSAAgD0gAICAbQEAgXkBAIIZAACDpQEADSEAgIV1AACFBQAAESEAgAUhAIAhIACAzMgCAM3cAgCsDQCAzR4AgIA5AACBOQAA1R4AgN0eAIDRHgCA2R4AgIAdAACBDQAA4R4AgCUgAICAxQAAgdUAAM3AAADMJAIAgNUAAIHFAACFOQAAg8kAACUhAICvDQCAgNUAAIEJAACFBQAALSEAgP0eAICBIACAgAkAAIERAAAFHwCAk5kAAJS5AAANHwCAhWUAAIU9AACJIACAk10AABUfAICFEQAAzXAFAMx0BQCUATwAkSAAgHkgAIDNKAEAhSAAgI0gAICFGQAAlSAAgH0gAIA1IQCAKSEAgCkgAICFJQAAhTkAAMz4AgDNxAMAzTwBALINAICBlQMAgI0DAM3EAQCCpQMAhVEAAIVJAADMKAEAzSwBAM04AQDMPAEAgGk+AIFpPgBJIQCARSEAgM04PADMVDwAgdE8AJOdPgDMSAEAzcgCAM00AQBNIQCAlLk+AFgNAICAoT4AgaE+AIKhPgCIjTwAVSEAgIWtAACALQAAgSEAAIXVPwCVHwCAgO0AAIHxAACGpQAARR8AgISpAADNJAEAzSgBAE0fAICI+T4AhfE/AFUfAIBJHwCAhcU/AM0wAQDNEAEAzfQGAIDdAQCB6QEAzbwGAM1wBgDM4AYAzVwBAMxoBgDNkAYAzWQGAM14BgDMrAcAzagHAMzoBwDNyAcAgk0/AIP9AgCANQIAgekCAFEfAIBZHwCAgAU9AIV9AQBRIQCALSAAgM0UAQApDgCAge0BAIDhAQDNPAEAgs0BAM0sAQCCdQEAgW0BAIBZAQCAZQEAgcUAAIUfAIDNJAEAzTgBAILxAACB+QAAgFkBAIApAACBcQAAzBgBAM18AQDNLAEAjR8AgIEdAACAHQAAiR8AgJEfAIBxIQCAzSQBAMzkPQDNXA8AzegAAMwMAQCA1QEAgckBAIKZAACD5T8ACR8AgBEfAIAZHwCAMSEAgCMOAIB1IQCAPR8AgDEgAIBBHwCALA4AgIBNPwCBQT8AfR8AgGkhAICBHwCAZSEAgIAlPwCBKT8Ak5E/AIN9AAAmDgCAlEEAAMzYAgDNrAIAbSEAgJNVAACACQAAgR0AALUNAIB9IQCAlEEAAK0fAICAnQAAgaEAAIAdAACBEQAAhKUAALUfAICGpQAAvR8AgIjxAACC0QAAgdkAAIDNAACAJQAAgSkAAIIFAADFHwCAsR8AgLkfAIDBHwCAk7EAAJQRAADJHwCAgB0AAIEVAACAJQAAgS0AAII9AAB5IQCAgO0AAIHRAACCFQAAg4EAAIHQPQA1IACAzCACAM3cAQCFeAIAkSEAgC8OAICZIQCAiRgDAN0fAICALQAAgTUAAIAJAACBbQAA5R8AgMEgAICRsQAAkKkAAJPdOwCSAQQAlaUAAJSVOwDtHwCAlqEAAIUJAACTQQAAySAAgPUfAICFBQAA0SAAgJT1AAC5IACAgLkAAIHdAACC5QAA4R8AgOkfAICF6QAAgAkAAIE1AACFBQAAxSAAgPEfAICFHQAAzSAAgPkfAICFBQAA1SAAgLHBBQCwxQMAvSAAgLLFAwC12QUAtM0DAJ0hAICFOQAAuf0DAKEhAICVIQCAuw0AgM0NAIAXDgCAAR8AgAUOAIDTDQCAzIgCAAsOAIDN4D4AzZABAMwkAQBwDQCAjg0AgEEOAIB9DgCAgLEAAM3UPgDN5D4Agw4AgMy8PgDNuD4AgNEDAIHtAwCC/QMAhmkAAD4OAICFnQMAzTwBADgOAIDM6AIAzTw/AIjlAADNGAEAiQ4AgIhBAAA7DgCAdw4AgM0sAQCVDgCAgNUAAJsOAICG4QAAhukAAEcOAIDNJAEAoQ4AgM0QAQCI0QAAiCkAAMz4AgBNDgCAzfgCAMwkAQCnDgCAhS0DAMygPgDNbD4AgNUDAIHNAwCCAQMAg/kDAMxkAwDNzAIARA4AgM0kAQDMDAIAzQgCAIERAADMnAMAzLA+AM20PgDMxD4AzcA+AMyAPgDNuD4ArQ4AgMyEAgDMmD8AzVA+AMwgPgDNoD4AzQw/AM0wPwDNeD8AzQQ/AIhZAAC/DgCAzfgBAMzEAQBKDgCAxQ4AgMsOAIDMFAIAzAgBAM3IAQCIBQAA0Q4AgNcOAIDMKAIAuQ4AgIgNAACG0QAAgB0BAITNAACI9QAAzDwCAIQ1AQDMRAIAhikBAIAOAICIZQEAhg4AgKdEBQBiDgCAi+0AAIjtAACBDQAAiCUAAIZlAADMcAIAzXQCAMwwAgDN2AUAXA4AgIwOAICAOQAAXw4AgMzgBQB6DgCAzCgBAM0UAQCGJQAAiFUAAAgOAICGhDAAxA0AgIDVBwCG/QcAmA4AgMwkAgCIPQAAng4AgGsOAICIPQAApA4AgMxIAgDNeAIAUA4AgKoOAICXwAUAlnAFAJUYBQCAaQAAk1gFAIE5AACIZQAAkPg8AIZZAACeqAUAhEUAAGgOAIDM1AIAmrQFAIBdAACYrAUAp+wEAIgRAADM2AIAzdwCAKO8BACwDgCAzGACAMIOAIBuDgCAyA4AgK0IBADODgCAq/QEAMwsAgCIBQAA1A4AgLfoAwC2HAQAtSgEAMwAAgCzKAQAi3kAAIh9AACwdAQAhkEAAL6kAwCEdQAAiB0AANoOAIC6TAMAzNwDALj8AwCDqAIAiA0AALwOAICIFQAAh5QCAMw4AgBlDgCAzAQCAIvcAgCPDQAAcQ4AgI8ZAADMIAIAdA4AgI3wAgCIdQAAmCADAJksAwCPDgCAlA0AgMxMAgCWcAMAzCQCAIg9AACSDgCAzCwCAIgFAACzDgCAzCQCAIgNAAC2DgCAh/UAAKjUAwCpxAMA3Q4AgNlgAgDSDwCA1Q8AgNsPAICUNQAAkzEAANloAgDYDwCA2UwCAJQFAADeDwCAlSEAAJQpAABQEACAdBYAgEMXAIDSFgCA2WACADcXAIC12AMAtPADAJQ1AADZWAIAWhcAgJQFAADZVAIAlA0AADEXAIDgdAEAisgAALwVAACIyAAA4IACAIcXAICBoAAApOwCAKTIAgCoXAAAvA0AAJkXAIDghAIAvAUAAJ0XAICk+AIA4PQCALDMAwCV0AAAXRcAgLPgAwCmyAIAp2ACAJLYAABkFwCAvsEAAGsXAICXwQAAchcAgHkXAICAFwCAzXg/AMy8PwC+gA0AixcAgLx4DAC9gA0AuvQMALtUDAC49AwAkhcAgLYXAIC3uAwAuhcAgLWMDACyoAMAs6AMAKEXAICxQAMArnACAK9kAwC4BQMArUgDAKgXAICvFwCAqEQDAKnYAwDaFwCAp9gDAKRoAgCliAMAtjUDALc9AwCSyAIAtT0DAJldAQCYTQEAm2UBAJppAQCdZQEAnGUBAJ+FAQCemQEAh5wCAL6tAACWpQAAl70AAMw0BQDNjDcAzLg4AM2sOACflQEAth0AAJ2ZAQCc9QEAs7EBAK54AgDhFwCAvhcAgJk9AADFFwCAmxkAAJoJAADMFwCA0xcAgOBIAgCeCQAArFwCAK30AgD6FwCA9hcAgP4XAIDoFwCAh2ADAO8XAICvVAIAvhEAAJcFAAACGACA4KwCAAYYAICG+AMAh+wDAOC0AgAOGACAr0gCAK6QAgDgPAIAvg0AAAoYAICXGQAA4NgCAIaEAwCWEQAAvwAMAJ1tAACcYQAAEhgAgLFMAgCzUAIAlQ0AABYYAICGnAMA4MgCALMEAgCCBQAAIhgAgLNQAgCVDQAAJhgAgBoYAIAeGACA4LQCAIaMAwCH3AMAvg0AAJVpAACWeQAAKhgAgLToAgC1UAIAlwUAADIYAIDg1AIAtPQCAL4ZAADgoAIALhgAgODUAgCZjAMAt9QCAIoFAAA2GACAOhgAgIoVAAC3NAIAjx0AAD4YAIBCGACAswUAAEYYAICzBQAAWxgAgJwJAACdCQAATRgAgFQYAICMBQAAYhgAgG0YAIB0GACAexgAgJ9JAACCGACAiRgAgGYYAICQGACAlxgAgNkYAIDPGACA6hgAgOAYAICeGACAg8kBAIH5AQCsGACAsxgAgLoYAIDBGACAyBgAgKUYAICAtAIApYgDAOEIAgCuHQAA8RgAgLwJAACN9QEA9RgAgOEAAgCSlQEA45QQAJNFAACXiQEAhRQAAId4AQCGAAQARjoAgEo6AIBOOgCAUjoAgFY6AICdeQAA74xoAJyhAQBaOgCAXjoAgKKZAABiOgCAZjoAgGo6AIBuOgCAp4kAAHI6AIB2OgCAqUkBAHo6AICsqQAAfjoAgII6AICGOgCAsyUBAIo6AICOOgCAkjoAgLchAQC2OQEAtTEBAJY6AICaOgCAufkAALkRAQC4GQEAnjoAgKI6AICmOgCAqjoAgICwAQCEiAIArjoAgIPIAQCEVAMAhFwEALI6AICEXAUAgN0DAIEtAACCMQAAvjwCALo6AIC+OgCAh4gDAIacBACzLQMAwjoAgMY6AIC+AAQAvhwFALbRAwC12QMAyjoAgLv5AwC68QMAmljTAYTgBwC/xQMAvtkDAL3dAwC83QMAvgAYAKUFAwCmDQMAzjoAgIQcGADSOgCA1joAgKPxAwCsAQMArQEDAK4FAwCvGQMArKQbAq3cGgKqLQMAqyUDAL5MGQC+SBoA2joAgL6AGwC04BoCtdQdArYwHgLvCAIA3joAgOGgAQC6OBoC4/gCALoAAAC9ZBwCvvQcAr8AEAKRBNMBkOT2AeBEAQCSCD4C4joAgOY6AIDqOgCA7joAgL6sHADyOgCA9joAgPo6AID+OgCAAjsAgAY7AIAKOwCAgbBtAICAAQCDHFIAgth3AIUgmgCEkL4AhwjPAIaM5gCJbDcBiOAsAYsYfgGK2BMBjeClAYzwWgGP/OsBjliPAbDVFwCxAWgAso1rALOdawC0SWsAtZVvAA47AIDgcAEAEjsAgBY7AIAaOwCAHjsAgIAZAACBGQAAggUAACI7AIAqOwCAoaUCAKJJBwCjQQcApEEGAKXVGwCm3RsAp8EaAKgBHACp4R8AqkkfAKsBEACs9RMAra0TAK4BFACv+RcAqDEGAKkxBgCqTQYAq0UGAKxNBgCtmQYAro0GAK+FBgCGgAMAhxgDAC47AIAyOwCANjsAgDo7AIA+OwCAQjsAgLhtBwC5dQcAun0HALt1BwC8bQcAvc0HAL75BwC/+QcAsKkGALGFBgCyeQcAs3kHALRpBwC1aQcAtl0HALdVBwC2OgCAs8EGAEY7AIAmOwCAth0GAEo7AIBOOwCAtcEGALppBgC7RQYAUjsAgFY7AIC+qQcAv6kHALypBwC9qQcAo4UGAFo7AIBeOwCAYjsAgGY7AICmWQYApYUGAGo7AICrAQYAqi0GAG47AIByOwCAr+0HAK7tBwCt7QcArO0HAKjBBgCpLQEAqiUBAKs9AQCsJQEArS0BAK4lAQCvlQEAdjsAgHo7AIB+OwCAgjsAgIY7AICCvQAAgb0AAIC9AAC4nQEAua0BALqlAQC7bQAAvHUAAL19AAC+dQAAv20AALD1AQCx/QEAssEBALPBAQC0tQEAtb0BALa1AQC3rQEAijsAgI47AICSOwCAs6EBAJY7AIC1oQEAtqEBAJo7AICGgAEAh8QBALo9AQC7NQEAvBkBAL0ZAQC+fQEAv3UBAKPtAQCeOwCAojsAgKY7AICqOwCApu0BAKXtAQCuOwCAq3kBAKpxAQCyOwCAtjsAgK85AQCuMQEArVUBAKxVAQC6OwCAvjsAgMI7AIDGOwCAyjsAgOGsAQDOOwCA42AGANI7AIDWOwCA2jsAgO9UBgDeOwCA4jsAgL60GgDmOwCA6jsAgO47AICGaBwAh4wDAPI7AID2OwCA+jsAgP47AICAOQAAgTkAAIIFAAACPACACjwAgA48AIASPACAFjwAgKgdAwCpQQMAqkEDAKtBAwCsQQMArUkDAK5xAwCvcQMAhCAdABo8AIAePACAIjwAgCY8AIAqPACALjwAgDI8AIC46QAAufUAALr9AAC78QAAvJEAAL2RAAC+iQAAv4kAALDhAACx4QAAsuEAALPhAAC04QAAte0AALbZAAC32QAA4wwHAOEgBwDhMAEA4wgHADY8AIA6PACAPjwAgEI8AIBGPACASjwAgE48AIBSPACA75gHAFY8AIBaPACA74gHALOJAgBePACAYjwAgL6AGgBmPACAtokCALWJAgBqPACAu2UBALplAQBuPACAcjwAgL9pAQC+ZQEAvXUBALx1AQC3PQYAtj0GALU9BgC0IQYAszUGALI1BgCxAQYAsAkGAL9ZBgC+UQYAvVkGALxNBgC7bQYAunkGALlxBgC4eQYAgJ0AAIGtAACCpQAAejwAgH48AICCPACAhjwAgIo8AICvcQYArmkGAK1tBgCsbQYAq4EGAKqZBgCpkQYAqJkGAAY8AIB2PACAjjwAgKPFHQCSPACApcUdAKbFHQCWPACAhgADAIdkAwCqKR4AqykeAKw5HgCtOR4ArikeAK8lHgCzOR4AmjwAgJ48AICiPACApjwAgLb9HgC1/R4AqjwAgLvZHgC60R4ArjwAgLI8AIC/aR8AvmEfAL1pHwC8wR4AqPEeAKnxHgCq8R4Aq/EeAKw1HgCtPR4ArjUeAK8tHgC2PACAujwAgL48AIDCPACAxjwAgMo8AIDOPACA0jwAgLjlHwC57R8AuuUfALv5HwC86R8AvZEfAL6RHwC/jR8AsFUeALFdHgCyVR4As/0fALTlHwC17R8AtuUfALfdHwCjeR8A1jwAgNo8AIDePACA4jwAgKa9HwClvR8A5jwAgKuZHwCqkR8AhogAAIdMAQCvKR4AriEeAK0pHgCsgR8AgEkAAIFJAACCWQAAs5keAOo8AIC1iR4AtlEBAO48AIDyPACA9jwAgLotAQC7JQEAvD0BAL0lAQC+JQEAvxUBAKhNHgCpVR4Aql0eAKtVHgCsTR4ArZ0BAK6JAQCvgQEAhKwBAPo8AID+PACAAj0AgAY9AIAKPQCADj0AgBI9AIC4ZQEAuW0BALplAQC7fQEAvGUBAL1tAQC+ZQEAv9kAALClAQCxrQEAsqUBALO9AQC0rQEAtZ0BALaVAQC3XQEAo9UdABY9AIAaPQCAHj0AgCI9AICmHQIApcUdACY9AICraQIAqmECACo9AIAuPQCAr1kCAK5pAgCtaQIArHECADI9AIA2PQCAOj0AgD49AIBCPQCARj0AgEo9AIBOPQCAgDkAAIE5AACCBQAAUj0AgFo9AIBePQCAh0ADAIZcBACETAQAYj0AgGY9AICEBAUA4yABAGo9AIDhqAEAbj0AgO+UGgByPQCAdj0AgHo9AIB+PQCAgj0AgIY9AICKPQCAs6EDAI49AICSPQCAlj0AgJo9AIC2fQMAtX0DAJ49AIC7WQMAulEDAKI9AICmPQCAv/0AAL79AAC9/QAAvEEDAKhRAgCpWQIAqmkCAKtpAgCstQIArb0CAK61AgCvrQIAhKgHAKo9AICuPQCAsj0AgIKpAAC2PQCAgKkAAIGpAAC4aQEAuWkBALoJAQC7CQEAvBkBAL0ZAQC+CQEAvwkBALDVAgCx3QIAstUCALNpAQC0eQEAtXkBALZpAQC3YQEA4bgBAOHUHwDjOB8A4wwbALo9AIC+PQCAwj0AgMo9AIDOPQCA0j0AgNY9AIDaPQCAvjwJAN49AIDvhBsA74QbAKOhAgDiPQCAhugEAIe8BQDmPQCApn0CAKV9AgDqPQCAq1kCAKpRAgDuPQCA8j0AgK/9AQCu/QEArf0BAKxBAgCzhQYAxj0AgPY9AID6PQCA/j0AgLaJBgC1jQYAAj4AgLuRBgC6iQYABj4AgAo+AIC/9QYAvokGAL2BBgC8iQYADj4AgBI+AIAWPgCAGj4AgB4+AIAiPgCAJj4AgO+EHQAqPgCA4QAEAC4+AIDj/AQAgBEAAIEdAACCBQAAMj4AgKjxBgCp8QYAqg0GAKsFBgCsBQYArQkGAK49BgCvNQYANj4AgDo+AICGiAAAhxADAD4+AIBCPgCARj4AgEo+AIC4EQYAuRkGALohBgC7IQYAvPUHAL39BwC+9QcAv+kHALBNBgCxVQYAsl0GALNVBgC0TQYAtTEGALYxBgC3MQYAo4UHAE4+AIBSPgCAVj4AgFo+AICmiQcApY0HAF4+AICrkQcAqokHAGI+AIBmPgCAr/UHAK6JBwCtgQcArIkHAGo+AICz4QYAbj4AgHI+AIC25QYAdj4AgHo+AIC18QYAur0GALuNBgB+PgCAgj4AgL59AQC/ZQEAvJUGAL11AQCoHQYAqSUGAKotBgCrJQYArD0GAK0hBgCuXQYAr00GAIY+AICKPgCAjj4AgJI+AICWPgCAgrkDAIGxAwCAuQMAuO0BALmFAQC6jQEAu4UBALydAQC9hQEAvo0BAL+FAQCwPQYAsQ0GALIFBgCz5QEAtP0BALXlAQC25QEAt9UBAKOlBQCaPgCAnj4AgKI+AICqPgCApqEFAKW1BQCuPgCAq8kFAKr5BQCGCAwAhxwDAK8hAgCuOQIArTECAKzRBQCyPgCAs/ECALY+AIC6PgCAtlUDAL4+AIDCPgCAteECALpxAwC7eQMAxj4AgMo+AIC+MQMAvz0DALxRAwC9UQMAqCUCAKk1AgCqPQIAqzUCAKwtAgCtkQMArpEDAK+RAwDOPgCA0j4AgNY+AIDaPgCArAAAAN4+AIDiPgCA5j4AgLiZAwC5rQMAuqUDALttAwC8dQMAvX0DAL51AwC/bQMAsPEDALH5AwCywQMAs8EDALSxAwC1vQMAtrUDALepAwDqPgCA7j4AgPI+AID2PgCA+j4AgP4+AIACPwCA76gaAL5oDADhlAEABj8AgOMcBgCADQAAgXEAAIJxAAAKPwCAo/UDAA4/AIASPwCAhEwCABo/AICmUQIApeUDAB4/AICrfQIAqnUCAIbIDACHLA0ArzkCAK41AgCtVQIArFUCAOFQBgAiPwCA4xQHAITADAAmPwCAKj8AgC4/AIAyPwCANj8AgDo/AIA+PwCAQj8AgEY/AIBKPwCA73gbAL74DwBOPwCAUj8AgFY/AICzjQEAWj8AgLWZAQC2jQEAXj8AgFY9AIBiPwCAuoUBALtNAQC8VQEAvV0BAL5VAQC/SQEAo0EOABY/AIBmPwCAaj8AgG4/AICmQQ4ApVUOAHI/AICrgQ4AqkkOAHY/AIB6PwCAr4UOAK6ZDgCtkQ4ArJkOAIBtAACBCQAAgh0AAH4/AIDvGAkAgj8AgIY/AICKPwCA4zwNAI4/AIDhWAwAkj8AgIbQAACHvAMAlj8AgJo/AICokQ4AqZkOAKrJDgCrxQ4ArN0OAK3BDgCuwQ4Ar/UOAIToAACePwCAoj8AgKY/AICqPwCArj8AgLI/AIC2PwCAuMEPALnBDwC6wQ8Au8EPALzBDwC9wQ8AvsEPAL/1DwCwjQ4AsUUOALJNDgCzRQ4AtF0OALVBDgC2QQ4At0EOAKhRDgCpWQ4Aqo0OAKudDgCshQ4ArY0OAK6FDgCvvQ4Auj8AgL4/AIDCPwCAxj8AgMo/AIDOPwCA0j8AgNY/AIC4kQ4AuZkOALqtDgC7RQEAvF0BAL1FAQC+RQEAv3UBALDFDgCxzQ4AssUOALPdDgC0xQ4AtbUOALa9DgC3tQ4AswUOANo/AIDePwCA4j8AgOY/AIC2DQ4AtQ0OAOo/AIC7CQ4AugEOAO4/AIDyPwCAv3EOAL4BDgC9CQ4AvBEOAIJtAACjQQ4AgFUAAIFlAACmSQ4A+j8AgP4/AIClSQ4AqkUOAKtNDgCGSAAAh3gAAK5FDgCvNQ4ArFUOAK1NDgCoXQIAqWECAKplAgCrdQIArG0CAK2xAgCusQIAr7ECAITsBAACQACABkAAgApAAIAOQACAEkAAgBZAAIAaQACAuHEDALlxAwC6cQMAu3EDALzVAwC93QMAvtUDAL/NAwCw0QIAsdECALLRAgCz0QIAtFEDALVRAwC2UQMAt1EDAB5AAICz6QIAIkAAgL6ABAC2NQIAJkAAgCpAAIC14QIAuhECALsRAgAuQACAMkAAgL6RAwC/kQMAvAECAL0BAgA2QACAOkAAgKOlAgA+QACApa0CAEJAAIBGQACApnkCAEpAAIBOQACAq10CAKpdAgCtTQIArE0CAK/dAwCu3QMAqNUCAKndAgCqLQEAqyUBAKw9AQCtJQEAri0BAK8lAQBSQACAVkAAgFpAAIBeQACAYkAAgGpAAIBuQACAckAAgLiFAQC5iQEAup0BALuVAQC8sQEAvbEBAL55AAC/eQAAsF0BALHlAQCy4QEAs/kBALTpAQC13QEAttUBALe9AQDh8A4AdkAAgOMUDgB6QACAgb0AAIC9AAB+QACAgq0AAIYABACH7AUAgkAAgIZAAICKQACAjkAAgO9gDgCSQACAlkAAgJpAAICFXH0AnkAAgKJAAIDjZAEApkAAgOG0AQCqQACA76AOAK5AAICmPgCAhPgFALJAAIC2QACAukAAgLMlBgBmQACAvkAAgMJAAIDGQACAtiUGALU1BgDKQACAu6EGALoZBgDOQACA0kAAgL+ZBgC+rQYAva0GALy1BgCCbQAA7zAEAIBVAACBZQAAvlwDANZAAICG+AAAh2wDANpAAIDeQACA4kAAgOZAAIDqQACA40QEAO5AAIDhjAcAo6UGAPJAAID2QACA+kAAgP5AAICmpQYApbUGAAJBAICrIQYAqpkGAAZBAIAKQQCArxkGAK4tBgCtLQYArDUGAA5BAICz+QcAEkEAgBZBAIC2SQcAGkEAgB5BAIC1UQcAulEHALtRBwAiQQCAJkEAgL41BwC/OQcAvEUHAL09BwCoNQYAqT0GAKo1BgCriQYArJ0GAK2NBgCusQYAr7EGACpBAIAuQQCAMkEAgDZBAICADQAAgbEAAIKxAAA6QQCAuKEGALmtBgC6vQYAu7UGALytBgC9XQEAvlUBAL9NAQCw0QYAsdEGALLVBgCzrQYAtLUGALW5BgC2qQYAt6UGAKO9BgA+QQCAQkEAgISEAgC+kAEApg0GAKUVBgBKQQCAqxUGAKoVBgCGCAAAh3wBAK99BgCucQYArXkGAKwBBgBOQQCAs60BAFJBAIBWQQCAtqkBAFpBAIBeQQCAta0BALptAQC7dQEAYkEAgGZBAIC+XQEAvzUBALxlAQC9VQEAqGECAKlhAgCqYQIAq2ECAKxhAgCtbQIArp0CAK+VAgBqQQCAbkEAgHJBAIB2QQCAekEAgH5BAICCQQCAhkEAgLiVAgC5nQIAuqECALuhAgC8cQMAvXEDAL5xAwC/cQMAsO0CALH1AgCy9QIAs8UCALTdAgC1tQIAtrECALexAgCKQQCAjkEAgJJBAICj5QIAlkEAgKXlAgCm4QIAmkEAgJ5BAICiQQCAqiUCAKs9AgCsLQIArR0CAK4VAgCvfQIApkEAgKpBAICuQQCAhEB8AIAVAACBHQAAggUAALJBAIC+7HwAukEAgIZIfQCHCAMAvkEAgMJBAIDGQQCAykEAgKidAgCpxQIAqsECAKvBAgCsxQIArc0CAK7xAgCv8QIAzkEAgNJBAIDWQQCA2kEAgMkAAADeQQCA4kEAgOZBAIC4wQEAucEBALrBAQC73QEAvM0BAL31AQC+/QEAv50BALBBAQCxQQEAskEBALNBAQC0QQEAtUEBALZBAQC3QQEA4TgGAOpBAIDjaAYA7kEAgPJBAID2QQCA+kEAgISUfQC+rHwA/kEAgAJCAIAGQgCAvrh/AApCAIDvEAEADkIAgBJCAIAWQgCAGkIAgB5CAIDhkAEAIkIAgONEAAAqQgCAgS0AAIAtAADvgAAAgjkAAC5CAIAyQgCA9j8AgDZCAIDhsH8AtkEAgOPUfAA6QgCAJkIAgD5CAICGuAAAh9QCAEJCAIBGQgCASkIAgE5CAIBSQgCAVkIAgO8gfABaQgCAs4l9AF5CAIBiQgCAZkIAgGpCAIC2jX0AtY19AG5CAIC7RX4AukV+AHJCAIB2QgCAv0V+AL5FfgC9VX4AvFV+AKNJfQB6QgCAfkIAgIJCAICGQgCApk19AKVNfQCKQgCAq4V+AKqFfgCOQgCAkkIAgK+FfgCuhX4ArZV+AKyVfgCCbQAAszF+AIBVAACBZQAAtvF/AITcAwCWQgCAtSF+ALrNfwC70X8AhgAEAIfUAAC+dX8Av3l/ALzBfwC9wX8AqOV/AKn1fwCq/X8Aq/V/AKztfwCtNX4Arj1+AK81fgCaQgCAnkIAgKJCAICmQgCAqkIAgK5CAICyQgCAtkIAgLjZfgC54X4AuuF+ALvhfgC85X4Avel+AL6ZfgC/mX4AsE1+ALFRfgCyUX4As1F+ALT1fgC1+X4Atul+ALfpfgCjdX8AukIAgL5CAIDCQgCAxkIAgKa1fgClZX8AykIAgKuVfgCqiX4AzkIAgNJCAICvPX4ArjF+AK2FfgCshX4A1kIAgLMxfgDaQgCA3kIAgLbFAQDiQgCA5kIAgLXRAQC6yQEAu8kBAOpCAIDuQgCAvs0BAL+xAQC8yQEAvckBAKjdfQCp9X0Aqv19AKvxfQCsHQIArQECAK45AgCvOQIA8kIAgPZCAID6QgCA/kIAgIIFAAACQwCAgBEAAIERAAC4EQIAuRkCALohAgC7IQIAvNUCAL3dAgC+1QIAv80CALBJAgCxSQIAslkCALNZAgC0TQIAtTECALYxAgC3MQIAvgADAKNxfQCEiAIAvoAEAKaFAgAKQwCADkMAgKWRAgCqiQIAq4kCAIYoBACHDAMAro0CAK/xAgCsiQIArYkCABJDAICEyAMAhcwFALPlAwAWQwCAteUDALbtAwAaQwCAHkMAgCJDAIC6bQMAu2UDALx9AwC9ZQMAvmUDAL9VAwAmQwCAKkMAgL8ABACjJQIALkMAgKUlAgCmLQIAMkMAgDZDAIA6QwCAqq0CAKulAgCsvQIAraUCAK6lAgCvlQIAPkMAgEJDAIBGQwCASkMAgE5DAIDjzAMAUkMAgOGsAQBWQwCA7xwDAFpDAIBeQwCAYkMAgGZDAIBqQwCAbkMAgOFwfwBGQQCA4wR+AHJDAIB6QwCA4ZQBAH5DAIDjWAEAgNkAAIHZAACCJQAA7+R+AIJDAICGQwCA7+B+AIpDAICzAQEAjkMAgIboBwCHLAQAkkMAgLY1AQC1BQEAlkMAgLvxAAC64QAAmkMAgJ5DAIC/sQAAvtEAAL3ZAAC84QAABkMAgHZDAICiQwCApkMAgKEBBACgEQQAoxkAAKLFBACotQYAqb0GAKrpBgCr/QYArO0GAK3VBgCu3QYArz0HALBFBwCxVQcAslUHALNtBwC0dQcAtRUHALYdBwC3FQcAuC0HALk1BwC6MQcAuw0HALwZBwC9GQcAvgkHAL8JBwCjQQYAqkMAgK5DAICyQwCAtkMAgKZ1BgClRQYAukMAgKuxBwCqoQcAj8ltAL5DAICv8QcArpEHAK2ZBwCsoQcAld11AJTBdACXzXAAli1zAJFdaACQVWgAk9l0AJJNaQCd5XgAnB17AJ9tBwCeuXgAmR1/AJhVcACboXwAmvl8AIJhbACDhWkAwkMAgMZDAICGEXUAhxF1AISVaQCFjWgAij10AIvFcgDKQwCAzkMAgI7dfgCPMX0AjD1xAI2dcQCSGX0Ak716ANJDAIDvkAkAltUGAJdRBQCUXXkAlQl5AJpxBQCbvQUA1kMAgNpDAIDeQwCA4agFAJx5AQDjuAgAoYUBAOJDAICjqQ0AogEMAKUBCACkOQ0Ap6kJAKa9CQCppRUAqAEUAKsBFACq/RUArbkRAKyxEQCvARwArqEQALH9HACw5R0As+kZALIBGAC1ASQAtH0ZAIQUAAC+FAAAgI0AAIGVAACCbQAA6kMAgIZQDwCHZAAA7kMAgPJDAIC61QcAu90HALjBBwC5wQcAvjEEAL8xBAC88QcAvfEHALKtBwCztQcAsK0HALGlBwC2nQcAt/UHALSlBwC1lQcAqmkHAKtpBwCoaQcAqWkHAK5pBwCvaQcArGkHAK1pBwD2QwCA+kMAgP5DAIACRACABkQAgApEAIAORACAEkQAgKgRBQCpHQUAqjkFAKs5BQCsLQUArVEFAK5JBQCvQQUAFkQAgBpEAIAeRACAIkQAgCZEAIAqRACALkQAgDJEAIC4XQIAuWkCALrBAwC7wQMAvPkDAL35AwC+kQMAv7UDALAJBQCxCQUAsuECALPhAgC0dQIAtX0CALZ1AgC3bQIAs7EEAIQAAgC+BA0ANkQAgDpEAIC20QQAtaUEAD5EAIC7zQQAus0EAEJEAIBGRACAv7kDAL6xAwC9NQMAvDUDAEpEAICj9QQATkQAgFJEAICmlQQAWkQAgF5EAICl4QQAqokEAKuJBACHqA0AhswMAK71AwCv/QMArHEDAK1xAwDhUAYA4TQHAONAAADjWAcAgNEAAIHdAACC1QAAYkQAgGZEAIBqRACAbkQAgHJEAIB2RACAekQAgO+cAADvyAcAfkQAgIJEAICzNQIAhkQAgLW1AQCKRACAjkQAgLa1AQC+7AwAkkQAgLuRAQC6mQEAvVEBALyJAQC/UQEAvlkBAKjtDQCp/Q0AqvUNAKttDgCsdQ4ArX0OAK51DgCvbQ4AVkQAgJZEAICaRACAnkQAgKJEAICmRACAqkQAgK5EAIC49Q4Auf0OALr1DgC7QQ8AvEEPAL1JDwC+cQ8Av3EPALAVDgCxHQ4AshUOALPNDgC01Q4Atd0OALbVDgC3zQ4Ao30NALJEAIC2RACAukQAgL5EAICm/Q4Apf0OAMJEAICr2Q4AqtEOAISoAgDGRACArxkOAK4RDgCtGQ4ArMEOAIBNAACBVQAAglUAALNRDwDKRACAtXEPALZxDwDORACAhuAAAIcEAwC6XQ8Auy0PALw1DwC9OQ8Avi0PAL8lDwCoVQ4AqV0OAKqVDgCrrQ4ArLUOAK29DgCutQ4Ar60OANJEAIDWRACA2kQAgN5EAIDiRACA5kQAgOpEAIDuRACAuGkBALlpAQC6eQEAu3kBALxpAQC9aQEAvt0BAL/VAQCw1Q4AsaUOALKtDgCzoQ4AtKUOALWtDgC2nQ4At1kBAKMdDgDyRACA9kQAgOZDAID6RACApj0OAKU9DgD+RACAq2EOAKoRDgACRQCABkUAgK9pDgCuYQ4ArXUOAKx5DgAKRQCADkUAgBJFAIAWRQCAGkUAgB5FAIAiRQCAJkUAgIANAACBFQAAgh0AACpFAIAuRQCAMkUAgIR4AQC+FAAA4xQPADpFAIDh4A0AhAADAIawBACHFAMAPkUAgEJFAIBGRQCASkUAgE5FAIBSRQCA78APAFZFAIBaRQCAXkUAgGJFAIBmRQCAakUAgLNtAwBuRQCAtX0DALZ1AwByRQCAdkUAgHpFAIC6UQMAu1EDALz1AwC9/QMAvukDAL/hAwB+RQCAgkUAgIZFAICKRQCAjkUAgJJFAICWRQCAmkUAgKhxAgCpeQIAqokDAKuJAwCsmQMArZkDAK6JAwCviQMAsPkDALH5AwCyTQMAs0UDALRBAwC1SQMAtnEDALdxAwC4IQMAuSEDALohAwC7IQMAvCEDAL0hAwC+IQMAvyEDAICdAQCBEQAAghEAAIQEBQDvFAAAnkUAgKJFAIC+EAUA48gAAKpFAIDh0AEArkUAgLJFAIC2RQCAukUAgL5FAICqeQIAq3kCAIboBACHYAUArsECAK/JAgCs3QIArdUCAMJFAICjRQIAxkUAgMpFAICmXQIAzkUAgNJFAIClVQIA1kUAgNpFAIDeRQCA4kUAgOZFAIDqRQCA7kUAgO+EDgC+rAQA4dAOAPJFAIDjFAEA9kUAgPpFAID+RQCAAkYAgLPdAQAGRgCACkYAgA5GAIASRgCAtv0BALX9AQAaRgCAu90BALrdAQCE4AQAHkYAgL+hAQC+vQEAvb0BALy9AQCoBQYAqR0GAKoVBgCrLQYArDUGAK09BgCuNQYArykGAKZFAICC9QcAgeUHAIDlBwAWRgCAIkYAgIYcAACHsAMAuCUGALnFBgC6zQYAu8UGALzdBgC9xQYAvs0GAL/FBgCwWQYAsVkGALIpBgCzKQYAtDkGALUlBgC2JQYAtx0GAKOdBgAmRgCAKkYAgC5GAIAyRgCApr0GAKW9BgA2RgCAq50GAKqdBgA6RgCAPkYAgK/hBgCu/QYArf0GAKz9BgBCRgCAs/UHAEZGAIBKRgCAtu0HAE5GAIBSRgCAteUHALqNBwC7kQcAVkYAgFpGAIC+dQcAv30HALyBBwC9fQcAqCUGAKkpBgCqOQYAqzkGAKwpBgCtKQYArnkGAK91BgBeRgCAYkYAgGZGAIBqRgCAbkYAgHJGAIB2RgCAekYAgLjVBgC53QYAuuEGALv9BgC85QYAve0GAL7lBgC/mQYAsA0GALERBgCyEQYAs+0GALT1BgC1/QYAtvUGALftBgCjsQYAgi0AAIEVAACAsQAANkUAgKapBgCloQYAfkYAgKvVBgCqyQYAgkYAgL5oAQCvOQYArjEGAK05BgCsxQYAikYAgLPxAQCGaAAAh3wBALZdAQCORgCAkkYAgLVVAQC6SQEAu0kBAJZGAICaRgCAvj0BAL8hAQC8OQEAvTUBAJ5GAICiRgCAhAQDAL6AHACmRgCA4RwGAKpGAIDjAAYAvwguAK5GAICyRgCA78gHALZGAIC6RgCAvkYAgMJGAIDGRgCAykYAgKN9AgDORgCApdkCANJGAIDWRgCAptECANpGAIDeRgCAq8UCAKrFAgCtuQIArLUCAK+tAgCusQIAqW0FAKhZBQCrDQIAqrkCAK0dAgCsHQIArwUCAK4NAgC+aB0A4kYAgOZGAIDqRgCAgB0AAIEJAACCmQEA7kYAgLnhAwC4KQIAu+EDALrpAwC94QMAvPkDAL/hAwC+6QMAsU0CALBNAgCzIQIAsi0CALUlAgC0OQIAtxECALYlAgCowQIAqdECAKrRAgCr5QIArP0CAK0VAQCuHQEArw0BAPJGAID6RgCA/kYAgAJHAIAGRwCACkcAgA5HAIASRwCAuAUBALkJAQC6HQEAuxUBALwxAQC9MQEAvv0BAL/1AQCweQEAsUEBALJBAQCzXQEAtEUBALVNAQC2RQEAtz0BAIagHQCHxB0AFkcAgO/YAAAaRwCAHkcAgCJHAIDvxAYAhGwcAOH0BgAmRwCA47AGACpHAIDhlAEALkcAgONEBgCzGQIAMkcAgDZHAIA6RwCAhewsALbVAQC1NQIAPkcAgLvFAQC6/QEAQkcAgEZHAIC/yQEAvsEBAL3JAQC81QEAo9kdAPZGAIBKRwCATkcAgFJHAICmFR4ApfUdAFZHAICrBR4Aqj0eAFpHAIBeRwCArwkeAK4BHgCtCR4ArBUeAIBpAACBaQAAggUAAGJHAIBmRwCAakcAgIcQAwCGfAMAbkcAgHJHAIB2RwCAekcAgH5HAICCRwCAhkcAgIpHAICopR8Aqa0fAKqlHwCrvR8ArKUfAK2tHwCupR8ArxUfAI5HAICSRwCAlkcAgJpHAICeRwCAokcAgKZHAICqRwCAuA0fALkZHwC6IR8AuyEfALzZAAC92QAAvskAAL/BAACwcR8AsXEfALJxHwCzRR8AtEEfALVNHwC2PR8AtzUfALMtHgCuRwCAskcAgLZHAIC6RwCAti0eALUtHgC+RwCAu7UeALq1HgDCRwCAxkcAgL+JHgC+hR4AvZEeALylHgCCKQAAo2keAIAdAACBFQAApmkeAMpHAIDORwCApWkeAKrxHgCr8R4A0kcAgITgAQCuwR4Ar80eAKzhHgCt1R4AqNUBAKnlAQCq7QEAq+UBAKz9AQCt5QEAru0BAK/lAQC+oAEAhkYAgNZHAIDaRwCAhhAAAId0AQDeRwCA4kcAgLh9AQC5wQAAusEAALvBAAC8wQAAvckAAL7xAAC/8QAAsJ0BALFFAQCyTQEAs0UBALRdAQC1RQEAtk0BALdFAQDmRwCA6kcAgO5HAIDyRwCA9kcAgO80AgDv7B4A+kcAgOHwHQDj4AIA4zAeAOGEAQD+RwCAAkgAgAZIAIAKSACAsyUCAJQAAAAOSACAEkgAgBZIAIC2JQIAtTUCABpIAIC7wQIAuhkCAB5IAIAiSACAv8ECAL7ZAgC90QIAvNkCACZIAIAqSACALkgAgKPpAgAySACApfkCAKbpAgA2SACAOkgAgD5IAICq1QIAqw0CAKwVAgCtHQIArhUCAK8NAgCAYQAAgWEAAIIFAABCSACASkgAgIQABAC+FAQATkgAgIbABACHUAMAUkgAgFZIAIBaSACAXkgAgGJIAIBmSACAqK0CAKm9AgCqtQIAqw0BAKwVAQCtHQEArhUBAK8NAQCE7AQAakgAgG5IAIBySACAdkgAgHpIAIB+SACAgkgAgLgdAQC5LQEAuiUBALvNAQC81QEAvd0BAL7JAQC/wQEAsH0BALFVAQCyXQEAs1UBALRNAQC1PQEAtjUBALctAQDhGB4AhkgAgOM4HgCKSACAjkgAgJJIAICWSACAmkgAgJ5IAICiSACAvmAEAKZIAICBdQAAgHUAAO/gHwCCbQAAqkgAgK5IAICG6AQAh3wFALJIAIDhkAEAukgAgOOgAAC+SACAwkgAgMZIAIDvtAAAykgAgM5IAIDSSACA1kgAgLUFBgBGSACAtkgAgLYFBgDaSACA3kgAgLOlBQDiSACAvRkGALwRBgC/YQYAvhEGAOZIAIDqSACAuwkGALohBgCj/QUA7kgAgPJIAID2SACA+kgAgKZdBgClXQYA/kgAgKtRBgCqeQYAAkkAgAZJAICvOQYArkkGAK1BBgCsSQYAqFEGAKlZBgCqYQYAq2EGAKxhBgCtYQYArmEGAK9hBgAKSQCADkkAgBJJAIAWSQCAgA0AAIGxAQCCsQEAGkkAgLhNBwC5VQcAul0HALtVBwC8TQcAvXUHAL59BwC/cQcAsMUHALHNBwCyxQcAs90HALTFBwC1zQcAtsUHALd5BwCz6QcAHkkAgCJJAICEwAEAvtgBALbhBwC16QcAJkkAgLsJBgC6AQYAhogAAIesAQC/CQYAvgEGAL0JBgC8EQYAKkkAgKOtBwAuSQCAMkkAgKalBwA2SQCAOkkAgKWtBwCqRQYAq00GAD5JAIBCSQCArkUGAK9NBgCsVQYArU0GAKhZBgCpZQYAqm0GAKtlBgCsYQYArWEGAK5hBgCvYQYAhKwBAEZJAIBKSQCATkkAgFJJAIBWSQCAWkkAgF5JAIC4kQEAuZkBALqhAQC7oQEAvHEBAL1xAQC+cQEAv3EBALDxAQCx8QEAsvUBALPdAQC0xQEAtbEBALaxAQC3sQEAs+UFAGJJAIBmSQCAakkAgG5JAIC24QUAtekFAHJJAIC7NQIAujUCAHZJAIB6SQCAv3UCAL4BAgC9CQIAvCECAH5JAICjoQUAgkkAgIZJAICmpQUAikkAgI5JAIClrQUAqnECAKtxAgCSSQCAvigDAK5FAgCvMQIArGUCAK1NAgCA1QAAgd0AAILhAACaSQCA4yABAJ5JAIDhqAEAokkAgO80AgCmSQCAhggMAIdoAwCsAAAAqkkAgK5JAICySQCAs40DALZJAIC6SQCAhIAMAL5JAIC2vQMAtYEDAMJJAIC7TQMAuk0DAMZJAIDKSQCAv00DAL5NAwC9TQMAvE0DAKhBAgCpTQIAqkUCAKtZAgCsSQIArX0CAK51AgCvuQIAvmgNAM5JAIDSSQCA1kkAgIRsDADaSQCA3kkAgOJJAIC4TQEAuVUBALpVAQC7ZQEAvH0BAL0VAQC+EQEAvxEBALDJAgCxyQIAstkCALPZAgC0yQIAtckCALZ9AQC3dQEA4XgHAOOYAADjuAYA4VwGAOZJAIDqSQCA7kkAgPJJAID2SQCA+kkAgP5JAIACSgCA7AAAAO9cAADv6AYACkoAgIFpAACAYQAAo4UCAIJhAACliQIADkoAgBJKAICmtQIAhkAMAIfEDACrRQIAqkUCAK1FAgCsRQIAr0UCAK5FAgCojQ4AqZEOAKqVDgCrqQ4ArKUOAK2tDgCupQ4Ar9kOAAZKAIAWSgCAGkoAgB5KAIAiSgCAJkoAgCpKAIAuSgCAuHUPALl9DwC6dQ8Au90PALzFDwC9zQ8AvsUPAL/9DwCwqQ4AsbUOALK1DgCzhQ4AtJ0OALVRDwC2UQ8At1EPALMdDgAySgCANkoAgDpKAIA+SgCAti0OALUtDgBCSgCAu3EOALptDgBGSgCASkoAgL+VDwC+WQ4AvVEOALxhDgBOSgCAo1kOAFJKAIBWSgCApmkOAFpKAIBeSgCApWkOAKopDgCrNQ4AYkoAgGZKAICuHQ4Ar9EPAKwlDgCtFQ4AqL0OAKnRDgCq0Q4AqykBAKw5AQCtOQEArikBAK8pAQCADQAAgRUAAIIdAABqSgCAbkoAgHJKAIC+dAIAdkoAgLjtAQC5hQEAuoEBALuBAQC8hQEAvY0BAL6xAQC/sQEAsFkBALFZAQCy7QEAs+UBALT9AQC15QEAtuUBALfVAQB6SgCAtqkBALWhAQB+SgCAs0kOAIJKAICGOAAAh9wBAL8xAQC+KQEAvSEBALwpAQC7jQEAuo0BAJZJAICGSgCAoxkOAIpKAICOSgCAkkoAgJZKAICm+QEApfEBAJpKAICr3QEAqt0BAJ5KAICiSgCAr2EBAK55AQCtcQEArHkBAKZKAIDv3A8AqkoAgK5KAICySgCAtkoAgLpKAIC+SgCAwkoAgMZKAIDKSgCAzkoAgNJKAIDj6A4A1koAgOGMDgCAEQAAgREAAIIRAACEQAIA2koAgN5KAIDiSgCAvhADAIbABACHRAMA6koAgO5KAIDySgCA9koAgPpKAID+SgCA7yQCAAJLAIAGSwCACksAgA5LAIASSwCAFksAgBpLAICE7AQAHksAgCJLAIAmSwCA4+wCACpLAIDhOAEALksAgLNVAwAySwCANksAgDpLAIA+SwCAth0DALUdAwBCSwCAuwkDALo5AwBGSwCASksAgL/9AAC+/QAAvfkAALwRAwCogQIAqYkCAKqdAgCrsQIArNUCAK3dAgCu1QIAr80CAIDNAQCBCQAAghkAAE5LAIBSSwCAWksAgL5wBQBeSwCAuFkBALlZAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9lAQCwvQIAsY0CALKFAgCzbQEAtHkBALV5AQC2aQEAt2kBAIYgBACHCAUAYksAgGZLAIBqSwCAbksAgHJLAIDvXAAAhOwEAOFcDgB2SwCA44wOAHpLAIB+SwCAgksAgIZLAICjVQIAiksAgI5LAICSSwCAlksAgKYdAgClHQIAmksAgKsJAgCqOQIAnksAgKJLAICv/QEArv0BAK35AQCsEQIAqGkGAKlpBgCqeQYAq3kGAKxpBgCtaQYArp0GAK+VBgBWSwCApksAgKpLAICuSwCAsksAgLZLAIC6SwCAvksAgLj1BgC5+QYAuo0GALuFBgC8nQYAvYUGAL6FBgC/tQYAsO0GALH1BgCy/QYAs/UGALTtBgC10QYAttEGALfRBgCz8QYAghUAAIG1AACAtQAAwksAgLbpBgC14QYAvtQDALsxBgC6KQYAxksAgMpLAIC/FQYAvikGAL0hBgC8KQYAzksAgKO1BgCGyAAAh8gAAKatBgDSSwCA1ksAgKWlBgCqbQYAq3UGANpLAIDeSwCArm0GAK9RBgCsbQYArWUGAKg1BgCpOQYAqoEGAKuBBgCsgQYArYEGAK6BBgCvtQYA4ksAgOZLAIDqSwCA7ksAgPJLAID2SwCA+ksAgP5LAIC4nQYAua0GALqlBgC7aQEAvHkBAL15AQC+aQEAv2kBALDRBgCx0QYAstEGALPRBgC0tQYAtb0GALa1BgC3rQYAswkGAAJMAIAGTACACkwAgA5MAIC2AQYAtQkGABJMAIC7FQYAuhUGABZMAIAaTACAv3kGAL5xBgC9BQYAvAUGAB5MAICjTQYAIkwAgOZKAICmRQYAJkwAgCpMAIClTQYAqlEGAKtRBgAuTACAMkwAgK41BgCvPQYArEEGAK1BBgCB6QMAgN0DAISIAwCC4QMAhrA8AIeIAgC+VAMAOkwAgD5MAIBCTACARkwAgEpMAIBOTACAUkwAgFZMAIBaTACA4/AGAF5MAIDhMAYAhAA8AGJMAIBmTACAakwAgG5MAIByTACAhTQ9AHZMAIB6TACA77AHAH5MAICCTACAhkwAgIpMAICOTACAkkwAgL7EPACWTACAgp0BAIGdAQCAnQEAqA0CAKllAgCqfQIAq3UCAKxZAgCtWQIArpkDAK+ZAwCw6QMAsekDALL5AwCz+QMAtOkDALXpAwC2XQMAt1UDALhtAwC5dQMAunUDALtFAwC8XQMAvTUDAL4xAwC/KQMAmkwAgJ5MAICiTACAqkwAgOFgAwDv9AMA40QCAK5MAICyTACA4zwDAO/0NwDh/AEAtkwAgLpMAIC+TACAwkwAgIZkPwCHaD0AhTQhALOZAwDGTACAtb0DALa1AwDKTACAzkwAgNJMAIC6QQIAu0ECALxBAgC9QQIAvkECAL9BAgDWTACA2kwAgN5MAIDiTACA5kwAgOpMAIDuTACA7/gBAIRoPADhPAYA8kwAgOMcBgD2TACA+kwAgP5MAIACTQCAoxUDAAZNAIAKTQCADk0AgBJNAICmOQMApTEDABpNAICrzQIAqs0CAL5kPgAeTQCAr80CAK7NAgCtzQIArM0CAKgdPgCpJT4Aqi0+AKslPgCsPT4ArSU+AK4tPgCvJT4ApkwAgIL1PwCB5T8AgOU/ABZNAIAiTQCAhgAEAIecAwC4LT4AuTE+ALoxPgC7MT4AvNE+AL3RPgC+0T4Av80+ALBdPgCxIT4Asjk+ALM5PgC0KT4AtSk+ALYZPgC3FT4As6U+ACZNAIAqTQCALk0AgDJNAIC2pT4AtbU+ADZNAIC75T4Aupk+ADpNAIA+TQCAv+0+AL7tPgC97T4AvO0+AEJNAICj4T4ARk0AgEpNAICm4T4ATk0AgFJNAICl8T4Aqt0+AKuhPgBWTQCAWk0AgK6pPgCvqT4ArKk+AK2pPgCPBSUAsyU+AF5NAIBiTQCAtik+AGZNAIBqTQCAtSk+ALp9PgC7RT4Abk0AgHJNAIC+tT4Av70+ALxdPgC9vT4An304AJ5lOQCd8TgAnFE0AJtZNQCaUTUAmfEwAJgNMQCXZTEAlsEwAJVZLQCUTS0Ak+EsAJLZKQCRWSkAkPEoALSlGQC13RgAdk0AgIQIAACwkRUAsQEVALIBGACzvRkAgA0AAIGtAwCCpQMAek0AgKNhAACiHT0AoZk9AKBxPACkxQUApUEEAKYBCACn4QkANkwAgKH1AQCi6QEAo90FAKwBEACtxREArtkRAK85EACoZQgAqQEMAKrZDQCrCQ0AijEuAIuhMwB+TQCAgk0AgI65MwCPETYAjB0yAI1NMgCCJSYAg6krAL5kAwCEYAQAhqEvAIcVLgCEGSoAhZEqAJphPgCb7T4AhsgEAIfcAwCKTQCA4Vw+AJyJAwDjAD4Akmk2AJN5NwCOTQCA7xg+AJZNOwCXuT8AlME7AJVdOgCpnT0AqIk9AKu5PQCqrT0Arak9AKyhPQCvyT0ArqE9AL7oBACSTQCAlk0AgJpNAICeTQCAok0AgKZNAICqTQCAuVk9ALhRPQC7eT0AumU9AL1pPQC8YT0Avx09AL5hPQCxgT0AsLk9ALNpPQCyiT0AtXk9ALRxPQC3aT0AtnE9AKMhPACuTQCAsk0AgLZNAIC6TQCApi08AKUtPAC+TQCAq0E8AKp5PADCTQCAxk0AgK+5PACusTwArbk8AKxZPADKTQCAzk0AgLN9AwDSTQCAtdkDANZNAIDaTQCAttEDAN5NAIDiTQCAu8UDALrFAwC9uQMAvLUDAL+tAwC+sQMA5k0AgOpNAIDuTQCA71wDAIAVAACBHQAAgjEAAO+MPgCE7AQA4fw+APJNAIDjHD4A+k0AgOGUAQD+TQCA4yAAAKP1AwACTgCAh+gEAIZsBAAGTgCAplkDAKVRAwAKTgCAq00DAKpNAwAOTgCAEk4AgK8lAwCuOQMArTEDAKw9AwCGTQCA9k0AgBZOAIAaTgCAHk4AgCJOAIAmTgCAKk4AgKhxBgCpTQYAqo0GAKuFBgCsnQYArYUGAK6NBgCvhQYAsP0GALFBBwCyQQcAs0EHALRBBwC1SQcAtnEHALdxBwC4IQcAuSEHALolBwC7OQcAvCkHAL0VBwC+HQcAv/0HALMlBgAuTgCAMk4AgDZOAIA6TgCAtiUGALU1BgA+TgCAu6UHALoZBgBCTgCARk4AgL+tBwC+pQcAvbUHALy1BwBKTgCAo2EGAE5OAIBSTgCApmEGAFZOAIBaTgCApXEGAKpdBgCr4QcAXk4AgGJOAICu4QcAr+kHAKzxBwCt8QcAqLEGAKm9BgCqzQYAq90GAKzNBgCt/QYArvUGAK8VAQCA+QEAgc0BAILFAQC+ZAIAhpAAAIcAAQBqTgCAbk4AgLjRAQC52QEAuuEBALvhAQC8kQEAvZ0BAL6VAQC/iQEAsG0BALF1AQCyfQEAs3UBALRtAQC18QEAtvEBALfxAQCzRQYAZk4AgHJOAIB2TgCAek4AgLZ9BgC1RQYAfk4AgLuxAQC6qQEAgk4AgIZOAIC/NQEAvqkBAL2hAQC8qQEAik4AgKMBBgCOTgCAkk4AgKY5BgCWTgCAmk4AgKUBBgCq7QEAq/UBAJ5OAICiTgCAru0BAK9xAQCs7QEAreUBAOEoAQCmTgCA41ACAKpOAICuTgCAsk4AgLZOAIC6TgCAvk4AgMJOAIDGTgCAyk4AgIFxAACAGQAA75wCAIJ5AADOTgCA0k4AgITIAgCzxQMA2k4AgLXFAwC2xQMAvhADAIbADACHRAwAuqkDALulAwC8vQMAvaEDAL6hAwC/lQMArhEGAK8ZBgCsAQYArQEGAKqlBgCrEQYAqEU5AKlxOQDeTgCA4k4AgOZOAIDqTgCA7k4AgPJOAID2TgCA+k4AgL7tBwC/TQcAvNEHAL3lBwC63QcAu8EHALg1BgC51QcAtjkGALcNBgC0JQYAtTkGALIxBgCzPQYAsFEGALFRBgCoOQIAqTkCAKqBAgCrgQIArIECAK2JAgCusQIAr7ECAIRsDQD+TgCAvmANAAJPAIAGTwCACk8AgA5PAIASTwCAuE0BALlVAQC6XQEAu1UBALxNAQC9dQEAvn0BAL91AQCwoQIAsa0CALKlAgCzuQIAtKkCALWdAgC2lQIAt3kBAOFUBgDh1AcA4zgGAOOwBwAWTwCAGk8AgB5PAIAiTwCAhOQMACZPAIAqTwCALk8AgDJPAIA2TwCA72wAAO/kBwCjSQIAOk8AgD5PAIBCTwCASk8AgKZJAgClSQIATk8AgKspAgCqJQIAhkgMAIfcDACvGQIAri0CAK0tAgCsMQIAqFEOAKmlDgCqrQ4Aq6UOAKy9DgCtpQ4Arq0OAK+lDgCA5Q8Age0PAILlDwBGTwCAUk8AgFZPAIBaTwCAXk8AgLjVDwC53Q8AutUPALvpDwC8+Q8AvfkPAL7pDwC/6Q8AsN0OALFBDwCyRQ8As10PALRFDwC1TQ8AtkUPALftDwCzJQ4AYk8AgGZPAIBqTwCAbk8AgLYlDgC1NQ4Ack8AgLuFDwC6GQ4Adk8AgHpPAIC/iQ8AvoEPAL2JDwC8kQ8Afk8AgKNhDgCCTwCAhk8AgKZhDgCKTwCAjk8AgKVxDgCqXQ4Aq8EPAJJPAICWTwCArsUPAK/NDwCs1Q8Arc0PAKjRDgCp2Q4AqjkBAKs5AQCsKQEArSkBAK6dAQCvlQEAmk8AgJ5PAICiTwCApk8AgIANAACBtQAAgr0AAKpPAIC4lQEAuZ0BALqhAQC7oQEAvHEAAL1xAAC+cQAAv3EAALDtAQCx9QEAsvUBALPFAQC03QEAtbUBALaxAQC3sQEArk8AgLJPAICzuQEAvsACALWpAQC2TwCAuk8AgLahAQCGgAEAh8QBALs5AQC6IQEAvRkBALwpAQC/eQEAvhEBAKPxAQC+TwCA1k4AgMJPAIDGTwCApukBAKXhAQDKTwCAq3EBAKppAQDOTwCA0k8AgK8xAQCuWQEArVEBAKxhAQDWTwCA2k8AgN5PAIDiTwCA4agBAOZPAIDjQAIA6k8AgL8oFQDuTwCA73QCAPJPAID2TwCA+k8AgP5PAIACUACABlAAgON0DwCEiAMA4TQOAApQAIAOUACAElAAgBZQAICADQAAgRUAAIIRAAAaUACAHlAAgO+kDwAiUACAKlAAgKgZAwCpQQMAqkUDAKtdAwCsTQMArX0DAK51AwCvnQAAhaQVAL58AwCGCAQAhxwDAC5QAIAyUACANlAAgDpQAIC49QAAuf0AALr1AAC7jQAAvIEAAL2BAAC+gQAAv4EAALDlAACx7QAAsuUAALP5AAC07QAAtdEAALbVAAC3zQAAPlAAgEJQAIBGUACAs8ECAEpQAIC1yQIAtvECAE5QAIBSUACAVlAAgLotAQC7JQEAvD0BAL0hAQC+JQEAvxkBAKapAgCESAIAWlAAgKWRAgBeUACAo5kCAGJQAIBmUACArn0BAK9BAQCsZQEArXkBAKp1AQCrfQEAalAAgG5QAIByUACAdlAAgHpQAIB+UACA7+QAAIJQAICGUACAilAAgOMQDgCOUACA4VgOAJJQAICALQAAgREAAIIVAAC+sAUAs3UBAJpQAICHFAUAhmwEAJ5QAIC21QAAtWUBAKJQAIC7/QAAuvUAAKZQAICqUACAv6EAAL69AAC93QAAvN0AAKh9BgCptQYAqr0GAKu1BgCsrQYArRUHAK4dBwCvFQcAllAAgK5QAICyUACAtlAAgLpQAIC+UACAwlAAgMZQAIC4OQcAuTkHALrJBwC7yQcAvNkHAL3ZBwC+zQcAv8UHALBxBwCxeQcAskkHALNJBwC0OQcAtSUHALYhBwC3IQcAozUGAMpQAIDOUACA0lAAgNZQAICmlQcApSUGANpQAICrvQcAqrUHAN5QAIDiUACAr+EHAK79BwCtnQcArJ0HAOZQAIDqUACA7lAAgPJQAID2UACAgj0AAIE9AACAPQAA+lAAgP5QAIACUQCAhKADAL6kAwAGUQCAhvgAAIfgAACoxQYAqdUGAKrVBgCr5QYArP0GAK0xAQCuMQEArzEBAApRAIAOUQCAElEAgBZRAIAaUQCAHlEAgCJRAIAmUQCAuN0BALntAQC65QEAu40BALyVAQC9nQEAvpUBAL+NAQCwUQEAsVEBALJRAQCzUQEAtPUBALX9AQC29QEAt+0BALNdBgAqUQCALlEAgDJRAIA2UQCAtrEBALV1BgA6UQCAu5UBALqVAQA+UQCAQlEAgL85AQC+MQEAvYUBALyFAQClLQYARlEAgEpRAICm6QEATlEAgFJRAICjBQYAVlEAgK3dAQCs3QEAr2EBAK5pAQBaUQCAJlAAgKvNAQCqzQEAXlEAgGJRAICExAMAvwD0AGZRAICCPQAAgT0AAIA9AABqUQCAblEAgHJRAIC+YAMAelEAgH5RAICCUQCAhlEAgIbgHACHAAMA7wwHAIpRAICOUQCAklEAgJZRAICaUQCAnlEAgKJRAICmUQCAqlEAgOHABgCuUQCA4ywHALJRAIC2UQCAulEAgL5RAIDCUQCAxlEAgMpRAIDOUQCA0lEAgKiBAwCpgQMAqoEDAKuBAwCsgQMArYEDAK6BAwCvgQMAsEUDALFNAwCyRQMAs10DALRNAwC1fQMAtnUDALcZAwC4KQMAuTUDALo9AwC7MQMAvAEDAL31AAC+/QAAv+0AALMpAgDWUQCA2lEAgN5RAIDiUQCAtiECALUpAgCEUB0Au6kCALqhAgDqUQCA7lEAgL+ZAgC+qQIAvakCALyxAgCBTQAAgE0AAO+cAwCCXQAAhvAcAId4HQC+EB0A8lEAgPZRAID6UQCA/lEAgAJSAIDhkAEABlIAgONgAwAKUgCADlIAgBJSAIAWUgCAGlIAgB5SAIAiUgCAJlIAgO+UAQCE7BwA4XAGACpSAIDjUAEALlIAgDJSAIA2UgCAOlIAgKPpAgA+UgCAQlIAgEZSAIBKUgCApuECAKXpAgBOUgCAq2kCAKphAgBSUgCAvqgcAK9ZAgCuaQIArWkCAKxxAgCoMR4AqTEeAKoxHgCrMR4ArF0eAK1FHgCuTR4Ar0UeAOZRAICCzR8AgfUfAID9HwBWUgCAWlIAgIYcAACH+AMAuMUeALnNHgC6xR4Au90eALzFHgC9zR4AvsUeAL9ZHwCwPR4AsQUeALINHgCzBR4AtB0eALUBHgC2BR4At/0eALO5HgBeUgCAYlIAgGZSAIBqUgCAtsUeALXVHgBuUgCAu8EeALr5HgByUgCAdlIAgL/FHgC+2R4AvdEeALzZHgB6UgCAo/0eAH5SAICCUgCApoEeAIZSAICKUgCApZEeAKq9HgCrhR4AjlIAgJJSAICunR4Ar4EeAKydHgCtlR4AqCkeAKkpHgCqVR4Aq20eAKx1HgCtfR4ArnUeAK9pHgCWUgCAmlIAgJ5SAICiUgCAplIAgKpSAICuUgCAslIAgLjpHgC59R4Auv0eALv1HgC87R4AvZEeAL6RHgC/kR4AsB0eALHlHgCy7R4As+UeALT9HgC15R4Atu0eALflHgCz3R4AtlIAgLpSAIC+UgCAwlIAgLb9HgC1/R4AhFgBALshHgC62R4AvigAAMpSAIC/IR4AvjkeAL0xHgC8OR4AgU0AAIBNAACjlR4Agl0AAKW1HgDGUgCAzlIAgKa1HgB2UQCA0lIAgKtpHgCqkR4ArXkeAKxxHgCvaR4ArnEeAIYABACHRAMAs4ECANZSAIC1gQIA2lIAgN5SAIC2gQIAiAAAAOJSAIC74QIAuu0CAL3lAgC8+QIAv9ECAL7lAgDmUgCA6lIAgIREAwC+jAMA4UgCAO5SAIDjAAIA7/wfAPJSAIDhPB4A79wCAONgHwD2UgCA+lIAgP5SAIACUwCAqQUCAKixAgCrBQIAqgUCAK0NAgCsBQIArzUCAK41AgCEbAUABlMAgApTAIAOUwCAElMAgBZTAIAaUwCAHlMAgLnpAwC44QMAu/kDALrhAwC96QMAvOEDAL9dAwC+4QMAsSkCALAlAgCzPQIAsiECALUZAgC0LQIAt9kDALYRAgAiUwCAJlMAgCpTAICjhQMALlMAgKWFAwCmhQMAMlMAgDpTAIA+UwCAqukDAKvlAwCs/QMAreEDAK7hAwCv1QMAgEkAAIFVAACCVQAAo6kCAL6YBAClQQEApkEBAEJTAICG4AUAh+AFAKotAQCrOQEArBEBAK0FAQCuDQEArwUBAEZTAIBKUwCATlMAgO/cAABSUwCAVlMAgFpTAIDviB4AhCwHAOHsHgBeUwCA4xweAGJTAIDhlAEAZlMAgOMwAACzJQIAhWDmAGpTAIBuUwCAclMAgLbNAQC1zQEAdlMAgLu1AQC6oQEAelMAgH5TAIC/iQEAvoEBAL2JAQC8nQEANlMAgIJTAICGUwCAilMAgI5TAICSUwCAllMAgJpTAICoAQcAqQEHAKp1BwCrrQcArLUHAK29BwCuqQcAr6kHALDZBwCx7QcAsvkHALP1BwC0mQcAtZkHALaJBwC3gQcAuIkHALmJBwC6bQAAu2UAALx9AAC9ZQAAvm0AAL9lAACBCQAAgJkAAJ5TAICCHQAAolMAgKZTAICqUwCArlMAgKgNBQCpfQUAqk0FAKuhBgCspQYAra0GAK6dBgCv/QYAsIUGALGRBgCyqQYAs70GALSlBgC1rQYAtqUGALd5BgC4SQYAuUkGALpZBgC7WQYAvEkGAL1JBgC++QcAv/kHALNdBgCyUwCAhigCAIcsAQC2UwCAtp0GALWdBgC6UwCAu4kGALq9BgC+UwCAwlMAgL/9BgC+/QYAvYEGALyNBgDGUwCAoxkGAMpTAIDOUwCAptkGANJTAIDWUwCApdkGAKr5BgCrzQYA2lMAgN5TAICuuQYAr7kGAKzJBgCtxQYAqBkBAKkZAQCqjQAAq50AAKyNAACtvQAArrUAAK/dAADiUwCA5lMAgOpTAIDuUwCA8lMAgPZTAID6UwCA/lMAgLhpAAC5aQAAunkAALt5AAC8aQAAvWkAAL7dAwC/1QMAsKkAALGpAACyvQAAs7UAALSZAAC1mQAAtlkAALdZAAC+LAIAAlQAgAZUAIAKVACADlQAgBJUAIAaVACAHlQAgIAtAACBNQAAgj0AACJUAICGkAwAh+gCACZUAIAqVACAs0UDAC5UAIAyVACANlQAgDpUAIC2fQMAtUUDAD5UAIC7LQMAui0DAEJUAIBGVACAvx0DAL4dAwC9IQMAvCkDAKvNAwCqzQMASlQAgE5UAICv/QMArv0DAK3BAwCsyQMAo6UDAFJUAIBWVACAWlQAgF5UAICmnQMApaUDAGJUAIBmVACAalQAgG5UAIByVACAdlQAgII9AACBPQAAgD0AAHpUAIB+VACAglQAgIRgAwCG0AwAhzADAIpUAICOVACAvkQCAJJUAICWVACAmlQAgOEAAACeVACA46gGAKJUAICE7AwAplQAgO/QAwCqVACArlQAgLJUAIC2VACAulQAgLNtAQC+VACAwlQAgMZUAIDKVACAthEBALVlAQDOVACAuz0BALo1AQDSVACA1lQAgL/9AQC+/QEAvRUBALwVAQDaVACA4fwGAN5UAIDjPAcA4lQAgOZUAIDqVACA7lQAgPJUAIC+bAwA+lQAgP5UAIACVQCABlUAgApVAIDvFAYAgV0AAIBdAACj5QEAgm0AAKXtAQAOVQCAElUAgKaZAQCHqAwAhuQMAKu1AQCqvQEArZ0BAKydAQCvdQEArnUBAKgZDgCpGQ4AqiUOAKs1DgCsLQ4ArVEOAK5RDgCvUQ4AhlQAgPZUAIAWVQCAGlUAgB5VAIAiVQCAJlUAgCpVAIC47Q4AufUOALr1DgC7jQ4AvJUOAL2dDgC+lQ4Av40OALAxDgCxOQ4AsgEOALMBDgC0+Q4AtfkOALbdDgC31Q4AqHkOAKl5DgCqjQ8Aq4UPAKydDwCtgQ8AroUPAK+5DwAuVQCAMlUAgDZVAIA6VQCAPlUAgEJVAIBGVQCASlUAgLiRDwC5mQ8AuqEPALuhDwC8UQ8AvV0PAL5JDwC/SQ8AsM0PALHVDwCy3Q8As9UPALTNDwC1sQ8AtrEPALexDwCzBQ4ATlUAgFJVAIBWVQCAWlUAgLYBDgC1FQ4AXlUAgLsRDgC6CQ4AYlUAgISgAQC/dQ4AvgkOAL0BDgC8CQ4AgmkAAKNBDgCAWQAAgVEAAKZFDgC+WAEAZlUAgKVRDgCqTQ4Aq1UOAIbIAACHrAEArk0OAK8xDgCsTQ4ArUUOAGpVAIBuVQCAclUAgHZVAIB6VQCAflUAgBZUAICCVQCAqAkOAKkJDgCqGQ4AqxkOAKwJDgCtYQ4ArmEOAK+VAQCw7QEAsfUBALL9AQCz9QEAtO0BALV1AQC2fQEAt3UBALhNAQC5VQEAul0BALtVAQC8TQEAvfEAAL7xAAC/8QAAhlUAgIpVAICOVQCAklUAgJZVAIDj6A4AmlUAgOE0DgC+AAQA79wPAJ5VAICiVQCAplUAgKpVAICuVQCAslUAgLPxDQC2VQCAulUAgL5VAIDCVQCAtoENALXhDQDGVQCAu1ECALpJAgDKVQCAzlUAgL/RAgC+SQIAvUECALxJAgCjMQ0A0lUAgISIAwDaVQCA3lUAgKZBDQClIQ0A4lUAgKuRAgCqiQIA5lUAgOpVAICvEQIArokCAK2BAgCsiQIAgKkAAIGpAACCTQAA7lUAgOFkEgDjTAIA4wgLAOGsAQDyVQCA7zwCAO8YFgD2VQCAhlAGAIdIAwD6VQCA/lUAgKiBAgCpgQIAqoECAKuBAgCsgQIArYECAK6FAgCvHQEAAlYAgAZWAIAKVgCADlYAgBJWAIAWVgCAGlYAgIS4BQC4dQEAuX0BALp1AQC7CQEAvBkBAL0ZAQC+CQEAvwEBALBlAQCxbQEAsmUBALN9AQC0aQEAtV0BALZVAQC3TQEAHlYAgCJWAIAmVgCAKlYAgC5WAIAyVgCA7zQAAO/ADgDhXA4A4UwPAOOUAADjnA4ANlYAgIJlAACBfQAAgH0AADpWAIA+VgCAvsQHALNFAgBCVgCAtUUCALZNAgBKVgCAhkAGAIeQBAC67QEAu+UBALz9AQC95QEAvuEBAL/VAQCflQgAngUIAJ3dDQCcPQwAmzEMAJr1DQCZ7RAAmD0QAJfVEQCWsRUAlQUUAJTlFQCTtRkAkjEYAJE5GACQDRwAj2EcANZVAICz1QYATlYAgLX9BgBGVgCAUlYAgLaRBgBWVgCAWlYAgLuVBgC6lQYAvVUHALxVBwC/VQcAvlUHAF5WAIBiVgCAqo0GAKuFBgCsnQYArYUGAK6BBgCvtQYAhKgAAGZWAIBqVgCAoyUFAG5WAIClJQUApi0FAHJWAIB2VgCAelYAgH5WAICCVgCAhlYAgIpWAICOVgCAklYAgJZWAICaVgCAnlYAgKJWAICjqQUAotEEAKHZBACgZQUAgiEdAIM1HQCmVgCAqlYAgIaVGACH3RQAhBkZAIUZGQCKDRUAi7EUAK5WAICyVgCAjsURAI/VDACMzRAAjR0RAJJhDQCTdQ0AvkwAALpWAICWxQkAl80EAJSNDACVXQkAmkEFAJtBBQCGyP8Ah0wAAIFZAACAeQAAnCEEAIJRAAChxQEAvlYAgKMB/ACi2QEApRX9AKS1/QCnufkApgH4AKkJ+AColfkAqwX1AKqt9QCtsfEArAHwAK8d8ACurfEAseHtALAB7ACzAegAsv3sALVd6QC09ekAwlYAgMZWAIDKVgCAzlYAgNJWAIDWVgCA2lYAgN5WAIDiVgCA5lYAgKiNBACplQQAqpUEAKulBACsvQQArdkEAK75BACv8QQAhGz8AOpWAIDuVgCA8lYAgPZWAID6VgCA/lYAgAJXAIC4eQUAucUFALrNBQC7xQUAvN0FAL3FBQC+zQUAv+0FALCZBACxmQQAskkFALNJBQC0WQUAtVkFALZJBQC3SQUAox0EAL7M/AAGVwCAClcAgA5XAICmWQQApTUEABJXAICrXQQAql0EABZXAIAaVwCAr50FAK6dBQCtnQUArJ0FAB5XAICznQIAIlcAgCpXAIC2UQIALlcAgDJXAIC1uQIAukkCALtVAgCGSP0Ah8D8AL41AgC/PQIAvEUCAL09AgCo3QQAqUkDAKpRAwCrbQMArHUDAK2VAwCunQMAr7kDAICNAQCB5QEAguEBADZXAIA6VwCAPlcAgEJXAIBGVwCAuJUDALmdAwC6lQMAu60DALy1AwC9vQMAvrUDAL9VAgCwyQMAsdUDALLVAwCzrQMAtLUDALW9AwC2tQMAt60DAEpXAIBOVwCAo9EDAFJXAICl9QMAVlcAgFpXAICmHQMAXlcAgGJXAICrGQMAqgUDAK1xAwCsCQMAr3EDAK55AwDhKAcAZlcAgOPkBgBqVwCA4SgGAG5XAIDjaAEAclcAgHZXAIB6VwCA71gAAH5XAICCVwCAhlcAgO/IBgCKVwCAqE39AKmB/QCq0f0Aq9H9AKzx/QCt8f0ArvH9AK/x/QAmVwCAghEAAIEZAACA0f8AjlcAgJJXAICEdAMAvnQDALh1/gC5ff4AunX+ALvF/gC83f4AvcX+AL7F/gC/9f4AsJH9ALGR/QCykf0As5H9ALRV/gC1Xf4AtlX+ALdN/gCzWf0AllcAgIasAACHRAMAmlcAgLZx/QC1ef0AnlcAgLtV/QC6Vf0AolcAgKZXAIC/mf4AvpH+AL1F/QC8Rf0AqlcAgKMd/QCuVwCAslcAgKY1/QC2VwCAulcAgKU9/QCqEf0AqxH9AL5XAIDCVwCArtX+AK/d/gCsAf0ArQH9AKjN/wCp0f8AqtH/AKsh/gCsIf4ArSH+AK4h/gCvIf4AxlcAgMpXAIDOVwCA0lcAgNZXAIDaVwCA3lcAgOJXAIC4jf4AuZH+ALqV/gC7rf4AvLX+AL25/gC+qf4Av6n+ALDh/gCx4f4AsuX+ALP5/gC06f4AtdX+ALbd/gC3uf4As1n/AOZXAIC2VgCA6lcAgO5XAIC2of4Atan+APJXAIC7Jf4AuiX+APZXAID6VwCAvxH+AL4t/gC9Lf4AvDH+AIIZAACjHf8AgGUAAIEZAACm5f4A/lcAgAJYAICl7f4AqmH+AKth/gCEZAEAviAAAK5p/gCvVf4ArHX+AK1p/gAKWACA4zT+AA5YAIDhfP0AhrAEAIcIAwASWACAFlgAgBpYAIAeWACAhCQDAIQkBAAiWACA70j+ACZYAIAqWACAs+kCAC5YAIC+RAQAvkAFADJYAIC2nQIAtZkCADZYAIC7iQIAur0CADpYAIA+WACAv1kDAL5RAwC9WQMAvJECAKkdAgCoFQIAqyUCAKolAgCtWQIArFUCAK9NAgCuUQIAvmQGAEJYAIBGWACASlgAgE5YAIBSWACAVlgAgFpYAIC5+QMAuPEDALtNAwC68QMAvUEDALxZAwC/cQMAvkEDALEJAgCwPQIAs8kDALIBAgC12QMAtNEDALfJAwC20QMA4ZABAF5YAIDj8AAAYlgAgGZYAICCPQAAgT0AAIA9AABqWACAblgAgHJYAIB6WACAflgAgIJYAIDvLAAAhlgAgKPpAwCKWACAhugEAIdgBQCOWACApp0DAKWZAwCSWACAq4kDAKq9AwCWWACAmlgAgK9ZAgCuUQIArVkCAKyRAwCeWACAolgAgKZYAICqWACArlgAgLJYAIC2WACA71gBAISgBADhVP8AulgAgOOEAQC+WACAwlgAgMZYAIDKWACAs9kBAM5YAICFzBkA0lgAgNZYAIC28QEAtfkBANpYAIC7pQEAutkBAN5YAIDiWACAv50BAL6dAQC9pQEAvK0BAKgBBgCpDQYAqhEGAKsRBgCsMQYArTEGAK4pBgCvJQYAdlgAgILJBwCBwQcAgPEHAOZYAIDqWACAhhwAAIf8AwC47QYAufUGALr9BgC79QYAvO0GAL1RBwC+VQcAv00HALBdBgCxIQYAsjkGALMxBgC0GQYAtRkGALbdBgC31QYAo5kGAO5YAIDyWACA9lgAgPpYAICmsQYApbkGAP5YAICr5QYAqpkGAAJZAIAGWQCAr90GAK7dBgCt5QYArO0GAApZAICz8QcADlkAgBJZAIC2gQcAFlkAgBpZAIC1mQcAuo0HALtlBwAeWQCAIlkAgL59BwC/ZQcAvH0HAL11BwCoLQYAqTUGAKo9BgCrMQYArFUGAK1FBgCuRQYAr3UGACZZAIAqWQCALlkAgDJZAIA2WQCAOlkAgD5ZAIBCWQCAuOkGALn1BgC6/QYAu/UGALztBgC9kQYAvpUGAL+NBgCwDQYAseUGALLtBgCz5QYAtP0GALXlBgC27QYAt+UGAKO1BgBGWQCASlkAgE5ZAIBSWQCApsUGAKXdBgAGWACAqyEGAKrJBgBWWQCAWlkAgK8hBgCuOQYArTEGAKw5BgCASQAAgUkAAIJZAACzRQEAXlkAgLVFAQC2RQEAYlkAgIZAAACHZAAAuikBALslAQC8PQEAvSEBAL4hAQC/FQEAZlkAgGpZAICEBAMAvgAMAOMoBgDv4AIA4RAGAG5ZAIDvkAYA4zwCAHJZAIDh1AEAdlkAgHpZAIB+WQCAglkAgIZZAICKWQCAo8ECAI5ZAIClwQIAklkAgJZZAICmwQIAmlkAgJ5ZAICroQIAqq0CAK2lAgCsuQIAr5ECAK6lAgCpBQIAqLECAKsFAgCqBQIArQ0CAKwFAgCvNQIArjUCAISoDACiWQCAplkAgKpZAICuWQCAslkAgLZZAIC6WQCAuekDALjhAwC7+QMAuuEDAL3pAwC84QMAv10DAL7hAwCxKQIAsCUCALM9AgCyIQIAtRkCALQtAgC32QMAthECAKitAgCp1QIAqtUCAKsNAQCsFQEArQkBAK4xAQCvLQEAvlkAgMJZAIDKWQCAzlkAgNJZAIDWWQCA2lkAgN5ZAIC4IQEAuSEBALrtAQC75QEAvP0BAL3lAQC+7QEAv+UBALBVAQCxXQEAslUBALMtAQC0NQEAtTkBALYtAQC3JQEAgD0BAIGlAACCrQAA79QHAOJZAIDmWQCA6lkAgO8oBwC+LAwA4fQGAO5ZAIDjkAcA8lkAgOGUAQD2WQCA4wwGALMdAgD6WQCAh0QNAIZMDQD+WQCAtskBALXdAQACWgCAu9kBALrRAQAGWgCACloAgL+9AQC+sQEAvbkBALzBAQDGWQCADloAgBJaAIAWWgCAGloAgB5aAIAiWgCAJloAgKgJDwCpCQ8AqhkPAKsZDwCsCQ8ArQkPAK6pDwCvqQ8AsNkPALHtDwCy+Q8As/UPALSVDwC1hQ8AtoUPALe1DwC4jQ8AuWEAALphAAC7YQAAvGEAAL1hAAC+YQAAv2EAAKNdDQCCLQAAgRUAAIAdAAAqWgCApokOAKWdDgAuWgCAq5kOAKqRDgAyWgCANloAgK/9DgCu8Q4ArfkOAKyBDgA6WgCAs/UPAIboAwCHvAMAtu0PAD5aAIBCWgCAteUPALp5DwC7TQ8ARloAgEpaAIC+NQ8AvyUPALxJDwC9RQ8AozEOAE5aAIBSWgCAVloAgFpaAICmKQ4ApSEOAF5aAICriQ4Aqr0OAGJaAIBmWgCAr+EOAK7xDgCtgQ4ArI0OAGpaAIBuWgCAcloAgHZaAIB6WgCAfloAgIJaAICGWgCAiloAgI5aAICSWgCAlloAgIANAACB1QAAgt0AAJpaAICoQQEAqVEBAKpRAQCrZQEArH0BAK2RAACukQAAr5EAAJ5aAICiWgCAhGQBAL5kAQCGkAEAh4QAAKpaAICuWgCAuJEAALmRAAC6kQAAu5EAALyxAAC9sQAAvrEAAL+xAACw8QAAsfkAALLBAACzwQAAtLEAALWxAAC2sQAAt7EAALPZAgCyWgCAvnADAL5EBAC2WgCAthEDALX1AgC6WgCAuz0DALo1AwC+WgCAwloAgL91AwC+dQMAvRUDALwVAwDGWgCAo50CAMpaAIDOWgCAplUDANJaAIDWWgCApbECAKpxAwCreQMA2loAgN5aAICuMQMArzEDAKxRAwCtUQMAqDkDAKk5AwCqjQAAq50AAKyNAACtvQAArrUAAK/dAADiWgCA5loAgOpaAIDuWgCA8loAgPZaAID6WgCA/loAgLhpAAC5aQAAunkAALt5AAC8aQAAvWkAAL7ZAQC/2QEAsKkAALGpAACyvQAAs7UAALSZAAC1mQAAtlkAALdZAAACWwCABlsAgApbAIAOWwCA70QAABJbAICGmAUAh+QCAOOYAACEqAIA4fgBABpbAICAOQAAgTkAAIItAAAeWwCAs0UBACJbAIAmWwCAKlsAgC5bAIC2fQEAtUUBADJbAIC7LQEAui0BADZbAIA6WwCAvx0BAL4dAQC9IQEAvCkBAD5bAIDhUA4AQlsAgOM8DwBGWwCASlsAgE5bAIBSWwCAVlsAgFpbAIDjAAAAXlsAgGJbAIBmWwCAhPQFAO/kDgCuqQEAr6kBAKydAQCtlQEAqpkBAKuZAQBqWwCAblsAgKbJAQByWwCAdlsAgKXxAQCC/QcAo/EBAID9BwCB9QcAFlsAgHpbAIB+WwCAglsAgIZbAICKWwCAhrgDAIeQAwCoDQcAqRkHAKptBwCrZQcArH0HAK1lBwCuZQcAr1UHALAtBwCxxQcAssEHALPdBwC0xQcAtc0HALbFBwC3/QcAuMUHALnJBwC62QcAu9kHALypBwC9qQcAvp0HAL+VBwCzxQcAjlsAgJJbAICWWwCAmlsAgLbFBwC11QcAnlsAgLshBwC6yQcAolsAgKZbAIC/KQcAviEHAL0pBwC8NQcAqlsAgKOBBwCuWwCAslsAgKaBBwC2WwCAulsAgKWRBwCqjQcAq2UHAL5bAIDCWwCArmUHAK9tBwCscQcArW0HAKgVAQCpgQEAqoEBAKuBAQCsgQEArYkBAK6xAQCvsQEAxlsAgMpbAIDOWwCA0lsAgNZbAIDaWwCA3lsAgOJbAIC4ZQAAuW0AALplAAC7fQAAvGUAAL1tAAC+ZQAAv90AALChAQCxrQEAsqUBALO5AQC0qQEAtZ0BALaVAQC3XQAA5lsAgIIdAACBHQAAgB0AAOpbAIDuWwCA8lsAgL5YAQCErAIA9lsAgIcIAQCGjAEA+lsAgKZaAID+WwCAAlwAgLNJAQAGXACAClwAgA5cAIASXACAtkkBALVJAQAWXACAuykBALolAQAaXACAHlwAgL8ZAQC+LQEAvS0BALwxAQC+2AMAIlwAgO/4BgAmXACAKlwAgC5cAIDv4AIAMlwAgOGUAQA2XACA43QCADpcAIDhmAUAPlwAgOMMBwBCXACARlwAgEpcAICjwQIAhIwDAKXBAgBOXACAUlwAgKbBAgBWXACAWlwAgKuhAgCqrQIAraUCAKy5AgCvkQIArqUCAKgxAwCpPQMAqjUDAKtJAwCsWQMArVkDAK5JAwCvQQMAgMUAAIEJAACCGQAAXlwAgGJcAIBqXACAh2wDAIYcHAC47QAAufEAALr1AAC7jQAAvJUAAL2BAAC+gQAAv70AALAJAwCxCQMAsu0AALPhAAC04QAAteEAALblAAC32QAAblwAgHJcAIB2XACAs7ECAHpcAIC13QIAttUCAH5cAICCXACAhlwAgLrBAgC7wQIAvDUBAL05AQC+KQEAvykBAKaNAgCKXACAjlwAgKWFAgCSXACAo+kCAJZcAICaXACArnEBAK9xAQCsbQEArWEBAKqZAgCrmQIAnlwAgKJcAICmXACA4YQGAKpcAIDjJAYArlwAgOGUAQCyXACA4ywAAL7oHQC2XACAulwAgO/IAACE/B0AvvAcAL5cAIDvSAcAwlwAgMZcAIDKXACAzlwAgIEdAACAHQAA0lwAgIIFAACGQBwAh8QcANpcAIDeXACA4lwAgOZcAIDqXACA7lwAgKi1HgCpBR8Aqg0fAKsFHwCsAR8ArQkfAK45HwCvOR8A1lwAgPJcAID2XACA+lwAgP5cAIACXQCABl0AgApdAIC4yR8AudUfALrRHwC76R8AvPkfAL3tHwC+mR8Av5kfALAlHwCxLR8AsjkfALM1HwC0LR8AtQ0fALYFHwC3/R8As4UfAA5dAIASXQCAFl0AgBpdAIC2iR8AtYkfAB5dAIC76R8AuuEfACJdAIAmXQCAv8kfAL7pHwC94R8AvO0fACpdAICjwR8ALl0AgDJdAICmzR8ANl0AgDpdAIClzR8AqqUfAKutHwA+XQCAQl0AgK6tHwCvjR8ArKkfAK2lHwCo6R4AqekeAKr5HgCr+R4ArOkeAK3pHgCuPQEArzUBAID5AQCBzQEAgsUBAIRgAgBGXQCASl0AgIdoAQCGnAAAuNEBALnZAQC64QEAu+EBALyRAQC9nQEAvpUBAL+JAQCwTQEAsVUBALJdAQCzVQEAtE0BALXxAQC28QEAt/EBALNxHgBOXQCAUl0AgFZdAIBaXQCAtmkeALVhHgBeXQCAu5EBALqJAQBiXQCAZl0AgL81AQC+iQEAvYEBALyJAQBqXQCAZlwAgKM5HgBuXQCApSkeAHJdAIB2XQCApiEeAHpdAIB+XQCAq9kBAKrBAQCtyQEArMEBAK99AQCuwQEAgl0AgIZdAICKXQCAjl0AgJJdAICWXQCAml0AgJ5dAICiXQCApl0AgKpdAICuXQCAsl0AgLpdAIC+XQCAvnADAOHkHgCESAIA4+gfAIQABACAeQAAgXkAAIJpAADCXQCAhsAEAIdEAwDGXQCAyl0AgM5dAIDSXQCA7yAfANZdAIDaXQCA3l0AgOJdAIDvSAIA5l0AgOpdAIDuXQCA8l0AgL7oBAD2XQCA+l0AgP5dAIACXgCA4ZABAAZeAIDj6AIAs0kDAApeAIAOXgCAEl4AgBZeAIC2SQMAtUkDABpeAIC7LQMAuiUDAB5eAIAiXgCAvxUDAL4VAwC9IQMAvCkDAKg1AgCpgQIAqoECAKuBAgCsgQIArYkCAK6xAgCvsQIAgP0BAIHNAQCCxQEAKl4AgIaQBACHBAUALl4AgIRwBAC4SQEAuUkBALpZAQC7WQEAvEkBAL1JAQC+eQEAv3kBALChAgCxqQIAsr0CALO1AgC0kQIAtZECALZ5AQC3eQEAMl4AgDZeAIA6XgCAPl4AgEJeAIBGXgCASl4AgO/QHgC+6AQA4VweAE5eAIDjkAAAUl4AgFZeAIBaXgCAXl4AgKNJAgBiXgCAZl4AgGpeAIBuXgCApkkCAKVJAgByXgCAqy0CAKolAgB2XgCAel4AgK8VAgCuFQIArSECAKwpAgCoNQYAqT0GAKpVBgCrZQYArH0GAK1lBgCubQYAr2EGACZeAIB+XgCAgl4AgIZeAICADQAAgbEAAIKxAACKXgCAuOkGALnpBgC6+QYAu/UGALyVBgC9nQYAvpUGAL+NBgCw4QYAseEGALLhBgCz/QYAtOUGALXtBgC25QYAt9kGALPdBgCOXgCAkl4AgJZeAICaXgCAtuUGALX1BgCeXgCAuyUGALolBgCGmAAAh6wAAL8pBgC+IQYAvSkGALw1BgCiXgCAo5kGAKZeAICqXgCApqEGAK5eAICyXgCApbEGAKphBgCrYQYAtl4AgLpeAICuZQYAr20GAKxxBgCtbQYAqC0GAKk9BgCqiQYAq4kGAKyZBgCtmQYArokGAK+JBgC+XgCAwl4AgMZeAIDKXgCAzl4AgNJeAIDWXgCA2l4AgLiNBgC5lQYAupUGALulBgC8vQYAvXEBAL5xAQC/cQEAsPkGALHNBgCy2QYAs9kGALTJBgC1yQYAtr0GALe1BgCzAQYA3l4AgOJeAIDmXgCA6l4AgLYZBgC1EQYA7l4AgLsJBgC6PQYA8l4AgPZeAIC/DQYAvg0GAL0NBgC8DQYA+l4AgKNFBgC2XQCA/l4AgKZdBgACXwCAhFgAAKVVBgCqeQYAq00GAL5oAQAGXwCArkkGAK9JBgCsSQYArUkGAIDBAwCByQMAgt0DAKPNAgAKXwCApdkCAKbNAgAOXwCAhoANAIeUAwCqxQIAqw0DAKwVAwCtHQMArhUDAK8NAwDhnBcA4xgGAOMUAwDhNAYA7xgCABJfAIAWXwCAGl8AgOPQAgAeXwCA4VACACJfAIAmXwCA7ywGAO/kJQAqXwCArE0CAK1RAgCuUQIAr2UCAKgBAgCpCQIAqlkCAKtVAgCE7A0ALl8AgDJfAIA2XwCAvvgNADpfAIA+XwCAQl8AgLxRAwC9WQMAvmEDAL9hAwC47QMAuVEDALpRAwC7UQMAtM0DALXVAwC23QMAt9UDALAdAgCx1QMAst0DALPVAwDjyAAARl8AgOG4AQBKXwCAhFQPAE5fAIBSXwCAVl8AgKHpAgCgFQYAo6UDAKINAwDvIAAAWl8AgF5fAIBiXwCAZl8AgGpfAICFNCYAs40DAG5fAIC1mQMAto0DAHJfAICGwA8Ah5QNALqFAwC7TQIAvFUCAL1dAgC+VQIAv00CAHpfAIB+XwCAgl8AgIZfAICKXwCAjl8AgI/d6wDvxAYAvuAPAOGMBgCSXwCA44AGAID1AACB5QAAguUAAJZfAICZbR8AmMUfAJvJGwCaeRoAnXUaAJzFGwCf+QcAnhkGAJFpFgCQsesAk20XAJLNFwCV0RMAlGkSAJdREgCWzRMAg1XkAIJB5AB2XwCAml8AgIeNHQCGkRgAhTkYAISVGQCLERwAigUcAJ5fAICiXwCAj4UVAI6ZEACNORAAjJUdAJNRFACSRRQApl8AgKpfAICXYQkAlnUIAJWdCQCU+RUAm0EMAJqtDQCuXwCAsl8AgLZfAIC6XwCAvl8AgJzxDAChbQ0Awl8AgKMBBACihQAApZkEAKSRBACnGTgApsUFAKkJOACoKTgAq4k8AKoBPACtATAArB08AK8pMACunTAAseE0ALABNACzASgAsv00ALXZKAC00SgAxl8AgMpfAIDOXwCA0l8AgNZfAIDaXwCAgB0AAIEJAACC2QEA3l8AgKgRDwCpGQ8Aql0PAKtVDwCsTQ8ArXEPAK51DwCvbQ8A4l8AgOpfAICGiAAAhxABAO5fAIDyXwCA9l8AgPpfAIC4TQ4AuVEOALpRDgC7UQ4AvGUOAL1tDgC+ZQ4Avx0OALAdDwCxwQ8AssEPALPBDwC0xQ8Atc0PALbFDwC3eQ4As9UPAP5fAIACYACABmAAgApgAIC28Q8AtcUPAA5gAIC7BQ8AutkPABJgAIAWYACAvwkPAL4BDwC9FQ8AvBUPABpgAICjkQ8AHmAAgCJgAICmtQ8AJmAAgCpgAIClgQ8Aqp0PAKtBDwAuYACAMmAAgK5FDwCvTQ8ArFEPAK1RDwCogQ0AqYENAKqBDQCrgQ0ArIENAK2BDQCusQ0Ar6ENADZgAIA6YACAPmAAgEJgAIBGYACAgrkAAIG9AACAvQAAuDUCALk9AgC6zQIAu5UCALyNAgC9tQIAvr0CAL+1AgCwbQIAsU0CALJFAgCzJQIAtD0CALUdAgC2FQIAtw0CAEpgAIBOYACAswENAFJgAIC1AQ0AWmAAgISUAwC2CQ0AviwEAF5gAIC7gQIAuqECAL35AgC8mQIAv9ECAL7xAgBiYACAZmAAgGpgAICjRQ0AbmAAgKVFDQCmTQ0AcmAAgIbgBACHpAQAquUCAKvFAgCs3QIArb0CAK61AgCvlQIAqCUCAKk1AgCqPQIAqzUCAKwtAgCtkQIArpECAK+RAgB2YACAemAAgH5gAICCYACAzAAAAIZgAICKYACAjmAAgLiZAgC5rQIAuqUCALttAQC8dQEAvX0BAL51AQC/bQEAsPECALH5AgCywQIAs8ECALSxAgC1vQIAtrUCALepAgCSYACA44QOAJZgAIDh9A4AmmAAgJ5gAICiYACApmAAgIQgBQCqYACArmAAgLJgAIC2YACA7+wOALpgAIC+YACAs/UCAMJgAICG6AQAh4wEAL5cBAC2UQIAteUCAMpgAIC7fQIAunUCAM5gAIDSYACAvzkCAL41AgC9VQIAvFUCAKM1BQBWYACAxmAAgNZgAIDaYACAppEFAKUlBQDeYACAq70FAKq1BQDiYACA5mAAgK/5BQCu9QUArZUFAKyVBQCA+QcAgfkHAIKNBwCzjQYA6mAAgLWdBgC2iQYA7mAAgPJgAID2YACAuk0HALtFBwC8XQcAvUEHAL5BBwC/QQcA+mAAgP5gAIDmXwCAAmEAgAZhAIAKYQCADmEAgBJhAICoNQYAqQEGAKppBgCraQYArHkGAK1lBgCuZQYAr50HALDlBwCx7QcAsuUHALP5BwC06QcAtekHALZZBwC3VQcAuHEHALlxBwC6cQcAu3EHALxVBwC9XQcAvlUHAL9NBwCjwQcAFmEAgBphAIAeYQCAImEAgKbFBwCl0QcAJmEAgKsJBgCqAQYAKmEAgC5hAICvDQYArg0GAK0NBgCsEQYAgGkAAIFpAACCBQAAMmEAgL6YAQCEmAEANmEAgDphAICGADwAh8QBAD5hAIBCYQCARmEAgEphAIBOYQCAUmEAgKhdBgCpbQYAqmUGAKuBAQCsgQEArYkBAK6xAQCvsQEAVmEAgFphAIBeYQCAYmEAgGZhAIBqYQCAbmEAgHJhAIC4VQEAuV0BALpVAQC7yQAAvNkAAL3ZAAC+yQAAv8EAALCxAQCxuQEAsokBALOJAQC0cQEAtXEBALZ1AQC3bQEAs+0FAHZhAIB6YQCAfmEAgIJhAIC2CQIAtQkCAIZhAIC7fQIAunUCAIphAICOYQCAv7UCAL61AgC9XQIAvF0CAL5gAgCjqQUAkmEAgJZhAICmTQIAmmEAgJ5hAIClTQIAqjECAKs5AgCiYQCAhOADAK7xAgCv8QIArBkCAK0ZAgC+iDwAqmEAgKotAwCrJQMArD0DAK0lAwCuLQMAryUDAID1AACB/QAAgsEAAKPBAwCuYQCApcEDAKbBAwCyYQCAhmA8AIdUAwC2YQCAumEAgL5hAIDjqAIAwmEAgOGkAQDGYQCA71wCAMphAIDOYQCA0mEAgNZhAIDaYQCA3mEAgOJhAIDjjAcA5mEAgOE8BADqYQCA7mEAgPJhAID2YQCAhCACAPphAID+YQCAAmIAgAZiAIDvbAcACmIAgA5iAICzLQIAhEQ9ABJiAIAaYgCAHmIAgLYtAgC1LQIAImIAgLvJAgC6wQIAJmIAgCpiAIC/yQIAvsECAL3JAgC80QIA4XgHAOPAAADjOAYA4VwGAICpAACBqQAAgtEAAC5iAIAyYgCANmIAgL6kPAA6YgCAPmIAgO8cAADvkAYAQmIAgIZgPACHBD0ARmIAgLNxAQBKYgCAtRkBALYJAQBOYgCAUmIAgFZiAIC6AQEAuwEBALwBAQC9AQEAvgEBAL8BAQCohT4AqbU+AKq1PgCrxT4ArN0+AK3FPgCuwT4Ar/0+AFpiAIBeYgCAYmIAgGZiAIBqYgCAbmIAgHJiAIB2YgCAuFE/ALlRPwC6UT8Au1E/ALx1PwC9fT8AvnU/AL9tPwCwiT4AsYk+ALKZPgCzmT4AtIk+ALWJPgC2eT8At3U/AKZhAICjOT4AemIAgBZiAICmQT4AfmIAgIJiAIClUT4Aqkk+AKtJPgCGYgCAimIAgK5JPgCvST4ArEk+AK1JPgCASQAAgVEAAIJRAACzkT8AjmIAgLW5PwC2RT8AkmIAgIZAAACHBAMAukU/ALtdPwC8TT8AvT0/AL4pPwC/IT8AqE0+AKlVPgCqVT4Aq2U+AKx9PgCtiT4Arrk+AK+5PgCWYgCAmmIAgJ5iAICiYgCApmIAgKpiAICuYgCAsmIAgLhhAQC5YQEAumEBALthAQC8YQEAvWEBAL5hAQC/YQEAsM0+ALHVPgCy1T4As6U+ALShPgC1qT4Atpk+ALeZPgCj3T4AtmIAgLpiAIC+YgCAwmIAgKYJPgCl9T4AxmIAgKsRPgCqCT4AymIAgM5iAICvbT4ArmU+AK1xPgCsAT4A0mIAgNZiAIDaYgCA3mIAgOJiAIDmYgCA6mIAgO5iAICAOQAAgTkAAIIFAADyYgCAvrgBAIS4AQD6YgCA/mIAgKitAgCp1QIAqtUCAKstAwCsNQMArT0DAK41AwCvLQMAAmMAgAZjAIAKYwCADmMAgBJjAIAWYwCAGmMAgB5jAIC46QMAuekDALqJAwC7iQMAvJkDAL2ZAwC+iQMAv4kDALBVAwCxXQMAslUDALPpAwC0+QMAtfkDALbpAwC34QMAs10CACJjAICGKAQAh8wDACZjAIC2vQMAtb0DACpjAIC7mQMAupEDAC5jAIAyYwCAvz0DAL49AwC9PQMAvIEDAIUAFACjGQIANmMAgDpjAICm+QMAPmMAgEJjAICl+QMAqtUDAKvdAwBGYwCASmMAgK55AwCveQMArMUDAK15AwDjVD4A4dw/AOHQPgDjPD4ATmMAgO8cAABSYwCAVmMAgFpjAIDjwAAAXmMAgOHUAQDvYD4AYmMAgGpjAIDvRD8AgGEAAIFtAACCfQAAhAAFAIbwBACHnAUAvhAFAG5jAIByYwCAdmMAgHpjAIB+YwCAgmMAgIZjAICKYwCAjmMAgLiJPQC5iT0Aupk9ALuRPQC8uT0Avbk9AL7RPQC/0T0AsAU+ALENPgCyBT4Asx0+ALQFPgC1DT4AtgU+ALe5PQConT4Aqa0+AKqlPgCrvT4ArKU+AK2tPgCupT4Ar30+AISsBAC+rAQAkmMAgJZjAICaYwCAnmMAgKJjAICmYwCAqPkFAKn5BQCqKQYAqykGAKw5BgCtOQYArikGAK8pBgBmYwCAqmMAgK5jAICyYwCAtmMAgLpjAIC+YwCAwmMAgLiNBgC5kQYAupEGALulBgC8vQYAvUUHAL5BBwC/QQcAsFkGALFZBgCy7QYAs/0GALTtBgC13QYAttUGALe1BgCzoQYAxmMAgMpjAIDOYwCA0mMAgLa5BgC1sQYA2mMAgLudBgC6nQYA1mMAgPZiAIC/GQYAvikGAL0pBgC8OQYAglEAAKPlBgCAQQAAgUEAAKb9BgDeYwCA4mMAgKX1BgCq2QYAq9kGAIZIAACHbAAArm0GAK9dBgCsfQYArW0GAKg5BgCpWQYAqmkGAKtpBgCseQYArXkGAK5pBgCvaQYA5mMAgOpjAIDuYwCA8mMAgPZjAID6YwCA/mMAgAJkAIC4ZQEAuW0BALplAQC7fQEAvGUBAL1tAQC+ZQEAv9kBALAZBgCxGQYAsoEGALOBBgC0gQYAtYEGALaBBgC3gQYAs+EGAAZkAIAKZACADmQAgBJkAIC2+QYAtfEGABZkAIC73QYAut0GABpkAIAeZACAv0UGAL5FBgC9VQYAvFUGACJkAICjpQYAJmQAgCpkAICmvQYALmQAgDJkAICltQYAqpkGAKuZBgA2ZACAOmQAgK4BBgCvAQYArBEGAK0RBgConQIAqdECAKrRAgCrLQMArDUDAK09AwCuNQMAry0DAD5kAIBCZACAvmQCAEpkAIBOZACAUmQAgFZkAIBaZACAuOkDALnpAwC6iQMAu4UDALydAwC9gQMAvoEDAL+1AwCwVQMAsV0DALJVAwCz6QMAtPkDALX5AwC26QMAt+EDAIBtAwCBpQAAgq0AALNVAgBeZACAtbEDALaxAwBiZACAhOACAGZkAIC6nQMAu5UDALyNAwC9MQMAvjEDAL8xAwCjGQIAamQAgIVwaQBuZACAcmQAgKb9AwCl/QMAdmQAgKvZAwCq0QMAhkgMAIe8AwCvfQMArn0DAK19AwCswQMAemQAgH5kAICCZACAhmQAgO+wBgDvxAMAimQAgI5kAIDjfAYA45QDAOG4BwDh3AEAkmQAgJZkAICaZACAnmQAgKJkAICmZACAhEQCAL5YDQCADQAAgTUAAII9AACqZACArmQAgLJkAICGyAwAh1wNALpkAIC+ZACAwmQAgMZkAIDKZACAzmQAgNJkAIDWZACA2mQAgN5kAIDiZACA74AGAISsDQDh7AYA5mQAgONcBgDqZACA7mQAgPJkAID2ZACAs/UBAPpkAID+ZACAAmUAgAZlAIC2RQEAteUBAAplAIC7LQEAuiEBAA5lAIASZQCAv/UAAL71AAC9JQEAvC0BAKgtDgCpNQ4Aqj0OAKs1DgCsLQ4ArYUOAK6FDgCvuQ4AtmQAgBZlAIAaZQCAHmUAgIAZAACBGQAAggUAACJlAIC4WQ8AuVkPALp5DwC7eQ8AvGkPAL1pDwC+GQ8AvxkPALClDgCxqQ4AsrkOALOxDgC0cQ8AtXEPALZxDwC3cQ8Apb0OAL6IAwAqZQCAph0OACZlAIAuZQCAo60OADJlAICtfQ4ArHUOAK+tDwCurQ8ARmQAgDZlAICrdQ4AqnkOALO5DwA6ZQCAhmgAAIcMAwA+ZQCAtlEPALVZDwBCZQCAu3UPALp1DwBGZQCASmUAgL9FDwC+RQ8AvVEPALxlDwCocQ4AqXEOAKpxDgCrcQ4ArJEOAK2RDgCukQ4Ar5EOAE5lAIBSZQCAVmUAgFplAIBeZQCAYmUAgGZlAIBqZQCAuIUOALmNDgC6hQ4Au50OALyNDgC9vQ4AvrUOAL95AQCw8Q4AsfEOALLxDgCzxQ4AtMEOALXBDgC2wQ4At8EOAKP5DgBuZQCAcmUAgHZlAIB6ZQCAphEOAKUZDgB+ZQCAqzUOAKo1DgCCZQCAhmUAgK8FDgCuBQ4ArREOAKwlDgCADQAAgRUAAIIdAACKZQCAjmUAgJJlAICElAEAvpQBAIZABwCH5AAAmmUAgJ5lAICiZQCApmUAgKplAICuZQCAqIkCAKmRAgCqlQIAq7kCAKzVAgCtxQIArsUCAK/1AgCyZQCAtmUAgLplAIC+ZQCAvnwDAMJlAIDGZQCAymUAgLh9AwC5wQMAusEDALvBAwC8wQMAvckDAL7xAwC/8QMAsI0CALFFAwCyTQMAs0UDALRdAwC1RQMAtk0DALdFAwCzHQIAzmUAgNJlAIDWZQCA2mUAgLZFAgC1XQIA3mUAgLuBAwC6SQIA4mUAgOZlAIC/gQMAvpkDAL2RAwC8mQMA6mUAgKNZAgDuZQCA8mUAgKYBAgD2ZQCA+mUAgKUZAgCqDQIAq8UDAP5lAIACZgCArt0DAK/FAwCs3QMArdUDAIDZAQCB7QEAguUBAO+4DgAKZgCA4cQBAISYAgDj1AAADmYAgL7sBAASZgCA7wgAABZmAIDhxA8AGmYAgONkDgCGAAUAh2gFAB5mAICzvQIAImYAgLWtAgC2pQIAJmYAgCpmAIAuZgCAukEBALtBAQC8RQEAvU0BAL5FAQC/+QEAMmYAgDZmAIA6ZgCAPmYAgEJmAIBGZgCASmYAgO/gAQCEbAQA4dQOAE5mAIDjHA4AUmYAgFZmAIBaZgCAXmYAgKMxAgBiZgCAhCQHAGZmAIBqZgCApikCAKUhAgBuZgCAq80BAKrNAQByZgCAemYAgK91AQCuyQEArcEBAKzJAQCo6QUAqekFAKr5BQCr+QUArOkFAK3pBQCuOQYArzkGAAZmAICCzQcAgfUHAID9BwB2ZgCAfmYAgIYYAwCHkAMAuNEGALnZBgC64QYAu+EGALyRBgC9nQYAvpUGAL+JBgCwSQYAsUkGALJdBgCzVQYAtE0GALXxBgC28QYAt/EGALDhBwCx4QcAsgkHALMJBwC0GQcAtRkHALYJBwC3CQcAuDkHALkNBwC6GQcAuxkHALwJBwC9CQcAvn0HAL9xBwCCZgCAlmUAgIZmAICKZgCAjmYAgJJmAICWZgCAmmYAgKjxBwCpxQcAqsEHAKvdBwCsyQcArb0HAK6pBwCvoQcAsykGAJ5mAICiZgCApmYAgKpmAIC2XQYAtSEGAK5mAIC7RQYAukUGALJmAIC2ZgCAv70GAL69BgC9vQYAvL0GALpmAICjbQYAvmYAgMJmAICmGQYAxmYAgMpmAIClZQYAqgEGAKsBBgDOZgCA0mYAgK75BgCv+QYArPkGAK35BgCobQYAqbEBAKpJAQCrRQEArF0BAK1FAQCuTQEAr0UBANZmAICCHQAAgR0AAIAdAADaZgCA3mYAgOJmAIC+VAEAuIEAALmNAAC6hQAAu5kAALyJAAC9vQAAvrUAAL99AACwPQEAseEAALLhAACz4QAAtOEAALXpAAC20QAAt9EAALsFAwC62QIAhiwCAIcsAwC/DQMAvgUDAL0VAwC8FQMAs+ECAOpmAIDuZgCAhCwDAPJmAIC25QIAtfUCAPZmAICqnQIAq0EDAPpmAID+ZgCArkEDAK9JAwCsUQMArVEDAAJnAICjpQIABmcAgApnAICmoQIADmcAgBJnAIClsQIAqakAAKihAACrtQAAqr0AAK3dAACs3QAAr/EAAK79AAC+LBwAFmcAgBpnAIAeZwCAImcAgCZnAIAqZwCALmcAgLl9AAC4fQAAu80BALrNAQC93QEAvN0BAL/NAQC+zQEAsZUAALCJAACzTQAAspUAALVdAAC0XQAAt00AALZNAAAyZwCANmcAgDpnAIA+ZwCAQmcAgEZnAIBKZwCATmcAgIA5AACBOQAAggUAAFJnAIBaZwCAXmcAgIf4AgCGfB0A4bgEAL7IHADjQAYAYmcAgGZnAIBqZwCAbmcAgHJnAIB2ZwCAemcAgH5nAICCZwCAhmcAgIpnAIDvsAcAjmcAgJJnAICWZwCAmmcAgO/IAACeZwCAomcAgKZnAIDvQAYAqmcAgOH8BgCuZwCA4xwGALJnAIDhlAEAtmcAgONkBgCAEQAAgRkAAIIpAACz/QEAumcAgLWdAQC2lQEAvmcAgMJnAICEbB0AuoUBALuZAQC8iQEAvVEBAL5RAQC/UQEAozEeAFZnAIDGZwCAymcAgM5nAICmWR4ApVEeANJnAICrVR4AqkkeAIYIAwCHbAMAr50eAK6dHgCtnR4ArEUeANZnAICzCR8A2mcAgN5nAIC2CR8A4mcAgOZnAIC1CR8AugUfALsNHwDqZwCA7mcAgL4FHwC/CR8AvBUfAL0NHwCw5R8Ase0fALLlHwCz/R8AtOUfALXpHwC2GR8AtxkfALgpHwC5NR8Auj0fALs1HwC8ER8AvR0fAL4JHwC/BR8A8mcAgPZnAIDmZgCA+mcAgP5nAIACaACABmgAgApoAICo0R8AqdEfAKqlHwCrvR8ArKUfAK2tHwCupR8Ar50fAKNNHgAOaACAEmgAgBZoAIAaaACApk0eAKVNHgAeaACAq0keAKpBHgAiaACAJmgAgK9NHgCuQR4ArUkeAKxRHgCADQAAgRUAAIIdAAAqaACALmgAgDJoAICEtAEAvrQBAL/oAQA6aACAhkgHAIc0AACEvAYAPmgAgEJoAIC+tAYAqI0BAKmVAQCqlQEAq80BAKzZAQCt2QEArs0BAK/FAQBGaACASmgAgE5oAIBSaACAVmgAgFpoAIBeaACAYmgAgLgdAQC5wQAAusEAALvBAAC8wQAAvckAAL7xAAC/8QAAsIkBALGJAQCyKQEAsykBALQ9AQC1JQEAti0BALclAQC7bQIAum0CAGZoAIBqaACAv8ECAL7ZAgC93QIAvN0CALM9AgBuaACAcmgAgHZoAICE/AYAtnkCALVxAgB6aACAqikCAKspAgB+aACAgmgAgK6dAgCvhQIArJkCAK2ZAgCGaACAo3kCAIpoAICOaACApj0CAJJoAICWaACApTUCAIJtJwCDjSoAhqgFAIdsAwCGmS4Ah80vAIQRLgCFmS4AiiESAIspEgCaaACAnmgAgI6RFgCPHRYAjBESAI0RFgCScRoAk+UaAKJoAIDvlHYAlvEeAJflHgCUSRoAlRkeAJopAgCb4QIAqmgAgK5oAICyaACA4SASAJzxAgDjIBYAnyEfAJ7BHwCdmRsAnC0bAJuhGwCavRcAmTkXAJixFwCXiRMAlqkTAJWpEwCUdS4AkzkvAJIxLwCRsS8AkDUrAI+tJgDjeB8A0gAAAOFcHwCCmQEAtmgAgIDxAQCB8QEAvqgHALpoAIC+aACAwmgAgIS8BgDvLB8AxmgAgMpoAIDhpB4A48wAAON8HgDhvAEAzmgAgNJoAIDWaACAhJwGANpoAIC+bAYA3mgAgOJoAIDmaACA7xAAAO8EHgDqaACA7mgAgPJoAID2aACA+mgAgP5oAIACaQCABmkAgAppAICAPQAAgQkAAILJBwAOaQCAo/kDAKLxAwChMQMAoM0fALBJcQCxAXwAsgl8ALMhfQC0AXgAtRV4ADZoAICmaACAEmkAgL4oDgCGDAAAh4wDABZpAIAaaQCAHmkAgCJpAIAmaQCAoV0AAKJVAACjfQAApAEMAKUVDACm9QwApwEIAKghCACpxQgAqgF0AKsJdACsAXQArR11AK55cACveXAAqOUFAKnxBQCq8QUAqy0FAKw1BQCtPQUArjUFAK8tBQAqaQCALmkAgDJpAIA2aQCAOmkAgD5pAIBCaQCARmkAgLj9BgC5jQYAuoUGALutBgC8uQYAvbkGAL6tBgC/pQYAsFUFALFdBQCyVQUAs+UGALT9BgC10QYAttEGALfRBgCzeQQASmkAgE5pAIBSaQCAVmkAgLa9BAC1vQQAWmkAgLuZBAC6kQQAXmkAgGJpAIC/FQcAvjkHAL0xBwC8gQQAZmkAgKM9BABqaQCAbmkAgKb5BAByaQCAdmkAgKX5BACq1QQAq90EAHppAIB+aQCArn0HAK9RBwCsxQQArXUHAKhpBwCpaQcAqnkHAKvZBgCs9QYArf0GAK71BgCv5QYAgMkAAIHJAACCBQAAgmkAgIZwDwCHNAAAimkAgI5pAIC4fQYAuQUGALoNBgC7BQYAvB0GAL0FBgC+DQYAvwUGALCdBgCxdQYAsn0GALN1BgC0UQYAtV0GALZVBgC3TQYAs/EEAJJpAICWaQCAmmkAgJ5pAIC2fQUAtX0FAKJpAIC7sQUAulkFAKZpAICqaQCAv5kFAL6VBQC9oQUAvKkFAK5pAICjtQQAsmkAgLZpAICmOQUAumkAgL5pAIClOQUAqh0FAKv1BQDCaQCAxmkAgK7RBQCv3QUArO0FAK3lBQCpuQIAqLECAKvJAgCqsQIArTUCAKw1AgCvNQIArjUCAMppAIDOaQCA0mkAgNZpAIDaaQCA3mkAgOJpAIDmaQCAuekDALjZAwC7iQMAuuEDAL2dAwC8nQMAv4EDAL6JAwCxVQIAsFUCALNVAgCyVQIAtfkDALTxAwC36QMAtvEDALM9AwDqaQCA7mkAgPJpAID6aQCAtrEDALW5AwD+aQCAu5UDALqVAwCGiAwAh6ANAL85AgC+MQIAvYUDALyFAwACagCAo3kDAAZqAIAKagCApvUDAA5qAIASagCApf0DAKrRAwCr0QMAFmoAgBpqAICudQIAr30CAKzBAwCtwQMAgIUAAIGNAACChQAA79AGAOOwBwDj9AQA4QgHAOHsBADvOAYA7yAEAL6kDAAeagCAImoAgOGEAQAmagCA49wGACpqAIAuagCAhMANALPJAQAyagCAtdkBALbJAQA2agCAOmoAgD5qAIC6xQEAu60BALy5AQC9uQEAvq0BAL+lAQCwLQ4AsUUOALJBDgCzQQ4AtEUOALVNDgC2cQ4At3EOALiBDgC5gQ4AuoEOALuBDgC8gQ4AvYEOAL6BDgC/gQ4A9mkAgEJqAIBGagCASmoAgIZpAIBOagCAUmoAgFZqAICo2Q0AqdkNAKptDgCrZQ4ArH0OAK1lDgCuZQ4Ar1UOAKOFDgCCLQAAgRUAAIAdAABaagCApoUOAKWVDgBeagCAq+EOAKqJDgBiagCAZmoAgK/pDgCu4Q4ArfUOAKz1DgBqagCAs4UPAIZoAACHHAMAtoUPAG5qAIByagCAtZEPALqNDwC7SQ8AdmoAgHpqAIC+MQ8AvzEPALxJDwC9RQ8AqBEOAKkZDgCqSQ4Aq0UOAKxdDgCtQQ4ArkEOAK91DgB+agCAgmoAgIZqAICKagCAjmoAgJJqAICWagCAmmoAgLihDgC5oQ4Aug0BALsFAQC8HQEAvQEBAL4BAQC/AQEAsA0OALHJDgCy2Q4As9UOALSxDgC1sQ4AtqkOALehDgCjwQ4AnmoAgKJqAICmagCAqmoAgKbBDgCl1Q4ArmoAgKsNDgCqyQ4AsmoAgLZqAICvdQ4ArnUOAK0BDgCsDQ4AumoAgL5qAIDCagCAxmoAgIANAACBNQAAgj0AAMpqAIDOagCA0moAgISEAQC+hAEAhjAHAIf4AADaagCA3moAgKjBAgCp0QIAqtECAKvlAgCs/QIArTUDAK49AwCvNQMA4moAgOZqAIDqagCA7moAgPJqAID2agCA+moAgP5qAIC40QMAudkDALrhAwC74QMAvJEDAL2RAwC+kQMAv5EDALBNAwCxVQMAsl0DALNVAwC0TQMAtfEDALbxAwC38QMAu7EDALqpAwACawCAvoQDAL8VAwC+qQMAvaEDALypAwCzeQIABmsAgAprAIAOawCAEmsAgLaVAwC1VQIAFmsAgKrtAwCr9QMAGmsAgB5rAICu7QMAr1EDAKztAwCt5QMAImsAgKM9AgAmawCAKmsAgKbRAwAuawCAMmsAgKURAgA2awCAgiEAAIEVAACAFQAA7wQAAISUAgA6awCAPmsAgOPYAABCawCA4fgBAEprAIBOawCAUmsAgFZrAIBaawCAhmAFAIcIBQBeawCAs20BAGJrAIC1fQEAtnUBAGZrAIBqawCAbmsAgLpRAQC7UQEAvPkBAL3RAQC+0QEAv9EBAHJrAICjpQEAdmsAgHprAICmvQEAfmsAgIJrAICltQEAqpkBAKuZAQCGawCAimsAgK4ZAQCvGQEArDEBAK0ZAQCOawCA4fQOAJJrAIDjFA4A9AAAAOF8DACWawCA41AKAJprAICeawCAviAEAO8wDQCiawCApmsAgIQ0BADvrA4AsDkGALE5BgCygQYAs6kGALS5BgC1uQYAtqkGALehBgC46QYAuekGALrJBgC7xQYAvN0GAL3BBgC+wQYAvz0HAEZrAICCHQAAgR0AAIAdAACqawCArmsAgLJrAIDWagCAqJkFAKmZBQCqSQYAq0kGAKxZBgCtWQYArkkGAK9JBgCorQcAqbUHAKq9BwCrtQcArK0HAK3dBwCuyQcAr8EHALZrAIC6awCAhogDAIcQAwC+awCAwmsAgMZrAIDKawCAuG0HALkFBwC6AQcAuxUHALwxBwC9MQcAvikHAL8pBwCwgQcAsYEHALJpBwCzZQcAtH0HALVhBwC2YQcAt1UHALM1BgDOawCA0msAgNZrAIDaawCAtl0GALUlBgDeawCAu0UGALpFBgDiawCA5msAgL+lBgC+uQYAvbEGALy9BgDqawCAo3EGAO5rAIDyawCAphkGAPZrAID6awCApWEGAKoBBgCrAQYA/msAgAJsAICu/QYAr+EGAKz5BgCt9QYAqCUBAKk1AQCqPQEAqzUBAKwtAQCtkQAArpEAAK+RAAAGbACACmwAgA5sAIASbACAFmwAgIK9AwCBvQMAgL0DALiZAAC5rQAAuqUAALttAAC8dQAAvX0AAL51AAC/bQAAsPEAALH5AACywQAAs8EAALSxAAC1vQAAtrUAALepAAAabACAHmwAgCJsAICEgAIAvhwCACpsAICG+HwAh8wCAISsAwAubACAMmwAgDZsAIA6bACAPmwAgEJsAIBGbACAs/UCAEpsAIBObACAkgAAAFJsAIC2UQMAteUCAFZsAIC7fQMAunUDAFpsAIBebACAvzkDAL41AwC9VQMAvFUDAKM1AgBibACAZmwAgGpsAIBubACAppEDAKUlAgBybACAq70DAKq1AwB2bACAemwAgK/5AwCu9QMArZUDAKyVAwC+wAMAfmwAgIJsAICGbACAgA0AAIE1AACCPQAAimwAgI5sAICSbACAhsh8AIcAAwCabACAnmwAgKJsAICmbACAqmwAgK5sAICybACAtmwAgLpsAIC+bACAwmwAgO/0AwCE7HwA4ZQBAMZsAIDjMAMAymwAgM5sAIDSbACA1mwAgLNpAQDabACA3mwAgOJsAIDmbACAtmEBALVpAQDqbACAuykBALohAQDubACA8mwAgL8dAQC+HQEAvSUBALwtAQD2bACA+mwAgP5sAICjpQEAAm0AgKWlAQCmrQEAvlR8AIaAfACH7HwAqu0BAKvlAQCs4QEArekBAK7RAQCv0QEACm0AgOGcBgCEBH8A4yQGAOPUBgAObQCA4TAEABJtAIDvlAcAgnUAAIFhAACAaQAAFm0AgBptAIAebQCA7+wGALiNfgC5lX4AupV+ALulfgC8vX4AvdF+AL7RfgC/0X4AsGV+ALFtfgCyeX4As3F+ALRZfgC1WX4Atr1+ALe1fgCoVX4AqWF+AKphfgCrYX4ArGF+AK1hfgCuYX4Ar2F+ACJtAICWbACAJmwAgCZtAIAGbQCAKm0AgC5tAIAybQCAqHF+AKlxfgCqcX4Aq3F+AKyRfwCtkX8ArpF/AK+RfwA2bQCAOm0AgD5tAIBCbQCARm0AgEptAIBObQCAUm0AgLiFfwC5jX8AuoV/ALudfwC8jX8Avb1/AL61fwC/XX8AsPF/ALHxfwCy8X8As8V/ALTBfwC1wX8AtsF/ALfBfwCz+X8AVm0AgFptAIBebQCAYm0AgLYRfgC1GX4AZm0AgLs1fgC6NX4Aam0AgG5tAIC/BX4AvgV+AL0RfgC8JX4AghUAAKO9fwCAYQAAgWEAAKZVfgBybQCAvpABAKVdfgCqcX4Aq3F+AHZtAIB6bQCArkF+AK9BfgCsYX4ArVV+AKhBfgCpUX4AqlV+AKt9fgCsZX4ArW1+AK75AQCv8QEAhgAAAIc0AQB+bQCAgm0AgIZtAICKbQCAjm0AgJJtAIC4dQEAuX0BALp1AQC7yQAAvNkAAL3ZAAC+yQAAv8EAALCVAQCxnQEAspUBALNNAQC0VQEAtV0BALZVAQC3TQEAs919AJZtAICabQCAnm0AgKJtAIC27X0Ate19AKZtAIC7WQIAulECAKptAICubQCAv5kCAL6RAgC9mQIAvEECALJtAICjmX0Atm0AgLptAICmqX0Avm0AgMJtAIClqX0AqhUCAKsdAgDGbQCAym0AgK7VAgCv3QIArAUCAK3dAgDObQCA0m0AgNZtAIDabQCAgB0AAIEJAACCOQAA3m0AgOJtAIC+AAQA6m0AgO5tAIDybQCA9m0AgPptAID+bQCAhIwDAAJuAICHCAMAhuwEAAZuAIDviAIACm4AgA5uAICEbAQA4zQCABJuAIDhVAEAFm4AgBpuAIAebgCAIm4AgKhtAgCprQIAqqUCAKu9AgCspQIAra0CAK6lAgCvGQEAvqwEACZuAIAqbgCALm4AgDJuAIA2bgCAOm4AgD5uAIC4DQEAuREBALoRAQC7JQEAvD0BAL3VAQC+3QEAv9UBALBpAQCxaQEAsnkBALNxAQC0WQEAtVkBALY5AQC3NQEAsy0CAEJuAIBGbgCASm4AgE5uAIC2LQIAtS0CAFJuAIC7rQEAuq0BAFpuAIBebgCAv50BAL6dAQC9pQEAvK0BAIBNAACBVQAAglUAAO9sAABibgCA7+x/AO+8fgBmbgCA4RB/AOPUfwDj2H4A4ex/AGpuAIDhTH4Abm4AgOMkfgDmbQCAVm4AgKsFBgCqBQYArQ0GAKwFBgCvNQYArjUGAIYAAwCHKAMAo4UFAHJuAIClhQUAdm4AgHpuAICmhQUAs/EGAH5uAICCbgCAhm4AgIpuAIC26QYAteEGAI5uAIC7vQYAur0GAJJuAICWbgCAv4kGAL6BBgC9iQYAvJUGAKgpBgCpKQYAqjkGAKs5BgCsKQYArSkGAK5dBgCvTQYAmm4AgJ5uAICibgCApm4AgKpuAICubgCAsm4AgLZuAIC46QcAuekHALr5BwC7+QcAvOkHAL3pBwC+XQcAv1UHALA5BgCxOQYAsgEGALMdBgC0BQYAtQ0GALYFBgC32QcAo7EHAIItAACBFQAAgB0AALpuAICmqQcApaEHAL5uAICr/QcAqv0HAMJuAICEpAIAr8kHAK7BBwCtyQcArNUHAL7MAQCzlQYAxm4AgMpuAIC2qQYAzm4AgNJuAIC1rQYAulkBALshAQCGyAAAhwwBAL4hAQC/KQEAvDEBAL0xAQCoKQYAqSkGAKpZBgCrUQYArGEGAK1tBgCutQEAr6kBAITgAQDWbgCA2m4AgN5uAIDibgCA5m4AgOpuAIDubgCAuGEBALlhAQC6YQEAu2EBALxhAQC9YQEAvmEBAL9hAQCw2QEAsaEBALKhAQCzoQEAtKEBALWpAQC2kQEAt5EBAKPRBQDybgCA9m4AgPpuAID+bgCApu0FAKXpBQACbwCAq2UCAKodAgAGbwCACm8AgK9tAgCuZQIArXUCAKx1AgAObwCAEm8AgBZvAIAabwCAHm8AgCJvAIAmbwCAKm8AgIA9AACBCQAAghkAAC5vAIAybwCAOm8AgL48AwA+bwCAhgAMAIcUAwBCbwCAs9UDAEZvAIC1PQMAtjUDAEpvAIBObwCAv4wKALoRAwC7EQMAvLUAAL29AAC+tQAAv60AAFJvAIDjdAEAVm8AgOG8AQBabwCAXm8AgGJvAIBmbwCAam8AgG5vAIBybwCAdm8AgHpvAIDvdAIAfm8AgIJvAICoTQIAqVECAKpRAgCrqQIArLkCAK25AgCuqQIAr6kCAIRsDQCGbwCAim8AgI5vAICSbwCAlm8AgJpvAIC+dA0AuG0BALkFAQC6DQEAuwUBALwdAQC9BQEAvg0BAL8FAQCw2QIAsdkCALJtAQCzZQEAtH0BALVlAQC2ZQEAt1UBAOG4AQDhUAcA47QAAON8BwCAqQAAgQkAAII5AACebwCAom8AgKpvAICubwCAsm8AgO4AAAC2bwCA7wAAAO9kBgCGYAwAh+QMAKORAgC6bwCApXkCAL5vAIDCbwCApnECAMZvAIDKbwCAq1UCAKpVAgCt+QEArPEBAK/pAQCu8QEApm8AgDZvAIDObwCA0m8AgNZvAIDabwCA3m8AgOJvAICoVQ4AqVkOAKqhDgCrvQ4ArK0OAK2VDgCu+Q4Ar/UOALCRDgCxkQ4AspEOALORDgC0sQ4AtbEOALaxDgC3sQ4AuJEOALmdDgC6lQ4Au0kPALxZDwC9WQ8AvkkPAL9JDwCzCQ4A5m8AgOpvAIDubwCA8m8AgLY1DgC1BQ4A9m8AgLt1DgC6dQ4A+m8AgP5vAIC/VQ4AvlUOAL1lDgC8ZQ4AAnAAgKNNDgAGcACACnAAgKZxDgAOcACAEnAAgKVBDgCqMQ4AqzEOAISkAwC+pAMArhEOAK8RDgCsIQ4ArSEOAKilDgCprQ4AqqUOAKu5DgCs3Q4ArcEOAK7BDgCv/Q4AgO0BAIHxAQCC8QEAFnAAgIaQAQCHtAEAGnAAgB5wAIC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALCFDgCxbQEAsmUBALN9AQC0ZQEAtW0BALZlAQC3+QEAsy0OACJwAIAmcACAKnAAgC5wAIC2QQ4AtVUOADJwAIC7qQEAukEOADZwAIA6cACAv6kBAL6hAQC9qQEAvLEBAD5wAICjaQ4AQnAAgEZwAICmBQ4ASnAAgE5wAIClEQ4AqgUOAKvtAQBScACAVnAAgK7lAQCv7QEArPUBAK3tAQCoOQMAqTkDAKqNAwCrhQMArJ0DAK2FAwCuhQMAr7UDAFpwAIBecACAYnAAgGZwAIBqcACAbnAAgHJwAIB2cACAuGEAALlhAAC6YQAAu2EAALxhAAC9YQAAvmEAAL9hAACwzQMAsaUDALKhAwCzoQMAtKUDALWtAwC2kQMAt5EDAIANAACBEQAAghEAAHpwAIDv9AIAfnAAgIJwAIC+HAMA4xQCAISIAgDhgAEAinAAgI5wAICScACAh8gDAIY8BAC7AQMAumkDAJZwAICacACAvwkDAL4BAwC9FQMAvBUDALNlAwCecACAonAAgKZwAICqcACAtmUDALV1AwCucACAsnAAgLZwAIC6cACAo4kCAL5wAIClmQIApokCAMJwAICELAIAxnAAgKqFAgCr7QIArPkCAK35AgCu7QIAr+UCAMpwAIDOcACAvkQFAIRMBQDScACA1nAAgNpwAIDecACA4nAAgOZwAIDqcACA7nAAgIAZAACBGQAAggUAAPJwAIDhGA8A4VwOAOO4DgDjdAEA+nAAgP5wAIACcQCABnEAgIYABACHZAUACnEAgA5xAIAScQCAFnEAgO98DgDvqAEAs3UBABpxAIAecQCAInEAgCZxAIC2MQEAtRUBACpxAIC7HQEAuhUBAC5xAIAycQCAv+EAAL79AAC9/QAAvP0AAPZwAIA2cQCAOnEAgD5xAICGcACAQnEAgEZxAIBKcQCAqI0GAKmVBgCqnQYAq+UGAKz9BgCt0QYArtEGAK/RBgCwsQYAsbkGALJJBwCzSQcAtFkHALVFBwC2RQcAt3kHALghBwC5IQcAujkHALs5BwC8KQcAvSkHAL4ZBwC/GQcAozUGAE5xAIBScQCAVnEAgFpxAICmcQYApVUGAF5xAICrXQYAqlUGAGJxAIC+oAMAr6EHAK69BwCtvQcArL0HAIBRAACBWQAAgmEAALNVBwCF9AAAtX0HALZ1BwBmcQCAhgAcAIfkAQC6LQcAuyUHALw9BwC9JQcAviUHAL8VBwCokQYAqZEGAKqRBgCrkQYArLkGAK25BgCuqQYAr6kGAGpxAIBucQCAcnEAgHZxAICiIQEAozUBAKA5BQChEQQAuEkBALlJAQC6XQEAu1UBALxNAQC90QEAvtEBAL/RAQCwpQYAsa0GALKlBgCzvQYAtK0GALWdBgC2lQYAt3kBAKMZBgCPnXkAenEAgH5xAICCcQCApjkGAKUxBgCGcQCAq2kGAKphBgCKcQCAjnEAgK9ZBgCuaQYArWkGAKxxBgCeiQgAn8EFAJzJCQCdyQkAmqENAJu9DACYsQ0AmbkNAJahcQCXRXEAlEV1AJWxcQCSoXUAk7V1AJDleQCRzXkAil1yAItFcgCScQCAvoAcAI51DgCPZQ4AjLlyAI11DgCCOXoAgzl6AJZxAICacQCAhnF2AIeZdgCECXoAhW12AJptBwCbVQIAnnEAgKJxAICmcQCA4ZAAAJxZAgDjCBoAkgkPAJNlCgCqcQCA7zgWAJZ1BgCXdQYAlH0KAJU1CwCpjRYAqIUWAKsBEACqMRYArXESAKy1EgCvuS4ArgEsAKF9AgCucQCAo6EeAKKpHgClsRoApPUfAKflGwCmsRoAhMwDAIRMHACycQCAtnEAgLpxAIC+cQCAwnEAgMZxAICxASgAsNkuALONKgCy6SoAtfUmALQBJACEcB0AynEAgID9AQCBFQAAgh0AAL6AHADOcQCA0nEAgIe4AgCGPB0A2nEAgN5xAIDicQCA5nEAgOpxAIDucQCA8nEAgPZxAID6cQCA/nEAgAJyAIAGcgCA44ADAApyAIDhoAEADnIAgO+UAwAScgCAFnIAgBpyAIAecgCAInIAgCZyAIAqcgCALnIAgOE8BgAycgCA49AGADZyAIDhMAcAOnIAgOOsBgCAOQAAgRUAAIIdAADvHAYAPnIAgEJyAIC+uB8A7+gBALPpAgBKcgCAh8QcAIbsHABOcgCAtlkCALVRAgBScgCAu00CALpNAgBWcgCAWnIAgL+5AQC+2QEAvdEBALz1AQCjKR0A1nEAgEZyAIBecgCAYnIAgKaZHQClkR0AZnIAgKuNHQCqjR0AanIAgG5yAICveR4ArhkeAK0RHgCsNR4AcnIAgLNtHwB2cgCAenIAgLZlHwB+cgCAgnIAgLVtHwC6IR8AuyEfAIZyAICKcgCAviUfAL8pHwC8MR8AvTEfAKihHwCpoR8AqqEfAKuhHwCsoR8AraEfAK6hHwCvoR8AjnIAgJJyAICWcgCAmnIAgJ5yAICicgCApnIAgKpyAIC4rR8AubUfALq9HwC7tR8AvK0fAL1VHwC+UR8Av00fALChHwCxoR8AsqEfALOhHwC0pR8AtakfALadHwC3lR8AoykeAIIZAACBGQAAgLEBAK5yAICmIR4ApSkeALJyAICrZR4AqmUeAIaIAACH/AEAr20eAK5hHgCtdR4ArHUeALZyAICzmR4AunIAgL5yAIC2XQEAwnIAgMZyAIC1sR4AukkBALtJAQDKcgCAznIAgL49AQC/IQEAvDkBAL01AQCoRR4AqVUeAKpVHgCrZR4ArH0eAK2ZAQCuiQEAr4EBAISsAADScgCA1nIAgNpyAIDecgCA4nIAgOZyAIDqcgCAuK0BALllAQC6bQEAu2UBALx9AQC9ZQEAvm0BAL9lAQCwyQEAsckBALKpAQCzpQEAtL0BALWhAQC2oQEAt5UBALhpHAC5oRwAusEcALvBHAC8wRwAvcEcAL7BHAC/wRwAsIkfALGJHwCyIRwAswUcALQdHAC1fRwAtnUcALdtHACoYR8AqWEfAKphHwCrYR8ArNkfAK3ZHwCuyR8Ar8EfAO5yAIDycgCA9nIAgPpyAID+cgCAAnMAgAZzAIAKcwCADnMAgBJzAIC+AAQAo1EdABZzAICleR0AppUCABpzAIAecwCAInMAgKqBAgCrgQIArPECAK39AgCu9QIAr+kCACpzAIDh9AEALnMAgON8AQCATQAAgXUAAIJ9AAAycwCAhsAEAIekBAA2cwCAOnMAgD5zAIBCcwCARnMAgO+MAgCoSQIAqUkCAKpdAgCrVQIArHkCAK15AgCuvQIAr7UCAISgBQBKcwCATnMAgFJzAIC+vAQAVnMAgFpzAIBecwCAuC0BALk1AQC6PQEAuzUBALwtAQC91QEAvt0BAL/NAQCwzQIAsdUCALLdAgCz1QIAtM0CALUVAQC2HQEAtxUBAOGEHgDjbB8A41wfAOFYHgBicwCAZnMAgGpzAIBucwCAcnMAgHZzAIB6cwCAfnMAgOkAAADv9B4A70weAIJzAICzlQIAhnMAgIpzAICOcwCAknMAgLa5AgC1sQIAmnMAgLtRAgC6SQIAhsgEAIesBAC/kQEAvkkCAL1BAgC8SQIAJnMAgKNRBQCecwCAlnMAgKZ9BQCicwCApnMAgKV1BQCqjQUAq5UFAKpzAICucwCAro0FAK9VBgCsjQUArYUFAICJBwCBiQcAgpkHALORBgCycwCAtbkGALapBgC2cwCAunMAgL5zAIC6TQcAu0UHALxdBwC9QQcAvkEHAL9BBwCoQQYAqU0GAKpVBgCrZQYArH0GAK1lBgCubQYAr2UGAMJzAIDGcwCAynMAgM5zAIDScwCA1nMAgNpzAIDecwCAuFkHALlZBwC6aQcAu2kHALx5BwC9eQcAvmUHAL8ZBwCwxQcAsc0HALLFBwCz2QcAtMkHALXJBwC2aQcAt2kHAKPdBwDicwCA5nMAgOpzAIDucwCApuUHAKX1BwDycwCAqwkGAKoBBgD2cwCA+nMAgK8NBgCuDQYArQ0GAKwRBgCAbQAAgQkAAIIZAAD+cwCAAnQAgISYAQC+kAEABnQAgIbAAACH5AEACnQAgA50AIASdACAFnQAgBp0AIAedACAqF0GAKmNAQCqnQEAq5UBAKy5AQCtuQEArskBAK/BAQCEoAAAInQAgCZ0AIAqdACALnQAgDJ0AIA2dACAOnQAgLh5AQC5eQEAus0AALvFAAC83QAAvcUAAL7FAAC/9QAAsIEBALGBAQCySQEAs0kBALRZAQC1WQEAtkkBALdJAQCzFQIAPnQAgEJ0AIBGdACASnQAgLY5AgC1MQIATnQAgLtFAgC6RQIAUnQAgFZ0AIC/nQIAvp0CAL2dAgC8nQIAhXw+AKNRAgBadACAXnQAgKZ9AgBidACAZnQAgKV1AgCqAQIAqwECAGp0AIBudACArtkCAK/ZAgCs2QIArdkCAIDpAACB6QAAggUAAHJ0AIC+AAwAenQAgIeoAwCGvAwAfnQAgIJ0AICGdACAinQAgI50AICSdACAlnQAgJp0AICedACAonQAgKZ0AICqdACA42ABAK50AIDhoAEAsnQAgO+IAgC2dACAunQAgL50AIDCdACAxnQAgMp0AIDOdACAqGkCAKlpAgCqeQIAq3kCAKxpAgCtaQIArr0CAK+1AgC+rAwA0nQAgNZ0AIDadACAgB0AAIEJAACCqQAA3nQAgLhRAQC5WQEAumEBALthAQC8GQEAvRkBAL4NAQC/BQEAsM0CALHVAgCy3QIAs9UCALTNAgC1cQEAtnEBALdxAQDjxAAA4XwHAOF4BgDjvAYA4nQAgIQYDQCGuAwAhzwNAL4sDwDqdACA7nQAgPJ0AIDvEAAA9nQAgPp0AIDvdAYA/nQAgAJ1AIAGdQCAs70CAAp1AIC1rQIAtqUCAA51AIASdQCAFnUAgLpFAgC7XQIAvEUCAL1NAgC+RQIAv/kBAHZ0AIClfQ0ApnUNAOZ0AIAadQCAHnUAgCJ1AICjbQ0ArJUNAK2dDQCulQ0ArykOACZ1AIAqdQCAqpUNAKuNDQCz5Q4ALnUAgDJ1AIA2dQCAOnUAgLblDgC19Q4APnUAgLuhDgC62Q4AQnUAgEZ1AIC/pQ4AvrkOAL2xDgC8uQ4AqBUOAKklDgCqLQ4AqyUOAKw9DgCtJQ4Ari0OAK8lDgCADQAAgRUAAIIdAABKdQCATnUAgFJ1AICEMAMAVnUAgLgpDgC5KQ4AujkOALs5DgC8KQ4AvSkOAL79DwC/9Q8AsF0OALElDgCyLQ4AsyUOALQ9DgC1IQ4AtiUOALcZDgCjpQ8AWnUAgIYoAQCHTAEAXnUAgKalDwCltQ8AYnUAgKvhDwCqmQ8AZnUAgGp1AICv5Q8ArvkPAK3xDwCs+Q8AbnUAgLPpDgBydQCAdnUAgLaRDgB6dQCAfnUAgLXlDgC6sQ4Au7kOAIJ1AICGdQCAvmEBAL9hAQC8mQ4AvZkOAKglDgCpLQ4AqiUOAKs5DgCsKQ4ArVUOAK5dDgCvVQ4AinUAgI51AICSdQCAlnUAgJp1AICedQCAonUAgKZ1AIC49QEAuYEBALqBAQC7gQEAvIEBAL2JAQC+sQEAv7EBALAxDgCxOQ4AsgkOALMJDgC04QEAteEBALbhAQC3zQEAo60NAKp1AICudQCAsnUAgLZ1AICm1Q0ApaENALp1AICr/Q0AqvUNAL51AIDCdQCAryUCAK4lAgCt3Q0ArN0NAIBdAACBbQAAgmUAALNRAwC+nAMAtXkDALYZAwDKdQCAhOACAM51AIC6PQMAuzUDALwZAwC9GQMAvtkDAL/ZAwCohQMAqZUDAKqVAwCrpQMArL0DAK3VAwCu0QMAr9EDAIYABACHNAMAv6AzANJ1AIDWdQCA2nUAgN51AIDidQCAuHEDALlxAwC6cQMAu3EDALzVAAC93QAAvtUAAL/NAACwtQMAsb0DALKBAwCzgQMAtFEDALVRAwC2UQMAt1EDAO+oAwDmdQCA6nUAgO51AICEHAIA8nUAgPZ1AID6dQCAviwFAP51AIACdgCABnYAgONAAwAKdgCA4SgAAA52AICjXQIAEnYAgBZ2AIAadgCAHnYAgKYVAgCldQIAInYAgKs5AgCqMQIAJnYAgCp2AICv1QIArtUCAK0VAgCsFQIA4ygBAOEADwDhCA4A4wgOAID9AACBCQAAgjkAAC52AIAydgCAOnYAgD52AIBCdgCA7+gOAEZ2AIBKdgCA72QOALNtAQBOdgCAhugEAIcMBQBSdgCAtm0BALVtAQBWdgCAu+0AALrtAABadgCAXnYAgL/VAAC+6QAAveEAALzpAACoXQYAqWEGAKqlBgCrvQYArKUGAK2tBgCupQYArxkHADZ2AIBidgCAZnYAgGp2AIBudgCAcnYAgHZ2AIB6dgCAuHUHALl5BwC6DQcAuwUHALwdBwC9BQcAvgUHAL81BwCwaQcAsWkHALJ9BwCzdQcAtG0HALVRBwC2UQcAt1EHAKMtBgB+dgCAgnYAgIZ2AICKdgCApi0GAKUtBgCOdgCAq60HAKqtBwCSdgCAlnYAgK+VBwCuqQcAraEHAKypBwCADQAAgRUAAIIdAACadgCAnnYAgKJ2AICEVAMAvlwAAKZ2AICqdgCAhugAAIdMAwCudgCAsnYAgLZ2AIC6dgCAvnYAgOMEBADCdgCA4bQFAMZ2AIDKdgCAznYAgNJ2AIDWdgCA2nYAgN52AIDidgCA5nYAgO/sBADqdgCA7nYAgLPtBgDydgCA9nYAgPp2AID+dgCAtpEGALXhBgACdwCAu40GALqNBgAGdwCACncAgL9BAQC+WQEAvVEBALxZAQCoJQYAqS0GAKolBgCrOQYArCkGAK1RBgCuSQYAr0EGAIDNAACBCQAAghkAAA53AIASdwCAhCwBAL40AAAadwCAuP0BALlBAQC6QQEAu0EBALxBAQC9SQEAvnEBAL9xAQCwCQYAsQkGALLNAQCzxQEAtN0BALXFAQC2zQEAt8UBAIagPACHRAMAHncAgKOhBQAidwCApa0FAKbdBQAmdwCAKncAgL4oPACqwQUAq8EFAKwVAgCtHQIArhUCAK8NAgC2QQMALncAgDJ3AIC1sQIANncAgLOhAgA6dwCAPncAgL5FAwC/TQMAvHUDAL1NAwC6ZQMAu20DAEJ3AIBGdwCASncAgE53AIDGdQCAUncAgFZ3AIBadwCAXncAgGJ3AICoRQIAqVUCAKpdAgCrVQIArE0CAK21AwCusQMAr60DALDVAwCx3QMAstUDALPtAwC09QMAtf0DALb1AwC37QMAuNkDALnZAwC6rQMAu6UDALy9AwC9pQMAvqUDAL+VAwCj9QMAZncAgGp3AIBudwCAcncAgKYVAgCl5QMAdncAgKs5AgCqMQIAencAgH53AICvGQIArhECAK0ZAgCsIQIAgGkAAIFpAACCBQAAgncAgIp3AICOdwCAkncAgO8cAACEbAIA4ZQBAJZ3AIDjyAAAmncAgJ53AICGWDwAh1A9AKJ3AICmdwCAqncAgISEPQCudwCAsncAgLZ3AIDvuAEAvmw8AOF0BgC6dwCA42QBAL53AIDCdwCAxncAgMp3AICz0QEAzncAgNJ3AIDWdwCA2ncAgLaRAQC1+QEA3ncAgLu9AQC6vQEA4ncAgOZ3AIC/dQEAvnUBAL2FAQC8hQEAqL09AKkNPgCqGT4AqxE+AKwxPgCtUT4ArlE+AK9NPgCGdwCAgh0AAIEdAACAHQAA6ncAgO53AIDydwCA9ncAgLjVPgC53T4AutU+ALtJPwC8WT8AvVk/AL5JPwC/QT8AsDk+ALE5PgCyET4AsxE+ALTxPgC18T4AtvU+ALftPgCjkT4A+ncAgIYoAACHwAMA/ncAgKbRPgCluT4AAngAgKv9PgCq/T4ABngAgAp4AICvNT4ArjU+AK3FPgCsxT4ADngAgLOdPwASeACAFngAgLalPwAaeACAHngAgLWtPwC6aT8Au3U/ACJ4AIAmeACAvlk/AL9FPwC8bT8AvWU/ACp4AIAueACAMngAgDZ4AIDjYDwAOngAgOEAPQA+eACA7/w9AEJ4AIBGeACASngAgE54AIBSeACAVngAgFp4AICjGT4AghkAAIEZAACAcQAAXngAgKYhPgClKT4AYngAgKvxPgCq7T4AhCQBAL4kAQCvwT4Art0+AK3hPgCs6T4AqNE+AKnRPgCq0T4Aq+U+AKzhPgCt4T4Arhk+AK8ZPgCGAAAAh4QAAGp4AIBueACAcngAgHZ4AIB6eACAfngAgLh9PgC5AT4AugE+ALsBPgC8AT4AvQk+AL4xPgC/MT4AsGk+ALF1PgCyfT4As3U+ALRZPgC1RT4Atk0+ALdFPgCohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAIJ4AICGeACAingAgL8k5gGOeACAkngAgJZ4AICaeACAuFUDALlZAwC6bQMAu2UDALx9AwC9ZQMAvm0DAL9lAwCwtQIAsb0CALKBAgCzgQIAtHEDALVxAwC2cQMAt3EDALMdAgCeeACAongAgKZ4AICEiAMAtlUCALU1AgAWdwCAu3kCALpxAgCqeACArngAgL+1AwC+tQMAvVUCALxVAgCyeACAo1kCALZ4AIC6eACAphECAL54AIDCeACApXECAKo1AgCrPQIAxngAgMp4AICu8QMAr/EDAKwRAgCtEQIAqKkCAKmpAgCquQIAq7kCAKypAgCtqQIArjkBAK85AQCAzQEAgQkAAIIZAADOeACA0ngAgL64BQDaeACA3ngAgLjpAQC56QEAuokBALuFAQC8nQEAvYEBAL6BAQC/tQEAsEkBALFVAQCyXQEAs1UBALRNAQC18QEAtvEBALfxAQDvFAAA4ngAgIaoBQCH3AUA5ngAgIRYBADqeACA78Q+AO54AIDhxD4A8ngAgOMwPgDjyAAA9ngAgOEoAQD6eACAtn0CAP54AIACeQCAtXUCAAZ5AICzZQIACnkAgA55AIC+3QEAv2EBALzdAQC91QEAutkBALvFAQASeQCAFnkAgKOxBQDWeACAGnkAgB55AIAieQCApqkFAKWhBQAmeQCAqxEGAKoNBgAqeQCALnkAgK+1BgCuCQYArQEGAKwJBgAyeQCANnkAgDp5AIA+eQCAgBkAAIEZAACCBQAAQnkAgL5sAwBGeQCAhsgAAIccAwBKeQCATnkAgFJ5AIBWeQCAqLkHAKm5BwCqDQcAqx0HAKwJBwCtNQcArjEHAK8pBwCEqAMAWnkAgF55AIBieQCAZnkAgGp5AIBueQCAcnkAgLjJAAC5yQAAutkAALvRAAC8+QAAvfkAAL6ZAAC/mQAAsF0HALEhBwCyIQcAsz0HALQpBwC1KQcAtgEHALcBBwCzhQYAdnkAgHp5AIB+eQCAgnkAgLa1BgC1gQYAhnkAgLvlBgC6mQYAinkAgI55AIC/7QYAvu0GAL3pBgC89QYAknkAgJZ5AICaeQCAnnkAgKJ5AICmeQCAqnkAgO+QBACueQCA4dwGALJ5AIDj7AUAgCkAAIEVAACCEQAAvnwBAKMFBgC6eQCAhigAAIdMAQC+eQCApjUGAKUBBgDCeQCAq2UGAKoZBgDGeQCAynkAgK9tBgCubQYArWkGAKx1BgDOeQCAs70BANJ5AIDWeQCAtnkBANp5AIDeeQCAtXkBALpVAQC7XQEA4nkAgOZ5AIC++QAAv/kAALxFAQC9+QAAqHECAKlxAgCqcQIAq3ECAKy1AgCtvQIArrUCAK+tAgCE7AwA6nkAgO55AIDyeQCA9nkAgPp5AID+eQCAAnoAgLhpAwC5aQMAugkDALsJAwC8GQMAvRkDAL4JAwC/CQMAsNUCALHdAgCy1QIAs2kDALR5AwC1eQMAtmkDALdhAwAGegCACnoAgA56AICj9QIAEnoAgKUxAgCmMQIAFnoAgBp6AIAeegCAqh0CAKsVAgCsDQIArbEDAK6xAwCvsQMAgGEAAIFhAACCBQAAInoAgIbwDACHYAMAvhAMACp6AIBmeACALnoAgDJ6AIA2egCAOnoAgD56AIBCegCARnoAgKiFAgCplQIAqpUCAKulAgCsvQIArdUCAK7RAgCv0QIASnoAgE56AIBSegCAVnoAgFp6AIBeegCAYnoAgGZ6AIC4dQEAuX0BALp1AQC7zQEAvNUBAL3dAQC+yQEAv8EBALC1AgCxvQIAsoECALOBAgC0VQEAtV0BALZVAQC3TQEA4RAGAIRIDADjDAYAanoAgISYDABuegCAcnoAgHZ6AIB6egCAfnoAgIJ6AICGegCAgXUAAIB1AADvIAEAgnUAAIp6AICOegCAknoAgL7ADACFtA4A4RACAO9cAADjABYA4ZABAJp6AIDjWAEA7zwHAJ56AICiegCAhgAIAIe4DACznQ0AJnoAgKZ6AICqegCArnoAgLbVDQC1tQ0AsnoAgLv5DQC68Q0AtnoAgLp6AIC/GQ4AvhEOAL3VDQC81Q0AvnoAgKPZDQDCegCAxnoAgKaRDQDKegCAznoAgKXxDQCqtQ0Aq70NANJ6AIDWegCArlUOAK9dDgCskQ0ArZENAKhdDgCpYQ4AqmEOAKthDgCsYQ4ArWEOAK5hDgCvYQ4A2noAgN56AIDiegCA5noAgOp6AIDuegCA8noAgPZ6AIC4TQ8AuVEPALpRDwC7UQ8AvHEPAL1xDwC+cQ8Av3EPALDBDwCxwQ8AssEPALPBDwC0wQ8AtcEPALbBDwC3wQ8As+kPAPp6AIC+gAEA/noAgJZ6AIC24Q8AtekPAAJ7AIC7BQ4AugUOAAp7AIAGewCAvwUOAL4FDgC9FQ4AvBUOAIFNAACAQQAA72gNAIJRAACG8AcAh9QBAA57AIASewCAFnsAgIRwAQAaewCAHnsAgOHgDgAiewCA40gNACZ7AICjaQ8AKnsAgC57AIAyewCANnsAgKZhDwClaQ8AOnsAgKuFDgCqhQ4APnsAgEJ7AICvhQ4AroUOAK2VDgCslQ4ARnsAgLMxDgBKewCATnsAgLbBAQBSewCAVnsAgLXRAQC6zQEAu6UBAFp7AIBeewCAvqUBAL+tAQC8sQEAvbEBAI/dJgCj8Q0AYnsAgGZ7AICmAQIAansAgG57AIClEQIAqg0CAKtlAgByewCAviAEAK5lAgCvbQIArHECAK1xAgCfoQwAnnkKAJ1pCgCc0QgAm7E2AJp1NgCZ0TQAmOEyAJdtMgCWZTIAlTU/AJRhPgCTcT4AkjU7AJFxOgCQeToAgJUAAIGdAACCoQAAensAgO9EAgDhdA8AfnsAgOMcDwDj1AEAgnsAgOHgAQDvXAEAo7UCAKJBAACh3Q4AoLkOALWpAwCGewCAhMAEALahAwCG8AUAh+QEALOFAwCKewCAvXEDALxpAwC/QQMAvnEDAI57AIC2eQCAu3EDALp5AwCC3ScAgwE7AL6EBwC+wAYAhhE/AIcZPwCEETsAhV06AIp9PgCLJTMAknsAgJZ7AICOuTUAjxU3AIw1MwCNgTMAkqE3AJPZCQC+xBkAmnsAgJaxDQCXUQ8AlHkLAJVhCwCaBQ8Am5EBAJ57AICiewCApnsAgN0AAACcfQMAqnsAgOFIDwCuewCA4xwOALJ7AIC2ewCAunsAgL57AIDCewCAsUEXALChFwCzqesBsgHoAbUB7AG0EesB74wOAMZ7AICpxR8AqAEcAKsBEACqkR8ArdkTAKzREwCv2RcArgUTAKHxAgDKewCAo8kHAKLBAgClARgApGUHAKehGwCm+RsAqCkFAKldBQCqVQUAq20FAKx5BQCteQUArm0FAK9hBQB2ewCAznsAgNJ7AIDWewCAgA0AAIGxAACCsQAA2nsAgLiJBQC5iQUAup0FALuVBQC8uQUAvbkFAL5RBgC/UQYAsOUFALHtBQCy5QUAs/0FALTtBQC13QUAttUFALe9BQCj3QUA3nsAgOJ7AICEDAAA5nsAgKb5BQCl8QUA6nsAgKspBQCqIQUAhpgAAIegAACvGQUArikFAK0pBQCsMQUA7nsAgLNhBgDyewCA9nsAgLYhBgD6ewCA/nsAgLUBBgC6rQcAu40HAAJ8AIAGfACAvo0HAL9xBwC8lQcAvY0HAL65BQC/uQUAvLkFAL25BQC6uQUAu7kFALi5BQC5uQUAtkkFALdJBQC0fQUAtXUFALJ5BQCzeQUAsBUFALF9BQCuXQUAr20FAKxFBQCtXQUAqqUKAKtdBQCovQoAqa0KAAp8AIAOfACAEnwAgBZ8AIAafACAHnwAgCJ8AIAmfACAqA0HAKkdBwCqLQcAq0kHAKxNBwCtZQcArrEGAK+xBgAqfACALnwAgDJ8AIA2fACAOnwAgD58AIBCfACARnwAgLhVBgC5XQYAulUGALtxBgC8NQYAvfEBAL7xAQC/8QEAsK0GALGNBgCyhQYAs50GALSNBgC1cQYAtnUGALdtBgCjpQQAgi0AAIEVAACAHQAASnwAgKblBAClxQQATnwAgKtJBQCqaQUAUnwAgFp8AICvtQUArkkFAK1JBQCsUQUAhmAcAIcIAwBefACAs4UCAGJ8AIC1gQIAtoECAGZ8AIBqfACAbnwAgLoJAwC7CQMAvBkDAL0ZAwC+CQMAvwkDAKxVAgCtXQIArmECAK9hAgCoDQIAqVUCAKpRAgCrUQIAhKwDAHJ8AIB2fACAenwAgIT8HQB+fACAgnwAgIZ8AIC8cQMAvXEDAL5xAwC/cQMAuHEDALlxAwC6cQMAu3EDALSRAwC1kQMAtpEDALeRAwCwkQMAsZEDALKRAwCzkQMAinwAgI58AICSfACAlnwAgJp8AIDhpAEAnnwAgOOAAQC+aBwAonwAgKZ8AIDv2AYAqnwAgK58AICyfACAtnwAgKOJAwCCLQAAgRUAAIAdAAC6fACApo0DAKWNAwC+fACAqwUCAKoFAgDCfACAynwAgK8FAgCuBQIArRUCAKwVAgCGIBwAh8QdAM58AIDSfACA1nwAgNp8AIDefACA72wGAOJ8AIDhbAcA5nwAgON0BwDqfACA7nwAgPJ8AID2fACAs5EBAPp8AID+fACAAn0AgAZ9AIC2sQEAtbkBAAp9AIC7VQEAukkBAA59AIASfQCAv/UAAL71AAC9RQEAvEUBAKNRHgDGfACAFn0AgBp9AIAefQCApnEeAKV5HgAifQCAq5UeAKqJHgAmfQCAKn0AgK81HwCuNR8ArYUeAKyFHgCAbQAAgRUAAIIdAADv/BkALn0AgDJ9AIA2fQCAOn0AgIbAAACHrAMAPn0AgEJ9AIBGfQCA4SwcAEp9AIDjzBwAqK0eAKnNHgCq2R4Aq9EeAKzxHgCt8R4Arj0eAK81HgCE7AAATn0AgFJ9AIBWfQCAWn0AgF59AIBifQCAZn0AgLjRHwC53R8Auu0fALvlHwC84R8AveEfAL7hHwC/4R8AsE0eALFRHgCyUR4As1EeALTxHwC18R8AtvEfALfxHwCobR4AqY0eAKqFHgCrnR4ArIUeAK2NHgCuuR4Ar7UeAGp9AIBufQCAcn0AgHZ9AIB6fQCAfn0AgIJ9AICGfQCAuJ0eALmtHgC6pR4Au0UBALxdAQC9RQEAvkUBAL91AQCw0R4AsdEeALLRHgCz0R4AtLUeALW9HgC2tR4At60eALMNHgCKfQCAjn0AgJJ9AICWfQCAtg0eALUNHgCafQCAuxUeALoVHgCefQCAon0AgL95HgC+cR4AvQUeALwFHgCCbQAAo0keAIBVAACBZQAApkkeAL6cAQCqfQCApUkeAKpRHgCrUR4Ah3wAAIZMAACuNR4Arz0eAKxBHgCtQR4AqF0CAKltAgCqZQIAq30CAKxpAgCtsQIArrECAK+xAgCE7AQArn0AgLJ9AIC2fQCAun0AgL59AIDCfQCAxn0AgLhxAwC5cQMAunEDALtxAwC81QMAvd0DAL7VAwC/zQMAsNECALHRAgCy0QIAs9ECALRRAwC1UQMAtlEDALdRAwCz7QIAyn0AgM59AIC+gAQA0n0AgLYxAgC14QIA1n0AgLsVAgC6FQIA2n0AgN59AIC/lQMAvpUDAL0FAgC8BQIA4n0AgKOpAgDmfQCA6n0AgKZ1AgDufQCA8n0AgKWlAgCqUQIAq1ECAPZ9AID6fQCArtEDAK/RAwCsQQIArUECAKjZAgCpIQEAqiEBAKshAQCsIQEArSEBAK4hAQCvIQEA/n0AgAJ+AIAGfgCAviAEAAp+AIAOfgCAEn4AgBp+AIC4jQEAuZEBALqRAQC7pQEAvL0BAL11AAC+fQAAv3UAALDlAQCx7QEAsvkBALPxAQC02QEAtdkBALa5AQC3tQEA4RgeAB5+AIDjKB8AIn4AgIGlAACApQAAJn4AgIKlAACGAAQAh/QFACp+AIAufgCAMn4AgDZ+AIDvYB4AOn4AgD5+AIBCfgCAhfD0AUZ+AIBKfgCA42QBAE5+AIDhpAEAUn4AgO/IAABWfgCAWn4AgFZ8AICE/AUAXn4AgGJ+AICzKQYAFn4AgGZ+AIBqfgCAbn4AgLYhBgC1KQYAcn4AgLupBgC6oQYAdn4AgHp+AIC/nQYAvp0GAL2lBgC8rQYA4bQHAH5+AIDjeAQAgn4AgIB9AACBEQAAghUAAIZ+AICGwAAAh1gDAIp+AICOfgCAkn4AgJZ+AIDvDAQAmn4AgKOpBgCefgCAon4AgKZ+AICqfgCApqEGAKWpBgCufgCAqykGAKohBgCyfgCAtn4AgK8dBgCuHQYArSUGAKwtBgC6fgCAs0kHAL5+AIDCfgCAtn0HAMZ+AIDKfgCAtXUHALpdBwC7JQcAzn4AgNJ+AIC+IQcAvy0HALw9BwC9MQcAqD0GAKmBBgCqhQYAq5UGAKy5BgCtuQYArqkGAK+pBgDWfgCA2n4AgN5+AIDifgCA5n4AgIK5AACBsQAAgLkAALitBgC5vQYAurUGALtFAQC8XQEAvUUBAL5FAQC/dQEAsN0GALGlBgCyrQYAs6EGALShBgC1rQYAtpkGALeVBgCjDQYA6n4AgO5+AIDyfgCAhJgCAKY5BgClMQYAvpwBAKthBgCqGQYAhggAAId8AQCvaQYArmUGAK11BgCseQYA+n4AgLO1AQD+fgCAAn8AgLZVAQAGfwCACn8AgLWhAQC6cQEAu3kBAA5/AIASfwCAvjEBAL89AQC8UQEAvVEBAKhpAgCpaQIAqnkCAKt5AgCsbQIArZECAK6RAgCvkQIAFn8AgBp/AIAefwCAIn8AgCZ/AIAqfwCALn8AgDJ/AIC4mQIAua0CALqlAgC7bQMAvHUDAL19AwC+dQMAv20DALDxAgCx+QIAssECALPBAgC0sQIAtb0CALa1AgC3qQIANn8AgDp/AIA+fwCAo/0CAEJ/AICl6QIAph0CAEZ/AIBKfwCATn8AgKo5AgCrMQIArBkCAK0ZAgCueQIAr3UCAFJ/AIBWfwCAWn8AgIQADACAGQAAgQkAAII5AABefwCAYn8AgGp/AIBufwCAvuAMAHJ/AIB2fwCAhlgNAIcMAwCowQIAqc0CAKrFAgCr2QIArMkCAK39AgCu9QIArz0BAHp/AIB+fwCAgn8AgIZ/AICKfwCAjn8AgJJ/AIC+MAwAuMUBALnNAQC62QEAu9EBALzxAQC98QEAvpkBAL+ZAQCwRQEAsU0BALJFAQCzXQEAtEUBALVNAQC2RQEAt/0BAOE4BgCWfwCA42wGAJp/AICefwCAon8AgKZ/AICqfwCAhKgNAK5/AICyfwCAtn8AgL6wDwC6fwCA72wGAL5/AIDCfwCApn0AgMZ/AIDKfwCA41AAAM5/AIDhoAEA0n8AgO+EAADafwCAhyANAIZMDwCAPQAAgSEAAIIlAADefwCAs80NAGZ/AIDWfwCA4n8AgOZ/AIC2/Q0AtcENAOp/AIC7CQ4AugEOAO5/AIDyfwCAvwkOAL4BDgC9CQ4AvBEOAPZ/AIDjmAwA+n8AgOH8DwD+fwCAAoAAgAaAAIAKgACADoAAgBKAAIAWgACAGoAAgB6AAIDvYAwAIoAAgCaAAICjTQ0AKoAAgC6AAIAygACANoAAgKZ9DQClQQ0AOoAAgKuJDgCqgQ4APoAAgEKAAICviQ4AroEOAK2JDgCskQ4Agm0AALM1DgCAVQAAgWUAALb1DwCE3AMARoAAgLX9DwC60Q8Au9EPAIYABACH3AAAvn0PAL9lDwC8wQ8AvXkPAKjlDwCp7Q8AqvkPAKv5DwCsMQ4ArTEOAK4xDgCvMQ4ASoAAgE6AAIBSgACAVoAAgFqAAIBegACAYoAAgGaAAIC43Q4AueEOALrhDgC74Q4AvOUOAL3pDgC+mQ4Av5UOALBRDgCxUQ4AslEOALPpDgC0/Q4AteUOALbtDgC35Q4Ao3EPAGqAAIBugACAcoAAgHaAAICmsQ4ApbkOAHqAAICrlQ4AqpUOAH6AAICCgACAryEOAK45DgCtPQ4ArIUOAIaAAICzyQEAioAAgI6AAIC2+QEAkoAAgJaAAIC1wQEAuqkBALu1AQCagACAnoAAgL6tAQC/lQEAvK0BAL2lAQCo5Q0AqfkNAKoFAgCrHQIArA0CAK09AgCuNQIAr10CAKKAAICmgACAqoAAgK6AAICAGQAAgRkAAIIFAACygACAuC0CALk1AgC6MQIAuzECALzVAgC93QIAvtUCAL/NAgCwKQIAsTUCALI9AgCzNQIAtC0CALUVAgC2HQIAtxUCALqAAICEnAIAvoAAgKOBAgDCgACApYkCAKaxAgDGgACAhiAEAIfUAwCq4QIAq/0CAKzlAgCt7QIAruUCAK/dAgC29QMAvkQDAIWM/QG1/QMAyoAAgLP9AwDOgACA0oAAgL59AwC/TQMAvGUDAL19AwC6dQMAu30DANaAAIDagACA3oAAgOKAAICEBAIAoyUCAOaAAIClJQIApi0CAOqAAIDugACA8oAAgKqtAgCrpQIArL0CAK2lAgCupQIAr5UCAPaAAID6gACA/oAAgAKBAIAGgQCA48ADAAqBAIDhrAEADoEAgO9YAwASgQCAFoEAgIANAACB5QAAgu0AABqBAIDhYA8A40ABAOM4DgDheA4AHoEAgCKBAIC+lAUAKoEAgIYABACHZAUALoEAgDKBAIA2gQCA7/wOAO98DgA6gQCAs1EBAD6BAID2fgCAQoEAgEaBAIC2DQEAtQkBAEqBAIC74QAAuhkBAE6BAIBSgQCAv9EAAL7pAAC96QAAvPkAALaAAIAmgQCAVoEAgFqBAIBegQCAYoEAgGaBAIBqgQCAqKEGAKmtBgCquQYAq7EGAKzhBgCt7QYAruUGAK/FBgCwvQYAsUUHALJNBwCzXQcAtE0HALV1BwC2fQcAtx0HALglBwC5LQcAuiUHALs9BwC8KQcAvRUHAL4RBwC/EQcAoxEGAG6BAIBygQCAdoEAgHqBAICmTQYApUkGAH6BAICroQcAqlkGAIKBAICGgQCAr5EHAK6pBwCtqQcArLkHAIANAACBFQAAgh0AAIqBAICOgQCAkoEAgISUAwC+lAMAloEAgJqBAICGyAAAh4wAAJ6BAICigQCApoEAgKqBAIConQYAqa0GAKqlBgCrvQYArK0GAK3RBgCu1QYAr80GAK6BAICygQCAtoEAgLqBAIC+gQCAwoEAgMaBAIDKgQCAuF0BALnBAQC6wQEAu8EBALzBAQC9yQEAvvEBAL/xAQCwvQYAsY0GALKFBgCzZQEAtH0BALVlAQC2bQEAt2UBALMtBgDOgQCA0oEAgNaBAIDagQCAtlEGALUlBgDegQCAu0kGALp5BgDigQCA5oEAgL+hAQC+uQEAvbEBALxRBgDqgQCAo2kGAO6BAIDygQCAphUGAPaBAID6gQCApWEGAKo9BgCrDQYA/oEAgAKCAICu/QEAr+UBAKwVBgCt9QEAutUHALvdBwC4wQcAucEHAL4xBAC/MQQAvPEHAL3xBwCyrQcAs7UHALCtBwCxpQcAtp0HALf1BwC0pQcAtZUHAKppBwCraQcAqGkHAKlpBwCuaQcAr2kHAKxpBwCtaQcAgLkDAIGNAwCChQMAhKgDAIZQ/AGHCAMAvjQDAAqCAICoZQIAqXUCAKp9AgCrdQIArG0CAK21AwCuvQMAr7UDAA6CAIASggCAFoIAgBqCAIAeggCAIoIAgCaCAIAqggCAuFEDALlZAwC6YQMAu2EDALwRAwC9HQMAvhUDAL8JAwCwzQMAsdUDALLdAwCz1QMAtM0DALVxAwC2cQMAt3EDAC6CAIAyggCAs/0DADaCAIC17QMAOoIAgD6CAIC2PQIAQoIAgEaCAIC7GQIAugECAL0JAgC8AQIAv70CAL4BAgBKggCAToIAgITE/QG+wPwBUoIAgFaCAIBaggCA79wDAF6CAIDhlAEAYoIAgOMQAwBmggCAgu0AAIHtAACA7QAA4TgGAOE8BwDjQAEA45QGAGqCAIBuggCAcoIAgHqCAICGgPwBh+j9AX6CAICCggCAhoIAgIqCAIDvnAEA79wGAKM1AwCOggCAkoIAgJaCAICaggCApvUCAKUlAwCeggCAq9ECAKrJAgCiggCApoIAgK91AgCuyQIArcECAKzJAgB2ggCAqoIAgK6CAICyggCA76T9AbaCAIC6ggCAvoIAgON4/QHCggCA4UD8AcaCAIDKggCAzoIAgNKCAIDWggCAs+X+AYItAACBFQAAgB0AANqCAIC25f4BtfX+Ad6CAIC7Yf8Butn+AeKCAICE5AMAv2n/Ab5h/wG9df8BvHn/Aaj9/gGpJf4Bqi3+Aasl/gGsPf4BrSX+Aa4t/gGvJf4BviwAAOaCAICGiAAAh+wAAOqCAIDuggCA8oIAgPaCAIC4gf8BuYH/AbqZ/wG7mf8BvIn/Ab21/wG+sf8Bv63/AbBd/gGx5f8Bsu3/AbPh/wG05f8Bte3/AbbZ/wG32f8Bo6X/AfqCAID+ggCAAoMAgAaDAICmpf8BpbX/AQqDAICrIf4Bqpn/AQ6DAIASgwCAryn+Aa4h/gGtNf4BrDn+ARaDAICz6f4BGoMAgB6DAIC2lf4BIoMAgCaDAIC16f4BurH+Abu5/gEqgwCALoMAgL51AQC/fQEAvJH+Ab2R/gGoHf4BqS3+Aaol/gGrPf4BrCX+Aa1R/gGuUf4Br1H+ATKDAIA2gwCAOoMAgD6DAIBCgwCARoMAgEqDAIBOgwCAuNkBALnZAQC67QEAu+EBALzhAQC94QEAvuEBAL/hAQCwMf4BsTn+AbIB/gGzAf4BtPUBALX9AQC29QEAt+kBAKOt/QFSgwCAvkwDAFqDAIBegwCAptH9AaWt/QFigwCAq/39Aar1/QFmgwCAaoMAgK85AgCuMQIArdX9AazV/QGA+QMAgfkDAIJNAACFdCAAboMAgITYAwCE1AQAcoMAgIZABACHVAMAdoMAgHqDAIB+gwCAgoMAgIaDAIC+8AUAqDECAKkxAgCqMQIAqzECAKyVAwCtnQMArpUDAK+NAwCKgwCAjoMAgJKDAICWgwCAhHwHAJqDAICegwCAooMAgLipAwC5qQMAumkDALtpAwC8eQMAvXkDAL5pAwC/aQMAsP0DALHNAwCyxQMAs60DALS5AwC1uQMAtq0DALelAwCmgwCAqoMAgK6DAICygwCAtoMAgLqDAIDv6AMAvoMAgOGQAQDCgwCA42wDAMqDAICAJQAAgSkAAIIdAADOgwCAs/kDANKDAICGaAcAh1wFANaDAIC2XQIAtV0CANqDAIC7SQIAunkCAN6DAIDigwCAvz0CAL49AgC9OQIAvFECAOaDAIDhPP4BvkAGAOPwAQDqgwCA7oMAgPKDAID2gwCA+oMAgP6DAIAChACABoIAgAaEAIAKhACADoQAgO/kAQAShACAFoQAgKNxAwAahACApdUCAB6EAIAihACAptUCACaEAIAqhACAq8ECAKrxAgCtsQIArNkCAK+1AgCutQIA4dz8AcaDAIDjUAQA74gEAID1BwCBCQAAgj0AAC6EAICEJAEAMoQAgDaEAIA6hACAPoQAgOFMBADv5BwA43QEALNdBgBChACAhgAMAIfgAwBGhACAtgUGALV1BgBKhACAuxEGALoJBgBOhACAUoQAgL/VBgC+1QYAvQEGALwJBgCojQYAqZUGAKqVBgCrpQYArL0GAK3FBgCuxQYAr/UGAFaEAIBahACAXoQAgGKEAIBmhACAaoQAgG6EAIByhACAuHUGALl9BgC6dQYAu80HALzVBwC93QcAvtUHAL/NBwCwjQYAsZUGALKdBgCzlQYAtFEGALVRBgC2UQYAt1EGAKMdBwCPFewBdoQAgHqEAIB+hACApkUHAKU1BwCChACAq1EHAKpJBwCGhACAioQAgK+VBwCulQcArUEHAKxJBwCeRfkBn6X5AZyR/QGdTfkBmlX9AZtd/QGYBfEBmZX+AZal8gGXYfEBlG31AZU19QGS4ekBk4X2AZBV7AGRXekBsbEdALClHQCziRkAskEcALUBJAC09RkAjoQAgJKEAICWhACAgqkDAIGhAwCAaQAAohUFAKMFAgCgFQYAob0FAKHFAQCahACAo80NAKLlAQClAQgApN0NAKfRCQCm2QkAqQEUAKilCACrxRQAqs0VAK3REQCsARAArwEcAK51EQCCEe8BgynvAZ6EAICihACAhuH1AYcR9gGEOeoBhY3qAYp59gGL4fEBvqQMAKqEAICO+f0BjzH+AYw98gGNYfIBkkn+AZOd/gGHCAwAhmwMAJax+gGX+QUAlFn6AZVZ+gGaYQYAm8EGAK6EAICyhACAtoQAgLqEAICcyQEAvoQAgKitBQCpuQUAqs0FAKvdBQCszQUArf0FAK71BQCvHQUAwoQAgMaEAIDKhACAzoQAgNKEAIDWhACA2oQAgN6EAIC4dQUAuX0FALoJBQC7CQUAvB0FAL0BBQC+AQUAvz0FALBxBQCxcQUAsnEFALNxBQC0UQUAtVEFALZRBQC3TQUAs0UEAOKEAIDmhACA6oQAgO6EAIC2fQQAtUUEAPKEAIC7tQQAurUEAPaEAID6hACAv5UEAL6VBAC9pQQAvKUEAP6EAICjAQQAAoUAgAaFAICmOQQACoUAgA6FAIClAQQAqvEEAKvxBAAShQCAhOwNAK7RBACv0QQArOEEAK3hBADh0AYAhAwMAOMoBwC+AAwAGoUAgO9EAwCGuAwAhywNAB6FAIDjlAEAIoUAgOH8AQBWgwCAJoUAgO/IBgAqhQCALoUAgDKFAICzjQMANoUAgLWNAwA6hQCAPoUAgLa1AwBChQCARoUAgLtBAwC6SQMAvUEDALxZAwC/QQMAvkkDAKNFDACmhACAFoUAgEqFAIBOhQCApn0MAKVFDABShQCAq4kMAKqBDABWhQCAWoUAgK+JDACugQwArYkMAKyRDACAFQ8AgR0PAIIhDwCzIQ4AXoUAgLUhDgC2JQ4AYoUAgGaFAIBqhQCAusEOALvBDgC8wQ4AvcEOAL7BDgC/wQ4AqK0OAKntDgCq5Q4Aq/0OAKzlDgCt6Q4ArjkOAK85DgBuhQCAcoUAgHaFAIB6hQCAgB0AAIEJAACCvQEAfoUAgLjNDwC51Q8AutUPALvlDwC8/Q8AvZUPAL6RDwC/kQ8AsEkOALFJDgCyWQ4As1kOALRJDgC1SQ4Atv0PALf1DwCjbQ8AgoUAgL6EAQCKhQCAjoUAgKZpDwClbQ8AkoUAgKuNDwCqjQ8AhogAAIdsAQCvjQ8Aro0PAK2NDwCsjQ8AloUAgLPtDgCahQCAnoUAgLaRDgCihQCApoUAgLXhDgC6tQ4Au70OAKqFAICuhQCAvn0BAL9lAQC8mQ4AvZkOAKgRDgCpJQ4AqiEOAKs5DgCsLQ4ArVUOAK5dDgCvUQ4AhKgAALKFAIC2hQCAuoUAgL6FAIDChQCAxoUAgMqFAIC47QEAuZUBALqVAQC7rQEAvLUBAL11AQC+fQEAv3UBALA1DgCxPQ4AsgkOALMJDgC0/QEAteUBALblAQC31QEAo6kNAM6FAIDShQCA1oUAgNqFAICm1Q0ApaUNAN6FAICr+Q0AqvENAOKFAIDmhQCAryECAK45AgCt3Q0ArN0NAIANAACBFQAAgh0AAOqFAIDuhQCA8oUAgIeQAwCGfAQAvuwEAPqFAID+hQCAAoYAgAaGAIAKhgCADoYAgBKGAICyLQ4AszUOALAtDgCxJQ4Ati0OALedDwC0LQ4AtSUOALq9DwC7jQ8AuKUPALm9DwC+LQ8AvxUPALyVDwC9JQ8AFoYAgBqGAIAehgCAIoYAgCaGAIAqhgCALoYAgDKGAICqpQ4Aq7UOAKjFDgCp3Q4Arp0OAK9VDgCspQ4ArZUOAKgNAgCpFQIAqhUCAKtNAgCsWQIArVkCAK5NAgCvRQIAhKgFADaGAIA6hgCAPoYAgIS4BABChgCARoYAgEqGAIC4/QIAuUEBALpBAQC7QQEAvEEBAL1JAQC+cQEAv3EBALAJAgCxCQIAss0CALPFAgC03QIAtcUCALbNAgC3xQIA4dQPAOMQDgDj9A4A4QwOAE6GAIBShgCAVoYAgFqGAIBehgCAYoYAgL4kBABqhgCA7AAAAO9EAADvzA4AboYAgIJlAACz2QIAgFUAAIFtAAC2nQIAcoYAgHaGAIC1lQIAuokCALuJAgCGqAQAh+AEAL5dAgC/RQIAvF0CAL1VAgCjHQUA9oUAgGaGAIB6hgCAfoYAgKZZBQClUQUAgoYAgKtNBQCqTQUAhoYAgIqGAICvgQUArpkFAK2RBQCsmQUAjoYAgLMpBgCShgCAloYAgLYpBgCahgCAnoYAgLUpBgC6pQYAu60GAKKGAICmhgCAvqUGAL+tBgC8tQYAva0GAKjlBgCp7QYAquUGAKv9BgCs5QYAre0GAK7lBgCvXQYAqoYAgK6GAICyhgCAtoYAgLqGAIC+hgCAwoYAgMaGAIC46QcAuekHALr9BwC79QcAvO0HAL1FBwC+TQcAv0UHALAlBgCxLQYAsiUGALM9BgC0JQYAtS0GALYlBgC32QcAo20HAIItAACBFQAAgB0AAMqGAICmbQcApW0HAM6GAICr6QcAquEHANKGAIC+oAEAr+kHAK7hBwCt6QcArPEHANaGAICzkQYAhugAAIcsAQC2QQEA2oYAgN6GAIC1UQEAuk0BALslAQDihgCA5oYAgL4lAQC/LQEAvDEBAL0xAQCwrQEAscUBALLBAQCzwQEAtMUBALXNAQC28QEAt/EBALgBAQC5AQEAugEBALsBAQC8AQEAvQEBAL4BAQC/AQEA6oYAgO6GAIDyhgCA9oYAgIaFAID6hgCA/oYAgAKHAICoTQYAqVkGAKo9BgCrNQYArP0BAK3lAQCu5QEAr9UBAKPVBQAGhwCACocAgA6HAIAShwCApgUCAKUVAgAWhwCAq2ECAKoJAgAahwCAHocAgK9pAgCuYQIArXUCAKx1AgAihwCAJocAgCqHAIAuhwCAMocAgOFkBQA2hwCA4+wFAIARAACBEQAAghEAAO/0BgA6hwCAPocAgEKHAIC+MAMAhMQCAEqHAICz4QMAhMAcALVRAwBOhwCAUocAgLZZAwBWhwCAWocAgLtxAwC6eQMAvbUAALxpAwC/tQAAvrUAAF6HAIDhlAEAYocAgONcAgCGcBwAh0QDAGaHAIBqhwCAbocAgHKHAIB2hwCAeocAgH6HAICChwCAhocAgO94AgCoVQIAqV0CAKphAgCrYQIArNECAK3RAgCu0QIAr9ECAIqHAICOhwCAkocAgJaHAICahwCAnocAgKKHAICmhwCAuGkBALlpAQC6CQEAuwkBALwZAQC9GQEAvgkBAL8FAQCwtQIAsb0CALK1AgCzaQEAtHkBALV5AQC2aQEAt2EBAOHEBwDjpAYA47gGAOF8BgCADQAAgTUAAII9AACqhwCArocAgLKHAIC+4B0AuocAgL6HAIDvYAAA7+gGAMKHAICjqQIAxocAgMqHAIDOhwCA0ocAgKYRAgClGQIA1ocAgKs5AgCqMQIAhkgcAIfMHACv/QEArv0BAK39AQCsIQIAqIUeAKmRHgCqkR4Aq60eAKy1HgCt1R4ArtEeAK/FHgC2hwCA2ocAgN6HAIDihwCA5ocAgOqHAIDuhwCA8ocAgLhhHwC5YR8AumEfALthHwC8YR8AvWEfAL5hHwC/YR8AsL0eALGFHgCyjR4As4UeALSdHgC1hR4Ato0eALeFHgCzGR4A9ocAgPqHAID+hwCAAogAgLZVHgC1PR4ABogAgLtBHgC6eR4ACogAgA6IAIC/QR4AvlkeAL1RHgC8WR4AEogAgKNdHgAWiACAGogAgKYRHgAeiACAIogAgKV5HgCqPR4AqwUeAISkAwC+qAMArh0eAK8FHgCsHR4ArRUeAKitHgCptR4AqrUeAKvJHgCs2R4ArdkeAK7JHgCvwR4AgO0BAIHxAQCC8QEAJogAgIaQAACHdAEAKogAgC6IAIC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALBFAQCxTQEAskUBALNdAQC0RQEAtU0BALZFAQC3+QEAsz0eADKIAIA2iACAOogAgD6IAIC2WR4AtVEeAEKIAIC7iQEAuoEBAEaIAIBKiACAv4kBAL6BAQC9iQEAvJEBAE6IAIBSiACAo3UeAFaIAIClGR4AWogAgF6IAICmER4ARocAgGKIAICrwQEAqskBAK3BAQCs2QEAr8EBAK7JAQBmiACAaogAgG6IAIByiACAdogAgIQYAgB6iACAfogAgIKIAICGiACAiogAgI6IAICSiACAmogAgJ6IAIC+cAMAgGkAAIFpAACCeQAAhAAEAIbwBACHdAMAoogAgO8MHwCmiACA4aweAKqIAIDj8B4ArogAgLKIAIC2iACAuogAgL6IAIDCiACAxogAgMqIAIDvVAIAzogAgNKIAIDWiACA46QCANqIAIDhgAEA3ogAgOKIAIDmiACA6ogAgO6IAICzRQMA8ogAgPaIAID6iACA/ogAgLZFAwC1VQMAAokAgLshAwC6SQMAvqAEAAqJAIC/KQMAviEDAL01AwC8OQMAqDkCAKk5AgCqjQIAq4UCAKydAgCthQIAroUCAK+1AgCA7QEAgfUBAIL1AQAOiQCAhpAEAIcEBQASiQCAFokAgLhFAQC5TQEAukUBALtdAQC8SQEAvUkBAL55AQC/eQEAsM0CALGlAgCyrQIAs6ECALSlAgC1rQIAtp0CALd9AQAaiQCAHokAgCKJAIAmiQCAKokAgC6JAIAyiQCA74gBAITsBADhVB4ANokAgONUAQA6iQCAPokAgEKJAIBGiQCAo0UCAEqJAIBOiQCAUokAgFaJAICmRQIApVUCAFqJAICrIQIAqkkCAF6JAIBiiQCArykCAK4hAgCtNQIArDkCAKg1BgCpPQYAqlEGAKttBgCseQYArWUGAK5tBgCvZQYABokAgGaJAIBqiQCAbokAgIAZAACBGQAAggUAAHKJAIC45QYAuekGALr5BgC7+QYAvOkGAL3pBgC+nQYAv5UGALAdBgCx5QYAsu0GALPlBgC0/QYAteEGALbhBgC34QYAs9kGAL7QAwB2iQCAeokAgH6JAIC25QYAtfEGAIKJAIC7IQYAutkGAIaYAACHeAMAvyUGAL45BgC9MQYAvDkGAIaJAICjnQYAiokAgI6JAICmoQYAkokAgJaJAICltQYAqp0GAKtlBgCaiQCAnokAgK59BgCvYQYArH0GAK11BgCo7QcAqSkGAKoxBgCrMQYArJEGAK2RBgCukQYAr5EGAKKJAICmiQCAqokAgK6JAICyiQCAtokAgLqJAIC+iQCAuIUGALmNBgC6hQYAu50GALyNBgC9vQYAvrUGAL95AQCw8QYAsfEGALLxBgCzxQYAtMEGALXBBgC2wQYAt8EGALO5BgDCiQCAxokAgMqJAIDOiQCAthEGALUZBgDSiQCAuzUGALo1BgDWiQCA2okAgL8FBgC+BQYAvREGALwlBgClQQYA3okAgOKJAICmSQYAgRUAAIB5AACj4QYAghUAAK1JBgCsfQYAr10GAK5dBgCENAEAlogAgKttBgCqbQYAvswDAOqJAICzlQIA7okAgLXZAgDyiQCA9okAgLbRAgCGgAwAhzgDALvFAgC6xQIAvRUDALwVAwC/FQMAvhUDAPqJAID+iQCA71gGAIRAAwACigCABooAgAqKAIAOigCAEooAgBaKAIAaigCAHooAgOE4BgAiigCA4yQGAL5wDACsSQIArUkCAK5dAgCvVQIAqB0CAKkFAgCqBQIAq10CAISoDAAmigCAKooAgC6KAIC+vA0AMooAgDaKAIA6igCAvE0DAL1VAwC+VQMAv2UDALjpAwC56QMAul0DALtVAwC0yQMAtckDALbZAwC32QMAsBkCALEZAgCy2QMAs9kDAD6KAIDj5AAAQooAgOG8AQBGigCAgj0AAIE9AACAPQAASooAgE6KAIBSigCAWooAgF6KAIDvzAMAYooAgGaKAICj3QMAaooAgIboDACHYA0AbooAgKaZAwClkQMAcooAgKuNAwCqjQMAdooAgHqKAICvXQIArl0CAK1dAgCsXQIAfooAgIKKAICGigCAiooAgI6KAICSigCAlooAgO/gAQCEvAwA4YwGAJqKAIDjHAYAnooAgKKKAICmigCAqooAgLPVAQCuigCAsooAgLaKAIC6igCAtpEBALWZAQC+igCAu70BALq9AQDCigCAyooAgL+dAQC+nQEAvZ0BALydAQCoBQ4AqQkOAKodDgCrFQ4ArFEOAK1RDgCuSQ4Ar0kOAFaKAICCzQ8AgfUPAID9DwDGigCAzooAgIYcAACHsAMAuOkOALnpDgC6/Q4Au/UOALztDgC9VQ8AvlEPAL9NDwCwOQ4AsTkOALIJDgCzCQ4AtBkOALUZDgC2DQ4At9kOAKOVDgDSigCA1ooAgNqKAIDeigCAptEOAKXZDgDiigCAq/0OAKr9DgDmigCA6ooAgK/dDgCu3Q4Ard0OAKzdDgDuigCAs/0PAPKKAID2igCAtoEPAPqKAID+igCAtZkPALqNDwC7ZQ8AAosAgAaLAIC+fQ8Av2UPALx9DwC9dQ8AqC0OAKk1DgCqMQ4AqzEOAKxVDgCtRQ4ArkUOAK91DgAKiwCADosAgBKLAIAWiwCAGosAgB6LAIAiiwCAJosAgLjpDgC59Q4Auv0OALv1DgC87Q4AvZEOAL6RDgC/kQ4AsA0OALHlDgCy7Q4As+UOALT9DgC15Q4Atu0OALflDgCjuQ4Agi0AAIEVAACAHQAAKosAgKbFDgCl3Q4ALosAgKshDgCqyQ4AMosAgL4sAQCvIQ4ArjkOAK0xDgCsOQ4AOosAgLZVAQC1RQEANosAgLNVAQA+iwCAhngAAIdcAAC/OQEAvjEBAL0lAQC8JQEAuzEBALpZAQDmiQCAQosAgEaLAIBKiwCAhAQDAKOJAgBOiwCApZkCAKaJAgBSiwCAvyg5AFaLAICqhQIAq+0CAKz5AgCt+QIAru0CAK/lAgDjWAIA78AOAOGIAQBaiwCAXosAgGKLAIBmiwCAaosAgG6LAIByiwCAdosAgHqLAIDvKAIA4ygOAH6LAIDhRA4AqbUCAKhpDQCrAQIAqgkCAK0BAgCsGQIArzECAK4BAgC+AAQAgosAgIaLAICKiwCAjosAgJKLAICWiwCAmosAgLnlAwC45QMAu+UDALrlAwC95QMAvOUDAL/lAwC+5QMAsSECALBJAgCzJQIAsiUCALUpAgC0IQIAtxUCALYVAgCowQIAqdECAKr1AgCrDQEArBUBAK0FAQCuBQEArzkBAJ6LAICiiwCAqosAgK6LAICyiwCAtosAgLqLAIC+iwCAuC0BALk9AQC67QEAu+UBALz9AQC95QEAvu0BAL/lAQCwLQEAsTUBALI9AQCzNQEAtC0BALUVAQC2HQEAtxUBAIA9AQCBpQAAgq0AAO/YAACGsAUAh9gFAMKLAIDv1A8AhGwEAOH0DgDGiwCA4xwPAMqLAIDhlAEAzosAgOMMDgCzPQIA0osAgNaLAIDaiwCA3osAgLbFAQC13QEA4osAgLuxAQC6qQEA5osAgOqLAIC/kQEAvqkBAL2hAQC8qQEAposAgO6LAICqRQYAq10GAKxFBgCtTQYArkUGAK99BgDyiwCA9osAgPqLAICj0QUA/osAgKUxBgCmKQYAAowAgAaMAICCHQAAgR0AAIAdAAAKjACADowAgBKMAIC+lAMAFowAgBqMAICGSAMAh8wDAB6MAIAijACAJowAgCqMAICoqQcAqakHAKq5BwCruQcArKkHAK2pBwCuAQcArzUHAC6MAIAyjACANowAgDqMAIA+jACAQowAgEaMAIBKjACAuC0HALnBAAC66QAAu+kAALz5AAC95QAAvuUAAL+dAACwUQcAsV0HALItBwCzJQcAtD0HALUlBwC2JQcAtxUHALMxBgBOjACAUowAgFaMAIBajACAtikGALUhBgBejACAu5kGALqVBgBijACAZowAgL/hBgC++QYAvfEGALz5BgBqjACAo3UGAG6MAIByjACApm0GAHaMAIB6jACApWUGAKrRBgCr3QYAfowAgIKMAICuvQYAr6UGAKy9BgCttQYAqOUBAKn1AQCq/QEAq/UBAKztAQCtNQEArj0BAK81AQCA+QAAgc0AAILFAACEYAEAvngBAIqMAICHrAAAhpABALjRAAC52QAAuuEAALvhAAC8kQAAvZ0AAL6VAAC/iQAAsE0BALFVAQCyXQEAs1UBALRNAQC18QAAtvEAALfxAACzdQIAjowAgJKMAICWjACAmowAgLa1AgC1ZQIAnowAgLuRAgC6iQIAoowAgKaMAIC/NQMAvokCAL2BAgC8iQIAqowAgKMxAgCujACAhMADAKbxAgCyjACAtowAgKUhAgCqzQIAq9UCALqMAIC+jACArs0CAK9xAwCszQIArcUCAKuNAACqjQAAqY0AAKg5AwCvvQAArr0AAK2FAACsjQAAqgAAAKsAAADCjACAxowAgMqMAIDOjACA0owAgNaMAIC7fQAAun0AALl9AAC4fQAAv90BAL7dAQC93QEAvN0BALO5AACysQAAsaEAALCtAAC3XQAAtl0AALWVAAC0lQAA2owAgN6MAIDijACA5owAgIE1AACADQAA6owAgII1AAC+rD0A7owAgPKMAICFaD0A+owAgP6MAICGODwAh8ACALNJAQACjQCA0AAAAAaNAIAKjQCAtkkBALVJAQAOjQCAuykBALolAQASjQCAFo0AgL8dAQC+HQEAvSEBALwpAQDjNDYA4QwGAOGwAgDjPAYAGo0AgB6NAIAijQCAJo0AgIQsPwC+oD8AKo0AgC6NAIDvfDcAMo0AgDaNAIDvGAEAOo0AgD6NAICGaD4Ah8w/AEKNAIBGjQCASo0AgO+UAABOjQCA4ZQBAFKNAIDjUAAAVo0AgILpPwCB6T8AgPE/AKMJPgCPASQA9owAgFqNAIBejQCApgk+AKUJPgBijQCAq2k+AKplPgBmjQCAao0AgK9dPgCuXT4ArWE+AKxpPgCeYTgAn3U4AJzBNACdtTkAmqU1AJt1NACYeTAAmXExAJYhLQCXhTEAlG0sAJVlLACSeSgAk6UtAJBRJACReSgAsQ0UALAFFACzARgAslUUALV5GAC0tRgAbo0AgHKNAIB2jQCAeo0AgH6NAICCjQCAotE8AKMlAQCgdTkAob08AKHJAACGjQCAowEEAKLlAAClHQQApPUEAKf5CACmAQgAqQEMAKhtCACrzQwAqs0MAK3REACsARAAr9URAK7ZEACCBSUAgy0lAIqNAICOjQCAhsEsAIcRLQCEHSkAhRUpAIopLQCLZSwAko0AgJaNAICOHTAAj8E0AIzZMACNHTEAkmE1AJPNNQCajQCAno0AgJZhOQCXmTgAlKE4AJV9OQCaYT0AmwU9AKKNAICmjQCAqo0AgK6NAICc6QAAso0AgLaNAIC6jQCAvo0AgMKNAICGjACAxo0AgMqNAIDOjQCAqJE+AKmRPgCq7T4Aq+E+AKzhPgCt6T4ArtE+AK/RPgCwUT4AsVE+ALJRPgCzUT4AtHk+ALV5PgC2bT4At2U+ALghPgC5IT4Aujk+ALs5PgC8KT4AvRU+AL4RPgC/DT4AgJkDAIGZAwCCBQAA0o0AgL5UAwDhsD0A2o0AgONAPgCEOAIA3o0AgOKNAIDv9D8A5o0AgOqNAICGmAQAhxwDALMFPQCECAQA7o0AgPKNAID2jQCAtgk9ALUJPQD6jQCAu/U9ALr1PQD+jQCAAo4AgL/dPQC+3T0AveU9ALzlPQAGjgCACo4AgKPNPQC+xAQApcE9AA6OAIASjgCApsE9ABaOAIAajgCAqz09AKo9PQCtLT0ArC09AK8VPQCuFT0AtmkCAB6OAIAijgCAtWkCACaOAICzSQIAKo4AgC6OAIC+qQMAv6kDALzBAwC9wQMAuvkDALv5AwAyjgCANo4AgKgtAwCpnQMAqpUDAKutAwCstQMArb0DAK61AwCv2QMAgA0AAIEVAACCHQAAOo4AgD6OAIBCjgCAh7QFAIacBAC4MQIAuTECALo1AgC7zQIAvNUCAL3dAgC+1QIAv8kCALBpAgCxaQIAskECALNBAgC0OQIAtTkCALYRAgC3EQIASo4AgOM0PgBOjgCA4aw+AFKOAIDvfAMAVo4AgFqOAIBejgCA45QDAGKOAIDhfD4AZo4AgO/oPgBqjgCAbo4AgHKOAIB2jgCAo1UDAHqOAICldQMAfo4AgIKOAICmdQMAho4AgIqOAICr5QIAquUCAK3dAgCs3QIAr7UCAK61AgCoGQYAqSEGAKohBgCrPQYArCUGAK1dBgCuVQYAr00GAEaOAICOjgCAko4AgJaOAICajgCAno4AgKKOAICmjgCAuOUGALmBBgC6gQYAu50GALyJBgC9iQYAvqEGAL+hBgCwPQYAsQ0GALIFBgCz7QYAtPUGALXhBgC24QYAt90GALOpBgCCLQAAgRUAAIAdAACqjgCAtt0GALWtBgCujgCAu8kGALr5BgCyjgCAhOADAL8lBgC+MQYAvTkGALzRBgC+iAMAo+0GANaNAIC2jgCAppkGALqOAIC+jgCApekGAKq9BgCrjQYAhkgAAIdsAACudQYAr2EGAKyVBgCtfQYAqIEGAKmNBgCqmQYAq5UGAKyNBgCttQYArrEGAK+tBgDCjgCAxo4AgMqOAIDOjgCA0o4AgNaOAIDajgCA3o4AgLilBgC5YQEAumEBALthAQC8YQEAvWEBAL5hAQC/YQEAsNkGALHZBgCyqQYAs6kGALS9BgC1oQYAtqEGALedBgCzEQYA4o4AgOaOAIDqjgCA7o4AgLY1BgC1BQYA8o4AgLsdBgC6HQYA9o4AgPqOAIC/ZQYAvnkGAL19BgC8fQYA/o4AgKNVBgACjwCABo8AgKZxBgAKjwCADo8AgKVBBgCqWQYAq1kGABKPAIAWjwCArj0GAK8hBgCsOQYArTkGAKjVAgCp3QIAqikDAKspAwCsOQMArTkDAK4pAwCvKQMAGo8AgB6PAIAijwCAKo8AgC6PAIAyjwCAvrgDADaPAIC47QMAuYUDALqBAwC7gQMAvIUDAL2NAwC+sQMAv7EDALBZAwCxWQMAsu0DALPlAwC0/QMAteUDALblAwC31QMAgKEAAIGhAACCoQAAvoAMADqPAICEmAIAPo8AgEKPAICGAAwAh/QDAEaPAIBKjwCATo8AgFKPAIBWjwCAhLADALPhAwBajwCAXo8AgGKPAIBmjwCAtvkDALXxAwBqjwCAu90DALrdAwBujwCAco8AgL9hAwC+eQMAvXEDALx5AwB2jwCAeo8AgH6PAICjLQIAgo8AgKU9AgCmNQIAho8AgIqPAICOjwCAqhECAKsRAgCstQIArb0CAK61AgCvrQIA48QDAOMQBwDhuAEA4WwHAIBxAACBcQAAggUAAJKPAICGwAwAh1QNAJqPAICejwCA77ADAO8ABwCijwCApo8AgKqPAICujwCAso8AgLaPAIC6jwCAvo8AgMKPAIDvpAEAhKANAOGABgDGjwCA4xABAMqPAIDOjwCA0o8AgNaPAICz9QEA2o8AgN6PAIDijwCA5o8AgLZNAQC1SQEA6o8AgLtRAQC6SQEA7o8AgPKPAIC/OQEAvjEBAL1BAQC8SQEAqC0OAKk1DgCqPQ4AqzEOAKyBDgCtjQ4AroUOAK+1DgCWjwCA9o8AgPqPAID+jwCAgBkAAIEZAACCBQAAApAAgLidDgC5rQ4AuqUOALtNDwC8VQ8AvV0PAL5JDwC/QQ8AsM0OALHVDgCy3Q4As9UOALS1DgC1vQ4AtrUOALetDgCjtQ4AvogDAAaQAIAKkACADpAAgKYNDgClCQ4AEpAAgKsRDgCqCQ4AhggAAIdsAwCveQ4ArnEOAK0BDgCsCQ4AFpAAgBqQAIAekACAs7UPACKQAIC1VQ8Atl0PACaPAIAmkACAKpAAgLp5DwC7eQ8AvGkPAL1dDwC+SQ8Av0kPAKhpDgCpaQ4AqnEOAKtxDgCskQ4ArZEOAK6RDgCvkQ4ALpAAgDKQAIA2kACAOpAAgD6QAIBCkACARpAAgEqQAIC4hQ4AuY0OALqFDgC7nQ4AvI0OAL29DgC+tQ4Av3kBALDxDgCx8Q4AsvEOALPFDgC0wQ4AtcEOALbBDgC3wQ4Ao/kOAE6QAIBSkACAVpAAgFqQAICmEQ4ApRkOAF6QAICrNQ4AqjUOAGKQAIBmkACArwUOAK4FDgCtEQ4ArCUOAIANAACBFQAAgh0AAGqQAIBukACAcpAAgISUAQC+lAEAhkAHAIf0AAB6kACAfpAAgIKQAICGkACAipAAgI6QAICojQIAqZUCAKqVAgCrzQIArNUCAK3dAgCuyQIAr/0CAJKQAICWkACAmpAAgJ6QAIC/ABQAopAAgKaQAICqkACAuH0DALnBAwC6wQMAu8EDALzBAwC9yQMAvvEDAL/xAwCwhQIAsUUDALJNAwCzRQMAtF0DALVFAwC2TQMAt0UDALMdAgCukACAspAAgLaQAIC6kACAtl0CALVdAgC+kACAu4EDALpBAgDCkACAxpAAgL+BAwC+mQMAvZEDALyZAwDKkACAo1kCAM6QAIDSkACAphkCANaQAIDakACApRkCAKoFAgCrxQMA3pAAgOKQAICu3QMAr8UDAKzdAwCt1QMA6pAAgOPMAACEBAIA4bwBAIDJAQCB/QEAgvUBAL4QBQDukACAvigEAPKQAID2kACA+pAAgO8QAAD+kACAApEAgIbgBACH9AIABpEAgAqRAIDj/A8ADpEAgOHgDwASkQCA7xQPABaRAIAakQCAHpEAgCKRAIAmkQCAKpEAgC6RAIAykQCANpEAgDqRAIA+kQCAQpEAgEaRAIBKkQCA7+ABAIUEEgDh3A4ATpEAgOMcDgCAKQAAgR0AAIIFAABSkQCAszECAFqRAICEzAUAXpEAgGKRAIC2KQIAtSECAGaRAIC7zQEAus0BAGqRAIBukQCAv3UBAL7JAQC9wQEAvMkBAKjpBQCp6QUAqvkFAKv5BQCs6QUArekFAK45BgCvOQYA5pAAgFaRAICGiAAAhwADAHKRAIB2kQCAepEAgH6RAIC40QYAudkGALrhBgC74QYAvJEGAL2dBgC+lQYAv4kGALBJBgCxSQYAsl0GALNVBgC0TQYAtfEGALbxBgC38QYAo3EFAIKRAICGkQCAipEAgI6RAICmaQUApWEFAJKRAICrjQYAqo0GAJaRAICakQCArzUGAK6JBgCtgQYArIkGAJ6RAICikQCAs+EHAKaRAIC14QcAqpEAgK6RAIC25QcAdpAAgLKRAIC7vQcAuqEHAL2VBwC8qQcAv5UHAL6VBwCoAQYAqSUGAKohBgCrIQYArCEGAK0tBgCuJQYAr1UGALaRAICCHQAAgR0AAIAdAAC6kQCAvpEAgMKRAIC+MAEAuDkGALk5BgC6yQYAu8kGALzZBgC92QYAvskGAL/JBgCwLQYAsTEGALI1BgCzCQYAtBkGALUZBgC2CQYAtwkGAKOpBgCEjAIAhigfAIdEAQDKkQCApq0GAKWpBgDOkQCAq/UGAKrpBgDSkQCA1pEAgK/dBgCu3QYArd0GAKzhBgDakQCAsxUGAN6RAIDikQCAtj0GAOaRAIDqkQCAtTUGALrZAQC72QEA7pEAgPKRAIC+fQEAv2UBALx9AQC9dQEAqMUFAKnJBQCq2QUAq9EFAKz5BQCt+QUArikCAK8pAgD2kQCA+pEAgP6RAIACkgCAjAAAAAaSAIAKkgCADpIAgLjtAgC5hQIAuo0CALuBAgC8hQIAvY0CAL69AgC/fQMAsFkCALFZAgCy7QIAs+UCALT9AgC15QIAtuUCALfVAgCjUQUAEpIAgBaSAIAakgCAHpIAgKZ5BQClcQUAIpIAgKudAgCqnQIAJpIAgCqSAICvIQIArjkCAK0xAgCsOQIAghEAAC6SAICAZQAAgQkAADKSAIC+mAMAOpIAgD6SAICEJAMAQpIAgIdoAwCGjBwARpIAgEqSAIBOkgCAUpIAgFaSAIBakgCAs6ECAITAHAC10QIAXpIAgGKSAIC21QIAZpIAgGqSAIC7wQIAuvUCAL0RAQC82QIAvxEBAL4ZAQBukgCAcpIAgHaSAIB6kgCAfpIAgIKSAICGkgCA77gGAIqSAIDhnAQAjpIAgON0BgCSkgCAlpIAgJqSAICekgCAgPkAAIH5AACCBQAAopIAgL5YHACEWB8A71wAAO9ABgDhkAEA4fwGAOM8AADjdAYAqpIAgK6SAICGmBwAh/QcAKNpAgC+DB8AspIAgLaSAIC6kgCAph0CAKUZAgC+kgCAqwkCAKo9AgDCkgCAxpIAgK/ZAQCu0QEArdkBAKwRAgCokR0AqZkdAKqhHQCroR0ArNEdAK3dHQCu1R0Ar8kdADaSAICmkgCAypIAgM6SAIDSkgCA1pIAgNqSAIDekgCAuHkeALl5HgC6zR4Au8UeALzdHgC9xR4AvsUeAL/1HgCwuR0AsY0dALKFHQCzTR4AtFUeALVdHgC2VR4At0keALjNHwC51R8Aut0fALvVHwC88R8Avf0fAL7pHwC/6R8AsKUfALGxHwCysR8As40fALSVHwC19R8Atv0fALf1HwCoGR4AqRkeAKotHgCrPR4ArCUeAK0tHgCuJR4Ar90fAOKSAIDmkgCA6pIAgO6SAIDykgCAxpEAgPaSAID6kgCAs+UfAP6SAIACkwCABpMAgAqTAIC27R8Ate0fAA6TAIC7NR4AuiEeABKTAIAWkwCAv3EeAL4RHgC9GR4AvCUeAIJpAACjoR8AgFkAAIFRAACmqR8AGpMAgB6TAIClqR8AqmUeAKtxHgCGAAQAh+wBAK5VHgCvNR4ArGEeAK1dHgCoMR4AqTEeAKpBHgCrQR4ArEEeAK1JHgCucR4Ar3EeACKTAIAmkwCAKpMAgC6TAIAykwCANpMAgDqTAIA+kwCAuCkBALkpAQC6OQEAuzUBALwtAQC90QAAvtEAAL/RAACwyQEAsckBALLZAQCz2QEAtMkBALXJAQC2GQEAtxkBALPJHQBCkwCARpMAgEqTAIBOkwCAtskdALXJHQBSkwCAuw0CALoNAgBWkwCAWpMAgL8NAgC+DQIAvQ0CALwNAgBekwCAo40dAGKTAIBmkwCApo0dAGqTAIBukwCApY0dAKpJAgCrSQIAcpMAgHaTAICuSQIAr0kCAKxJAgCtSQIAgA0AAIERAACCEQAAepMAgO/MAgB+kwCAgpMAgISQAgDjLAIAvigDAOHYAQCKkwCAhhAEAIfUAwCOkwCAkpMAgLNhAwCWkwCAmpMAgJ6TAICikwCAtnkDALVxAwCmkwCAu10DALpdAwCqkwCArpMAgL/hAAC++QAAvfEAALz5AACjoQIAspMAgLaTAIC6kwCAvpMAgKa5AgClsQIAwpMAgKudAgCqnQIAxpMAgMqTAICvIQEArjkBAK0xAQCsOQEAzpMAgNKTAIDvZB8A1pMAgNqTAIDekwCA4pMAgOaTAICADQAAgREAAIIVAADqkwCA4eAcAO6TAIDjiB8A8pMAgISAAgC+jAUAh0gFAIYsBAD6kwCA/pMAgO+kHgDv9B4A4QAeAOFQHwDjLB4A47AeAAKUAIAGlACACpQAgA6UAIASlACAFpQAgISEBACzcQEAGpQAgLUdAQC2FQEAHpQAgCKUAIAmlACAugEBALsBAQC89QAAvf0AAL71AAC/7QAAqK0GAKm9BgCqtQYAq8kGAKzZBgCt2QYArskGAK/BBgAqlACALpQAgDKUAIA2lACAOpQAgD6UAIBClACARpQAgLhtBwC5BQcAug0HALsBBwC8AQcAvQEHAL4BBwC/AQcAsIkGALGJBgCybQcAs2UHALR9BwC1ZQcAtmUHALdVBwCGkwCAozkGAEqUAID2kwCApl0GAE6UAIBSlACApVUGAKpJBgCrSQYAVpQAgFqUAICuvQcAr6UHAKy9BwCttQcAgG0AAIEJAACCGQAAXpQAgGKUAIC+nAMAZpQAgGqUAICGQAAAh2AAAG6UAIBylACAdpQAgHqUAIB+lACAgpQAgKiRBgCpkQYAqrkGAKu5BgCsqQYArakGAK7ZBgCv2QYAhpQAgIqUAICOlACAkpQAgJaUAICalACAnpQAgKKUAIC4cQEAuXEBALpxAQC7cQEAvNkBAL3BAQC+wQEAv/UBALCxBgCxuQYAsokGALOJBgC0UQEAtVEBALZRAQC3UQEAszEGAKaUAICqlACArpQAgLKUAIC2KQYAtSEGALaUAIC7fQYAunUGALqUAIC+lACAv5UBAL6VAQC9XQYAvF0GAMKUAICjdQYAxpQAgMqUAICmbQYAzpQAgNKUAIClZQYAqjEGAKs5BgCErAEAvqABAK7RAQCv0QEArBkGAK0ZBgCo3QIAqe0CAKrlAgCr/QIArOUCAK3tAgCu5QIArz0DANqUAIDelACA4pQAgL5kDADmlACA6pQAgO6UAIDylACAuMkDALnJAwC62QMAu9EDALz5AwC9+QMAvpkDAL+VAwCwRQMAsU0DALJFAwCzXQMAtEUDALVNAwC2RQMAt/kDAIFVAwCASQMAs2UCAIJVAwC1ZQIA9pQAgPqUAIC2ZQIAhgAMAIfkAwC7gQMAuokDAL2BAwC8mQMAv4EDAL6JAwCjLQIA/pQAgAKVAIAGlQCACpUAgKYtAgClLQIADpUAgKvJAwCqwQMAEpUAgBaVAICvyQMArsEDAK3JAwCs0QMA49gGAOGsBwDhnAYA45wGABqVAICEWA0AHpUAgCKVAIAmlQCAKpUAgC6VAIAylQCA7xwBADaVAIA6lQCA70AGAIB5AACBFQAAghEAAIQADAA+lQCA46wAAEKVAIDhpAEASpUAgO9wAACGyAwAh6QNAE6VAIBSlQCAVpUAgFqVAIC6yQUAu8kFALilBQC5zQUAvvkFAL/5BQC8zQUAvcUFALKlBQCzrQUAsBEGALERBgC2rQUAt50FALS1BQC1rQUAqmEGAKthBgConQYAqZUGAK5hBgCvYQYArHEGAK1xBgBelQCAYpUAgGaVAIBqlQCAbpUAgHKVAIC+sAwAdpUAgKghDgCpIQ4AqiEOAKs9DgCsJQ4ArS0OAK4lDgCviQ4ARpUAgHqVAIB+lQCAgpUAgIaVAICKlQCAjpUAgJKVAIC4UQ8AuV0PALpVDwC7bQ8AvHUPAL19DwC+dQ8Av2kPALD5DgCxoQ4AsqEOALOhDgC0oQ4AtakOALaRDgC3kQ4As6kOAJaVAIDWlACAmpUAgJ6VAIC2rQ4Ata0OAKKVAIC7ZQ4Auj0OAKaVAICqlQCAv20OAL5lDgC9dQ4AvHUOAIIZAACj7Q4AgGUAAIEZAACm6Q4ArpUAgLKVAICl6Q4AqnkOAKshDgC2lQCAupUAgK4hDgCvKQ4ArDEOAK0xDgCoYQ4AqXUOAKp9DgCrdQ4ArG0OAK31DgCu/Q4Ar/UOAIaAAQCHpAEAvpUAgMKVAIDGlQCAypUAgM6VAIDSlQCAuHUBALl9AQC6dQEAu8kBALzdAQC9xQEAvsUBAL/1AQCwjQ4AsZUOALKdDgCzkQ4AtFUBALVdAQC2VQEAt00BALP1DgDWlQCA2pUAgN6VAIDilQCAtnUOALXlDgDmlQCAu1EOALpJDgDqlQCA7pUAgL+ZAQC+kQEAvUUOALxJDgDylQCAo7EOAPaVAID6lQCApjEOAP6VAIAClgCApaEOAKoNDgCrFQ4ABpYAgAqWAICu1QEAr90BAKwNDgCtAQ4AqO0CAKktAwCqJQMAqz0DAKwlAwCtLQMAriUDAK+ZAwAOlgCAEpYAgBaWAIAalgCAHpYAgCKWAIC+dAIAKpYAgLiNAwC5kQMAupEDALulAwC8vQMAvXUAAL59AAC/dQAAsOkDALHpAwCy+QMAs/EDALTZAwC12QMAtrkDALe1AwCArQAAgbUAAIK9AACzoQMALpYAgLWhAwC2oQMAMpYAgITgAgA2lgCAuiEDALshAwC8IQMAvSkDAL4RAwC/EQMAo+0DAIXABACFtG8AOpYAgD6WAICm7QMApe0DAEKWAICrbQMAqm0DAIZIBQCHbAMAr10DAK5dAwCtZQMArG0DAEaWAIDjAA4A71hsAOG0DwBKlgCATpYAgFKWAIBWlgCAoakDAKD9DwCjwQMAog0DAOHgAwDv4A8A4+QDAFqWAIBelgCAYpYAgIQEBAC+BAQAZpYAgO+UAwBqlgCAbpYAgHKWAIDj1AMAdpYAgOFUAAB6lgCAfpYAgIKWAICGlgCAgA0AAIEVAACCHQAAipYAgI6WAICSlgCAj5EbAO+cDgCE4AcA4dQOAJqWAIDj8A4AnpYAgKKWAICGGAcAh5AEAJnlFwCY5RcAm+kLAJo5CwCd/QoAnPELAJ9VDwCeXQ8AkSkfAJDNGwCTJR8Aks0fAJXREwCUKRMAlxkXAJZ1EwCM4RAAjSUQAI4tEACP+QwAJpYAgJaWAICKORQAi5UUAITpGACFBRgAhuUYAIfxFACmlgCAqpYAgIIxHACDFRwAnKkEAK6WAICylgCAtpYAgLqWAIC+lgCAmtEEAJt9BACUTQ0AleUIAJblCACXtQgAwpYAgMaWAICSWQwAk1kMAKGRAADKlgCAowF8AKKZAACluXwApJF8AKeZeACm4X0AqYF5AKiheACriXQAqgF0AK0BcACsWXQAr4VwAK6dcACx4WwAsAFsALMBaACyHWwAtfVoALT1aADOlgCA0pYAgNaWAIDalgCA3pYAgOKWAIDmlgCA6pYAgO6WAIDylgCAqD0HAKmVBwCqlQcAq6kHAKzdBwCtxQcArsUHAK8dBgD2lgCAgh0AAIEdAACAHQAA+pYAgP6WAIAClwCAvmABALgZBgC5GQYAuikGALslBgC8IQYAvSEGAL4hBgC/IQYAsHEGALFxBgCycQYAs3EGALRNBgC1NQYAtj0GALctBgCzHQcACpcAgIYoAACHqAAADpcAgLZFBwC1VQcAEpcAgLu1BgC6tQYAFpcAgBqXAIC/8QYAvokGAL2lBgC8pQYAHpcAgKNZBwAilwCAJpcAgKYBBwAqlwCALpcAgKURBwCq8QYAq/EGADKXAIA2lwCArs0GAK+1BgCs4QYAreEGAKipBQCptQUAqr0FAKs9AgCsJQIArVECAK5RAgCvUQIAOpcAgD6XAIBClwCARpcAgIQ8AwBKlwCATpcAgFKXAIC4pQIAua0CALqlAgC7vQIAvKUCAL2tAgC+pQIAv30DALAxAgCxMQIAshkCALMZAgC09QIAta0CALalAgC3nQIAVpcAgFqXAIBelwCAszkFAGKXAIC1oQIAtt0CAGaXAIBqlwCAbpcAgLr5AgC7+QIAvMECAL3BAgC+PQIAv2UCAHKXAICmgQIApf0CAHqXAICjZQUAvlh8AIbYfACHnHwArzkCAK5hAgCtnQIArJ0CAKulAgCqpQIAfpcAgIKXAICohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAIGFAQCAhQEAhpcAgILtAQCKlwCAjpcAgJKXAICWlwCAuHUBALl9AQC6dQEAu80BALzVAQC93QEAvskBAL/BAQCwtQIAsb0CALKBAgCzgQIAtFEBALVRAQC2UQEAt1EBAJqXAICelwCAopcAgKaXAIDhMAYA4WQHAOMoBgDjxAYAhCB9AKqXAIDvbAAA7xgGAK6XAICylwCAtpcAgLqXAICzXQIAvkh8AL6XAIDClwCAxpcAgLYVAgC1dQIAypcAgLs5AgC6MQIAzpcAgNKXAIC/1QEAvtUBAL0VAgC8FQIAo519AHaXAIDWlwCA2pcAgN6XAICm1X0ApbV9AOKXAICr+X0AqvF9AOaXAIDqlwCArxV+AK4VfgCt1X0ArNV9AIBNAACBVQAAglUAALOxfgDulwCAtWV/ALZtfwDylwCAhkADAIcEAwC66X8Au+l/ALz5fwC9+X8Avt1/AL/NfwD2lwCA+pcAgAaXAID+lwCAApgAgAaYAIAKmACADpgAgKhtfgCpXX4AqlV+AKuFfwCsgX8ArYF/AK6BfwCvgX8AsEF/ALFBfwCyQX8As0F/ALR1fwC1ZX8Atm1/ALdlfwC4XX8AuS1/ALolfwC7PX8AvC1/AL0dfwC+FX8Av/UAAKP9fwASmACAFpgAgBqYAIAemACApiF+AKUpfgAimACAq6V+AKqlfgAmmACAKpgAgK+BfgCukX4ArbV+AKy1fgAumACAMpgAgDaYAIA6mACAPpgAgEKYAIBGmACASpgAgIA9AACBCQAAghkAAE6YAIBSmACAhLgBAL6wAQBWmACAqK0BAKnVAQCq1QEAqw0BAKwVAQCtGQEArgkBAK8JAQCGAAQAhwQBAFqYAIBemACAYpgAgGaYAIBqmACAbpgAgLjtAAC5hQAAuo0AALuFAAC8nQAAvYUAAL6NAAC/hQAAsHkBALF5AQCy7QAAs+UAALT9AAC15QAAtuUAALfVAACzXQIAcpgAgHaYAIB6mACAfpgAgLaZAgC1nQIAgpgAgLu9AgC6vQIAhpgAgIqYAIC/IQMAvjkDAL0xAwC8OQMAvigDAKMZAgCOmACAkpgAgKbdAgCWmACAmpgAgKXZAgCq+QIAq/kCAJ6YAICimACArn0DAK9lAwCsfQMArXUDAL7IBACmmACAqpgAgL7EBQCumACAspgAgLaYAIC6mACAgD0AAIEJAACCGQAAvpgAgMKYAICEOAMAypgAgM6YAIDveAIA0pgAgIZIBACHVAMA1pgAgNqYAIDemACA4pgAgOaYAIDqmACA7pgAgPKYAIDjVAIA9pgAgOFAAQD6mACA/pgAgOMkfwACmQCA4Zx8AAaZAIAKmQCADpkAgBKZAICEbAUAFpkAgBqZAIAemQCAIpkAgO8YfwAmmQCAKpkAgLPxAgAumQCAMpkAgDqZAIA+mQCAtukCALXhAgBCmQCAu3EBALppAQCHoAUAhswEAL85AQC+WQEAvVEBALxhAQDhQH8ARpkAgOM4fgCEwAQAgtkAAO8UAACApQAAgdkAAEqZAIDjwAAATpkAgOHUAQBSmQCAVpkAgO+EfgBamQCAqs0BAKvVAQBemQCAYpkAgK79AQCvnQEArMUBAK31AQBmmQCAo1UCAGqZAIBumQCApk0CAHKZAIB2mQCApUUCAMaYAIA2mQCAepkAgH6ZAICCmQCAhpkAgIqZAICOmQCAqJkGAKmZBgCq7QYAq/0GAKzlBgCt7QYAruUGAK/dBgCwpQYAsa0GALKlBgCzuQYAtK0GALVVBwC2UQcAt00HALh1BwC5fQcAunUHALtJBwC8WQcAvVkHAL5JBwC/RQcAs0UGAJKZAICWmQCAmpkAgJ6ZAIC2TQYAtU0GAKKZAIC7SQYAukEGAIYIAACHjAAAv7EHAL5JBgC9TQYAvFEGAIJdAACjAQYAgEUAAIFdAACmCQYAqpkAgK6ZAIClCQYAqgUGAKsNBgCymQCAtpkAgK4NBgCv9QcArBUGAK0JBgCoTQYAqVUGAKpVBgCriQYArLEGAK29BgCuqQYAr6kGAKaZAIC6mQCAvpkAgMKZAIDGmQCAypkAgM6ZAIDSmQCAuEkBALlJAQC6WQEAu1kBALxJAQC9SQEAvt0BAL/VAQCw3QYAsa0GALKlBgCzjQYAtJkGALWZBgC2jQYAt4UGALPdBgDWmQCA2pkAgN6ZAIDimQCAtj0GALU5BgDmmQCAu2kGALoZBgDqmQCA7pkAgL9dBgC+XQYAvVkGALxxBgDymQCAo5kGAPaZAID6mQCApnkGAP6ZAIACmgCApX0GAKpdBgCrLQYABpoAgAqaAICuGQYArxkGAKw1BgCtHQYAqNUCAKndAgCq4QIAq+ECAKw1AwCtPQMArjUDAK8tAwCAzQMAgQkAAIIZAAAOmgCAEpoAgIQYAgC+dAMAGpoAgLjpAwC56QMAuokDALuFAwC8nQMAvYEDAL6BAwC/tQMAsFUDALFdAwCyVQMAs+kDALT5AwC1+QMAtukDALfhAwCGIAwAhxADAB6aAIAimgCAJpoAgCqaAIAumgCA71wCADKaAIDhFAAANpoAgOOIAgC++AwAOpoAgD6aAIBCmgCAu/kDALrxAwC+gA0ARpoAgL9dAwC+XQMAvV0DALzhAwCzCQIASpoAgE6aAIBSmgCAVpoAgLbdAwC13QMAWpoAgKipBgCpqQYAqrkGAKu5BgCsqQYArakGAK4dBQCvFQUAXpoAgGKaAIBmmgCAapoAgG6aAIBymgCAdpoAgHqaAIC4GQUAuS0FALolBQC7yQUAvNkFAL3FBQC+zQUAv8UFALBtBQCxdQUAsnUFALNFBQC0XQUAtT0FALY1BQC3KQUA4fQGAOFUBwDjFAYA47wGAIEJAACAqQAAfpoAgII5AACE7A0AgpoAgIeIDACGDAwAipoAgI6aAIDvzAcA78QHAKMpAwCSmgCAlpoAgJqaAICemgCApv0CAKX9AgCimgCAq9kCAKrRAgCmmgCAqpoAgK99AgCufQIArX0CAKzBAgCoPQ4AqY0OAKqFDgCrnQ4ArIUOAK2NDgCuuQ4Ar7UOAIaaAICumgCAspoAgLaaAIC6mgCAvpoAgMKaAIDGmgCAuL0OALllDwC6bQ8Au2UPALx9DwC9ZQ8Avm0PAL9lDwCw1Q4Asd0OALLVDgCzoQ4AtJUOALWdDgC2lQ4At40OALMNDgDKmgCAzpoAgNKaAIDWmgCAtg0OALUNDgDamgCAuxkOALoRDgDemgCAFpoAgL9ZDgC+UQ4AvXUOALwBDgDimgCAo0kOAOaaAIDqmgCApkkOAO6aAIDymgCApUkOAKpVDgCrXQ4AhKQDAPaaAICuFQ4Arx0OAKxFDgCtMQ4AqLEOAKmxDgCqzQ4Aq8UOAKzdDgCtxQ4ArsUOAK/1DgCA7QEAgfEBAILxAQD6mgCAhpABAIe0AQD+mgCAApsAgLjFAQC5zQEAusUBALvdAQC8zQEAvf0BAL6ZAQC/lQEAsI0OALFBAQCyQQEAs0EBALRBAQC1QQEAtkEBALdBAQCzRQ4ABpsAgAqbAIAOmwCAEpsAgLZFDgC1VQ4AFpsAgLuFAQC6SQ4AGpsAgB6bAIC/hQEAvoUBAL2VAQC8lQEAIpsAgKMBDgAmmwCAKpsAgKYBDgAumwCAMpsAgKURDgCqDQ4Aq8EBADabAIA6mwCArsEBAK/BAQCs0QEArdEBAKgtAwCpPQMAqjUDAKuJAwCsmQMArZkDAK6JAwCvgQMAPpsAgEKbAIBGmwCASpsAgE6bAIBSmwCAVpsAgFqbAIC4rQMAuWUAALptAAC7ZQAAvH0AAL1lAAC+bQAAv2UAALDJAwCxyQMAsqkDALOlAwC0vQMAtaEDALahAwC3lQMAgL0AAIEJAACCGQAAXpsAgGKbAIC+2AMAapsAgG6bAICErAIAcpsAgIfoAwCGDAQAdpsAgHqbAIB+mwCAgpsAgLP9AwCGmwCAipsAgI6bAICSmwCAtlkDALVRAwCWmwCAu00DALpNAwCamwCAnpsAgL8lAwC+OQMAvTEDALw9AwCimwCAppsAgKqbAICumwCA71gPALKbAIC2mwCAupsAgOOQDgC+mwCA4bAPAMKbAIDGmwCAypsAgM6bAIDSmwCAgHUAAIF9AACCdQAAhBgFAO88AwDamwCAvhQFAN6bAIDj0AMA4psAgOFAAADmmwCAhtAEAIdYBQDqmwCA7psAgPKbAID2mwCA+psAgP6bAIACnACABpwAgAqcAIDvrA8AhOwEAOEQDgAOnACA41QBABKcAIAWnACAGpwAgB6cAICj/QIAIpwAgCacAIAqnACALpwAgKZZAgClUQIAMpwAgKtNAgCqTQIANpwAgDqcAICvJQIArjkCAK0xAgCsPQIAqJkGAKmZBgCqrQYAq70GAKylBgCtrQYArqUGAK/ZBgDWmwCAghEAAIEZAACAwQcAPpwAgEKcAIC+cAMARpwAgLhJBwC5SQcAul0HALtVBwC8TQcAvXEHAL51BwC/bQcAsKkGALGpBgCyuQYAs7EGALSZBgC1mQYAtnkHALd5BwC1NQYASpwAgE6cAIC2NQYAhjAAAIdcAwCzPQYAUpwAgL19BgC8dQYAv0UGAL5FBgBmmwCAVpwAgLt1BgC6dQYAo2UGAFqcAIBenACAYpwAgGacAICmbQYApW0GAGqcAICrLQYAqi0GAG6cAIBynACArx0GAK4dBgCtJQYArC0GAKhVBgCpWQYAqm0GAKthBgCsaQYArWkGAK6ZBgCvmQYAdpwAgHqcAIB+nACAgpwAgIacAICKnACAjpwAgJKcAIC4+QYAufkGALqNBgC7hQYAvJ0GAL2FBgC+hQYAv7UGALDpBgCx6QYAsvkGALP5BgC06QYAtd0GALbJBgC3yQYAs+UGAJacAICanACAnpwAgKKcAIC26QYAteEGAKacAIC7LQYAui0GAKqcAICunACAvxkGAL4tBgC9LQYAvC0GAIIVAACjoQYAgGEAAIFhAACmrQYAspwAgL6QAQClpQYAqmkGAKtpBgCEpAEAupwAgK5pBgCvXQYArGkGAK1pBgCohQIAqY0CAKqVAgCruQIArNUCAK3dAgCu1QIAr80CAIaAHACHZAMAvpwAgL5gAwDCnACAxpwAgMqcAIDOnACAuHUDALl9AwC6dQMAu8kDALzZAwC92QMAvskDAL/BAwCwvQIAsY0CALKFAgCzTQMAtFUDALVdAwC2VQMAt00DALMdAgDSnACAhAgDANacAIDanACAtl0CALVdAgDenACAu0kCALp5AgDinACA5pwAgL+ZAwC+kQMAvZkDALxRAgCwAAAAo1kCAOqcAIDunACAphkCAPKcAID2nACApRkCAKo9AgCrDQIA+pwAgP6cAICu1QMAr90DAKwVAgCt3QMAAp0AgAadAIAKnQCA76wGAA6dAIASnQCAFp0AgBqdAIC+6BwAHp0AgCKdAIAqnQCALp0AgOGABwAynQCA42AGAIBdAACBYQAAgmEAALN9AQA2nQCAtW0BALZlAQA6nQCAhiAdAIdYHQC6+QEAu/EBALzZAQC92QEAvrEBAL+xAQDvoAAAPp0AgEKdAIBGnQCASp0AgE6dAIBSnQCA71wBAIRsHADhzAYAVp0AgOMcBgDjSAAAWp0AgOEwAQBenQCAo/EBAGKdAICFABQAZp0AgGqdAICm6QEApeEBAG6dAICrfQEAqnUBAHKdAIB2nQCArz0BAK49AQCtVQEArFUBAKjtHQCpLR4AqjkeAKs5HgCsKR4ArSkeAK6dHgCvkR4AJp0AgHqdAIB+nQCAgp0AgIadAICC+QAAgfEAAID9AAC4qR4AuakeALpJHwC7SR8AvFkfAL1FHwC+TR8Av0UfALDxHgCx+R4AssEeALPBHgC0uR4AtbkeALatHgC3pR4AsBEfALERHwCyER8AsyUfALQlHwC1KR8Atl0fALdRHwC4cR8AuXkfALpBHwC7QR8AvJUAAL2dAAC+lQAAv40AAIqdAIC2nACAjp0AgJKdAICWnQCAmp0AgIb4AwCH0AAAqM0fAKnVHwCq0R8Aq70fAKytHwCtcR8ArnEfAK9xHwCzOR4Anp0AgKKdAICmnQCAqp0AgLaRHgC1RR4Arp0AgLu1HgC6tR4Asp0AgLadAIC/jR4AvoEeAL2RHgC8pR4Aup0AgKN9HgC+nQCAwp0AgKbVHgDGnQCAyp0AgKUBHgCq8R4Aq/EeAM6dAIDSnQCArsUeAK/JHgCs4R4ArdUeAKhVAQCpgQAAqoEAAKuBAACsgQAArYkAAK6xAACvsQAA1p0AgNqdAIDenQCA4p0AgOadAIDqnQCA7p0AgPKdAIC4ZQAAuW0AALplAAC7fQAAvGUAAL1tAAC+ZQAAv90DALChAACxrQAAsqUAALO5AAC0qQAAtZ0AALaVAAC3XQAA9p0AgIIdAACBHQAAgB0AAPqdAID+nQCAAp4AgL4UAgAKngCAhKgCAA6eAIASngCAFp4AgBqeAIAengCAjwAAALNJAwAingCAhugEAIesAgAmngCAtkkDALVJAwAqngCAuykDALolAwAungCAMp4AgL8ZAwC+LQMAvS0DALwxAwA2ngCAo40DADqeAIA+ngCApo0DAEKeAIBGngCApY0DAKrhAwCr7QMASp4AgE6eAICu6QMAr90DAKz1AwCt6QMAvoQDAFKeAIBWngCAWp4AgF6eAIBingCAZp4AgGqeAICAPQAAgQkAAIIZAABungCAcp4AgHqeAICENAMAfp4AgLMtAQCCngCAh8wCAIZMBQCGngCAti0BALUtAQCKngCAu0kBALp5AQCOngCAkp4AgL+9AQC+vQEAvbkBALxRAQDheB8Alp4AgOPQHwCangCAnp4AgOGUAQCingCA42gDAKaeAICqngCArp4AgO+IAwCyngCAtp4AgO+sHwC6ngCAvp4AgMKeAIDGngCAyp4AgM6eAIDSngCA1p4AgO9EHgDangCA4dweAN6eAIDjHB4A4p4AgOqeAIDungCA8p4AgIFpAACAZQAAo+UBAIJ9AACl5QEA9p4AgIQUBACm5QEAvigEAPqeAICrgQEAqrEBAK1xAQCsmQEAr3UBAK51AQCoIQYAqS0GAKolBgCrPQYArCUGAK0tBgCuXQYAr00GAHaeAIDmngCAhggDAIeMAwD+ngCAAp8AgAafAIAKnwCAuOkGALnpBgC6jQYAu4UGALydBgC9hQYAvo0GAL+FBgCwPQYAsQ0GALIFBgCz7QYAtPkGALX5BgC27QYAt+UGALDNBwCx1QcAstEHALPtBwC09QcAtf0HALbpBwC36QcAuN0HALklBwC6LQcAuyUHALw9BwC9JQcAvi0HAL8lBwAOnwCAEp8AgAaeAIAWnwCAGp8AgB6fAIAinwCAJp8AgKgVBgCpGQYAqu0HAKv9BwCs7QcArd0HAK7VBwCvuQcAswUGACqfAIAunwCAMp8AgDafAIC2PQYAtQUGADqfAIC7cQYAumkGAD6fAIBCnwCAv1kGAL5RBgC9WQYAvGUGAEafAICjQQYASp8AgE6fAICmeQYAUp8AgIS0AQClQQYAqi0GAKs1BgC+gAEAWp8AgK4VBgCvHQYArCEGAK0dBgCoNQYAqT0GAKo1BgCrWQYArHUGAK2lAQCurQEAr6UBAIDpAACB6QAAgv0AAL8kAQCGMA8Ah+QAAF6fAIBinwCAuMUAALnNAAC6xQAAu90AALzNAAC9/QAAvvUAAL+dAACw3QEAsSUBALItAQCzIQEAtCEBALUhAQC2IQEAtyEBALvBAgC6OQIAZp8AgGqfAIC/xQIAvsUCAL3VAgC82QIAs50FAG6fAIBynwCAdp8AgIwAAAC2BQIAtd0FAHqfAICqfQIAq4UCAH6fAICCnwCAroECAK+BAgCsnQIArZECAIafAICj2QUAip8AgI6fAICmQQIAkp8AgJafAIClmQUAgpFqAIORagCanwCAnp8AgIa5FgCH6RcAhBEWAIWZFgCKoRIAi6ESAKKfAICmnwCAjpEeAI9ZHgCMmRMAjREeAJJxGgCT5RoAqp8AgO/oJACW8QYAlwUGAJTlGgCVGQYAmikCAJvFAgCunwCAsp8AgLafAIDhKBsAnN0CAOMgDwCfIQcAnsEHAJ01GwCcLRsAm6EbAJr5HwCZOR8AmLEfAJcBEgCWIRMAlSkTAJRRFgCTGRcAkjEXAJGxFwCQKWsAj1FrAOOsBwCEBA0A4RwHAIANAACBNQAAgj0AALqfAIC+nwCAwp8AgL4gDQDKnwCAzp8AgO9MBwCGWAwAh2ANANKfAIDWnwCA2p8AgN6fAICEXA8A4p8AgO8IAADvhAYA4ZABAOGwBgDj4AAA42QGAOafAIDqnwCA7p8AgPKfAID2nwCA+p8AgL4ADwCEQA4A/p8AgAKgAIAGoACACqAAgA6gAIASoACAFqAAgBqgAICj1QMAotUDAKExAwCgLQcAVp8AgMafAIAeoACAIqAAgCagAICCmQAAgZEAAICZAACoTQ0AqZ0NAKqVDQCrJQ4ArD0OAK0RDgCuEQ4ArxEOALB9DgCxDQ4AsgUOALMtDgC0OQ4AtTkOALYtDgC3JQ4AuOkOALnpDgC6wQ4Au8EOALy5DgC9nQ4AvpUOAL+NDgCzPQ0AKqAAgC6gAIAyoACANqAAgLaxDgC1lQ4AOqAAgLvpDgC6mQ4AhogAAIfkAAC/3Q4Avt0OAL3ZDgC88Q4APqAAgKN5DQC+hAEAhIAGAKb1DgBCoACARqAAgKXRDgCq3Q4Aq60OAEqgAIBOoACArpkOAK+ZDgCstQ4ArZ0OALIFNQCzGTQAsG0wALENNQBSoACAVqAAgLQBKAC1PSkAWqAAgF6gAIBioACAZqAAgGqgAIBuoACAcqAAgHagAICiRQEAo9UBAHqgAIChTQEAps0FAKcBOACkAQQApX0FAKoBPACrRT0AqEk5AKnlOQCudTEAr30xAKxdPQCtATAAqO0OAKn1DgCqCQ4AqwkOAKwZDgCtGQ4Arg0OAK8tDgB+oACAgqAAgIagAICKoACAjqAAgJKgAICWoACAmqAAgLgdDgC5JQ4Aui0OALslDgC8PQ4Avd0BAL7VAQC/zQEAsFUOALFdDgCyVQ4Asy0OALQ1DgC1JQ4Ati0OALclDgCzgQ0AnqAAgKKgAICqoACArqAAgLaZDQC1kQ0AvlQEALuZDQC6kQ0AhogEAIe8AwC/4Q0AvvENAL35DQC8gQ0AgkkAAKPFDQCA9QMAgUkAAKbdDQCyoACAtqAAgKXVDQCq1Q0Aq90NALqgAIC+oACArrUNAK+lDQCsxQ0Arb0NAKgdAgCpRQIAql0CAKtVAgCseQIArXkCAK6JAwCviQMAwqAAgMagAIDKoACAzqAAgIT8BQDSoACA1qAAgNqgAIC4iQMAuWUDALptAwC7ZQMAvH0DAL1lAwC+bQMAv2UDALDBAwCxwQMAssEDALPBAwC0wQMAtcEDALbBAwC3wQMA3qAAgOKgAIDmoACA6qAAgO6gAIDhpAEA8qAAgOPADgC+aAQA9qAAgPqgAIDvHAEA/qAAgAKhAIAGoQCACqEAgLOVAwAOoQCAEqEAgBqhAIAeoQCAtrkDALWxAwAioQCAu0UCALpFAgCGqAQAh6QFAL9FAgC+RQIAvVUCALxVAgDh4A4A4SwMAOMIDgDj1A4AgK0AAIHRAACC0QAAJqEAgCqhAIAuoQCAMqEAgDahAIA6oQCAPqEAgO+IDgDvLA4AoxUDAEKhAICFxCsARqEAgEqhAICmOQMApTEDAE6hAICrxQIAqsUCAFKhAIBWoQCAr8UCAK7FAgCt1QIArNUCAKgNBgCpFQYAql0GAKtVBgCseQYArXkGAK65BgCvuQYAFqEAgFqhAIBeoQCAYqEAgGahAIBqoQCAbqEAgHKhAIC4TQcAuVUHALpRBwC7aQcAvHkHAL1lBwC+bQcAv2UHALDJBgCxyQYAst0GALPVBgC0zQYAtXUHALZ9BwC3dQcAs9UGAHahAIB6oQCAfqEAgIKhAIC2+QYAtfEGAIahAIC7DQYAug0GAIYIAACHLAAAv7EHAL4JBgC9AQYAvAkGAIJRAACjkQYAgEEAAIFBAACmvQYAiqEAgI6hAICltQYAqkkGAKtJBgCSoQCAlqEAgK5NBgCv9QcArE0GAK1FBgCwsQYAsbEGALLNBgCzwQYAtMEGALXJBgC28QYAt/EGALgFAQC5DQEAugUBALsdAQC8BQEAvQ0BAL4FAQC/uQEAmqEAgJ6hAICioQCApqEAgKqhAICuoQCApqAAgLKhAICoLQYAqTUGAKo1BgCr8QYArNEGAK3RBgCu0QYAr9EGALPdBgC2oQCAuqEAgL6hAIDCoQCAtjEGALU5BgDGoQCAuxUGALoVBgDKoQCAzqEAgL9tBgC+ZQYAvXUGALx5BgDSoQCAo5kGANahAIDaoQCApnUGAN6hAIDioQCApX0GAKpRBgCrUQYA5qEAgOqhAICuIQYArykGAKw9BgCtMQYAqNUCAKndAgCq4QIAq+ECAKxRAwCtUQMArlEDAK9RAwDuoQCA8qEAgL7sAwD6oQCA/qEAgAKiAIAGogCACqIAgLjpAwC56QMAuokDALuFAwC8nQMAvYEDAL6BAwC/tQMAsDEDALExAwCyNQMAs+kDALT5AwC1+QMAtukDALfhAwCAbQMAgaUAAIKtAACzZQIADqIAgLXVAwC23QMAEqIAgITgAgAWogCAuvkDALv5AwC87QMAvTEDAL4xAwC/MQMAh+wDAIZkPACyAAAAGqIAgB6iAIDjCAQAIqIAgOHsBgAmogCA7wAGACqiAIAuogCAMqIAgDaiAIA6ogCAPqIAgEKiAIBGogCASqIAgE6iAIDjoAMAUqIAgOGoAQBWogCA7/ADAIIdAACBHQAAgB0AAFqiAIBeogCAYqIAgGqiAIC+TD0AbqIAgKOhAwC+QDwApRECAHKiAIB2ogCAphkCAIRsAgB6ogCAqz0CAKo9AgCt9QIArCkCAK/1AgCu9QIAhkA8AIe0PQB+ogCAgqIAgIaiAICKogCAjqIAgO9EBgCSogCA4dQGAJaiAIDjDAcAmqIAgJ6iAICiogCApqIAgLP1AQCqogCArqIAgLKiAIC2ogCAtkUBALXlAQC6ogCAuzEBALopAQC+ogCAwqIAgL8dAQC+HQEAvRkBALwlAQCoLT4AqTU+AKo9PgCrNT4ArC0+AK2FPgCuhT4Ar7k+AGaiAIDGogCAyqIAgM6iAICAGQAAgRkAAIIFAADSogCAuLk+ALm5PgC6ST8Au0k/ALxZPwC9WT8Avk0/AL9BPwCwrT4AsbU+ALKxPgCzjT4AtJk+ALWZPgC2iT4At4k+AKO1PgCEjAIA1qIAgNqiAIDeogCApgU+AKWlPgDiogCAq3E+AKppPgCGCAAAh2gDAK9dPgCuXT4ArVk+AKxlPgDmogCAs5E/AOqiAIDuogCAtlk/APKiAID2ogCAtbk/ALp1PwC7fT8A+qIAgP6iAIC+QT8Av0E/ALxZPwC9VT8AsJU+ALGdPgCyqT4As6U+ALShPgC1oT4AtqE+ALehPgC45T4Aue0+ALrlPgC7/T4AvO0+AL3dPgC+1T4AvxkBAAKjAIAGowCACqMAgA6jAIASowCA9qEAgBajAIAaowCAqF0+AKkhPgCqPT4AqzU+AKwVPgCt/T4ArvU+AK/tPgCj1T4AHqMAgCKjAIAmowCAKqMAgKYdPgCl/T4ALqMAgKs5PgCqMT4AMqMAgDajAICvBT4ArgU+AK0RPgCsHT4AgREAAIANAAA6owCAghkAAD6jAIBCowCAhJQBAL4QAACGQAcAhwABAEqjAIBOowCAUqMAgFajAIBaowCAXqMAgKiNAgCplQIAqpUCAKvNAgCs2QIArdkCAK7NAgCvxQIAYqMAgGajAIBqowCAbqMAgIwAAAByowCAdqMAgHqjAIC4HQMAucEDALrBAwC7wQMAvMEDAL3JAwC+8QMAv/EDALCJAgCxiQIAsikDALMpAwC0OQMAtTkDALYpAwC3JQMAsx0CAH6jAICCowCAhqMAgIqjAIC2WQIAtVECAI6jAIC7TQIAuk0CAJKjAICWowCAv/0DAL79AwC9/QMAvP0DAJqjAICeowCAoqMAgKajAIDhDD4AqqMAgOOoPwCuowCAgT0AAIAxAADvUD8Agh0AALKjAIC++AQAhhgFAIdMAwCEDAIA48wAALqjAIDhvAEAvqMAgMKjAIDGowCAyqMAgM6jAICELAUA0qMAgNajAIDaowCA7xAAAN6jAIDiowCAo90DAOajAIDqowCA7qMAgPKjAICmmQMApZEDAPajAICrjQMAqo0DAPqjAID+owCArz0CAK49AgCtPQIArD0CAAKkAIAGpACACqQAgA6kAIASpACAFqQAgBqkAIDvKD4AHqQAgOE8PgAipACA4zgBAIApAACBFQAAghEAACqkAICzMQIAvsgEAITABAAupACAMqQAgLYpAgC1IQIANqQAgLvNAQC6zQEAOqQAgD6kAIC/dQEAvskBAL3BAQC8yQEAqOkFAKnpBQCq+QUAq/kFAKzpBQCt6QUArjkGAK85BgC2owCAJqQAgIaIAACHQAMAQqQAgEakAIBKpACATqQAgLjRBgC52QYAuuEGALvhBgC8kQYAvZEGAL6RBgC/kQYAsEkGALFJBgCyXQYAs1UGALRNBgC18QYAtvEGALfxBgCjcQUAUqQAgFakAIBapACAXqQAgKZpBQClYQUAYqQAgKuNBgCqjQYAZqQAgGqkAICvNQYArokGAK2BBgCsiQYAbqQAgLPRBwBypACAdqQAgLbxBwB6pACAfqQAgLXBBwC60QcAu90HAIKkAICGpACAvrkHAL+5BwC8xQcAvbkHALhpBgC5aQYAuokGALuJBgC8mQYAvZkGAL6JBgC/iQYAsBEGALEdBgCyFQYAs2kGALR5BgC1eQYAtmkGALdhBgCoSQYAqVUGAKpdBgCrVQYArE0GAK11BgCucQYAr3EGAEajAICCHQAAgR0AAIAdAACKpACAjqQAgJKkAIC+cAEAo5UGAJqkAICGKAAAh0gBAJ6kAICmtQYApYUGAKKkAICrmQYAqpUGAKakAICqpACAr/0GAK79BgCt/QYArIEGAK6kAICzFQYAsqQAgLakAIC2PQYAuqQAgL6kAIC1NQYAutkBALvZAQDCpACAxqQAgL59AQC/ZQEAvH0BAL11AQCovQUAqckFAKrZBQCr0QUArPkFAK35BQCuKQIArykCAMqkAIDOpACA0qQAgNakAICMAAAA2qQAgN6kAIDipACAuO0CALmFAgC6gQIAu4ECALyFAgC9jQIAvrECAL+xAgCwWQIAsVkCALLtAgCz5QIAtP0CALXlAgC25QIAt9UCAKNRBQDmpACA6qQAgO6kAIDypACApnkFAKVxBQD2pACAq50CAKqdAgD6pACA/qQAgK8hAgCuOQIArTECAKw5AgCBbQAAgG0AAAKlAICCBQAAvlwMAAqlAIAOpQCA79AGAITsAwDhHAUAEqUAgOP8BwAWpQCAGqUAgIbYDACHvAwAqIUCAKmVAgCqlQIAq6UCAKy9AgCt1QIArtECAK/RAgAepQCAIqUAgCalAIAqpQCALqUAgDKlAIA2pQCAOqUAgLh1AQC5fQEAunUBALvJAQC82QEAvdkBAL7JAQC/wQEAsLUCALG9AgCygQIAs4ECALRRAQC1UQEAtlEBALdRAQA+pQCAhAQNAEKlAIBGpQCAvhwMAEqlAIDvHAAA76AGAOGQAQDhRAcA43AGAOOYBgBOpQCAUqUAgFalAIBapQCAs10CAF6lAIBipQCAZqUAgGqlAIC2FQIAtXUCAG6lAIC7OQIAujECAHKlAIB6pQCAv9UBAL7VAQC9FQIAvBUCAKOdDQAGpQCAdqUAgH6lAICCpQCAptUNAKW1DQCGpQCAq/kNAKrxDQCGCAMAh2ADAK8VDgCuFQ4ArdUNAKzVDQCAkQ8AgZkPAIKhDwCzpQ4AiqUAgLWhDgC2eQ8AjqUAgJKlAICWpQCAukUPALtdDwC8RQ8AvU0PAL5FDwC//Q8AqFUOAKldDgCqYQ4Aq30OAKxlDgCttQ8Arr0PAK+1DwCapQCAnqUAgKKlAICmpQCAqqUAgK6lAICypQCAtqUAgLhVDwC5dQ8Aun0PALt1DwC8bQ8AvREPAL4RDwC/EQ8AsM0PALHVDwCy3Q8As9UPALTNDwC1dQ8AtnEPALdxDwCj6Q8AuqUAgL6lAIDCpQCAxqUAgKY1DgCl7Q8AyqUAgKsRDgCqCQ4AzqUAgNKlAICvsQ4ArgkOAK0BDgCsCQ4A1qUAgIIdAACBHQAAgB0AANqlAIDepQCA4qUAgL6UAQCErAEA5qUAgIfgAQCGzAAA6qUAgO6lAIDypQCAlqQAgKhtDgCpiQEAqpkBAKuRAQCswQEArckBAK75AQCv+QEAhKAAAPalAID6pQCA/qUAgAKmAIAGpgCACqYAgA6mAIC4xQAAuc0AALrFAAC73QAAvM0AAL39AAC+9QAAv50AALBBAQCxQQEAskEBALNBAQC0QQEAtUEBALZBAQC3QQEAsxECABKmAIAWpgCAGqYAgB6mAIC2SQIAtUkCACKmAIC7hQIAuoUCACamAIAqpgCAv4UCAL6FAgC9lQIAvJUCAIU8GgCjVQIALqYAgDKmAICmDQIANqYAgDqmAIClDQIAqsECAKvBAgA+pgCAQqYAgK7BAgCvwQIArNECAK3RAgCCGQAARqYAgIAZAACBGQAASqYAgE6mAIBSpgCAWqYAgL4ABABepgCAYqYAgGamAIBqpgCAbqYAgHKmAIB2pgCA7+gOAHqmAICG6AQAh1ADAH6mAICCpgCA74ACAIamAIDhlAEAiqYAgONYAQCOpgCA4wAOAJKmAIDhaA0AlqYAgKhxAgCpcQIAqnECAKupAgCsuQIArbkCAK6pAgCvqQIAhKwFAJqmAICepgCAoqYAgKamAICqpgCArqYAgLKmAIC4bQEAuQ0BALoFAQC7GQEAvAkBAL09AQC+NQEAv9kBALDZAgCx2QIAsm0BALNlAQC0fQEAtWUBALZlAQC3VQEA4WAPAOP0AADjHA4A4bwBALamAICCOQAAgTEAAIA9AAC6pgCAvigEAL6mAIDCpgCAvjwHAO8QAADv0A4AyqYAgIbgBACHyAQAzqYAgLO1AgDSpgCAtX0CALZ1AgDWpgCA2qYAgN6mAIC6UQIAu1ECALz1AQC9/QEAvvUBAL/tAQBWpgCAxqYAgKqxBQCrsQUArBUGAK0dBgCuFQYArw0GAOKmAIDmpgCA6qYAgKNVBQDupgCApZ0FAKaVBQDypgCAs+kGAPamAID6pgCA/qYAgAKnAIC24QYAtekGAAanAIC7sQYAuqEGAAqnAIAOpwCAv50GAL6RBgC9pQYAvKkGAKgdBgCpIQYAqiEGAKshBgCsIQYArSEGAK4hBgCvIQYAEqcAgBanAIAapwCAHqcAgCKnAIAmpwCAKqcAgC6nAIC45QcAue0HALrlBwC7/QcAvOUHAL3tBwC+5QcAv00HALAlBgCxNQYAsj0GALMxBgC0FQYAtRkGALYNBgC3AQYAo6kHAIIVAACBtQEAgLUBADKnAICmoQcApakHADanAICr8QcAquEHAISgAgA6pwCAr90HAK7RBwCt5QcArOkHAD6nAICzlQYAhugAAIcYAQC2tQYAQqcAgEanAIC1vQYAukkBALtVAQBKpwCATqcAgL45AQC/OQEAvEUBAL05AQCoPQYAqU0GAKpZBgCrUQYArHEGAK1xBgCuuQEAr7kBAISsAQBSpwCAVqcAgFqnAIBepwCAYqcAgGanAIBqpwCAuKkBALmpAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9pAQCwyQEAsdUBALLVAQCzqQEAtLkBALW5AQC2qQEAt6EBAKPRBQBupwCAcqcAgHanAIB6pwCApvEFAKX5BQB+pwCAqxECAKoNAgCCpwCAhqcAgK99AgCufQIArX0CAKwBAgCKpwCAjqcAgJKnAICWpwCAgTEAAIANAACapwCAgjkAAJ6nAICipwCAviQDAKqnAICupwCAsqcAgIbYHACHTAMAtqcAgLqnAIC+pwCAhMAcAOMgAQDCpwCA4cgBAManAIDvMAIAyqcAgM6nAIDSpwCA1qcAgNqnAIDepwCA4qcAgLOVAwDmpwCA6qcAgO6nAIDypwCAtrkDALWxAwD2pwCAu1EDALpJAwD6pwCA/qcAgL/1AAC+SQMAvUEDALxJAwCoLQIAqUUCAKpdAgCrVQIArHkCAK15AgCuvQIAr7UCAL5oHQACqACABqgAgAqoAICAHQAAgQkAAIKpAAAOqACAuFEBALlZAQC6YQEAu2EBALwRAQC9EQEAvhEBAL8RAQCwzQIAsdUCALLdAgCz1QIAtM0CALVxAQC2cQEAt3EBAOFYBgDhVAcA47AAAOO8BgASqACAGqgAgIYYHACHVB0AHqgAgCKoAIAmqACAKqgAgL74HAAuqACA7/AGAO/gBgCjlQIAMqgAgDaoAIA6qACAPqgAgKa5AgClsQIAQqgAgKtRAgCqSQIARqgAgEqoAICv9QEArkkCAK1BAgCsSQIAqG0eAKl1HgCqfR4Aq40eAKyVHgCtnR4Aro0eAK+BHgAWqACATqgAgFKoAIBWqACAWqgAgF6oAIBiqACAZqgAgLiJHgC5iR4AupkeALuRHgC8uR4AvbkeAL59HwC/dR8AsMUeALHNHgCyxR4As90eALTFHgC1zR4AtsUeALe5HgCz9R4AaqgAgG6oAIByqACAdqgAgLYdHgC1HR4AeqgAgLsJHgC6AR4AfqgAgIKoAIC/CR4AvgEeAL0JHgC8ER4Agm0AAKOxHgCAVQAAgWUAAKZZHgCEmAMAv9ABAKVZHgCqRR4Aq00eAIYABACHmAEArkUeAK9NHgCsVR4ArU0eAIqoAICOqACAhCQAAJKoAICWqACAmqgAgKanAICGqACAqLUeAKmFHgCqjR4Aq4UeAKydHgCtgR4Arv0eAK/1HgCwjR4AsZUeALKVHgCzpR4AtL0eALVxAQC2cQEAt3EBALhRAQC5UQEAulEBALtRAQC89QEAvf0BAL71AQC/7QEAsyUeAL4IBwCeqACAoqgAgKaoAIC2IR4AtTUeAKqoAIC7cR4AumkeAK6oAICyqACAv5UBAL5ZHgC9UR4AvGEeALaoAICjYR4AuqgAgL6oAICmZR4AwqgAgMaoAIClcR4Aqi0eAKs1HgDKqACAzqgAgK4dHgCv0QEArCUeAK0VHgDhVBoA0qgAgONcCgDWqACA2qgAgN6oAIDiqACA5qgAgOqoAIC+qAUA7qgAgPKoAICPMSoA+qgAgO/E+wD+qACAk2EuAJIdLwCR2SoAkEkqAJfZEgCWdRIAlQ0TAJTBLgCbHRsAmkEWAJlJFgCYDRcAn3EeAJ4RGwCdcRoAnHkaAKOhAgCinQMAoZUfAKCJHgDjiAEA4wgeAOFoAADh/B4A79wBAO98HwC1if4AtAH8ALMB+gCylfoAsQH4ALAR9gCv4fYArgH0AK0l8gCs7fIAqwHwAKrpDwCp1Q4AqN0OAKcBDACmyQoApe0KAKQBCACj4QYAovEGAKHlAwACqQCAggErAIMBKwAGqQCACqkAgIYxLwCHiS8AhIkrAIVFLgCKdRIAiwUTAIYIBQCHbAUAjhEXAI8RFwCMsRMAjV0WAJI9GgCTQRsAhMgFAIQABwCWUR8Al1EfAJRRGwCVORoAmn0eAJt9AgAOqQCAEqkAgIFZAQCAVQEAnFkDAIJRAQC+yAcAFqkAgBqpAIAeqQCAIqkAgCapAIAqqQCA79QeAC6pAIDhJB4AMqkAgONoAQA2qQCAOqkAgD6pAIBCqQCAu2kCALpZAgBGqQCASqkAgL8dAgC+HQIAvRkCALxxAgCz7QIATqkAgFKpAIBWqQCAWqkAgLZ9AgC17QIAXqkAgKMNBQD2qACAYqkAgGqpAIBmqQCApp0FAKUNBQBuqQCAq4kFAKq5BQCGCAMAh3wDAK/9BQCu/QUArfkFAKyRBQCAsQcAgbkHAIJBAACzsQYAcqkAgLVZBwC2MQcAdqkAgHqpAIB+qQCAuuEHALvhBwC84QcAveEHAL7hBwC/3QcAqLUGAKm5BgCqdQYAq4UHAKydBwCt/QcArvUHAK8ZBwCCqQCAhqkAgIqpAICOqQCAkqkAgJapAICaqQCAnqkAgLh1BwC5fQcAunUHALsFBwC8HQcAvTEHAL4xBwC/MQcAsGkHALFpBwCyeQcAs3kHALRpBwC1VQcAtlEHALdNBwCj/QcAoqkAgKapAICqqQCArqkAgKZ9BgClFQYAsqkAgKutBgCqrQYAtqkAgLqpAICvkQYArq0GAK2tBgCsrQYAvqkAgMKpAIDGqQCAyqkAgIAdAACBCQAAgjkAAM6pAIDSqQCA2qkAgIbIAACHpAEA3qkAgOKpAIDmqQCA6qkAgKiNAQCpmQEAqtkBAKvRAQCs8QEArfEBAK45AQCvOQEAhKAAAO6pAIDyqQCA9qkAgPqpAID+qQCAAqoAgAaqAIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+nQAAv5UAALBJAQCxSQEAslkBALNZAQC0SQEAtUkBALb9AAC39QAAugUEALsJBAC44QcAueEHAL4JBAC/CQQAvAkEAL0JBACyjQcAs+UHALC1BwCxhQcAtuUHALftBwC08QcAtfEHAKpNBwCrVQcAqEkHAKlJBwCu3QcAr8UHAKxNBwCt1QcACqoAgA6qAIASqgCAFqoAgBqqAIAeqgCAIqoAgCaqAICz0QIAKqoAgC6qAIC+AAwAMqoAgLbxAgC1+QIANqoAgLsNAgC6DQIAOqoAgD6qAIC/DQIAvg0CAL0NAgC8DQIAghUAAKOVAgCAYQAAgWEAAKa1AgBCqgCASqoAgKW9AgCqSQIAq0kCAIbIDACHrAwArkkCAK9JAgCsSQIArUkCAKhlAgCpdQIAqn0CAKt1AgCsbQIArbECAK6xAgCvsQIAhKANAE6qAIBSqgCAVqoAgFqqAIBeqgCAYqoAgGaqAIC4MQEAuTEBALoxAQC7MQEAvNUBAL3dAQC+yQEAv8EBALDRAgCx0QIAstECALPRAgC0EQEAtREBALYRAQC3EQEA4bAGAGqqAIDj0AYAhEAPAG6qAIDhpAEAcqoAgOPABgB2qgCAeqoAgH6qAIDv1AYA7AAAAIKqAIDvZAcAhqoAgIqqAICOqgCAkqoAgLO5AgCWqgCAtakCALZ9AgCaqgCAnqoAgKKqAIC6WQIAu1kCALxJAgC9SQIAvpkBAL+ZAQCjdQ0ARqoAgKaqAICqqgCArqoAgKaxDQClZQ0AsqoAgKuVDQCqlQ0AvqQDALaqAICvVQ4ArlUOAK2FDQCshQ0AgE0AAIFVAACCVQAAs2UPALqqAIC1ZQ8Atm0PAL6qAICGQAMAhxQDALrtDwC7/Q8AvOkPAL3VDwC+3Q8Av9UPAKhZDgCpoQ8AqqEPAKuhDwCsoQ8AraEPAK6hDwCvoQ8AwqoAgMaqAIDKqgCAzqoAgNKqAIDWqgCA2qoAgN6qAIC4AQ8AuQEPALoBDwC7HQ8AvA0PAL01DwC+PQ8Av9UAALBlDwCxdQ8AsnEPALNNDwC0VQ8AtV0PALZNDwC3QQ8AoykOAOKqAIDmqgCA6qoAgO6qAICmIQ4ApSkOAPKqAICrsQ4AqqEOAPaqAID6qgCAr5kOAK6RDgCtmQ4ArKUOAP6qAIACqwCABqsAgAqrAIDvJA0ADqsAgBKrAIAWqwCA49AOABqrAIDhGA4AHqsAgIAVAACBGQAAggUAACKrAICo0QEAqdkBAKopAQCrKQEArDkBAK05AQCuKQEArykBAL5oAQAqqwCAhsgBAIesAAAuqwCAMqsAgDarAIA6qwCAuO0AALmFAAC6jQAAu4UAALydAAC9gQAAvoEAAL+BAACwWQEAsVkBALLtAACz5QAAtP0AALXlAAC25QAAt9UAALOhAgA+qwCAQqsAgEarAIBKqwCAtrkCALWxAgBOqwCAu50CALqdAgBSqwCAVqsAgL8hAwC+OQMAvTEDALw5AwCF+PUAo+UCAFqrAIBeqwCApv0CAGKrAIBmqwCApfUCAKrZAgCr2QIAaqsAgG6rAICufQMAr2UDAKx9AwCtdQMAuOkAALnpAAC6aQAAu2kAALx5AAC9ZQAAvm0AAL9lAACwsQAAsbkAALKBAACzgQAAtPkAALX5AAC27QAAt+UAAKhlAwCpdQMAqn0DAKt1AwCsbQMArdEAAK7RAACv0QAAcqsAgHarAIB6qwCA1qkAgH6rAICCqwCAhqsAgIqrAICA/QEAgQkAAIIZAACOqwCAkqsAgL5EAgCaqwCAnqsAgISsAgCiqwCAh/gCAIasBQCmqwCAqqsAgK6rAICyqwCAs/UCALarAIC6qwCAvqsAgMKrAIC2UQEAteUCAMarAIC7fQEAunUBAMqrAIDOqwCAvz0BAL49AQC9VQEAvFUBAOFwDwDSqwCA47gOAITABQDvyAAA1qsAgNqrAIDeqwCA4zwOAOKrAIDh0AEA5qsAgIR0BwDqqwCA72gBAO6rAIDyqwCApXkCAKbNAQD2qwCAgCEAAIEhAACC3QcAo2kCAKzJAQCtyQEArqEBAK+hAQD6qwCA/qsAgKrpAQCr4QEAlqsAgAKsAIC+QAIABqwAgIYwAwCHMAMACqwAgA6sAICoOQcAqTkHAKoNBwCrHQcArAUHAK0NBwCuBQcAr3kHALAJBwCxCQcAshkHALMRBwC0OQcAtTkHALbdBwC3yQcAuPkHALn5BwC6zQcAu8EHALzFBwC9yQcAvrkHAL+xBwCzpQcAEqwAgBasAIAarACAHqwAgLatBwC1rQcAIqwAgLvtBwC67QcAJqwAgCqsAIC/3QcAvt0HAL3lBwC87QcALqwAgKPhBwAyrACANqwAgKbpBwA6rACAPqwAgKXpBwCqqQcAq6kHAEKsAIBGrACArpkHAK+ZBwCsqQcAraEHAEqsAIBOrACAUqwAgFasAIBarACAXqwAgGKsAIBmrACAgREAAIANAABqrACAghkAAG6sAIByrACAvuQBAHasAICG4AAAhxgBAHqsAIB+rACAgqwAgIasAICKrACA77AEAI6sAIDh1AYAkqwAgONcBACWrACAmqwAgJ6sAICirACAqJkBAKmZAQCqDQEAqwUBAKwdAQCtBQEArgUBAK81AQCEiAEApqwAgKqsAICurACAsqwAgLasAIC6rACAvqwAgLjBAAC5wQAAusEAALvBAAC8wQAAvcEAAL7BAAC/wQAAsE0BALElAQCyIQEAsyEBALQlAQC1LQEAthEBALcRAQDCrACAxqwAgLONAgDKrACAtZ0CAM6sAIDSrACAto0CANasAIDarACAu+kCALqBAgC9/QIAvP0CAL/hAgC+6QIA3qwAgKbVAgClxQIAvggDAKPVAgCCLQAAgRkAAIB5AACvuQIArrECAK2lAgCspQIAq7ECAKrZAgDirACA6qwAgO80AgDurACAhxgDAIYs/ADyrACA9qwAgPqsAID+rACAAq0AgAatAIAKrQCADq0AgOMAAQASrQCA4eABABatAIC6tQMAu70DABqtAIAerQCAvnkDAL95AwC8pQMAvXkDACarAICztQMAIq0AgCatAIC2kQMAKq0AgC6tAIC1pQMAqEkCAKlJAgCqWQIAq1kCAKxJAgCtdQIArnECAK9tAgC+aP0AvqT/ADKtAIA2rQCAOq0AgD6tAIBCrQCARq0AgLj5AgC5+QIAukkBALtJAQC8XQEAvUEBAL5BAQC/fQEAsBUCALEdAgCyFQIAs8kCALTZAgC12QIAtskCALfJAgDjIAYA4bAGAOGAAQDjEAYAgA0AAIE1AACCPQAASq0AgE6tAIBSrQCAWq0AgF6tAIDvcAAAYq0AgGatAIDvTAEAhIz9AGqtAICjmQIAbq0AgKWJAgByrQCAdq0AgKa9AgCGwPwAh+T8AKuRAgCqmQIArVUCAKyJAgCvVQIArlUCAKh9/gCpgf4Aqpn+AKuZ/gCsif4ArYn+AK65/gCvuf4AVq0AgHqtAIB+rQCAgq0AgIatAICKrQCAjq0AgJKtAIC4tf4Aub3+ALph/wC7Yf8AvGH/AL1h/wC+Yf8Av2H/ALDJ/gCxyf4Ast3+ALPR/gC0uf4Atbn+ALaR/gC3kf4AsxH+AJatAICarQCAnq0AgKKtAIC2Cf4AtQH+AKatAIC7Df4Aug3+AKqtAICurQCAv33+AL59/gC9Bf4AvAn+ALKtAICjVf4Atq0AgLqtAICmTf4Avq0AgMKtAIClRf4Aqkn+AKtJ/gCEKAMAxq0AgK45/gCvOf4ArE3+AK1B/gCAzQEAgdEBAILRAQCzuf4Ayq0AgLXR/gC21f4Azq0AgIZgAQCHYAEAug0BALsFAQC8HQEAvQUBAL4NAQC/BQEA0q0AgNatAIDarQCA3q0AgOKtAIDhwP0A5q0AgOOM/ADqrQCA7q0AgPKtAIDvtPwA9q0AgPqtAID+rQCAAq4AgKgp/gCpKf4Aqj3+AKs1/gCsVf4ArVn+AK5N/gCvRf4ABq4AgAquAIAOrgCAEq4AgBauAIAargCAHq4AgCKuAIC4SQEAuUkBALpZAQC7UQEAvHkBAL15AQC+GQEAvxUBALDFAQCxzQEAssUBALPdAQC0xQEAtc0BALbFAQC3eQEAJq4AgCquAIAurgCAo7n9ADKuAICl0f0AptX9AITQAwBBrgCAvuACAKoNAgCrBQIArB0CAK0FAgCuDQIArwUCAIFJAACAQQAAowkDAIJdAAClGQMARa4AgEmuAICmEQMAhsAEAIfkAwCrDQMAqg0DAK0BAwCsHQMArwEDAK4JAwCw4QMAseEDALLhAwCz/QMAtOUDALXtAwC25QMAtz0DALgFAwC5DQMAugUDALsdAwC8BQMAvQ0DAL4FAwC/vQAATa4AgFGuAIBVrgCAWa4AgOasAIBdrgCAYa4AgGWuAICo8QMAqfkDAKqpAwCrqQMArLkDAK25AwCuqQMAr6UDALNBAgBprgCAba4AgHGuAIB1rgCAtlkCALVRAgB5rgCAu0UCALpFAgB9rgCAga4AgL9JAgC+QQIAvUkCALxVAgCFrgCAia4AgI2uAICRrgCA74wDAJWuAICZrgCAna4AgONsAwChrgCA4VAAAKWuAICprgCAvngFALGuAICEcAIAgOUAAIHpAACC+QAAta4AgIawBACHVAUAua4AgO9A/gC9rgCA4Vz+AMGuAIDjVAEAxa4AgMmuAIDNrgCA0a4AgLOZAQDVrgCA2a4AgN2uAIDhrgCAth0BALUdAQDlrgCAuz0BALo9AQDprgCA7a4AgL/hAAC++QAAvfEAALz5AACoIQYAqVEGAKpRBgCrzQYArNUGAK3dBgCu1QYAr8kGAK2uAIDxrgCA9a4AgPmuAID9rgCAAa8AgAWvAIAJrwCAuG0HALkFBwC6DQcAuwUHALwdBwC9AQcAvgEHAL8BBwCwuQYAsbkGALJtBwCzZQcAtH0HALVlBwC2ZQcAt1UHAKPZBgANrwCAEa8AgBWvAIAZrwCApl0GAKVdBgCEnAIAq30GAKp9BgC+JAMAHa8AgK+hBwCuuQcArbEHAKy5BwCASQAAgUkAAIJZAACzVQcAIa8AgLV9BwC2aQcAJa8AgIZAAACHVAMAulUHALspBwC8OQcAvTkHAL4pBwC/IQcAo5kGACmvAIAtrwCAMa8AgDWvAICmpQYApbEGADmvAICr5QYAqpkGAD2vAIBBrwCAr+0GAK7lBgCt9QYArPUGAOE4BQBFrwCA4yQEAEmvAIBNrwCAUa8AgFWvAIBZrwCAXa8AgGGvAIBlrwCAaa8AgG2vAIBxrwCA7/QEAHWvAICo+QYAqQkGAKoRBgCrLQYArDkGAK0lBgCuLQYAryUGAHmvAIB9rwCAga8AgIWvAICAGQAAgRkAAIIFAACJrwCAuOUBALntAQC65QEAu/0BALzlAQC97QEAvuUBAL9ZAQCwXQYAsSEGALIhBgCzIQYAtCEGALUpBgC2EQYAtxEGAKjRAgCp2QIAqg0DAKsFAwCsHQMArQUDAK4FAwCvNQMAvmQCAJGvAICVrwCAma8AgJ2vAIChrwCApa8AgKmvAIC4JQMAuS0DALolAwC7PQMAvCUDAL0pAwC++QMAv/kDALBNAwCxIQMAsiUDALM9AwC0JQMAtS0DALYlAwC3HQMAs4UDAITIAgCtrwCAhAgDALGvAIC2hQMAtZUDALWvAIC75QMAuokDAIYIDACHnAMAv+kDAL7hAwC96QMAvPEDAIXsCgA2rgCAo80DALmvAICl3QMAva8AgMGvAICmzQMAxa8AgMmvAICrrQMAqsEDAK2hAwCsuQMAr6EDAK6pAwDNrwCA0a8AgNWvAIDZrwCA78gDAN2vAIDhrwCA5a8AgOO0AwDprwCA4dABAO2vAICADQAAgXUAAIJ9AADxrwCA9a8AgPmvAICzZQEAvgQCALVlAQABsACABbAAgLZlAQCGQA0Ah1gNALv1AQC6/QEAvaUBALy5AQC/mQEAvqUBAAmwAIANsACAEbAAgIQADAAVsACAGbAAgB2wAIDvzAEAIbAAgOEsBgAlsACA4yABAOwAAAApsACALbAAgDGwAIA1sACAo+kBADmwAIA9sACApukBAEGwAIBFsACApekBAKpxAQCreQEASbAAgE2wAICuKQEArxUBAKw1AQCtKQEAqCUOAKktDgCqJQ4Aqz0OAKwlDgCtLQ4AriUOAK+VDgD9rwCAUbAAgFWwAIBZsACAXbAAgIKdAACBnQAAgJ0AALhFDwC5TQ8AukUPALtZDwC8SQ8AvUkPAL59DwC/cQ8AsPEOALH5DgCypQ4As7kOALSpDgC1lQ4Atp0OALd9DwCo1Q8Aqd0PAKoJDwCrCQ8ArBkPAK0FDwCuDQ8ArwUPAGGwAIBlsACAabAAgL6gAwBtsACAcbAAgId4AwCGEAAAuBUPALkdDwC6IQ8AuyEPALz1AAC9/QAAvvUAAL/tAACwQQ8AsU0PALJdDwCzVQ8AtE0PALU1DwC2MQ8AtzEPAHWwAIDvsAwAebAAgH2wAICBsACAhbAAgImwAICNsACAkbAAgJWwAICZsACAnbAAgKGwAIDjqA0ApbAAgOGMDQCzwQ4AqbAAgK2wAICxsACAtbAAgLbFDgC10Q4AubAAgLvJDgC6xQ4AvbAAgMGwAIC/sQ4AvskOAL3BDgC8yQ4AowEOAMWwAIDJsACAzbAAgNGwAICmBQ4ApREOANWwAICrCQ4AqgUOANmwAICErAIAr3EOAK4JDgCtAQ4ArAkOAIBRAACBWQAAgmEAALPFAAC+zAEAtcUAALbNAADhsACAhkAHAIcUAQC6yQAAu8kAALzZAAC92QAAvskAAL/FAACrDQMAqg0DAKkJAwCouQIArw0DAK4NAwCtDQMArA0DAL5gAwDlsACA6bAAgO2wAIDxsACA9bAAgPmwAIC+MAUAuykDALoZAwC5GQMAuAEDAL/dAwC+3QMAvd0DALwxAwCzTQMAsk0DALFNAwCwTQMAtzkDALYxAwC1QQMAtE0DAP2wAICmkQMApZkDAAGxAICjmQMABbEAgAmxAIANsQCAr5kDAK6VAwCthQMArIUDAKuVAwCqlQMAja8AgBGxAIAVsQCAGbEAgB2xAIAhsQCAJbEAgCmxAIAtsQCAMbEAgDWxAIA5sQCAPbEAgEGxAICAHQAAgQkAAIL9AQBFsQCAvwgHAEmxAIBRsQCA7yQAAFWxAICElAIAWbEAgF2xAICH4AIAhgQFAL4AGABhsQCAZbEAgOGQAQBpsQCA44AAAG2xAIBxsQCAdbEAgLNlAQB5sQCAtWUBALZtAQB9sQCAgbEAgIWxAIC65QEAu/kBALzpAQC96QEAvsUBAL+9AQCJsQCAjbEAgJGxAIC+xBkAlbEAgJmxAICdsQCA78gBAKGxAIDh3A4ApbEAgOMwDgCpsQCArbEAgLGxAICEMAQAgHkAAIEVAACCFQAAo+UBALWxAICl5QEApu0BALmxAICGQAYAh5AHAKplAQCreQEArGkBAK1pAQCuRQEArz0BAKjdBQCpIQYAqiEGAKshBgCsIQYArSEGAK4hBgCvnQYATbEAgL2xAIDBsQCAhDABAMWxAIDJsQCAzbEAgNGxAIC4jQYAuZUGALqdBgC7lQYAvI0GAL21BgC+vQYAv7UGALDtBgCx8QYAsvEGALPxBgC0zQYAtbUGALa9BgC3tQYAqIkHAKmVBwCqkQcAq5EHAKy9BwCtpQcArqEHAK/dBwDVsQCA2bEAgN2xAIDhsQCA5bEAgOmxAIDtsQCA8bEAgLhJBwC5VQcAul0HALtVBwC8cQcAvX0HAL5pBwC/aQcAsKUHALGtBwCyuQcAs7EHALSRBwC1kQcAtnkHALd5BwD1sQCA+bEAgP2xAIABsgCA78gFAOHACQAFsgCA48AZAOMkBAAJsgCA4dAGAO/cKACinQMAoxUBAKAZBQChjQUAs1kGAA2yAIARsgCAFbIAgBmyAIC2ZQYAtXUGAB2yAIC7KQYAuiEGACGyAIAlsgCAvxUGAL4VBgC9JQYAvC0GAKOZBgCPmfwAKbIAgDGyAIA1sgCApqUGAKW1BgA5sgCAq+kGAKrhBgCGKB8Ah5wAAK/VBgCu1QYAreUGAKztBgCebQkAn30HAJwNCwCd7QkAmvENAJs5DQCY5fAAmQ0PAJbh8QCX6fEAlMX1AJUN8wCSHfcAk/H1AJD9+QCR7fkAgh3/AIMB+gA9sgCAQbIAgIYV9gCHOfYAhAn6AIXx9ACKwfAAiyXyAEWyAIBJsgCAjuEMAI8VDgCMNfIAjQHzAJKtDgCTgQgATbIAgFGyAICW6QQAl3UGAJR5CgCV8QoAmtEGAJvJAABVsgCAWbIAgIEdAwCAHQMAnFkCAIL1AwCrARAAqpUWAKmNFgCojRYAr5UuAK4BLACt/RIArJkSAKOlHgCipR4AoY0CAN2wAICnGRoAppUaAKUBGACknR8AXbIAgGGyAIBlsgCAabIAgG2yAIBxsgCAdbIAgHmyAICz5SoAsuUqALGtLwCw5S4AfbIAgIGyAIC1ASQAtBEqAKgpAwCpNQMAqj0DAKs1AwCsLQMArbUDAK69AwCvtQMAhbIAgImyAICNsgCAkbIAgIAdAACBCQAAgrkAAJWyAIC4TQIAuV0CALptAgC7CQIAvBkCAL0ZAgC+CQIAvwECALDNAwCx1QMAst0DALPVAwC0zQMAtXUCALZ9AgC3dQIAmbIAgITIHQChsgCAvgwfAKWyAICpsgCA70gGAO9YBwDhWAYA4ZgGAOOUAQDjAAYAhhAcAId8HQC+9B4ArbIAgLGyAIC2ZQMAtfUDALWyAICz5QMAubIAgL2yAIDBsgCAv+ECAL5ZAwC9UQMAvFkDALtBAwC6WQMAxbIAgMmyAIAtsgCAnbIAgM2yAIDRsgCA1bIAgNmyAIDdsgCA4bIAgKitHQCptR0AqrUdAKslHgCsPR4ArR0eAK4VHgCvdR4AsA0eALEtHgCyJR4As40eALSVHgC1nR4AtpUeALeNHgC4tR4Aub0eALq1HgC7nR4AvIUeAL1VHwC+XR8Av1UfALMdHQDlsgCA6bIAgO2yAIDxsgCAtr0eALWVHgD1sgCAu8keALrpHgD5sgCA/bIAgL95HgC+cR4AvXkeALzRHgCCKQAAo1kdAIAdAACBFQAApvkeAAGzAIAFswCApdEeAKqtHgCrjR4ACbMAgITgAwCuNR4Arz0eAKyVHgCtPR4AqIkeAKmVHgCqnR4Aq7EeAKzRHgCt2R4Ars0eAK/FHgANswCAEbMAgIaIAACHbAEAFbMAgBmzAIAdswCAIbMAgLhdAQC5wQEAusEBALvBAQC8wQEAvckBAL7xAQC/8QEAsL0eALGdHgCylR4As2UBALR9AQC1ZQEAtm0BALdlAQCqLR0AqzUdACWzAIApswCAri0dAK+VHACsLR0ArSUdAISMAQCjkR0ALbMAgDGzAICmER0ANbMAgDmzAIClgR0As1UeAD2zAIBBswCARbMAgEmzAIC2GR4AtRkeAE2zAIC7GR4AujkeAFGzAIBVswCAv+EBAL75AQC98QEAvAEeAFmzAIBdswCAYbMAgKOZHQBlswCApdUdAKbVHQBpswCAbbMAgHGzAICq9R0Aq9UdAKzNHQCtPQIArjUCAK8tAgCAZQAAgRUAAIIdAACEAAQAdbMAgHmzAICHcAMAhvwEAIGzAICFswCAibMAgI2zAICRswCAlbMAgJmzAICdswCAvsgEAKGzAIClswCAqbMAgK2zAICxswCAtbMAgO/cHwC5swCA4ZQBAL2zAIDjHAEAwbMAgMWzAIDJswCAzbMAgLt1AwC6aQMAvkgGANGzAIC/HQMAvh0DAL0dAwC8ZQMAs9UDANWzAIDZswCA3bMAgOGzAIC2fQMAtcUDAIRwBQCoJQIAqTUCAKo9AgCrNQIArC0CAK2dAgCulQIAr7UCAIIVAADlswCAgNkBAIEJAADEAAAA6bMAgPGzAID1swCAuKkCALmpAgC6SQEAu0kBALxZAQC9RQEAvkUBAL99AQCwzQIAsdECALLRAgCzqQIAtLkCALW5AgC2qQIAt6ECAOEoHgDhNBwA43QBAOMYHgD5swCA/bMAgIa4BACHVAUAhDgHAAG0AIAFtACACbQAgL6sBwANtACA78weAO/IGgCj9QIAEbQAgBW0AIAZtACAHbQAgKZdAgCl5QIAIbQAgKtVAgCqSQIAJbQAgCm0AICvPQIArj0CAK09AgCsRQIAqGEGAKlhBgCqYQYAq2EGAKxhBgCtYQYArmEGAK9hBgDtswCALbQAgDG0AIA1tACAObQAgD20AIBBtACARbQAgLjxBgC58QYAuvEGALvxBgC8nQYAvbEGAL6xBgC/sQYAsOUGALHtBgCy5QYAs/0GALTlBgC17QYAttkGALfVBgCz6QYASbQAgE20AIBRtACAVbQAgLbhBgC16QYAWbQAgLspBgC6IQYAXbQAgGG0AIC/KQYAviEGAL0pBgC8MQYAgl0AAKOtBgCARQAAgV0AAKalBgBltACAabQAgKWtBgCqZQYAq20GAIYADACHQAMArmUGAK9tBgCsdQYArW0GAG20AIDvfAUAcbQAgHW0AIB5tACAfbQAgIG0AICFtACAibQAgI20AICRtACAlbQAgJm0AIDjaAUAnbQAgOF4BQCz0QYAobQAgKW0AICptACArbQAgLb9BgC1/QYAsbQAgLupBgC6oQYAtbQAgLm0AIC/mQYAvqkGAL2pBgC8sQYAqLkGAKm5BgCqGQYAqxkGAKw1BgCtPQYArjUGAK8pBgC9tACAgh0AAIEdAACAHQAAwbQAgMW0AIDJtACA0bQAgLjpAQC56QEAuvkBALv5AQC86QEAvekBAL5dAQC/VQEAsCUGALEtBgCyJQYAsz0GALQtBgC1HQYAthUGALfZAQCGgAwAh+QCANW0AICjnQUA2bQAgKWxBQCmsQUA3bQAgOG0AIDltACAqu0FAKvlBQCs/QUAreUFAK7lBQCv1QUAtk0DAOm0AICExAMAtUUDAO20AICzjQIA8bQAgPW0AIC+SQMAv0kDALxJAwC9SQMAumkDALtpAwD5tACA/bQAgAG1AICmiQMApYEDAAW1AICjSQIACbUAgA21AIARtQCAr40DAK6NAwCtjQMArI0DAKutAwCqrQMAfbMAgBW1AIAZtQCAHbUAgIW0PQAhtQCAJbUAgCm1AIAttQCAMbUAgIA9AACBCQAAgh0AADW1AIC+sAMAObUAgIc4AwCG3AwAQbUAgEW1AIBJtQCATbUAgFG1AIDvXAYAVbUAgFm1AIC+6AwA45QGAF21AIDh3AEAYbUAgGW1AIBptQCAbbUAgLNRAQBxtQCAdbUAgHm1AIB9tQCAtnEBALV5AQCBtQCAuz0BALo9AQCFtQCAibUAgL/9AQC+9QEAvQUBALwFAQCNtQCAkbUAgJW1AICEQAwAmbUAgJ21AIChtQCA76wHAKW1AIDhJAYAqbUAgONABwCGkAwAh/wMALG1AIC1tQCAgFkAAIFlAACCYQAAo90BALm1AICl9QEApv0BAL21AIDBtQCAxbUAgKqxAQCrsQEArIkBAK2JAQCueQEAr3EBAM20AIA9tQCAybUAgM21AICttQCA0bUAgNW1AIDZtQCAqJ0NAKktDgCqOQ4AqzEOAKwRDgCtEQ4Arn0OAK9tDgCwGQ4AsRkOALIxDgCzMQ4AtNEOALXZDgC2zQ4At8UOALj9DgC52Q4AuqkOALupDgC8vQ4AvaUOAL6tDgC/pQ4AqIEPAKmBDwCqgQ8Aq4EPAKyBDwCtjQ8AroUPAK+1DwDdtQCA4bUAgOW1AIDptQCA7bUAgPG1AID1tQCA+bUAgLidDwC5rQ8AuqUPALtNDwC8VQ8AvV0PAL5JDwC/SQ8AsNEPALHRDwCy0Q8As9EPALS1DwC1vQ8AtrUPALetDwCzCQ4A/bUAgAG2AIAFtgCACbYAgLYNDgC1CQ4ADbYAgLsVDgC6FQ4AEbYAgBW2AIC/eQ4AvnEOAL0FDgC8BQ4AghUAAKNNDgCAYQAAgWEAAKZJDgAZtgCAvhABAKVNDgCqUQ4Aq1EOAIQkAQAhtgCArjUOAK89DgCsQQ4ArUEOAKg5DgCpOQ4AqlkOAKtRDgCscQ4ArXEOAK6RAQCvkQEAhgAAAIeEAAAltgCAKbYAgC22AIAxtgCANbYAgDm2AIC4dQEAuX0BALp1AQC7yQAAvNkAAL3ZAAC+yQAAv8EAALD1AQCx/QEAsvUBALNNAQC0VQEAtV0BALZVAQC3TQEAuk0PALtVDwC4TQ8AuUUPAL59DwC/tQ8AvEUPAL11DwCyAQ8AswEPALAxDwCxMQ8AtgEPALcNDwC0EQ8AtREPAKqZDgCrRQ8AqOUOAKmZDgCuQQ8Ar0EPAKxRDwCtUQ8APbYAgEG2AIBFtgCASbYAgE22AIBRtgCAVbYAgFm2AICzUQ0AXbYAgGG2AIBltgCAabYAgLZxDQC1eQ0AbbYAgLu5AgC6sQIAcbYAgHW2AIC/GQIAvhECAL0ZAgC8oQIAebYAgKMVDQB9tgCAgbYAgKY1DQCFtgCAibYAgKU9DQCq9QIAq/0CAIToAwCRtgCArlUCAK9dAgCs5QIArV0CAKhtAgCprQIAqqUCAKu9AgCspQIAra0CAK6lAgCvfQEAgO0BAIHxAQCC8QEAvqAFAJW2AICZtgCAh2gFAIYcBQC4yQEAuckBALrZAQC70QEAvPkBAL35AQC+mQEAv5UBALAFAQCxDQEAsgUBALMdAQC0BQEAtQ0BALYFAQC3+QEA4WQPAOGcDwDjFA4A49QPAJ22AIDhPA4AobYAgOPkAAC+rAQApbYAgKm2AIDvDAAArbYAgLG2AIDvYA4A77QPALW2AIC5tgCAhEQEALNhAgC9tgCAtWECALZhAgDBtgCAxbYAgMm2AIC6jQEAu4UBALydAQC9hQEAvo0BAL+FAQCjrQUAjbYAgM22AIDRtgCA1bYAgKatBQClrQUA2bYAgKtJBgCqQQYA3bYAgOG2AICvSQYArkEGAK1JBgCsUQYA5bYAgOm2AIDttgCA8bYAgIAdAACBCQAAgjkAAPW2AID5tgCA/bYAgIbIAACHIAMAAbcAgAW3AIAJtwCADbcAgKhtBgCptQcAqr0HAKsdBwCsCQcArTEHAK4xBwCvLQcAhKgDABG3AIAVtwCAGbcAgB23AIAhtwCAJbcAgCm3AIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+nQAAv5UAALBVBwCxJQcAsi0HALM9BwC0LQcAtRUHALYdBwC39QAALbcAgOG8BgAxtwCA4/QFADW3AIA5twCAPbcAgEG3AIBFtwCASbcAgE23AIBRtwCAVbcAgFm3AIBdtwCA7+gEALN1BgCCLQAAgRUAAIAdAABhtwCAtvEGALXBBgBltwCAu6EGALrRBgBptwCAvmwBAL+RBgC+qQYAvakGALy5BgCjtQYAcbcAgIYoAACHTAEAdbcAgKYxBgClAQYAebcAgKthBgCqEQYAfbcAgIG3AICvUQYArmkGAK1pBgCseQYAhbcAgLO9AQCJtwCAjbcAgLZ5AQCRtwCAlbcAgLV5AQC6VQEAu10BAJm3AICdtwCAvvkAAL/lAAC8RQEAvf0AAKhxAgCpcQIAqnECAKtxAgCstQIArb0CAK61AgCvrQIAhOw8AKG3AICltwCAqbcAgK23AICxtwCAtbcAgLm3AIC4XQMAuWUDALptAwC7ZQMAvH0DAL1lAwC+bQMAv2UDALDVAgCx3QIAstUCALNtAwC0eQMAtWUDALZtAwC3ZQMAHbYAgL23AIDBtwCAo/UCAMW3AIClMQIApjECAMm3AIDNtwCA0bcAgKodAgCrFQIArA0CAK21AwCusQMAr60DAIBlAACBCQAAghkAANW3AIDZtwCA4bcAgL4QPADltwCAhsA8AIcgAwDptwCA7bcAgPG3AID1twCA+bcAgP23AICohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAAG4AIAFuACACbgAgA24AIARuACAFbgAgBm4AIAduACAuHUBALl9AQC6dQEAu8kBALzZAQC9xQEAvsUBAL/9AQCwtQIAsb0CALKBAgCzgQIAtFUBALVdAQC2VQEAt00BAOGkBgAhuACA41AGAL6APACEHDwAvoA/ACW4AIApuACALbgAgDG4AIA1uACAObgAgD24AIBBuACA7+AGAEW4AICBfQAAgHEAAEm4AICCBQAAUbgAgFW4AIDvTAAAWbgAgOGQAQBduACA41gBAGG4AIBluACAabgAgIZYPwCH/DwAs509AN23AIBNuACAbbgAgHG4AIC21T0AtbU9AHW4AIC7+T0AuvE9AHm4AIB9uACAvxk+AL4RPgC91T0AvNU9AIG4AICj2T0AhbgAgIm4AICmkT0AjbgAgJG4AICl8T0AqrU9AKu9PQCVuACAmbgAgK5VPgCvXT4ArJE9AK2RPQCoVT4AqVk+AKphPgCrYT4ArGE+AK1hPgCuYT4Ar2E+AISoAwCduACAobgAgKW4AICpuACArbgAgLG4AIC1uACAuEU/ALldPwC6VT8Au20/ALx1PwC9fT8AvnU/AL9tPwCwwT8AscE/ALLBPwCzwT8AtME/ALXBPwC2wT8At8E/AIC5AQCBuQEAggUAALm4AIDhgD4AwbgAgOMoPQDFuACAhoAAAIcEAQDvCD0AybgAgM24AIDRuACA1bgAgNm4AICzqT8AvbgAgN24AIDhuACA5bgAgLahPwC1qT8A6bgAgLtFPgC6RT4A7bgAgPG4AIC/RT4AvkU+AL1VPgC8VT4Ao2k/APW4AID5uACA/bgAgAG5AICmYT8ApWk/AAW5AICrhT4AqoU+AAm5AIANuQCAr4U+AK6FPgCtlT4ArJU+ABG5AICzGT4AFbkAgBm5AIC2IT4AHbkAgCG5AIC1MT4AuvEBALv5AQAluQCAKbkAgL6xAQC/vQEAvNEBAL3RAQCo0T0AqdE9AKrVPQCr6T0ArP09AK3lPQCu7T0ArxECAID5AwCBzQMAgsUDAIQkAwC+AAQAMbkAgIesAwCGvAQAuBkCALktAgC6JQIAu+kCALz5AgC9+QIAvukCAL/pAgCwcQIAsXkCALJBAgCzQQIAtDECALU9AgC2NQIAtykCAKVtPQA1uQCAObkAgKZ9PQA9uQCAbbcAgKNFPQBBuQCArY0CAKyNAgCv4QIAru0CAKwAAABFuQCAq6UCAKqtAgDh+AEASbkAgOP0AgCEwAQATbkAgFG5AIBVuQCAWbkAgF25AIBhuQCAZbkAgGm5AIBtuQCAcbkAgO8wAgB1uQCAqBUCAKkZAgCqJQIAqz0CAKwlAgCtLQIAriUCAK9VAgB5uQCAfbkAgIG5AICFuQCAibkAgI25AICEsAQAkbkAgLjRAgC52QIAuuECALvhAgC8kQIAvZ0CAL6VAgC/iQIAsC0CALE1AgCyNQIAswUCALQdAgC18QIAtvECALfxAgDheD8A4zQBAOMIPgDhbD4AgQkAAICpAACVuQCAgj0AAJm5AIChuQCApbkAgL4gBACpuQCA79g+AO/MPgCtuQCAsbkAgLPpAgCG6AQAh8AEALbpAgC1uQCAubkAgLXpAgC6rQIAu7UCAL25AIDBuQCAvp0CAL9xAgC8pQIAvZUCAC25AICduQCAxbkAgMm5AIDNuQCA0bkAgNW5AIDZuQCAqBUGAKmhBgCqoQYAq70GAKytBgCtgQYArv0GAK/tBgCwlQYAsZ0GALKVBgCzrQYAtLUGALW9BgC2tQYAt60GALiVBgC5mQYAukkHALtJBwC8WQcAvVkHAL5JBwC/SQcArN0FAK3tBQCu5QUArwkFAN25AIDhuQCAqtUFAKvNBQDluQCApZEFAKaRBQDpuQCA7bkAgPG5AID1uQCAo5EFALNJBgD5uQCA/bkAgAG6AIAFugCAtmEGALVFBgAJugCAuzkGALoxBgC+ZAAADboAgL8ZBgC+EQYAvRkGALwhBgCjiQcAgtkBAIHZAQCAwQEAEboAgKahBwClhQcAFboAgKv5BwCq8QcAhggBAId8AQCv2QcArtEHAK3ZBwCs4QcAGboAgLP1BgAdugCAIboAgLaFBgAlugCAKboAgLWdBgC6jQYAu20BAC26AIAxugCAvmUBAL9tAQC8dQEAvW0BAKglBgCpLQYAqjkGAKsxBgCsUQYArUEGAK5BBgCvdQYANboAgDm6AIA9ugCAQboAgEW6AIBJugCATboAgFG6AIC4VQEAuWUBALplAQC7fQEAvGUBAL1tAQC+HQEAvxUBALANBgCx7QEAsuUBALP9AQC05QEAte0BALblAQC3bQEAo7EFAFW6AIBZugCAvkgDAL5YDACmwQUApdkFAF26AICrKQIAqskFAGG6AIBlugCArykCAK4hAgCtKQIArDECAGm6AIBtugCAcboAgHW6AICAGQAAgRkAAIIFAAB5ugCAhKwDAIG6AICHGAMAhswMAIW6AICJugCAjboAgJG6AICokQMAqZkDAKrJAwCrxQMArN0DAK3BAwCuwQMAr/UDAJW6AICZugCAnboAgKG6AIClugCAqboAgK26AICxugCAuH0DALnBAAC6wQAAu9EAALz5AAC9+QAAvpkAAL+ZAACwjQMAsUUDALJNAwCzRQMAtF0DALVFAwC2TQMAt0UDALNBAgC1ugCAuboAgL8EDwC9ugCAtkECALVVAgDBugCAu4ECALpJAgDFugCAyboAgL+BAgC+mQIAvZECALyZAgDNugCA0boAgNW6AIDZugCA76QDAN26AIDhugCA5boAgOMQAwDpugCA4VgAAIQgDQCAKQAAgSkAAIIdAADxugCA4VAGAOGgBwDjoAYA41AHAIWUDAD1ugCA70gbAPm6AIDhJAIA/boAgONwGgABuwCABbsAgAm7AIDvqAEA7+gGAIagDwCHDA0Ao4kCAA27AIClnQIAEbsAgBW7AICmiQIAGbsAgB27AICrSQIAqoECAK1ZAgCsUQIAr0kCAK5RAgCoZQ4AqXUOAKp9DgCrdQ4ArG0OAK21DgCuvQ4Ar7UOAO26AIAhuwCAJbsAgCm7AIAtuwCAOLsAgDy7AIBAuwCAuF0PALltDwC6ZQ8Auw0PALwVDwC9HQ8AvhUPAL8JDwCwzQ4AsdUOALLdDgCz1Q4AtM0OALVxDwC2cQ8At20PALP1DgBEuwCASLsAgEy7AIBQuwCAtjUOALXlDgBUuwCAuxEOALoJDgBYuwCAXLsAgL+1DwC+CQ4AvQEOALwJDgCCFQAAo7EOAIBhAACBYQAApnEOAGC7AIC+EAEApaEOAKpNDgCrVQ4AaLsAgIQgAQCuTQ4Ar/EPAKxNDgCtRQ4An0UIAJ4NCQCdDQkAnJkLAJt1NQCaETUAmZk3AJgNMQCXJTEAliUxAJWBPQCUDT0Ak4k/AJIVOACRPTkAkD05AI9lJQDvrA0AhgAEAIegAQBsuwCAcLsAgHS7AIDv6AEAeLsAgOE0AgB8uwCA4zQBAIC7AIDjCAwAhLsAgOEIDQChoQEAiLsAgKMJBQCibQMApc0EAKQRBQCnHRkAph0ZAKmhHQCoORkAq+kcAKqpHQCtkREArAEQAK8BFACuUREAsfkVALDlFQCz6WkAsgFoALUBbAC0eWkAjLsAgJC7AICUuwCAmLsAgJy7AICguwCAowkDAKIZDQCh/Q0AoP0NAIIlJgCDBToApLsAgKi7AICGqTwAhzU+AIQdOgCFPTsAiok+AIslMgCsuwCAsLsAgI6xNACPMTYAjD0yAI0tMgCSJTYAk9EIAIREAwC+wAQAlhULAJdVDgCUXQoAlVUKAJplDgCbiQ4AtLsAgLi7AIC8uwCAwLsAgJyBAADEuwCAuLUCALm9AgC6tQIAuwkCALwZAgC9GQIAvgkCAL8BAgCwdQ0AsX0NALJJDQCzSQ0AtJUCALWdAgC2lQIAt40CAKi9DQCpUQ0AqlUNAKtpDQCsfQ0ArWUNAK5tDQCvEQ0AZLsAgILtAQCBHQAAgB0AAMi7AIDMuwCAfboAgL5wBQCznQwAhIwFANC7AIDYuwCA3LsAgLalDAC1tQwA4LsAgLv5DAC68QwAhigFAIcgBQC/GQMAvhEDAL3dDAC83QwA5LsAgKPZDADouwCA7LsAgKbhDADwuwCA9LsAgKXxDACqtQwAq70MAPi7AID8uwCArlUDAK9dAwCsmQwArZkMAAC8AIAEvACACLwAgAy8AIAQvACAFLwAgBi8AIDvvAEAHLwAgOF8DgAgvACA41ABACS8AIAovACALLwAgDC8AICzlQIANLwAgDi8AIA8vACAQLwAgLa9AgC1uQIASLwAgLs5AgC6YQIAhsgEAIesBAC/GQIAvhECAL0ZAgC8IQIAo1UFAILVBwCBxQcAgMUHAEy8AICmfQUApXkFAFC8AICr+QUAqqEFAFS8AIBYvACAr9kFAK7RBQCt2QUArOEFAFy8AICzWQcAYLwAgGS8AIC2HQcAaLwAgGy8AIC1FQcAugkHALsJBwBwvACAdLwAgL75BwC/+QcAvPkHAL35BwDUuwCARLwAgHi8AIB8vACAgLwAgIS8AICIvACAjLwAgKitBwCptQcAqrUHAKvtBwCs+QcArfkHAK7tBwCv5QcAsKkHALGpBwCySQcAs0kHALRZBwC1WQcAtkkHALdJBwC4eQcAuUUHALpBBwC7XQcAvEUHAL1NBwC+RQcAvzkHAKMdBgCQvACAlLwAgJi8AICcvACAplkGAKVRBgCgvACAq00GAKpNBgCkvACAqLwAgK+9BgCuvQYArb0GAKy9BgCAbQAAgQkAAIIZAACsvACAsLwAgISYAQC+kAEAtLwAgIYAHACHxAEAuLwAgLy8AIDAvACAxLwAgMi8AIDMvACAqF0GAKmVAQCqlQEAq6UBAKy9AQCt1QEArtEBAK/RAQDQvACA1LwAgNi8AIDcvACA4LwAgOS8AIDovACA7LwAgLhZAQC5WQEAus0AALvFAAC83QAAvcUAAL7FAAC/9QAAsLUBALG9AQCygQEAs4EBALR5AQC1eQEAtmkBALdpAQCzHQIA8LwAgPS8AIC+gBwA+LwAgLZVAgC1NQIA/LwAgLt5AgC6cQIAAL0AgAS9AIC/vQIAvr0CAL1VAgC8VQIACL0AgKNZAgAMvQCAEL0AgKYRAgAUvQCAGL0AgKVxAgCqNQIAqz0CABy9AIAgvQCArvkCAK/5AgCsEQIArRECACi9AIAsvQCAvgQdAL4AHgAwvQCANL0AgDi9AIA8vQCAgPkAAIHNAACCxQAAhCADAIawHACHlAMAQL0AgES9AIBIvQCATL0AgFC9AIBUvQCA42wCAFi9AIDhoAEAXL0AgO8UAgBgvQCAZL0AgGi9AIBsvQCAcL0AgHS9AIB4vQCA4fAGAOE0BgDjTAAA4xgGAHy9AICAvQCAhL0AgIi9AICAPQAAgQkAAIIZAACMvQCAkL0AgIS8HQDvmAAA7zgHALMxAgDRAAAAh9gdAIZsHACYvQCAtikCALUhAgCcvQCAu80CALrNAgCgvQCApL0AgL/NAgC+zQIAvc0CALzNAgCyXQYAs2UGALANBgCxVQYAtn0GALedBQC0fQYAtXUGALqNBQC7zQUAuKUFALmFBQC+xQUAv8kFALzVBQC9zQUAqL0AgKy9AICwvQCAtL0AgLi9AIC8vQCAwL0AgMS9AICqtQYAq70GAKgBBwCpvQYAroEGAK+NBgCsmQYArZUGAKNxHQDIvQCAzL0AgNC9AIDUvQCApmkdAKVhHQDYvQCAq40dAKqNHQDcvQCA4L0AgK+NHQCujR0ArY0dAKyNHQDkvQCAs9UeAOi9AIDsvQCAts0eAPC9AID0vQCAtcUeALqhHgC7oR4A+L0AgPy9AIC+pR4Av6keALyxHgC9sR4AJL0AgJS9AIAAvgCAhAQDAID5AACB+QAAghEAAAS+AICoIR4AqSEeAKo5HgCrOR4ArCkeAK0pHgCuAR4ArwEeALABHgCxAR4AsgEeALMBHgC0BR4AtQkeALY9HgC3NR4AuA0eALkVHgC6HR4AuxUeALwNHgC95R8Avu0fAL/lHwCjkR8ACL4AgIYoAQCHSAEADL4AgKaJHwClgR8AEL4AgKvlHwCq5R8AFL4AgBi+AICv7R8AruEfAK31HwCs9R8AHL4AgLMtHgAgvgCAJL4AgLaVHgAovgCALL4AgLWdHgC6sR4Au7EeADC+AIA0vgCAvnUBAL99AQC8oR4AvaEeAKjRHgCp2R4AquEeAKvhHgCsUR4ArVEeAK5RHgCvUR4AOL4AgDy+AIBAvgCARL4AgEi+AIBMvgCAUL4AgFS+AIC43QEAue0BALrlAQC7jQEAvJkBAL2ZAQC+jQEAv4UBALAxHgCxMR4AsjEeALMxHgC09QEAtf0BALb1AQC37QEAo2kdAFi+AIBcvgCAYL4AgGS+AICm0R0ApdkdAGi+AICr9R0AqvUdAGy+AIBwvgCArzkCAK4xAgCt5R0ArOUdAIFpAACAWQAAvgAEAIJhAAB4vgCAfL4AgIC+AICEvgCAhOwDAIi+AICHiAMAhuwEAIy+AICQvgCAlL4AgJi+AICohQMAqZUDAKqVAwCrpQMArL0DAK3VAwCu0QMAr9EDAJy+AICgvgCApL4AgKi+AICsvgCAsL4AgLS+AIC4vgCAuHEDALlxAwC6cQMAu3EDALzVAAC93QAAvtUAAL/NAACwtQMAsb0DALKBAwCzgQMAtFEDALVRAwC2UQMAt1EDAOFUHgDhrB8A45QBAOMoHgDjYAMAvL4AgOEIAADAvgCA75ADAMS+AIDIvgCAzL4AgNC+AIDUvgCA70wfAO9MHwCzXQIA2L4AgNy+AIDgvgCA6L4AgLYVAgC1dQIA7L4AgLs5AgC6MQIAhCQFAL7gBAC/1QIAvtUCAL0VAgC8FQIAuJEdALmZHQC6oR0Au6EdALzRHQC93R0AvtUdAL/JHQCwCR4AsQkeALIZHgCzGR4AtAkeALUJHgC2vR0At7UdAKipHgCpqR4AqrkeAKu5HgCsqR4ArakeAK55HgCveR4AgKUAAIGtAACCpQAA8L4AgIbQBACH+AQA9L4AgPi+AIB0vgCA5L4AgPy+AIAAvwCABL8AgAi/AIAMvwCAEL8AgKhxBgCpcQYAqnEGAKtxBgCsVQYArUUGAK5NBgCvRQYAsD0GALHlBgCy7QYAs+UGALT9BgC15QYAtu0GALflBgC43QYAuXEHALp1BwC7SQcAvFkHAL1ZBwC+SQcAv0kHALPZBgAUvwCAGL8AgBy/AIAgvwCAtuUGALX9BgAkvwCAuwEGALrZBgAovwCALL8AgL8BBgC+GQYAvREGALwZBgAwvwCAo9kFADS/AIA4vwCAppEFADy/AIBAvwCApfEFAKq1BQCrvQUARL8AgEi/AICuUQUAr1EFAKyRBQCtkQUAo1kHAIIZAACBGQAAgOEBAEy/AICmZQcApX0HAFC/AICrgQcAqlkHAISgAgC+rAEAr4EHAK6ZBwCtkQcArJkHAFS/AICzqQYAhugAAIcsAQC2WQEAWL8AgFy/AIC1oQYAunUBALt9AQBgvwCAZL8AgL75AQC/+QEAvGUBAL35AQCo0QYAqdkGAKplBgCrdQYArG0GAK2dAQCulQEAr40BAITsAQBovwCAbL8AgHC/AIB0vwCAeL8AgHy/AICAvwCAuGkBALlpAQC6CQEAuwUBALwdAQC9AQEAvgEBAL81AQCw9QEAsf0BALL1AQCzaQEAtHkBALV5AQC2aQEAt2EBAIS/AICIvwCAjL8AgKPhBQCQvwCApekFAKYRAgCUvwCAmL8AgJy/AICqPQIAqzUCAKwtAgCtsQIArrECAK+xAgCgvwCApL8AgL4EAwCEAAwAqL8AgKy/AICwvwCAtL8AgIANAACBFQAAgh0AALi/AIC8vwCAwL8AgIdEAwCG3AwAs+kDAMi/AIDMvwCA0L8AgNS/AIC2PQMAtT0DANi/AIC7GQMAuhEDANy/AIDgvwCAv7kAAL6xAAC9uQAAvAEDAOS/AIDhlAEA6L8AgON8AQDsvwCA8L8AgPS/AID4vwCA/L8AgADAAIAEwACACMAAgAzAAIAQwACAFMAAgO9MAgCoVQIAqV0CAKphAgCrYQIArLUCAK29AgCutQIAr60CAL5oDQAYwACAHMAAgCDAAIAkwACAgq0AAIGtAACArQAAuGEBALlhAQC6CQEAuwkBALwBAQC9AQEAvgEBAL8BAQCw1QIAsd0CALLVAgCzbQEAtHUBALV9AQC2aQEAt2EBAOFoBgDh8AcA47AAAOP0BgAowACALMAAgDDAAIA4wACAPMAAgEDAAIBEwACASMAAgL78DABMwACA72wAAO8oBgCjqQIAUMAAgIZoDACHBA0AVMAAgKZ9AgClfQIAWMAAgKtZAgCqUQIAXMAAgGDAAICv+QEArvEBAK35AQCsQQIAqIUOAKmNDgCqhQ4Aq50OAKyNDgCtvQ4ArrUOAK/dDgA0wACAZMAAgGjAAIBswACAcMAAgHTAAIB4wACAfMAAgLitDgC5tQ4Aur0OALu1DgC8dQ8AvX0PAL51DwC/bQ8AsKkOALG1DgCyvQ4As7UOALStDgC1lQ4Atp0OALeVDgCzDQ4AgMAAgITAAICIwACAjMAAgLY9DgC1BQ4AkMAAgLtxDgC6bQ4AlMAAgJjAAIC/UQ4AvmkOAL1hDgC8aQ4AghkAAKNJDgCAZQAAgRkAAKZ5DgCcwACAoMAAgKVBDgCqKQ4AqzUOAIS8AwCkwACAri0OAK8VDgCsLQ4ArSUOAKidDgCppQ4Aqq0OAKulDgCsvQ4AraEOAK7dDgCvzQ4AhiABAIdkAQCowACArMAAgLDAAIC0wACAuMAAgLzAAIC4eQEAuXkBALrNAQC7xQEAvN0BAL3FAQC+xQEAv/UBALC9DgCxjQ4AsoUOALNJAQC0WQEAtVkBALZJAQC3SQEAtS0OAMDAAIDEwACAtjkOAMjAAIDMwACAsz0OANDAAIC9hQEAvEkOAL+FAQC+hQEA1MAAgMS/AIC7UQ4AumEOAKNlDgDYwACA3MAAgODAAIDkwACApmEOAKV1DgDowACAqwkOAKo5DgDswACA8MAAgK/dAQCu3QEArd0BAKwRDgD0wACA+MAAgO/QDwD8wACAAMEAgATBAIAIwQCADMEAgBDBAIC+aAMAGMEAgBzBAIDhVA4AIMEAgONkDgAkwQCAgFkAAIFZAACCaQAAhIwDAIbwBACHFAMAKMEAgCzBAIAwwQCANMEAgDjBAIA8wQCAQMEAgETBAIBIwQCATMEAgFDBAIBUwQCAWMEAgFzBAIBgwQCAZMEAgGjBAIBswQCAqIkDAKmJAwCqmQMAq5kDAKyJAwCtiQMArj0DAK81AwCwUQMAsVEDALJVAwCzfQMAtBUDALUdAwC2FQMAtw0DALg9AwC5DQMAugUDALvtAAC89QAAvfkAAL7pAAC/6QAAcMEAgHTBAIB4wQCAsz0CAHzBAIC1LQIAtiUCAIDBAIC+aAUAiMEAgLq5AgC7uQIAvK0CAL2FAgC+/QIAv/UCAIBJAACBVQAAglUAAIQABQDvjAMAvhgEAId0BQCG/AQA4zwDAIzBAIDhUAAAkMEAgJTBAICYwQCAnMEAgKDBAICkwQCAqMEAgKzBAICwwQCAtMEAgLjBAIC8wQCA79QOAL4oBgDhdA4AwMEAgONUAQDEwQCAyMEAgMzBAIDQwQCAo/ECANTBAIDYwQCA3MEAgODBAICm6QIApeECAOTBAICrdQIAqnUCAOjBAIDswQCArzkCAK4xAgCtSQIArGECAKgpBgCpKQYAqj0GAKsxBgCsSQYArUkGAK55BgCveQYAhMEAgIIVAACBxQcAgMUHAPDBAICEaAMA9MEAgPjBAIC4yQYAuckGALrZBgC72QYAvMkGAL3JBgC+WQcAv1kHALAJBgCxCQYAshkGALMZBgC0CQYAtQkGALb5BgC3+QYAs7UGAPzBAICGrAAAh0ADAADCAIC2yQYAtcEGAATCAIC7zQYAus0GAAjCAIAMwgCAv80GAL7NBgC9zQYAvM0GABDCAICj8QYAFMIAgBjCAICmjQYAHMIAgCDCAIClhQYAqokGAKuJBgAkwgCAKMIAgK6JBgCviQYArIkGAK2JBgCoJQYAqWEGAKplBgCrfQYArGUGAK1tBgCuZQYAr50GACzCAIAwwgCANMIAgDjCAIA8wgCAQMIAgETCAIBIwgCAuPUGALn9BgC69QYAu4kGALyZBgC9mQYAvokGAL+BBgCw5QYAse0GALLlBgCz/QYAtOUGALXtBgC20QYAt80GAEzCAIC2/QYAtf0GAFDCAICz/QYAVMIAgFjCAIBcwgCAvzkGAL4xBgC9OQYAvCEGALs5BgC6MQYAFMEAgGDCAICjrQYAgnkAAIFVAACAVQAAhFwBAKatBgClrQYAaMIAgKtpBgCqYQYAhkh/AIfkAACvaQYArmEGAK1pBgCscQYAbMIAgO/cBwBwwgCAdMIAgHjCAIB8wgCAgMIAgITCAICIwgCAhKADAIzCAIC/JHkAkMIAgONoBwCUwgCA4XQGALPRAgCYwgCAvgQDAISAfQCcwgCAtvkCALXxAgCgwgCAu7UCALqpAgCkwgCAqMIAgL9RAwC+mQIAvZECALylAgCpBQIAqLkCAKsVAgCqHQIArT0CAKw9AgCvUQIArl0CAL5ofQCswgCAsMIAgLTCAIC4wgCAvMIAgMDCAIDEwgCAufEDALjpAwC78QMAuvkDAL1RAwC86QMAv00DAL5RAwCxNQIAsCkCALMBAgCyNQIAtdEDALQZAgC30QMAttkDAIIpAACjlQMAgB0AAIEVAACmvQMAyMIAgMzCAICltQMAqu0DAKvxAwDQwgCA2MIAgK7dAwCvFQIArOEDAK3VAwCGYH0Ah3h9ALNBAQCEAH8AtUEBANzCAIDgwgCAtkkBAOTCAIDowgCAu0EBALpNAQC9SQEAvEUBAL8pAQC+OQEA7MIAgO/cBgDwwgCA9MIAgPjCAID8wgCAAMMAgO8wBgCELH4A4eAGAATDAIDjiAEACMMAgON0AAAMwwCA4SwBAKPJAQAQwwCAFMMAgIVweQAYwwCApsEBAKXJAQAcwwCAq8kBAKrFAQAgwwCAJMMAgK+hAQCusQEArcEBAKzNAQCo3X0AqQV+AKoBfgCrAX4ArAF+AK0BfgCuAX4ArwF+ANTCAIAowwCALMMAgDDDAIA0wwCAgp0AAIGdAACAnQAAuC1+ALnhfgC64X4Au+F+ALzhfgC94X4AvuF+AL/hfgCwQX4AsU1+ALJZfgCzVX4AtDV+ALUlfgC2JX4AtxV+AKitfwCp0X8AqtF/AKvtfwCs9X8ArRV/AK4RfwCvEX8AOMMAgDzDAIBAwwCARMMAgIbwAwCHuAAASMMAgEzDAIC4EX8AuRl/ALohfwC7IX8AvPUAAL39AAC+9QAAv+0AALBxfwCxcX8AsnF/ALNFfwC0QX8AtU1/ALY9fwC3NX8As1l+AFDDAIBUwwCAWMMAgFzDAIC2lX4AtX1+AGDDAIC7tX4AurV+AGTDAIBowwCAv4l+AL6FfgC9kX4AvKV+AGzDAICjHX4AcMMAgHTDAICm0X4AeMMAgHzDAIClOX4AqvF+AKvxfgCAwwCAhMMAgK7BfgCvzX4ArOF+AK3VfgCwrQAAscUAALLBAACzwQAAtMUAALXNAAC28QAAt/EAALhhAAC5YQAAumEAALt9AAC8ZQAAvW0AAL5lAAC/vQMAiMMAgIzDAICQwwCAZMIAgJTDAICYwwCAnMMAgKDDAICoWQEAqVkBAKrtAACr5QAArP0AAK3lAACu5QAAr9UAAKTDAICCHQAAgR0AAIAdAACowwCArMMAgLDDAIC+VAIAhoAEAIfsAgC4wwCAvMMAgMDDAIDEwwCAyMMAgL54AwDjdH4AzMMAgOG4fQDQwwCA1MMAgNjDAIDcwwCA4MMAgOTDAIDowwCA7MMAgPDDAIDvwH4A9MMAgPjDAID8wwCAs4UDAADEAIAExACACMQAgAzEAIC2hQMAtZUDABDEAIC74QMAuokDAL4kBgAUxACAv+kDAL7hAwC99QMAvPUDAIIpAACjwQMAgB0AAIEVAACmwQMAGMQAgBzEAICl0QMAqs0DAKulAwAgxACAheAFAK6lAwCvrQMArLEDAK2xAwDh+AMAKMQAgONcHwAsxACA7/QDADDEAICGPAcAh6wCAON8fgA0xACA4YABADjEAIA8xACAQMQAgO/kEwBExACAs3EBAEjEAIBMxACAUMQAgFTEAIC2EQEAtWEBAFjEAIC7OQEAujEBAFzEAIBgxACAvxkBAL4RAQC9GQEAvCEBAGTEAIBoxACAbMQAgHDEAIB0xACAeMQAgHzEAIDvxH8AgMQAgOH8fgCExACA4/B/AIANAACBdQAAgn0AAIjEAICMxACAkMQAgKP5AQC+AAgApekBAJjEAICcxACAppkBAISoBQCgxACAq7EBAKq5AQCtkQEArKkBAK+RAQCumQEAqCkGAKkpBgCqOQYAqzkGAKwpBgCtUQYArlUGAK9NBgAkxACAhCABAKTEAICUxACAo+EBAKKZBAChGQQAoPEFALg5BgC5OQYAus0GALvFBgC83QYAvcUGAL7FBgC/8QYAsDUGALE9BgCyNQYAsw0GALQVBgC1HQYAthUGALcJBgCPoWwAs5EHAIYoAQCHfAMAtqEHAKjEAICsxACAtbEHALrlBwC77QcAsMQAgLTEAIC+7QcAv90HALz1BwC97QcAn/l4AJ7leACdcXkAnCF8AJvxfACaYX0AmZlxAJjZcACX4XAAlnl0AJVtdACUbXQAk61pAJJxaACReWgAkB1uAIIhbQCD5W8AuMQAgLzEAICGTWgAh5V1AISZaQCFmWkAiqV1AIu5dQDAxACAxMQAgI5xcACPgXwAjDlxAI05cQCSYX0Ak6l9AMjEAIDMxACAlml5AJeZBACU4XgAlX15AJpBBQCbyQUA0MQAgNTEAIDYxACA3MQAgJypAADgxACAo4ENAKKpAQChqQEA5MQAgKexCQCmAQgApU0NAKSZDQCrkRUAqoUVAKkBFACocQkArx0QAK7pEQCtvREArAEQALMBGACy8RwAscEdALDJHQC0wwCA6MQAgLXhGAC0/RkA7MQAgPDEAID0xACA+MQAgIAdAACBCQAAgv0DAPzEAICjFQUAAMUAgIaIDACHPAMACMUAgKYlBQClNQUADMUAgKtpBQCqYQUAEMUAgBTFAICvWQUArmkFAK1pBQCscQUAGMUAgBzFAICEBAwAIMUAgCTFAIDhbAYAKMUAgOPsewAsxQCAMMUAgDTFAIDvqAYAOMUAgDzFAIBAxQCARMUAgKmNBQCogQUAq60FAKqZBQCtoQUArLkFAK+lBQCuqQUAhGgNAEjFAIBMxQCAUMUAgFTFAIBYxQCAXMUAgL70DAC5SQUAuEEFALtZBQC6QQUAvUkFALxBBQC/cQUAvn0FALGpBQCwoQUAs7kFALKhBQC1mQUAtKkFALd5BQC2kQUAqNUEAKndBACq7QQAqyUDAKyFAwCtjQMArrEDAK+xAwBgxQCAZMUAgGjFAIBsxQCAgBkAAIEZAACCBQAAcMUAgLgxAgC5MQIAujUCALvBAgC8hQIAvbUCAL69AgC/tQIAsGkCALFpAgCyQQIAs0ECALQ5AgC1OQIAthECALcRAgCGoAwAh0wNAHjFAIB8xQCA76QGAIDFAICExQCA78wHAOOUAQDhpAYA4TgBAONcBgCIxQCAjMUAgJDFAICUxQCAmMUAgJzFAICzLQQAoMUAgLVFAwCkxQCAqMUAgLZFAwCsxQCAsMUAgLvlAgC65QIAvd0CALzdAgC/tQIAvrUCAATFAIB0xQCAtMUAgLjFAIC8xQCAwMUAgMTFAIDIxQCAqDEOAKk5DgCqAQ4AqwEOAKxxDgCtcQ4ArnUOAK9tDgCwGQ4AsSUOALItDgCzJQ4AtCEOALUhDgC2IQ4AtyEOALjFDgC5zQ4AusUOALvdDgC8xQ4Avc0OAL5ZDwC/WQ8As6kOAMzFAIDQxQCA1MUAgNjFAIC20Q4AtdkOANzFAIC7wQ4Auv0OAODFAIC+LAAAv8UOAL7FDgC90Q4AvNkOAIJpAACj7Q4AgFkAAIFRAACmlQ4A5MUAgOjFAIClnQ4AqrkOAKuFDgCGyAAAh6wAAK6BDgCvgQ4ArJ0OAK2VDgDsxQCAs5EOAPDFAID0xQCAtqUOAPjFAID8xQCAta0OALrhDgC74Q4AAMYAgATGAIC+6Q4Av9UOALz1DgC96Q4Ao6UKAAjGAIAMxgCAEMYAgBTGAICmzQ0Apc0NABjGAICrbQwAqm0MABzGAIAgxgCArz0MAK49DACtVQwArFUMAKgJDgCpCQ4Aqh0OAKsVDgCsIQ4ArSEOAK4hDgCvIQ4AJMYAgCjGAIAsxgCAMMYAgDTGAIA4xgCAPMYAgEDGAIC4zQEAudUBALrdAQC71QEAvM0BAL1RAQC+UQEAv1EBALAhDgCxIQ4AsiUOALM5DgC0KQ4AtRUOALYdDgC39QEARMYAgEjGAIBMxgCAo5kNAFDGAIClpQ0Apq0NAL7cAgCE7AMAWMYAgKrpDQCr6Q0ArP0NAK3hDQCu4Q0Ar90NAIBFAACBTQAAglkAAKNFAwBcxgCApUEDAKZBAwBgxgCAhsAEAIcAAwCqLQMAqyUDAKw9AwCtJQMAriUDAK8VAwCoWQIAqYUDAKqBAwCrgQMArIUDAK2NAwCusQMAr7EDAGTGAIBoxgCAbMYAgHDGAIB0xgCAeMYAgHzGAICAxgCAuGUDALltAwC6ZQMAu30DALxlAwC9bQMAvmUDAL/dAACwpQMAsa0DALKlAwCzvQMAtK0DALWdAwC2lQMAt10DALMJAgCExgCAiMYAgIzGAICQxgCAtg0CALUNAgCUxgCAu2kCALphAgCYxgCAnMYAgL9ZAgC+aQIAvWkCALxxAgCgxgCApMYAgKjGAICsxgCA4aABALDGAIDjaAMAtMYAgIEVAACAFQAA74wDAIIVAAC4xgCAvMYAgMDGAIC+cAUA4RgOAOGUDwDjOA8A49QPAISUAgDIxgCAzMYAgNDGAIDUxgCA2MYAgNzGAIDgxgCA5MYAgOjGAIDv7AEA7/gPAIZgBACHBAUAs5UBAITMBQC1dQEA7MYAgPDGAIC2dQEA9MYAgPjGAIC7UQEAulkBAL31AAC8SQEAv/UAAL71AACoJQYAqVUGAKpVBgCrrQYArLUGAK29BgCutQYAr60GAMTGAID8xgCAAMcAgATHAIAIxwCADMcAgBDHAIAUxwCAuGkHALlpBwC6CQcAuwkHALwZBwC9GQcAvg0HAL8BBwCw1QYAsd0GALLVBgCzaQcAtHkHALV5BwC2aQcAt2EHAKPdBgAYxwCAHMcAgCDHAIAkxwCApj0GAKU9BgAoxwCAqxkGAKoRBgAsxwCAMMcAgK+9BwCuvQcArb0HAKwBBgCAXQAAgW0AAIJlAACzUQcAvtgDALVxBwC2cQcANMcAgIbgAACHFAMAul0HALs5BwC8KQcAvRUHAL4dBwC/2QAAqJUGAKmdBgCqlQYAq60GAKy1BgCtvQYArrUGAK+tBgA4xwCAPMcAgEDHAIBExwCASMcAgEzHAIBQxwCAVMcAgLhxAQC5cQEAunEBALtxAQC81QEAvd0BAL7VAQC/zQEAsNUGALGxBgCysQYAs40GALSVBgC1UQEAtlEBALdRAQBYxwCAoxkGAFzHAIBgxwCApjkGAFTGAIBkxwCApTkGAKoVBgCrcQYAaMcAgGzHAICuVQYAr5EBAKxhBgCtXQYAcMcAgHTHAIB4xwCAfMcAgIDHAICExwCAiMcAgIzHAICQxwCAlMcAgJjHAICcxwCAgBkAAIEZAACCBQAAoMcAgISAAgC+gAMAhwwDAIasHADhaAYAqMcAgOOYBwCsxwCAsMcAgLTHAIDvrAcAuMcAgLzHAIDAxwCAxMcAgMjHAIDMxwCA0McAgNTHAICzZQMA2McAgLVlAwC2bQMA3McAgODHAIDkxwCAuukDALvlAwC8/QMAve0DAL7RAwC/0QMA6McAgOzHAIDwxwCA9McAgPjHAID8xwCAAMgAgATIAICogQMAqYEDAKqBAwCrgQMArIEDAK2BAwCugQMAr4EDALBBAwCxTQMAskUDALNVAwC0eQMAtXkDALYZAwC3GQMAuCkDALkpAwC6OQMAuzkDALwpAwC9KQMAvhkDAL8ZAwCBGQAAgBEAAKMhAgCCLQAApSECAAjIAIAMyACApikCABDIAIAYyACAq6ECAKqtAgCtqQIArLkCAK+VAgCulQIAhEwCAL5IHQCHZB0AhuwcAONAAwAcyACA4aABACDIAIDvnAMAJMgAgCjIAIAsyACAMMgAgDTIAIA4yACAPMgAgEDIAIBEyACASMgAgEzIAIBQyACAVMgAgFjIAIDvtAEAhKgdAOF8BgBcyACA43AGAGDIAIBkyACAaMgAgGzIAICz4QEAcMgAgHTIAIB4yACAfMgAgLblAQC19QEAgMgAgLuhAQC62QEAvuQcAIjIAIC/rQEAvqUBAL2xAQC8uQEAqBUeAKkZHgCqKR4AqykeAKw9HgCtJR4Ari0eAK8lHgAUyACAgvkfAIH5HwCA4R8AhMgAgIzIAICGHAAAh7ADALjBHgC5wR4AusEeALvBHgC8wR4AvcEeAL7BHgC/wR4AsF0eALElHgCyLR4AsyUeALQhHgC1KR4AthkeALcZHgCjoR4AkMgAgJTIAICYyACAnMgAgKalHgCltR4AoMgAgKvhHgCqmR4ApMgAgKjIAICv7R4AruUeAK3xHgCs+R4ArMgAgLOZHwCwyACAtMgAgLa9HwC4yACAvMgAgLW1HwC6mR8Au5kfAMDIAIDEyACAvnkfAL95HwC8eR8AvXkfAKglHgCpUR4AqlUeAKtpHgCseR4ArXkeAK5pHgCvaR4AyMgAgMzIAIDQyACA1MgAgNjIAIDcyACA4MgAgOTIAIC42R4Aue0eALr5HgC7+R4AvOkeAL3pHgC+nR4Av5UeALAZHgCxGR4AsukeALPpHgC0+R4AtfkeALbpHgC36R4Ao90eAIIpAACBFQAAgB0AAOjIAICm+R4ApfEeAOzIAICr3R4Aqt0eAKTHAIDwyACArz0eAK49HgCtPR4ArD0eAITIAgCzQQEAvgwBAPjIAIC2QQEA/MgAgADJAIC1UQEAuk0BALslAQCGSAAAh1ABAL4lAQC/LQEAvDEBAL0xAQAEyQCACMkAgIQEAwC+gAQADMkAgO+oHwAQyQCAFMkAgL8oMQDjdB8AGMkAgOE4HgAcyQCAIMkAgCTJAIAoyQCALMkAgDDJAICjzQIANMkAgKXdAgA4yQCAPMkAgKbNAgBAyQCARMkAgKupAgCqwQIArb0CAKy9AgCvoQIArqkCAKm1AgCoaR0AqwECAKoJAgCtAQIArBkCAK8xAgCuAQIAhGwFAEjJAIBMyQCAUMkAgFTJAICCnQEAgZ0BAICdAQC55QMAuOUDALvlAwC65QMAveUDALzlAwC/5QMAvuUDALEhAgCwSQIAsyUCALIlAgC1KQIAtCECALcVAgC2FQIAqM0CAKnRAgCq0QIAqw0BAKwVAQCtBQEArgEBAK8BAQBYyQCAXMkAgGDJAIBoyQCAvvgEAGzJAIBwyQCAdMkAgLgVAQC5HQEAuikBALspAQC89QEAvf0BAL71AQC/7QEAsEkBALFVAQCyXQEAs1UBALRNAQC1NQEAtj0BALcxAQCGoAUAh8gFAHjJAIDvvAAAfMkAgIDJAICEyQCA74weAIQsBwDh8B4AiMkAgOMcHgCMyQCA4ZQBAJDJAIDjbAAAsxkCAJTJAICYyQCAnMkAgIQACAC2xQEAtd0BAKDJAIC70QEAus0BAKTJAICoyQCAv7EBAL7JAQC9wQEAvMkBAKPZBQBkyQCArMkAgLDJAIC0yQCApgUGAKUdBgC4yQCAqxEGAKoNBgC8yQCAwMkAgK9xBgCuCQYArQEGAKwJBgDEyQCAgh0AAIEdAACAHQAAyMkAgMzJAIDQyQCA1MkAgIZAAwCHxAMA2MkAgNzJAIDgyQCA5MkAgOjJAIDsyQCAqK0HAKmxBwCqsQcAq7EHAKwZBwCtBQcArg0HAK8FBwDwyQCA9MkAgPjJAID8yQCAAMoAgATKAIAIygCADMoAgLgtBwC5zQAAusUAALvdAAC8zQAAvf0AAL71AAC/nQAAsEkHALFVBwCyUQcAsykHALQ5BwC1OQcAtiUHALcVBwCzOQYAEMoAgBTKAIAYygCAHMoAgLaFBgC1kQYAIMoAgLuRBgC6jQYAJMoAgCjKAIC//QYAvv0GAL39BgC8hQYALMoAgKN9BgAwygCANMoAgKbBBgA4ygCAPMoAgKXVBgCqyQYAq9UGAEDKAIC+bAEArrkGAK+5BgCswQYArbkGAKjpAQCp6QEAqvkBAKv5AQCs6QEArekBAK45AQCvOQEAgPUAAIH9AACCwQAARMoAgIYQAACHdAEASMoAgPTIAIC4zQAAudUAALrVAAC75QAAvP0AAL2VAAC+kQAAv5EAALBJAQCxSQEAslkBALNZAQC0SQEAtUkBALb9AAC39QAA7/QGAEzKAIBQygCAVMoAgO8wAgBYygCAXMoAgGDKAIDj4AcAZMoAgOGAAQBoygCA4ygGAGzKAIDhyAUAcMoAgLMxAgB0ygCAeMoAgJYAAAB8ygCAtikCALUhAgCAygCAu80CALrNAgCEygCAiMoAgL/NAgC+zQIAvc0CALzNAgCMygCAkMoAgJTKAICj/QIAmMoAgKXtAgCm5QIAnMoAgKDKAICkygCAqgECAKsBAgCsAQIArQECAK4BAgCvAQIAgA0AAIEVAACCHQAAqMoAgKzKAICwygCAvlQMALjKAICGwAwAhyQDALzKAIDAygCAxMoAgMjKAIDMygCA0MoAgKi5AgCpAQEAqgEBAKsBAQCsBQEArQ0BAK4FAQCvOQEAhKgNANTKAIDYygCA3MoAgODKAIDkygCA6MoAgOzKAIC4LQEAucUBALrNAQC7xQEAvMEBAL3JAQC++QEAv/kBALBNAQCxUQEAslUBALMpAQC0OQEAtSUBALYlAQC3FQEA4RgGAPDKAIDjOAcA9MoAgPjKAIC+WAwA/MoAgADLAICEbA8ABMsAgL5gDwAIywCADMsAgBDLAIDvcAYAFMsAgIAVAACBGQAAgi0AAITMDwDjYAYAGMsAgOGgAQAcywCA73QAACDLAICGyAwAh/wMACjLAIAsywCAMMsAgDTLAICjCQ4AtMoAgCTLAIA4ywCAPMsAgKYNDgClDQ4AQMsAgKsVDgCqCQ4ARMsAgEjLAICvYQ4Arn0OAK19DgCsAQ4ATMsAgLOpDgBQywCAVMsAgLapDgBYywCAXMsAgLWpDgC6SQ8Au0kPAGDLAIBkywCAvkkPAL9JDwC8SQ8AvUkPAKhdDgCpbQ4AqmUOAKt9DgCsZQ4ArW0OAK5lDgCvuQ8AaMsAgGzLAIBwywCAdMsAgHjLAIB8ywCAgMsAgITLAIC4UQ8AuV0PALpVDwC7aQ8AvH0PAL1lDwC+bQ8Av2EPALDJDwCxyQ8AstkPALPZDwC0yQ8AtckPALZ9DwC3cQ8AiMsAgLURDwC2EQ8AjMsAgIARAACBGQAAgikAALMVDwC8HQ8AvWEPAL5hDwC/fQ8AkMsAgJTLAIC6FQ8AuwkPAKOtDwCYywCAhugAAIfIAQCcywCApq0PAKWtDwCgywCAq00OAKpNDgCkywCAqMsAgK9NDgCuTQ4ArU0OAKxNDgCocQ4AqXEOAKpxDgCrcQ4ArJ0BAK2FAQCuhQEAr7UBAL7sAACsywCAsMsAgLTLAIC4ywCAvMsAgMDLAIDEywCAuGEBALlhAQC6YQEAu2EBALxhAQC9YQEAvmEBAL9hAQCwzQEAsaUBALKhAQCzoQEAtKUBALWtAQC2kQEAt5EBALP5DQDIywCAzMsAgNDLAIDUywCAtgUCALUVAgDYywCAu2ECALoJAgDcywCA4MsAgL9pAgC+YQIAvXUCALx1AgDkywCAo70NAOjLAIDsywCApkECAPDLAID0ywCApVECAKpNAgCrJQIA+MsAgPzLAICuJQIAry0CAKwxAgCtMQIAge0AAIDtAADv0AEAgh0AAADMAIAIzACAhjgEAIdQAwAMzACAEMwAgBTMAIAYzACA4eABABzMAIDjZA8AIMwAgCTMAIAozACALMwAgLORAwAwzACAtbkDALZ9AwA0zACAOMwAgDzMAIC6WQMAu1kDALxJAwC9SQMAvv0AAL/1AACoRQIAqVUCAKpVAgCrZQIArH0CAK2xAgCusQIAr7ECAL5oBQBAzACARMwAgEjMAIBMzACAUMwAgFTMAIBYzACAuF0BALltAQC6ZQEAuw0BALwZAQC9GQEAvg0BAL8FAQCw0QIAsdECALLRAgCz0QIAtHUBALV9AQC2dQEAt20BAOF4DwDjNA4A47gOAOF8DgBczACAYMwAgGTMAIBozACAbMwAgHDMAIB4zACAfMwAgIDMAIDv5A4A79QOAITMAICjnQIAgmEAAIFpAACAUQAAhJwFAKZxAgCltQIAiMwAgKtVAgCqVQIAhkgEAIfMBACv+QEArvEBAK1FAgCsRQIAqJUGAKmlBgCqrQYAq6UGAKy9BgCtoQYArqUGAK/dBgB0zACAjMwAgJDMAICUzACAmMwAgJzMAICgzACApMwAgLhtBwC5dQcAun0HALt1BwC8bQcAvcUHAL7NBwC/xQcAsKUGALGtBgCyuQYAs7EGALSRBgC1kQYAtl0HALdVBwCzJQYAqMwAgKzMAICwzACAtMwAgLYhBgC1NQYAuMwAgLtpBgC6YQYAvMwAgMDMAIC/VQYAvlUGAL1lBgC8bQYAxMwAgKNhBgDIzACAzMwAgKZlBgDQzACA1MwAgKVxBgCqJQYAqy0GANjMAIDczACArhEGAK8RBgCsKQYArSEGAKipBgCpqQYAqrkGAKuxBgCszQYArTEBAK4xAQCvMQEAgMkBAIHJAQCCBQAA4MwAgL54AgCEeAIA5MwAgOjMAIC43QEAue0BALrlAQC7jQEAvJkBAL2ZAQC+jQEAv4UBALBRAQCxUQEAslEBALNRAQC09QEAtf0BALb1AQC37QEAszEGAOzMAICGKAAAh9wBAPDMAIC2sQEAtUUGAPTMAIC7lQEAupUBAPjMAID8zACAvzkBAL4xAQC9hQEAvIUBAATMAICjdQYAAM0AgATNAICm9QEACM0AgAzNAIClAQYAqtEBAKvRAQAQzQCAFM0AgK51AQCvfQEArMEBAK3BAQAYzQCAHM0AgCDNAIAkzQCAKM0AgCzNAIAwzQCANM0AgDjNAIA8zQCAQM0AgETNAIBIzQCATM0AgFDNAIC+cAMAhQA8AOHEBgCERAIA44wHAIBhAACBYQAAgmEAAO9oAwCFRDwA4RACAFjNAIDj2CsAhlA9AIf0AwBczQCA76QHAGDNAIDvQAIAZM0AgGjNAIBszQCAcM0AgHTNAIB4zQCAhDw8AHzNAICAzQCAhM0AgIjNAIDj7AIAjM0AgOEsAQCzUQMAkM0AgJTNAICYzQCAnM0AgLZ5AwC1cQMAoM0AgLs5AwC6MQMApM0AgKjNAIC/9QAAvvUAAL0VAwC8FQMAqD0CAKmBAgCqmQIAq5ECAKy5AgCtuQIArtECAK/RAgCEqD8Avqg/AKzNAICwzQCAtM0AgLjNAIC8zQCAwM0AgLhRAQC5UQEAulEBALtRAQC8cQEAvXEBAL5xAQC/cQEAsLUCALG9AgCygQIAs4ECALRxAQC1cQEAtnEBALdxAQCAtQAAgb0AAIK1AADIzQCAhrA/AIfgPADMzQCA71QAAL4sPgDhVAYA0M0AgOOIAADUzQCA2M0AgNzNAIDgzQCAo1ECAOTNAIC/2CYA6M0AgOzNAICmeQIApXECAPDNAICrOQIAqjECAPTNAID4zQCAr/UBAK71AQCtFQIArBUCAJAtJACRBSgAkg0oAJPZKACUhS0AlTUsAJbFLACXtTEAmAEwAJkVMACalTUAmyk0AJxtNACdmTUAnj04AJ81OABUzQCAttU+ALXFPgDEzQCAs9E+APzNAIAAzgCABM4AgL/ZPgC+1T4AvcU+ALzFPgC71T4Auuk+AAjOAICPXSQAqeUJAKgVCACrBQwAqg0MAK0BEACsAQwAr0EQAK69EACh4QAADM4AgKMBBACi4QAApZ0EAKSVBACnuQgApgEIAKD1OQChBT0Aouk8AKP1PQAQzgCAFM4AgBjOAIAczgCAscEUALABFACzARgAsn0UALXVGAC01RgAIM4AgCTOAICCISUAgyklACjOAIAszgCAhsUpAIeBLACEGSkAhRkpAIoBLQCL+S0AMM4AgDjOAICOATEAj4k0AIyRMACNHTEAkkU1AJMZNQCG6AcAh+wBAJZZOQCXYTgAlPU0AJVZOQCaoTwAm0U9ADzOAIBAzgCAgX0AAIB9AACcQTwAglUAAKjpPwCp/T8Aqgk/AKsFPwCsHT8ArQU/AK4NPwCvBT8ARM4AgEjOAIBMzgCAUM4AgFTOAIBYzgCAXM4AgGDOAIC4DT8AuRU/ALoVPwC7JT8AvD0/AL39PgC+9T4Av+0+ALB9PwCxQT8AskE/ALNBPwC0QT8AtU0/ALY9PwC3NT8Ao4E8AGTOAIBozgCAbM4AgHDOAICmhTwApZU8AHTOAICrhTwAqrk8AHjOAIB8zgCAr4k8AK6FPACtlTwArJU8AITIAwCz7T0AgM4AgITOAIC26T0AiM4AgIzOAIC16T0Auq09ALu1PQCQzgCAlM4AgL6dPQC/IQIAvKU9AL2VPQCoDT0AqR09AKohPQCrPT0ArCU9AK0tPQCuJT0Ar1k9AIANAACBFQAAgh0AAJjOAICczgCAoM4AgKjOAIC+uAMAuLkCALlhAgC6GQIAuxkCALwJAgC9CQIAviECAL8hAgCwLT0AsTU9ALI1PQCzBT0AtB09ALWhAgC2oQIAt6ECAKOpPACszgCAhigFAIfsAgCwzgCApq08AKWtPAC0zgCAq/E8AKrpPAC4zgCAvM4AgK9lAwCu2TwArdE8AKzhPADAzgCAsykCAMTOAIDIzgCAtvkCAMzOAIDQzgCAtfkCALrVAgC73QIA1M4AgNjOAIC+eQEAv3kBALzFAgC9eQEA3M4AgODOAICj5QIA5M4AgKU1AgDozgCA7M4AgKY1AgDwzgCA9M4AgKsRAgCqGQIArbUBAKwJAgCvtQEArrUBAOPwPgDhrD8A4UA+AON8PwD4zgCA/M4AgADPAIAEzwCAgA0AAIERAACCEQAACM8AgO+oPgAMzwCAEM8AgO8gPgCoLQUAqW0FAKplBQCrrQUArLUFAK29BQCutQUAr60FAKTOAICE6AMAvuADABTPAICGEAMAh5gDABjPAIAczwCAuGkGALlpBgC6AQYAuwEGALwFBgC9DQYAvjEGAL8xBgCw1QUAsd0FALLVBQCzaQYAtHkGALV5BgC2aQYAt2EGAKg5BgCpgQcAqpkHAKuRBwCsuQcArbkHAK7ZBwCv1QcAIM8AgCTPAIA0zgCAKM8AgCzPAIAwzwCANM8AgDjPAIC4VQcAuV0HALppBwC7aQcAvAEHAL0BBwC+AQcAvwEHALCtBwCxsQcAsrEHALOFBwC0nQcAtXUHALZ9BwC3cQcAsxEGADzPAIBAzwCARM8AgEjPAIC2OQYAtTEGAEzPAIC7dQYAumkGAFDPAIBUzwCAv7EGAL5ZBgC9UQYAvGUGAFjPAICjVQYAXM8AgGDPAICmfQYAZM8AgGjPAICldQYAqi0GAKsxBgBszwCAcM8AgK4dBgCv9QYArCEGAK0VBgCouQEAqbkBAKopAQCrKQEArD0BAK0lAQCuLQEAryUBAHTPAICCHQAAgR0AAIAdAAB4zwCAfM8AgIDPAIC+cAEAuIEAALmNAAC6hQAAu5kAALyJAAC9vQAAvrUAAL99AACwXQEAseEAALLhAACz4QAAtOEAALXpAAC20QAAt9EAAITIAgCzpQIAhzgDAIYoAgC2oQIAiM8AgIzPAIC1sQIAup0CALshAwC+bAMAkM8AgL4hAwC/KQMAvDEDAL0xAwCj4QIAlM8AgJjPAICczwCAoM8AgKblAgCl9QIApM8AgKtlAwCq2QIAqM8AgKzPAICvbQMArmUDAK11AwCsdQMAqZkAAKiRAACrzQAAqqEAAK3dAACs3QAAr8UAAK7NAAC+LA0AsM8AgLTPAIC4zwCAvM8AgMDPAIDEzwCAyM8AgLnBAQC4eQAAu8EBALrJAQC9wQEAvNkBAL/FAQC+xQEAsY0AALCNAACzQQAAskkAALVBAAC0WQAAt0EAALZJAADMzwCA0M8AgNTPAIDYzwCA3M8AgO9QBwDgzwCA5M8AgL74DwDjdAcA6M8AgOF8BACAGQAAgQkAAIJ5AADszwCA8M8AgLNpAQD4zwCAhMQCALYdAQD8zwCAANAAgLUVAQC6CQEAuwkBAIboDQCH6A0Avt0BAL/FAQC83QEAvdUBAATQAIAI0ACADNAAgBDQAIDv1AAAFNAAgBjQAIDvTAEA47ADAOG0BgDhgAEA45gBABzQAIAg0ACAJNAAgCjQAIAs0ACAMNAAgKPlAQCEwA0ApZkBADTQAIA40ACAppEBADzQAIBA0ACAq4UBAKqFAQCtWQEArFEBAK9JAQCuUQEA9M8AgETQAIBI0ACATNAAgFDQAIBU0ACAWNAAgFzQAICoaQ8AqXEPAKpxDwCrrQ8ArLUPAK29DwCutQ8Ar6kPALDZDwCx9Q8Asv0PALP1DwC07Q8AtZUPALadDwC3iQ8AuLkPALmFDwC6jQ8Au2kAALx5AAC9eQAAvmkAAL9pAACBnQAAgJ0AAGDQAICCBQAAZNAAgGjQAIBs0ACAcNAAgIaAAwCH9AMAdNAAgHjQAIB80ACAgNAAgITQAICEzwCAs5kPAIjQAICM0ACAkNAAgJTQAIC2XQ8AtV0PAJjQAIC7UQ8Aun0PAJzQAICg0ACAvzEPAL5JDwC9QQ8AvEkPAKNZDgCk0ACAqNAAgKzQAICw0ACApp0OAKWdDgC00ACAq5EOAKq9DgC40ACAvNAAgK/xDgCuiQ4ArYEOAKyJDgDA0ACAxNAAgMjQAIDM0ACAgBkAAIEZAACCBQAA0NAAgISgAQDU0ACAh+gBAIYABADY0ACA3NAAgODQAIDk0ACAqBUBAKkdAQCqFQEAqyUBAKw9AQCtJQEAri0BAK8lAQDo0ACA7NAAgPDQAID00ACA+NAAgPzQAIAA0QCABNEAgLjJAAC5yQAAutkAALvRAAC8+QAAvfkAAL6ZAAC/mQAAsCUBALEtAQCyJQEAsz0BALQtAQC1HQEAthUBALf5AAAI0QCADNEAgBDRAICzkQIAFNEAgLW5AgC2qQIAGNEAgBzRAIAg0QCAuu0CALvlAgC8/QIAveUCAL7lAgC/1QIApvECACTRAIAo0QCApeECACzRAICjyQIAMNEAgDTRAICuvQIAr40CAKylAgCtvQIAqrUCAKu9AgA40QCAPNEAgID5AACB+QAAggUAAEDRAIC+yAMAhBgDAEjRAIBM0QCAUNEAgFTRAIBY0QCAXNEAgGDRAIBk0QCAhhgEAIecAwBo0QCAbNEAgHDRAIB00QCAeNEAgHzRAIDvsAIAgNEAgOGUAQCE0QCA42wCAIjRAICM0QCAkNEAgJTRAICY0QCA79APAJzRAICg0QCApNEAgKjRAIDhrAEArNEAgONsAACAMQAAgT0AAIIdAADv9A4A42wOALDRAIDhLA8AvnAFALM5AgCEDAUAhugEAIdgBQDcAAAAtvECALX5AgC40QCAu9UCALrVAgC80QCAwNEAgL91AQC+dQEAvcUCALzFAgDE0QCA4fQOAMjRAIDjUA4AzNEAgNDRAIDU0QCA2NEAgNzRAIDg0QCA5NEAgOjRAIDs0QCA8NEAgPTRAIDv5A8ApmUCAPjRAID80QCApW0CAADSAICjrQIABNIAgAjSAICu4QEAr+EBAKxRAgCtUQIAqkECAKtBAgAM0gCAENIAgKiZBgCpmQYAqqkGAKupBgCsuQYArbkGAK6pBgCvqQYAFNIAgIIdAACBHQAAgB0AABjSAIAc0gCAINIAgL50AwC4rQYAubUGALq9BgC7tQYAvK0GAL1RBwC+UQcAv1EHALChBgCxoQYAsqEGALOhBgC0oQYAtaEGALalBgC3mQYARNEAgLMlBgCExAMAtNEAgLY9BgAk0gCAKNIAgLU1BgC6YQYAu2EGAIYIAACHiAAAvmEGAL9hBgC8cQYAvXEGAKNhBgAs0gCAMNIAgDTSAIA40gCApnkGAKVxBgA80gCAqyUGAKolBgBA0gCARNIAgK8lBgCuJQYArTUGAKw1BgCoXQYAqW0GAKplBgCrjQYArJkGAK2FBgCujQYAr4UGAEjSAIBM0gCAUNIAgFTSAIBY0gCAXNIAgGDSAIBk0gCAuIUGALmNBgC6mQYAu5UGALyNBgC9rQYAvqUGAL99AQCw/QYAscUGALLNBgCzxQYAtN0GALXFBgC2zQYAt8UGALPtBgBo0gCAbNIAgHDSAIB00gCAtgUGALURBgB40gCAuwEGALo5BgB80gCAgNIAgL8BBgC+GQYAvREGALwZBgCE0gCAo6kGAIjSAICM0gCApkEGAJDSAICElAEApVUGAKp9BgCrRQYAvqABAJjSAICuXQYAr0UGAKxdBgCtVQYAqJkCAKnBAgCqwQIAq8ECAKzBAgCtyQIArvECAK/xAgCB7QMAgO0DAJzSAICC+QMAhpAcAId0AwCg0gCApNIAgLjFAwC5zQMAusUDALvdAwC8zQMAvf0DAL71AwC/nQMAsEEDALFBAwCyQQMAs0EDALRBAwC1QQMAtkEDALdBAwCzSQIAqNIAgKzSAICw0gCAtNIAgLZJAgC1SQIAuNIAgLuFAwC6hQMAvNIAgMDSAIC/hQMAvoUDAL2VAwC8lQMAxNIAgKMNAgDI0gCAzNIAgKYNAgDQ0gCA1NIAgKUNAgCqwQMAq8EDANjSAIDc0gCArsEDAK/BAwCs0QMArdEDAOOYAQDhpAcA4VgGAONYBgDhoAEA4NIAgOPQAADk0gCA6NIAgOzSAIDvOAAA8NIAgO/0AQD00gCA+NIAgO/4BgCAeQAAgRUAAIIdAACEAB0A/NIAgADTAIC+EB0ACNMAgIbAHACHrB0ADNMAgBDTAIAU0wCAGNMAgBzTAIAg0wCAu8UFALqhBQC5qQUAuJEFAL/NBQC+zQUAvckFALzVBQCzHQYAsh0GALEdBgCwHQYAt6EFALa9BQC1vQUAtL0FAKu9BgCqvQYAqb0GAKi9BgCvfQYArn0GAK19BgCsfQYAJNMAgCjTAIAs0wCAMNMAgDTTAIA40wCAPNMAgEDTAICo7R0AqS0eAKoxHgCrMR4ArJUeAK2dHgCulR4Ar40eAATTAIBE0wCASNMAgEzTAIBQ0wCAVNMAgFjTAIBc0wCAuKkeALmpHgC6XR8Au1EfALxxHwC9cR8AvnUfAL9pHwCw/R4Asc0eALLFHgCzrR4AtLkeALW5HgC2rR4At6UeALO5HgBg0wCAZNMAgGjTAICU0gCAth0eALUdHgBs0wCAuwkeALo5HgBw0wCAhOADAL99HgC+fR4AvXkeALwRHgCCaQAAo/0eAIBFAACBUQAAplkeAL6cAwB00wCApVkeAKp9HgCrTR4AhkgAAIdsAACuOR4ArzkeAKxVHgCtPR4AqF0eAKltHgCqZR4Aq30eAKxlHgCtbR4ArmUeAK/9HgB40wCAfNMAgIDTAICE0wCAiNMAgIzTAICQ0wCAlNMAgLhpAQC5aQEAunkBALt5AQC8aQEAvWkBAL7dAQC/1QEAsIUeALGNHgCyhR4As50eALSFHgC1jR4AtoUeALdZAQCz7R4AmNMAgJzTAICg0wCApNMAgLbtHgC17R4AqNMAgLtJHgC6QR4ArNMAgLDTAIC/SR4AvkEeAL1JHgC8UR4AtNMAgKOpHgC40wCAvNMAgKapHgDA0wCAxNMAgKWpHgCqBR4Aqw0eAMjTAIDM0wCArgUeAK8NHgCsFR4ArQ0eAKghAwCpIQMAqiEDAKshAwCsIQMArSEDAK4hAwCvIQMA0NMAgNTTAIDY0wCAvmACANzTAIDg0wCA6NMAgOzTAIC4iQMAuYkDALqdAwC7lQMAvLkDAL25AwC+eQAAv3kAALDlAwCx7QMAsuUDALP9AwC07QMAtd0DALbVAwC3vQMAgKkAAIG1AACCvQAAs6UDAPDTAIC1pQMAtq0DAPTTAICE4AIA+NMAgLotAwC7JQMAvD0DAL0lAwC+JQMAvxUDAKPpAwD80wCAhmgEAIeAAwAA1ACApuEDAKXpAwAE1ACAq2kDAKphAwAI1ACADNQAgK9ZAwCuaQMArWkDAKxxAwAQ1ACAFNQAgBjUAIAc1ACAINQAgOE8HwAk1ACA40AeACjUAIAs1ACAMNQAgO+MHgA01ACAONQAgDzUAIBA1ACARNQAgIIlAACBEQAAgB0AAEjUAIDj5AMATNQAgOGsAQBQ1ACA77ADAIRkAgC+YAUAhtAEAIdEBQBY1ACAXNQAgGDUAIBk1ACAaNQAgGzUAIBw1ACAdNQAgHjUAIDvsAEAhKQFAOHcHgB81ACA4xABAIDUAICE1ACAiNQAgIzUAICzUQEAkNQAgJTUAICY1ACAnNQAgLYRAQC1fQEAoNQAgLsNAQC6DQEApNQAgKjUAIC//QAAvv0AAL39AAC8/QAAqDkGAKk5BgCqmQYAq5EGAKy1BgCt0QYArskGAK/BBgBU1ACArNQAgLDUAIC01ACAgA0AAIGxAACCsQAAuNQAgLhhBwC5YQcAumEHALt9BwC8ZQcAvW0HAL5lBwC/HQcAsIkGALGJBgCyaQcAs2kHALR5BwC1eQcAtmkHALdlBwCjEQYAvNQAgMDUAIC+gAMAxNQAgKZRBgClPQYAyNQAgKtNBgCqTQYAhggAAId8AwCvvQcArr0HAK29BwCsvQcAzNQAgNDUAICzSQcA1NQAgLVZBwDY1ACA3NQAgLZRBwDg1ACA5NMAgLtBBwC6dQcAvUUHALxFBwC/RQcAvkUHAKh5BgCpeQYAqokGAKuJBgCsmQYArZkGAK6JBgCviQYA5NQAgOjUAIDs1ACA8NQAgPTUAID41ACA/NQAgADVAIC4jQYAuZUGALqVBgC7pQYAvL0GAL1xAQC+cQEAv3EBALD5BgCxzQYAstkGALPZBgC0yQYAtckGALa9BgC3tQYAowEGAATVAIAI1QCADNUAgBDVAICmGQYApREGABTVAICrCQYAqj0GABjVAIAc1QCArw0GAK4NBgCtDQYArA0GACDVAIAk1QCAKNUAgCzVAICAGQAAgRkAAIIFAAAw1QCAhKwBAL6sAQCH6AAAhkwPADjVAIA81QCAQNUAgETVAIConQIAqcUCAKrNAgCrwQIArMUCAK3NAgCu+QIArz0DAEjVAIBM1QCAUNUAgFTVAIC+PAwAWNUAgFzVAIBg1QCAuMkDALnJAwC62QMAu9EDALz5AwC9+QMAvpkDAL+ZAwCwRQMAsU0DALJFAwCzXQMAtEUDALVNAwC2RQMAt/kDALNFAgBk1QCAaNUAgGzVAIBw1QCAtk0CALVNAgB01QCAu4kDALqBAwB41QCAfNUAgL+JAwC+gQMAvYkDALyRAwCA1QCAowECAITVAICI1QCApgkCAIzVAICQ1QCApQkCAKrFAwCrzQMAlNUAgJjVAICuxQMAr80DAKzVAwCtzQMAgO0BAIEVAACCEQAAhAACAJzVAIDhpAEAoNUAgOPsAACo1QCArNUAgLDVAIDvMAAAtNUAgLjVAIC81QCAwNUAgIbgDACH9AIAxNUAgMjVAIDM1QCA0NUAgO/MBgDU1QCA4bAHANjVAIDjEAYA3NUAgODVAIDk1QCA6NUAgOzVAIDw1QCA9NUAgPjVAID81QCAANYAgATWAIAI1gCA7+gBAIUYDwDhzAYADNYAgOMcBgCAKQAAgR0AAIIFAAAQ1gCAszkCAITMDQCGaA8Ah/wMAOHQ0gO28QEAtfkBABjWAIC72QEAutEBAL7kDAAc1gCAv30BAL59AQC9fQEAvMEBAKjxDQCp8Q0AqvENAKvxDQCsMQ4ArTEOAK4xDgCvMQ4ApNUAgBTWAIAg1gCAJNYAgCjWAIAs1gCAMNYAgDTWAIC46Q4AuekOALqJDgC7hQ4AvJ0OAL2BDgC+gQ4Av7UOALBVDgCxXQ4AslUOALPpDgC0+Q4AtfkOALbpDgC34Q4Ao3kNADjWAIA81gCAQNYAgETWAICmsQ4ApbkOAEjWAICrmQ4AqpEOAEzWAIBQ1gCArz0OAK49DgCtPQ4ArIEOAFTWAICz7Q8AWNYAgFzWAIC26Q8AYNYAgGTWAIC16Q8Auq0PALu1DwA01QCAaNYAgL6VDwC/mQ8AvK0PAL2hDwCoIQ4AqSEOAKohDgCrPQ4ArCUOAK0tDgCuJQ4Ar1UOAGzWAIBw1gCAdNYAgHjWAICAHQAAgQkAAIK9AAB81gCAuDkOALk5DgC6yQ4Au8kOALzZDgC92Q4AvskOAL/JDgCwLQ4AsTUOALI9DgCzMQ4AtBUOALUZDgC2CQ4AtwkOAKOpDgCA1gCAhIACAL6AAQCFAAQApq0OAKWtDgCI1gCAq/EOAKrpDgCGKAcAhxgAAK/dDgCu0Q4AreUOAKzpDgCM1gCAs+0BAJDWAICU1gCAtuUBAJjWAICc1gCAte0BALplAQC7bQEAoNYAgKTWAIC+bQEAv10BALx1AQC9bQEAqN0NAKnpDQCqIQIAqyECAKwhAgCtIQIAriECAK8hAgCo1gCArNYAgLDWAIC01gCAohECAKMRAgCgqQ4AodUCALiJAgC5iQIAup0CALuVAgC8vQIAvXUDAL59AwC/dQMAsOUCALHtAgCy5QIAs/0CALTtAgC13QIAttUCALe9AgCjqQIAj8UaALjWAIC81gCAwNYAgKahAgClqQIAxNYAgKspAgCqIQIAyNYAgMzWAICvGQIArikCAK0pAgCsMQIAniUOAJ/lDgCc6QoAnRUKAJpFFgCbRQoAmFkWAJlRFgCWcRIAl4ETAJRVEgCV7RIAktEeAJPZHgCQtRoAkVUeAISpHwCFJR8AhiUfAIexEwDQ1gCA1NYAgIJZGwCDURsAjEUSAI2lFwCOpRcAj7kXAIA5+wHY1gCAijkTAIutEwCUmQsAlaEPAJZpDwCX3Q8A3NYAgO+cDwCSyQsAk30LAJxFAwDjeA4A4NYAgOGYDADk1gCAhHgCAJqRAwCbXQMA4QQAAL6IBQDj3OoD6NYAgOzWAIDw1gCA7+wAAO+MDgDhcA4A4fwOAOMwAADjeA4AgSEAAIA5AADvtO0DgikAALMJAgD41gCAhmgEAIcsBQD81gCAtg0CALUNAgAA1wCAu8UBALrFAQAE1wCACNcAgL99AQC+fQEAvdUBALzVAQCE1gCA9NYAgAzXAIAQ1wCAFNcAgBjXAIAc1wCAINcAgKi9BQCp5QUAquEFAKvhBQCs5QUAre0FAK7RBQCv0QUAsGEGALFhBgCyYQYAs2EGALTZBgC12QYAtskGALfBBgC4yQYAuckGALp5BwC7eQcAvEUHAL0lBwC+EQcAvw0HAKNJBQAk1wCAKNcAgCzXAIAw1wCApk0FAKVNBQA01wCAq4UGAKqFBgA41wCAPNcAgK89BgCuPQYArZUGAKyVBgBA1wCARNcAgEjXAIBM1wCAUNcAgFTXAIBY1wCAXNcAgIA5AACBOQAAggUAAGDXAIC+uAMAhLgDAGjXAIBs1wCAqMUGAKnVBgCq1QYAq+UGAKz9BgCtHQEArhUBAK8NAQBk1wCAcNcAgIaIAQCHHAEAdNcAgHjXAIB81wCAgNcAgLjpAQC56QEAuokBALuJAQC8mQEAvZkBAL6JAQC/iQEAsHUBALF9AQCydQEAs+kBALT5AQC1+QEAtukBALfhAQCzXQYAhNcAgIjXAICM1wCAhLwBALadAQC1dQYAkNcAgLu5AQC6sQEAlNcAgJjXAIC/PQEAvj0BAL09AQC8oQEAnNcAgKMZBgCg1wCApNcAgKbZAQCo1wCArNcAgKUxBgCq9QEAq/0BALDXAIC01wCArnkBAK95AQCs5QEArXkBAKj5AgCp+QIAqi0DAKs9AwCsJQMArS0DAK4lAwCvmQMAuNcAgLzXAIDA1wCAxNcAgIANAACBsQAAgrEAAMjXAIC4lQMAuZ0DALqhAwC7oQMAvHEAAL1xAAC+cQAAv3EAALDpAwCx6QMAsvUDALPFAwC03QMAtbUDALaxAwC3sQMAvswDAMzXAIDQ1wCA2NcAgNzXAIDg1wCA5NcAgO/kAgDo1wCA4ZQBAOzXAIDjLAEA8NcAgPTXAICHGAMAhhz8A7tNAwC6TQMA+NcAgPzXAIC/EQMAvnkDAL1xAwC8QQMAs8UDAITo/AMA2ACABNgAgAjYAIC2zQMAtc0DAAzYAICkAfwDpSX/A6bZ/wOnAfgDENgAgKEVAwCiHQMAoz0CAKwR9wOtAfADri3zA68B8wOoEfsDqZn7A6oB9AOrHfcDtAHoA7Vl6wO+xPwDhMT8A7AB7AOxVe8Dsk3vA7Nx7gMU2ACAGNgAgBzYAIAg2ACAJNgAgCjYAIAs2ACAMNgAgOFQBgDhNAQA42wBAOPoBgA02ACAONgAgDzYAIBA2ACAgDUAAIE9AACCNQAASNgAgEzYAIBQ2ACA77ABAO/ABgCj5QIAVNgAgIbo/AOHfP0DWNgAgKbtAgCl7QIAXNgAgKttAgCqbQIAYNgAgGTYAICvMQIArlkCAK1RAgCsYQIAqI3+A6mV/gOqnf4Dq5X+A6yx/gOtvf4Drqn+A6+p/gNE2ACAaNgAgGzYAIBw2ACAdNgAgHjYAIB82ACAgNgAgLgl/wO5Lf8DuiX/A7s9/wO8Jf8DvS3/A74l/wO/zf8DsKn+A7Gp/gOygf4Ds4H+A7SB/gO1if4Dtmn/A7cd/wOE2ACA4SD8A4jYAIDjePwDjNgAgJDYAICU2ACAmNgAgJzYAICg2ACApNgAgKjYAICAHQAAgXEAAIJxAADvDP0Ds1X+A6zYAICw2ACAvkAAALTYAIC2ff4DtXn+A7jYAIC7Lf4Dui3+A4boAACHrAAAvw3+A74F/gO9Ff4DvBX+A6OV/wO82ACAwNgAgMTYAIDI2ACApr3/A6W5/wPM2ACAq+3/A6rt/wPQ2ACA1NgAgK/N/wOuxf8DrdX/A6zV/wPY2ACAs/H+A9zYAIDg2ACAto3+A+TYAIDo2ACAtY3+A7pFAQC7TQEA7NgAgPDYAIC+RQEAv00BALxVAQC9TQEAqC3+A6k1/gOqPf4Dq0n+A6xB/gOtSf4DrnH+A69x/gP02ACA+NgAgPzYAIAA2QCABNkAgAjZAIAM2QCAENkAgLhJAQC5VQEAul0BALtVAQC8TQEAvXUBAL59AQC/dQEAsMUBALHNAQCyxQEAs90BALTFAQC1zQEAtsUBALd9AQCjtf0DFNkAgBjZAICExAMAHNkAgKbJ/QOlyf0DINkAgKsJAgCqAQIAKNkAgL7sAgCvCQIArgECAK0JAgCsEQIAgEkAAIFVAACCVQAAo0UDACzZAIClRQMApkUDADDZAICGwAQAhxQDAKopAwCrJQMArD0DAK0hAwCuIQMArxUDADTZAIA42QCAPNkAgEDZAIBE2QCASNkAgEzZAIBQ2QCAqH0CAKmhAwCqoQMAq6EDAKyhAwCtqQMArpEDAK+RAwCwgQMAsY0DALKFAwCzmQMAtIkDALW9AwC2tQMAt30DALhFAwC5TQMAukUDALtdAwC8RQMAvU0DAL5FAwC/+QAA1NcAgLMNAgBU2QCAWNkAgLYNAgBc2QCAYNkAgLUNAgC6YQIAu20CAGTZAIBo2QCAvmkCAL9dAgC8dQIAvWkCAGzZAIBw2QCAdNkAgHjZAIB82QCA4aQBAIDZAIDjQAMAhNkAgIjZAICM2QCA77gDAIAVAACBHQAAggUAAJDZAICEgAIAvsgFAIcYBQCGLAQAmNkAgJzZAICg2QCA76gBAKTZAIDhdP4DqNkAgOPw/gOs2QCAsNkAgLTZAIC42QCAvNkAgMDZAIDE2QCAs5EBAMjZAIC1UQEAtlEBAMzZAIDQ2QCA1NkAgLp9AQC7dQEAvG0BAL39AAC+9QAAv+kAAKgpBgCpVQYAqlUGAKuNBgCslQYArZ0GAK6VBgCvjQYAlNkAgNjZAIDc2QCA4NkAgOTZAIDo2QCA7NkAgPDZAIC4bQcAuQUHALoNBwC7BQcAvB0HAL0FBwC+AQcAvz0HALD1BgCx/QYAsvUGALNlBwC0fQcAtWEHALZhBwC3VQcA4xAFAPTZAIDh8AQA+NkAgIAdAACBCQAAgjkAAPzZAIAA2gCAhOgDAL7gAwAE2gCA78wFAAjaAICHOAAAhhgAAKOdBgAM2gCAENoAgBTaAIAY2gCApl0GAKVdBgAc2gCAq3kGAKpxBgAg2gCAJNoAgK/lBwCu+QcArfEHAKxhBgCokQYAqZEGAKqRBgCrrQYArLkGAK2lBgCurQYAr6UGACjaAIAs2gCAMNoAgDTaAIA42gCAPNoAgEDaAIBE2gCAuGUBALltAQC6ZQEAu30BALxlAQC9bQEAvmUBAL/ZAQCw3QYAsaUGALKtBgCzpQYAtKEGALWpBgC2mQYAt5kGALMZBgBI2gCATNoAgFDaAIBU2gCAtiUGALUxBgBY2gCAu2EGALoZBgBc2gCAYNoAgL9tBgC+ZQYAvXEGALx5BgBk2gCAo10GAGjaAIBs2gCApmEGAHDaAICEmAEApXUGAKpdBgCrJQYAvqQBAHjaAICuIQYArykGAKw9BgCtNQYAqcUCAKixAgCrxQIAqsUCAK3NAgCsxQIAr/UCAK71AgB82gCAgNoAgITaAICI2gCAjNoAgJDaAICU2gCAmNoAgLnJAwC4wQMAu9kDALrBAwC9+QMAvMkDAL+ZAwC+8QMAsUUDALBFAwCzRQMAskUDALVFAwC0RQMAt0UDALZFAwCASQMAgUkDAIJdAwCzRQIAvtwMALVFAgC2RQIAnNoAgIYADACH5AMAuokDALuJAwC8mQMAvZkDAL6JAwC/iQMAowkCAKDaAICk2gCAqNoAgKzaAICmCQIApQkCALDaAICrxQMAqsUDALTaAIC42gCAr8UDAK7FAwCt1QMArNUDALzaAIDA2gCAxNoAgCTZAIDvAAAAyNoAgMzaAIDQ2gCA4+gAANTaAIDhjAEA2NoAgNzaAIDg2gCA6NoAgOzaAICAbQAAgXUAAIJ9AACEQAIAhvAMAId4DQDw2gCA9NoAgPjaAID82gCAANsAgATbAIAI2wCADNsAgBDbAIAU2wCAGNsAgBzbAIAg2wCAJNsAgCjbAIAs2wCAMNsAgO/MAQCE7AwA4TAGADTbAIDjGAEAONsAgDzbAIBA2wCARNsAgLPlAQBI2wCAhIQPAEzbAIBQ2wCAtuUBALX1AQBY2wCAu30BALrZAQC+oAwAXNsAgL8hAQC+OQEAvTEBALw5AQCo7Q0AqSUOAKotDgCrJQ4ArD0OAK0lDgCuLQ4AryUOAOTaAICC9Q8AgeUPAIDpDwBU2wCAYNsAgIaYAACHDAMAuK0OALlFDwC6TQ8Au0UPALxFDwC9TQ8AvkUPAL95DwCwXQ4AsfkOALKtDgCzpQ4AtL0OALWlDgC2pQ4At5UOAGTbAIDv7AwAaNsAgGzbAIBw2wCAdNsAgHjbAIB82wCAvugAAIDbAICE2wCAiNsAgIzbAIDj6A0AkNsAgOEEDACj5Q4AlNsAgJjbAICc2wCAoNsAgKblDgCl9Q4ApNsAgKt9DgCq2Q4AqNsAgKzbAICvIQ4ArjkOAK0xDgCsOQ4AqDkOAKk5DgCqUQ4Aq1EOAKxxDgCtcQ4ArnEOAK9xDgCw2wCAtNsAgLjbAIC82wCAgBkAAIEZAACCBQAAwNsAgLjRDgC50Q4AutEOALvlDgC84Q4AveEOAL7hDgC/4Q4AsBEOALERDgCyEQ4AsxEOALTxDgC18Q4AtvEOALfxDgCz2Q4AyNsAgIYoAACHuAAAzNsAgLbxDgC1+Q4A0NsAgLvVDgC61Q4A1NsAgNjbAIC/NQ4AvjUOAL3FDgC8xQ4A3NsAgKOdDgDg2wCA5NsAgKa1DgDo2wCA7NsAgKW9DgCqkQ4Aq5EOAPDbAID02wCArnEOAK9xDgCsgQ4ArYEOAKjdDQCp6Q0Aqj0CAKuNAgCsmQIArZkCAK6JAgCviQIAvqwEAPjbAID82wCAhCADAADcAIAE3ACACNwAgAzcAIC4iQIAuYkCALqZAgC7kQIAvLkCAL25AgC+eQMAv3kDALD5AgCx+QIAss0CALPFAgC03QIAtcUCALbBAgC3uQIAs7UCABDcAIAU3ACAGNwAgBzcAIC2GQIAtRECACDcAIC7PQIAuj0CACTcAIAo3ACAvwECAL4ZAgC9EQIAvBkCACzcAICj8QIAMNwAgDjcAICmXQIAPNwAgEDcAIClVQIAqnkCAKt5AgCGSAUAh6wEAK5dAgCvRQIArF0CAK1VAgCohQIAqZUCAKqVAgCrpQIArL0CAK3VAgCu0QIAr9ECAETcAIBI3ACATNwAgFDcAICB8QEAgJkBAHTaAICC9QEAuHkBALl5AQC6zQEAu8UBALzdAQC9xQEAvsUBAL/1AQCwtQIAsb0CALKBAgCzgQIAtFUBALVdAQC2SQEAt0kBAFTcAIBY3ACAXNwAgO/UAQCEEAUAYNwAgGTcAIDvjA4AvuwFAOHsDgBo3ACA4xwOAGzcAIDhlAEAcNwAgONkDgCzXQIAdNwAgHjcAIB83ACAgNwAgLYVAgC1dQIAhNwAgLs5AgC6MQIAiNwAgIzcAIC/2QEAvtEBAL0VAgC8FQIAo50FADTcAICQ3ACAlNwAgJjcAICm1QUApbUFAJzcAICr+QUAqvEFAKDcAICk3ACArxkGAK4RBgCt1QUArNUFAIBRAACBWQAAgmEAALOVBgCo3ACAtXEHALZxBwCs3ACAhkADAIdUAwC67QcAu+UHALzlBwC97QcAvtEHAL/NBwCw3ACAtNwAgLjcAIC83ACAwNwAgMTcAIDvQAQAyNwAgOEwBwDM3ACA45QEANDcAIDU3ACA2NwAgNzcAIDg3ACAoxkGAOTcAIDo3ACA7NwAgPDcAICm/QcApf0HAPTcAICraQcAqmEHAPjcAID83ACAr0EHAK5dBwCtYQcArGkHAKjNBwCp0QcAqtEHAKstBgCsNQYArT0GAK41BgCvnQYAAN0AgATdAIAI3QCADN0AgIAZAACBGQAAggUAABDdAIC4iQYAuYkGALqZBgC7kQYAvLkGAL25BgC+UQEAv1EBALDlBgCx7QYAsv0GALP1BgC02QYAtcUGALbBBgC3uQYAqNEBAKnZAQCqCQEAqwkBAKwZAQCtGQEArgkBAK8JAQCEYAEAvnwBAIeoAACGjAEAGN0AgBzdAIAg3QCAJN0AgLgJAQC5CQEAuhkBALsRAQC8OQEAvTkBAL75AAC/+QAAsH0BALFBAQCyRQEAs10BALRFAQC1TQEAtkUBALc5AQAo3QCALN0AgDDdAICzjQIANN0AgLWdAgC2lQIAON0AgDzdAIBA3QCAurUCALuJAgC8nQIAvYUCAL6NAgC/hQIAps0CAETdAIBI3QCApcUCAEzdAICj1QIAUN0AgFTdAICu1QIAr90CAKzFAgCt3QIAqu0CAKvRAgCE9AMAWN0AgKgxAwCpMQMAqjEDAKsxAwCskQAArZEAAK6RAACvjQAAXN0AgGDdAIBk3QCAaN0AgGzdAIBw3QCAdN0AgHjdAIC4vQAAuWUAALptAAC7ZQAAvH0AAL1lAAC+bQAAv2UAALD9AACxxQAAss0AALOpAAC0uQAAtaUAALahAAC3oQAAgL0BAIEJAACCGQAAfN0AgIDdAIC+WAIAhxQdAIacHQCEbB0AxNsAgIjdAICM3QCAvrwcAJDdAICU3QCAmN0AgLP5AgCc3QCAoN0AgKTdAICo3QCAtlEBALVZAQC+3B8Au0EBALp5AQCs3QCAsN0AgL8hAQC+PQEAvT0BALxZAQDhcAcAtN0AgOMIBgC43QCA78wAALzdAIDA3QCAxN0AgOMQAADI3QCA4dABAMzdAICGkBwAh/QcAO/gBgDQ3QCAo3kCANTdAIDY3QCA3N0AgODdAICm0QEApdkBAOTdAICrwQEAqvkBAOjdAIDs3QCAr6EBAK69AQCtvQEArNkBAITdAICCFQAAgeUfAIDlHwDw3QCA9N0AgPjdAID83QCAqAkfAKkJHwCqHR8AqxUfAKwNHwCtcR8ArnEfAK9xHwCwER8AsS0fALIlHwCzyR8AtN0fALXBHwC2wR8At8EfALjFHwC5yR8AutUfALupHwC8uR8AvbkfAL6pHwC/oR8As7UfAADeAIAE3gCACN4AgAzeAIC20R8AtaUfABDeAIC7yR8AuvUfABTeAIAY3gCAvyUfAL45HwC9PR8AvNEfABzeAIAg3gCAJN4AgCjeAIAs3gCA4WAfADDeAIDjtBwANN4AgDjeAIA83gCA7wAdAEDeAIBE3gCASN4AgEzeAICjNR4AUN4AgFTeAIBY3gCAXN4AgKZRHgClJR4AYN4AgKtJHgCqdR4AhKgCAGTeAICvpR4ArrkeAK29HgCsUR4AgE0AAIFVAACCVQAAs8kBAGjeAIC12QEAtskBAGzeAICGoAAAhwQBALrFAQC7rQEAvLUBAL29AQC+tQEAv60BAKiZAQCpmQEAqg0BAKsFAQCsHQEArQUBAK4FAQCvNQEAcN4AgHTeAIB43gCAfN4AgIDeAICE3gCAiN4AgIzeAIC4JQEAuS0BALo5AQC7OQEAvCkBAL0pAQC+3QAAv9UAALBNAQCxJQEAsi0BALMlAQC0PQEAtSUBALYhAQC3HQEAkN4AgJTeAICY3gCAo4kCAJzeAIClmQIApokCAKDeAICk3gCAqN4AgKqFAgCr7QIArPUCAK39AgCu9QIAr+0CAKzeAICw3gCAtN4AgIRAAgC43gCAvN4AgMDeAIDE3gCAgA0AAIEVAACCHQAAyN4AgMzeAIDQ3gCAh7QDAIbcBAC+zAMA2N4AgNzeAIDg3gCA7+gCAOTeAIDo3gCA7N4AgOP8AgDw3gCA4dABAPTeAID43gCA/N4AgADfAIAE3wCAs2EDAAjfAIAM3wCAEN8AgBTfAIC2eQMAtXEDABjfAIC7XQMAul0DABzfAIAg3wCAv+EAAL79AAC9/QAAvP0AALC5AgCxuQIAsgkBALMJAQC0GQEAtQUBALYFAQC3PQEAuAUBALllAQC6bQEAu2UBALxhAQC9YQEAvmEBAL9hAQCFXAcAJN8AgCjfAIAs3wCAFN0AgDDfAIA03wCAON8AgKgxAgCpOQIAqskCAKvJAgCs2QIArdkCAK7JAgCvyQIAhMwFAOGAHgA83wCA47weAOE4HgBA3wCA46AAAL4QBABI3wCATN8AgO8MHgBQ3wCAVN8AgFjfAIBc3wCA73QeAKNhAgCCUQAAgUEAAICRAABg3wCApnkCAKVxAgBk3wCAq10CAKpdAgCGyAQAhzwFAK/hAQCu/QEArf0BAKz9AQCohQYAqY0GAKqFBgCrmQYArIkGAK2JBgCuvQYAr7EGAETfAIBo3wCAbN8AgHDfAIB03wCAeN8AgHzfAICA3wCAuJ0GALmtBgC6pQYAuwkHALwZBwC9GQcAvg0HAL8FBwCw0QYAsdEGALLRBgCz0QYAtLUGALW9BgC2tQYAt60GALMNBgCE3wCAiN8AgIzfAICQ3wCAtgkGALUBBgCU3wCAuxUGALoVBgCY3wCAnN8AgL95BgC+cQYAvQUGALwFBgCg3wCA4aAEAKTfAIDjXAUAgA0AAIE1AACCPQAAqN8AgKzfAICw3wCAhGADAL5sAAC/8AEAhZAAALTfAIDvmAUAo40HAIQIAACGAAwAh4wAALjfAICmiQcApYEHALzfAICrlQcAqpUHAMDfAIDE3wCAr/kHAK7xBwCthQcArIUHAMjfAICz6QYAzN8AgNDfAIC26QYA1N8AgNjfAIC16QYAukUBALtNAQDc3wCA4N8AgL5FAQC/TQEAvFUBAL1NAQCoIQYAqSEGAKolBgCrPQYArCUGAK0tBgCuSQYAr0EGAOTfAIDo3wCA7N8AgPDfAID03wCA+N8AgPzfAIAA4ACAuEkBALlJAQC6WQEAu1EBALx5AQC9eQEAvhkBAL8VAQCwxQEAsc0BALLFAQCz3QEAtMUBALXNAQC2xQEAt3kBAATgAIAI4ACADOAAgKOhBQAQ4ACApaEFAKahBQAU4ACAjyHqAxjgAICqDQIAqwUCAKwdAgCtBQIArg0CAK8FAgCX7RIAlmUSAJVFEQCUnRYAk3EWAJJVFQCReesDkFnqA59hBgCeNQUAnUUaAJxpGgCbVRkAmkUeAJlZHgCYRR0A4WAAABzgAIDjTD4AIOAAgKOxAgCi1QEAobUHAKCJBgCxATgAsAk+ALOVOgCyjToAtbUmALQBJADvaDoAvjAMAKnJNgCowTYAqwEwAKrhNwCtzTMArPUyAK/5PgCuATwAoRkCACjgAICjbQ4Aom0OAKX1CgCkAQgAp4ULAKaZCgCGAA0Ah0QNAIIJ6wODCesDhDHqA4UVFACGORcAh80XAISgDQAs4ACAiiUQAIsNEwCMnRMAjQ0cAI4ZHwCPDR8A1N4AgO8AAwCSbRgAk0kbAJR9GwCVBQQAllkHAJdJBwAw4ACANOAAgJpFBgCbLQAAnFEDAONgAAA44ACA4WwAAIClAQCBAQEAggUBAL4ADAA84ACAQOAAgETgAIDviAEASOAAgOFUBgBM4ACA41QBAFDgAIBU4ACAWOAAgFzgAICz6QIAYOAAgGTgAIBo4ACAbOAAgLadAgC1mQIAcOAAgLuJAgC6vQIAdOAAgHjgAIC/WQIAvlECAL1ZAgC8kQIAoykNAHzgAICA4ACAhOAAgIjgAICmXQ0ApVkNAIzgAICrSQ0Aqn0NAJDgAICY4ACAr5kNAK6RDQCtmQ0ArFENAIBRAACBWQAAgmEAALMtDwCc4ACAtS0PALbJDwCg4ACAhkADAIcIAwC6yQ8Au8UPALzBDwC9wQ8AvsEPAL/BDwAk4ACAlOAAgKTgAICo4ACArOAAgLDgAIC04ACAuOAAgKhFDgCpgQ8AqskPAKvJDwCsyQ8ArSUPAK4tDwCvJQ8AsGEPALFtDwCyeQ8As3kPALRpDwC1aQ8Ath0PALcVDwC4LQ8AuTUPALo1DwC7BQ8AvB0PAL3xAAC+8QAAv/EAAKNhDgC84ACAhMQBAMDgAIDE4ACApoUOAKVhDgDI4ACAq4kOAKqFDgDM4ACA0OAAgK+NDgCujQ4ArY0OAKyNDgDU4ACA2OAAgNzgAIDg4ACA5OAAgOjgAIDs4ACA8OAAgPTgAICCHQAAgR0AAIAdAAD44ACA/OAAgADhAIC+tAEAqK0BAKnVAQCq1QEAqwUBAKwdAQCtBQEArg0BAK8FAQCGgAEAhxgBAAjhAIAM4QCAEOEAgBThAIAY4QCAHOEAgLiFAAC5jQAAuoUAALudAAC8hQAAvY0AAL6FAAC/vQAAsH0BALHhAACy5QAAs/0AALTtAAC13QAAttUAALe9AACzXQIAIOEAgCThAIAo4QCALOEAgLaFAgC1lQIAMOEAgLslAwC6uQIANOEAgDjhAIC/GQMAvikDAL0pAwC8MQMAvswEAKMZAgA84QCAQOEAgKbBAgBE4QCASOEAgKXRAgCq/QIAq2EDAEzhAIBQ4QCArm0DAK9dAwCsdQMArW0DAKgpAwCpKQMAqjkDAKs5AwCsKQMArSkDAK6dAACvlQAAVOEAgFjhAIBc4QCAYOEAgGThAICCqQEAga0BAICtAQC4mQAAua0AALqlAAC7bQAAvHUAAL19AAC+dQAAv20AALDtAACx9QAAsvUAALPFAAC03QAAtb0AALa1AAC3qQAA4XgBAOEcDgDjEAAA4zwOAGjhAIBs4QCAvhQEAHDhAICErAIAeOEAgId4BQCGDAUAfOEAgIDhAIDvvAAA70gOALPxAgCE4QCAiOEAgIzhAICQ4QCAtukCALXhAgCU4QCAu3EBALppAQCY4QCAhKAEAL85AQC+WQEAvVEBALxhAQCc4QCAhIwEAKDhAICEADgApOEAgKjhAICs4QCAsOEAgKqJDgCriQ4AqLkOAKmxDgCu/Q4Ar+EOAKz5DgCt9Q4Asq0OALNlDgCwkQ4AsaUOALZ9DgC3ZQ4AtH0OALV1DgC6XQ4Au+UNALhdDgC5VQ4AvuENAL/pDQC8/Q0AvfUNAKOxBQB04QCAtOEAgLjhAIC84QCApqkFAKWhBQDA4QCAqzEGAKopBgDE4QCAyOEAgK95BgCuGQYArREGAKwhBgDM4QCA0OEAgNThAIDY4QCAgB0AAIEJAACCOQAA3OEAgODhAIDk4QCAhsgAAIcMAwDo4QCA7OEAgPDhAID04QCAqKUHAKm1BwCqvQcAq8kHAKzZBwCt2QcArskHAK/BBwC+oAAA+OEAgPzhAIAA4gCABOIAgAjiAIAM4gCAEOIAgLjNAAC51QAAutUAALvlAAC8/QAAvZUAAL6dAAC/lQAAsIkHALFlBwCyYQcAs30HALRlBwC1bQcAtmUHALf1AACzNQYAFOIAgBjiAIAc4gCAIOIAgLZZBgC1UQYAJOIAgLuhBgC6TQYAKOIAgCziAIC/qQYAvqEGAL2pBgC8tQYAMOIAgDTiAIDv8AUAOOIAgDziAIBA4gCAROIAgEjiAICAPQAAgQkAAIIdAABM4gCA4cgGAFDiAIDjSAQAVOIAgKO1BgBY4gCAhigAAIdAAQBc4gCAptkGAKXRBgBg4gCAqyEGAKrNBgBk4gCAaOIAgK8pBgCuIQYArSkGAKw1BgBs4gCAs70BAHDiAIB04gCAtnkBAHjiAIB84gCAtXkBALpVAQC7XQEAgOIAgITiAIC++QAAv/kAALxFAQC9+QAAqHECAKlxAgCqcQIAq3ECAKy1AgCtvQIArrUCAK+tAgC+rDwAiOIAgIziAICQ4gCAlOIAgJjiAICc4gCAoOIAgLhpAwC5aQMAugkDALsJAwC8HQMAvQUDAL4NAwC/BQMAsNUCALHdAgCy1QIAs2kDALR5AwC1eQMAtmkDALdhAwCk4gCAqOIAgKziAICj9QIAsOIAgKUxAgCmMQIAtOIAgLjiAIC84gCAqh0CAKsVAgCsDQIArbEDAK6xAwCvsQMA7xgCAIIVAACBbQAAgG0AAMDiAIDI4gCAhvg8AIcYAwDM4gCA0OIAgNTiAIDY4gCA42wHAAThAIDhaAEA3OIAgKiFAgCplQIAqpUCAKulAgCsvQIArdUCAK7RAgCv0QIA4OIAgOTiAIDo4gCA7OIAgPDiAID04gCA+OIAgPziAIC4dQEAuX0BALp1AQC7zQEAvNUBAL3dAQC+yQEAv8EBALC1AgCxvQIAsoECALOBAgC0VQEAtV0BALZVAQC3TQEA4bQGAADjAIDj9AYABOMAgIQYPQAI4wCADOMAgBDjAIAU4wCAGOMAgBzjAIAg4wCAJOMAgCjjAIDvWAYALOMAgIF9AACAcQAAMOMAgIIFAAA44wCAPOMAgO+AAQC+VDwA4ZABAEDjAIDjfAYAROMAgEjjAIBM4wCAhtg8AIf0PACjnT0AxOIAgDTjAIBQ4wCAVOMAgKbVPQCltT0AWOMAgKv5PQCq8T0AXOMAgGDjAICvGT4ArhE+AK3VPQCs1T0AZOMAgLOhPgBo4wCAbOMAgLatPgBw4wCAdOMAgLWxPgC6ST8Au0k/AHjjAIB84wCAvkk/AL9JPwC8ST8AvUk/AKhVPgCpZT4Aqm0+AKtlPgCsfT4ArWk+AK65PwCvuT8AgOMAgITjAICI4wCAjOMAgJDjAICU4wCAmOMAgJzjAIC4VT8AuV0/ALpVPwC7bT8AvHU/AL19PwC+dT8Av20/ALDJPwCxyT8Astk/ALPZPwC0yT8Atck/ALZ9PwC3cT8AghUAAKPhPwCAsQEAgbEBAKbtPwCg4wCAvtABAKXxPwCqCT4Aqwk+AITkAQCk4wCArgk+AK8JPgCsCT4ArQk+ALPdPACo4wCAhugAAIfMAQCs4wCAtpU8ALX1PACw4wCAu7k8ALqxPAC04wCAuOMAgL9ZPwC+UT8AvZU8ALyVPACoUT4AqVE+AKptPgCrYT4ArGE+AK1hPgCulQEAr40BAISgAQC84wCAwOMAgMTjAIDI4wCAzOMAgNDjAIDU4wCAuKkBALmpAQC6aQEAu2kBALx5AQC9eQEAvmkBAL9pAQCw/QEAsc0BALLFAQCzrQEAtLkBALW5AQC2rQEAt6UBALPlPQDY4wCA3OMAgODjAIDk4wCAtuE9ALXpPQDo4wCAuwkCALo5AgDs4wCA8OMAgL99AgC+fQIAvXkCALwRAgD04wCAo6E9APjjAID84wCApqU9AADkAIAE5ACApa09AKp9AgCrTQIACOQAgAzkAICuOQIArzkCAKxVAgCtPQIAgOkAAIHpAACCHQAAvsADAO/kAgAQ5ACAh1QDAIY8BADjEAEAGOQAgOH4AQAc5ACAIOQAgCTkAIAo5ACALOQAgDDkAIA05ACAOOQAgLORAwA85ACAtbkDALZ9AwBA5ACAROQAgEjkAIC6WQMAu1kDALxJAwC9SQMAvv0AAL/1AACoRQIAqVUCAKpVAgCrZQIArH0CAK2xAgCusQIAr7ECAIRsBQBM5ACAUOQAgFTkAIBY5ACAXOQAgL5wBQBg5ACAuF0BALltAQC6ZQEAuw0BALwZAQC9GQEAvg0BAL8FAQCw0QIAsdECALLRAgCz0QIAtHUBALV9AQC2dQEAt20BAOFAPwDjvAAA4wg+AOFsPgBk5ACAaOQAgGzkAIBw5ACAdOQAgHjkAIB85ACAgOQAgL5sBwDvVAAA75w+AIjkAICjnQIAgmkAAIFhAACAaQAAjOQAgKZxAgCltQIAkOQAgKtVAgCqVQIAhsgEAIfsBACv+QEArvEBAK1FAgCsRQIAqKUGAKmpBgCquQYAq7kGAKypBgCtqQYArtkGAK/ZBgCE5ACAlOQAgJjkAICc5ACAoOQAgKTkAICo5ACArOQAgLhxBwC5cQcAunUHALvdBwC8xQcAvc0HAL7FBwC//QcAsKkGALG1BgCytQYAs40GALSVBgC1UQcAtlEHALdRBwCzMQYAsOQAgLTkAIC45ACAvOQAgLYpBgC1IQYAwOQAgLtxBgC6bQYAxOQAgMjkAIC/lQcAvlEGAL1ZBgC8YQYAzOQAgKN1BgDQ5ACA1OQAgKZtBgDY5ACA3OQAgKVlBgCqKQYAqzUGAODkAIDk5ACArhUGAK/RBwCsJQYArR0GAIANAACBFQAAgh0AAOjkAIDs5ACA8OQAgITcAQD05ACAhoAAAIcgAQD45ACA/OQAgADlAIAE5QCACOUAgAzlAIAQ5QCA43QEABTlAIDhyAUAGOUAgBzlAIAg5QCAJOUAgCjlAIAs5QCAMOUAgDTlAIA45QCA77QEADzlAIBA5QCAqD0GAKlVBgCqVQYAq6kBAKy5AQCtuQEArqkBAK+pAQCErAEAROUAgEjlAIBM5QCAUOUAgFTlAIBY5QCAXOUAgLhtAQC5BQEAugEBALsBAQC8BQEAvQ0BAL4xAQC/MQEAsNkBALHZAQCybQEAs2UBALR9AQC1ZQEAtmUBALdVAQCBvQMAgL0DALPVBQCCGQAAtTkCAGDlAIC+VAMAtjECAGjlAIBs5QCAuxUCALoVAgC9uQIAvLECAL+pAgC+sQIAcOUAgKZpAgClYQIAhAAMAKONBQB05QCAhvgMAId8AwCv8QIArukCAK3hAgCs6QIAq00CAKpNAgB45QCAfOUAgIDlAICE5QCAiOUAgIzlAIDjIAEAkOUAgOGgAQCU5QCA70ACAJjlAICc5QCAoOUAgKTlAICo5QCArOUAgLDlAICz8QMAtOUAgBTkAIC45QCAvOUAgLbpAwC14QMAwOUAgLu1AwC6tQMAxOUAgMjlAIC/lQMAvpUDAL2lAwC8pQMAqCkCAKkpAgCqOQIAqzkCAKwpAgCtKQIArlkCAK9VAgCAzQEAgQkAAIIZAADM5QCA0OUAgL58DQCHtA0AhhwMALgxAgC5PQIAujUCALvpAgC8+QIAvfkCAL7pAgC/6QIAsDECALExAgCyMQIAszECALQRAgC1EQIAthECALcRAgDY5QCA3OUAgODlAIDk5QCA6OUAgOzlAIDw5QCA79QGAPTlAIDhVAYA+OUAgOOkAACsDBUA/OUAgADmAIAE5gCAo/ECAAjmAIAM5gCAEOYAgBTmAICm6QIApeECABjmAICrtQIAqrUCABzmAIAg5gCAr5UCAK6VAgCtpQIArKUCAKghDgCpIQ4AqkkOAKtZDgCsaQ4ArWkOAK6ZDgCvmQ4A1OUAgCTmAIAo5gCALOYAgDDmAIA05gCAOOYAgDzmAIC49Q4Auf0OALr1DgC7iQ4AvJ0OAL2FDgC+hQ4Av7UOALDpDgCx6Q4Asv0OALPxDgC01Q4Atd0OALbVDgC3zQ4As8EOAIIVAACBtQAAgLUAAEDmAIC26Q4AteEOAL4QAAC7LQ4Aui0OAIRkAwBE5gCAvxkOAL4RDgC9JQ4AvCkOAEjmAICjhQ4AhogAAIdsAwCmrQ4ATOYAgFDmAIClpQ4AqmkOAKtpDgBU5gCAWOYAgK5VDgCvXQ4ArG0OAK1hDgCziQ4AXOYAgGDmAIBk5gCAaOYAgLaBDgC1iQ4AbOYAgLuVDgC6jQ4AcOYAgHTmAIC/+Q4AvvEOAL2FDgC8hQ4AeOYAgHzmAICA5gCAhOYAgOMMDQCI5gCA4RgNAIzmAIDvrAwAkOYAgJTmAICY5gCAnOYAgKDmAICk5gCAqOYAgKgBDgCpAQ4AqgEOAKsBDgCsAQ4ArQEOAK4BDgCvPQ4AgN0AAIEJAACCGQAArOYAgLDmAICEPAEAvnQAALjmAIC4HQ4AuS0OALolDgC76QEAvPkBAL35AQC+6QEAv+kBALBJDgCxUQ4AslEOALNRDgC0NQ4AtT0OALY1DgC3LQ4Ao4kNALzmAICGrAQAhzwDAMDmAICmgQ0ApYkNAMTmAICrlQ0Aqo0NAMjmAIDM5gCAr/kNAK7xDQCthQ0ArIUNANDmAICznQIAhEgDAL5ABAC2VQMA1OYAgNjmAIC1sQIAunEDALt5AwDc5gCA4OYAgL4xAwC/MQMAvFEDAL1RAwCwkQMAsZkDALKhAwCzoQMAtNEDALXRAwC20QMAt9EDALj1AwC5+QMAus0DALvFAwC83QMAvcUDAL7NAwC/xQMA5OYAgOjmAIDs5gCA8OYAgIV8GQD05gCA+OYAgGTlAICoIQIAqTECAKoxAgCrBQIArB0CAK3xAwCu8QMAr/EDAPzmAIAA5wCABOcAgAjnAIDvUAAADOcAgBDnAIAU5wCA44QAABjnAIDh+AEAHOcAgIAVAACBGQAAggUAACDnAICjmQMAKOcAgIZoBACHYAUALOcAgKZRAgCltQMAMOcAgKt9AgCqdQIANOcAgDjnAICvNQIArjUCAK1VAgCsVQIAPOcAgEDnAIBE5wCASOcAgEznAIBQ5wCAVOcAgO/4AQC+bAQA4YAOAFjnAIDjFAEAXOcAgGDnAIBk5wCAaOcAgGznAIBw5wCAdOcAgLPdAQB45wCAtf0BALb1AQB85wCAgOcAgITnAIC6sQEAu4UBALydAQC9NQEAvj0BAL81AQCpBQYAqLkFAKsVBgCqHQYArT0GAKw9BgCvTQYArl0GACTnAICCHQAAgR0AAIAdAACI5wCAjOcAgJDnAICU5wCAuUEHALidBgC7QQcAukkHAL1FBwC8WQcAv0UHAL5FBwCxCQYAsD0GALOpBgCyAQYAtbkGALSxBgC3rQYAtrEGAKORBgCEjAIAhigAAIfAAwCY5wCAprkGAKWxBgCc5wCAq8kGAKr9BgCg5wCApOcAgK95BgCucQYArXkGAKzRBgCo5wCAs5kHAKznAICw5wCAtlEHALTnAIC45wCAtbEHALptBwC7dQcAvOcAgMDnAIC+WQcAv0UHALxtBwC9ZQcAxOcAgMjnAIDM5wCA0OcAgNTnAIDY5wCA3OcAgO+oBQDg5wCA4TQFAOTnAIDjdAUA6OcAgOznAIDw5wCA9OcAgKMdBgCCLQAAgRUAAIAdAAD45wCAptUGAKU1BgD85wCAq/EGAKrpBgAA6ACAhCgBAK/BBgCu3QYAreEGAKzpBgCoxQYAqdUGAKrVBgCr5QYArP0GAK0VBgCuHQYArxUGAL7sAQAI6ACAhggAAIcgAAAM6ACAEOgAgBToAIAY6ACAuH0GALkFBgC6DQYAuwUGALwBBgC9CQYAvjkGAL85BgCwbQYAsXUGALJ9BgCzdQYAtFkGALVFBgC2TQYAt0UGAKiRAgCpmQIAqqECAKuhAgCs0QIArd0CAK7VAgCvyQIAHOgAgCDoAIAk6ACAvyweACjoAIAs6ACAMOgAgDToAIC4VQMAuV0DALppAwC7ZQMAvGEDAL1hAwC+YQMAv2EDALC5AgCxjQIAsoUCALNtAwC0dQMAtX0DALZ1AwC3bQMAOOgAgDzoAICzIQIAQOgAgLVRAgCEiAMAROgAgLZVAgC05gCAvigcALtBAgC6dQIAvbEDALxZAgC/sQMAvrkDAKNpAgBI6ACATOgAgFDoAIBU6ACAph0CAKUZAgBY6ACAqwkCAKo9AgBc6ACAYOgAgK/5AwCu8QMArfkDAKwRAgCopQIAqbUCAKq9AgCrtQIArK0CAK01AQCuPQEArzUBAL4sHABk6ACAaOgAgGzoAIBw6ACAeOgAgIdoHQCGHB0AuIUBALmNAQC6hQEAu50BALyNAQC9vQEAvrUBAL95AACwUQEAsVEBALJRAQCzUQEAtPEBALXxAQC29QEAt+UBAO/YAACCtQAAgaUAAIClAAB86ACAgOgAgIToAIDvxAYAiOgAgOH0BgCM6ACA4zgBAOPMAACQ6ACA4SgBAJToAICY6ACAtuUBALV1AgCEQBwAs2UCAJzoAICg6ACApOgAgL9lAQC+ZQEAvdUBALzVAQC7xQEAusUBAKjoAICs6ACAo7UdAHToAICw6ACAtOgAgLjoAICmNR4ApaUdALzoAICrFR4AqhUeAMDoAIDE6ACAr7UeAK61HgCtBR4ArAUeAMjoAIDM6ACA0OgAgNToAICADQAAgTUAAII9AADY6ACA3OgAgODoAIC1BQAAcRoAgOG0AgCs2AIAtQUAAHUaAICotR8AqRUfAKodHwCrFR8ArDEfAK09HwCuLR8AryEfAOG0AgCs2AIAtQUAAHkaAIDhtAIArNgCALUFAAB9GgCAuNEAALnZAAC64QAAu+EAALyRAAC9kQAAvpEAAL+RAACwIR8AsTEfALIxHwCzMR8AtAkfALUJHwC28QAAt/EAAOG0AgCs3AIA71QdALUdAACBGgCA4bwCAKzQAgC1KQAAoyUBAKKRAwChFR0AoA0dAOGAHgCFGgCA47wdAOHEAgCz1R4AtQkAAKzYAgCJGgCA4bwCALb9HgC1+R4ArOACALu1HgC6pR4AtQUAAI0aAIC/jR4Avo0eAL2lHgC8pR4AoxUeAOG8AgCs0AIAtREAAI9pJQCmPR4ApTkeAJEaAICrdR4AqmUeAOG0AgCseAEAr00eAK5NHgCtZR4ArGUeAJvdFACa5RUAmQEXAJjhEACfcR8AnnkZAJ35GQCcARsAk+UtAJIRLwCRbSkAkG0pAJf5EQCW8REAlYUsAJSZLQC1JQAA4ZQCAILxJgCDjSoAhJUqAIXhLACGHS4Ah3kuAKy0AgCVGgCAilUvAIspEgCMORIAjRkTAI7xFACPHRYAtQUAAJkaAICSVRcAk5EYAJRxGgCV+RoAlvkcAJd9HgCC4AMAkwsAgJpVHgCb2QAAnHUCAIMMAICzDACAuIkKAKwBBACthQYAroEGAMwQAgDMfAMAtgwAgJ0aAIDCDACAxQwAgMgMAIAACwCAgaUyArwMAIAE6ACAmpUGAJtVIwK8kQYAvbEAAL6RBgC/rQYAuOkGALmVBgC6kQYAoRoAgLTBBgC1zQYAts0GALfdBgCw/QYAseUGALKdAACz5QYAhVTHA6UaAICH/AAAuAEKAK0aAIDpDACAsRoAgIyRcwCNpAEAzPACAL4NAIDBDQCAiRQAALgZCgCLDAAAGg4AgFMOAIC5DACAvwwAgBkKAICRwAEAywwAgLhtCgDODACA1AwAgNoMAIDdDACA4AwAgLUaAIAoDQCA5gwAgLkaAIDhpB4AKw0AgONUHgCvIXMAzCgCAO8MAIDsDACA8gwAgPUMAID4DACAzIACAJS4AwD7DACAkhQCAO9gHgCQAAIA/gwAgAoNAIC48QoADQ0AgJ8LAIAQDQCAiSkLABMNAICpGgCAvDABAL/EAQC+7AEAFg0AgMzsAgC4xQoAukQBAK0JAIAZDQCAygYAgN8GAIDyBgCAHA0AgPoGAIAfDQCACgcAgC0HAIAYBwCA9gcAgC8HAICpDQCAOgcAgK8NAIBKBwCAtXkAAGcHAIC3cSoCcgcAgLFhAAB0BwCAsw0pAo0HAIC96QAAoAcAgPoHAICtBwCAuRkrAsMHAIC7WRQCHwgAgFoJAIA8CACALw4AgFsIAIA5AACAgQgAgHEAAIDHCACAKwAAgCAJAIA9AACAXAkAgEMAAIBeCQCARQgAgGoIAIBJAACAAAgAgFMAAIB5CQCAWQAAgCINAIBfAACAuw0iAtANAIDMFDYCHwAAgL9lAAC+EQAAvW0AAOUHAICAaQEAgXUBAIJxAQCD3SEChGkHAIWBBwCGgQcAh3EBAIihAQCJrQEAirUHAIuNBwCMlQcAjaUBAE8AAICPpQEAkOEBAJHtBwCSsSECk/0HAJSNBwCVUQYAlvEBAJfZAQCY0QEAmXUGAJp9BgCb1QEAnGkGAJ2ZFAKeUQYAn1EGAKB1FAKhuQYAokkBAKOFLQKkIQEApS0BAKZ1FAKntQYAqKERAqlRFAKqlQYAsSEAgMy8NQLNPDUCbQAAgKoDAICsAwCArwMAgL0hAIDEIQCA2yEAgOIhAIDJAACADwAAgLihBgC6BgCAtwYAgMwAAIDOIQCAtQMAgN0FAIAYBgCAugUCALvVAgC46QUAuf0FAL7JAgC/5RcCvA0CAL0BAgCy4QUAs+EFALCNBQCxnQUAtuUFALfpBQC09QUAte0FAKo9BQCrwQUAqD0FAKk1BQCuzQUAr/UFAKzNBQCtxQUAoj0FAKMFBQCg1QIAoTkFAKYdBQCnBQUApB0FAKUVBQC/BgCAm8EFAD4GAIBVBgCAnt0FAJ8xBACcUQIAndUFAHIGAICJBgCApAMAgDAiAIDbAACAoAMAgI8HAIDuBwCA8gcAgJAJAIACCACABggAgJYLAICUCQCArwoAgG8HAICLBwCAlwcAgKIHAICqBwCAqgkAgPsOAIASDwCAHw8AgMwEMwLNsDACzCAzAs3gMALMEDACzGgwAsxYMALNjDACzGgxAs0UMQLM1DECzRQ2AsxwIALN0CcCzDA2AswkMQLMDDwCzWg/AswYPwLNND8CzBg9As3AMgLMRDwCzBg5Asw4MgLNqDICzIgyAs34MwLMfDMCzUAzAswoMwLNCDMCzMghAs0kJgLMrCYCzEA4AsyYJQLNyDoCzBwkAs0QJALMhDsCzag7AsysJQLNvDoCzKw4Asz4JwLM4DgCzXQ4AicPAID2BgCAYQ0AgIgNAIDNICoCzBwrAqoGAIAsIgCAzKQgAs2gJwLMOCYCygQAgMw4OgLNPDsCzBA5As1gPgLMoAMAvj0NAL3tLALWBACAu1UjAgQJAIC5PSICzwYAgNkHAIClBACAoA0AgLIEAIBvBQCA9AYAgL4EAIB1BQCAr70MAK6ZLgKtpQwAwgUAgKvFIgIDBgCAxAQAgCMGAIDQBACAyAUAgCkGAIBdBgCAowEYAqAEAIAaBwCAHQcAgJ9dDACeUQwAnUUMACcHAICbWSECrwcAgLEHAIC0BwCAuAcAgCoHAIDOBwCA0AcAgJMtJgLTBwCAbAgAgG8IAICPBQwAjnEMAI1lDAB5CACAi0UgAmAJAICJNS8CYwkAgGcJAIB8CACAcAkAgHMJAIC9AwCAACIAgIFdDACAYQwAgAABAIEYAACCAAQABCIAgIQQBwCFFAYAhuQIAIc8AgCILAUAiaQFAIoAeAAIIgCAjCQAAAwiAIAUIgCAECIAgLgRAACRxHsAkkh6AJNMeQAcIgCAzOgCAJbwCQC4OQAAkMAJACQiAICS8AkAzPgCAJS0CQC4DQAAKCIAgMwcAgC4BQAANCIAgMzkAgC4HQAAOCIAgDwiAIBDIgCAWiIAgKiMCACp5HsAYSIAgKvUBgDM5AIAuA0AAGsiAIDMlAIAbyIAgLGAewC4CQAAuBUAAMz8AgC15AgAcyIAgMzYAgB3IgCAuAUAALqcBQC7XAUAvAB8AL30fwC++H0Av/xyAIAJOgKBDToCggE6AoMFOgKEGToChR06AoYROgKHFToCiCk6AoktOgKKIToCiyU6Aow5OgKNPToCjjE6Ao81OgLM8AIAkekPAIMiAIDMzAIAuBkAAH8iAIDM3AIAl+UPALg1AAC4DQAAjyIAgMz8AgC4BQAAkyIAgMwwAgCXIgCAzNACAJsiAICfIgCAzIgCAKQtDwClVQ8Apl0PAMyUAgCoqToCqa06ArjVAACjIgCAuDUAAKciAIDMUAMAr7U6AswsAwCrIgCAzBgDALMFDwC0HQ8AzyIAgLYJDwC3CQ8Avmh9ALhtAAC4RQAAzDgDALwpDwDTIgCAviUPAMxYAwCH5Q4AzOg6Ari9AQC4yQEAzPA1As2kMwLMgCICzXwlAs2UNgLMBCkCzew7AsxkOgK45QEAuMEBAInVDgCI1Q4Al7EOALgNAACvIgCAsyIAgLciAIC4GQAAuyIAgNciAICfaTsC2yIAgL8iAIC4PQAAzMQCAMz4AgDDIgCAxyIAgLjZAADLIgCA3yIAgLjRAADjIgCAuPEAAMzMMwLnIgCAuMkAAMzoMwLrIgCAuNUAAKllAAC4yQAAzNgCAKq5BgC3TQ0Atk0NALU1DgC0NQ4AuFUAABUjAICxGQ8AsCkOAL/1AwC+UQ0AvVkNALw1DAC7XQ0Aul0NALldDQC4XQ0AgL0KAIHFCgCCFQQAg8kKAMx8BQCF3QoAhtUKAIfNCgDMVAUAifEKAIq5CACLDQgAjBEIAI0VCACOtScCj+UKAJBpCACRbQgAknEIAJNtJALMEAUAlR0IAJaFCgDMEAUAzDQFAJk9CACaiQoAmw0IAJwRCACdFQgAzEgFAMwQAgCgZQoAoW0KAKJlCgC4BQcApLEEAMzoAgCmsQQAuA0HAKiBBADM/AIAqpkIAKtdCgCsuQgArakEALglBwCvNQgAsNEIALHxBADMwAIAs40IALQpKAK1IQoAtiEKALchCgC4IQsAuSUIALhBBwC7KQsAvA0dAr3dDwC+MQsAvzELAIDdCgAZIwCAnKF9ANADAIDpAwCAhRkJAIaZCQCHlQkAiOEJAIklJQICBACAGwQAgC4EAIBBBACAVAQAgGcEAICQrQoAkUkFAJJtBQCTYQUAlGEFAJVtBQCWZQUAlxEFAJg1BQCZPQUAmjUFAJsNBQCcFQUAnR0FAJ4VBQCfCQUAoKkJAKH9BQCi9QUAowEFAKQFBQClDQUApgUFAKc9BQCoBQUAqQ0FAKoFBQCrGQUArIkJAK2pBQCutQkAr/0JALABCQCxfQUAsnUFALMBBQC0aQkAtQEFALYFBQC3PQUAuAUFALnhJQK6AQUAuwEFALzRJQK9PQkAvnkJAL9dCQCDMAUAoXgHAJ+xfgB6BACApHgHAKVIBwCNBACA8wQAgIt8BADdAACAEwEAgIhIBAAcAQCAIAEAgCQBAIAoAQCALAEAgDABAICyAAcAs/wHADQBAIDhAACAtuQHALfwBwDmAACA6wAAgLrgBwC7nAcAvIgHAL2oBwDwAACAs8F+AKPMBAD1AACA+gAAgIMABAD/AACAhXQEAKUgBAAEAQCAiEwEAAkBAIAOAQCAFwEAgK8tBwCNxAcArSEHAKwpBwDNAwCA8AQAgI8FAICwZQcA4gUAgB0GAIBDBgCAWgYAgHcGAICOBgCA0wMAgOwDAIAFBACAHgQAgDEEAIC8fAQAgt0rAoPlKwKA/QoAgfkrAoaZCQCHmQkAhOEKAIXhCgCKiQkAi4kJAIiJCQCJiQkAjoUJAEQEAICM4QgAjY0JAJK5KwKTQScCkJkrApHFCwCWyQsAl3UnApTFDQCV0SQCmskLAJvZKgKYyQsAmXkHAFcEAIBqBACAnP0LAH0EAICQBACA9gQAgKABAICkAQCAqAEAgONkAgCsAQCAsAEAgLQBAIDvvAcAqBEJALgBAIC8AQCAwAEAgMQBAIDIAQCAzAEAgNABAIDUAQCA2AEAgNwBAIDgAQCA5AEAgOgBAIDsAQCA8AEAgPQBAID4AQCA/AEAgAACAICCnH4ABAIAgKD1VAKh2VQCoulUAqP1dQCk7XUApZ12AKaVdgCnvXYAqIV2AKkpfQCqOX0AqwV9AKwdfQCtBX0Arg19AK8FfQCwfX0AsUl+ALJRfgCzUX4AtHV+ALV9fgC2aX4At2l+ALhZfgC5WX4Auil+ALspfgC8IX4AvSF+AL4ZfgC/GX4AkgcAgDkJAIDXBwCATSIAgLQNAAC1NQAAtj0AAKIGAICsBgCArwYAgAMjAIAJIwCAvSV4ALy1WALGMQCALjoAgJkqAIC9KgCAySoAgNkqAIDhKgCA7SoAgPUqAID9KgCACSsAgF0rAIB1KwCAhSsAgJUrAIClKwCAtSsAgNUrAICAeX8AgYF/AIKBfwCDnX8AhI1/AIWxfwCGsX8Ah7F/AIjhfwCJ4X8AiuF/AIv9fwCM5X8Aje1/AI7lfwCP3X8AkKV/AJGtfwCSpX8Ak71/AJSlfwCVrX8Alm1+AJctfgCYFX4AmRl+AJrpfgCb6X4AnPl+AJ35fgCe6X4An+V+AKAdfgChJX4AoiV+AKM9fgCkJX4ApS1+AKYlfgCnXX4AqGV+AKltfgCqZX4Aq31+AKxlfgCtbX4ArmV+AK9dfgCwJX4AsS1+ALIlfgCzPX4AtCV+ALUpfgC2WXcAt9V1ALj9eQC56XUAuvl1ALvZeQC86XUAvdV1AL7RdQC/2XUAgDF2AIE9dgCCSXYAg0V2AIRBdgCFTXYAhvl0AId9dgCIoQIAiU12AIpZdgCLuXoAjEl2AI2degCOsQIAjx16AJCRVgKRKXYAkoF2AJPNdgCU2XYAlel2AJbJdgCX0VkCmKF2AJllWgKa8XYAm01aApzRdgCdYXoAnoFWAp/VdgCgBQIAoY1aAqI1VwKjCXYApCF2AKUtdgCmiVoCp5laAqi5WgKpdXYAql13ANkrAIDdKwCAESwAgDksAIBJLACAUSwAgFUsAIBhLACAfSwAgIEsAICZLACAnSwAgKUsAIC1LACAUS0AgGUtAIClLQCAuS0AgMEtAIDFLQCA1S0AgJl1CgD4LQCAJC4AgDAuAIBQLgCAXC4AgGAuAIBkLgCAgux6AINkewB8LgCAgC4AgIZ0ewCHvHsArC4AgLguAIDALgCAyC4AgNguAIDnLgCA7y4AgBsvAIAfLwCAJy8AgJJwfAArLwCAMy8AgJFMfAA7LwCASy8AgGcvAIDfLwCA8y8AgKvMfACo5HwAqdx8APcvAIB3MACAezAAgI8wAICiwHwAkzAAgJswAICjMACAzEBJAs0ASQLM/EoCzWhLAqswAIC3MACA7TAAgP0wAIARMQCAjjEAgJoxAICqMQCAsqx8ALNAfAC2MQCAwjEAgMoxAIDOMQCAtGx8ALUEfACAlQcAgZ0HAIKVBwCDqQcAhLkHAIW5BwCG2QcAh9kHAIjpBwCJ6QcAivkHAIv5BwCM6QcAjekHAI7RBwCP0QcAkLEHAJGxBwCSSQEAk0kBAJRZAQCVWQEAlkkBAJdJAQCYeQEAmXkBAJpJAQCbSQEAnFkBAJ1ZAQCeSQEAn0kBAKC5AQChuQEAoskBAKPJAQCk2QEApdkBAKbJAQCnyQEAqPkBAKn5AQCqyQEAq8kBAKzZAQCt2QEArskBAK/JAQCwuQEAsbkBALJJAQCzSQEAtFkBALVZAQC2SQEAt0kBALh5AQC5eQEAukkBALtJAQC8WQEAvVkBAL5JAQC/SQEA0jEAgNYxAIDaMQCAkjIAgNoyAIDmMgCA6jIAgO4yAIDyMgCA+jIAgP4yAIASMwCALjMAgDYzAIB2MwCAejMAgIIzAICGMwCAjjMAgJIzAIC2MwCAujMAgNYzAIDaMwCA3jMAgOIzAID2MwCAGjQAgB40AIAiNACARjQAgIY0AICKNACAqjQAgLo0AIDCNACA4jQAgAY1AIBKNQCAUjUAgGY1AIByNQCAejUAgII1AICGNQCAijUAgKI1AICmNQCAwjUAgMo1AIDSNQCA1jUAgOI1AIDqNQCA7jUAgPI1AID6NQCA/jUAgJ42AICyNgCAnoUMAOY2AIDqNgCA8jYAgIC5AwCBuQMAgskDAIPJAwCE2QMAhdkDAIbJAwCHyQMAiPkDAIn5AwCKyQMAi8kDAIzZAwCN2QMAjs0DAI/FAwCQvQMAkQEMAJJJDgCTSQ4AlFkOAJVZDgCWSQ4Al0kOAJh5DgCZeQ4AmkkOAJtJDgCcWQ4AnVkOAJ5JDgCfSQ4AoLkOAKG5DgCiyQ4Ao8kOAKTZDgCl2Q4ApskOAKfJDgCo+Q4AqfkOAKrJDgCryQ4ArNkOAK3ZDgCuyQ4Ar8kOALC5DgCxuQ4AskkOALNJDgC0WQ4AtVkOALZJDgC3SQ4AuHkOALl5DgC6SQ4Au0kOALxZDgC9WQ4AvkkOAL9JDgC8eQQAvXkEAL6JBAC/nQQAuHUEALl9BAC6aQQAu2kEALRxBAC1cQQAtnEEALdxBACwcQQAsXEEALJxBACzcQQArGkEAK1pBACucQQAr3EEAKhBBACpQQQAqkEEAKtBBACknQUApWEEAKZhBACnYQQAoJ0FAKGFBQCijQUAo4UFAJxdBQCdZQUAnm0FAJ9lBQCYXQUAmUUFAJpNBQCbRQUAlB0FAJVlBQCWbQUAl2UFAJAdBQCRBQUAkg0FAJMFBQCMMQcAjTEHAI4xBwCPMQcAiDEHAIkxBwCKMQcAizEHAIQxBwCFMQcAhjEHAIcxBwCAMQcAgTEHAIIxBwCDMQcAJjcAgC43AIA2NwCAcjcAgHY3AIB+NwCAgjcAgIY3AICyNwCAtjcAgL43AIDSNwCA1jcAgPI3AID6NwCA/jcAgCI4AIBCOACAUjgAgFY4AIBeOACAijgAgI44AICeOACAwjgAgM44AIDeOACA9jgAgP44AIACOQCABjkAgAo5AIAWOQCAGjkAgCI5AIA+OQCAQjkAgEY5AIBeOQCAYjkAgGo5AIB+OQCAgjkAgIY5AICOOQCAkjkAgJY5AICaOQCAnjkAgK45AIDGOQCAyjkAgNY5AIDaOQCA3jkAgOI5AIDqOQCA7jkAgPI5AID+OQCABjoAgA46AIASOgCAGjoAgIC5AQCBuQEAgskBAIPJAQCE2QEAhdkBAIbJAQCHyQEAiPkBAIn5AQCKyQEAi8kBAIzZAQCN2QEAjskBAI/JAQCQuQEAkbkBAJIRAACTEQAAlDEAAJUxAAAeOgCAIjoAgCo6AIAyOgCAPSMAgGUsAIBpLACAJSQAgIJgAgCZ4QAAgIAAAIGYAACC5AYAg4gEAITUGwCFlBoAhhgfALMjAICIxB4AiQAQAIqoEwCLrBEAjAAoAI20KwCOuCoAj7wpAOOwAgC+dAIAnlUAAOMUAgCCbAIAtyMAgJkNAAC+RAIAnjUAAIJoAgCZBQAAuyMAgO/MAgC+oAAAgoQAAO/YAgDj7AEA4/QBAL8jAIDjCAMAwyMAgOM4AwDHIwCA44gDAMsjAIDv4AMAzyMAgO+IAwDvPAEA78QDANMjAIDv1AMA4+wDAB43AIDXIwCA4+wDAOPsAwDj5AMA2yMAgOO4AwDvXAMA70wDAN8jAIDvSAMA7/QDAOMjAIDnIwCA7zQDAON8AwDjlAQA6yMAgO8jAIDzIwCA47QEAPcjAID7IwCA/yMAgO9sBAADJACAByQAgO9YBADvUAQACyQAgBYkAIAaJACAvQAAgOP4BADCAACAMSQAgB4kAIBtKQCA45wEAAglAIBrJQCAriUAgO9QBADaJQCABCYAgO88BAApJgCAgAlLAoYcdwC+RAIAgnQCAL5QAgA+JgCAmREBAJkNAQCPrAIAggQCAI1oAQCewQIAi3wBAJ49AQCeKQEAvggCAJfQAgCZXQEAldACAJ5VAQCT0AIAmXUBAJHQAgC+SAIAn7gCAEYmAICdtAIAnk0BAJuwAgCZXQEAmbQCAL6EAgCeqQEApowCAGImAICkgAIAmakBAGomAIChSAIAgqwCAK/kAgCCtAIAglwCAJnlAQC+CAIAgnwCAIIABACopAIAnvkBAL5wAgC1HAQAnoUBAL6oBQCyhAIAtrECAL6sBQC4KQkAuYkCALqZAgCCjAUAu+gEAIKcBQByJgCAuPAEAJ5ZBgCZbQYAnmEGAJl5BgC+fAIAnmEGAIJcAgC+QAIAmVkGAJ5dBgCCYAIAmaUGAL58AgCevQYAghwCAL4UAgCZzQYAvkwCAIJMAgCa3QYAnt0GAJ/FBgDjDAIAgrwCAJn5BgC+ZAIA7/QCAJrxBgCe6QYAn+kGAJ7ZBgCf1QYA4wQCAJklBgCaIQYAgngCAJk9BgDjBAIAgkQCAJolBgC+cAIA75wCAJ4FBgCfFQYA7+gCAJp1BgCZBQYAggQCAL5wAgDjcAIAnnUGAJ8NBgCeAQYAvnwCAOM0AgCZDQYAvmACAIJsAgDv8AIAmTUGAIKQAwDv2AIAniEGAIQmAICbxQcAmeUHAL58AgCe7QcAn8UHAOPsAwCdUAIAnNEHAIJsAgDv1AIAmc0HAIJ8AgC+cAIAmd0HAJ7dBwC+AAIA42gCAJ6tBwCZuQcA42gCAIJ8AgDjDAIAvkgCAJmpBwCCWAIA78QCAJ6ZBwC+bAIA77gCAIKUAgCejQcA77gCALsAAACZeQcAuQwAAJ5xBwC/AAAAglQCAL0EAAC+aAIAs9QDAJmxBgCxcAMAggQCALc4AACeoQYAtTQAAL5wAgCrWAMAnqEGAO9cAgCZqQYArxADAIJQAgCtFAMAmYUHAJlpBgC+WAIAnmEGAL58AgCCaAIApqACAOOQAgCZaQYA43wBAOOYAQDjrAEA49ABAOPoAQC+dAIAno0FAOMwAgDvzAIAgmgCAJnRBQDvlAIA71QBAO9wAQDvJAEA7ygBAL58AgCevQUA4wwCAIJ4AgCZrQIAvnQCAJ6lAgDjNAIAgmACAJkZAAC+YAIA7/wCAJ4NAACClAIA79QCAJAmAIDj/AIAmQkAAL5gAgCYJgCAnh0AAOMAAgCwJSoAglgCAJkNAADv9AIAvmQCAK4mAIDvwAIAnhkAAIIYAgCCOAIA43ACAJkRAACaNQAAmSkBAL50AgDsJgCAnyUAAJ4JAACZ6QEAvrQDAL7gAwCazQEA79gCAJ4RAQCC2AMA/SYAgIHEAgDjsAMAHycAgOP8AwC+/AIAhMQCAIIoAgCGEAIAKicAgIg8AgCeIQAAnw0AAHonAIDvKAMAj3QCAO8sAwCCiAIAmXUAAJoVAACSxAMAldADAJktAACa0QAAjicAgL7IAgCYaAMAm3wDAILEAwCeQQAAnykAALAnAICChAIA45ACAL4IAwC+JwCABigAgJ8ZAACe7QAA49ACAJlxAACaFQAAvhQCAO8wAgCZIQAA71gCABQoAICv7AMAggQCALFMHACwABwAniUAALJMHACeXQAAn2EAAOO8AgCZIQAA+QAAAHEpAIDvlAIAdSkAgL08HACCgB0Av8EfAHkpAIDjtB0AvnQCAJ71HwDj8B0AmQUAAH0pAIC+fAIAngkAAIJgAgCZDQAAiSkAgL5gAgDvzAIAnh0AAOklAIDv3AIA42gCAPkYAIDjPB0AIRoAgP0YAIABGQCAJRoAgCkaAIAtGgCAMRoAgDUaAIA5GgCA76QCAD0aAIDvJB0AQRoAgLHFAAAFGQCAs8UAALLdAAC1yQAAtMEAALcdAAC2wQAAuWUAALhlAAC7zQAAus0AAL3dAAC83QAAv8UAAL7JAAAJGQCADRkAgE0ZAIBhGQCAERkAgBUZAIDvFHgD7wBIA+HYTQPhOKgC41x5A+O0UAOtGQCAsRkAgLUZAIC5GQCAgMkBAIHVAQCC3QEAg20CAITdAQCFcQIAhgEEAIcdBQCIJQUAiTUFAIo9BQCLbQUAjHUFAI1lBQCObQUAj80BAJC1AQCRvQEAkrUBAJNNAwCUVQMAlV0DAJZVAwCXTQMAmHUDAJl9AwCadQMAm00DAJxVAwCdWQMAnkkDAJ9JAwCguQMAobkDAKLBAwCj3QMApMUDAKXNAwCmxQMAp/0DAKjJAwCpyQMAqtEDAKvRAwCsMQMArTEDAK4xAwCvMQMAsFEDALFRAwCyUQMAs1EDALRxAwC1cQMAtnEDALdxAwC4UQMAuVEDALpRAwC7UQMAvDEDAL0xAwC+MQMAvzEDAL0ZAIDBGQCAxRkAgMkZAIDNGQCA0RkAgNUZAIDZGQCA3RkAgOEZAIDwIAIA5RkAgOkZAIDtGQCA8RkAgPUZAICc9TYAnf02APkZAICRkAIA/RkAgKkZAIBFGQCASRkAgEUaAIC6adgASRoAgE0aAIC4sTYAubE2AFEaAIBVGgCAWRoAgF0aAIBRGQCAYRoAgGUaAIBVGQCAWRkAgF0ZAIBlGQCAaRkAgG0ZAIBxGQCAdRkAgHkZAIB9GQCAgRkAgIUZAICJGQCAjRkAgJEZAICVGQCAglgCAJkZAIBpGgCA8FgCAG0aAICdGQCAoRkAgKUZAIABGgCABRoAgJF0AwDhtDsCCRoAgOPYIgINGgCAERoAgBUaAIAZGgCAHRoAgKUqAIBVLQCAqSoAgMEqAICtKgCAljMAgO/IPwK1KgCA4ZTzAuGY0gLjlPcC4xDGAuGUtgLhkJ0C44SiAuMIhwIZGQCAHRkAgO+4swLvOIsCnSoAgOAtAIDvIJcC7+DgAoLkAgBpLQCACAIAgLrF2QAOAgCAFAIAgBoCAIAgAgCAJgIAgCwCAIAyAgCAOAIAgD4CAIBEAgCASgIAgFACAIDhgHgC8OQGAOMUagKCgAgA4aAPAuEIEwLjhA4C4xgeAlYCAIA0AwCA7zQ7Au8wHwI6AwCAQAMAgO8MEgJGAwCAJRkAgCkZAIBMAwCAUgMAgC0ZAIAxGQCAWAMAgF4DAIB2AwCAggMAgIgDAICOAwCAlAMAgJoDAIB8AwCAZAMAgDUZAIA5GQCAbQMAgFwCAIA9GQCAQRkAgHQCAIBoAgCAvAIAgHoCAICYAgCAYgIAgJICAIBuAgCApAIAgNQCAICAUQYAgV0GAIJVBgCDaQYAhHkGAIV5BgCGaQYAh2kGAIhZBgCJoQcAiqUHAIu9BwCMpQcAja0HAI6lBwDyAgCA7AIAgOACAICSCRQAkxUUAJTxBwCV8QcAlvEHAJfxBwCY0QcAmdEHAJo5FACb0QcAnIEHAJ2BBwCefQcAnx0UAJktAQCYLQEAmz0BAJo9AQCdLQEAnC0BACEZAICeVQEAkd0GAJDRBgCTJQEAkiUBAJUtAQCULQEAlx0BAJYdAQCJ8QYAiOkGAIvxBgCK+QYAjbEGAIzpBgCPqQYAjrkGAIHxBgCA7QYAg/EGAIL5BgCF0QYAhOkGAIfRBgCG2QYAua0DALitAwC7vQMAur0DAL2tAwC8rQMAv90DAL7dAwCxrQMAsK0DALO9AwCyvQMAta0DALStAwC3nQMAtp0DAKm5AQCosQEAq3UBAKqxAQCtFQEArBUBAK/dAwCu3QMAobkBAKCpAQCjiQEAorEBAKWZAQCkkQEAp4kBAKaRAQAuAwCAwgIAgM4CAIDmAgCA2gIAgAQDAICwAgCA+AIAgCIDAIAKAwCAngIAgIACAIC2AgCAyAIAgP4CAICGAgCAKAMAgKoCAIAQAwCAjAIAgBYDAIAcAwCACS0AgOsuAIDKNACAhAcAgAYFAIAVBQCAJAUAgDMFAIBCBQCASwUAgPAsOABUBQCAXQUAgGYFAICSBQCA40huA5sFAIDhTG4DpAUAgO/0AQOnBQCAqgUAgK0FAIBGOgCApkwAgNZVAIA2aACAZnEAgJZ6AID2jACAVp8AgIaoAIDtugCAJMQAgFTNAICE1gCAtN8AgDG7AIA6rgCABqUAgPkqAICJKwCAoSoAgOUqAIBBMQCAATEAgE40AIDVLACABjMAgIo3AIBiNACAHSwAgJI0AICeMwCAEjgAgFkrAICFLACA+jEAgCY5AIAdKwCArSsAgJ4xAIC8LgCAySwAgFksAIA4LgCALC4AgJGgBgDuMwCAGSsAgJ43AIB1LACAzS0AgLAFAIDh1D8D4VgaA+PcLwPjUA4D4RTyA+FA0wPjQOoD40DDA7MFAIC2BQCA73jrA+9c8gO5BQCA5QUAgO9E3gPvmCUD4bSLA+E8lwPjfKID45iLA+EwQQDhUKwD4xx/AOOIRgDoBQCA6wUAgO84ewDv4EEA7gUAgPEFAIDvzIoD7yCHA4DBGACB3RgAgikLAIMpCwCE6Q4AhekOAIYZDwCH8RgAiCUPAIntGgCK5RsAiyEdAIw5HQCN5RsAjmkQAI/VGgCQhRsAkU0PAJJFDwCTXQ8AlEUPAJVNDwCWRQ8Al30PAJhFDwCZTQ8AmkUPAJtpGwCcQQ8AnUEPAJ5BDwCfQQ8AoMEPAKHBDwCiwQ8Ao8EPAKS5CwCluQsApqkLAKfNDwCo9Q8Aqf0PAKr1DwCrzQ8ArNkPAK3ZDwCuyQ8Ar8kPALC5DwCxuQ8AsmkPALNpDwC0YQ8AtWEPALY5DwC3OQ8AuBEPALkRDwC66QEAu+kBALz5AQC9+QEAvukBAL/pAQD0BQCA9wUAgPoFAID9BQCAAAYAgCAGAIDhBACAgAUAgNMFAIAOBgCANAYAgEsGAIBoBgCAfwYAgJYGAIDdAwCA9gMAgA8EAIASBwCAQQgAgD4IAIA/BwCAOSQAgHIkAICjJACAyCQAgLkmAIDEJgCAyCYAgMwmAIDQJgCALygAgG4oAICWKACAmigAgL8oAIDHKACA4ygAgPUoAID5KACA/SgAgLrp0wAVKQCAMCkAgEspAIA9JACASiQAgFckAIBkJACAdiQAgIMkAICVJACApyQAgLckAIDMJACA1iQAgOQkAIDuJACA+yQAgAwlAIAWJQCAbyUAgHYlAIAkJQCAgBkDAIEZAwCCKQMAgykDAIQ5AwCFOQMAhikDAIcpAwCIGQMAiRkDAIppAwCLaQMAjHkDAI15AwCOaQMAj2kDAJAZAwCRGQMAkgEEAJMtAwCUNQMAlVUGAJZdBgCXVQYAmG0GAJl1BgCafQYAm3UGAJxtBgCdNQYAnj0GAJ81BgCgzQYAodUGAKLdBgCj1QYApPkDAKX5AwCm6QMAp+kDAKjZAwCp+QYAqikGAKspBgCsOQYArTkGAK7FAwCvPQMAsEUDALFNAwCyRQMAs10DALRFAwC1TQMAtkUDALd9AwC4SQMAuUkDALpZAwC7fQYAvGUGAL1tBgC+ZQYAgCUAgKkVDwCoAQ8Aq00PAKpNDwCtRQ8ArEUPAK+hDQCuqQ0AoXULAKBhCwCj7QsAoqkLAKXlCwCk5QsApzkPAKZZCAC5oQ0AuJkNALuhDQC6qQ0AvaENALy5DQAxJQCAvqkNALGhDQCw2Q0As6ENALKpDQC1oQ0AtLkNALehDQC2qQ0AOCUAgEglAIBbJQCAsiUAgLwlAICRJQCAoSUAgNAlAICB7Q0AgO0NAIP9DQCC/Q0Ahe0NAITtDQCH2Q0AhiEYAJlNDQCYTQ0Am1ENAJpdDQCdeQ0AnHUNAJ9pDQCecQ0AkYkNAJCBDQCTmQ0AkoENAJWJDQCUgQ0Al30NAJaBDQDgJACAICUAgI0lAIDMJQCA3iUAgAgmAIAtJgCAQiYAgPAlAID6JQCADCYAgBkmAIAxJgCATiYAgFgmAIB2JgCASiYAgGYmAIBuJgCAgCYAgIwmAICUJgCAoyYAgN4mAICcJgCAsiYAgKcmAIC9JgCA1CYAgOImAIABJwCAEScAgBsnAIBPJwCAkicAgOcnAIBPKQCAXSkAgGEpAIBlKQCA8CYAgC4nAIA+JwCASCcAgCMnAIBTJwCAYycAgH4nAIBwJwCAlicAgMInAIDJJwCApicAgNMnAIDdJwCAtCcAgBgoAIAKKACA6ycAgCUoAIDyJwCA/CcAgDMoAIBAKACASigAgFQoAIBeKACAcigAgH8oAICGKACAnigAgKUoAICyKACAyygAgNUoAIDnKACAASkAgA4pAIAZKQCAIykAgDQpAIA7KQCAUykAgMMDAIDmBACAhQUAgNgFAIATBgCAOQYAgFAGAIBtBgCAhAYAgJsGAIDjAwCA/AMAgBUEAIAoBACAOwQAgE4EAIBhBACAdAQAgIcEAICaBACAAAUAgA8FAIAeBQCALQUAgDwFAIBjCACAJAgAgMEGAID8BwCAHQkAgOMoEwAzCQCAKggAgC0IAIAxCACAJAcAgNwuAIDKMACA2S0AgLswAIBFMQCAJwkAgO/sEwAGCQCA3A0AgM8IAICDCACAMQcAgEwHAID8BgCACggAgJQIAIAqCQCACQkAgOANAIDsDQCA2wgAgJkIAIAVBwCAhggAgFUHAID/BgCApgcAgJEkAIDwDQCA4ggAgCcIAICcCACAWAgAgBUJAID0DQCA5QgAgBQIAICfCACA6AgAgBcIAIDJCACAoggAgOwIAIAbCACAzAgAgKYIAID3CACA/QgAgIgHAICKCACAWQcAgAMHAIA9CQCAQQkAgEkJAIA2CQCAGAkAgPgNAID0CACALQkAgAwJAIDkDQCA0ggAgI4IAIBdBwCAMAkAgA8JAIDoDQCA1QgAgJEIAIBgBwCArQgAgGMHAIDjSBIA4xQSAOP4EwDjuBMA4+wSAOOgEgDjbBIA43gSAO/ADQDv2A0A73QSAO9QEgDvqBIA79wSAO8oEwDvIBMA6QcAgMwGAIAOCACAEQgAgNgGAIDUBgCAIQgAgAcHAIBnCACADAcAgHYIAIA0BwCANwcAgKoIAIC2CACAuQgAgOPYEADjoBAA46AQAON0EQDjNBAA4wgQAOPkEADj9BAA77wQAO/gEADvzBAA7zgQAO8QEADvcBAA73AQAO9MEADjhBMA4+gTAOMwEADjEBAA42ATAONAEwDjpBMA47QTAO/IEwDvtBMA75gTAO98EwDvXBMA70wTAO8UEwDv6BAAgO08AIH1PACC/TwAg/U8AITtPACFFT0Ahh09AIcVPQCILT0AiTU9AIo9PQCLNT0AjC09AI0VPQCOHT0AjxU9AJBtPQCRdT0Akn09AJN1PQCUbT0AlRU9AJYdPQCXFT0AmC09AJk1PQCaPT0AmzU9AJwtPQCdFT0Anh09AJ8VPQCg7T0AofU9AKL9PQCj9T0ApO09AKUVPQCmHT0ApxU9AKgtPQCpNT0Aqj09AKs1PQCsLT0ArRU9AK4dPQCvFT0AsG09ALF1PQCyfT0As3U9ALRtPQC1FT0AthE9ALcRPQC4MT0AuTE9ALoxPQC7MT0AvBE9AL0RPQC+ET0AvxE9AIDxPACB/TwAgvU8AIMNPwCEFT8AhR0/AIYVPwCHDT8AiDU/AIk9PwCKNT8Aiw0/AIwVPwCNHT8AjhU/AI8NPwCQdT8AkX0/AJJ1PwCTDT8AlBU/AJUZPwCWCT8Alwk/AJg5PwCZOT8Amgk/AJsJPwCcGT8AnRk/AJ4JPwCfCT8AoPk/AKH5PwCiCT8Aowk/AKQZPwClGT8Apgk/AKcJPwCoOT8AqTk/AKoJPwCrCT8ArBk/AK0ZPwCuCT8Arwk/ALB5PwCxeT8Asgk/ALMJPwC0GT8AtRk/ALYJPwC3CT8AuDk/ALk5PwC6CT8Auwk/ALwZPwC9GT8Avgk/AL8JPwCA+TwAgfk8AIJJPQCDST0AhFk9AIVZPQCGST0Ah0k9AIh5PQCJeT0Aikk9AItJPQCMWT0AjVk9AI5JPQCPST0AkDk9AJE5PQCSAQQAk00GAJRVBgCVXQYAllUGAJdNBgCYdQYAmX0GAJp1BgCbTQYAnFUGAJ1dBgCeVQYAn00GAKC1BgChvQYAorUGAKPNBgCk1QYApd0GAKbVBgCnzQYAqPUGAKn9BgCq9QYAq80GAKzVBgCt3QYArtUGAK/NBgCwtQYAsb0GALK1BgCzTQYAtFUGALVdBgC2VQYAt00GALh1BgC5fQYAunUGALtNBgC8VQYAvV0GAL5VBgC/TQYArH0/AK2lPwCurT8Ar6U/AKh9PwCpZT8Aqm0/AKtlPwCkHT8ApUU/AKZNPwCnRT8AoB0/AKEFPwCiDT8AowU/ALydPwC9pT8Avq0/AL+lPwC4nT8AuYU/ALqNPwC7hT8AtN0/ALWlPwC2rT8At6U/ALDdPwCxxT8Ass0/ALPFPwCMZToAjW06AI5lOgCPfToAiEU6AIlNOgCKRToAi306AIRlOgCFbToAhmU6AId9OgCABToAgQ06AIIFOgCDfToAnF04AJ3lPwCe7T8An+U/AJhdOACZRTgAmk04AJtFOACUuTgAlWU4AJZtOACXZTgAkAU6AJENOgCSBToAkwE5AMAIAIDYCACA3ggAgPAIAIB2BwCAIgkAgHkHAICBBwCAVAkAgJ0HAIDLBwCAvQcAgMQGAIDcBACAewUAgM4FAIAJBgCALwYAgEYGAIBjBgCAegYAgJEGAIDXAwCA8AMAgAkEAIAiBACANQQAgEgEAIBbBACAbgQAgIEEAICUBACA+gQAgAkFAIAYBQCAJwUAgDYFAIBFBQCATgUAgFcFAIBgBQCAaQUAgJUFAICeBQCAXQgAgFYOAIBZDgCAOjoAgKwKAIAVCwCANjoAgD46AICcGQAAnRkAAJ45AACfOQAA4wwAgEI6AIB6NwCA8TAAgKI3AIBaMgCAxSoAgLksAICaMDUA7C0AgB0tAIDoLQCA1y8AgJ+ENQDSMwCAnUQpAGI1AICaNgCA1jYAgAo3AIAeOACAdjEAgAIyAICuMgCARjMAgGI2AIBGOACAcjkAgOkqAICNLACAijEAgNIyAICWNgCAwjkAgJQuAIB6MgCAhjYAgBo3AIALMACAvjUAgLSAGgC1hBkAtojmALeM5ACwABwAsZQeALIAGACznBsAvADsAL2k7wC+qO4Av6TtALgA4AC5tOMAurjiALu84QCkwAAApQAMAKbIDgCnAAgA4jYAgAcvAIAFMQCArXwDAKwAEACt5BMArugSAK9gEQCo8AoAqRwJAKr4FgCr/BQAGjIAgB4zAIAqOACAKSsAgMErAIAtLACAczAAgIIxAIDOMgCA8jMAgI42AICmNgCAyjcAgO44AICiOQCAvjkAgC40AIBuNACAvAgAgCY1AIBGNgCAejgAgE43AIChLQCAIy8AgN40AICeNQCAAjMAgDY0AICaNwCA5jgAgJ0tAIBwLgCAejEAgC4yAIBiMgCAFjUAgD41AICmOACAKSwAgJwAAACqNQCAzSsAgMkrAICaNACAKjUAgF42AICuOACAajcAgA8wAIBaNwCA0SoAgEQuAIB7LwCAMjMAgLIzAIBNLACAPjQAgDkrAIBfLwCAsSoAgO4xAICLMACAEjUAgIDpAwCB6QMAgjkvAIP9AwCE5QMAhe0DAIblAwCHfS4AiEEuAIkhAgCKeS8AiyUCAIw9AgCNJQIAjiECAI8dAgCQZQIAkW0CAJJlAgCTfQIAlGUCAJVtAgCWZQIAlx0CAJglAgCZLQIAmiUCAJs9AgCcJQIAnS0CAJ4lAgCfHQIAoOUCAKHtAgCi5QIAo/0CAKTlAgCl7QIApuUCAKdNAgCodQIAqX0CAKqpAQCrqQEArLkBAK25AQCuqQEAr6kBALDZAQCx2QEAsukBALPpAQC0eSIAtf0BALb1AQC37QEAuNUBALndAQC61QEAu60BALy1AQC9uQEAvqkBAL+pAQChLACAjS0AgP4zAIBmNgCAPjcAgLoxAIDmMQCAHzAAgB42AIA/MACArjMAgAUrAICBKwCAxSsAgFYxAID+NACA9jUAgEo3AIBaOACANSwAgOksAIAXLwCApzAAgH4yAIBCNACAljgAgHo5AIDOOQCA5jkAgOkwAICmMQCA7jcAgOMuAIC/LwCA2y8AgGswAIBuMgCAujIAgGozAICONACAMjUAgJY1AIDeNwCAbjYAgAY4AIB+OACA6SsAgBUsAID9LACAqjIAgPY2AIADLwCAcy8AgDcwAICyMQCA2jQAgCYzAIAVKwCAWS0AgKguAIB/LwCAQjMAgF4zAIBuNQCAgFEBAIEBKgCCXQEAg1UBAIRNAQCFdQEAhn0BAId1AQCITQEAiVUBAIqdKwCLWQEAjEkBAI1JAQCOuQEAj7kBAJDJAQCRyQEAktkBAJPZAQCUyQEAlckBAJb5AQCX+QEAmMkBAJnJAQCa2QEAm9kBAJzJAQCdyQEAnrkBAJ+5AQCgSQEAoZUBAKJFAQCjXQEApEUBAKVNAQCmRQEAp30BAKhFAQCpTQEAqnkPAKtBAQCsQQEArUEBAK5BAQCvQQEAsMEDALHBAwCywQMAs8EDALTBAwC1wQMAtsEDALfBAwC4wQMAucEDALrBAwC7wQMAvMEDAL3BAwC+wQMAv8kMAI41AIBiOACA4jgAgPI4AIAuOQCALSsAgII0AIBOOACAyjgAgJcvAIDxKgCAUSsAgEguAIBoLgCAlzAAgMYyAIDOMwCAejYAgBo4AIDZMACAojgAgA0sAIAlMQCAMTEAgBIyAIBKMgCATjMAgKozAIAqNACADjUAgDo5AIDrLwCAsjgAgEErAICMLgCAMjIAgOI3AIBPLwCAny8AgDkxAIC6OACA8SsAgNksAIB4LgCAwjAAgBUxAIBiMQCA9jEAgEozAIC+MwCAWjUAgPo2AIAGNwCA1jgAgF0sAIBOMgCA3SwAgMoyAIBuMwCAijYAgL44AICqOQCA0jkAgC0xAICxOSMAsBEDALMVAwCyFQMAtTUDALQ1AwC3NQMAtjUDALkVAwC4FQMAuxUDALoVAwC9dQMAvHUDAL91AwC+dQMAoZkNAKCRDQCjqQ0AopENAKW5DQCksQ0Ap6kNAKaxDQCpmQ0AqJENAKtpAwCqkQ0ArXkDAKxxAwCvaQMArnEDAJEZDQCQEQ0Aky0NAJIRDQCVPQ0AlD0NAJctDQCWLQ0AmR0NAJgdDQCbbQ0Amm0NAJ15DQCcgQ4An2kNAJ5xDQCBmQ0AgAkjAIOpDQCCkQ0AhbkNAISxDQCHqQ0AhrENAImZDQCIkQ0Ai2kNAIqRDQCNeQ0AjHENAI9pDQCOcQ0AKjIAgMY1AIDGNACA6jQAgBozAICiMgCAZjcAgA0rAIAuNgCA9SsAgOUrAIDzLgCAEzAAgPY0AIA0LgCABjIAgOUwAIDqNwCAqjgAgA8vAIBhKwCANS0AgIktAIDVMACA0SsAgCIzAIDmMwCASjQAgGY0AIBqNACAfjQAgPo4AIDuNACAkjYAgFY3AIAKOACANjgAgE45AIBSOQCAVjkAgLo5AIAuOACAxjgAgDErAIBVKwCAaSsAgCUsAIAxLACAcSwAgCUtAIBBLQCASS0AgIUtAICRLQCAdC4AgIsvAICzLwCAuy8AgJH4EADTLwCAfzAAgK8wAIDdMACAWjEAgIApAQCBKQEAgjkBAIM5AQCEKQEAhSkBAIZZAQCHWQEAiNkoAIltAQCKKSUAi2EBAIxhAQCNYQEAHjIAgDoyAICQGQEAajIAgJIVAQC+MgCA3jIAgJU1AQCWPQEAlzUBAJgNAQCZFQEAmh0BAJsVAQCcDQEAnfUBAJ7dKABSMwCAoAUBADI0AICiAQEAVjQAgFI0AIClGQEApgkBAFo0AIBeNACAdjQAgKo9AQCrNQEArC0BAK0VAQCuHQEArxUBALBtAQCxdQEAsn0BALN1AQC0bQEAtRUBALYdAQC3FQEAuC0BALk1AQC6PQEAuzUBALzZLgC9KQEAvhkBAL8ZAQC6eR4Au3keALjNAgC5eR4AvpUeAL+dHgC8QQIAvZ0eALJ9HgCzRR4AsH0eALF1HgC2XR4At0UeALRdHgC1VR4AqgUeAKsNHgCodR4AqQ0eAHo0AICeNACArBUeAK0NHgCiSR4Ao0keAKBJHgChSR4ApkkeAKf5AgCkSR4ApUkeAJqNHgCblR4AmI0eAJmFHgCeiR4An4keAJyNHgCdhR4AkgUDAJP1AACQCQMAkY05AJaxHgCXFQYAlO0AAJUBHACKvQMAi0EDAIiFAwCJnQMAjkEDAI9JAwCMyTkAjVEDAIIVAgCDHQIAgAUCAIEdAgCGzQMAh7EDAIQFAgCFxQMAs/kFALLxBQCx+QUAsOEFALeZKgC2EQMAtRkDALThBQC7NQMAujUDALklAwC4JQMAvxUDAL4VAwC9JQMAvCUDAKP9BQCi/QUAof0FAKD9BQCnnQUApp0FAKWdBQCknQUAq7kFAKqxBQCpJScAqL0FAK+ZBQCukQUArZkFAKyhBQCTAQUAkvkFAJF1OQCQ9QUAlwEFAJYZBQCVEQUAlBkFAJt5CQCaOQUAmTEFAJg5BQCfHQUAnh0FAJ0dBQCcHQUAg4kFAIKBBQCBiQUAgPEFAIeFBQCGhQUAhZUFAISBJgCLhQUAioUFAIm1BQCItQUAj4UFAI6FBQCNlQUAjJUFAM40AIA6NQCAQjUAgFY1AIB+NQCAzjUAgAI2AIBqNgCAEjcAgCo3AIBeNwCAYjcAgKY3AICqNwCAAjgAgNo4AIAeOQCANjkAgIMvAICQ6gCA5jUAgLkqAIC9KwCAfSsAgCUrAIBlKwCAkSsAgCEsAIA9LACAES0AgCEtAIA9LQCAmS0AgOQtAIDwLQCADC4AgBwuAIALLwCAEy8AgEMvAIBjLwCAky8AgKsvAICbLwCAry8AgO8vAIBHMACAUzAAgFswAICDMACACTEAgB0xAIBeMgCAVjIAgIYyAIAWNACA4jIAgBYzAIBiMwCAfjMAgKIzAIDGMwCAyjMAgOozAICAjQEAgZUBAIKdAQCDlQEAhI0BAIW1AQCGvQEAh7UBAIiNAQCJwR0AipkBAIvBHQCMhQEAjY0BAI6FAQCP/QEAkIUBAJEZHQCSkRQAk4UBAJSdAQCViTIAlk0ZAJc9GwCYsQEAmbEBAJotHACbtQEAnD0cAJ2pAQCemQEAn5kBAKDlHQChbQEAomUBAKN9AQCkZQEApW0BAKbxHQCnYQEAqKEDAKmhAwCqoQMAq6EDAKyhAwCttQEArq0DAK+lAwCwYRkAsdkDALLZAQCz7QMAtPUDALX9AwC29QMAt+0DALjFAQC50QMAumEdALvVAwC82QEAvT0XAL7FAwC/0QEA+jMAgA40AIAKNACAOjQAgLY0AIDmNACAHjUAgE41AIAyNgCAWjYAgM42AIAWNwCAIjcAgEI3AIBGNwCAUjcAgG43AIDmNwCAFjgAgEo4AIBqOACAtjgAgA45AIAqOQCAijkAgCfqAIAi6gCAVOoAgOEpAIAJKgCADSoAgNbqAIAD6wCAe+sAgBY6AIAmOgCARwgAgFIIAIBVCACASggAgE4IAIBXCQCA8Q4AgOIOAIDnDgCA9g4AgOwOAICyNACASw8AgMoPAICBDwCALw8AgFoPAIBnDwCAbw8AgJ0PAIDCDwCAuA8AgL0PAICqDwCAsQ8AgP4OAIADDwCACA8AgIBBAQCBMQMAgk0BAINFAQCEXQEAhUUBAIZNAQCHIQMAiF0fAIl9AQCKaQMAi3EBAIx1AwCNVQEAjlk6AI9ZAQCQKQEAkSkBAJI5AQCTOQEAlCkBAJUpAQCW2QEAl9kBAJjpAQCZ6QEAFQ8AgCIPAIAqDwCAMg8AgDwPAIBBDwCARg8AgFAPAIBVDwCAXQ8AgGoPAIByDwCAdw8AgHwPAICEDwCAiQ8AgJMPAICYDwCAoA8AgKUPAIDFDwCANw8AgBoPAIBiDwCAjg8AgA0PAIDdFgCA5hYAgOkWAIDvFgCA4xYAgOwWAIDgFgCAExcAgBYXAID1FgCA8hYAgPgWAICAmQcAgZkHAPsWAICDrQcAhLUHAAQXAICGsQcAh7EHAIiRBwCJkQcAipEHAIuRBwCM8QcAjfEHAI7xBwCP8QcAkJEHAJGVBwCSnQcAk5kHAJSFBwCVgQcAloEHAJeFBwCYuQcAmb0HAJq1BwCbsQcAnK0HAJ2pBwCemQcAn50HAKBhBwChZQcAom0HAKNpBwCkdQcApXEHAKZxBwCndQcAqEkHAKlNBwCqRQcAq0EHAKxdBwCtWQcArkkHAK9NBwCwMQcAsTUHALI9BwCzOQcAtCUHALUhBwC2IQcAtyUHALgZBwC5HQcAuhUHALsRBwC8DQcAvQkHAL7xAAC/9QAAgAkBAIENAQCCHQEAgxkBAITZAACF3QAAhtUAAIfRAACI8QAAifUAAIr9AACL+QAAjOkAAI3tAACO5QAAj+EAAJCdAACRmQAAkq0AAJOpAACUtQAAlbEAAJaxAACXtQAAmIkAAJmNAACahQAAm4EAAJydAACdmQAAnokAAJ+NAACgdQAAoXEAAKJ9AACjeQAApGlQAqVtUAKmYQAAp2UAAKhZAACpXQAAqlUAAKtRAACsTQAArUkAAK49AwCvOQMAsClQArEtUAIBFwCABxcAgP4WAIANFwCAChcAgBkXAIDZXFICHxcAgCUXAIAiFwCAKBcAgCsXAIA0FwCALhcAgKOhAACipQAAoZEAAKCVAACntQAAprEAAKW9AACkuQAAq40AAKqJAACpgQAAqIUAAK+FAACugQAArYkAAKyNAACz/QAAsvkAALHxAACw9QAAt5kAALadAAC1nQAAtJkAALutAAC6qQAAuaUAALilAAC/ZQEAvmEBAL1tAQC8aQEAHBcAgFcXAIBAFwCAPRcAgEgXAIBOFwCAOhcAgNksUQJLFwCAVBcAgHkWAIDhDwCAMRAAgA4QAIAiEACAHRAAgJNBAAAnEACALBAAgBMQAICXWQAAllUAAJVZAACUXQAAm3EAAJppAACZZQAAmGUAAJ9lAACeYQAAnTFTApxtAAC4gQQAuYEEALqBBAC7gQQAvIEEAFEXAIC+jQQA5g8AgLDdBQCxTQQAskUEALNdBAC0RQQAtU0EALZFBADrDwCAqKEFAKntQQCqrQUAq6UFAKy9BQCtpQUArq0FAK+lBQCgqQUAoZFBAKKpQACjoQUApKEFAKWhBQCmoQUAp6EFAP8PAIAYEACAWBAAgF0QAIBpEACAnVUFAH8QAICfWQUAjhAAgJMQAICeEACAkwUFAJQdBQCVBQUAlg0FAJcFBQC4EACAyxAAgO8QAIAhEQCAJhEAgC4RAIA9EQCATBEAgIBxBQCBcQUAgnEFAINxBQCEUQUAhVEFAIZdBQBREQCAWREAgHwRAICjEQCArxEAgM8RAIDUEQCA2REAgBMSAIAmEgCAMhIAgEoSAIDEEgCAGhMAgDMTAIA4EwCASxMAgFwTAIBuEwCAcxMAgJoTAICiEwCAtxMAgN4TAIDjEwCAPRQAgEIUAIBHFACAUxQAgF8UAIBkFACAbBQAgHgUAICSFACAlxQAgJ8UAICkFACAqRQAgK4UAICzFACAuBQAgMsUAIDQFACA7BQAgAYVAIAgFQCALBUAgEQVAIBJFQCAVhUAgHcVAICaFQCAtBUAgMAVAIDFFQCAzRUAgO4VAIAIFgCAFxYAgDQWAIA5FgCAQRYAgEYWAIBZFgCAXhYAgICtAQCBtQEAgr0BAIO1AQCErQEAhdUBAIbdAQCH1QEAiO0BAIn1AQCK/QEAi/UBAIztAQCN1QEAjt0BAI/VAQCQrQEAkbUBAJK9AQCTtQEAlK0BAJVVAwCWXQMAl1UDAJhtAwCZdQMAmn0DAJt1AwCcbQMAnVUDAJ5dAwCfVQMAoK0DAKG1AwCivQMAo7UDAKStAwCl1QMAphkOAKfZAwCobQ8AqSEOAKrhAwCr4QMArCkOAK3lAwCuGQ4ArxkOALCVAwCxnQMAsgEOALORAwC0HQ4AtQUOALa5AwC3uQMAuDkOALmNAwC6NQ4AuxEOALyBAQC9gQEAvnkBAL95AQCEFgCAkBYAgJwWAICrFgCAyBYAgM0WAIDuEQCA/xEAgHwWAICBAACAiwAAgJUAAICfAACAqQAAgLMAAID1DwCA+g8AgAQQAIB1EACAehAAgIQQAIDlEACA6hAAgBcRAIAzEQCAOBEAgEIRAIBRFQCADRYAgBIWAIAqFgCAoRYAgKYWAIC+FgCA8A8AgAkQAICJEACAHBEAgNcSAIA/FQCALxYAgGMWAIDDFgCARxEAgGQSAICfEgCAshIAgBEUAIAdFACAKRQAgI0TAICSEwCA0RMAgNYTAID9EwCAAhQAgGkSAIBuEgCAtxIAgLwSAIDCEQCAxxEAgJYRAICbEQCApD0DAKVFAwCmTQMAp0UDAKA9AwChJQMAoi0DAKMlAwCsfQMArUUDAK5NAwCvRQMAqH0DAKllAwCqbQMAq2UDALQ9AwC1xQMAts0DALfFAwCwPQMAsSUDALItAwCzJQMAvP0DAL3FAwC+zQMAv8UDALj9AwC55QMAuu0DALvlAwCEBQwAhQ0MAIYFDACHHQwAgI0MAIGpDACCGQwAg1ENAIxhDACNYQwAjmEMAI9hDACIKQwAiRUMAIodDACLFQwAlD0MAJXFAwCWzQMAl8UDAJABDACRAQwAkgEMAJMBDACc/QMAncUDAJ7NAwCfxQMAmP0DAJnlAwCa7QMAm+UDAIBpBACBaQQAgnEEAINxBACEnQQAhYUEAIaNBACHhQQAiL0EAImNBACKhQQAi50EAIyFBACNqQYAjvkEAI/5BACQiQQAkYkEAJKRBACTkQQAlLEEAJWxBACW+QYAl60EAJiVBACZwQYAmmkGAJtpBgCceQYAnXkGAJ7RBgCf/QsAoA0GAKEdCwCiGQYAo0ULAKQFBgClTQsApjUGAKe1BACoEQYAqREGAKoRBgCrNQQArC0EAK0BBACuXQQArx0GALDNBgCxbQYAsnUGALMNBgC0FQYAtR0GALYVBgC3DQYAuDUGALk9BgC6NQYAuw0GALwVBgC9HQYAvhUGAL8NBgCA9QcAgf0HAIL1BwCD9QAAhO0AAIURAwCGEQMAhxEDAIgxAwCJMQMAijEDAIsxAwCMhQcAjRUDAI4dAwCPFQMAkG0DAJGNBwCShQcAk50HAJSFBwCVjQcAloUHAJe9BwCYhQcAmY0HAJqFBwCbnQcAnIUHAJ2NBwCehQcAn4UAAKB9AAChgQMAooEDAKOBAwCkgQMApYEDAKaBAwCngQMAqBUHAKmFAwCqjQMAq4UDAKydAwCtoQMArqEDAK+hAwCwdQcAsXUHALJxBwCzhQUAtM0FALX1BQC2/QUAt8kDALj5AwC5+QMAuqEFALuhBQC8wQMAvcUDAN4RAIDjEQCAhJz7ACYTAIArEwCAYRMAgGYTAIB2EgCAghIAgJUSAICaEgCARRIAgNwSAIBXEwCASxAAgKMQAIC9EACAxBAAgJB1AACRfQAAknEAAJNxAACUAfwAlVX+AJZd/gCXVf4AmG3+AJlp/gCaef4Am3n+AJxp/gCdaf4Anln+AJ9Z/gCgpf4Aoa3+AKKl/gCjof4ApKH+AKWl/gCmrf4Ap6X+AKiZ/gCpmf4Aqun+AKvt/gCs9f4ArfH+AK7x/gCv8f4AsI3+ALGV/gCymf4As5n+ALSJ/gC1if4Atrn+ALe9/gC4hf4AuY3+ALqF/gC7nf4AvIX+AL2B/gC+gf4Av4H+AKbZCACnBQcApMEIAKWZBQCi0QgAo9EIAKCJBQChtQgArgEHAK8BBwCsMQcArTEHAKo9BwCrJQcAqD0HAKk1BwC2fQcAtwUHALR9BwC1dQcAsskFALNlBwCwcQcAsXEHAL4BBwC/AQcAvDEHAL0xBwC6IQcAuyEHALg9BwC5MQcAhjkHAIc5BwCELQcAhTkHAIINBwCDNQcAgBEHAIEFBwCOSQcAj0kHAIxNBwCN1QUAisEFAIvBBQCI1QUAiXEHAJbVBQCX2QgAlE0FAJXdBQCSUQUAk9kFAJD5BQCRoQUAnnEIAJ99CACcYQgAnWEIAJpxCACbeQUAmMUIAJl1BQD0EACA+xAAgAIRAICBEQCAuxEAgLQRAIArEgCAGBIAgB8SAIBWEgCATxIAgF0SAIDJEgCAHxMAgIcSAIB7EgCApBIAgKsSAIA9EwCAUBMAgHgTAIB/EwCAhhMAgKcTAIC8EwCAwxMAgOgTAID2EwCA7xMAgEwUAIB9FACAhBQAgAsVAIAZFQCAEhUAgPEUAIAlFQCAMRUAgHwVAICDFQCAkxUAgFsVAIBpFQCAnxUAgKYVAIBiFQCASxYAgFIWAIDzFQCA+hUAgNkVAIDgFQCAIxYAgBwWAICwFgCAbhAAgLEQAICqEACA3hAAgNcQAIAQEQCACREAgI8RAIBeEQCAgIEBAIGBAQCCgQEAg4EBAISdAQCFhQEAhokBAIeJAQCItQEAib0BAIq1AQCLjQEAjJUBAI2dAQCOlQEAj40BAIgRAIA3EgCAkv0BAJP1AQCU7QEAlZUBAJadAQCXlQEAmKkBAJmpAQCauQEAm7kBAJypAQCdrQEAnqUBAJ+dAQCgZQEAoW0BAKJlAQCjfQEApGUBAKVtAQCmZQEAp90AAKjlAACppQMAqq0DAKulAwCsvQMAraUDAK6tAwCvpQMAsN0DALHlAwCy7QMAs+UDALSpAQC1VQEAtvUDALftAwC41QMAud0DALrVAwC7rQMAvM0DAL3BAwC+vQMAv7UDANASAICOEgCARBMAgP8UAIA4FQCAlRYAgIkWAIC3FgCAuRUAgIsUAIABFgCAyhMAgMQUAIDSFQCArRUAgPgUAIC9FACAZREAgKgRAIBwFQCA0BAAgFgUAIBiEACAPhIAgOcVAIATEwCAcRQAgEIQAIA5EACAihUAgOESAID2EQCArhMAgGsWAIDqEgCA8RIAgGwRAIAEEgCApgMAgA0jAIARIwCAoAYAgMcAAIC1BgCAqyMAgK8jAIC5IQCAtSEAgOMHAIB7CQCAfwkAgEEjAICnIwCANSMAgDkjAIAdIwCAISMAgCUjAIApIwCALSMAgDEjAIDbBwCA3wcAgNEAAICATQEAgVEBAIJRAQCDTQEAhE0DAIUhAwCGRQEAh30BANcAAICiAwCAqAMAgN0HAIDTAACA1QAAgL0GAIB5AACABxQAgH0AAICHAACAkQAAgAwUAICbAACAGBQAgKUAAIAkFACArwAAgDAUAIC5AACANRQAgM8PAIBVEACAmBAAgJsQAIArEQCAVhEAgKARAIDMEQCA6BEAgOsRAIDzEQCADRIAgBASAIBzEgCAwRIAgDATAIBrEwCAlxMAgJ8TAICwpQEAsa0BALKlAQCzvQEAtKUBALWtAQC2pQEAt10BALhlAQC5bQEAumUBALt9AQC8ZQEA2xMAgDoUAIBpFACAgAW5AIHhBgCC4QYAg+EGAIThBgCoBgCAswYAgIfpBgCI2QYAifmxAIr1sQCL8bEAjO2xAI31BgCO+QYAj/0GAJDZBgCR2QYAkvWxAJwUAICUiZIClfEGAJb1BgCX9QYAmNkGAJnVsgCa3bIAm6kGAJy5BgCduQYAnqkGAJ+BBgCgoQcAoaEHAKIhsgCjpQcApIUAAKWNAACmQbMA1RQAgKiNBwCplQcAqp0HAKuVBwBOFQCAyhUAgDYQAIA+FgCAsP0HALGFBwCyjQcAaBYAgLSZBwCBFgCAtpUHALeNBwC4tQcAub0HALq1BwC7jQcAvJUHAL2dBwC+lQcAv40HAIB1BgCBlaACgpmgAoOZoAKEhaAChb2gAoaxoAKHhaACiLmgAomRoAKKnaACi5mgAoyFoAKNjQEAjoEBAI9FBgCQOQYAkT0GAJIxBgCTMQYAlC0GAJXVBgCW2QYAl90GAJjhBgCZ4QYAmu0GAJvpBgCc9QYAnf0GAJ7xBgCf9QYAoAkGAKEJBgCiBQYAowEGAKQdBgClBQYApgkGAKcNBgCoMQYAqTEGAKo9BgCrNQYArCkGAK0pBgCuJQYArx0GALBhBgCxYQYAsm0GALNpBgC0dQYAtX0GALZxBgC3dQYAuEkGALlJBgC6RQYAu0EGALxdBgC9RQYAvkkGAL9NBgCAsQUAgbEFAIK9BQCDuQUAhKUFAIWtBQCGoQUAh6UFAIiZBQCJmQUAipUFAIuRBQCMjQUAjcEFAI7NBQCPyQUAkLUFAJG9BQCSsQUAk7UFAJSpBQCVqQUAlqUFAJehBQCYnQUAmSkCAJolAgCbIQIAnD0CAJ3pAgCe5QIAn+ECAKAdAgChNQIAojkCAKM9AgCkIQIApSECAKYtAgCnKQIAqBUCAKkZAgCqFQIAqxECAKwNAgCteQIArnUCAK8V8ACwafAAsRECALIdAgCzGQIAtAUCALUhAAC2LQAAtyUAALgZAAC54QEAuu0BALvlAQC8+QEA2BQAgN0UAIC/9YYCp2kNAOIUAIDnFACAzwAAgNkAAICzAwCA4QcAgH0JAID7IgCAzNSFAszghQL/IgCAgSkAgDUkAIBuJACAjSQAgLyZBQC9mQUAvqkFAL+ZvAC4mQUAuZkFALqJBQC7iQUAtKEFALXVsQC23bEAt6kFALCxsgCxzQUAssUFALO9BQCfJACAxCQAgMMoAIDfKACA8SgAgIgmAICFKQCAaSkAgCkkAIAtJACA2WSgAoEJAIDZUKAChAkAgI0JAICKCQCAhwkAgOwhAIDvIgCA9CEAgJhlBQCZEbIA/CEAgNkwoAKUOZEClU0FAJZFBQCXXQUAkGkFAJFpBQCSWQUAk1kFAID9vACB1ZwCgmW8AIPFvACEkbwAhZ28AIalvACHjbwAiK2TAonlvACKKZACi7W8AIwRkAKNlbwAji2wAI/FnAKQ6bwAkcHIAJJBkAKT8Z0ClNW8AJXlvACW4bwAl02QAphlkAKZfZACmrm8AJupCgCcbQ8Anb0KAPMiAICfXQ8AoK0PAKElCgCibQoAo2UKAKQNCgClpQ8ApgXUAKepDwComQ8AqZkPAKopDwCrKQ8ArDkPAK05DwCuKQ8ArykPALBZDwCxndEAspXRALOF1gC0sdEAtbHRALbZ1AC32dQAuOnUALnp1AC6+dQAu/nUALzp1AC96dQAvrnUAL+51ACASdUAgUnVAIJZ1QCDWdUAhEnVAIV90ACGddAAh23QAIhV0ACJXdAAinXVAIut1QCMtdUAjb3VAI611QCPQdAAkMHQAJHB0ACSwdAAk8HQAJTB0ACVwdAAlsHQAJfB0ACYwdAAmc3QAJrF0ACb3dAAnOHVAJ3pDgCe2Q4An9kOAKDV2wChwdkAotnZAKPB2QCkxdkApc3ZAKbF2QCnGdkAqGHZAKlh2QCqydkAq8nZAKzZ2QCt2dkArs3ZAK/B2QCwCdkAsRXZALId2QCzrdoAtB3ZALWx2gC2wdwAt93dALjl3QC59d0Auv3dALut3QC8td0AvaXdAL6t3QDwIQCAgvHaAIPx2gD3IgCA5OgAgIYR2ACHEdgAhOHaAIXh2gCKKdgAiynYAK9AEwClKNoAjinYAI8p2ACMKdgAjSnYAJJh2ACTYdgA6egAgO7oAICWZdgAl23YAJR12ACVbdgAml3YAJst2ADz6ACA8FwCALEw3wCR8AIAnCnYALLQAwCiOQ0Ao1GeAqAlDQChOQ0AplUNAIS8AgCkJQ0ApV0NAKptDQCrAQQAqGENAKlRAwCuuQAAp3UAAKxhDQCtxQIA+OgAgIfMAwDwVAIAzFC6AJHYBACb9NsAkRgCAJk02wCddAQAvh0AAJ9gBQCejAUAjOwCAI2sBAD96ACAvfWKAqghvwCpLb8Aqi2/AKs9vwCsKb8ArVW/AK5RvwCvTb8AoBkIAKGlvQCiIb8AozGzAKQ9vwClJb8Apg2zAKclvwC46bMAuc3LALppswC7uQkAvH0IAL2tCQC+QQwAv50JALA5vwCxhb0Asgm/ALPtywC0Gb8AtQW/ALbtswC3Bb8AiDG9AIkxvQCKrQgAiyW9AIwJCQCNvQgAjiW+AI+JDAAC6QCAgQ0JAIKlDACDUQkAhIEIAIWBCACGmQgAh60MAJhhvQCZYb0Amm0JAJsVnQKcxQ8AnQ28AJ7BDwCfcQkAkBW+AJERnwKSNZ8Ckw2fApQJvgCVCb4AlnG9AJdxvQCCuAQAl6UHALnEAwDwWAIAkUwCAJLIAgCErAQAsD0AAAzpAIAH6QCAvQUAABHpAIDwTAIAuhEAAJEkAgCN5AQAkqwCAJasAgC4uAMAudADAJb4AgCvDQAAFukAgPB4AgCRXAIAlrACAK8FAAAb6QCAIOkAgCnpAIAy6QCAP+kAgIX4AwBM6QCAh4ADAIbAAgBZ6QCAZukAgHPpAICW6QCAuzkAAHzpAICf6QCAiekAgL8dAAC+HQAAvR0AALwhAACVwB0AlMQfAJfIGgCWABgAkSAAAJDUAQCT2B4AkgAcAJ3gEgCcABAAn+gRAJ7sEwCZ8BkAmPQbAJv4FwCaABQAnnEBAJ9xAQCABQAArOkAgM0KAICwDACAXg0AgGQNAIBqDQCAdg0AgHkNAIB8DQCAfw0AgIINAICRDQCAlw0AgJoNAICdDQCAICIAgMcNAIDWDQCA/A0AgP8NAIAODgCAEQ4AgB0OAIAYIgCAMg4AgDUOAIDXFgCAEBcAgNoWAIC4ACwAuYwvALqILgC6AwCAhpwXAMx4vACEmC0AhVwXALcDAIDKAwCAiAAoAIksFADtBACAjAUAgN8FAIAaBgCAQAYAgFcGAIB0BgCAiwYAgDgBAIA8AQCAQAEAgEQBAIBIAQCATAEAgKR9AQBQAQCAonUBAKNlAQCggQEAoYEBALxxugC9kbYAvnG6AL+ltgC48bgAuXW6ALqZzgC7dboAtGG6ALVtugC2eboAt3W6ALAZugCxEboAsgm6ALMFugCsUboArXG2AK5RugCvbboAqNG4AKldugCqRbYAq1G6AKRxlgKlYZYCpnGWAqe9ugCgzZsCofG6AKLJugCjxboAnHmaAp0tugCeDc4An4WWApgJugCZtZYCmjm6AJuJtgCUMboA+CEAgJZpugCXrZYCkHm6AJE1ugCSMboAkwG6AIxJzgCN5bYAjhmaAo+hugCIoboAiUG2AIqhugCLdbYAhAG4AIWFugCGac4Ah4W6AICxugCBvboAgqm6AIOlugCAgbkAgQ27AIIVtwCDAbsAhAG7AIUhtwCGAbsAhz27AIgJuwCJAbsAihm7AIsVuwCMcbsAjX27AI5puwCPZbsAkKG5AJEluwCSyc8AkyW7AJQhuwCVwbcAliG7AJf1twCY6c8AmUW3AJq5mwKbAbsAnLm7AJ31uwCe8bsAn8G7AKARuwChCZQCokm7AKONlwKkCbsApbWXAqY5uwCnibcAqFmbAqkNuwCqLc8Aq6WXAqwNmgKtMbsArgm7AK8FuwCw0ZcCscGXArLRlwKzHbsAtFG5ALXduwC2xbcAt9G7ALjxuwC50bcAuvG7ALvNuwC82bsAvdG7AL7JuwC/xbsAgJmkAIEliAKCqaQAgxmoAFsNAICFvaQAhp3QAIcViAKInYUCiaGkAIqZpACLlaQAjCGIAo0xiAKOIYgCj+2kAJDBpgCRTaQAklWoAJNBpACUQaQAlWGoAJZBpACXfaQAmEmkAJlBpACaWaQAm1WkAJwxpACdPaQAnimkAJ8lpACgYaYAoeWkAKIJ0ACj5aQApOGkAKUBqACm4aQApzWoAKgp0ACphagAqnmEAqvBpACseaQArTWkAK4xpACvAaQAsFGkALFJiwKyCaQAs82IArRJpAC19YgCtnmkALfJqAC4GYQCuU2kALpt0AC75YgCvE2FAr1xpAC+SaQAv0WkAIARiQKBAYkCghGJAoPdpQCEkacAhR2lAFQBAICHEaUAiDGlAIkRqQCKMaUAWAEAgFwBAICNEaUAjgmlAI8FpQCQAaUAkQ2lAJIZpQCTFaUAlLGnAGABAICW2dEAlzWlAJgRpQCZ8akAmhGlAJvFqQCc+dEAZAEAgJ6phQKfEaUAoEmlAKEFpQCiAaUAozGlAKQBpQClGYoCplmlAKediQKoOaUAqYWJAqoJpQCruakArEmFAq0dpQCuPdEAr7WJArB9hAKxQaUAsnmlALN1pQC0wYkCtdGJArbBiQK3DaUAuGGnALntpQBoAQCAu+GlALzhpQC9wakAvuGlAGwBAIC3baYAttWGArUpqgC0hdIAs7mqALJtpgCxjaoAsG2mAL8higK+5aYAvaWJAnABAIC7jaYAdAEAgLm5pgC49aYAeAEAgKZ1pgClbaYAfAEAgIABAICiTaYAhAEAgIgBAICvCaYAruXSAIwBAICsjaQAqymmAKolpgCpMaYAkAEAgJc5pgCWNaYAlQ2mAJQxhwKTmYoCkhHSAJExpgCQZYYCn62mAJ65qgCUAQCAnC2kAJthpgCarYoCmb2KApitigKHfaYAhk2mAIVJpgCEBaYAg72mAIIFhgKB+aoAgFXSAI/1qgCORaYAjcmKAox1pgCL8YoCijWmAIl1iQKIbaYAgCmnAIEhpwCCOacAgzWnAIRRpwCYAQCAhkmnAJwBAIDMSIkCzYiJAoqp0wCLRacAjEGnAI2hqwCOQacAj5WrAJDJ0wBFIwCAkpmHApMhpwCUmacAldWnAJbRpwCX4acAmPGnAJnpiAKaqacAm22LApzppwCdVYsCntmnAJ9pqwCgeYcCoS2nAKIN0wCjhYsCpC2GAqURpwCmKacApyWnAKixiwKpoYsCqrGLAqt9pwCsMaUArb2nAK6lqwCvsacAsNGnALHxqwCy0acAs+2nALT5pwC18acAtumnALflpwC4oacAua2nALq5pwC7tacAvBGlAL2VpwC+edMAv5WnAICRoACBiY8CgsmgAIMNjAKEiaAAhTWMAoa5oACHCawAiNmAAomNoACKrdQAiyWMAoyNgQKNsaAAjomgAI+FoACQUYwCkUGMApJRjAKTnaAAlNGiAJVdoACWRawAl1GgAJhxoACZUawAmnGgAJtNoACcWaAAnVGgAJ5JoACfRaAAoMGgAKHNoACi2aAAo9WgAKRxogCl9aAAphnUAKf1oACo0aAAqTGsAKrRoACrBawArDnUAK2VrACuaYACr9GgALAJoACxRaAAskGgALNxoAC0QaAAtVmPArYZoAC33YwCuHmgALnFjAK6SaAAu/msALwJgAK9XaAAvn3UAL/1jAKAvYACgYGhAIK5oQCDtaEAhAGNAoURjQKGAY0Ch82hAIihowCJLaEAijWtAIshoQCMIaEAjQGtAI4hoQCPHaEAkGmhAJFhoQCSeaEAk3WhAJQRoQCVHaEAlgmhAJcFoQCYgaMAmQWhAJrp1QCbBaEAnAGhAJ3hrQCeAaEAn9WtAKAJ1QChpa0AolmBAqPhoQCkWaEApRWhAKYRoQCnIaEAqDGhAKkpjgKqaaEAq62NAqwpoQCtlY0CrhmhAK+prQCwOYECsW2hALJN1QCzxY0CtG2AArVRoQC2aaEAt2WhALjxjQK54Y0CuvGNArs9oQC8caMAvf2hAL7lrQC/8aEAs2miALKF1gCxaaIAsO2gALe5rgC2baIAtY2uALRtogC7TaIAuvWCArkJrgC4pdYAv42iAL69ogC9uaIAvPWiAKNNogCiWa4AoUGiAKDNoACncaIApk2iAKVtrgCkTaIAq1miAKpVogCpTaIAqEWiAK8pogCuJaIArTGiAKw9ogCTla4AkiWiAJGpjgKQFaIAl5mOApYR1gCVMaIAlGWCApsZogCaFaIAmS2iAJgRgwKfYaIAnq2OAp29jgKcrY4Cg2muAIK9ogCBXa4AgL2iAIe9ogCGBYIChfmuAIRV1gCLXaIAim2iAIlpogCIJaIAj/GOAo41ogCNdY0CjG2iAIARowCBMa8AghGjAIMtowCEOaMAhTGjAIYpowCHJaMAiGGjAIltowCKeaMAi3WjAIzRoQCNVaMAjrnXAI9VowCQMaMAkdGvAJIxowCT5a8AlNnXAJV1rwCWiYMClzGjAJipowCZ5aMAmuGjAJvRowCc4aMAnfmMAp65owCffY8CoBmjAKGljwKiKaMAo5mvAKRpgwKlPaMAph3XAKeVjwKoHYICqSGjAKoZowCrFaMArKGPAq2xjwKuoY8Cr22jALBBoQCxzaMAstWvALPBowC0waMAteGvALbBowC3/aMAuMmjALnBowC62aMAu9WjALyxowC9vaMAvqmjAL+lowBnDQCA0QYAgG0NAIDIBwCAcw0AgA8HAICFDQCAlAcAgIsNAICaBwCAuA0AgH0HAIDKDQCAxQcAgAIOAIBPBwCAFA4AgFIHAIAgDgCAkB0AAOEGAIAPJACA4iUAgCguAICtLACAyS0AgKpVAACrKQAAMjcAgAErAIDGMACAsjIAgAEsAIBTLwCAmSsAgJ8wAIDtKwCAGjUAgI43AICtLQCA5SwAgGYyAIADMACALzAAgA44AIAjMACA+y8AgHI0AICAIa4AgaWsAIJJ2ACDpawAhKGsAIVBoACGoawAh3WgAIhp2ACJxaAAiv0AAIsxxgCM7QAAjdEAAI7VAACPyQAAgCmhAIFNFACCIQEAg+G4AoQ5qgCFOaoAhhG9AodRFACIEQEAidW4AorNrQCLLbsCjGEUAI3ZjQKObRQAj2UUAJB5AQCRubgCkkm9ApNFuwKUDRQAlTUUAJYZAQCXqbgCmF2qAJkBFACaIQEAmwUUAJx5vQKdhbgCnnm7Ap+JuAKggb0CoXm4AqKZCQCjlRQApFmuAKWJFACmmQEAp70UAKipAQCpvbsCqrkBAKuJFACsmRQArZkUAK6JFACviRQAsNkBALEJrgCy6QEAs9W7ArTNuwK17RQAtpW8ArfhFAC4oRQAuaEUALrBoQC7pRQAvNkBAL0ZuAK+0aoAv9GqAL9FFwC+RRcAvTUXALxBvwK7KRcAugm4ArkBuAK4PQIAt+2tALY9AgC1HRcAtB0XALMdFwCyHRcAsR0XALAtAgCvWbgCrk0CAK1pFwCsTQIAq00XAKqdrQCpQRcAqE0KAK40AIDRLACApX0XAKR9FwCjoa4Aom2CAqF9ggKgbYICnzmuAJ41rgCdDa4AnDGPApuZggKaEdoAmTGuAJhljgKXtaIAlgWuAJWJggKUNa4Ak7GCApJ1rgCRNYECkC2uAI99rgCOTa4AjUmuAIwFrgCLva4AigWOAon5ogCIVdoAh0miAIadrgCFfaIAhJ2uAIOZrgCCddoAgZmuAIAdrADMqIQCzUyGAswguQLNTLkCzECOAkYyAIDMmIUCzTyEAswQgwLNUIMCzKCDAs2MgwLMMIACzSSAAswYgALNhIACmjMAgAUsAIAxLQCAiSMAgE0jAIBXIwCAayMAgJMjAIB1IwCAnSMAgGEjAIB/IwCAzPC5As2EuQLMULgCzay7AoDNAACB1QAAgt0AAIPVAACEzQAAhfUAAIb9AACH9QAAiM0AAFcvAIDBLACA1SoAgM0qAIDdKgCAuekAgCErAICQZQAAkW0AAKiIKgA1KwCAPSsAgEUrAIBJKwCATSsAgKIAMACjzDMAoOg9AKHsPACm8DYAp/QoAKQANACl/DUAgFERAIHpiAKCXREAg1URAIQpBACF6b0Chhm4AocVvgKIfREAiUURAIppBACL2b0CjA2vAI1REQCOcQQAj1URAJBJuAKRtb0Ckkm+ApO5vQKUUbgClam9ApZJDACXRREAmKmrAJl5EQCaaQQAm00RAJx5BACdbb4CnmkEAJ9ZEQCgqREAoakRAKK5EQCjuREApIkEAKVZqwCmuQQAp4W+Aqi9vgKpnREAquW5AquREQCs8REArfERAK6RpACv9REAsOkEALEpvQKy4a8As+GvALTZuAK1mREAtukEALctvQK4BagAueW+Arq5EQC7AYgCvKURAL2tEQC+wQQAvwG9AoABuQKBDb8CglUQAINtEACEUQUAheG8AoYlrgCHeRAAiGkFAIlNEACKIbkCi928AowxvwKNwbwCjjm5Ao/BvAKQUQ0AkV0QAJKBqgCTURAAlFEFAJV1EACWUQUAl0W/AphxBQCZQRAAmkEQAJtBEACcQRAAnUEQAJ5hBQCfsaoAoKEFAKGdvwKilb8Co7UQAKTduAKlqRAAptkQAKfZEACoiaUAqe0QAKqBBQCrQbwCrJmuAK2ZrgCusbkCr/EQALDxBQCxNbwCsi2pALPNvwK0gRAAtTmJAraNEAC3hRAAuNkFALkZvAK66bkCu+W/ArytEAC9lRAAvrkFAL8JvAK5La0AuC2tALtFEwC6BboCveG/ArwlBgC/GbwCvvmqALEdEwCwabsCs20TALJtEwC1eRMAtB2mALfVvwK2FQYAqXUTAKh1EwCrhakAqlUGAK1JvAKsdQYAr2ETAK5BvAKhQRMAoGUGAKNxvAKiZQYApVUTAKRlBgCnVRMAplUTAJl1vwKYhbwCm3W/ApqNugKdiRMAnIUOAJ+FEwCeVakAkVW/ApDlBgCTzRMAkpGtAJXZEwCU/QYAl0m/Apa1ugKJmRMAiJETAIs1vwKK9QYAjdm8AozVugKPuRMAjoETAIGtEwCA7boCgxm/AoLdBgCF8bwChBGqAIcVigKGrRMAgD2sAIFhEgCCQQcAg2USAIQZuwKF5b4Chhm9AofpvgKIIbsCidm+AopFEgCLXRIAjSkAgM3pAICOzaoAj8mLApCdiwKRpYsCkrGqAJOxqgCU2akAldmpAJb5qQCX+akAmJWqAJmRiwKatYsCm42LApyJqgCdiaoAnvGpAJ/xqQCgIakAoSGpAKJ9qgCjeYsCpE2LAqV1iwKmYaoAp2GqAKgpqQCpKakAqgmpAKsJqQCsRaoArUGLAq5liwKvXYsCsDmqALE5qgCyQakAs0GpALRxqQC1cakAti2qALcpiwK4PYsCuQWLAroRqgC7EaoAvHmpAL15qQC+WakAv1mpAIKJIwBtKwCAcSsAgI0rAIC+6QCAh5kjAJEpAIB5KwCAyOkAgIu5JACpKwCAifkkAI6VIwCPiSMAsSsAgI2JJACSvSMAESsAgLkrAICR4SMAo+sAgJfFIwCU8SMA4SsAgJkpAICbkSMA+SsAgJndIwD9KwCAnwktAAksAICdjdUAogkjAJ0pAIBBLACAofUjAEUsAICnGSMApCUkAG0sAICq7SQAeSwAgKgdIwCpeSQArhUjAK8JIwCsCSQArQkkALI9IwCJLACAsDEjALFhIwC2VSMAt0UjALRxIwC1XSMAulkjALsRIwCRLACAuV0jAL6JLQCVLACAvI0tANzpAICAuSUAgX0iAIKBIgCDmSIAhK0lAIXZJQCGuSIAh5EiAIiVIgCJ8SUAljIAgIuxJQCMgSUAjYElAI6dIgCPgSIAkLkiAJHpIgCStSIAk9EiAJT5IgCV1SIAlt0iAJfNIgCY+SIAmdUiAJrRIgCbmSIAqSwAgLEsAIDh6QCAvSwAgGUAAACh/SIAogEiAKMZIgDFLACApVklAKY5IgCnESIAqBUiAKlxJQDNLACAqzElAKwBJQCtASUArh0iAK8BIgCwOSIAsWkiALI1IgCzUSIAtHkiALVVIgC2XSIAt00iALh5IgC5VSIAulEiALsZIgD1LACA4SwAgO0sAIDxLACAgI0vAIGlLwCCrS8Ag70vAISlLwCFrS8AhqUvAIfdLwCI5S8Aie0vAIrlLwD5LACAAS0AgAUtAIANLQCAFS0AgJCRLwCRkS8AkpEvAJORLwCUsS8AlbEvAJa1LwCXRTMAmE0zAJlVMwCaPTMAmxkzAJyZMwCdiTMAnlUwAJ9JMACgwTAAockwAKLZMACj1TAApM0wAKX9MACm5TAApzUwAKi1MQCpuTEAqu0xAKuxmgCs0ZYArbE6AK61OgAZLQCAsEGUALHNlgCy1ZoAs8GWALTBlgC14ZoAtsGWALf9lgC4yZYAucGWALrZlgC71ZYAvLGWAL29lgC+qZYAv6WWAMUAAAChfSAAooEgACktAICkrScALS0AgDktAICnkSAAXS0AgKnxJwCqZScAq7EnAKyBJwCtgScArp0gAK+BIACwuSAAsekgALK1IABhLQCAtPkgALXVIAC23SAAt80gAEUtAIC51SAATS0AgLuZIACpLQCAcS0AgHUtAIB5LQCAgDknAIH9IACCASAAgxkgAG0tAICFWScAhjkgAIcRIACIFSAAiXEnAIrlJwCLMScAjAEnAI0BJwCOHSAAjwEgAJA5IACRaSAAkjUgAJNRIACUeSAAlVUgAJZdIACXTSAAmHkgAJlVIACaUSAAmxkgAJyFLgCdBdYAnoEuAJ+BLgCArT8AgbU/AIK9PwCDtT8AhK0/AIW5yACG1T8Ah80/AIj1PwCJ/T8AipnIAIvxPwCMATsAjQE7AI6NyACPOQQAkEkEAJFJBACSWQQAk1UEAJRNBACV3TwAlnkEAJd1BACYWQQAmSEEAJohBACbNdQAnCEEAJ3Z5gCeJQQAnx0EAKDpBACh9QQAos0/AKP1BACkFQQApfnUAKYhyACnIcgAqNHUAKktBACqOQQAq03CAKwtBACtdcgArh0EAK95BACwKQQAsTEEALI9BACzOQQAtC0EALX9BQC2qQUAt6kFALiZBQC5mQUAunkFALtFBQC8AQUAvQEFAL4BBQC/AQUAgC0HAIE1BwCCPQcAgzUHAIQtBwCFqQcAhqUHAIdl1QCILQYAiTEGAIoxBgCLDQYAjPnJAI15BgCOWQYAj1UGAJBpyQCRNQYAkj0GAJM1BgCULQYAlcUGAJZdAwCXVQMAmG0DAJl1AwCafQMAm3UDAJxtAwCdET0AnlkDAJ9ZAwCgqQMAoakDAKK5AwCjuQMApKkDAKWpAwCm2QMAp9kDAKjpAwCp6QMAqvkDAKv9AwCs5QMAre0DAK7lAwCvbcMAsKEDALGhAwCyoQMAs6EDALShAwC1zeYAtq0DALelAwC4yeYAuZkDALppAwC7aQMAvHkDAL15AwC+aQMAv2kDAIAAAACBLQCAfS0AgJUtAIDm6QCAsS0AgLUtAIC9LQCA0S0AgPQtAIDr6QCA8OkAgAAuAIAELgCACC4AgPwtAIAQLgCAoSkAgKUpAIAYLgCAIC4AgPXpAIA8LgCAQC4AgEwuAID66QCAVC4AgFguAIA3LwCAqSkAgGwuAICILgCAhC4AgATqAICQLgCACeoAgJwuAICYLgCAoC4AgLAuAIC0LgCArSkAgMQuAIDMLgCA0C4AgNQuAICxKQCADuoAgLUpAID3LgCA+y4AgP8uAIDV6wCAGOoAgNo1AIAvLwCAuSkAgDvqAIAN6wCAPy8AgEcvAIC9KQCAWy8AgGsvAICqIfQAq7U/AKilPwCpzecArkXwAK+hPwCsSfAArTH0AKJl4gCjvT8AoLk/AKG5PwCmlT8Ap50/AKSlPwClnT8Augk8AG8vAIC4CTwAuQk8AHcvAICHLwCAxSkAgMEpAICy3T8AswU9ALBN7wCx1T8Atn3wALe55AC0HT0AtWk8AB3qAICPLwCAoy8AgKcvAIC3LwCAyy8AgMMvAIDHLwCAgrX7AM8vAICA/T8AgfU/AOMvAIDnLwCA/y8AgAcwAICavT8Am/3NAJi9PwCZtT8Anlk/AJ9ZPwCcWT8AnVk/AJKBPwCTaekAkHnkAJGxPwCWgT8Al4H0AJQh5wCVmT8AFzAAgCswAIAs6gCAJzAAgBswAIAzMACAOzAAgE8wAIAx6gCAVzAAgEoAAABLMACAQzAAgMkpAIBfMACAZzAAgG8wAIBjMACAzSkAgIcwAIA26gCAszAAgPUwAIDRMACA2SkAgNUpAIDRKQCAnSsAgKErAID5MACA4TAAgK41AIA9KgCADTEAgCExAIAZMQCAT+oAgN0pAIA1MQCAKTEAgFIxAIBZ6gCAXjEAgD0xAIBmMQCAajEAgG4xAIByMQCAfjEAgF7qAICGMQCA5SkAgJIxAIBj6gCAljEAgOkpAICiMQCArjEAgL4xAIBo6gCA/+kAgG3qAIDeMQCAcuoAgLgJAQC5CQEAuhkBALsZAQC8CQEAvQkBAL45AQC/OQEAsM3FALE1zACymQ4As5kOALSJDgC1iQ4AtjkBALc5AQCo6dkAqckOAKrZDgCrqcUArMUOAK3NDgCuxQ4Ar/kOAKA1DgChPQ4AojUOAKOxxQCk8Q4ApfEOAKbxDgCn8Q4AmGkPAJlpDwCaeQ8Am3kPAJxpDwCdaQ8Ant0OAJ/NDgCQ+eoAkXEPAJJ9DwCTdQ8AlG0PAJVpDwCWWQ8Al1kPAIh5DwCJeQ8AigkPAIsJDwCMGQ8AjRkPAI4NzACPDQ8AgHkPAIF5DwCCSQ8Ag0kPAIRZDwCFWQ8AhkkPAIdJDwCKUQIAi1ECAIj5xgCJQQIAjnECAI/txgCMQQIAjUECAIIVAgCDHQIAgAUCAIEdAgCGdQIAh30CAIQFAgCFfQIAmsUCAJvNAgCYkc8AmYXaAJ7FAgCfzQIAnNUCAJ3NAgCSDQIAkxUCAJANAgCRBQIAlg0CAJf1AgCUDQIAlQUCAKo9AgCrRQIAqD0CAKk1AgCuXQIAr0UCAKxdAgCtVQIAol3GAKMBAgCgNQIAoQ0CAKYBAgCnxdgApBECAKURAgC6OQIAuzkCALg5AgC5OQIAvtkBAL/ZAQC82QEAvdkBALI9AgCzBQIAsD0CALE1AgC2GQIAtxkCALQdAgC16cIA6jEAgPIxAIDiMQCA/jEAgA4yAIAWMgCAIjIAgCYyAIB36gCACjIAgD4yAIBCMgCA7SkAgFIyAIB86gCANjIAgHIyAICB6gCAhuoAgHYyAICKMgCAgjIAgPEpAICOMgCAnjIAgJoyAICmMgCAw+kAgLYyAICL6gCAwjIAgJXqAIDWMgCA9jIAgJrqAIAKMwCADjMAgJ/qAICk6gCAKjMAgDozAID1KQCAPjMAgPkpAIBWMwCAWjMAgGYzAIByMwCA/SkAgIozAICp6gCApjMAgK7qAIAT6gCAwjMAgLPqAIC4AAAAuOoAgL3qAIABKgCABSoAgMfqAIDC6gCAzOoAgIAB3gCB8QcAgvEHAIPxBwCEFQIAhR0CAIYVAgCHEQIAiCXeAIld3gCKOQIAizkCAIwpAgCNKQIAjhkCAI99ygCQTd4AkWECAJJhAgCT7cEAlH0CAJVlAgCWIcAAl2kCAJhZAgCZMcIAmlUCAJstAgCcNQIAnT0CAJ4xAgCfMQIAoNECAKHRAgCi0QIAo9ECAKTxAgCl8QIApvECAKfxAgCo0QIAqdECAKrRAgCr0QIArDECAK0xAgCuMQIArzECALBRAgCxUQIAslECALNRAgC0cQIAtXECALZxAgC3cQIAuFECALlRAgC6+dwAu1UCALxNAgC9NQIAvj0CAL81AgC+7QYAv/UGALztBgC95QYAuskGALvJBgC4xcsAuckGALbtBgC39QYAtO0GALXlBgCyjQYAs/UGALDR3QCxhQYArvEGAK/xBgCs5QYAreEGAKr1BgCr/QYAqMUGAKn9BgCm9QYAp/0GAKTlBgCl/QYAovUGAKP9BgCg+QYAoZ3dAJ75BgCf+QYAnPkGAJ35BgCa+QYAm/kGAJj5BgCZ+QYAlvkGAJf5BgCUcd0AlfkGAJL9BgCT5QYAkP0GAJH1BgCO/QYAj4UGAIz9BgCN9QYAiuEGAIsB3QCI8QYAifEGAIbBBgCHwQYAhPEGAIXxBgCCkccAg+EGAIDpBgCBxcAAgAAAANHqAIACNACABjQAgBI0AIARKgCAFSoAgNvqAIAmNACAGSoAgODqAIDl6gCA6uoAgJY0AIAdKgCAojQAgKY0AIDv6gCA9OoAgL40AIAhKgCA+eoAgNI0AIDWNACAJSoAgP7qAIDyNACAKSoAgAI1AID6NACACjUAgAjrAIAiNQCALSoAgC41AIA2NQCARjUAgDEqAIAS6wCAF+sAgDUqAIAc6wCAXjUAgCHrAIBqNQCAdjUAgCbrAIAr6wCAkjUAgDDrAICaNQCAQOoAgDkqAICyNQCAtjUAgEEqAIC6NQCAFC4AgDXrAIA66wCAReoAgErqAIDeNQCA9jcAgIDNAQCB1QEAgt0BAIPVAQCEzQEAhfUBAIb9AQCH9QEAiM0BAInVAQCK3QEAi/UJAIzJAQCNyQEAjgEcAI89HwCQRR8AkU0fAJJFHwCTXR8AlEUfAJVNHwCWRR8Al30fAJhBxwCZQR8AmkEfAJtBHwCcQR8AnUEfAJ5BHwCfYd8AoL0fAKHFHwCizR8Ao8UfAKTdHwClxR8Aps0fAKfFHwCo/R8AqcUfAKrNHwCrxR8ArN0fAK3FHwCuzR8Ar8UfALC9HwCxRR8Ask0fALNFHwC0/ckAtVkfALZJHwC3SR8AuHkfALl5HwC6SR8Au8XdALxVHwC9XR8AvlUfAL9NHwAKNgCABjYAgA42AIAZLACAEjYAgBY2AIAaNgCAIjYAgD/rAIAmNgCAOjYAgD42AIAqNgCAQjYAgFY2AIA2NgCASjYAgE42AIBSNgCAROsAgE7rAIBJ6wCASSoAgHI2AIB2NgCAfjYAgGLrAICCNgCAU+sAgE0qAIBRKgCAWOsAgF3rAIBVKgCAojYAgKo2AICuNgCAujYAgLY2AIDCNgCAvjYAgMY2AIDKNgCA0jYAgFkqAIDaNgCA3jYAgF0qAIDuNgCAZ+sAgP42AIACNwCAYSoAgA43AICVKQCAbOsAgHHrAIBlKgCAaSoAgDo3AIB26wCAkjcAgJY3AICuNwCAgLUBAIG9AQCCtQEAg80BAITt9ACF0QEAhtEBAIfRAQCI8QEAifEBAIrxAQCL8QEAjNEBAI3RAQCO0QEAj9EBAJB9wwCRBcMAkl35AJO9AQCUpQEAla0BAJalAQCXXQMAmGUDAJltAwCaZQMAm30DAJxlAwCdbQMAnmUDAJ85wwCgoQMAoaEDAKKhAwCjoQMApKEDAKWhAwCmoQMAp6EDAKjhAwCp4QMAquEDAKvhAwCs4QMAreEDAK7hAwCv4QMAsKEDALGhAwCyoQMAs6EDALShAwC1oQMAtqEDALehAwC4YQMAuWEDALphAwC7YQMAvGEDAL1hAwC+pcMAv6HDALo3AICA6wCA0ukAgMY3AIDCNwCAzjcAgNfpAIDaNwCAhesAgIrrAIAmOACAMjgAgDo4AICP6wCAPjgAgGY4AIByOACAdjgAgG44AICCOACAhjgAgJTrAICSOACAbSoAgJo4AICZ6wCAcSoAgNI4AICkLgCA6jgAgJ7rAICo6wCAdSoAgHkqAIASOQCAresAgH0qAICy6wCAMjkAgLfrAIBKOQCAgSoAgFo5AIBmOQCAbjkAgHY5AICFKgCAvOsAgKY5AICyOQCAiSoAgI0qAIC2OQCAwesAgJEqAIDG6wCAy+sAgNDrAICVKgCA9jkAgPo5AIACOgCACjoAgNrrAICQ1QEAkd0BAJLVAQCT7QEAlPUBAJXB+wCW8QEAl/n7AJjNAQCZ1QEAmt0BAJvVAQCcyfsAnckBAEUqAICPAAAAgNkBAIHZAQCC6QEAg+kBAIT5AQCF+QEAhukBAIfpAQCI2QEAidkBAIoJwQCLrQEAjLUBAI29AQCOtQEAj60BAKAAAAChAAAAogAAAKMAAACkAAAApQAAAKYAAACnAAAAqAAAAKkAAACqAAAAqwAAAKwAAACtAAAArgAAAK8AAACwAAAAsQAAALIAAACzAAAAtAAAALUAAAC2AAAAtwAAALgAAAC5AAAAugAAALsAAAC8AAAAvQAAAL4AAAC/AAAAACAAIMyBACDMgwAgzIQAIMyFACDMhgAgzIcAIMyIACDMiMyAACDMiMyBACDMiM2CACDMigAgzIsAIMyTACDMk8yAACDMk8yBACDMk82CACDMlAAgzJTMgAAgzJTMgQAgzJTNggAgzKcAIMyoACDMswAgzYIAIM2FACDZiwAg2YwAINmM2ZEAINmNACDZjdmRACDZjgAg2Y7ZkQAg2Y8AINmP2ZEAINmQACDZkNmRACDZkQAg2ZHZsAAg2ZIAIOOCmQAg44KaACEAISEAIT8AIgAjACQAJQAmACcAKAAoMSkAKDEwKQAoMTEpACgxMikAKDEzKQAoMTQpACgxNSkAKDE2KQAoMTcpACgxOCkAKDE5KQAoMikAKDIwKQAoMykAKDQpACg1KQAoNikAKDcpACg4KQAoOSkAKEEpAChCKQAoQykAKEQpAChFKQAoRikAKEcpAChIKQAoSSkAKEopAChLKQAoTCkAKE0pAChOKQAoTykAKFApAChRKQAoUikAKFMpAChUKQAoVSkAKFYpAChXKQAoWCkAKFkpAChaKQAoYSkAKGIpAChjKQAoZCkAKGUpAChmKQAoZykAKGgpAChpKQAoaikAKGspAChsKQAobSkAKG4pAChvKQAocCkAKHEpAChyKQAocykAKHQpACh1KQAodikAKHcpACh4KQAoeSkAKHopACjhhIApACjhhIIpACjhhIMpACjhhIUpACjhhIYpACjhhIcpACjhhIkpACjhhIspACjhhIwpACjhhI4pACjhhI8pACjhhJApACjhhJEpACjhhJIpACjkuIApACjkuIMpACjkuIkpACjkuZ0pACjkuowpACjkupQpACjku6MpACjkvIEpACjkvJEpACjlhaspACjlha0pACjlirQpACjljYEpACjljZQpACjlkI0pACjlkbwpACjlm5spACjlnJ8pACjlraYpACjml6UpACjmnIgpACjmnIkpACjmnKgpACjmoKopACjmsLQpACjngaspACjnibkpACjnm6MpACjnpL4pACjnpZ0pACjnpa0pACjoh6opACjoh7MpACjosqEpACjos4cpACjph5EpACjqsIApACjrgpgpACjri6QpACjrnbwpACjrp4gpACjrsJQpACjsgqwpACjslYQpACjsmKTsoIQpACjsmKTtm4QpACjsnpApACjso7wpACjssKgpACjsubQpACjtg4ApACjtjIwpACjtlZgpACkAKgArACwALQAuAC4uAC4uLgAvADAAMCwAMC4AMOKBhDMAMOeCuQAxADEsADEuADEwADEwLgAxMOaXpQAxMOaciAAxMOeCuQAxMQAxMS4AMTHml6UAMTHmnIgAMTHngrkAMTIAMTIuADEy5pelADEy5pyIADEy54K5ADEzADEzLgAxM+aXpQAxM+eCuQAxNAAxNC4AMTTml6UAMTTngrkAMTUAMTUuADE15pelADE154K5ADE2ADE2LgAxNuaXpQAxNueCuQAxNwAxNy4AMTfml6UAMTfngrkAMTgAMTguADE45pelADE454K5ADE5ADE5LgAxOeaXpQAxOeeCuQAx4oGEADHigYQxMAAx4oGEMgAx4oGEMwAx4oGENAAx4oGENQAx4oGENgAx4oGENwAx4oGEOAAx4oGEOQAx5pelADHmnIgAMeeCuQAyADIsADIuADIwADIwLgAyMOaXpQAyMOeCuQAyMQAyMeaXpQAyMeeCuQAyMgAyMuaXpQAyMueCuQAyMwAyM+aXpQAyM+eCuQAyNAAyNOaXpQAyNOeCuQAyNQAyNeaXpQAyNgAyNuaXpQAyNwAyN+aXpQAyOAAyOOaXpQAyOQAyOeaXpQAy4oGEMwAy4oGENQAy5pelADLmnIgAMueCuQAzADMsADMuADMwADMw5pelADMxADMx5pelADMyADMzADM0ADM1ADM2ADM3ADM4ADM5ADPigYQ0ADPigYQ1ADPigYQ4ADPml6UAM+aciAAz54K5ADQANCwANC4ANDAANDEANDIANDMANDQANDUANDYANDcANDgANDkANOKBhDUANOaXpQA05pyIADTngrkANQA1LAA1LgA1MAA14oGENgA14oGEOAA15pelADXmnIgANeeCuQA2ADYsADYuADbml6UANuaciAA254K5ADcANywANy4AN+KBhDgAN+aXpQA35pyIADfngrkAOAA4LAA4LgA45pelADjmnIgAOOeCuQA5ADksADkuADnml6UAOeaciAA554K5ADoAOjo9ADsAPAA9AD09AD09PQA+AD8APyEAPz8AQABBAEFVAEHiiJVtAEIAQnEAQwBDRABDby4AQ+KIlWtnAEQAREoARFoARHoARMW9AETFvgBFAEYARkFYAEcAR0IAR0h6AEdQYQBHeQBIAEhQAEhWAEhnAEh6AEkASUkASUlJAElKAElVAElWAElYAEoASwBLQgBLSwBLTQBMAExKAExURABMagBMwrcATQBNQgBNQwBNRABNSHoATVBhAE1WAE1XAE3OqQBOAE5KAE5qAE5vAE8AUABQSABQUE0AUFBWAFBSAFBURQBQYQBRAFIAUnMAUwBTRABTTQBTUwBTdgBUAFRFTABUSHoAVE0AVQBWAFZJAFZJSQBWSUlJAFbiiJVtAFcAV0MAV1oAV2IAWABYSQBYSUkAWQBaAFsAXABdAF4AXwBgAGEAYS5tLgBhL2MAYS9zAGHKvgBiAGJhcgBjAGMvbwBjL3UAY2FsAGNjAGNkAGNtAGNtMgBjbTMAZABkQgBkYQBkbABkbQBkbTIAZG0zAGR6AGTFvgBlAGVWAGVyZwBmAGZmAGZmaQBmZmwAZmkAZmwAZm0AZwBnYWwAaABoUGEAaGEAaQBpaQBpaWkAaWoAaW4AaXYAaXgAagBrAGtBAGtIegBrUGEAa1YAa1cAa2NhbABrZwBrbABrbQBrbTIAa20zAGt0AGvOqQBsAGxqAGxtAGxuAGxvZwBseABswrcAbQBtMgBtMwBtQQBtVgBtVwBtYgBtZwBtaWwAbWwAbW0AbW0yAG1tMwBtb2wAbXMAbeKIlXMAbeKIlXMyAG4AbkEAbkYAblYAblcAbmoAbm0AbnMAbwBvVgBwAHAubS4AcEEAcEYAcFYAcFcAcGMAcHMAcQByAHJhZAByYWTiiJVzAHJhZOKIlXMyAHMAc3IAc3QAdAB1AHYAdmkAdmlpAHZpaWkAdwB4AHhpAHhpaQB5AHoAewB8AH0AwqIAwqMAwqUAwqYAwqwAwrBDAMKwRgDCtwDDgADDgQDDggDDgwDDhADDhQDDhgDDhwDDiADDiQDDigDDiwDDjADDjQDDjgDDjwDDkQDDkgDDkwDDlADDlQDDlgDDmQDDmgDDmwDDnADDnQDDoADDoQDDogDDowDDpADDpQDDpwDDqADDqQDDqgDDqwDDrADDrQDDrgDDrwDDsADDsQDDsgDDswDDtADDtQDDtgDDuQDDugDDuwDDvADDvQDDvwDEgADEgQDEggDEgwDEhADEhQDEhgDEhwDEiADEiQDEigDEiwDEjADEjQDEjgDEjwDEkgDEkwDElADElQDElgDElwDEmADEmQDEmgDEmwDEnADEnQDEngDEnwDEoADEoQDEogDEowDEpADEpQDEpgDEpwDEqADEqQDEqgDEqwDErADErQDErgDErwDEsADEsQDEtADEtQDEtgDEtwDEuQDEugDEuwDEvADEvQDEvgDFgwDFhADFhQDFhgDFhwDFiADFiwDFjADFjQDFjgDFjwDFkADFkQDFkwDFlADFlQDFlgDFlwDFmADFmQDFmgDFmwDFnADFnQDFngDFnwDFoADFoQDFogDFowDFpADFpQDFqADFqQDFqgDFqwDFrADFrQDFrgDFrwDFsADFsQDFsgDFswDFtADFtQDFtgDFtwDFuADFuQDFugDFuwDFvADFvQDFvgDGjgDGkADGoADGoQDGqwDGrwDGsADHjQDHjgDHjwDHkADHkQDHkgDHkwDHlADHlQDHlgDHlwDHmADHmQDHmgDHmwDHnADHngDHnwDHoADHoQDHogDHowDHpgDHpwDHqADHqQDHqgDHqwDHrADHrQDHrgDHrwDHsADHtADHtQDHuADHuQDHugDHuwDHvADHvQDHvgDHvwDIgADIgQDIggDIgwDIhADIhQDIhgDIhwDIiADIiQDIigDIiwDIjADIjQDIjgDIjwDIkADIkQDIkgDIkwDIlADIlQDIlgDIlwDImADImQDImgDImwDIngDInwDIogDIpgDIpwDIqADIqQDIqgDIqwDIrADIrQDIrgDIrwDIsADIsQDIsgDIswDItwDJkADJkQDJkgDJlADJlQDJmQDJmwDJnADJnwDJoQDJowDJpQDJpgDJqADJqQDJqgDJqwDJrQDJrwDJsADJsQDJsgDJswDJtADJtQDJuADJuQDJuwDKgQDKggDKgwDKiQDKigDKiwDKjADKkADKkQDKkgDKlQDKnQDKnwDKuQDKvG4AzIAAzIEAzIjMgQDMkwDOhgDOiADOiQDOigDOjADOjgDOjwDOkADOkQDOkgDOkwDOlADOlQDOlgDOlwDOmADOmQDOmgDOmwDOnADOnQDOngDOnwDOoADOoQDOowDOpADOpQDOpgDOpwDOqADOqQDOqgDOqwDOrADOrQDOrgDOrwDOsADOsQDOsgDOswDOtADOtQDOtgDOtwDOuADOuQDOugDOuwDOvADOvEEAzrxGAM68VgDOvFcAzrxnAM68bADOvG0AzrxzAM69AM6+AM6/AM+AAM+BAM+CAM+DAM+EAM+FAM+GAM+HAM+IAM+JAM+KAM+LAM+MAM+NAM+OAM+cAM+dANCAANCBANCDANCHANCMANCNANCOANCZANC5ANC9ANGKANGMANGQANGRANGTANGXANGcANGdANGeANG2ANG3ANOBANOCANOQANORANOSANOTANOWANOXANOaANObANOcANOdANOeANOfANOiANOjANOkANOlANOmANOnANOqANOrANOsANOtANOuANOvANOwANOxANOyANOzANO0ANO1ANO4ANO5ANWl1oIA1bTVpQDVtNWrANW01a0A1bTVtgDVvtW2ANeQANeQ1rcA15DWuADXkNa8ANeQ15wA15EA15HWvADXkda/ANeSANeS1rwA15MA15PWvADXlADXlNa8ANeV1rkA15XWvADXlta8ANeY1rwA15nWtADXmda8ANea1rwA15sA15vWvADXm9a/ANecANec1rwA150A157WvADXoNa8ANeh1rwA16IA16PWvADXpNa8ANek1r8A16bWvADXp9a8ANeoANeo1rwA16nWvADXqda814EA16nWvNeCANep14EA16nXggDXqgDXqta8ANey1rcA2KEA2KIA2KMA2KQA2KUA2KYA2KbYpwDYptisANim2K0A2KbYrgDYptixANim2LIA2KbZhQDYptmGANim2YcA2KbZiADYptmJANim2YoA2KbbhgDYptuHANim24gA2KbbkADYptuVANinANin2YPYqNixANin2YTZhNmHANin2YsA2KfZtADYqADYqNisANio2K0A2KjYrdmKANio2K4A2KjYrtmKANio2LEA2KjYsgDYqNmFANio2YYA2KjZhwDYqNmJANio2YoA2KkA2KoA2KrYrADYqtis2YUA2KrYrNmJANiq2KzZigDYqtitANiq2K3YrADYqtit2YUA2KrYrgDYqtiu2YUA2KrYrtmJANiq2K7ZigDYqtixANiq2LIA2KrZhQDYqtmF2KwA2KrZhditANiq2YXYrgDYqtmF2YkA2KrZhdmKANiq2YYA2KrZhwDYqtmJANiq2YoA2KsA2KvYrADYq9ixANir2LIA2KvZhQDYq9mGANir2YcA2KvZiQDYq9mKANisANis2K0A2KzYrdmJANis2K3ZigDYrNmEINis2YTYp9mE2YcA2KzZhQDYrNmF2K0A2KzZhdmJANis2YXZigDYrNmJANis2YoA2K0A2K3YrADYrdis2YoA2K3ZhQDYrdmF2YkA2K3ZhdmKANit2YkA2K3ZigDYrgDYrtisANiu2K0A2K7ZhQDYrtmJANiu2YoA2K8A2LAA2LDZsADYsQDYsdiz2YjZhADYsdmwANix24zYp9mEANiyANizANiz2KwA2LPYrNitANiz2KzZiQDYs9itANiz2K3YrADYs9iuANiz2K7ZiQDYs9iu2YoA2LPYsQDYs9mFANiz2YXYrADYs9mF2K0A2LPZhdmFANiz2YcA2LPZiQDYs9mKANi0ANi02KwA2LTYrNmKANi02K0A2LTYrdmFANi02K3ZigDYtNiuANi02LEA2LTZhQDYtNmF2K4A2LTZhdmFANi02YcA2LTZiQDYtNmKANi1ANi12K0A2LXYrditANi12K3ZigDYtdiuANi12LEA2LXZhNi52YUA2LXZhNmJANi12YTZiSDYp9mE2YTZhyDYudmE2YrZhyDZiNiz2YTZhQDYtdmE25IA2LXZhQDYtdmF2YUA2LXZiQDYtdmKANi2ANi22KwA2LbYrQDYttit2YkA2LbYrdmKANi22K4A2LbYrtmFANi22LEA2LbZhQDYttmJANi22YoA2LcA2LfYrQDYt9mFANi32YXYrQDYt9mF2YUA2LfZhdmKANi32YkA2LfZigDYuADYuNmFANi5ANi52KwA2LnYrNmFANi52YTZitmHANi52YUA2LnZhdmFANi52YXZiQDYudmF2YoA2LnZiQDYudmKANi6ANi62KwA2LrZhQDYutmF2YUA2LrZhdmJANi62YXZigDYutmJANi62YoA2YDZiwDZgNmOANmA2Y7ZkQDZgNmPANmA2Y/ZkQDZgNmQANmA2ZDZkQDZgNmRANmA2ZIA2YEA2YHYrADZgditANmB2K4A2YHYrtmFANmB2YUA2YHZhdmKANmB2YkA2YHZigDZggDZgtitANmC2YTbkgDZgtmFANmC2YXYrQDZgtmF2YUA2YLZhdmKANmC2YkA2YLZigDZgwDZg9inANmD2KwA2YPYrQDZg9iuANmD2YQA2YPZhQDZg9mF2YUA2YPZhdmKANmD2YkA2YPZigDZhADZhNiiANmE2KMA2YTYpQDZhNinANmE2KwA2YTYrNisANmE2KzZhQDZhNis2YoA2YTYrQDZhNit2YUA2YTYrdmJANmE2K3ZigDZhNiuANmE2K7ZhQDZhNmFANmE2YXYrQDZhNmF2YoA2YTZhwDZhNmJANmE2YoA2YUA2YXYpwDZhdisANmF2KzYrQDZhdis2K4A2YXYrNmFANmF2KzZigDZhditANmF2K3YrADZhdit2YUA2YXYrdmF2K8A2YXYrdmKANmF2K4A2YXYrtisANmF2K7ZhQDZhdiu2YoA2YXZhQDZhdmF2YoA2YXZiQDZhdmKANmGANmG2KwA2YbYrNitANmG2KzZhQDZhtis2YkA2YbYrNmKANmG2K0A2YbYrdmFANmG2K3ZiQDZhtit2YoA2YbYrgDZhtixANmG2LIA2YbZhQDZhtmF2YkA2YbZhdmKANmG2YYA2YbZhwDZhtmJANmG2YoA2YcA2YfYrADZh9mFANmH2YXYrADZh9mF2YUA2YfZiQDZh9mKANmH2bAA2YgA2YjYs9mE2YUA2YjZtADZiQDZidmwANmKANmK2KwA2YrYrNmKANmK2K0A2YrYrdmKANmK2K4A2YrYsQDZitiyANmK2YUA2YrZhdmFANmK2YXZigDZitmGANmK2YcA2YrZiQDZitmKANmK2bQA2a4A2a8A2bEA2bkA2boA2bsA2b4A2b8A2oAA2oMA2oQA2oYA2ocA2ogA2owA2o0A2o4A2pEA2pgA2qEA2qQA2qYA2qkA2q0A2q8A2rEA2rMA2roA2rsA2r4A24AA24EA24IA24UA24YA24cA24fZtADbiADbiQDbiwDbjADbkADbkgDbkwDgpJXgpLwA4KSW4KS8AOCkl+CkvADgpJzgpLwA4KSh4KS8AOCkouCkvADgpKkA4KSr4KS8AOCkr+CkvADgpLEA4KS0AOCmoeCmvADgpqLgprwA4Kav4Ka8AOCniwDgp4wA4KiW4Ki8AOCol+CovADgqJzgqLwA4Kir4Ki8AOCosuCovADgqLjgqLwA4Kyh4Ky8AOCsouCsvADgrYgA4K2LAOCtjADgrpQA4K+KAOCviwDgr4wA4LGIAOCzgADgs4cA4LOIAOCzigDgs4sA4LWKAOC1iwDgtYwA4LeaAOC3nADgt50A4LeeAOC5jeC4sgDguqvgupkA4Lqr4LqhAOC7jeC6sgDgvIsA4L2A4L61AOC9guC+twDgvYzgvrcA4L2R4L63AOC9luC+twDgvZvgvrcA4L2x4L2yAOC9seC9tADgvbHgvoAA4L6Q4L61AOC+kuC+twDgvpzgvrcA4L6h4L63AOC+puC+twDgvqvgvrcA4L6y4L2x4L6AAOC+suC+gADgvrPgvbHgvoAA4L6z4L6AAOGApgDhg5wA4YSAAOGEgQDhhIIA4YSDAOGEhADhhIUA4YSGAOGEhwDhhIgA4YSJAOGEigDhhIsA4YSMAOGEjQDhhI4A4YSPAOGEkADhhJEA4YSSAOGElADhhJUA4YSaAOGEnADhhJ0A4YSeAOGEoADhhKEA4YSiAOGEowDhhKcA4YSpAOGEqwDhhKwA4YStAOGErgDhhK8A4YSyAOGEtgDhhYAA4YWHAOGFjADhhZcA4YWYAOGFmQDhhaAA4YWhAOGFogDhhaMA4YWkAOGFpQDhhaYA4YWnAOGFqADhhakA4YWqAOGFqwDhhawA4YWtAOGFrgDhha8A4YWwAOGFsQDhhbIA4YWzAOGFtADhhbUA4YaEAOGGhQDhhogA4YaRAOGGkgDhhpQA4YaeAOGGoQDhhqoA4YasAOGGrQDhhrAA4YaxAOGGsgDhhrMA4Ya0AOGGtQDhh4cA4YeIAOGHjADhh44A4YeTAOGHlwDhh5kA4YedAOGHnwDhh7EA4YeyAOGshgDhrIgA4ayKAOGsjADhrI4A4aySAOGsuwDhrL0A4a2AAOGtgQDhrYMA4bSCAOG0lgDhtJcA4bScAOG0nQDhtKUA4bW7AOG2hQDhuIAA4biBAOG4ggDhuIMA4biEAOG4hQDhuIYA4biHAOG4iADhuIkA4biKAOG4iwDhuIwA4biNAOG4jgDhuI8A4biQAOG4kQDhuJIA4biTAOG4lADhuJUA4biWAOG4lwDhuJgA4biZAOG4mgDhuJsA4bicAOG4nQDhuJ4A4bifAOG4oADhuKEA4biiAOG4owDhuKQA4bilAOG4pgDhuKcA4bioAOG4qQDhuKoA4birAOG4rADhuK0A4biuAOG4rwDhuLAA4bixAOG4sgDhuLMA4bi0AOG4tQDhuLYA4bi3AOG4uADhuLkA4bi6AOG4uwDhuLwA4bi9AOG4vgDhuL8A4bmAAOG5gQDhuYIA4bmDAOG5hADhuYUA4bmGAOG5hwDhuYgA4bmJAOG5igDhuYsA4bmMAOG5jQDhuY4A4bmPAOG5kADhuZEA4bmSAOG5kwDhuZQA4bmVAOG5lgDhuZcA4bmYAOG5mQDhuZoA4bmbAOG5nADhuZ0A4bmeAOG5nwDhuaAA4bmhAOG5ogDhuaMA4bmkAOG5pQDhuaYA4bmnAOG5qADhuakA4bmqAOG5qwDhuawA4bmtAOG5rgDhua8A4bmwAOG5sQDhubIA4bmzAOG5tADhubUA4bm2AOG5twDhubgA4bm5AOG5ugDhubsA4bm8AOG5vQDhub4A4bm/AOG6gADhuoEA4bqCAOG6gwDhuoQA4bqFAOG6hgDhuocA4bqIAOG6iQDhuooA4bqLAOG6jADhuo0A4bqOAOG6jwDhupAA4bqRAOG6kgDhupMA4bqUAOG6lQDhupYA4bqXAOG6mADhupkA4bqgAOG6oQDhuqIA4bqjAOG6pADhuqUA4bqmAOG6pwDhuqgA4bqpAOG6qgDhuqsA4bqsAOG6rQDhuq4A4bqvAOG6sADhurEA4bqyAOG6swDhurQA4bq1AOG6tgDhurcA4bq4AOG6uQDhuroA4bq7AOG6vADhur0A4bq+AOG6vwDhu4AA4buBAOG7ggDhu4MA4buEAOG7hQDhu4YA4buHAOG7iADhu4kA4buKAOG7iwDhu4wA4buNAOG7jgDhu48A4buQAOG7kQDhu5IA4buTAOG7lADhu5UA4buWAOG7lwDhu5gA4buZAOG7mgDhu5sA4bucAOG7nQDhu54A4bufAOG7oADhu6EA4buiAOG7owDhu6QA4bulAOG7pgDhu6cA4buoAOG7qQDhu6oA4burAOG7rADhu60A4buuAOG7rwDhu7AA4buxAOG7sgDhu7MA4bu0AOG7tQDhu7YA4bu3AOG7uADhu7kA4byAAOG8gQDhvIIA4byDAOG8hADhvIUA4byGAOG8hwDhvIgA4byJAOG8igDhvIsA4byMAOG8jQDhvI4A4byPAOG8kADhvJEA4bySAOG8kwDhvJQA4byVAOG8mADhvJkA4byaAOG8mwDhvJwA4bydAOG8oADhvKEA4byiAOG8owDhvKQA4bylAOG8pgDhvKcA4byoAOG8qQDhvKoA4byrAOG8rADhvK0A4byuAOG8rwDhvLAA4byxAOG8sgDhvLMA4by0AOG8tQDhvLYA4by3AOG8uADhvLkA4by6AOG8uwDhvLwA4by9AOG8vgDhvL8A4b2AAOG9gQDhvYIA4b2DAOG9hADhvYUA4b2IAOG9iQDhvYoA4b2LAOG9jADhvY0A4b2QAOG9kQDhvZIA4b2TAOG9lADhvZUA4b2WAOG9lwDhvZkA4b2bAOG9nQDhvZ8A4b2gAOG9oQDhvaIA4b2jAOG9pADhvaUA4b2mAOG9pwDhvagA4b2pAOG9qgDhvasA4b2sAOG9rQDhva4A4b2vAOG9sADhvbIA4b20AOG9tgDhvbgA4b26AOG9vADhvoAA4b6BAOG+ggDhvoMA4b6EAOG+hQDhvoYA4b6HAOG+iADhvokA4b6KAOG+iwDhvowA4b6NAOG+jgDhvo8A4b6QAOG+kQDhvpIA4b6TAOG+lADhvpUA4b6WAOG+lwDhvpgA4b6ZAOG+mgDhvpsA4b6cAOG+nQDhvp4A4b6fAOG+oADhvqEA4b6iAOG+owDhvqQA4b6lAOG+pgDhvqcA4b6oAOG+qQDhvqoA4b6rAOG+rADhvq0A4b6uAOG+rwDhvrAA4b6xAOG+sgDhvrMA4b60AOG+tgDhvrcA4b64AOG+uQDhvroA4b68AOG/ggDhv4MA4b+EAOG/hgDhv4cA4b+IAOG/igDhv4wA4b+QAOG/kQDhv5IA4b+WAOG/lwDhv5gA4b+ZAOG/mgDhv6AA4b+hAOG/ogDhv6QA4b+lAOG/pgDhv6cA4b+oAOG/qQDhv6oA4b+sAOG/sgDhv7MA4b+0AOG/tgDhv7cA4b+4AOG/ugDhv7wA4oCQAOKAkwDigJQA4oCy4oCyAOKAsuKAsuKAsgDigLLigLLigLLigLIA4oC14oC1AOKAteKAteKAtQDigqkA4oaQAOKGkQDihpIA4oaTAOKGmgDihpsA4oauAOKHjQDih44A4oePAOKIggDiiIQA4oiHAOKIiQDiiIwA4oiRAOKIkgDiiKQA4oimAOKIq+KIqwDiiKviiKviiKsA4oir4oir4oir4oirAOKIruKIrgDiiK7iiK7iiK4A4omBAOKJhADiiYcA4omJAOKJoADiiaIA4omtAOKJrgDiia8A4omwAOKJsQDiibQA4om1AOKJuADiibkA4oqAAOKKgQDiioQA4oqFAOKKiADiiokA4oqsAOKKrQDiiq4A4oqvAOKLoADii6EA4ouiAOKLowDii6oA4ourAOKLrADii60A4pSCAOKWoADil4sA4qaFAOKmhgDiq53MuADitaEA44CBAOOAggDjgIgA44CJAOOAigDjgIsA44CMAOOAjQDjgI4A44CPAOOAkADjgJEA44CSAOOAlADjgJRT44CVAOOAlOS4ieOAlQDjgJTkuozjgJUA44CU5Yud44CVAOOAlOWuieOAlQDjgJTmiZPjgJUA44CU5pWX44CVAOOAlOacrOOAlQDjgJTngrnjgJUA44CU55uX44CVAOOAlQDjgJYA44CXAOOBjADjgY4A44GQAOOBkgDjgZQA44GWAOOBmADjgZoA44GcAOOBngDjgaAA44GiAOOBpQDjgacA44GpAOOBsADjgbEA44GzAOOBtADjgbYA44G3AOOBuQDjgboA44G744GLAOOBvADjgb0A44KI44KKAOOClADjgpkA44KaAOOCngDjgqEA44KiAOOCouODkeODvOODiADjgqLjg6vjg5XjgqEA44Ki44Oz44Oa44KiAOOCouODvOODqwDjgqMA44KkAOOCpOODi+ODs+OCsADjgqTjg7Pjg4EA44KlAOOCpgDjgqbjgqnjg7MA44KnAOOCqADjgqjjgrnjgq/jg7zjg4kA44Ko44O844Kr44O8AOOCqQDjgqoA44Kq44Oz44K5AOOCquODvOODoADjgqsA44Kr44Kk44OqAOOCq+ODqeODg+ODiADjgqvjg63jg6rjg7wA44KsAOOCrOODreODswDjgqzjg7Pjg54A44KtAOOCreODpeODquODvADjgq3jg60A44Kt44Ot44Kw44Op44OgAOOCreODreODoeODvOODiOODqwDjgq3jg63jg6/jg4Pjg4gA44KuAOOCruOCrADjgq7jg4vjg7wA44Ku44Or44OA44O8AOOCrwDjgq/jg6vjgrzjgqTjg60A44Kv44Ot44O844ONAOOCsADjgrDjg6njg6AA44Kw44Op44Og44OI44OzAOOCsQDjgrHjg7zjgrkA44KyAOOCswDjgrPjgrMA44Kz44OIAOOCs+ODq+ODigDjgrPjg7zjg50A44K0AOOCtQDjgrXjgqTjgq/jg6sA44K144Oz44OB44O844OgAOOCtgDjgrcA44K344Oq44Oz44KwAOOCuADjgrkA44K6AOOCuwDjgrvjg7Pjg4EA44K744Oz44OIAOOCvADjgr0A44K+AOOCvwDjg4AA44OA44O844K5AOODgQDjg4IA44ODAOODhADjg4UA44OGAOODhwDjg4fjgrcA44OIAOODiOODswDjg4kA44OJ44OrAOODigDjg4rjg44A44OLAOODjADjg40A44OOAOODjuODg+ODiADjg48A44OP44Kk44OEAOODkADjg5Djg7zjg6zjg6sA44ORAOODkeODvOOCu+ODs+ODiADjg5Hjg7zjg4QA44OSAOODkwDjg5Pjg6sA44OUAOODlOOCouOCueODiOODqwDjg5Tjgq/jg6sA44OU44KzAOODlQDjg5XjgqHjg6njg4Pjg4kA44OV44Kj44O844OIAOODleODqeODswDjg5YA44OW44OD44K344Kn44OrAOODlwDjg5gA44OY44Kv44K/44O844OrAOODmOODq+ODhADjg5kA44OZ44O844K/AOODmgDjg5rjgr0A44Oa44OL44OSAOODmuODs+OCuQDjg5rjg7zjgrgA44ObAOODm+ODswDjg5vjg7zjg6sA44Ob44O844OzAOODnADjg5zjg6vjg4gA44OdAOODneOCpOODs+ODiADjg53jg7Pjg4kA44OeAOODnuOCpOOCr+ODrQDjg57jgqTjg6sA44Oe44OD44OPAOODnuODq+OCrwDjg57jg7Pjgrfjg6fjg7MA44OfAOODn+OCr+ODreODswDjg5/jg6oA44Of44Oq44OQ44O844OrAOODoADjg6EA44Oh44KsAOODoeOCrOODiOODswDjg6Hjg7zjg4jjg6sA44OiAOODowDjg6QA44Ok44O844OJAOODpOODvOODqwDjg6UA44OmAOODpuOCouODswDjg6cA44OoAOODqQDjg6oA44Oq44OD44OI44OrAOODquODqQDjg6sA44Or44OU44O8AOODq+ODvOODluODqwDjg6wA44Os44OgAOODrOODs+ODiOOCsuODswDjg60A44OvAOODr+ODg+ODiADjg7AA44OxAOODsgDjg7MA44O0AOODtwDjg7gA44O5AOODugDjg7sA44O8AOODvgDjkp4A45K5AOOSuwDjk58A45SVAOObrgDjm7wA456BAOOgrwDjoaIA46G8AOOjhwDjo6MA46ScAOOkugDjqK4A46msAOOrpADjrIgA46yZAOOtiQDjrp0A47CYAOOxjgDjtLMA47aWAOO6rADjurgA47ybAOO/vADkgIgA5ICYAOSAuQDkgYYA5IKWAOSDowDkhK8A5IiCAOSIpwDkiqAA5IyBAOSMtADkjZkA5I+VAOSPmQDkkIsA5JGrAOSUqwDklZ0A5JWhAOSVqwDkl5cA5Je5AOSYtQDkmr4A5JuHAOSmlQDkp6YA5KmuAOSptgDkqrIA5KyzAOSvjgDks44A5LOtAOSzuADktZYA5LiAAOS4gQDkuIMA5LiJAOS4igDkuIsA5LiNAOS4mQDkuKYA5LioAOS4rQDkuLIA5Li2AOS4uADkuLkA5Li9AOS4vwDkuYEA5LmZAOS5nQDkuoIA5LqFAOS6hgDkuowA5LqUAOS6oADkuqQA5LquAOS6ugDku4AA5LuMAOS7pADkvIEA5LyRAOS9oADkvoAA5L6GAOS+iwDkvq4A5L67AOS+vwDlgIIA5YCrAOWBugDlgpkA5YOPAOWDmgDlg6cA5YSqAOWEvwDlhYAA5YWFAOWFjQDlhZQA5YWkAOWFpQDlhacA5YWoAOWFqQDlhasA5YWtAOWFtwDlhoAA5YaCAOWGjQDlhpIA5YaVAOWGlgDlhpcA5YaZAOWGpADlhqsA5YasAOWGtQDlhrcA5YeJAOWHjADlh5wA5YeeAOWHoADlh7UA5YiAAOWIgwDliIcA5YiXAOWInQDliKkA5Yi6AOWIuwDliYYA5YmNAOWJsgDlibcA5YqJAOWKmwDliqMA5YqzAOWKtADli4cA5YuJAOWLkgDli54A5YukAOWLtQDli7kA5Yu6AOWMhQDljIYA5YyVAOWMlwDljJoA5Yy4AOWMuwDljL8A5Y2BAOWNhADljYUA5Y2JAOWNkQDljZQA5Y2aAOWNnADljakA5Y2wAOWNswDljbUA5Y29AOWNvwDljoIA5Y62AOWPgwDlj4gA5Y+KAOWPjADlj58A5Y+jAOWPpQDlj6sA5Y+vAOWPsQDlj7MA5ZCGAOWQiADlkI0A5ZCPAOWQnQDlkLgA5ZC5AOWRggDlkYgA5ZGoAOWSngDlkqIA5ZK9AOWTtgDllJAA5ZWPAOWVkwDllZUA5ZWjAOWWhADllocA5ZaZAOWWnQDllqsA5ZazAOWWtgDll4AA5ZeCAOWXogDlmIYA5ZmRAOWZqADlmbQA5ZuXAOWbmwDlm7kA5ZyWAOWclwDlnJ8A5ZywAOWeiwDln44A5Z+0AOWgjQDloLEA5aCyAOWhgADloZoA5aGeAOWiqADloqwA5aKzAOWjmADlo58A5aOrAOWjrgDlo7AA5aOyAOWjtwDlpIIA5aSGAOWkigDlpJUA5aSaAOWknADlpKIA5aSnAOWkp+atowDlpKkA5aWEAOWliADlpZEA5aWUAOWlogDlpbMA5aeYAOWnrADlqJsA5ainAOWpogDlqaYA5aq1AOWsiADlrKgA5ay+AOWtkADlrZcA5a2mAOWugADlroUA5a6XAOWvgwDlr5gA5a+nAOWvrgDlr7MA5a+4AOWvvwDlsIYA5bCPAOWwogDlsLgA5bC/AOWxoADlsaIA5bGkAOWxpQDlsa4A5bGxAOWyjQDls4AA5bSZAOW1gwDltZAA5bWrAOW1rgDltbwA5bayAOW2ugDlt5sA5behAOW3ogDlt6UA5bemAOW3sQDlt70A5be+AOW4qADluL0A5bmpAOW5sgDlubPmiJAA5bm0AOW5ugDlubwA5bm/AOW6pgDlurAA5bqzAOW6tgDlu4kA5buKAOW7kgDlu5MA5buZAOW7rADlu7QA5bu+AOW8hADlvIsA5byTAOW8ogDlvZAA5b2TAOW9oQDlvaIA5b2pAOW9qwDlvbMA5b6LAOW+jADlvpcA5b6aAOW+qQDlvq0A5b+DAOW/jQDlv5cA5b+1AOW/uQDmgJIA5oCcAOaBtQDmgoEA5oKUAOaDhwDmg5gA5oOhAOaEiADmhYQA5oWIAOaFjADmhY4A5oWgAOaFqADmhboA5oaOAOaGkADmhqQA5oavAOaGsgDmh54A5oeyAOaHtgDmiIAA5oiIAOaIkADmiJsA5oiuAOaItADmiLYA5omLAOaJkwDmiZ0A5oqVAOaKsQDmi4kA5ouPAOaLkwDmi5QA5ou8AOaLvgDmjIcA5oy9AOaNkADmjZUA5o2oAOaNuwDmjoMA5o6gAOaOqQDmj4QA5o+FAOaPpADmkJwA5pCiAOaRkgDmkakA5pG3AOaRvgDmkpoA5pKdAOaThADmlK8A5pS0AOaVjwDmlZYA5pWsAOaVuADmlocA5paXAOaWmQDmlqQA5pawAOaWuQDml4UA5pegAOaXogDml6MA5pelAOaYjuayuwDmmJMA5pigAOaYreWSjADmmYkA5pm0AOaaiADmmpEA5pqcAOaatADmm4YA5puwAOabtADmm7gA5pyAAOaciADmnIkA5pyXAOacmwDmnKEA5pyoAOadjgDmnZMA5p2WAOadngDmnbsA5p6FAOaelwDmn7MA5p+6AOaglwDmoJ8A5qCqAOagquW8j+S8muekvgDmoZIA5qKBAOaihQDmoo4A5qKoAOaklADmpYIA5qajAOanqgDmqIIA5qiTAOaqqADmq5MA5qubAOashADmrKAA5qyhAOatlADmraIA5q2jAOatsgDmrbcA5q25AOaunwDmrq4A5q6zAOauugDmrrsA5q+LAOavjQDmr5QA5q+bAOawjwDmsJQA5rC0AOaxjgDmsacA5rKIAOayvwDms4wA5rONAOazpQDms6gA5rSWAOa0mwDmtJ4A5rS0AOa0vgDmtYEA5rWpAOa1qgDmtbcA5rW4AOa2hQDmt4sA5reaAOa3qgDmt7kA5riaAOa4rwDmua4A5rqAAOa6nADmuroA5ruHAOa7iwDmu5EA5rubAOa8jwDmvJQA5ryiAOa8owDmva4A5r+GAOa/qwDmv74A54CbAOeAngDngLkA54GKAOeBqwDngbAA54G3AOeBvQDngpkA54KtAOeDiADng5kA54ShAOeFhQDnhYkA54WuAOeGnADnh44A54eQAOeIkADniJsA54ioAOeIqgDniKsA54i1AOeItgDniLsA54i/AOeJhwDniZAA54mZAOeJmwDniaIA54m5AOeKgADnipUA54qsAOeKrwDni4AA54u8AOeMqgDnjbUA5426AOeOhADnjocA546JAOeOiwDnjqUA546yAOePngDnkIYA55CJAOeQogDnkYcA55GcAOeRqQDnkbEA55KFAOeSiQDnkpgA55OKAOeTnADnk6YA55SGAOeUmADnlJ8A55SkAOeUqADnlLAA55SyAOeUswDnlLcA55S7AOeUvgDnlZkA55WlAOeVsADnlosA55aSAOeXogDnmJAA55idAOeYnwDnmYIA55mpAOeZtgDnmb0A55quAOeavwDnm4oA55ubAOebowDnm6cA55uuAOebtADnnIEA55yeAOecnwDnnYAA552KAOeeiwDnnqcA55+bAOefogDnn7MA56GOAOehqwDnoowA56KRAOejigDno4wA56O7AOekqgDnpLoA56S8AOekvgDnpYgA56WJAOelkADnpZYA56WdAOelngDnpaUA56W/AOemgQDnpo0A56aOAOemjwDnpq4A56a4AOemvgDnp4oA56eYAOenqwDnqJwA56mAAOepigDnqY8A56m0AOepugDnqoEA56qxAOeriwDnq64A56u5AOesoADnro8A56+AAOevhgDnr4kA57C+AOexoADnsbMA57G7AOeykgDnsr4A57OSAOezlgDns6MA57OnAOezqADns7gA57SAAOe0kADntKIA57SvAOe1ggDntZsA57WjAOe2oADntr4A57eHAOe3tADnuIIA57iJAOe4twDnuYEA57mFAOe8tgDnvL4A572RAOe9sgDnvbkA5726AOe+hQDnvooA576VAOe+mgDnvr0A57+6AOiAgQDogIUA6ICMAOiAkgDogLMA6IGGAOiBoADoga8A6IGwAOiBvgDogb8A6IKJAOiCiwDogq0A6IKyAOiEgwDohL4A6IeYAOiHowDoh6gA6IeqAOiHrQDoh7MA6Ie8AOiIgQDoiIQA6IiMAOiImADoiJsA6IifAOiJrgDoia8A6ImyAOiJuADoibkA6IqLAOiKkQDoip0A6IqxAOiKswDoir0A6IulAOiLpgDojJ0A6IyjAOiMtgDojZIA6I2TAOiNowDojq0A6I69AOiPiQDoj4oA6I+MAOiPnADoj6cA6I+vAOiPsQDokL0A6JGJAOiRlwDok64A6JOxAOiTswDok7wA6JSWAOiVpADol40A6Je6AOiYhgDomJIA6JitAOiYvwDomY0A6JmQAOiZnADomacA6JmpAOiZqwDomogA6JqpAOibogDonI4A6JyoAOidqwDonbkA6J6GAOieugDon6EA6KCBAOignwDooYAA6KGMAOihoADooaMA6KOCAOijjwDoo5cA6KOeAOijoQDoo7gA6KO6AOikkADopYEA6KWkAOilvgDopoYA6KaLAOimlgDop5IA6KejAOiogADoqqAA6KqqAOiqvwDoq4sA6KuSAOirlgDoq60A6Ku4AOirvgDorIEA6Ky5AOitmADoroAA6K6KAOiwtwDosYYA6LGIAOixlQDosbgA6LKdAOiyoQDosqkA6LKrAOizgQDos4IA6LOHAOiziADos5MA6LSIAOi0mwDotaQA6LWwAOi1twDotrMA6La8AOi3iwDot68A6LewAOi6qwDou4oA6LuUAOi8pgDovKoA6Ly4AOi8uwDovaIA6L6bAOi+ngDovrAA6L61AOi+tgDpgKMA6YC4AOmBigDpgakA6YGyAOmBvADpgo8A6YKRAOmClADpg44A6YOeAOmDsQDpg70A6YSRAOmEmwDphYkA6YWqAOmGmQDphrQA6YeGAOmHjADph48A6YeRAOmItADpiLgA6Ym2AOmJvADpi5cA6YuYAOmMhADpjYoA6Y+5AOmQlQDplbcA6ZaAAOmWiwDplq0A6Za3AOmYnADpmK4A6ZmLAOmZjQDpmbUA6Zm4AOmZvADpmoYA6ZqjAOmatgDpmrcA6Zq4AOmauQDpm4MA6ZuiAOmbowDpm6gA6Zu2AOmbtwDpnKMA6ZyyAOmdiADpnZEA6Z2WAOmdngDpnaIA6Z2pAOmfiwDpn5sA6Z+gAOmfrQDpn7MA6Z+/AOmggQDpoIUA6aCLAOmgmADpoKkA6aC7AOmhngDpoqgA6aObAOmjnwDpo6IA6aOvAOmjvADppKgA6aSpAOmmlgDpppkA6aanAOmmrADpp4IA6aexAOmnvgDpqaoA6aqoAOmrmADpq58A6aySAOmspQDprK8A6ayyAOmsvADprZoA6a2vAOmxgADpsZcA6bOlAOmzvQDptacA6ba0AOm3ugDpuJ4A6bm1AOm5vwDpupcA6bqfAOm6pQDpursA6buDAOm7jQDpu44A6buRAOm7uQDpu70A6bu+AOm8hQDpvI4A6byPAOm8kwDpvJYA6bygAOm8uwDpvYMA6b2KAOm9kgDpvo0A6b6OAOm+nADpvp8A6b6gAOqcpwDqna8A6qy3AOqtkgDqsIAA6rCBAOqwggDqsIMA6rCEAOqwhQDqsIYA6rCHAOqwiADqsIkA6rCKAOqwiwDqsIwA6rCNAOqwjgDqsI8A6rCQAOqwkQDqsJIA6rCTAOqwlADqsJUA6rCWAOqwlwDqsJgA6rCZAOqwmgDqsJsA6rCcAOqwnQDqsJ4A6rCfAOqwoADqsKEA6rCiAOqwowDqsKQA6rClAOqwpgDqsKcA6rCoAOqwqQDqsKoA6rCrAOqwrADqsK0A6rCuAOqwrwDqsLAA6rCxAOqwsgDqsLMA6rC0AOqwtQDqsLYA6rC3AOqwuADqsLkA6rC6AOqwuwDqsLwA6rC9AOqwvgDqsL8A6rGAAOqxgQDqsYIA6rGDAOqxhADqsYUA6rGGAOqxhwDqsYgA6rGJAOqxigDqsYsA6rGMAOqxjQDqsY4A6rGPAOqxkADqsZEA6rGSAOqxkwDqsZQA6rGVAOqxlgDqsZcA6rGYAOqxmQDqsZoA6rGbAOqxnADqsZ0A6rGeAOqxnwDqsaAA6rGhAOqxogDqsaMA6rGkAOqxpQDqsaYA6rGnAOqxqADqsakA6rGqAOqxqwDqsawA6rGtAOqxrgDqsa8A6rGwAOqxsQDqsbIA6rGzAOqxtADqsbUA6rG2AOqxtwDqsbgA6rG5AOqxugDqsbsA6rG8AOqxvQDqsb4A6rG/AOqygADqsoEA6rKCAOqygwDqsoQA6rKFAOqyhgDqsocA6rKIAOqyiQDqsooA6rKLAOqyjADqso0A6rKOAOqyjwDqspAA6rKRAOqykgDqspMA6rKUAOqylQDqspYA6rKXAOqymADqspkA6rKaAOqymwDqspwA6rKdAOqyngDqsp8A6rKgAOqyoQDqsqIA6rKjAOqypADqsqUA6rKmAOqypwDqsqgA6rKpAOqyqgDqsqsA6rKsAOqyrQDqsq4A6rKvAOqysADqsrEA6rKyAOqyswDqsrQA6rK1AOqytgDqsrcA6rK4AOqyuQDqsroA6rK7AOqyvADqsr0A6rK+AOqyvwDqs4AA6rOBAOqzggDqs4MA6rOEAOqzhQDqs4YA6rOHAOqziADqs4kA6rOKAOqziwDqs4wA6rONAOqzjgDqs48A6rOQAOqzkQDqs5IA6rOTAOqzlADqs5UA6rOWAOqzlwDqs5gA6rOZAOqzmgDqs5sA6rOcAOqznQDqs54A6rOfAOqzoADqs6EA6rOiAOqzowDqs6QA6rOlAOqzpgDqs6cA6rOoAOqzqQDqs6oA6rOrAOqzrADqs60A6rOuAOqzrwDqs7AA6rOxAOqzsgDqs7MA6rO0AOqztQDqs7YA6rO3AOqzuADqs7kA6rO6AOqzuwDqs7wA6rO9AOqzvgDqs78A6rSAAOq0gQDqtIIA6rSDAOq0hADqtIUA6rSGAOq0hwDqtIgA6rSJAOq0igDqtIsA6rSMAOq0jQDqtI4A6rSPAOq0kADqtJEA6rSSAOq0kwDqtJQA6rSVAOq0lgDqtJcA6rSYAOq0mQDqtJoA6rSbAOq0nADqtJ0A6rSeAOq0nwDqtKAA6rShAOq0ogDqtKMA6rSkAOq0pQDqtKYA6rSnAOq0qADqtKkA6rSqAOq0qwDqtKwA6rStAOq0rgDqtK8A6rSwAOq0sQDqtLIA6rSzAOq0tADqtLUA6rS2AOq0twDqtLgA6rS5AOq0ugDqtLsA6rS8AOq0vQDqtL4A6rS/AOq1gADqtYEA6rWCAOq1gwDqtYQA6rWFAOq1hgDqtYcA6rWIAOq1iQDqtYoA6rWLAOq1jADqtY0A6rWOAOq1jwDqtZAA6rWRAOq1kgDqtZMA6rWUAOq1lQDqtZYA6rWXAOq1mADqtZkA6rWaAOq1mwDqtZwA6rWdAOq1ngDqtZ8A6rWgAOq1oQDqtaIA6rWjAOq1pADqtaUA6rWmAOq1pwDqtagA6rWpAOq1qgDqtasA6rWsAOq1rQDqta4A6rWvAOq1sADqtbEA6rWyAOq1swDqtbQA6rW1AOq1tgDqtbcA6rW4AOq1uQDqtboA6rW7AOq1vADqtb0A6rW+AOq1vwDqtoAA6raBAOq2ggDqtoMA6raEAOq2hQDqtoYA6raHAOq2iADqtokA6raKAOq2iwDqtowA6raNAOq2jgDqto8A6raQAOq2kQDqtpIA6raTAOq2lADqtpUA6raWAOq2lwDqtpgA6raZAOq2mgDqtpsA6racAOq2nQDqtp4A6rafAOq2oADqtqEA6raiAOq2owDqtqQA6ralAOq2pgDqtqcA6raoAOq2qQDqtqoA6rarAOq2rADqtq0A6rauAOq2rwDqtrAA6raxAOq2sgDqtrMA6ra0AOq2tQDqtrYA6ra3AOq2uADqtrkA6ra6AOq2uwDqtrwA6ra9AOq2vgDqtr8A6reAAOq3gQDqt4IA6reDAOq3hADqt4UA6reGAOq3hwDqt4gA6reJAOq3igDqt4sA6reMAOq3jQDqt44A6rePAOq3kADqt5EA6reSAOq3kwDqt5QA6reVAOq3lgDqt5cA6reYAOq3mQDqt5oA6rebAOq3nADqt50A6reeAOq3nwDqt6AA6rehAOq3ogDqt6MA6rekAOq3pQDqt6YA6renAOq3qADqt6kA6reqAOq3qwDqt6wA6retAOq3rgDqt68A6rewAOq3sQDqt7IA6rezAOq3tADqt7UA6re2AOq3twDqt7gA6re5AOq3ugDqt7sA6re8AOq3vQDqt74A6re/AOq4gADquIEA6riCAOq4gwDquIQA6riFAOq4hgDquIcA6riIAOq4iQDquIoA6riLAOq4jADquI0A6riOAOq4jwDquJAA6riRAOq4kgDquJMA6riUAOq4lQDquJYA6riXAOq4mADquJkA6riaAOq4mwDquJwA6ridAOq4ngDquJ8A6rigAOq4oQDquKIA6rijAOq4pADquKUA6rimAOq4pwDquKgA6ripAOq4qgDquKsA6risAOq4rQDquK4A6rivAOq4sADquLEA6riyAOq4swDquLQA6ri1AOq4tgDquLcA6ri4AOq4uQDquLoA6ri7AOq4vADquL0A6ri+AOq4vwDquYAA6rmBAOq5ggDquYMA6rmEAOq5hQDquYYA6rmHAOq5iADquYkA6rmKAOq5iwDquYwA6rmNAOq5jgDquY8A6rmQAOq5kQDquZIA6rmTAOq5lADquZUA6rmWAOq5lwDquZgA6rmZAOq5mgDquZsA6rmcAOq5nQDquZ4A6rmfAOq5oADquaEA6rmiAOq5owDquaQA6rmlAOq5pgDquacA6rmoAOq5qQDquaoA6rmrAOq5rADqua0A6rmuAOq5rwDqubAA6rmxAOq5sgDqubMA6rm0AOq5tQDqubYA6rm3AOq5uADqubkA6rm6AOq5uwDqubwA6rm9AOq5vgDqub8A6rqAAOq6gQDquoIA6rqDAOq6hADquoUA6rqGAOq6hwDquogA6rqJAOq6igDquosA6rqMAOq6jQDquo4A6rqPAOq6kADqupEA6rqSAOq6kwDqupQA6rqVAOq6lgDqupcA6rqYAOq6mQDqupoA6rqbAOq6nADqup0A6rqeAOq6nwDquqAA6rqhAOq6ogDquqMA6rqkAOq6pQDquqYA6rqnAOq6qADquqkA6rqqAOq6qwDquqwA6rqtAOq6rgDquq8A6rqwAOq6sQDqurIA6rqzAOq6tADqurUA6rq2AOq6twDqurgA6rq5AOq6ugDqursA6rq8AOq6vQDqur4A6rq/AOq7gADqu4EA6ruCAOq7gwDqu4QA6ruFAOq7hgDqu4cA6ruIAOq7iQDqu4oA6ruLAOq7jADqu40A6ruOAOq7jwDqu5AA6ruRAOq7kgDqu5MA6ruUAOq7lQDqu5YA6ruXAOq7mADqu5kA6ruaAOq7mwDqu5wA6rudAOq7ngDqu58A6rugAOq7oQDqu6IA6rujAOq7pADqu6UA6rumAOq7pwDqu6gA6rupAOq7qgDqu6sA6rusAOq7rQDqu64A6ruvAOq7sADqu7EA6ruyAOq7swDqu7QA6ru1AOq7tgDqu7cA6ru4AOq7uQDqu7oA6ru7AOq7vADqu70A6ru+AOq7vwDqvIAA6ryBAOq8ggDqvIMA6ryEAOq8hQDqvIYA6ryHAOq8iADqvIkA6ryKAOq8iwDqvIwA6ryNAOq8jgDqvI8A6ryQAOq8kQDqvJIA6ryTAOq8lADqvJUA6ryWAOq8lwDqvJgA6ryZAOq8mgDqvJsA6rycAOq8nQDqvJ4A6ryfAOq8oADqvKEA6ryiAOq8owDqvKQA6rylAOq8pgDqvKcA6ryoAOq8qQDqvKoA6ryrAOq8rADqvK0A6ryuAOq8rwDqvLAA6ryxAOq8sgDqvLMA6ry0AOq8tQDqvLYA6ry3AOq8uADqvLkA6ry6AOq8uwDqvLwA6ry9AOq8vgDqvL8A6r2AAOq9gQDqvYIA6r2DAOq9hADqvYUA6r2GAOq9hwDqvYgA6r2JAOq9igDqvYsA6r2MAOq9jQDqvY4A6r2PAOq9kADqvZEA6r2SAOq9kwDqvZQA6r2VAOq9lgDqvZcA6r2YAOq9mQDqvZoA6r2bAOq9nADqvZ0A6r2eAOq9nwDqvaAA6r2hAOq9ogDqvaMA6r2kAOq9pQDqvaYA6r2nAOq9qADqvakA6r2qAOq9qwDqvawA6r2tAOq9rgDqva8A6r2wAOq9sQDqvbIA6r2zAOq9tADqvbUA6r22AOq9twDqvbgA6r25AOq9ugDqvbsA6r28AOq9vQDqvb4A6r2/AOq+gADqvoEA6r6CAOq+gwDqvoQA6r6FAOq+hgDqvocA6r6IAOq+iQDqvooA6r6LAOq+jADqvo0A6r6OAOq+jwDqvpAA6r6RAOq+kgDqvpMA6r6UAOq+lQDqvpYA6r6XAOq+mADqvpkA6r6aAOq+mwDqvpwA6r6dAOq+ngDqvp8A6r6gAOq+oQDqvqIA6r6jAOq+pADqvqUA6r6mAOq+pwDqvqgA6r6pAOq+qgDqvqsA6r6sAOq+rQDqvq4A6r6vAOq+sADqvrEA6r6yAOq+swDqvrQA6r61AOq+tgDqvrcA6r64AOq+uQDqvroA6r67AOq+vADqvr0A6r6+AOq+vwDqv4AA6r+BAOq/ggDqv4MA6r+EAOq/hQDqv4YA6r+HAOq/iADqv4kA6r+KAOq/iwDqv4wA6r+NAOq/jgDqv48A6r+QAOq/kQDqv5IA6r+TAOq/lADqv5UA6r+WAOq/lwDqv5gA6r+ZAOq/mgDqv5sA6r+cAOq/nQDqv54A6r+fAOq/oADqv6EA6r+iAOq/owDqv6QA6r+lAOq/pgDqv6cA6r+oAOq/qQDqv6oA6r+rAOq/rADqv60A6r+uAOq/rwDqv7AA6r+xAOq/sgDqv7MA6r+0AOq/tQDqv7YA6r+3AOq/uADqv7kA6r+6AOq/uwDqv7wA6r+9AOq/vgDqv78A64CAAOuAgQDrgIIA64CDAOuAhADrgIUA64CGAOuAhwDrgIgA64CJAOuAigDrgIsA64CMAOuAjQDrgI4A64CPAOuAkADrgJEA64CSAOuAkwDrgJQA64CVAOuAlgDrgJcA64CYAOuAmQDrgJoA64CbAOuAnADrgJ0A64CeAOuAnwDrgKAA64ChAOuAogDrgKMA64CkAOuApQDrgKYA64CnAOuAqADrgKkA64CqAOuAqwDrgKwA64CtAOuArgDrgK8A64CwAOuAsQDrgLIA64CzAOuAtADrgLUA64C2AOuAtwDrgLgA64C5AOuAugDrgLsA64C8AOuAvQDrgL4A64C/AOuBgADrgYEA64GCAOuBgwDrgYQA64GFAOuBhgDrgYcA64GIAOuBiQDrgYoA64GLAOuBjADrgY0A64GOAOuBjwDrgZAA64GRAOuBkgDrgZMA64GUAOuBlQDrgZYA64GXAOuBmADrgZkA64GaAOuBmwDrgZwA64GdAOuBngDrgZ8A64GgAOuBoQDrgaIA64GjAOuBpADrgaUA64GmAOuBpwDrgagA64GpAOuBqgDrgasA64GsAOuBrQDrga4A64GvAOuBsADrgbEA64GyAOuBswDrgbQA64G1AOuBtgDrgbcA64G4AOuBuQDrgboA64G7AOuBvADrgb0A64G+AOuBvwDrgoAA64KBAOuCggDrgoMA64KEAOuChQDrgoYA64KHAOuCiADrgokA64KKAOuCiwDrgowA64KNAOuCjgDrgo8A64KQAOuCkQDrgpIA64KTAOuClADrgpUA64KWAOuClwDrgpgA64KZAOuCmgDrgpsA64KcAOuCnQDrgp4A64KfAOuCoADrgqEA64KiAOuCowDrgqQA64KlAOuCpgDrgqcA64KoAOuCqQDrgqoA64KrAOuCrADrgq0A64KuAOuCrwDrgrAA64KxAOuCsgDrgrMA64K0AOuCtQDrgrYA64K3AOuCuADrgrkA64K6AOuCuwDrgrwA64K9AOuCvgDrgr8A64OAAOuDgQDrg4IA64ODAOuDhADrg4UA64OGAOuDhwDrg4gA64OJAOuDigDrg4sA64OMAOuDjQDrg44A64OPAOuDkADrg5EA64OSAOuDkwDrg5QA64OVAOuDlgDrg5cA64OYAOuDmQDrg5oA64ObAOuDnADrg50A64OeAOuDnwDrg6AA64OhAOuDogDrg6MA64OkAOuDpQDrg6YA64OnAOuDqADrg6kA64OqAOuDqwDrg6wA64OtAOuDrgDrg68A64OwAOuDsQDrg7IA64OzAOuDtADrg7UA64O2AOuDtwDrg7gA64O5AOuDugDrg7sA64O8AOuDvQDrg74A64O/AOuEgADrhIEA64SCAOuEgwDrhIQA64SFAOuEhgDrhIcA64SIAOuEiQDrhIoA64SLAOuEjADrhI0A64SOAOuEjwDrhJAA64SRAOuEkgDrhJMA64SUAOuElQDrhJYA64SXAOuEmADrhJkA64SaAOuEmwDrhJwA64SdAOuEngDrhJ8A64SgAOuEoQDrhKIA64SjAOuEpADrhKUA64SmAOuEpwDrhKgA64SpAOuEqgDrhKsA64SsAOuErQDrhK4A64SvAOuEsADrhLEA64SyAOuEswDrhLQA64S1AOuEtgDrhLcA64S4AOuEuQDrhLoA64S7AOuEvADrhL0A64S+AOuEvwDrhYAA64WBAOuFggDrhYMA64WEAOuFhQDrhYYA64WHAOuFiADrhYkA64WKAOuFiwDrhYwA64WNAOuFjgDrhY8A64WQAOuFkQDrhZIA64WTAOuFlADrhZUA64WWAOuFlwDrhZgA64WZAOuFmgDrhZsA64WcAOuFnQDrhZ4A64WfAOuFoADrhaEA64WiAOuFowDrhaQA64WlAOuFpgDrhacA64WoAOuFqQDrhaoA64WrAOuFrADrha0A64WuAOuFrwDrhbAA64WxAOuFsgDrhbMA64W0AOuFtQDrhbYA64W3AOuFuADrhbkA64W6AOuFuwDrhbwA64W9AOuFvgDrhb8A64aAAOuGgQDrhoIA64aDAOuGhADrhoUA64aGAOuGhwDrhogA64aJAOuGigDrhosA64aMAOuGjQDrho4A64aPAOuGkADrhpEA64aSAOuGkwDrhpQA64aVAOuGlgDrhpcA64aYAOuGmQDrhpoA64abAOuGnADrhp0A64aeAOuGnwDrhqAA64ahAOuGogDrhqMA64akAOuGpQDrhqYA64anAOuGqADrhqkA64aqAOuGqwDrhqwA64atAOuGrgDrhq8A64awAOuGsQDrhrIA64azAOuGtADrhrUA64a2AOuGtwDrhrgA64a5AOuGugDrhrsA64a8AOuGvQDrhr4A64a/AOuHgADrh4EA64eCAOuHgwDrh4QA64eFAOuHhgDrh4cA64eIAOuHiQDrh4oA64eLAOuHjADrh40A64eOAOuHjwDrh5AA64eRAOuHkgDrh5MA64eUAOuHlQDrh5YA64eXAOuHmADrh5kA64eaAOuHmwDrh5wA64edAOuHngDrh58A64egAOuHoQDrh6IA64ejAOuHpADrh6UA64emAOuHpwDrh6gA64epAOuHqgDrh6sA64esAOuHrQDrh64A64evAOuHsADrh7EA64eyAOuHswDrh7QA64e1AOuHtgDrh7cA64e4AOuHuQDrh7oA64e7AOuHvADrh70A64e+AOuHvwDriIAA64iBAOuIggDriIMA64iEAOuIhQDriIYA64iHAOuIiADriIkA64iKAOuIiwDriIwA64iNAOuIjgDriI8A64iQAOuIkQDriJIA64iTAOuIlADriJUA64iWAOuIlwDriJgA64iZAOuImgDriJsA64icAOuInQDriJ4A64ifAOuIoADriKEA64iiAOuIowDriKQA64ilAOuIpgDriKcA64ioAOuIqQDriKoA64irAOuIrADriK0A64iuAOuIrwDriLAA64ixAOuIsgDriLMA64i0AOuItQDriLYA64i3AOuIuADriLkA64i6AOuIuwDriLwA64i9AOuIvgDriL8A64mAAOuJgQDriYIA64mDAOuJhADriYUA64mGAOuJhwDriYgA64mJAOuJigDriYsA64mMAOuJjQDriY4A64mPAOuJkADriZEA64mSAOuJkwDriZQA64mVAOuJlgDriZcA64mYAOuJmQDriZoA64mbAOuJnADriZ0A64meAOuJnwDriaAA64mhAOuJogDriaMA64mkAOuJpQDriaYA64mnAOuJqADriakA64mqAOuJqwDriawA64mtAOuJrgDria8A64mwAOuJsQDribIA64mzAOuJtADribUA64m2AOuJtwDribgA64m5AOuJugDribsA64m8AOuJvQDrib4A64m/AOuKgADrioEA64qCAOuKgwDrioQA64qFAOuKhgDriocA64qIAOuKiQDriooA64qLAOuKjADrio0A64qOAOuKjwDripAA64qRAOuKkgDripMA64qUAOuKlQDripYA64qXAOuKmADripkA64qaAOuKmwDripwA64qdAOuKngDrip8A64qgAOuKoQDriqIA64qjAOuKpADriqUA64qmAOuKpwDriqgA64qpAOuKqgDriqsA64qsAOuKrQDriq4A64qvAOuKsADrirEA64qyAOuKswDrirQA64q1AOuKtgDrircA64q4AOuKuQDriroA64q7AOuKvADrir0A64q+AOuKvwDri4AA64uBAOuLggDri4MA64uEAOuLhQDri4YA64uHAOuLiADri4kA64uKAOuLiwDri4wA64uNAOuLjgDri48A64uQAOuLkQDri5IA64uTAOuLlADri5UA64uWAOuLlwDri5gA64uZAOuLmgDri5sA64ucAOuLnQDri54A64ufAOuLoADri6EA64uiAOuLowDri6QA64ulAOuLpgDri6cA64uoAOuLqQDri6oA64urAOuLrADri60A64uuAOuLrwDri7AA64uxAOuLsgDri7MA64u0AOuLtQDri7YA64u3AOuLuADri7kA64u6AOuLuwDri7wA64u9AOuLvgDri78A64yAAOuMgQDrjIIA64yDAOuMhADrjIUA64yGAOuMhwDrjIgA64yJAOuMigDrjIsA64yMAOuMjQDrjI4A64yPAOuMkADrjJEA64ySAOuMkwDrjJQA64yVAOuMlgDrjJcA64yYAOuMmQDrjJoA64ybAOuMnADrjJ0A64yeAOuMnwDrjKAA64yhAOuMogDrjKMA64ykAOuMpQDrjKYA64ynAOuMqADrjKkA64yqAOuMqwDrjKwA64ytAOuMrgDrjK8A64ywAOuMsQDrjLIA64yzAOuMtADrjLUA64y2AOuMtwDrjLgA64y5AOuMugDrjLsA64y8AOuMvQDrjL4A64y/AOuNgADrjYEA642CAOuNgwDrjYQA642FAOuNhgDrjYcA642IAOuNiQDrjYoA642LAOuNjADrjY0A642OAOuNjwDrjZAA642RAOuNkgDrjZMA642UAOuNlQDrjZYA642XAOuNmADrjZkA642aAOuNmwDrjZwA642dAOuNngDrjZ8A642gAOuNoQDrjaIA642jAOuNpADrjaUA642mAOuNpwDrjagA642pAOuNqgDrjasA642sAOuNrQDrja4A642vAOuNsADrjbEA642yAOuNswDrjbQA6421AOuNtgDrjbcA6424AOuNuQDrjboA6427AOuNvADrjb0A642+AOuNvwDrjoAA646BAOuOggDrjoMA646EAOuOhQDrjoYA646HAOuOiADrjokA646KAOuOiwDrjowA646NAOuOjgDrjo8A646QAOuOkQDrjpIA646TAOuOlADrjpUA646WAOuOlwDrjpgA646ZAOuOmgDrjpsA646cAOuOnQDrjp4A646fAOuOoADrjqEA646iAOuOowDrjqQA646lAOuOpgDrjqcA646oAOuOqQDrjqoA646rAOuOrADrjq0A646uAOuOrwDrjrAA646xAOuOsgDrjrMA6460AOuOtQDrjrYA6463AOuOuADrjrkA6466AOuOuwDrjrwA6469AOuOvgDrjr8A64+AAOuPgQDrj4IA64+DAOuPhADrj4UA64+GAOuPhwDrj4gA64+JAOuPigDrj4sA64+MAOuPjQDrj44A64+PAOuPkADrj5EA64+SAOuPkwDrj5QA64+VAOuPlgDrj5cA64+YAOuPmQDrj5oA64+bAOuPnADrj50A64+eAOuPnwDrj6AA64+hAOuPogDrj6MA64+kAOuPpQDrj6YA64+nAOuPqADrj6kA64+qAOuPqwDrj6wA64+tAOuPrgDrj68A64+wAOuPsQDrj7IA64+zAOuPtADrj7UA64+2AOuPtwDrj7gA64+5AOuPugDrj7sA64+8AOuPvQDrj74A64+/AOuQgADrkIEA65CCAOuQgwDrkIQA65CFAOuQhgDrkIcA65CIAOuQiQDrkIoA65CLAOuQjADrkI0A65COAOuQjwDrkJAA65CRAOuQkgDrkJMA65CUAOuQlQDrkJYA65CXAOuQmADrkJkA65CaAOuQmwDrkJwA65CdAOuQngDrkJ8A65CgAOuQoQDrkKIA65CjAOuQpADrkKUA65CmAOuQpwDrkKgA65CpAOuQqgDrkKsA65CsAOuQrQDrkK4A65CvAOuQsADrkLEA65CyAOuQswDrkLQA65C1AOuQtgDrkLcA65C4AOuQuQDrkLoA65C7AOuQvADrkL0A65C+AOuQvwDrkYAA65GBAOuRggDrkYMA65GEAOuRhQDrkYYA65GHAOuRiADrkYkA65GKAOuRiwDrkYwA65GNAOuRjgDrkY8A65GQAOuRkQDrkZIA65GTAOuRlADrkZUA65GWAOuRlwDrkZgA65GZAOuRmgDrkZsA65GcAOuRnQDrkZ4A65GfAOuRoADrkaEA65GiAOuRowDrkaQA65GlAOuRpgDrkacA65GoAOuRqQDrkaoA65GrAOuRrADrka0A65GuAOuRrwDrkbAA65GxAOuRsgDrkbMA65G0AOuRtQDrkbYA65G3AOuRuADrkbkA65G6AOuRuwDrkbwA65G9AOuRvgDrkb8A65KAAOuSgQDrkoIA65KDAOuShADrkoUA65KGAOuShwDrkogA65KJAOuSigDrkosA65KMAOuSjQDrko4A65KPAOuSkADrkpEA65KSAOuSkwDrkpQA65KVAOuSlgDrkpcA65KYAOuSmQDrkpoA65KbAOuSnADrkp0A65KeAOuSnwDrkqAA65KhAOuSogDrkqMA65KkAOuSpQDrkqYA65KnAOuSqADrkqkA65KqAOuSqwDrkqwA65KtAOuSrgDrkq8A65KwAOuSsQDrkrIA65KzAOuStADrkrUA65K2AOuStwDrkrgA65K5AOuSugDrkrsA65K8AOuSvQDrkr4A65K/AOuTgADrk4EA65OCAOuTgwDrk4QA65OFAOuThgDrk4cA65OIAOuTiQDrk4oA65OLAOuTjADrk40A65OOAOuTjwDrk5AA65ORAOuTkgDrk5MA65OUAOuTlQDrk5YA65OXAOuTmADrk5kA65OaAOuTmwDrk5wA65OdAOuTngDrk58A65OgAOuToQDrk6IA65OjAOuTpADrk6UA65OmAOuTpwDrk6gA65OpAOuTqgDrk6sA65OsAOuTrQDrk64A65OvAOuTsADrk7EA65OyAOuTswDrk7QA65O1AOuTtgDrk7cA65O4AOuTuQDrk7oA65O7AOuTvADrk70A65O+AOuTvwDrlIAA65SBAOuUggDrlIMA65SEAOuUhQDrlIYA65SHAOuUiADrlIkA65SKAOuUiwDrlIwA65SNAOuUjgDrlI8A65SQAOuUkQDrlJIA65STAOuUlADrlJUA65SWAOuUlwDrlJgA65SZAOuUmgDrlJsA65ScAOuUnQDrlJ4A65SfAOuUoADrlKEA65SiAOuUowDrlKQA65SlAOuUpgDrlKcA65SoAOuUqQDrlKoA65SrAOuUrADrlK0A65SuAOuUrwDrlLAA65SxAOuUsgDrlLMA65S0AOuUtQDrlLYA65S3AOuUuADrlLkA65S6AOuUuwDrlLwA65S9AOuUvgDrlL8A65WAAOuVgQDrlYIA65WDAOuVhADrlYUA65WGAOuVhwDrlYgA65WJAOuVigDrlYsA65WMAOuVjQDrlY4A65WPAOuVkADrlZEA65WSAOuVkwDrlZQA65WVAOuVlgDrlZcA65WYAOuVmQDrlZoA65WbAOuVnADrlZ0A65WeAOuVnwDrlaAA65WhAOuVogDrlaMA65WkAOuVpQDrlaYA65WnAOuVqADrlakA65WqAOuVqwDrlawA65WtAOuVrgDrla8A65WwAOuVsQDrlbIA65WzAOuVtADrlbUA65W2AOuVtwDrlbgA65W5AOuVugDrlbsA65W8AOuVvQDrlb4A65W/AOuWgADrloEA65aCAOuWgwDrloQA65aFAOuWhgDrlocA65aIAOuWiQDrlooA65aLAOuWjADrlo0A65aOAOuWjwDrlpAA65aRAOuWkgDrlpMA65aUAOuWlQDrlpYA65aXAOuWmADrlpkA65aaAOuWmwDrlpwA65adAOuWngDrlp8A65agAOuWoQDrlqIA65ajAOuWpADrlqUA65amAOuWpwDrlqgA65apAOuWqgDrlqsA65asAOuWrQDrlq4A65avAOuWsADrlrEA65ayAOuWswDrlrQA65a1AOuWtgDrlrcA65a4AOuWuQDrlroA65a7AOuWvADrlr0A65a+AOuWvwDrl4AA65eBAOuXggDrl4MA65eEAOuXhQDrl4YA65eHAOuXiADrl4kA65eKAOuXiwDrl4wA65eNAOuXjgDrl48A65eQAOuXkQDrl5IA65eTAOuXlADrl5UA65eWAOuXlwDrl5gA65eZAOuXmgDrl5sA65ecAOuXnQDrl54A65efAOuXoADrl6EA65eiAOuXowDrl6QA65elAOuXpgDrl6cA65eoAOuXqQDrl6oA65erAOuXrADrl60A65euAOuXrwDrl7AA65exAOuXsgDrl7MA65e0AOuXtQDrl7YA65e3AOuXuADrl7kA65e6AOuXuwDrl7wA65e9AOuXvgDrl78A65iAAOuYgQDrmIIA65iDAOuYhADrmIUA65iGAOuYhwDrmIgA65iJAOuYigDrmIsA65iMAOuYjQDrmI4A65iPAOuYkADrmJEA65iSAOuYkwDrmJQA65iVAOuYlgDrmJcA65iYAOuYmQDrmJoA65ibAOuYnADrmJ0A65ieAOuYnwDrmKAA65ihAOuYogDrmKMA65ikAOuYpQDrmKYA65inAOuYqADrmKkA65iqAOuYqwDrmKwA65itAOuYrgDrmK8A65iwAOuYsQDrmLIA65izAOuYtADrmLUA65i2AOuYtwDrmLgA65i5AOuYugDrmLsA65i8AOuYvQDrmL4A65i/AOuZgADrmYEA65mCAOuZgwDrmYQA65mFAOuZhgDrmYcA65mIAOuZiQDrmYoA65mLAOuZjADrmY0A65mOAOuZjwDrmZAA65mRAOuZkgDrmZMA65mUAOuZlQDrmZYA65mXAOuZmADrmZkA65maAOuZmwDrmZwA65mdAOuZngDrmZ8A65mgAOuZoQDrmaIA65mjAOuZpADrmaUA65mmAOuZpwDrmagA65mpAOuZqgDrmasA65msAOuZrQDrma4A65mvAOuZsADrmbEA65myAOuZswDrmbQA65m1AOuZtgDrmbcA65m4AOuZuQDrmboA65m7AOuZvADrmb0A65m+AOuZvwDrmoAA65qBAOuaggDrmoMA65qEAOuahQDrmoYA65qHAOuaiADrmokA65qKAOuaiwDrmowA65qNAOuajgDrmo8A65qQAOuakQDrmpIA65qTAOualADrmpUA65qWAOualwDrmpgA65qZAOuamgDrmpsA65qcAOuanQDrmp4A65qfAOuaoADrmqEA65qiAOuaowDrmqQA65qlAOuapgDrmqcA65qoAOuaqQDrmqoA65qrAOuarADrmq0A65quAOuarwDrmrAA65qxAOuasgDrmrMA65q0AOuatQDrmrYA65q3AOuauADrmrkA65q6AOuauwDrmrwA65q9AOuavgDrmr8A65uAAOubgQDrm4IA65uDAOubhADrm4UA65uGAOubhwDrm4gA65uJAOubigDrm4sA65uMAOubjQDrm44A65uPAOubkADrm5EA65uSAOubkwDrm5QA65uVAOublgDrm5cA65uYAOubmQDrm5oA65ubAOubnADrm50A65ueAOubnwDrm6AA65uhAOubogDrm6MA65ukAOubpQDrm6YA65unAOubqADrm6kA65uqAOubqwDrm6wA65utAOubrgDrm68A65uwAOubsQDrm7IA65uzAOubtADrm7UA65u2AOubtwDrm7gA65u5AOubugDrm7sA65u8AOubvQDrm74A65u/AOucgADrnIEA65yCAOucgwDrnIQA65yFAOuchgDrnIcA65yIAOuciQDrnIoA65yLAOucjADrnI0A65yOAOucjwDrnJAA65yRAOuckgDrnJMA65yUAOuclQDrnJYA65yXAOucmADrnJkA65yaAOucmwDrnJwA65ydAOucngDrnJ8A65ygAOucoQDrnKIA65yjAOucpADrnKUA65ymAOucpwDrnKgA65ypAOucqgDrnKsA65ysAOucrQDrnK4A65yvAOucsADrnLEA65yyAOucswDrnLQA65y1AOuctgDrnLcA65y4AOucuQDrnLoA65y7AOucvADrnL0A65y+AOucvwDrnYAA652BAOudggDrnYMA652EAOudhQDrnYYA652HAOudiADrnYkA652KAOudiwDrnYwA652NAOudjgDrnY8A652QAOudkQDrnZIA652TAOudlADrnZUA652WAOudlwDrnZgA652ZAOudmgDrnZsA652cAOudnQDrnZ4A652fAOudoADrnaEA652iAOudowDrnaQA652lAOudpgDrnacA652oAOudqQDrnaoA652rAOudrADrna0A652uAOudrwDrnbAA652xAOudsgDrnbMA6520AOudtQDrnbYA6523AOuduADrnbkA6526AOuduwDrnbwA6529AOudvgDrnb8A656AAOuegQDrnoIA656DAOuehADrnoUA656GAOuehwDrnogA656JAOueigDrnosA656MAOuejQDrno4A656PAOuekADrnpEA656SAOuekwDrnpQA656VAOuelgDrnpcA656YAOuemQDrnpoA656bAOuenADrnp0A656eAOuenwDrnqAA656hAOueogDrnqMA656kAOuepQDrnqYA656nAOueqADrnqkA656qAOueqwDrnqwA656tAOuergDrnq8A656wAOuesQDrnrIA656zAOuetADrnrUA6562AOuetwDrnrgA6565AOueugDrnrsA6568AOuevQDrnr4A656/AOufgADrn4EA65+CAOufgwDrn4QA65+FAOufhgDrn4cA65+IAOufiQDrn4oA65+LAOufjADrn40A65+OAOufjwDrn5AA65+RAOufkgDrn5MA65+UAOuflQDrn5YA65+XAOufmADrn5kA65+aAOufmwDrn5wA65+dAOufngDrn58A65+gAOufoQDrn6IA65+jAOufpADrn6UA65+mAOufpwDrn6gA65+pAOufqgDrn6sA65+sAOufrQDrn64A65+vAOufsADrn7EA65+yAOufswDrn7QA65+1AOuftgDrn7cA65+4AOufuQDrn7oA65+7AOufvADrn70A65++AOufvwDroIAA66CBAOugggDroIMA66CEAOughQDroIYA66CHAOugiADroIkA66CKAOugiwDroIwA66CNAOugjgDroI8A66CQAOugkQDroJIA66CTAOuglADroJUA66CWAOuglwDroJgA66CZAOugmgDroJsA66CcAOugnQDroJ4A66CfAOugoADroKEA66CiAOugowDroKQA66ClAOugpgDroKcA66CoAOugqQDroKoA66CrAOugrADroK0A66CuAOugrwDroLAA66CxAOugsgDroLMA66C0AOugtQDroLYA66C3AOuguADroLkA66C6AOuguwDroLwA66C9AOugvgDroL8A66GAAOuhgQDroYIA66GDAOuhhADroYUA66GGAOuhhwDroYgA66GJAOuhigDroYsA66GMAOuhjQDroY4A66GPAOuhkADroZEA66GSAOuhkwDroZQA66GVAOuhlgDroZcA66GYAOuhmQDroZoA66GbAOuhnADroZ0A66GeAOuhnwDroaAA66GhAOuhogDroaMA66GkAOuhpQDroaYA66GnAOuhqADroakA66GqAOuhqwDroawA66GtAOuhrgDroa8A66GwAOuhsQDrobIA66GzAOuhtADrobUA66G2AOuhtwDrobgA66G5AOuhugDrobsA66G8AOuhvQDrob4A66G/AOuigADrooEA66KCAOuigwDrooQA66KFAOuihgDroocA66KIAOuiiQDroooA66KLAOuijADroo0A66KOAOuijwDropAA66KRAOuikgDropMA66KUAOuilQDropYA66KXAOuimADropkA66KaAOuimwDropwA66KdAOuingDrop8A66KgAOuioQDroqIA66KjAOuipADroqUA66KmAOuipwDroqgA66KpAOuiqgDroqsA66KsAOuirQDroq4A66KvAOuisADrorEA66KyAOuiswDrorQA66K1AOuitgDrorcA66K4AOuiuQDroroA66K7AOuivADror0A66K+AOuivwDro4AA66OBAOujggDro4MA66OEAOujhQDro4YA66OHAOujiADro4kA66OKAOujiwDro4wA66ONAOujjgDro48A66OQAOujkQDro5IA66OTAOujlADro5UA66OWAOujlwDro5gA66OZAOujmgDro5sA66OcAOujnQDro54A66OfAOujoADro6EA66OiAOujowDro6QA66OlAOujpgDro6cA66OoAOujqQDro6oA66OrAOujrADro60A66OuAOujrwDro7AA66OxAOujsgDro7MA66O0AOujtQDro7YA66O3AOujuADro7kA66O6AOujuwDro7wA66O9AOujvgDro78A66SAAOukgQDrpIIA66SDAOukhADrpIUA66SGAOukhwDrpIgA66SJAOukigDrpIsA66SMAOukjQDrpI4A66SPAOukkADrpJEA66SSAOukkwDrpJQA66SVAOuklgDrpJcA66SYAOukmQDrpJoA66SbAOuknADrpJ0A66SeAOuknwDrpKAA66ShAOukogDrpKMA66SkAOukpQDrpKYA66SnAOukqADrpKkA66SqAOukqwDrpKwA66StAOukrgDrpK8A66SwAOuksQDrpLIA66SzAOuktADrpLUA66S2AOuktwDrpLgA66S5AOukugDrpLsA66S8AOukvQDrpL4A66S/AOulgADrpYEA66WCAOulgwDrpYQA66WFAOulhgDrpYcA66WIAOuliQDrpYoA66WLAOuljADrpY0A66WOAOuljwDrpZAA66WRAOulkgDrpZMA66WUAOullQDrpZYA66WXAOulmADrpZkA66WaAOulmwDrpZwA66WdAOulngDrpZ8A66WgAOuloQDrpaIA66WjAOulpADrpaUA66WmAOulpwDrpagA66WpAOulqgDrpasA66WsAOulrQDrpa4A66WvAOulsADrpbEA66WyAOulswDrpbQA66W1AOultgDrpbcA66W4AOuluQDrpboA66W7AOulvADrpb0A66W+AOulvwDrpoAA66aBAOumggDrpoMA66aEAOumhQDrpoYA66aHAOumiADrpokA66aKAOumiwDrpowA66aNAOumjgDrpo8A66aQAOumkQDrppIA66aTAOumlADrppUA66aWAOumlwDrppgA66aZAOummgDrppsA66acAOumnQDrpp4A66afAOumoADrpqEA66aiAOumowDrpqQA66alAOumpgDrpqcA66aoAOumqQDrpqoA66arAOumrADrpq0A66auAOumrwDrprAA66axAOumsgDrprMA66a0AOumtQDrprYA66a3AOumuADrprkA66a6AOumuwDrprwA66a9AOumvgDrpr8A66eAAOungQDrp4IA66eDAOunhADrp4UA66eGAOunhwDrp4gA66eJAOunigDrp4sA66eMAOunjQDrp44A66ePAOunkADrp5EA66eSAOunkwDrp5QA66eVAOunlgDrp5cA66eYAOunmQDrp5oA66ebAOunnADrp50A66eeAOunnwDrp6AA66ehAOunogDrp6MA66ekAOunpQDrp6YA66enAOunqADrp6kA66eqAOunqwDrp6wA66etAOunrgDrp68A66ewAOunsQDrp7IA66ezAOuntADrp7UA66e2AOuntwDrp7gA66e5AOunugDrp7sA66e8AOunvQDrp74A66e/AOuogADrqIEA66iCAOuogwDrqIQA66iFAOuohgDrqIcA66iIAOuoiQDrqIoA66iLAOuojADrqI0A66iOAOuojwDrqJAA66iRAOuokgDrqJMA66iUAOuolQDrqJYA66iXAOuomADrqJkA66iaAOuomwDrqJwA66idAOuongDrqJ8A66igAOuooQDrqKIA66ijAOuopADrqKUA66imAOuopwDrqKgA66ipAOuoqgDrqKsA66isAOuorQDrqK4A66ivAOuosADrqLEA66iyAOuoswDrqLQA66i1AOuotgDrqLcA66i4AOuouQDrqLoA66i7AOuovADrqL0A66i+AOuovwDrqYAA66mBAOupggDrqYMA66mEAOuphQDrqYYA66mHAOupiADrqYkA66mKAOupiwDrqYwA66mNAOupjgDrqY8A66mQAOupkQDrqZIA66mTAOuplADrqZUA66mWAOuplwDrqZgA66mZAOupmgDrqZsA66mcAOupnQDrqZ4A66mfAOupoADrqaEA66miAOupowDrqaQA66mlAOuppgDrqacA66moAOupqQDrqaoA66mrAOuprADrqa0A66muAOuprwDrqbAA66mxAOupsgDrqbMA66m0AOuptQDrqbYA66m3AOupuADrqbkA66m6AOupuwDrqbwA66m9AOupvgDrqb8A66qAAOuqgQDrqoIA66qDAOuqhADrqoUA66qGAOuqhwDrqogA66qJAOuqigDrqosA66qMAOuqjQDrqo4A66qPAOuqkADrqpEA66qSAOuqkwDrqpQA66qVAOuqlgDrqpcA66qYAOuqmQDrqpoA66qbAOuqnADrqp0A66qeAOuqnwDrqqAA66qhAOuqogDrqqMA66qkAOuqpQDrqqYA66qnAOuqqADrqqkA66qqAOuqqwDrqqwA66qtAOuqrgDrqq8A66qwAOuqsQDrqrIA66qzAOuqtADrqrUA66q2AOuqtwDrqrgA66q5AOuqugDrqrsA66q8AOuqvQDrqr4A66q/AOurgADrq4EA66uCAOurgwDrq4QA66uFAOurhgDrq4cA66uIAOuriQDrq4oA66uLAOurjADrq40A66uOAOurjwDrq5AA66uRAOurkgDrq5MA66uUAOurlQDrq5YA66uXAOurmADrq5kA66uaAOurmwDrq5wA66udAOurngDrq58A66ugAOuroQDrq6IA66ujAOurpADrq6UA66umAOurpwDrq6gA66upAOurqgDrq6sA66usAOurrQDrq64A66uvAOursADrq7EA66uyAOurswDrq7QA66u1AOurtgDrq7cA66u4AOuruQDrq7oA66u7AOurvADrq70A66u+AOurvwDrrIAA66yBAOusggDrrIMA66yEAOushQDrrIYA66yHAOusiADrrIkA66yKAOusiwDrrIwA66yNAOusjgDrrI8A66yQAOuskQDrrJIA66yTAOuslADrrJUA66yWAOuslwDrrJgA66yZAOusmgDrrJsA66ycAOusnQDrrJ4A66yfAOusoADrrKEA66yiAOusowDrrKQA66ylAOuspgDrrKcA66yoAOusqQDrrKoA66yrAOusrADrrK0A66yuAOusrwDrrLAA66yxAOussgDrrLMA66y0AOustQDrrLYA66y3AOusuADrrLkA66y6AOusuwDrrLwA66y9AOusvgDrrL8A662AAOutgQDrrYIA662DAOuthADrrYUA662GAOuthwDrrYgA662JAOutigDrrYsA662MAOutjQDrrY4A662PAOutkADrrZEA662SAOutkwDrrZQA662VAOutlgDrrZcA662YAOutmQDrrZoA662bAOutnADrrZ0A662eAOutnwDrraAA662hAOutogDrraMA662kAOutpQDrraYA662nAOutqADrrakA662qAOutqwDrrawA662tAOutrgDrra8A662wAOutsQDrrbIA662zAOuttADrrbUA6622AOuttwDrrbgA6625AOutugDrrbsA6628AOutvQDrrb4A662/AOuugADrroEA666CAOuugwDrroQA666FAOuuhgDrrocA666IAOuuiQDrrooA666LAOuujADrro0A666OAOuujwDrrpAA666RAOuukgDrrpMA666UAOuulQDrrpYA666XAOuumADrrpkA666aAOuumwDrrpwA666dAOuungDrrp8A666gAOuuoQDrrqIA666jAOuupADrrqUA666mAOuupwDrrqgA666pAOuuqgDrrqsA666sAOuurQDrrq4A666vAOuusADrrrEA666yAOuuswDrrrQA6661AOuutgDrrrcA6664AOuuuQDrrroA6667AOuuvADrrr0A666+AOuuvwDrr4AA66+BAOuvggDrr4MA66+EAOuvhQDrr4YA66+HAOuviADrr4kA66+KAOuviwDrr4wA66+NAOuvjgDrr48A66+QAOuvkQDrr5IA66+TAOuvlADrr5UA66+WAOuvlwDrr5gA66+ZAOuvmgDrr5sA66+cAOuvnQDrr54A66+fAOuvoADrr6EA66+iAOuvowDrr6QA66+lAOuvpgDrr6cA66+oAOuvqQDrr6oA66+rAOuvrADrr60A66+uAOuvrwDrr7AA66+xAOuvsgDrr7MA66+0AOuvtQDrr7YA66+3AOuvuADrr7kA66+6AOuvuwDrr7wA66+9AOuvvgDrr78A67CAAOuwgQDrsIIA67CDAOuwhADrsIUA67CGAOuwhwDrsIgA67CJAOuwigDrsIsA67CMAOuwjQDrsI4A67CPAOuwkADrsJEA67CSAOuwkwDrsJQA67CVAOuwlgDrsJcA67CYAOuwmQDrsJoA67CbAOuwnADrsJ0A67CeAOuwnwDrsKAA67ChAOuwogDrsKMA67CkAOuwpQDrsKYA67CnAOuwqADrsKkA67CqAOuwqwDrsKwA67CtAOuwrgDrsK8A67CwAOuwsQDrsLIA67CzAOuwtADrsLUA67C2AOuwtwDrsLgA67C5AOuwugDrsLsA67C8AOuwvQDrsL4A67C/AOuxgADrsYEA67GCAOuxgwDrsYQA67GFAOuxhgDrsYcA67GIAOuxiQDrsYoA67GLAOuxjADrsY0A67GOAOuxjwDrsZAA67GRAOuxkgDrsZMA67GUAOuxlQDrsZYA67GXAOuxmADrsZkA67GaAOuxmwDrsZwA67GdAOuxngDrsZ8A67GgAOuxoQDrsaIA67GjAOuxpADrsaUA67GmAOuxpwDrsagA67GpAOuxqgDrsasA67GsAOuxrQDrsa4A67GvAOuxsADrsbEA67GyAOuxswDrsbQA67G1AOuxtgDrsbcA67G4AOuxuQDrsboA67G7AOuxvADrsb0A67G+AOuxvwDrsoAA67KBAOuyggDrsoMA67KEAOuyhQDrsoYA67KHAOuyiADrsokA67KKAOuyiwDrsowA67KNAOuyjgDrso8A67KQAOuykQDrspIA67KTAOuylADrspUA67KWAOuylwDrspgA67KZAOuymgDrspsA67KcAOuynQDrsp4A67KfAOuyoADrsqEA67KiAOuyowDrsqQA67KlAOuypgDrsqcA67KoAOuyqQDrsqoA67KrAOuyrADrsq0A67KuAOuyrwDrsrAA67KxAOuysgDrsrMA67K0AOuytQDrsrYA67K3AOuyuADrsrkA67K6AOuyuwDrsrwA67K9AOuyvgDrsr8A67OAAOuzgQDrs4IA67ODAOuzhADrs4UA67OGAOuzhwDrs4gA67OJAOuzigDrs4sA67OMAOuzjQDrs44A67OPAOuzkADrs5EA67OSAOuzkwDrs5QA67OVAOuzlgDrs5cA67OYAOuzmQDrs5oA67ObAOuznADrs50A67OeAOuznwDrs6AA67OhAOuzogDrs6MA67OkAOuzpQDrs6YA67OnAOuzqADrs6kA67OqAOuzqwDrs6wA67OtAOuzrgDrs68A67OwAOuzsQDrs7IA67OzAOuztADrs7UA67O2AOuztwDrs7gA67O5AOuzugDrs7sA67O8AOuzvQDrs74A67O/AOu0gADrtIEA67SCAOu0gwDrtIQA67SFAOu0hgDrtIcA67SIAOu0iQDrtIoA67SLAOu0jADrtI0A67SOAOu0jwDrtJAA67SRAOu0kgDrtJMA67SUAOu0lQDrtJYA67SXAOu0mADrtJkA67SaAOu0mwDrtJwA67SdAOu0ngDrtJ8A67SgAOu0oQDrtKIA67SjAOu0pADrtKUA67SmAOu0pwDrtKgA67SpAOu0qgDrtKsA67SsAOu0rQDrtK4A67SvAOu0sADrtLEA67SyAOu0swDrtLQA67S1AOu0tgDrtLcA67S4AOu0uQDrtLoA67S7AOu0vADrtL0A67S+AOu0vwDrtYAA67WBAOu1ggDrtYMA67WEAOu1hQDrtYYA67WHAOu1iADrtYkA67WKAOu1iwDrtYwA67WNAOu1jgDrtY8A67WQAOu1kQDrtZIA67WTAOu1lADrtZUA67WWAOu1lwDrtZgA67WZAOu1mgDrtZsA67WcAOu1nQDrtZ4A67WfAOu1oADrtaEA67WiAOu1owDrtaQA67WlAOu1pgDrtacA67WoAOu1qQDrtaoA67WrAOu1rADrta0A67WuAOu1rwDrtbAA67WxAOu1sgDrtbMA67W0AOu1tQDrtbYA67W3AOu1uADrtbkA67W6AOu1uwDrtbwA67W9AOu1vgDrtb8A67aAAOu2gQDrtoIA67aDAOu2hADrtoUA67aGAOu2hwDrtogA67aJAOu2igDrtosA67aMAOu2jQDrto4A67aPAOu2kADrtpEA67aSAOu2kwDrtpQA67aVAOu2lgDrtpcA67aYAOu2mQDrtpoA67abAOu2nADrtp0A67aeAOu2nwDrtqAA67ahAOu2ogDrtqMA67akAOu2pQDrtqYA67anAOu2qADrtqkA67aqAOu2qwDrtqwA67atAOu2rgDrtq8A67awAOu2sQDrtrIA67azAOu2tADrtrUA67a2AOu2twDrtrgA67a5AOu2ugDrtrsA67a8AOu2vQDrtr4A67a/AOu3gADrt4EA67eCAOu3gwDrt4QA67eFAOu3hgDrt4cA67eIAOu3iQDrt4oA67eLAOu3jADrt40A67eOAOu3jwDrt5AA67eRAOu3kgDrt5MA67eUAOu3lQDrt5YA67eXAOu3mADrt5kA67eaAOu3mwDrt5wA67edAOu3ngDrt58A67egAOu3oQDrt6IA67ejAOu3pADrt6UA67emAOu3pwDrt6gA67epAOu3qgDrt6sA67esAOu3rQDrt64A67evAOu3sADrt7EA67eyAOu3swDrt7QA67e1AOu3tgDrt7cA67e4AOu3uQDrt7oA67e7AOu3vADrt70A67e+AOu3vwDruIAA67iBAOu4ggDruIMA67iEAOu4hQDruIYA67iHAOu4iADruIkA67iKAOu4iwDruIwA67iNAOu4jgDruI8A67iQAOu4kQDruJIA67iTAOu4lADruJUA67iWAOu4lwDruJgA67iZAOu4mgDruJsA67icAOu4nQDruJ4A67ifAOu4oADruKEA67iiAOu4owDruKQA67ilAOu4pgDruKcA67ioAOu4qQDruKoA67irAOu4rADruK0A67iuAOu4rwDruLAA67ixAOu4sgDruLMA67i0AOu4tQDruLYA67i3AOu4uADruLkA67i6AOu4uwDruLwA67i9AOu4vgDruL8A67mAAOu5gQDruYIA67mDAOu5hADruYUA67mGAOu5hwDruYgA67mJAOu5igDruYsA67mMAOu5jQDruY4A67mPAOu5kADruZEA67mSAOu5kwDruZQA67mVAOu5lgDruZcA67mYAOu5mQDruZoA67mbAOu5nADruZ0A67meAOu5nwDruaAA67mhAOu5ogDruaMA67mkAOu5pQDruaYA67mnAOu5qADruakA67mqAOu5qwDruawA67mtAOu5rgDrua8A67mwAOu5sQDrubIA67mzAOu5tADrubUA67m2AOu5twDrubgA67m5AOu5ugDrubsA67m8AOu5vQDrub4A67m/AOu6gADruoEA67qCAOu6gwDruoQA67qFAOu6hgDruocA67qIAOu6iQDruooA67qLAOu6jADruo0A67qOAOu6jwDrupAA67qRAOu6kgDrupMA67qUAOu6lQDrupYA67qXAOu6mADrupkA67qaAOu6mwDrupwA67qdAOu6ngDrup8A67qgAOu6oQDruqIA67qjAOu6pADruqUA67qmAOu6pwDruqgA67qpAOu6qgDruqsA67qsAOu6rQDruq4A67qvAOu6sADrurEA67qyAOu6swDrurQA67q1AOu6tgDrurcA67q4AOu6uQDruroA67q7AOu6vADrur0A67q+AOu6vwDru4AA67uBAOu7ggDru4MA67uEAOu7hQDru4YA67uHAOu7iADru4kA67uKAOu7iwDru4wA67uNAOu7jgDru48A67uQAOu7kQDru5IA67uTAOu7lADru5UA67uWAOu7lwDru5gA67uZAOu7mgDru5sA67ucAOu7nQDru54A67ufAOu7oADru6EA67uiAOu7owDru6QA67ulAOu7pgDru6cA67uoAOu7qQDru6oA67urAOu7rADru60A67uuAOu7rwDru7AA67uxAOu7sgDru7MA67u0AOu7tQDru7YA67u3AOu7uADru7kA67u6AOu7uwDru7wA67u9AOu7vgDru78A67yAAOu8gQDrvIIA67yDAOu8hADrvIUA67yGAOu8hwDrvIgA67yJAOu8igDrvIsA67yMAOu8jQDrvI4A67yPAOu8kADrvJEA67ySAOu8kwDrvJQA67yVAOu8lgDrvJcA67yYAOu8mQDrvJoA67ybAOu8nADrvJ0A67yeAOu8nwDrvKAA67yhAOu8ogDrvKMA67ykAOu8pQDrvKYA67ynAOu8qADrvKkA67yqAOu8qwDrvKwA67ytAOu8rgDrvK8A67ywAOu8sQDrvLIA67yzAOu8tADrvLUA67y2AOu8twDrvLgA67y5AOu8ugDrvLsA67y8AOu8vQDrvL4A67y/AOu9gADrvYEA672CAOu9gwDrvYQA672FAOu9hgDrvYcA672IAOu9iQDrvYoA672LAOu9jADrvY0A672OAOu9jwDrvZAA672RAOu9kgDrvZMA672UAOu9lQDrvZYA672XAOu9mADrvZkA672aAOu9mwDrvZwA672dAOu9ngDrvZ8A672gAOu9oQDrvaIA672jAOu9pADrvaUA672mAOu9pwDrvagA672pAOu9qgDrvasA672sAOu9rQDrva4A672vAOu9sADrvbEA672yAOu9swDrvbQA6721AOu9tgDrvbcA6724AOu9uQDrvboA6727AOu9vADrvb0A672+AOu9vwDrvoAA676BAOu+ggDrvoMA676EAOu+hQDrvoYA676HAOu+iADrvokA676KAOu+iwDrvowA676NAOu+jgDrvo8A676QAOu+kQDrvpIA676TAOu+lADrvpUA676WAOu+lwDrvpgA676ZAOu+mgDrvpsA676cAOu+nQDrvp4A676fAOu+oADrvqEA676iAOu+owDrvqQA676lAOu+pgDrvqcA676oAOu+qQDrvqoA676rAOu+rADrvq0A676uAOu+rwDrvrAA676xAOu+sgDrvrMA6760AOu+tQDrvrYA6763AOu+uADrvrkA6766AOu+uwDrvrwA6769AOu+vgDrvr8A67+AAOu/gQDrv4IA67+DAOu/hADrv4UA67+GAOu/hwDrv4gA67+JAOu/igDrv4sA67+MAOu/jQDrv44A67+PAOu/kADrv5EA67+SAOu/kwDrv5QA67+VAOu/lgDrv5cA67+YAOu/mQDrv5oA67+bAOu/nADrv50A67+eAOu/nwDrv6AA67+hAOu/ogDrv6MA67+kAOu/pQDrv6YA67+nAOu/qADrv6kA67+qAOu/qwDrv6wA67+tAOu/rgDrv68A67+wAOu/sQDrv7IA67+zAOu/tADrv7UA67+2AOu/twDrv7gA67+5AOu/ugDrv7sA67+8AOu/vQDrv74A67+/AOyAgADsgIEA7ICCAOyAgwDsgIQA7ICFAOyAhgDsgIcA7ICIAOyAiQDsgIoA7ICLAOyAjADsgI0A7ICOAOyAjwDsgJAA7ICRAOyAkgDsgJMA7ICUAOyAlQDsgJYA7ICXAOyAmADsgJkA7ICaAOyAmwDsgJwA7ICdAOyAngDsgJ8A7ICgAOyAoQDsgKIA7ICjAOyApADsgKUA7ICmAOyApwDsgKgA7ICpAOyAqgDsgKsA7ICsAOyArQDsgK4A7ICvAOyAsADsgLEA7ICyAOyAswDsgLQA7IC1AOyAtgDsgLcA7IC4AOyAuQDsgLoA7IC7AOyAvADsgL0A7IC+AOyAvwDsgYAA7IGBAOyBggDsgYMA7IGEAOyBhQDsgYYA7IGHAOyBiADsgYkA7IGKAOyBiwDsgYwA7IGNAOyBjgDsgY8A7IGQAOyBkQDsgZIA7IGTAOyBlADsgZUA7IGWAOyBlwDsgZgA7IGZAOyBmgDsgZsA7IGcAOyBnQDsgZ4A7IGfAOyBoADsgaEA7IGiAOyBowDsgaQA7IGlAOyBpgDsgacA7IGoAOyBqQDsgaoA7IGrAOyBrADsga0A7IGuAOyBrwDsgbAA7IGxAOyBsgDsgbMA7IG0AOyBtQDsgbYA7IG3AOyBuADsgbkA7IG6AOyBuwDsgbwA7IG9AOyBvgDsgb8A7IKAAOyCgQDsgoIA7IKDAOyChADsgoUA7IKGAOyChwDsgogA7IKJAOyCigDsgosA7IKMAOyCjQDsgo4A7IKPAOyCkADsgpEA7IKSAOyCkwDsgpQA7IKVAOyClgDsgpcA7IKYAOyCmQDsgpoA7IKbAOyCnADsgp0A7IKeAOyCnwDsgqAA7IKhAOyCogDsgqMA7IKkAOyCpQDsgqYA7IKnAOyCqADsgqkA7IKqAOyCqwDsgqwA7IKtAOyCrgDsgq8A7IKwAOyCsQDsgrIA7IKzAOyCtADsgrUA7IK2AOyCtwDsgrgA7IK5AOyCugDsgrsA7IK8AOyCvQDsgr4A7IK/AOyDgADsg4EA7IOCAOyDgwDsg4QA7IOFAOyDhgDsg4cA7IOIAOyDiQDsg4oA7IOLAOyDjADsg40A7IOOAOyDjwDsg5AA7IORAOyDkgDsg5MA7IOUAOyDlQDsg5YA7IOXAOyDmADsg5kA7IOaAOyDmwDsg5wA7IOdAOyDngDsg58A7IOgAOyDoQDsg6IA7IOjAOyDpADsg6UA7IOmAOyDpwDsg6gA7IOpAOyDqgDsg6sA7IOsAOyDrQDsg64A7IOvAOyDsADsg7EA7IOyAOyDswDsg7QA7IO1AOyDtgDsg7cA7IO4AOyDuQDsg7oA7IO7AOyDvADsg70A7IO+AOyDvwDshIAA7ISBAOyEggDshIMA7ISEAOyEhQDshIYA7ISHAOyEiADshIkA7ISKAOyEiwDshIwA7ISNAOyEjgDshI8A7ISQAOyEkQDshJIA7ISTAOyElADshJUA7ISWAOyElwDshJgA7ISZAOyEmgDshJsA7IScAOyEnQDshJ4A7ISfAOyEoADshKEA7ISiAOyEowDshKQA7ISlAOyEpgDshKcA7ISoAOyEqQDshKoA7ISrAOyErADshK0A7ISuAOyErwDshLAA7ISxAOyEsgDshLMA7IS0AOyEtQDshLYA7IS3AOyEuADshLkA7IS6AOyEuwDshLwA7IS9AOyEvgDshL8A7IWAAOyFgQDshYIA7IWDAOyFhADshYUA7IWGAOyFhwDshYgA7IWJAOyFigDshYsA7IWMAOyFjQDshY4A7IWPAOyFkADshZEA7IWSAOyFkwDshZQA7IWVAOyFlgDshZcA7IWYAOyFmQDshZoA7IWbAOyFnADshZ0A7IWeAOyFnwDshaAA7IWhAOyFogDshaMA7IWkAOyFpQDshaYA7IWnAOyFqADshakA7IWqAOyFqwDshawA7IWtAOyFrgDsha8A7IWwAOyFsQDshbIA7IWzAOyFtADshbUA7IW2AOyFtwDshbgA7IW5AOyFugDshbsA7IW8AOyFvQDshb4A7IW/AOyGgADshoEA7IaCAOyGgwDshoQA7IaFAOyGhgDshocA7IaIAOyGiQDshooA7IaLAOyGjADsho0A7IaOAOyGjwDshpAA7IaRAOyGkgDshpMA7IaUAOyGlQDshpYA7IaXAOyGmADshpkA7IaaAOyGmwDshpwA7IadAOyGngDshp8A7IagAOyGoQDshqIA7IajAOyGpADshqUA7IamAOyGpwDshqgA7IapAOyGqgDshqsA7IasAOyGrQDshq4A7IavAOyGsADshrEA7IayAOyGswDshrQA7Ia1AOyGtgDshrcA7Ia4AOyGuQDshroA7Ia7AOyGvADshr0A7Ia+AOyGvwDsh4AA7IeBAOyHggDsh4MA7IeEAOyHhQDsh4YA7IeHAOyHiADsh4kA7IeKAOyHiwDsh4wA7IeNAOyHjgDsh48A7IeQAOyHkQDsh5IA7IeTAOyHlADsh5UA7IeWAOyHlwDsh5gA7IeZAOyHmgDsh5sA7IecAOyHnQDsh54A7IefAOyHoADsh6EA7IeiAOyHowDsh6QA7IelAOyHpgDsh6cA7IeoAOyHqQDsh6oA7IerAOyHrADsh60A7IeuAOyHrwDsh7AA7IexAOyHsgDsh7MA7Ie0AOyHtQDsh7YA7Ie3AOyHuADsh7kA7Ie6AOyHuwDsh7wA7Ie9AOyHvgDsh78A7IiAAOyIgQDsiIIA7IiDAOyIhADsiIUA7IiGAOyIhwDsiIgA7IiJAOyIigDsiIsA7IiMAOyIjQDsiI4A7IiPAOyIkADsiJEA7IiSAOyIkwDsiJQA7IiVAOyIlgDsiJcA7IiYAOyImQDsiJoA7IibAOyInADsiJ0A7IieAOyInwDsiKAA7IihAOyIogDsiKMA7IikAOyIpQDsiKYA7IinAOyIqADsiKkA7IiqAOyIqwDsiKwA7IitAOyIrgDsiK8A7IiwAOyIsQDsiLIA7IizAOyItADsiLUA7Ii2AOyItwDsiLgA7Ii5AOyIugDsiLsA7Ii8AOyIvQDsiL4A7Ii/AOyJgADsiYEA7ImCAOyJgwDsiYQA7ImFAOyJhgDsiYcA7ImIAOyJiQDsiYoA7ImLAOyJjADsiY0A7ImOAOyJjwDsiZAA7ImRAOyJkgDsiZMA7ImUAOyJlQDsiZYA7ImXAOyJmADsiZkA7ImaAOyJmwDsiZwA7ImdAOyJngDsiZ8A7ImgAOyJoQDsiaIA7ImjAOyJpADsiaUA7ImmAOyJpwDsiagA7ImpAOyJqgDsiasA7ImsAOyJrQDsia4A7ImvAOyJsADsibEA7ImyAOyJswDsibQA7Im1AOyJtgDsibcA7Im4AOyJuQDsiboA7Im7AOyJvADsib0A7Im+AOyJvwDsioAA7IqBAOyKggDsioMA7IqEAOyKhQDsioYA7IqHAOyKiADsiokA7IqKAOyKiwDsiowA7IqNAOyKjgDsio8A7IqQAOyKkQDsipIA7IqTAOyKlADsipUA7IqWAOyKlwDsipgA7IqZAOyKmgDsipsA7IqcAOyKnQDsip4A7IqfAOyKoADsiqEA7IqiAOyKowDsiqQA7IqlAOyKpgDsiqcA7IqoAOyKqQDsiqoA7IqrAOyKrADsiq0A7IquAOyKrwDsirAA7IqxAOyKsgDsirMA7Iq0AOyKtQDsirYA7Iq3AOyKuADsirkA7Iq6AOyKuwDsirwA7Iq9AOyKvgDsir8A7IuAAOyLgQDsi4IA7IuDAOyLhADsi4UA7IuGAOyLhwDsi4gA7IuJAOyLigDsi4sA7IuMAOyLjQDsi44A7IuPAOyLkADsi5EA7IuSAOyLkwDsi5QA7IuVAOyLlgDsi5cA7IuYAOyLmQDsi5oA7IubAOyLnADsi50A7IueAOyLnwDsi6AA7IuhAOyLogDsi6MA7IukAOyLpQDsi6YA7IunAOyLqADsi6kA7IuqAOyLqwDsi6wA7IutAOyLrgDsi68A7IuwAOyLsQDsi7IA7IuzAOyLtADsi7UA7Iu2AOyLtwDsi7gA7Iu5AOyLugDsi7sA7Iu8AOyLvQDsi74A7Iu/AOyMgADsjIEA7IyCAOyMgwDsjIQA7IyFAOyMhgDsjIcA7IyIAOyMiQDsjIoA7IyLAOyMjADsjI0A7IyOAOyMjwDsjJAA7IyRAOyMkgDsjJMA7IyUAOyMlQDsjJYA7IyXAOyMmADsjJkA7IyaAOyMmwDsjJwA7IydAOyMngDsjJ8A7IygAOyMoQDsjKIA7IyjAOyMpADsjKUA7IymAOyMpwDsjKgA7IypAOyMqgDsjKsA7IysAOyMrQDsjK4A7IyvAOyMsADsjLEA7IyyAOyMswDsjLQA7Iy1AOyMtgDsjLcA7Iy4AOyMuQDsjLoA7Iy7AOyMvADsjL0A7Iy+AOyMvwDsjYAA7I2BAOyNggDsjYMA7I2EAOyNhQDsjYYA7I2HAOyNiADsjYkA7I2KAOyNiwDsjYwA7I2NAOyNjgDsjY8A7I2QAOyNkQDsjZIA7I2TAOyNlADsjZUA7I2WAOyNlwDsjZgA7I2ZAOyNmgDsjZsA7I2cAOyNnQDsjZ4A7I2fAOyNoADsjaEA7I2iAOyNowDsjaQA7I2lAOyNpgDsjacA7I2oAOyNqQDsjaoA7I2rAOyNrADsja0A7I2uAOyNrwDsjbAA7I2xAOyNsgDsjbMA7I20AOyNtQDsjbYA7I23AOyNuADsjbkA7I26AOyNuwDsjbwA7I29AOyNvgDsjb8A7I6AAOyOgQDsjoIA7I6DAOyOhADsjoUA7I6GAOyOhwDsjogA7I6JAOyOigDsjosA7I6MAOyOjQDsjo4A7I6PAOyOkADsjpEA7I6SAOyOkwDsjpQA7I6VAOyOlgDsjpcA7I6YAOyOmQDsjpoA7I6bAOyOnADsjp0A7I6eAOyOnwDsjqAA7I6hAOyOogDsjqMA7I6kAOyOpQDsjqYA7I6nAOyOqADsjqkA7I6qAOyOqwDsjqwA7I6tAOyOrgDsjq8A7I6wAOyOsQDsjrIA7I6zAOyOtADsjrUA7I62AOyOtwDsjrgA7I65AOyOugDsjrsA7I68AOyOvQDsjr4A7I6/AOyPgADsj4EA7I+CAOyPgwDsj4QA7I+FAOyPhgDsj4cA7I+IAOyPiQDsj4oA7I+LAOyPjADsj40A7I+OAOyPjwDsj5AA7I+RAOyPkgDsj5MA7I+UAOyPlQDsj5YA7I+XAOyPmADsj5kA7I+aAOyPmwDsj5wA7I+dAOyPngDsj58A7I+gAOyPoQDsj6IA7I+jAOyPpADsj6UA7I+mAOyPpwDsj6gA7I+pAOyPqgDsj6sA7I+sAOyPrQDsj64A7I+vAOyPsADsj7EA7I+yAOyPswDsj7QA7I+1AOyPtgDsj7cA7I+4AOyPuQDsj7oA7I+7AOyPvADsj70A7I++AOyPvwDskIAA7JCBAOyQggDskIMA7JCEAOyQhQDskIYA7JCHAOyQiADskIkA7JCKAOyQiwDskIwA7JCNAOyQjgDskI8A7JCQAOyQkQDskJIA7JCTAOyQlADskJUA7JCWAOyQlwDskJgA7JCZAOyQmgDskJsA7JCcAOyQnQDskJ4A7JCfAOyQoADskKEA7JCiAOyQowDskKQA7JClAOyQpgDskKcA7JCoAOyQqQDskKoA7JCrAOyQrADskK0A7JCuAOyQrwDskLAA7JCxAOyQsgDskLMA7JC0AOyQtQDskLYA7JC3AOyQuADskLkA7JC6AOyQuwDskLwA7JC9AOyQvgDskL8A7JGAAOyRgQDskYIA7JGDAOyRhADskYUA7JGGAOyRhwDskYgA7JGJAOyRigDskYsA7JGMAOyRjQDskY4A7JGPAOyRkADskZEA7JGSAOyRkwDskZQA7JGVAOyRlgDskZcA7JGYAOyRmQDskZoA7JGbAOyRnADskZ0A7JGeAOyRnwDskaAA7JGhAOyRogDskaMA7JGkAOyRpQDskaYA7JGnAOyRqADskakA7JGqAOyRqwDskawA7JGtAOyRrgDska8A7JGwAOyRsQDskbIA7JGzAOyRtADskbUA7JG2AOyRtwDskbgA7JG5AOyRugDskbsA7JG8AOyRvQDskb4A7JG/AOySgADskoEA7JKCAOySgwDskoQA7JKFAOyShgDskocA7JKIAOySiQDskooA7JKLAOySjADsko0A7JKOAOySjwDskpAA7JKRAOySkgDskpMA7JKUAOySlQDskpYA7JKXAOySmADskpkA7JKaAOySmwDskpwA7JKdAOySngDskp8A7JKgAOySoQDskqIA7JKjAOySpADskqUA7JKmAOySpwDskqgA7JKpAOySqgDskqsA7JKsAOySrQDskq4A7JKvAOySsADskrEA7JKyAOySswDskrQA7JK1AOyStgDskrcA7JK4AOySuQDskroA7JK7AOySvADskr0A7JK+AOySvwDsk4AA7JOBAOyTggDsk4MA7JOEAOyThQDsk4YA7JOHAOyTiADsk4kA7JOKAOyTiwDsk4wA7JONAOyTjgDsk48A7JOQAOyTkQDsk5IA7JOTAOyTlADsk5UA7JOWAOyTlwDsk5gA7JOZAOyTmgDsk5sA7JOcAOyTnQDsk54A7JOfAOyToADsk6EA7JOiAOyTowDsk6QA7JOlAOyTpgDsk6cA7JOoAOyTqQDsk6oA7JOrAOyTrADsk60A7JOuAOyTrwDsk7AA7JOxAOyTsgDsk7MA7JO0AOyTtQDsk7YA7JO3AOyTuADsk7kA7JO6AOyTuwDsk7wA7JO9AOyTvgDsk78A7JSAAOyUgQDslIIA7JSDAOyUhADslIUA7JSGAOyUhwDslIgA7JSJAOyUigDslIsA7JSMAOyUjQDslI4A7JSPAOyUkADslJEA7JSSAOyUkwDslJQA7JSVAOyUlgDslJcA7JSYAOyUmQDslJoA7JSbAOyUnADslJ0A7JSeAOyUnwDslKAA7JShAOyUogDslKMA7JSkAOyUpQDslKYA7JSnAOyUqADslKkA7JSqAOyUqwDslKwA7JStAOyUrgDslK8A7JSwAOyUsQDslLIA7JSzAOyUtADslLUA7JS2AOyUtwDslLgA7JS5AOyUugDslLsA7JS8AOyUvQDslL4A7JS/AOyVgADslYEA7JWCAOyVgwDslYQA7JWFAOyVhgDslYcA7JWIAOyViQDslYoA7JWLAOyVjADslY0A7JWOAOyVjwDslZAA7JWRAOyVkgDslZMA7JWUAOyVlQDslZYA7JWXAOyVmADslZkA7JWaAOyVmwDslZwA7JWdAOyVngDslZ8A7JWgAOyVoQDslaIA7JWjAOyVpADslaUA7JWmAOyVpwDslagA7JWpAOyVqgDslasA7JWsAOyVrQDsla4A7JWvAOyVsADslbEA7JWyAOyVswDslbQA7JW1AOyVtgDslbcA7JW4AOyVuQDslboA7JW7AOyVvADslb0A7JW+AOyVvwDsloAA7JaBAOyWggDsloMA7JaEAOyWhQDsloYA7JaHAOyWiADslokA7JaKAOyWiwDslowA7JaNAOyWjgDslo8A7JaQAOyWkQDslpIA7JaTAOyWlADslpUA7JaWAOyWlwDslpgA7JaZAOyWmgDslpsA7JacAOyWnQDslp4A7JafAOyWoADslqEA7JaiAOyWowDslqQA7JalAOyWpgDslqcA7JaoAOyWqQDslqoA7JarAOyWrADslq0A7JauAOyWrwDslrAA7JaxAOyWsgDslrMA7Ja0AOyWtQDslrYA7Ja3AOyWuADslrkA7Ja6AOyWuwDslrwA7Ja9AOyWvgDslr8A7JeAAOyXgQDsl4IA7JeDAOyXhADsl4UA7JeGAOyXhwDsl4gA7JeJAOyXigDsl4sA7JeMAOyXjQDsl44A7JePAOyXkADsl5EA7JeSAOyXkwDsl5QA7JeVAOyXlgDsl5cA7JeYAOyXmQDsl5oA7JebAOyXnADsl50A7JeeAOyXnwDsl6AA7JehAOyXogDsl6MA7JekAOyXpQDsl6YA7JenAOyXqADsl6kA7JeqAOyXqwDsl6wA7JetAOyXrgDsl68A7JewAOyXsQDsl7IA7JezAOyXtADsl7UA7Je2AOyXtwDsl7gA7Je5AOyXugDsl7sA7Je8AOyXvQDsl74A7Je/AOyYgADsmIEA7JiCAOyYgwDsmIQA7JiFAOyYhgDsmIcA7JiIAOyYiQDsmIoA7JiLAOyYjADsmI0A7JiOAOyYjwDsmJAA7JiRAOyYkgDsmJMA7JiUAOyYlQDsmJYA7JiXAOyYmADsmJkA7JiaAOyYmwDsmJwA7JidAOyYngDsmJ8A7JigAOyYoQDsmKIA7JijAOyYpADsmKUA7JimAOyYpwDsmKgA7JipAOyYqgDsmKsA7JisAOyYrQDsmK4A7JivAOyYsADsmLEA7JiyAOyYswDsmLQA7Ji1AOyYtgDsmLcA7Ji4AOyYuQDsmLoA7Ji7AOyYvADsmL0A7Ji+AOyYvwDsmYAA7JmBAOyZggDsmYMA7JmEAOyZhQDsmYYA7JmHAOyZiADsmYkA7JmKAOyZiwDsmYwA7JmNAOyZjgDsmY8A7JmQAOyZkQDsmZIA7JmTAOyZlADsmZUA7JmWAOyZlwDsmZgA7JmZAOyZmgDsmZsA7JmcAOyZnQDsmZ4A7JmfAOyZoADsmaEA7JmiAOyZowDsmaQA7JmlAOyZpgDsmacA7JmoAOyZqQDsmaoA7JmrAOyZrADsma0A7JmuAOyZrwDsmbAA7JmxAOyZsgDsmbMA7Jm0AOyZtQDsmbYA7Jm3AOyZuADsmbkA7Jm6AOyZuwDsmbwA7Jm9AOyZvgDsmb8A7JqAAOyagQDsmoIA7JqDAOyahADsmoUA7JqGAOyahwDsmogA7JqJAOyaigDsmosA7JqMAOyajQDsmo4A7JqPAOyakADsmpEA7JqSAOyakwDsmpQA7JqVAOyalgDsmpcA7JqYAOyamQDsmpoA7JqbAOyanADsmp0A7JqeAOyanwDsmqAA7JqhAOyaogDsmqMA7JqkAOyapQDsmqYA7JqnAOyaqADsmqkA7JqqAOyaqwDsmqwA7JqtAOyargDsmq8A7JqwAOyasQDsmrIA7JqzAOyatADsmrUA7Jq2AOyatwDsmrgA7Jq5AOyaugDsmrsA7Jq8AOyavQDsmr4A7Jq/AOybgADsm4EA7JuCAOybgwDsm4QA7JuFAOybhgDsm4cA7JuIAOybiQDsm4oA7JuLAOybjADsm40A7JuOAOybjwDsm5AA7JuRAOybkgDsm5MA7JuUAOyblQDsm5YA7JuXAOybmADsm5kA7JuaAOybmwDsm5wA7JudAOybngDsm58A7JugAOyboQDsm6IA7JujAOybpADsm6UA7JumAOybpwDsm6gA7JupAOybqgDsm6sA7JusAOybrQDsm64A7JuvAOybsADsm7EA7JuyAOybswDsm7QA7Ju1AOybtgDsm7cA7Ju4AOybuQDsm7oA7Ju7AOybvADsm70A7Ju+AOybvwDsnIAA7JyBAOycggDsnIMA7JyEAOychQDsnIYA7JyHAOyciADsnIkA7JyKAOyciwDsnIwA7JyNAOycjgDsnI8A7JyQAOyckQDsnJIA7JyTAOyclADsnJUA7JyWAOyclwDsnJgA7JyZAOycmgDsnJsA7JycAOycnQDsnJ4A7JyfAOycoADsnKEA7JyiAOycowDsnKQA7JylAOycpgDsnKcA7JyoAOycqQDsnKoA7JyrAOycrADsnK0A7JyuAOycrwDsnLAA7JyxAOycsgDsnLMA7Jy0AOyctQDsnLYA7Jy3AOycuADsnLkA7Jy6AOycuwDsnLwA7Jy9AOycvgDsnL8A7J2AAOydgQDsnYIA7J2DAOydhADsnYUA7J2GAOydhwDsnYgA7J2JAOydigDsnYsA7J2MAOydjQDsnY4A7J2PAOydkADsnZEA7J2SAOydkwDsnZQA7J2VAOydlgDsnZcA7J2YAOydmQDsnZoA7J2bAOydnADsnZ0A7J2eAOydnwDsnaAA7J2hAOydogDsnaMA7J2kAOydpQDsnaYA7J2nAOydqADsnakA7J2qAOydqwDsnawA7J2tAOydrgDsna8A7J2wAOydsQDsnbIA7J2zAOydtADsnbUA7J22AOydtwDsnbgA7J25AOydugDsnbsA7J28AOydvQDsnb4A7J2/AOyegADsnoEA7J6CAOyegwDsnoQA7J6FAOyehgDsnocA7J6IAOyeiQDsnooA7J6LAOyejADsno0A7J6OAOyejwDsnpAA7J6RAOyekgDsnpMA7J6UAOyelQDsnpYA7J6XAOyemADsnpkA7J6aAOyemwDsnpwA7J6dAOyengDsnp8A7J6gAOyeoQDsnqIA7J6jAOyepADsnqUA7J6mAOyepwDsnqgA7J6pAOyeqgDsnqsA7J6sAOyerQDsnq4A7J6vAOyesADsnrEA7J6yAOyeswDsnrQA7J61AOyetgDsnrcA7J64AOyeuQDsnroA7J67AOyevADsnr0A7J6+AOyevwDsn4AA7J+BAOyfggDsn4MA7J+EAOyfhQDsn4YA7J+HAOyfiADsn4kA7J+KAOyfiwDsn4wA7J+NAOyfjgDsn48A7J+QAOyfkQDsn5IA7J+TAOyflADsn5UA7J+WAOyflwDsn5gA7J+ZAOyfmgDsn5sA7J+cAOyfnQDsn54A7J+fAOyfoADsn6EA7J+iAOyfowDsn6QA7J+lAOyfpgDsn6cA7J+oAOyfqQDsn6oA7J+rAOyfrADsn60A7J+uAOyfrwDsn7AA7J+xAOyfsgDsn7MA7J+0AOyftQDsn7YA7J+3AOyfuADsn7kA7J+6AOyfuwDsn7wA7J+9AOyfvgDsn78A7KCAAOyggQDsoIIA7KCDAOyghADsoIUA7KCGAOyghwDsoIgA7KCJAOygigDsoIsA7KCMAOygjQDsoI4A7KCPAOygkADsoJEA7KCSAOygkwDsoJQA7KCVAOyglgDsoJcA7KCYAOygmQDsoJoA7KCbAOygnADsoJ0A7KCeAOygnwDsoKAA7KChAOygogDsoKMA7KCkAOygpQDsoKYA7KCnAOygqADsoKkA7KCqAOygqwDsoKwA7KCtAOygrgDsoK8A7KCwAOygsQDsoLIA7KCzAOygtADsoLUA7KC2AOygtwDsoLgA7KC5AOygugDsoLsA7KC8AOygvQDsoL4A7KC/AOyhgADsoYEA7KGCAOyhgwDsoYQA7KGFAOyhhgDsoYcA7KGIAOyhiQDsoYoA7KGLAOyhjADsoY0A7KGOAOyhjwDsoZAA7KGRAOyhkgDsoZMA7KGUAOyhlQDsoZYA7KGXAOyhmADsoZkA7KGaAOyhmwDsoZwA7KGdAOyhngDsoZ8A7KGgAOyhoQDsoaIA7KGjAOyhpADsoaUA7KGmAOyhpwDsoagA7KGpAOyhqgDsoasA7KGsAOyhrQDsoa4A7KGvAOyhsADsobEA7KGyAOyhswDsobQA7KG1AOyhtgDsobcA7KG4AOyhuQDsoboA7KG7AOyhvADsob0A7KG+AOyhvwDsooAA7KKBAOyiggDsooMA7KKEAOyihQDsooYA7KKHAOyiiADsookA7KKKAOyiiwDsoowA7KKNAOyijgDsoo8A7KKQAOyikQDsopIA7KKTAOyilADsopUA7KKWAOyilwDsopgA7KKZAOyimgDsopsA7KKcAOyinQDsop4A7KKfAOyioADsoqEA7KKiAOyiowDsoqQA7KKlAOyipgDsoqcA7KKoAOyiqQDsoqoA7KKrAOyirADsoq0A7KKuAOyirwDsorAA7KKxAOyisgDsorMA7KK0AOyitQDsorYA7KK3AOyiuADsorkA7KK6AOyiuwDsorwA7KK9AOyivgDsor8A7KOAAOyjgQDso4IA7KODAOyjhADso4UA7KOGAOyjhwDso4gA7KOJAOyjigDso4sA7KOMAOyjjQDso44A7KOPAOyjkADso5EA7KOSAOyjkwDso5QA7KOVAOyjlgDso5cA7KOYAOyjmQDso5oA7KObAOyjnADso50A7KOeAOyjnwDso6AA7KOhAOyjogDso6MA7KOkAOyjpQDso6YA7KOnAOyjqADso6kA7KOqAOyjqwDso6wA7KOtAOyjrgDso68A7KOwAOyjsQDso7IA7KOzAOyjtADso7UA7KO2AOyjtwDso7gA7KO5AOyjugDso7sA7KO8AOyjvOydmADso70A7KO+AOyjvwDspIAA7KSBAOykggDspIMA7KSEAOykhQDspIYA7KSHAOykiADspIkA7KSKAOykiwDspIwA7KSNAOykjgDspI8A7KSQAOykkQDspJIA7KSTAOyklADspJUA7KSWAOyklwDspJgA7KSZAOykmgDspJsA7KScAOyknQDspJ4A7KSfAOykoADspKEA7KSiAOykowDspKQA7KSlAOykpgDspKcA7KSoAOykqQDspKoA7KSrAOykrADspK0A7KSuAOykrwDspLAA7KSxAOyksgDspLMA7KS0AOyktQDspLYA7KS3AOykuADspLkA7KS6AOykuwDspLwA7KS9AOykvgDspL8A7KWAAOylgQDspYIA7KWDAOylhADspYUA7KWGAOylhwDspYgA7KWJAOyligDspYsA7KWMAOyljQDspY4A7KWPAOylkADspZEA7KWSAOylkwDspZQA7KWVAOyllgDspZcA7KWYAOylmQDspZoA7KWbAOylnADspZ0A7KWeAOylnwDspaAA7KWhAOylogDspaMA7KWkAOylpQDspaYA7KWnAOylqADspakA7KWqAOylqwDspawA7KWtAOylrgDspa8A7KWwAOylsQDspbIA7KWzAOyltADspbUA7KW2AOyltwDspbgA7KW5AOylugDspbsA7KW8AOylvQDspb4A7KW/AOymgADspoEA7KaCAOymgwDspoQA7KaFAOymhgDspocA7KaIAOymiQDspooA7KaLAOymjADspo0A7KaOAOymjwDsppAA7KaRAOymkgDsppMA7KaUAOymlQDsppYA7KaXAOymmADsppkA7KaaAOymmwDsppwA7KadAOymngDspp8A7KagAOymoQDspqIA7KajAOympADspqUA7KamAOympwDspqgA7KapAOymqgDspqsA7KasAOymrQDspq4A7KavAOymsADsprEA7KayAOymswDsprQA7Ka1AOymtgDsprcA7Ka4AOymuQDsproA7Ka7AOymvADspr0A7Ka+AOymvwDsp4AA7KeBAOynggDsp4MA7KeEAOynhQDsp4YA7KeHAOyniADsp4kA7KeKAOyniwDsp4wA7KeNAOynjgDsp48A7KeQAOynkQDsp5IA7KeTAOynlADsp5UA7KeWAOynlwDsp5gA7KeZAOynmgDsp5sA7KecAOynnQDsp54A7KefAOynoADsp6EA7KeiAOynowDsp6QA7KelAOynpgDsp6cA7KeoAOynqQDsp6oA7KerAOynrADsp60A7KeuAOynrwDsp7AA7KexAOynsgDsp7MA7Ke0AOyntQDsp7YA7Ke3AOynuADsp7kA7Ke6AOynuwDsp7wA7Ke9AOynvgDsp78A7KiAAOyogQDsqIIA7KiDAOyohADsqIUA7KiGAOyohwDsqIgA7KiJAOyoigDsqIsA7KiMAOyojQDsqI4A7KiPAOyokADsqJEA7KiSAOyokwDsqJQA7KiVAOyolgDsqJcA7KiYAOyomQDsqJoA7KibAOyonADsqJ0A7KieAOyonwDsqKAA7KihAOyoogDsqKMA7KikAOyopQDsqKYA7KinAOyoqADsqKkA7KiqAOyoqwDsqKwA7KitAOyorgDsqK8A7KiwAOyosQDsqLIA7KizAOyotADsqLUA7Ki2AOyotwDsqLgA7Ki5AOyougDsqLsA7Ki8AOyovQDsqL4A7Ki/AOypgADsqYEA7KmCAOypgwDsqYQA7KmFAOyphgDsqYcA7KmIAOypiQDsqYoA7KmLAOypjADsqY0A7KmOAOypjwDsqZAA7KmRAOypkgDsqZMA7KmUAOyplQDsqZYA7KmXAOypmADsqZkA7KmaAOypmwDsqZwA7KmdAOypngDsqZ8A7KmgAOypoQDsqaIA7KmjAOyppADsqaUA7KmmAOyppwDsqagA7KmpAOypqgDsqasA7KmsAOyprQDsqa4A7KmvAOypsADsqbEA7KmyAOypswDsqbQA7Km1AOyptgDsqbcA7Km4AOypuQDsqboA7Km7AOypvADsqb0A7Km+AOypvwDsqoAA7KqBAOyqggDsqoMA7KqEAOyqhQDsqoYA7KqHAOyqiADsqokA7KqKAOyqiwDsqowA7KqNAOyqjgDsqo8A7KqQAOyqkQDsqpIA7KqTAOyqlADsqpUA7KqWAOyqlwDsqpgA7KqZAOyqmgDsqpsA7KqcAOyqnQDsqp4A7KqfAOyqoADsqqEA7KqiAOyqowDsqqQA7KqlAOyqpgDsqqcA7KqoAOyqqQDsqqoA7KqrAOyqrADsqq0A7KquAOyqrwDsqrAA7KqxAOyqsgDsqrMA7Kq0AOyqtQDsqrYA7Kq3AOyquADsqrkA7Kq6AOyquwDsqrwA7Kq9AOyqvgDsqr8A7KuAAOyrgQDsq4IA7KuDAOyrhADsq4UA7KuGAOyrhwDsq4gA7KuJAOyrigDsq4sA7KuMAOyrjQDsq44A7KuPAOyrkADsq5EA7KuSAOyrkwDsq5QA7KuVAOyrlgDsq5cA7KuYAOyrmQDsq5oA7KubAOyrnADsq50A7KueAOyrnwDsq6AA7KuhAOyrogDsq6MA7KukAOyrpQDsq6YA7KunAOyrqADsq6kA7KuqAOyrqwDsq6wA7KutAOyrrgDsq68A7KuwAOyrsQDsq7IA7KuzAOyrtADsq7UA7Ku2AOyrtwDsq7gA7Ku5AOyrugDsq7sA7Ku8AOyrvQDsq74A7Ku/AOysgADsrIEA7KyCAOysgwDsrIQA7KyFAOyshgDsrIcA7KyIAOysiQDsrIoA7KyLAOysjADsrI0A7KyOAOysjwDsrJAA7KyRAOyskgDsrJMA7KyUAOyslQDsrJYA7KyXAOysmADsrJkA7KyaAOysmwDsrJwA7KydAOysngDsrJ8A7KygAOysoQDsrKIA7KyjAOyspADsrKUA7KymAOyspwDsrKgA7KypAOysqgDsrKsA7KysAOysrQDsrK4A7KyvAOyssADsrLEA7KyyAOysswDsrLQA7Ky1AOystgDsrLcA7Ky4AOysuQDsrLoA7Ky7AOysvADsrL0A7Ky+AOysvwDsrYAA7K2BAOytggDsrYMA7K2EAOythQDsrYYA7K2HAOytiADsrYkA7K2KAOytiwDsrYwA7K2NAOytjgDsrY8A7K2QAOytkQDsrZIA7K2TAOytlADsrZUA7K2WAOytlwDsrZgA7K2ZAOytmgDsrZsA7K2cAOytnQDsrZ4A7K2fAOytoADsraEA7K2iAOytowDsraQA7K2lAOytpgDsracA7K2oAOytqQDsraoA7K2rAOytrADsra0A7K2uAOytrwDsrbAA7K2xAOytsgDsrbMA7K20AOyttQDsrbYA7K23AOytuADsrbkA7K26AOytuwDsrbwA7K29AOytvgDsrb8A7K6AAOyugQDsroIA7K6DAOyuhADsroUA7K6GAOyuhwDsrogA7K6JAOyuigDsrosA7K6MAOyujQDsro4A7K6PAOyukADsrpEA7K6SAOyukwDsrpQA7K6VAOyulgDsrpcA7K6YAOyumQDsrpoA7K6bAOyunADsrp0A7K6eAOyunwDsrqAA7K6hAOyuogDsrqMA7K6kAOyupQDsrqYA7K6nAOyuqADsrqkA7K6qAOyuqwDsrqwA7K6tAOyurgDsrq8A7K6wAOyusQDsrrIA7K6zAOyutADsrrUA7K62AOyutwDsrrgA7K65AOyuugDsrrsA7K68AOyuvQDsrr4A7K6/AOyvgADsr4EA7K+CAOyvgwDsr4QA7K+FAOyvhgDsr4cA7K+IAOyviQDsr4oA7K+LAOyvjADsr40A7K+OAOyvjwDsr5AA7K+RAOyvkgDsr5MA7K+UAOyvlQDsr5YA7K+XAOyvmADsr5kA7K+aAOyvmwDsr5wA7K+dAOyvngDsr58A7K+gAOyvoQDsr6IA7K+jAOyvpADsr6UA7K+mAOyvpwDsr6gA7K+pAOyvqgDsr6sA7K+sAOyvrQDsr64A7K+vAOyvsADsr7EA7K+yAOyvswDsr7QA7K+1AOyvtgDsr7cA7K+4AOyvuQDsr7oA7K+7AOyvvADsr70A7K++AOyvvwDssIAA7LCBAOywggDssIMA7LCEAOywhQDssIYA7LCHAOywiADssIkA7LCKAOywiwDssIwA7LCNAOywjgDssI8A7LCQAOywkQDssJIA7LCTAOywlADssJUA7LCWAOywlwDssJgA7LCZAOywmgDssJsA7LCcAOywnQDssJ4A7LCfAOywoADssKEA7LCiAOywowDssKQA7LClAOywpgDssKcA7LCoAOywqQDssKoA7LCrAOywrADssK0A7LCuAOywrwDssLAA7LCxAOywsgDssLMA7LC0AOywtQDssLYA7LC3AOywuADssLjqs6AA7LC5AOywugDssLsA7LC8AOywvQDssL4A7LC/AOyxgADssYEA7LGCAOyxgwDssYQA7LGFAOyxhgDssYcA7LGIAOyxiQDssYoA7LGLAOyxjADssY0A7LGOAOyxjwDssZAA7LGRAOyxkgDssZMA7LGUAOyxlQDssZYA7LGXAOyxmADssZkA7LGaAOyxmwDssZwA7LGdAOyxngDssZ8A7LGgAOyxoQDssaIA7LGjAOyxpADssaUA7LGmAOyxpwDssagA7LGpAOyxqgDssasA7LGsAOyxrQDssa4A7LGvAOyxsADssbEA7LGyAOyxswDssbQA7LG1AOyxtgDssbcA7LG4AOyxuQDssboA7LG7AOyxvADssb0A7LG+AOyxvwDssoAA7LKBAOyyggDssoMA7LKEAOyyhQDssoYA7LKHAOyyiADssokA7LKKAOyyiwDssowA7LKNAOyyjgDsso8A7LKQAOyykQDsspIA7LKTAOyylADsspUA7LKWAOyylwDsspgA7LKZAOyymgDsspsA7LKcAOyynQDssp4A7LKfAOyyoADssqEA7LKiAOyyowDssqQA7LKlAOyypgDssqcA7LKoAOyyqQDssqoA7LKrAOyyrADssq0A7LKuAOyyrwDssrAA7LKxAOyysgDssrMA7LK0AOyytQDssrYA7LK3AOyyuADssrkA7LK6AOyyuwDssrwA7LK9AOyyvgDssr8A7LOAAOyzgQDss4IA7LODAOyzhADss4UA7LOGAOyzhwDss4gA7LOJAOyzigDss4sA7LOMAOyzjQDss44A7LOPAOyzkADss5EA7LOSAOyzkwDss5QA7LOVAOyzlgDss5cA7LOYAOyzmQDss5oA7LObAOyznADss50A7LOeAOyznwDss6AA7LOhAOyzogDss6MA7LOkAOyzpQDss6YA7LOnAOyzqADss6kA7LOqAOyzqwDss6wA7LOtAOyzrgDss68A7LOwAOyzsQDss7IA7LOzAOyztADss7UA7LO2AOyztwDss7gA7LO5AOyzugDss7sA7LO8AOyzvQDss74A7LO/AOy0gADstIEA7LSCAOy0gwDstIQA7LSFAOy0hgDstIcA7LSIAOy0iQDstIoA7LSLAOy0jADstI0A7LSOAOy0jwDstJAA7LSRAOy0kgDstJMA7LSUAOy0lQDstJYA7LSXAOy0mADstJkA7LSaAOy0mwDstJwA7LSdAOy0ngDstJ8A7LSgAOy0oQDstKIA7LSjAOy0pADstKUA7LSmAOy0pwDstKgA7LSpAOy0qgDstKsA7LSsAOy0rQDstK4A7LSvAOy0sADstLEA7LSyAOy0swDstLQA7LS1AOy0tgDstLcA7LS4AOy0uQDstLoA7LS7AOy0vADstL0A7LS+AOy0vwDstYAA7LWBAOy1ggDstYMA7LWEAOy1hQDstYYA7LWHAOy1iADstYkA7LWKAOy1iwDstYwA7LWNAOy1jgDstY8A7LWQAOy1kQDstZIA7LWTAOy1lADstZUA7LWWAOy1lwDstZgA7LWZAOy1mgDstZsA7LWcAOy1nQDstZ4A7LWfAOy1oADstaEA7LWiAOy1owDstaQA7LWlAOy1pgDstacA7LWoAOy1qQDstaoA7LWrAOy1rADsta0A7LWuAOy1rwDstbAA7LWxAOy1sgDstbMA7LW0AOy1tQDstbYA7LW3AOy1uADstbkA7LW6AOy1uwDstbwA7LW9AOy1vgDstb8A7LaAAOy2gQDstoIA7LaDAOy2hADstoUA7LaGAOy2hwDstogA7LaJAOy2igDstosA7LaMAOy2jQDsto4A7LaPAOy2kADstpEA7LaSAOy2kwDstpQA7LaVAOy2lgDstpcA7LaYAOy2mQDstpoA7LabAOy2nADstp0A7LaeAOy2nwDstqAA7LahAOy2ogDstqMA7LakAOy2pQDstqYA7LanAOy2qADstqkA7LaqAOy2qwDstqwA7LatAOy2rgDstq8A7LawAOy2sQDstrIA7LazAOy2tADstrUA7La2AOy2twDstrgA7La5AOy2ugDstrsA7La8AOy2vQDstr4A7La/AOy3gADst4EA7LeCAOy3gwDst4QA7LeFAOy3hgDst4cA7LeIAOy3iQDst4oA7LeLAOy3jADst40A7LeOAOy3jwDst5AA7LeRAOy3kgDst5MA7LeUAOy3lQDst5YA7LeXAOy3mADst5kA7LeaAOy3mwDst5wA7LedAOy3ngDst58A7LegAOy3oQDst6IA7LejAOy3pADst6UA7LemAOy3pwDst6gA7LepAOy3qgDst6sA7LesAOy3rQDst64A7LevAOy3sADst7EA7LeyAOy3swDst7QA7Le1AOy3tgDst7cA7Le4AOy3uQDst7oA7Le7AOy3vADst70A7Le+AOy3vwDsuIAA7LiBAOy4ggDsuIMA7LiEAOy4hQDsuIYA7LiHAOy4iADsuIkA7LiKAOy4iwDsuIwA7LiNAOy4jgDsuI8A7LiQAOy4kQDsuJIA7LiTAOy4lADsuJUA7LiWAOy4lwDsuJgA7LiZAOy4mgDsuJsA7LicAOy4nQDsuJ4A7LifAOy4oADsuKEA7LiiAOy4owDsuKQA7LilAOy4pgDsuKcA7LioAOy4qQDsuKoA7LirAOy4rADsuK0A7LiuAOy4rwDsuLAA7LixAOy4sgDsuLMA7Li0AOy4tQDsuLYA7Li3AOy4uADsuLkA7Li6AOy4uwDsuLwA7Li9AOy4vgDsuL8A7LmAAOy5gQDsuYIA7LmDAOy5hADsuYUA7LmGAOy5hwDsuYgA7LmJAOy5igDsuYsA7LmMAOy5jQDsuY4A7LmPAOy5kADsuZEA7LmSAOy5kwDsuZQA7LmVAOy5lgDsuZcA7LmYAOy5mQDsuZoA7LmbAOy5nADsuZ0A7LmeAOy5nwDsuaAA7LmhAOy5ogDsuaMA7LmkAOy5pQDsuaYA7LmnAOy5qADsuakA7LmqAOy5qwDsuawA7LmtAOy5rgDsua8A7LmwAOy5sQDsubIA7LmzAOy5tADsubUA7Lm2AOy5twDsubgA7Lm5AOy5ugDsubsA7Lm8AOy5vQDsub4A7Lm/AOy6gADsuoEA7LqCAOy6gwDsuoQA7LqFAOy6hgDsuocA7LqIAOy6iQDsuooA7LqLAOy6jADsuo0A7LqOAOy6jwDsupAA7LqRAOy6kgDsupMA7LqUAOy6lQDsupYA7LqXAOy6mADsupkA7LqaAOy6mwDsupwA7LqdAOy6ngDsup8A7LqgAOy6oQDsuqIA7LqjAOy6pADsuqUA7LqmAOy6pwDsuqgA7LqpAOy6qgDsuqsA7LqsAOy6rQDsuq4A7LqvAOy6sADsurEA7LqyAOy6swDsurQA7Lq1AOy6tgDsurcA7Lq4AOy6uQDsuroA7Lq7AOy6vADsur0A7Lq+AOy6vwDsu4AA7LuBAOy7ggDsu4MA7LuEAOy7hQDsu4YA7LuHAOy7iADsu4kA7LuKAOy7iwDsu4wA7LuNAOy7jgDsu48A7LuQAOy7kQDsu5IA7LuTAOy7lADsu5UA7LuWAOy7lwDsu5gA7LuZAOy7mgDsu5sA7LucAOy7nQDsu54A7LufAOy7oADsu6EA7LuiAOy7owDsu6QA7LulAOy7pgDsu6cA7LuoAOy7qQDsu6oA7LurAOy7rADsu60A7LuuAOy7rwDsu7AA7LuxAOy7sgDsu7MA7Lu0AOy7tQDsu7YA7Lu3AOy7uADsu7kA7Lu6AOy7uwDsu7wA7Lu9AOy7vgDsu78A7LyAAOy8gQDsvIIA7LyDAOy8hADsvIUA7LyGAOy8hwDsvIgA7LyJAOy8igDsvIsA7LyMAOy8jQDsvI4A7LyPAOy8kADsvJEA7LySAOy8kwDsvJQA7LyVAOy8lgDsvJcA7LyYAOy8mQDsvJoA7LybAOy8nADsvJ0A7LyeAOy8nwDsvKAA7LyhAOy8ogDsvKMA7LykAOy8pQDsvKYA7LynAOy8qADsvKkA7LyqAOy8qwDsvKwA7LytAOy8rgDsvK8A7LywAOy8sQDsvLIA7LyzAOy8tADsvLUA7Ly2AOy8twDsvLgA7Ly5AOy8ugDsvLsA7Ly8AOy8vQDsvL4A7Ly/AOy9gADsvYEA7L2CAOy9gwDsvYQA7L2FAOy9hgDsvYcA7L2IAOy9iQDsvYoA7L2LAOy9jADsvY0A7L2OAOy9jwDsvZAA7L2RAOy9kgDsvZMA7L2UAOy9lQDsvZYA7L2XAOy9mADsvZkA7L2aAOy9mwDsvZwA7L2dAOy9ngDsvZ8A7L2gAOy9oQDsvaIA7L2jAOy9pADsvaUA7L2mAOy9pwDsvagA7L2pAOy9qgDsvasA7L2sAOy9rQDsva4A7L2vAOy9sADsvbEA7L2yAOy9swDsvbQA7L21AOy9tgDsvbcA7L24AOy9uQDsvboA7L27AOy9vADsvb0A7L2+AOy9vwDsvoAA7L6BAOy+ggDsvoMA7L6EAOy+hQDsvoYA7L6HAOy+iADsvokA7L6KAOy+iwDsvowA7L6NAOy+jgDsvo8A7L6QAOy+kQDsvpIA7L6TAOy+lADsvpUA7L6WAOy+lwDsvpgA7L6ZAOy+mgDsvpsA7L6cAOy+nQDsvp4A7L6fAOy+oADsvqEA7L6iAOy+owDsvqQA7L6lAOy+pgDsvqcA7L6oAOy+qQDsvqoA7L6rAOy+rADsvq0A7L6uAOy+rwDsvrAA7L6xAOy+sgDsvrMA7L60AOy+tQDsvrYA7L63AOy+uADsvrkA7L66AOy+uwDsvrwA7L69AOy+vgDsvr8A7L+AAOy/gQDsv4IA7L+DAOy/hADsv4UA7L+GAOy/hwDsv4gA7L+JAOy/igDsv4sA7L+MAOy/jQDsv44A7L+PAOy/kADsv5EA7L+SAOy/kwDsv5QA7L+VAOy/lgDsv5cA7L+YAOy/mQDsv5oA7L+bAOy/nADsv50A7L+eAOy/nwDsv6AA7L+hAOy/ogDsv6MA7L+kAOy/pQDsv6YA7L+nAOy/qADsv6kA7L+qAOy/qwDsv6wA7L+tAOy/rgDsv68A7L+wAOy/sQDsv7IA7L+zAOy/tADsv7UA7L+2AOy/twDsv7gA7L+5AOy/ugDsv7sA7L+8AOy/vQDsv74A7L+/AO2AgADtgIEA7YCCAO2AgwDtgIQA7YCFAO2AhgDtgIcA7YCIAO2AiQDtgIoA7YCLAO2AjADtgI0A7YCOAO2AjwDtgJAA7YCRAO2AkgDtgJMA7YCUAO2AlQDtgJYA7YCXAO2AmADtgJkA7YCaAO2AmwDtgJwA7YCdAO2AngDtgJ8A7YCgAO2AoQDtgKIA7YCjAO2ApADtgKUA7YCmAO2ApwDtgKgA7YCpAO2AqgDtgKsA7YCsAO2ArQDtgK4A7YCvAO2AsADtgLEA7YCyAO2AswDtgLQA7YC1AO2AtgDtgLcA7YC4AO2AuQDtgLoA7YC7AO2AvADtgL0A7YC+AO2AvwDtgYAA7YGBAO2BggDtgYMA7YGEAO2BhQDtgYYA7YGHAO2BiADtgYkA7YGKAO2BiwDtgYwA7YGNAO2BjgDtgY8A7YGQAO2BkQDtgZIA7YGTAO2BlADtgZUA7YGWAO2BlwDtgZgA7YGZAO2BmgDtgZsA7YGcAO2BnQDtgZ4A7YGfAO2BoADtgaEA7YGiAO2BowDtgaQA7YGlAO2BpgDtgacA7YGoAO2BqQDtgaoA7YGrAO2BrADtga0A7YGuAO2BrwDtgbAA7YGxAO2BsgDtgbMA7YG0AO2BtQDtgbYA7YG3AO2BuADtgbkA7YG6AO2BuwDtgbwA7YG9AO2BvgDtgb8A7YKAAO2CgQDtgoIA7YKDAO2ChADtgoUA7YKGAO2ChwDtgogA7YKJAO2CigDtgosA7YKMAO2CjQDtgo4A7YKPAO2CkADtgpEA7YKSAO2CkwDtgpQA7YKVAO2ClgDtgpcA7YKYAO2CmQDtgpoA7YKbAO2CnADtgp0A7YKeAO2CnwDtgqAA7YKhAO2CogDtgqMA7YKkAO2CpQDtgqYA7YKnAO2CqADtgqkA7YKqAO2CqwDtgqwA7YKtAO2CrgDtgq8A7YKwAO2CsQDtgrIA7YKzAO2CtADtgrUA7YK2AO2CtwDtgrgA7YK5AO2CugDtgrsA7YK8AO2CvQDtgr4A7YK/AO2DgADtg4EA7YOCAO2DgwDtg4QA7YOFAO2DhgDtg4cA7YOIAO2DiQDtg4oA7YOLAO2DjADtg40A7YOOAO2DjwDtg5AA7YORAO2DkgDtg5MA7YOUAO2DlQDtg5YA7YOXAO2DmADtg5kA7YOaAO2DmwDtg5wA7YOdAO2DngDtg58A7YOgAO2DoQDtg6IA7YOjAO2DpADtg6UA7YOmAO2DpwDtg6gA7YOpAO2DqgDtg6sA7YOsAO2DrQDtg64A7YOvAO2DsADtg7EA7YOyAO2DswDtg7QA7YO1AO2DtgDtg7cA7YO4AO2DuQDtg7oA7YO7AO2DvADtg70A7YO+AO2DvwDthIAA7YSBAO2EggDthIMA7YSEAO2EhQDthIYA7YSHAO2EiADthIkA7YSKAO2EiwDthIwA7YSNAO2EjgDthI8A7YSQAO2EkQDthJIA7YSTAO2ElADthJUA7YSWAO2ElwDthJgA7YSZAO2EmgDthJsA7YScAO2EnQDthJ4A7YSfAO2EoADthKEA7YSiAO2EowDthKQA7YSlAO2EpgDthKcA7YSoAO2EqQDthKoA7YSrAO2ErADthK0A7YSuAO2ErwDthLAA7YSxAO2EsgDthLMA7YS0AO2EtQDthLYA7YS3AO2EuADthLkA7YS6AO2EuwDthLwA7YS9AO2EvgDthL8A7YWAAO2FgQDthYIA7YWDAO2FhADthYUA7YWGAO2FhwDthYgA7YWJAO2FigDthYsA7YWMAO2FjQDthY4A7YWPAO2FkADthZEA7YWSAO2FkwDthZQA7YWVAO2FlgDthZcA7YWYAO2FmQDthZoA7YWbAO2FnADthZ0A7YWeAO2FnwDthaAA7YWhAO2FogDthaMA7YWkAO2FpQDthaYA7YWnAO2FqADthakA7YWqAO2FqwDthawA7YWtAO2FrgDtha8A7YWwAO2FsQDthbIA7YWzAO2FtADthbUA7YW2AO2FtwDthbgA7YW5AO2FugDthbsA7YW8AO2FvQDthb4A7YW/AO2GgADthoEA7YaCAO2GgwDthoQA7YaFAO2GhgDthocA7YaIAO2GiQDthooA7YaLAO2GjADtho0A7YaOAO2GjwDthpAA7YaRAO2GkgDthpMA7YaUAO2GlQDthpYA7YaXAO2GmADthpkA7YaaAO2GmwDthpwA7YadAO2GngDthp8A7YagAO2GoQDthqIA7YajAO2GpADthqUA7YamAO2GpwDthqgA7YapAO2GqgDthqsA7YasAO2GrQDthq4A7YavAO2GsADthrEA7YayAO2GswDthrQA7Ya1AO2GtgDthrcA7Ya4AO2GuQDthroA7Ya7AO2GvADthr0A7Ya+AO2GvwDth4AA7YeBAO2HggDth4MA7YeEAO2HhQDth4YA7YeHAO2HiADth4kA7YeKAO2HiwDth4wA7YeNAO2HjgDth48A7YeQAO2HkQDth5IA7YeTAO2HlADth5UA7YeWAO2HlwDth5gA7YeZAO2HmgDth5sA7YecAO2HnQDth54A7YefAO2HoADth6EA7YeiAO2HowDth6QA7YelAO2HpgDth6cA7YeoAO2HqQDth6oA7YerAO2HrADth60A7YeuAO2HrwDth7AA7YexAO2HsgDth7MA7Ye0AO2HtQDth7YA7Ye3AO2HuADth7kA7Ye6AO2HuwDth7wA7Ye9AO2HvgDth78A7YiAAO2IgQDtiIIA7YiDAO2IhADtiIUA7YiGAO2IhwDtiIgA7YiJAO2IigDtiIsA7YiMAO2IjQDtiI4A7YiPAO2IkADtiJEA7YiSAO2IkwDtiJQA7YiVAO2IlgDtiJcA7YiYAO2ImQDtiJoA7YibAO2InADtiJ0A7YieAO2InwDtiKAA7YihAO2IogDtiKMA7YikAO2IpQDtiKYA7YinAO2IqADtiKkA7YiqAO2IqwDtiKwA7YitAO2IrgDtiK8A7YiwAO2IsQDtiLIA7YizAO2ItADtiLUA7Yi2AO2ItwDtiLgA7Yi5AO2IugDtiLsA7Yi8AO2IvQDtiL4A7Yi/AO2JgADtiYEA7YmCAO2JgwDtiYQA7YmFAO2JhgDtiYcA7YmIAO2JiQDtiYoA7YmLAO2JjADtiY0A7YmOAO2JjwDtiZAA7YmRAO2JkgDtiZMA7YmUAO2JlQDtiZYA7YmXAO2JmADtiZkA7YmaAO2JmwDtiZwA7YmdAO2JngDtiZ8A7YmgAO2JoQDtiaIA7YmjAO2JpADtiaUA7YmmAO2JpwDtiagA7YmpAO2JqgDtiasA7YmsAO2JrQDtia4A7YmvAO2JsADtibEA7YmyAO2JswDtibQA7Ym1AO2JtgDtibcA7Ym4AO2JuQDtiboA7Ym7AO2JvADtib0A7Ym+AO2JvwDtioAA7YqBAO2KggDtioMA7YqEAO2KhQDtioYA7YqHAO2KiADtiokA7YqKAO2KiwDtiowA7YqNAO2KjgDtio8A7YqQAO2KkQDtipIA7YqTAO2KlADtipUA7YqWAO2KlwDtipgA7YqZAO2KmgDtipsA7YqcAO2KnQDtip4A7YqfAO2KoADtiqEA7YqiAO2KowDtiqQA7YqlAO2KpgDtiqcA7YqoAO2KqQDtiqoA7YqrAO2KrADtiq0A7YquAO2KrwDtirAA7YqxAO2KsgDtirMA7Yq0AO2KtQDtirYA7Yq3AO2KuADtirkA7Yq6AO2KuwDtirwA7Yq9AO2KvgDtir8A7YuAAO2LgQDti4IA7YuDAO2LhADti4UA7YuGAO2LhwDti4gA7YuJAO2LigDti4sA7YuMAO2LjQDti44A7YuPAO2LkADti5EA7YuSAO2LkwDti5QA7YuVAO2LlgDti5cA7YuYAO2LmQDti5oA7YubAO2LnADti50A7YueAO2LnwDti6AA7YuhAO2LogDti6MA7YukAO2LpQDti6YA7YunAO2LqADti6kA7YuqAO2LqwDti6wA7YutAO2LrgDti68A7YuwAO2LsQDti7IA7YuzAO2LtADti7UA7Yu2AO2LtwDti7gA7Yu5AO2LugDti7sA7Yu8AO2LvQDti74A7Yu/AO2MgADtjIEA7YyCAO2MgwDtjIQA7YyFAO2MhgDtjIcA7YyIAO2MiQDtjIoA7YyLAO2MjADtjI0A7YyOAO2MjwDtjJAA7YyRAO2MkgDtjJMA7YyUAO2MlQDtjJYA7YyXAO2MmADtjJkA7YyaAO2MmwDtjJwA7YydAO2MngDtjJ8A7YygAO2MoQDtjKIA7YyjAO2MpADtjKUA7YymAO2MpwDtjKgA7YypAO2MqgDtjKsA7YysAO2MrQDtjK4A7YyvAO2MsADtjLEA7YyyAO2MswDtjLQA7Yy1AO2MtgDtjLcA7Yy4AO2MuQDtjLoA7Yy7AO2MvADtjL0A7Yy+AO2MvwDtjYAA7Y2BAO2NggDtjYMA7Y2EAO2NhQDtjYYA7Y2HAO2NiADtjYkA7Y2KAO2NiwDtjYwA7Y2NAO2NjgDtjY8A7Y2QAO2NkQDtjZIA7Y2TAO2NlADtjZUA7Y2WAO2NlwDtjZgA7Y2ZAO2NmgDtjZsA7Y2cAO2NnQDtjZ4A7Y2fAO2NoADtjaEA7Y2iAO2NowDtjaQA7Y2lAO2NpgDtjacA7Y2oAO2NqQDtjaoA7Y2rAO2NrADtja0A7Y2uAO2NrwDtjbAA7Y2xAO2NsgDtjbMA7Y20AO2NtQDtjbY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"", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "clean_up_tokenization_spaces": true, + "eos_token": "", + "extra_ids": 100, + "legacy": false, + "model_max_length": 512, + "pad_token": "", + "sp_model_kwargs": {}, + "tokenizer_class": "T5Tokenizer", + "unk_token": "" +} diff --git a/comfy/utils.py b/comfy/utils.py new file mode 100644 index 0000000..cc92e11 --- /dev/null +++ b/comfy/utils.py @@ -0,0 +1,850 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + + +import torch +import math +import struct +import comfy.checkpoint_pickle +import safetensors.torch +import numpy as np +from PIL import Image +import logging +import itertools + +def load_torch_file(ckpt, safe_load=False, device=None): + if device is None: + device = torch.device("cpu") + if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"): + sd = safetensors.torch.load_file(ckpt, device=device.type) + else: + if safe_load: + if not 'weights_only' in torch.load.__code__.co_varnames: + logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") + safe_load = False + if safe_load: + pl_sd = torch.load(ckpt, map_location=device, weights_only=True) + else: + pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) + if "global_step" in pl_sd: + logging.debug(f"Global Step: {pl_sd['global_step']}") + if "state_dict" in pl_sd: + sd = pl_sd["state_dict"] + else: + sd = pl_sd + return sd + +def save_torch_file(sd, ckpt, metadata=None): + if metadata is not None: + safetensors.torch.save_file(sd, ckpt, metadata=metadata) + else: + safetensors.torch.save_file(sd, ckpt) + +def calculate_parameters(sd, prefix=""): + params = 0 + for k in sd.keys(): + if k.startswith(prefix): + w = sd[k] + params += w.nelement() + return params + +def weight_dtype(sd, prefix=""): + dtypes = {} + for k in sd.keys(): + if k.startswith(prefix): + w = sd[k] + dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel() + + if len(dtypes) == 0: + return None + + return max(dtypes, key=dtypes.get) + +def state_dict_key_replace(state_dict, keys_to_replace): + for x in keys_to_replace: + if x in state_dict: + state_dict[keys_to_replace[x]] = state_dict.pop(x) + return state_dict + +def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False): + if filter_keys: + out = {} + else: + out = state_dict + for rp in replace_prefix: + replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) + for x in replace: + w = state_dict.pop(x[0]) + out[x[1]] = w + return out + + +def transformers_convert(sd, prefix_from, prefix_to, number): + keys_to_replace = { + "{}positional_embedding": "{}embeddings.position_embedding.weight", + "{}token_embedding.weight": "{}embeddings.token_embedding.weight", + "{}ln_final.weight": "{}final_layer_norm.weight", + "{}ln_final.bias": "{}final_layer_norm.bias", + } + + for k in keys_to_replace: + x = k.format(prefix_from) + if x in sd: + sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) + + resblock_to_replace = { + "ln_1": "layer_norm1", + "ln_2": "layer_norm2", + "mlp.c_fc": "mlp.fc1", + "mlp.c_proj": "mlp.fc2", + "attn.out_proj": "self_attn.out_proj", + } + + for resblock in range(number): + for x in resblock_to_replace: + for y in ["weight", "bias"]: + k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) + k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) + if k in sd: + sd[k_to] = sd.pop(k) + + for y in ["weight", "bias"]: + k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) + if k_from in sd: + weights = sd.pop(k_from) + shape_from = weights.shape[0] // 3 + for x in range(3): + p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] + k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) + sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] + + return sd + +def clip_text_transformers_convert(sd, prefix_from, prefix_to): + sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32) + + tp = "{}text_projection.weight".format(prefix_from) + if tp in sd: + sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp) + + tp = "{}text_projection".format(prefix_from) + if tp in sd: + sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous() + return sd + + +UNET_MAP_ATTENTIONS = { + "proj_in.weight", + "proj_in.bias", + "proj_out.weight", + "proj_out.bias", + "norm.weight", + "norm.bias", +} + +TRANSFORMER_BLOCKS = { + "norm1.weight", + "norm1.bias", + "norm2.weight", + "norm2.bias", + "norm3.weight", + "norm3.bias", + "attn1.to_q.weight", + "attn1.to_k.weight", + "attn1.to_v.weight", + "attn1.to_out.0.weight", + "attn1.to_out.0.bias", + "attn2.to_q.weight", + "attn2.to_k.weight", + "attn2.to_v.weight", + "attn2.to_out.0.weight", + "attn2.to_out.0.bias", + "ff.net.0.proj.weight", + "ff.net.0.proj.bias", + "ff.net.2.weight", + "ff.net.2.bias", +} + +UNET_MAP_RESNET = { + "in_layers.2.weight": "conv1.weight", + "in_layers.2.bias": "conv1.bias", + "emb_layers.1.weight": "time_emb_proj.weight", + "emb_layers.1.bias": "time_emb_proj.bias", + "out_layers.3.weight": "conv2.weight", + "out_layers.3.bias": "conv2.bias", + "skip_connection.weight": "conv_shortcut.weight", + "skip_connection.bias": "conv_shortcut.bias", + "in_layers.0.weight": "norm1.weight", + "in_layers.0.bias": "norm1.bias", + "out_layers.0.weight": "norm2.weight", + "out_layers.0.bias": "norm2.bias", +} + +UNET_MAP_BASIC = { + ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), + ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias") +} + +def unet_to_diffusers(unet_config): + if "num_res_blocks" not in unet_config: + return {} + num_res_blocks = unet_config["num_res_blocks"] + channel_mult = unet_config["channel_mult"] + transformer_depth = unet_config["transformer_depth"][:] + transformer_depth_output = unet_config["transformer_depth_output"][:] + num_blocks = len(channel_mult) + + transformers_mid = unet_config.get("transformer_depth_middle", None) + + diffusers_unet_map = {} + for x in range(num_blocks): + n = 1 + (num_res_blocks[x] + 1) * x + for i in range(num_res_blocks[x]): + for b in UNET_MAP_RESNET: + diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) + for t in range(num_transformers): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + n += 1 + for k in ["weight", "bias"]: + diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) + + i = 0 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) + for t in range(transformers_mid): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) + + for i, n in enumerate([0, 2]): + for b in UNET_MAP_RESNET: + diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) + + num_res_blocks = list(reversed(num_res_blocks)) + for x in range(num_blocks): + n = (num_res_blocks[x] + 1) * x + l = num_res_blocks[x] + 1 + for i in range(l): + c = 0 + for b in UNET_MAP_RESNET: + diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) + c += 1 + num_transformers = transformer_depth_output.pop() + if num_transformers > 0: + c += 1 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) + for t in range(num_transformers): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + if i == l - 1: + for k in ["weight", "bias"]: + diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) + n += 1 + + for k in UNET_MAP_BASIC: + diffusers_unet_map[k[1]] = k[0] + + return diffusers_unet_map + +def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + +MMDIT_MAP_BASIC = { + ("context_embedder.bias", "context_embedder.bias"), + ("context_embedder.weight", "context_embedder.weight"), + ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"), + ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"), + ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"), + ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"), + ("x_embedder.proj.bias", "pos_embed.proj.bias"), + ("x_embedder.proj.weight", "pos_embed.proj.weight"), + ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"), + ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"), + ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"), + ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"), + ("pos_embed", "pos_embed.pos_embed"), + ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), + ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), + ("final_layer.linear.bias", "proj_out.bias"), + ("final_layer.linear.weight", "proj_out.weight"), +} + +MMDIT_MAP_BLOCK = { + ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"), + ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"), + ("context_block.attn.proj.bias", "attn.to_add_out.bias"), + ("context_block.attn.proj.weight", "attn.to_add_out.weight"), + ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"), + ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"), + ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"), + ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"), + ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"), + ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"), + ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"), + ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"), + ("x_block.attn.proj.bias", "attn.to_out.0.bias"), + ("x_block.attn.proj.weight", "attn.to_out.0.weight"), + ("x_block.attn.ln_q.weight", "attn.norm_q.weight"), + ("x_block.attn.ln_k.weight", "attn.norm_k.weight"), + ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"), + ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"), + ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"), + ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"), + ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"), + ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"), + ("x_block.mlp.fc2.bias", "ff.net.2.bias"), + ("x_block.mlp.fc2.weight", "ff.net.2.weight"), +} + +def mmdit_to_diffusers(mmdit_config, output_prefix=""): + key_map = {} + + depth = mmdit_config.get("depth", 0) + num_blocks = mmdit_config.get("num_blocks", depth) + for i in range(num_blocks): + block_from = "transformer_blocks.{}".format(i) + block_to = "{}joint_blocks.{}".format(output_prefix, i) + + offset = depth * 64 + + for end in ("weight", "bias"): + k = "{}.attn.".format(block_from) + qkv = "{}.x_block.attn.qkv.{}".format(block_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) + + qkv = "{}.context_block.attn.qkv.{}".format(block_to, end) + key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset)) + key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset)) + key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) + + k = "{}.attn2.".format(block_from) + qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) + + for k in MMDIT_MAP_BLOCK: + key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) + + map_basic = MMDIT_MAP_BASIC.copy() + map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift)) + map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift)) + + for k in map_basic: + if len(k) > 2: + key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) + else: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + + return key_map + + +def auraflow_to_diffusers(mmdit_config, output_prefix=""): + n_double_layers = mmdit_config.get("n_double_layers", 0) + n_layers = mmdit_config.get("n_layers", 0) + + key_map = {} + for i in range(n_layers): + if i < n_double_layers: + index = i + prefix_from = "joint_transformer_blocks" + prefix_to = "{}double_layers".format(output_prefix) + block_map = { + "attn.to_q.weight": "attn.w2q.weight", + "attn.to_k.weight": "attn.w2k.weight", + "attn.to_v.weight": "attn.w2v.weight", + "attn.to_out.0.weight": "attn.w2o.weight", + "attn.add_q_proj.weight": "attn.w1q.weight", + "attn.add_k_proj.weight": "attn.w1k.weight", + "attn.add_v_proj.weight": "attn.w1v.weight", + "attn.to_add_out.weight": "attn.w1o.weight", + "ff.linear_1.weight": "mlpX.c_fc1.weight", + "ff.linear_2.weight": "mlpX.c_fc2.weight", + "ff.out_projection.weight": "mlpX.c_proj.weight", + "ff_context.linear_1.weight": "mlpC.c_fc1.weight", + "ff_context.linear_2.weight": "mlpC.c_fc2.weight", + "ff_context.out_projection.weight": "mlpC.c_proj.weight", + "norm1.linear.weight": "modX.1.weight", + "norm1_context.linear.weight": "modC.1.weight", + } + else: + index = i - n_double_layers + prefix_from = "single_transformer_blocks" + prefix_to = "{}single_layers".format(output_prefix) + + block_map = { + "attn.to_q.weight": "attn.w1q.weight", + "attn.to_k.weight": "attn.w1k.weight", + "attn.to_v.weight": "attn.w1v.weight", + "attn.to_out.0.weight": "attn.w1o.weight", + "norm1.linear.weight": "modCX.1.weight", + "ff.linear_1.weight": "mlp.c_fc1.weight", + "ff.linear_2.weight": "mlp.c_fc2.weight", + "ff.out_projection.weight": "mlp.c_proj.weight" + } + + for k in block_map: + key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k]) + + MAP_BASIC = { + ("positional_encoding", "pos_embed.pos_embed"), + ("register_tokens", "register_tokens"), + ("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"), + ("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"), + ("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"), + ("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"), + ("cond_seq_linear.weight", "context_embedder.weight"), + ("init_x_linear.weight", "pos_embed.proj.weight"), + ("init_x_linear.bias", "pos_embed.proj.bias"), + ("final_linear.weight", "proj_out.weight"), + ("modF.1.weight", "norm_out.linear.weight", swap_scale_shift), + } + + for k in MAP_BASIC: + if len(k) > 2: + key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) + else: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + + return key_map + +def flux_to_diffusers(mmdit_config, output_prefix=""): + n_double_layers = mmdit_config.get("depth", 0) + n_single_layers = mmdit_config.get("depth_single_blocks", 0) + hidden_size = mmdit_config.get("hidden_size", 0) + + key_map = {} + for index in range(n_double_layers): + prefix_from = "transformer_blocks.{}".format(index) + prefix_to = "{}double_blocks.{}".format(output_prefix, index) + + for end in ("weight", "bias"): + k = "{}.attn.".format(prefix_from) + qkv = "{}.img_attn.qkv.{}".format(prefix_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) + + k = "{}.attn.".format(prefix_from) + qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end) + key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) + key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) + key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) + + block_map = { + "attn.to_out.0.weight": "img_attn.proj.weight", + "attn.to_out.0.bias": "img_attn.proj.bias", + "norm1.linear.weight": "img_mod.lin.weight", + "norm1.linear.bias": "img_mod.lin.bias", + "norm1_context.linear.weight": "txt_mod.lin.weight", + "norm1_context.linear.bias": "txt_mod.lin.bias", + "attn.to_add_out.weight": "txt_attn.proj.weight", + "attn.to_add_out.bias": "txt_attn.proj.bias", + "ff.net.0.proj.weight": "img_mlp.0.weight", + "ff.net.0.proj.bias": "img_mlp.0.bias", + "ff.net.2.weight": "img_mlp.2.weight", + "ff.net.2.bias": "img_mlp.2.bias", + "ff_context.net.0.proj.weight": "txt_mlp.0.weight", + "ff_context.net.0.proj.bias": "txt_mlp.0.bias", + "ff_context.net.2.weight": "txt_mlp.2.weight", + "ff_context.net.2.bias": "txt_mlp.2.bias", + "attn.norm_q.weight": "img_attn.norm.query_norm.scale", + "attn.norm_k.weight": "img_attn.norm.key_norm.scale", + "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale", + "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale", + } + + for k in block_map: + key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) + + for index in range(n_single_layers): + prefix_from = "single_transformer_blocks.{}".format(index) + prefix_to = "{}single_blocks.{}".format(output_prefix, index) + + for end in ("weight", "bias"): + k = "{}.attn.".format(prefix_from) + qkv = "{}.linear1.{}".format(prefix_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) + key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4)) + + block_map = { + "norm.linear.weight": "modulation.lin.weight", + "norm.linear.bias": "modulation.lin.bias", + "proj_out.weight": "linear2.weight", + "proj_out.bias": "linear2.bias", + "attn.norm_q.weight": "norm.query_norm.scale", + "attn.norm_k.weight": "norm.key_norm.scale", + } + + for k in block_map: + key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) + + MAP_BASIC = { + ("final_layer.linear.bias", "proj_out.bias"), + ("final_layer.linear.weight", "proj_out.weight"), + ("img_in.bias", "x_embedder.bias"), + ("img_in.weight", "x_embedder.weight"), + ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"), + ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"), + ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"), + ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"), + ("txt_in.bias", "context_embedder.bias"), + ("txt_in.weight", "context_embedder.weight"), + ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"), + ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"), + ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"), + ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"), + ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"), + ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"), + ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"), + ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"), + ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), + ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), + ("pos_embed_input.bias", "controlnet_x_embedder.bias"), + ("pos_embed_input.weight", "controlnet_x_embedder.weight"), + } + + for k in MAP_BASIC: + if len(k) > 2: + key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) + else: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + + return key_map + +def repeat_to_batch_size(tensor, batch_size, dim=0): + if tensor.shape[dim] > batch_size: + return tensor.narrow(dim, 0, batch_size) + elif tensor.shape[dim] < batch_size: + return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size) + return tensor + +def resize_to_batch_size(tensor, batch_size): + in_batch_size = tensor.shape[0] + if in_batch_size == batch_size: + return tensor + + if batch_size <= 1: + return tensor[:batch_size] + + output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device) + if batch_size < in_batch_size: + scale = (in_batch_size - 1) / (batch_size - 1) + for i in range(batch_size): + output[i] = tensor[min(round(i * scale), in_batch_size - 1)] + else: + scale = in_batch_size / batch_size + for i in range(batch_size): + output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)] + + return output + +def convert_sd_to(state_dict, dtype): + keys = list(state_dict.keys()) + for k in keys: + state_dict[k] = state_dict[k].to(dtype) + return state_dict + +def safetensors_header(safetensors_path, max_size=100*1024*1024): + with open(safetensors_path, "rb") as f: + header = f.read(8) + length_of_header = struct.unpack(' max_size: + return None + return f.read(length_of_header) + +def set_attr(obj, attr, value): + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + setattr(obj, attrs[-1], value) + return prev + +def set_attr_param(obj, attr, value): + return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) + +def copy_to_param(obj, attr, value): + # inplace update tensor instead of replacing it + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + prev.data.copy_(value) + +def get_attr(obj, attr): + attrs = attr.split(".") + for name in attrs: + obj = getattr(obj, name) + return obj + +def bislerp(samples, width, height): + def slerp(b1, b2, r): + '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' + + c = b1.shape[-1] + + #norms + b1_norms = torch.norm(b1, dim=-1, keepdim=True) + b2_norms = torch.norm(b2, dim=-1, keepdim=True) + + #normalize + b1_normalized = b1 / b1_norms + b2_normalized = b2 / b2_norms + + #zero when norms are zero + b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 + b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 + + #slerp + dot = (b1_normalized*b2_normalized).sum(1) + omega = torch.acos(dot) + so = torch.sin(omega) + + #technically not mathematically correct, but more pleasing? + res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized + res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) + + #edge cases for same or polar opposites + res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] + res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] + return res + + def generate_bilinear_data(length_old, length_new, device): + coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") + ratios = coords_1 - coords_1.floor() + coords_1 = coords_1.to(torch.int64) + + coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 + coords_2[:,:,:,-1] -= 1 + coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") + coords_2 = coords_2.to(torch.int64) + return ratios, coords_1, coords_2 + + orig_dtype = samples.dtype + samples = samples.float() + n,c,h,w = samples.shape + h_new, w_new = (height, width) + + #linear w + ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) + coords_1 = coords_1.expand((n, c, h, -1)) + coords_2 = coords_2.expand((n, c, h, -1)) + ratios = ratios.expand((n, 1, h, -1)) + + pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h, w_new, c).movedim(-1, 1) + + #linear h + ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) + coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) + + pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) + return result.to(orig_dtype) + +def lanczos(samples, width, height): + images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] + images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] + images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] + result = torch.stack(images) + return result.to(samples.device, samples.dtype) + +def common_upscale(samples, width, height, upscale_method, crop): + orig_shape = tuple(samples.shape) + if len(orig_shape) > 4: + samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1]) + samples = samples.movedim(2, 1) + samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1]) + if crop == "center": + old_width = samples.shape[-1] + old_height = samples.shape[-2] + old_aspect = old_width / old_height + new_aspect = width / height + x = 0 + y = 0 + if old_aspect > new_aspect: + x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) + elif old_aspect < new_aspect: + y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) + s = samples.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2) + else: + s = samples + + if upscale_method == "bislerp": + out = bislerp(s, width, height) + elif upscale_method == "lanczos": + out = lanczos(s, width, height) + else: + out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) + + if len(orig_shape) == 4: + return out + + out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width)) + return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width)) + +def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): + rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap)) + cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap)) + return rows * cols + +@torch.inference_mode() +def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): + dims = len(tile) + + if not (isinstance(upscale_amount, (tuple, list))): + upscale_amount = [upscale_amount] * dims + + if not (isinstance(overlap, (tuple, list))): + overlap = [overlap] * dims + + def get_upscale(dim, val): + up = upscale_amount[dim] + if callable(up): + return up(val) + else: + return up * val + + def mult_list_upscale(a): + out = [] + for i in range(len(a)): + out.append(round(get_upscale(i, a[i]))) + return out + + output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device) + + for b in range(samples.shape[0]): + s = samples[b:b+1] + + # handle entire input fitting in a single tile + if all(s.shape[d+2] <= tile[d] for d in range(dims)): + output[b:b+1] = function(s).to(output_device) + if pbar is not None: + pbar.update(1) + continue + + out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device) + out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device) + + positions = [range(0, s.shape[d+2], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)] + + for it in itertools.product(*positions): + s_in = s + upscaled = [] + + for d in range(dims): + pos = max(0, min(s.shape[d + 2] - (overlap[d] + 1), it[d])) + l = min(tile[d], s.shape[d + 2] - pos) + s_in = s_in.narrow(d + 2, pos, l) + upscaled.append(round(get_upscale(d, pos))) + + ps = function(s_in).to(output_device) + mask = torch.ones_like(ps) + + for d in range(2, dims + 2): + feather = round(get_upscale(d - 2, overlap[d - 2])) + for t in range(feather): + a = (t + 1) / feather + mask.narrow(d, t, 1).mul_(a) + mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a) + + o = out + o_d = out_div + for d in range(dims): + o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + + o.add_(ps * mask) + o_d.add_(mask) + + if pbar is not None: + pbar.update(1) + + output[b:b+1] = out/out_div + return output + +def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): + return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar) + +PROGRESS_BAR_ENABLED = True +def set_progress_bar_enabled(enabled): + global PROGRESS_BAR_ENABLED + PROGRESS_BAR_ENABLED = enabled + +PROGRESS_BAR_HOOK = None +def set_progress_bar_global_hook(function): + global PROGRESS_BAR_HOOK + PROGRESS_BAR_HOOK = function + +class ProgressBar: + def __init__(self, total): + global PROGRESS_BAR_HOOK + self.total = total + self.current = 0 + self.hook = PROGRESS_BAR_HOOK + + def update_absolute(self, value, total=None, preview=None): + if total is not None: + self.total = total + if value > self.total: + value = self.total + self.current = value + if self.hook is not None: + self.hook(self.current, self.total, preview) + + def update(self, value): + self.update_absolute(self.current + value) diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py new file mode 100644 index 0000000..630f280 --- /dev/null +++ b/comfy_execution/caching.py @@ -0,0 +1,318 @@ +import itertools +from typing import Sequence, Mapping, Dict +from comfy_execution.graph import DynamicPrompt + +import nodes + +from comfy_execution.graph_utils import is_link + +NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {} + + +def include_unique_id_in_input(class_type: str) -> bool: + if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID: + return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values() + return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] + +class CacheKeySet: + def __init__(self, dynprompt, node_ids, is_changed_cache): + self.keys = {} + self.subcache_keys = {} + + def add_keys(self, node_ids): + raise NotImplementedError() + + def all_node_ids(self): + return set(self.keys.keys()) + + def get_used_keys(self): + return self.keys.values() + + def get_used_subcache_keys(self): + return self.subcache_keys.values() + + def get_data_key(self, node_id): + return self.keys.get(node_id, None) + + def get_subcache_key(self, node_id): + return self.subcache_keys.get(node_id, None) + +class Unhashable: + def __init__(self): + self.value = float("NaN") + +def to_hashable(obj): + # So that we don't infinitely recurse since frozenset and tuples + # are Sequences. + if isinstance(obj, (int, float, str, bool, type(None))): + return obj + elif isinstance(obj, Mapping): + return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())]) + elif isinstance(obj, Sequence): + return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj])) + else: + # TODO - Support other objects like tensors? + return Unhashable() + +class CacheKeySetID(CacheKeySet): + def __init__(self, dynprompt, node_ids, is_changed_cache): + super().__init__(dynprompt, node_ids, is_changed_cache) + self.dynprompt = dynprompt + self.add_keys(node_ids) + + def add_keys(self, node_ids): + for node_id in node_ids: + if node_id in self.keys: + continue + if not self.dynprompt.has_node(node_id): + continue + node = self.dynprompt.get_node(node_id) + self.keys[node_id] = (node_id, node["class_type"]) + self.subcache_keys[node_id] = (node_id, node["class_type"]) + +class CacheKeySetInputSignature(CacheKeySet): + def __init__(self, dynprompt, node_ids, is_changed_cache): + super().__init__(dynprompt, node_ids, is_changed_cache) + self.dynprompt = dynprompt + self.is_changed_cache = is_changed_cache + self.add_keys(node_ids) + + def include_node_id_in_input(self) -> bool: + return False + + def add_keys(self, node_ids): + for node_id in node_ids: + if node_id in self.keys: + continue + if not self.dynprompt.has_node(node_id): + continue + node = self.dynprompt.get_node(node_id) + self.keys[node_id] = self.get_node_signature(self.dynprompt, node_id) + self.subcache_keys[node_id] = (node_id, node["class_type"]) + + def get_node_signature(self, dynprompt, node_id): + signature = [] + ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id) + signature.append(self.get_immediate_node_signature(dynprompt, node_id, order_mapping)) + for ancestor_id in ancestors: + signature.append(self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping)) + return to_hashable(signature) + + def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping): + if not dynprompt.has_node(node_id): + # This node doesn't exist -- we can't cache it. + return [float("NaN")] + node = dynprompt.get_node(node_id) + class_type = node["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + signature = [class_type, self.is_changed_cache.get(node_id)] + if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type): + signature.append(node_id) + inputs = node["inputs"] + for key in sorted(inputs.keys()): + if is_link(inputs[key]): + (ancestor_id, ancestor_socket) = inputs[key] + ancestor_index = ancestor_order_mapping[ancestor_id] + signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket))) + else: + signature.append((key, inputs[key])) + return signature + + # This function returns a list of all ancestors of the given node. The order of the list is + # deterministic based on which specific inputs the ancestor is connected by. + def get_ordered_ancestry(self, dynprompt, node_id): + ancestors = [] + order_mapping = {} + self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping) + return ancestors, order_mapping + + def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping): + if not dynprompt.has_node(node_id): + return + inputs = dynprompt.get_node(node_id)["inputs"] + input_keys = sorted(inputs.keys()) + for key in input_keys: + if is_link(inputs[key]): + ancestor_id = inputs[key][0] + if ancestor_id not in order_mapping: + ancestors.append(ancestor_id) + order_mapping[ancestor_id] = len(ancestors) - 1 + self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping) + +class BasicCache: + def __init__(self, key_class): + self.key_class = key_class + self.initialized = False + self.dynprompt: DynamicPrompt + self.cache_key_set: CacheKeySet + self.cache = {} + self.subcaches = {} + + def set_prompt(self, dynprompt, node_ids, is_changed_cache): + self.dynprompt = dynprompt + self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache) + self.is_changed_cache = is_changed_cache + self.initialized = True + + def all_node_ids(self): + assert self.initialized + node_ids = self.cache_key_set.all_node_ids() + for subcache in self.subcaches.values(): + node_ids = node_ids.union(subcache.all_node_ids()) + return node_ids + + def _clean_cache(self): + preserve_keys = set(self.cache_key_set.get_used_keys()) + to_remove = [] + for key in self.cache: + if key not in preserve_keys: + to_remove.append(key) + for key in to_remove: + del self.cache[key] + + def _clean_subcaches(self): + preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys()) + + to_remove = [] + for key in self.subcaches: + if key not in preserve_subcaches: + to_remove.append(key) + for key in to_remove: + del self.subcaches[key] + + def clean_unused(self): + assert self.initialized + self._clean_cache() + self._clean_subcaches() + + def _set_immediate(self, node_id, value): + assert self.initialized + cache_key = self.cache_key_set.get_data_key(node_id) + self.cache[cache_key] = value + + def _get_immediate(self, node_id): + if not self.initialized: + return None + cache_key = self.cache_key_set.get_data_key(node_id) + if cache_key in self.cache: + return self.cache[cache_key] + else: + return None + + def _ensure_subcache(self, node_id, children_ids): + subcache_key = self.cache_key_set.get_subcache_key(node_id) + subcache = self.subcaches.get(subcache_key, None) + if subcache is None: + subcache = BasicCache(self.key_class) + self.subcaches[subcache_key] = subcache + subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache) + return subcache + + def _get_subcache(self, node_id): + assert self.initialized + subcache_key = self.cache_key_set.get_subcache_key(node_id) + if subcache_key in self.subcaches: + return self.subcaches[subcache_key] + else: + return None + + def recursive_debug_dump(self): + result = [] + for key in self.cache: + result.append({"key": key, "value": self.cache[key]}) + for key in self.subcaches: + result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()}) + return result + +class HierarchicalCache(BasicCache): + def __init__(self, key_class): + super().__init__(key_class) + + def _get_cache_for(self, node_id): + assert self.dynprompt is not None + parent_id = self.dynprompt.get_parent_node_id(node_id) + if parent_id is None: + return self + + hierarchy = [] + while parent_id is not None: + hierarchy.append(parent_id) + parent_id = self.dynprompt.get_parent_node_id(parent_id) + + cache = self + for parent_id in reversed(hierarchy): + cache = cache._get_subcache(parent_id) + if cache is None: + return None + return cache + + def get(self, node_id): + cache = self._get_cache_for(node_id) + if cache is None: + return None + return cache._get_immediate(node_id) + + def set(self, node_id, value): + cache = self._get_cache_for(node_id) + assert cache is not None + cache._set_immediate(node_id, value) + + def ensure_subcache_for(self, node_id, children_ids): + cache = self._get_cache_for(node_id) + assert cache is not None + return cache._ensure_subcache(node_id, children_ids) + +class LRUCache(BasicCache): + def __init__(self, key_class, max_size=100): + super().__init__(key_class) + self.max_size = max_size + self.min_generation = 0 + self.generation = 0 + self.used_generation = {} + self.children = {} + + def set_prompt(self, dynprompt, node_ids, is_changed_cache): + super().set_prompt(dynprompt, node_ids, is_changed_cache) + self.generation += 1 + for node_id in node_ids: + self._mark_used(node_id) + + def clean_unused(self): + while len(self.cache) > self.max_size and self.min_generation < self.generation: + self.min_generation += 1 + to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation] + for key in to_remove: + del self.cache[key] + del self.used_generation[key] + if key in self.children: + del self.children[key] + self._clean_subcaches() + + def get(self, node_id): + self._mark_used(node_id) + return self._get_immediate(node_id) + + def _mark_used(self, node_id): + cache_key = self.cache_key_set.get_data_key(node_id) + if cache_key is not None: + self.used_generation[cache_key] = self.generation + + def set(self, node_id, value): + self._mark_used(node_id) + return self._set_immediate(node_id, value) + + def ensure_subcache_for(self, node_id, children_ids): + # Just uses subcaches for tracking 'live' nodes + super()._ensure_subcache(node_id, children_ids) + + self.cache_key_set.add_keys(children_ids) + self._mark_used(node_id) + cache_key = self.cache_key_set.get_data_key(node_id) + self.children[cache_key] = [] + for child_id in children_ids: + self._mark_used(child_id) + self.children[cache_key].append(self.cache_key_set.get_data_key(child_id)) + return self + diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py new file mode 100644 index 0000000..0b5bf18 --- /dev/null +++ b/comfy_execution/graph.py @@ -0,0 +1,270 @@ +import nodes + +from comfy_execution.graph_utils import is_link + +class DependencyCycleError(Exception): + pass + +class NodeInputError(Exception): + pass + +class NodeNotFoundError(Exception): + pass + +class DynamicPrompt: + def __init__(self, original_prompt): + # The original prompt provided by the user + self.original_prompt = original_prompt + # Any extra pieces of the graph created during execution + self.ephemeral_prompt = {} + self.ephemeral_parents = {} + self.ephemeral_display = {} + + def get_node(self, node_id): + if node_id in self.ephemeral_prompt: + return self.ephemeral_prompt[node_id] + if node_id in self.original_prompt: + return self.original_prompt[node_id] + raise NodeNotFoundError(f"Node {node_id} not found") + + def has_node(self, node_id): + return node_id in self.original_prompt or node_id in self.ephemeral_prompt + + def add_ephemeral_node(self, node_id, node_info, parent_id, display_id): + self.ephemeral_prompt[node_id] = node_info + self.ephemeral_parents[node_id] = parent_id + self.ephemeral_display[node_id] = display_id + + def get_real_node_id(self, node_id): + while node_id in self.ephemeral_parents: + node_id = self.ephemeral_parents[node_id] + return node_id + + def get_parent_node_id(self, node_id): + return self.ephemeral_parents.get(node_id, None) + + def get_display_node_id(self, node_id): + while node_id in self.ephemeral_display: + node_id = self.ephemeral_display[node_id] + return node_id + + def all_node_ids(self): + return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys())) + + def get_original_prompt(self): + return self.original_prompt + +def get_input_info(class_def, input_name): + valid_inputs = class_def.INPUT_TYPES() + input_info = None + input_category = None + if "required" in valid_inputs and input_name in valid_inputs["required"]: + input_category = "required" + input_info = valid_inputs["required"][input_name] + elif "optional" in valid_inputs and input_name in valid_inputs["optional"]: + input_category = "optional" + input_info = valid_inputs["optional"][input_name] + elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]: + input_category = "hidden" + input_info = valid_inputs["hidden"][input_name] + if input_info is None: + return None, None, None + input_type = input_info[0] + if len(input_info) > 1: + extra_info = input_info[1] + else: + extra_info = {} + return input_type, input_category, extra_info + +class TopologicalSort: + def __init__(self, dynprompt): + self.dynprompt = dynprompt + self.pendingNodes = {} + self.blockCount = {} # Number of nodes this node is directly blocked by + self.blocking = {} # Which nodes are blocked by this node + + def get_input_info(self, unique_id, input_name): + class_type = self.dynprompt.get_node(unique_id)["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + return get_input_info(class_def, input_name) + + def make_input_strong_link(self, to_node_id, to_input): + inputs = self.dynprompt.get_node(to_node_id)["inputs"] + if to_input not in inputs: + raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all") + value = inputs[to_input] + if not is_link(value): + raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant") + from_node_id, from_socket = value + self.add_strong_link(from_node_id, from_socket, to_node_id) + + def add_strong_link(self, from_node_id, from_socket, to_node_id): + if not self.is_cached(from_node_id): + self.add_node(from_node_id) + if to_node_id not in self.blocking[from_node_id]: + self.blocking[from_node_id][to_node_id] = {} + self.blockCount[to_node_id] += 1 + self.blocking[from_node_id][to_node_id][from_socket] = True + + def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None): + node_ids = [node_unique_id] + links = [] + + while len(node_ids) > 0: + unique_id = node_ids.pop() + if unique_id in self.pendingNodes: + continue + + self.pendingNodes[unique_id] = True + self.blockCount[unique_id] = 0 + self.blocking[unique_id] = {} + + inputs = self.dynprompt.get_node(unique_id)["inputs"] + for input_name in inputs: + value = inputs[input_name] + if is_link(value): + from_node_id, from_socket = value + if subgraph_nodes is not None and from_node_id not in subgraph_nodes: + continue + input_type, input_category, input_info = self.get_input_info(unique_id, input_name) + is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"] + if (include_lazy or not is_lazy) and not self.is_cached(from_node_id): + node_ids.append(from_node_id) + links.append((from_node_id, from_socket, unique_id)) + + for link in links: + self.add_strong_link(*link) + + def is_cached(self, node_id): + return False + + def get_ready_nodes(self): + return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0] + + def pop_node(self, unique_id): + del self.pendingNodes[unique_id] + for blocked_node_id in self.blocking[unique_id]: + self.blockCount[blocked_node_id] -= 1 + del self.blocking[unique_id] + + def is_empty(self): + return len(self.pendingNodes) == 0 + +class ExecutionList(TopologicalSort): + """ + ExecutionList implements a topological dissolve of the graph. After a node is staged for execution, + it can still be returned to the graph after having further dependencies added. + """ + def __init__(self, dynprompt, output_cache): + super().__init__(dynprompt) + self.output_cache = output_cache + self.staged_node_id = None + + def is_cached(self, node_id): + return self.output_cache.get(node_id) is not None + + def stage_node_execution(self): + assert self.staged_node_id is None + if self.is_empty(): + return None, None, None + available = self.get_ready_nodes() + if len(available) == 0: + cycled_nodes = self.get_nodes_in_cycle() + # Because cycles composed entirely of static nodes are caught during initial validation, + # we will 'blame' the first node in the cycle that is not a static node. + blamed_node = cycled_nodes[0] + for node_id in cycled_nodes: + display_node_id = self.dynprompt.get_display_node_id(node_id) + if display_node_id != node_id: + blamed_node = display_node_id + break + ex = DependencyCycleError("Dependency cycle detected") + error_details = { + "node_id": blamed_node, + "exception_message": str(ex), + "exception_type": "graph.DependencyCycleError", + "traceback": [], + "current_inputs": [] + } + return None, error_details, ex + + self.staged_node_id = self.ux_friendly_pick_node(available) + return self.staged_node_id, None, None + + def ux_friendly_pick_node(self, node_list): + # If an output node is available, do that first. + # Technically this has no effect on the overall length of execution, but it feels better as a user + # for a PreviewImage to display a result as soon as it can + # Some other heuristics could probably be used here to improve the UX further. + def is_output(node_id): + class_type = self.dynprompt.get_node(node_id)["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: + return True + return False + + for node_id in node_list: + if is_output(node_id): + return node_id + + #This should handle the VAEDecode -> preview case + for node_id in node_list: + for blocked_node_id in self.blocking[node_id]: + if is_output(blocked_node_id): + return node_id + + #This should handle the VAELoader -> VAEDecode -> preview case + for node_id in node_list: + for blocked_node_id in self.blocking[node_id]: + for blocked_node_id1 in self.blocking[blocked_node_id]: + if is_output(blocked_node_id1): + return node_id + + #TODO: this function should be improved + return node_list[0] + + def unstage_node_execution(self): + assert self.staged_node_id is not None + self.staged_node_id = None + + def complete_node_execution(self): + node_id = self.staged_node_id + self.pop_node(node_id) + self.staged_node_id = None + + def get_nodes_in_cycle(self): + # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle. + # We're skipping some of the performance optimizations from the original TopologicalSort to keep + # the code simple (and because having a cycle in the first place is a catastrophic error) + blocked_by = { node_id: {} for node_id in self.pendingNodes } + for from_node_id in self.blocking: + for to_node_id in self.blocking[from_node_id]: + if True in self.blocking[from_node_id][to_node_id].values(): + blocked_by[to_node_id][from_node_id] = True + to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] + while len(to_remove) > 0: + for node_id in to_remove: + for to_node_id in blocked_by: + if node_id in blocked_by[to_node_id]: + del blocked_by[to_node_id][node_id] + del blocked_by[node_id] + to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] + return list(blocked_by.keys()) + +class ExecutionBlocker: + """ + Return this from a node and any users will be blocked with the given error message. + If the message is None, execution will be blocked silently instead. + Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's + possible, a lazy input will be more efficient and have a better user experience. + This functionality is useful in two cases: + 1. You want to conditionally prevent an output node from executing. (Particularly a built-in node + like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using + lazy evaluation to let it conditionally disable itself.) + 2. You have a node with multiple possible outputs, some of which are invalid and should not be used. + (I would recommend not making nodes like this in the future -- instead, make multiple nodes with + different outputs. Unfortunately, there are several popular existing nodes using this pattern.) + """ + def __init__(self, message): + self.message = message + diff --git a/comfy_execution/graph_utils.py b/comfy_execution/graph_utils.py new file mode 100644 index 0000000..8595e94 --- /dev/null +++ b/comfy_execution/graph_utils.py @@ -0,0 +1,139 @@ +def is_link(obj): + if not isinstance(obj, list): + return False + if len(obj) != 2: + return False + if not isinstance(obj[0], str): + return False + if not isinstance(obj[1], int) and not isinstance(obj[1], float): + return False + return True + +# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end +class GraphBuilder: + _default_prefix_root = "" + _default_prefix_call_index = 0 + _default_prefix_graph_index = 0 + + def __init__(self, prefix = None): + if prefix is None: + self.prefix = GraphBuilder.alloc_prefix() + else: + self.prefix = prefix + self.nodes = {} + self.id_gen = 1 + + @classmethod + def set_default_prefix(cls, prefix_root, call_index, graph_index = 0): + cls._default_prefix_root = prefix_root + cls._default_prefix_call_index = call_index + cls._default_prefix_graph_index = graph_index + + @classmethod + def alloc_prefix(cls, root=None, call_index=None, graph_index=None): + if root is None: + root = GraphBuilder._default_prefix_root + if call_index is None: + call_index = GraphBuilder._default_prefix_call_index + if graph_index is None: + graph_index = GraphBuilder._default_prefix_graph_index + result = f"{root}.{call_index}.{graph_index}." + GraphBuilder._default_prefix_graph_index += 1 + return result + + def node(self, class_type, id=None, **kwargs): + if id is None: + id = str(self.id_gen) + self.id_gen += 1 + id = self.prefix + id + if id in self.nodes: + return self.nodes[id] + + node = Node(id, class_type, kwargs) + self.nodes[id] = node + return node + + def lookup_node(self, id): + id = self.prefix + id + return self.nodes.get(id) + + def finalize(self): + output = {} + for node_id, node in self.nodes.items(): + output[node_id] = node.serialize() + return output + + def replace_node_output(self, node_id, index, new_value): + node_id = self.prefix + node_id + to_remove = [] + for node in self.nodes.values(): + for key, value in node.inputs.items(): + if is_link(value) and value[0] == node_id and value[1] == index: + if new_value is None: + to_remove.append((node, key)) + else: + node.inputs[key] = new_value + for node, key in to_remove: + del node.inputs[key] + + def remove_node(self, id): + id = self.prefix + id + del self.nodes[id] + +class Node: + def __init__(self, id, class_type, inputs): + self.id = id + self.class_type = class_type + self.inputs = inputs + self.override_display_id = None + + def out(self, index): + return [self.id, index] + + def set_input(self, key, value): + if value is None: + if key in self.inputs: + del self.inputs[key] + else: + self.inputs[key] = value + + def get_input(self, key): + return self.inputs.get(key) + + def set_override_display_id(self, override_display_id): + self.override_display_id = override_display_id + + def serialize(self): + serialized = { + "class_type": self.class_type, + "inputs": self.inputs + } + if self.override_display_id is not None: + serialized["override_display_id"] = self.override_display_id + return serialized + +def add_graph_prefix(graph, outputs, prefix): + # Change the node IDs and any internal links + new_graph = {} + for node_id, node_info in graph.items(): + # Make sure the added nodes have unique IDs + new_node_id = prefix + node_id + new_node = { "class_type": node_info["class_type"], "inputs": {} } + for input_name, input_value in node_info.get("inputs", {}).items(): + if is_link(input_value): + new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]] + else: + new_node["inputs"][input_name] = input_value + new_graph[new_node_id] = new_node + + # Change the node IDs in the outputs + new_outputs = [] + for n in range(len(outputs)): + output = outputs[n] + if is_link(output): + new_outputs.append([prefix + output[0], output[1]]) + else: + new_outputs.append(output) + + return new_graph, tuple(new_outputs) + diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py new file mode 100644 index 0000000..d48bc23 --- /dev/null +++ b/comfy_extras/chainner_models/model_loading.py @@ -0,0 +1,5 @@ +from spandrel import ModelLoader + +def load_state_dict(state_dict): + print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.") + return ModelLoader().load_from_state_dict(state_dict).eval() diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py new file mode 100644 index 0000000..820c250 --- /dev/null +++ b/comfy_extras/nodes_advanced_samplers.py @@ -0,0 +1,112 @@ +import comfy.samplers +import comfy.utils +import torch +import numpy as np +from tqdm.auto import trange, tqdm +import math + + +@torch.no_grad() +def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None): + extra_args = {} if extra_args is None else extra_args + + if upscale_steps is None: + upscale_steps = max(len(sigmas) // 2 + 1, 2) + else: + upscale_steps += 1 + upscale_steps = min(upscale_steps, len(sigmas) + 1) + + upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:] + + orig_shape = x.size() + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + x = denoised + if i < len(upscales): + x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled") + + if sigmas[i + 1] > 0: + x += sigmas[i + 1] * torch.randn_like(x) + return x + + +class SamplerLCMUpscale: + upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"] + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}), + "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}), + "upscale_method": (s.upscale_methods,), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, scale_ratio, scale_steps, upscale_method): + if scale_steps < 0: + scale_steps = None + sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method}) + return (sampler, ) + +from comfy.k_diffusion.sampling import to_d +import comfy.model_patcher + +@torch.no_grad() +def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): + extra_args = {} if extra_args is None else extra_args + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + sigma_hat = sigmas[i] + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x - denoised + temp[0], sigmas[i], denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + x = x + d * dt + return x + + +class SamplerEulerCFGpp: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"version": (["regular", "alternative"],),} + } + RETURN_TYPES = ("SAMPLER",) + # CATEGORY = "sampling/custom_sampling/samplers" + CATEGORY = "_for_testing" + + FUNCTION = "get_sampler" + + def get_sampler(self, version): + if version == "alternative": + sampler = comfy.samplers.KSAMPLER(sample_euler_pp) + else: + sampler = comfy.samplers.ksampler("euler_cfg_pp") + return (sampler, ) + +NODE_CLASS_MAPPINGS = { + "SamplerLCMUpscale": SamplerLCMUpscale, + "SamplerEulerCFGpp": SamplerEulerCFGpp, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SamplerEulerCFGpp": "SamplerEulerCFG++", +} diff --git a/comfy_extras/nodes_align_your_steps.py b/comfy_extras/nodes_align_your_steps.py new file mode 100644 index 0000000..3ffe531 --- /dev/null +++ b/comfy_extras/nodes_align_your_steps.py @@ -0,0 +1,53 @@ +#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html +import numpy as np +import torch + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + +NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582], + "SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], + "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} + +class AlignYourStepsScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model_type": (["SD1", "SDXL", "SVD"], ), + "steps": ("INT", {"default": 10, "min": 10, "max": 10000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model_type, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + + sigmas = NOISE_LEVELS[model_type][:] + if (steps + 1) != len(sigmas): + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas), ) + +NODE_CLASS_MAPPINGS = { + "AlignYourStepsScheduler": AlignYourStepsScheduler, +} diff --git a/comfy_extras/nodes_attention_multiply.py b/comfy_extras/nodes_attention_multiply.py new file mode 100644 index 0000000..4747eb3 --- /dev/null +++ b/comfy_extras/nodes_attention_multiply.py @@ -0,0 +1,120 @@ + +def attention_multiply(attn, model, q, k, v, out): + m = model.clone() + sd = model.model_state_dict() + + for key in sd: + if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, q) + if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, k) + if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, v) + if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, out) + + return m + + +class UNetSelfAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, q, k, v, out): + m = attention_multiply("attn1", model, q, k, v, out) + return (m, ) + +class UNetCrossAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, q, k, v, out): + m = attention_multiply("attn2", model, q, k, v, out) + return (m, ) + +class CLIPAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, clip, q, k, v, out): + m = clip.clone() + sd = m.patcher.model_state_dict() + + for key in sd: + if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"): + m.add_patches({key: (None,)}, 0.0, q) + if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"): + m.add_patches({key: (None,)}, 0.0, k) + if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"): + m.add_patches({key: (None,)}, 0.0, v) + if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"): + m.add_patches({key: (None,)}, 0.0, out) + return (m, ) + +class UNetTemporalAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "self_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "self_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "cross_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "cross_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, self_structural, self_temporal, cross_structural, cross_temporal): + m = model.clone() + sd = model.model_state_dict() + + for k in sd: + if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")): + if '.time_stack.' in k: + m.add_patches({k: (None,)}, 0.0, self_temporal) + else: + m.add_patches({k: (None,)}, 0.0, self_structural) + elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")): + if '.time_stack.' in k: + m.add_patches({k: (None,)}, 0.0, cross_temporal) + else: + m.add_patches({k: (None,)}, 0.0, cross_structural) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "UNetSelfAttentionMultiply": UNetSelfAttentionMultiply, + "UNetCrossAttentionMultiply": UNetCrossAttentionMultiply, + "CLIPAttentionMultiply": CLIPAttentionMultiply, + "UNetTemporalAttentionMultiply": UNetTemporalAttentionMultiply, +} diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py new file mode 100644 index 0000000..e5cc4df --- /dev/null +++ b/comfy_extras/nodes_audio.py @@ -0,0 +1,228 @@ +import torchaudio +import torch +import comfy.model_management +import folder_paths +import os +import io +import json +import struct +import random +import hashlib +from comfy.cli_args import args + +class EmptyLatentAudio: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/audio" + + def generate(self, seconds, batch_size): + length = round((seconds * 44100 / 2048) / 2) * 2 + latent = torch.zeros([batch_size, 64, length], device=self.device) + return ({"samples":latent, "type": "audio"}, ) + +class VAEEncodeAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent/audio" + + def encode(self, vae, audio): + sample_rate = audio["sample_rate"] + if 44100 != sample_rate: + waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100) + else: + waveform = audio["waveform"] + + t = vae.encode(waveform.movedim(1, -1)) + return ({"samples":t}, ) + +class VAEDecodeAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} + RETURN_TYPES = ("AUDIO",) + FUNCTION = "decode" + + CATEGORY = "latent/audio" + + def decode(self, vae, samples): + audio = vae.decode(samples["samples"]).movedim(-1, 1) + std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0 + std[std < 1.0] = 1.0 + audio /= std + return ({"waveform": audio, "sample_rate": 44100}, ) + + +def create_vorbis_comment_block(comment_dict, last_block): + vendor_string = b'ComfyUI' + vendor_length = len(vendor_string) + + comments = [] + for key, value in comment_dict.items(): + comment = f"{key}={value}".encode('utf-8') + comments.append(struct.pack('I', len(comment_data))[1:] + comment_data + + return comment_block + +def insert_or_replace_vorbis_comment(flac_io, comment_dict): + if len(comment_dict) == 0: + return flac_io + + flac_io.seek(4) + + blocks = [] + last_block = False + + while not last_block: + header = flac_io.read(4) + last_block = (header[0] & 0x80) != 0 + block_type = header[0] & 0x7F + block_length = struct.unpack('>I', b'\x00' + header[1:])[0] + block_data = flac_io.read(block_length) + + if block_type == 4 or block_type == 1: + pass + else: + header = bytes([(header[0] & (~0x80))]) + header[1:] + blocks.append(header + block_data) + + blocks.append(create_vorbis_comment_block(comment_dict, last_block=True)) + + new_flac_io = io.BytesIO() + new_flac_io.write(b'fLaC') + for block in blocks: + new_flac_io.write(block) + + new_flac_io.write(flac_io.read()) + return new_flac_io + + +class SaveAudio: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_audio" + + OUTPUT_NODE = True + + CATEGORY = "audio" + + def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + results = list() + + metadata = {} + if not args.disable_metadata: + if prompt is not None: + metadata["prompt"] = json.dumps(prompt) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + for (batch_number, waveform) in enumerate(audio["waveform"].cpu()): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.flac" + + buff = io.BytesIO() + torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC") + + buff = insert_or_replace_vorbis_comment(buff, metadata) + + with open(os.path.join(full_output_folder, file), 'wb') as f: + f.write(buff.getbuffer()) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "audio": results } } + +class PreviewAudio(SaveAudio): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"audio": ("AUDIO", ), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + +class LoadAudio: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"]) + return {"required": {"audio": (sorted(files), {"audio_upload": True})}} + + CATEGORY = "audio" + + RETURN_TYPES = ("AUDIO", ) + FUNCTION = "load" + + def load(self, audio): + audio_path = folder_paths.get_annotated_filepath(audio) + waveform, sample_rate = torchaudio.load(audio_path) + audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} + return (audio, ) + + @classmethod + def IS_CHANGED(s, audio): + image_path = folder_paths.get_annotated_filepath(audio) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, audio): + if not folder_paths.exists_annotated_filepath(audio): + return "Invalid audio file: {}".format(audio) + return True + +NODE_CLASS_MAPPINGS = { + "EmptyLatentAudio": EmptyLatentAudio, + "VAEEncodeAudio": VAEEncodeAudio, + "VAEDecodeAudio": VAEDecodeAudio, + "SaveAudio": SaveAudio, + "LoadAudio": LoadAudio, + "PreviewAudio": PreviewAudio, +} diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py new file mode 100644 index 0000000..d85e6b8 --- /dev/null +++ b/comfy_extras/nodes_canny.py @@ -0,0 +1,25 @@ +from kornia.filters import canny +import comfy.model_management + + +class Canny: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), + "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), + "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "detect_edge" + + CATEGORY = "image/preprocessors" + + def detect_edge(self, image, low_threshold, high_threshold): + output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) + img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) + return (img_out,) + +NODE_CLASS_MAPPINGS = { + "Canny": Canny, +} diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py new file mode 100644 index 0000000..3087b91 --- /dev/null +++ b/comfy_extras/nodes_clip_sdxl.py @@ -0,0 +1,56 @@ +import torch +from nodes import MAX_RESOLUTION + +class CLIPTextEncodeSDXLRefiner: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, ascore, width, height, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled, "aesthetic_score": ascore, "width": width,"height": height}]], ) + +class CLIPTextEncodeSDXL: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), + "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), + "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), + "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l): + tokens = clip.tokenize(text_g) + tokens["l"] = clip.tokenize(text_l)["l"] + if len(tokens["l"]) != len(tokens["g"]): + empty = clip.tokenize("") + while len(tokens["l"]) < len(tokens["g"]): + tokens["l"] += empty["l"] + while len(tokens["l"]) > len(tokens["g"]): + tokens["g"] += empty["g"] + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], ) + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner, + "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL, +} diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py new file mode 100644 index 0000000..48fe5e3 --- /dev/null +++ b/comfy_extras/nodes_compositing.py @@ -0,0 +1,215 @@ +import numpy as np +import torch +import comfy.utils +from enum import Enum + +def resize_mask(mask, shape): + return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1) + +class PorterDuffMode(Enum): + ADD = 0 + CLEAR = 1 + DARKEN = 2 + DST = 3 + DST_ATOP = 4 + DST_IN = 5 + DST_OUT = 6 + DST_OVER = 7 + LIGHTEN = 8 + MULTIPLY = 9 + OVERLAY = 10 + SCREEN = 11 + SRC = 12 + SRC_ATOP = 13 + SRC_IN = 14 + SRC_OUT = 15 + SRC_OVER = 16 + XOR = 17 + + +def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode): + # convert mask to alpha + src_alpha = 1 - src_alpha + dst_alpha = 1 - dst_alpha + # premultiply alpha + src_image = src_image * src_alpha + dst_image = dst_image * dst_alpha + + # composite ops below assume alpha-premultiplied images + if mode == PorterDuffMode.ADD: + out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1) + out_image = torch.clamp(src_image + dst_image, 0, 1) + elif mode == PorterDuffMode.CLEAR: + out_alpha = torch.zeros_like(dst_alpha) + out_image = torch.zeros_like(dst_image) + elif mode == PorterDuffMode.DARKEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image) + elif mode == PorterDuffMode.DST: + out_alpha = dst_alpha + out_image = dst_image + elif mode == PorterDuffMode.DST_ATOP: + out_alpha = src_alpha + out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.DST_IN: + out_alpha = src_alpha * dst_alpha + out_image = dst_image * src_alpha + elif mode == PorterDuffMode.DST_OUT: + out_alpha = (1 - src_alpha) * dst_alpha + out_image = (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.DST_OVER: + out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha + out_image = dst_image + (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.LIGHTEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image) + elif mode == PorterDuffMode.MULTIPLY: + out_alpha = src_alpha * dst_alpha + out_image = src_image * dst_image + elif mode == PorterDuffMode.OVERLAY: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image, + src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image)) + elif mode == PorterDuffMode.SCREEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = src_image + dst_image - src_image * dst_image + elif mode == PorterDuffMode.SRC: + out_alpha = src_alpha + out_image = src_image + elif mode == PorterDuffMode.SRC_ATOP: + out_alpha = dst_alpha + out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.SRC_IN: + out_alpha = src_alpha * dst_alpha + out_image = src_image * dst_alpha + elif mode == PorterDuffMode.SRC_OUT: + out_alpha = (1 - dst_alpha) * src_alpha + out_image = (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.SRC_OVER: + out_alpha = src_alpha + (1 - src_alpha) * dst_alpha + out_image = src_image + (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.XOR: + out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + else: + return None, None + + # back to non-premultiplied alpha + out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image)) + out_image = torch.clamp(out_image, 0, 1) + # convert alpha to mask + out_alpha = 1 - out_alpha + return out_image, out_alpha + + +class PorterDuffImageComposite: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "source": ("IMAGE",), + "source_alpha": ("MASK",), + "destination": ("IMAGE",), + "destination_alpha": ("MASK",), + "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}), + }, + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "composite" + CATEGORY = "mask/compositing" + + def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode): + batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha)) + out_images = [] + out_alphas = [] + + for i in range(batch_size): + src_image = source[i] + dst_image = destination[i] + + assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels + + src_alpha = source_alpha[i].unsqueeze(2) + dst_alpha = destination_alpha[i].unsqueeze(2) + + if dst_alpha.shape[:2] != dst_image.shape[:2]: + upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') + dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) + if src_image.shape != dst_image.shape: + upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') + src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0) + if src_alpha.shape != dst_alpha.shape: + upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center') + src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) + + out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode]) + + out_images.append(out_image) + out_alphas.append(out_alpha.squeeze(2)) + + result = (torch.stack(out_images), torch.stack(out_alphas)) + return result + + +class SplitImageWithAlpha: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + } + } + + CATEGORY = "mask/compositing" + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "split_image_with_alpha" + + def split_image_with_alpha(self, image: torch.Tensor): + out_images = [i[:,:,:3] for i in image] + out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image] + result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas)) + return result + + +class JoinImageWithAlpha: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "alpha": ("MASK",), + } + } + + CATEGORY = "mask/compositing" + RETURN_TYPES = ("IMAGE",) + FUNCTION = "join_image_with_alpha" + + def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor): + batch_size = min(len(image), len(alpha)) + out_images = [] + + alpha = 1.0 - resize_mask(alpha, image.shape[1:]) + for i in range(batch_size): + out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2)) + + result = (torch.stack(out_images),) + return result + + +NODE_CLASS_MAPPINGS = { + "PorterDuffImageComposite": PorterDuffImageComposite, + "SplitImageWithAlpha": SplitImageWithAlpha, + "JoinImageWithAlpha": JoinImageWithAlpha, +} + + +NODE_DISPLAY_NAME_MAPPINGS = { + "PorterDuffImageComposite": "Porter-Duff Image Composite", + "SplitImageWithAlpha": "Split Image with Alpha", + "JoinImageWithAlpha": "Join Image with Alpha", +} diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py new file mode 100644 index 0000000..4c3a1d5 --- /dev/null +++ b/comfy_extras/nodes_cond.py @@ -0,0 +1,25 @@ + + +class CLIPTextEncodeControlnet: + @classmethod + def INPUT_TYPES(s): + return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "_for_testing/conditioning" + + def encode(self, clip, conditioning, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['cross_attn_controlnet'] = cond + n[1]['pooled_output_controlnet'] = pooled + c.append(n) + return (c, ) + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet +} diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py new file mode 100644 index 0000000..2d20e1f --- /dev/null +++ b/comfy_extras/nodes_controlnet.py @@ -0,0 +1,60 @@ +from comfy.cldm.control_types import UNION_CONTROLNET_TYPES +import nodes +import comfy.utils + +class SetUnionControlNetType: + @classmethod + def INPUT_TYPES(s): + return {"required": {"control_net": ("CONTROL_NET", ), + "type": (["auto"] + list(UNION_CONTROLNET_TYPES.keys()),) + }} + + CATEGORY = "conditioning/controlnet" + RETURN_TYPES = ("CONTROL_NET",) + + FUNCTION = "set_controlnet_type" + + def set_controlnet_type(self, control_net, type): + control_net = control_net.copy() + type_number = UNION_CONTROLNET_TYPES.get(type, -1) + if type_number >= 0: + control_net.set_extra_arg("control_type", [type_number]) + else: + control_net.set_extra_arg("control_type", []) + + return (control_net,) + +class ControlNetInpaintingAliMamaApply(nodes.ControlNetApplyAdvanced): + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "vae": ("VAE", ), + "image": ("IMAGE", ), + "mask": ("MASK", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + + FUNCTION = "apply_inpaint_controlnet" + + CATEGORY = "conditioning/controlnet" + + def apply_inpaint_controlnet(self, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent): + extra_concat = [] + if control_net.concat_mask: + mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round() + image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3]) + extra_concat = [mask] + + return self.apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat) + + + +NODE_CLASS_MAPPINGS = { + "SetUnionControlNetType": SetUnionControlNetType, + "ControlNetInpaintingAliMamaApply": ControlNetInpaintingAliMamaApply, +} diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py new file mode 100644 index 0000000..c7ff9a4 --- /dev/null +++ b/comfy_extras/nodes_custom_sampler.py @@ -0,0 +1,725 @@ +import comfy.samplers +import comfy.sample +from comfy.k_diffusion import sampling as k_diffusion_sampling +import latent_preview +import torch +import comfy.utils +import node_helpers + + +class BasicScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "scheduler": (comfy.samplers.SCHEDULER_NAMES, ), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, scheduler, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = int(steps/denoise) + + sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu() + sigmas = sigmas[-(steps + 1):] + return (sigmas, ) + + +class KarrasScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, rho): + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) + return (sigmas, ) + +class ExponentialScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min): + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max) + return (sigmas, ) + +class PolyexponentialScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, rho): + sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) + return (sigmas, ) + +class LaplaceScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "mu": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step":0.1, "round": False}), + "beta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step":0.1, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta): + sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta) + return (sigmas, ) + + +class SDTurboScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "steps": ("INT", {"default": 1, "min": 1, "max": 10}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, steps, denoise): + start_step = 10 - int(10 * denoise) + timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] + sigmas = model.get_model_object("model_sampling").sigma(timesteps) + sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) + return (sigmas, ) + +class BetaSamplingScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "alpha": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}), + "beta": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, steps, alpha, beta): + sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta) + return (sigmas, ) + +class VPScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), #TODO: fix default values + "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, beta_d, beta_min, eps_s): + sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s) + return (sigmas, ) + +class SplitSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "step": ("INT", {"default": 0, "min": 0, "max": 10000}), + } + } + RETURN_TYPES = ("SIGMAS","SIGMAS") + RETURN_NAMES = ("high_sigmas", "low_sigmas") + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas, step): + sigmas1 = sigmas[:step + 1] + sigmas2 = sigmas[step:] + return (sigmas1, sigmas2) + +class SplitSigmasDenoise: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS","SIGMAS") + RETURN_NAMES = ("high_sigmas", "low_sigmas") + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas, denoise): + steps = max(sigmas.shape[-1] - 1, 0) + total_steps = round(steps * denoise) + sigmas1 = sigmas[:-(total_steps)] + sigmas2 = sigmas[-(total_steps + 1):] + return (sigmas1, sigmas2) + +class FlipSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas): + if len(sigmas) == 0: + return (sigmas,) + + sigmas = sigmas.flip(0) + if sigmas[0] == 0: + sigmas[0] = 0.0001 + return (sigmas,) + +class KSamplerSelect: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sampler_name": (comfy.samplers.SAMPLER_NAMES, ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, sampler_name): + sampler = comfy.samplers.sampler_object(sampler_name) + return (sampler, ) + +class SamplerDPMPP_3M_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_3m_sde" + else: + sampler_name = "dpmpp_3m_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + +class SamplerDPMPP_2M_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"solver_type": (['midpoint', 'heun'], ), + "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, solver_type, eta, s_noise, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_2m_sde" + else: + sampler_name = "dpmpp_2m_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) + return (sampler, ) + + +class SamplerDPMPP_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise, r, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_sde" + else: + sampler_name = "dpmpp_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) + return (sampler, ) + +class SamplerDPMPP_2S_Ancestral: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise): + sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + +class SamplerEulerAncestral: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise): + sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + +class SamplerEulerAncestralCFGPP: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}), + }} + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise): + sampler = comfy.samplers.ksampler( + "euler_ancestral_cfg_pp", + {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + +class SamplerLMS: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"order": ("INT", {"default": 4, "min": 1, "max": 100}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, order): + sampler = comfy.samplers.ksampler("lms", {"order": order}) + return (sampler, ) + +class SamplerDPMAdaptative: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"order": ("INT", {"default": 3, "min": 2, "max": 3}), + "rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise): + sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff, + "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta, + "s_noise":s_noise }) + return (sampler, ) + +class Noise_EmptyNoise: + def __init__(self): + self.seed = 0 + + def generate_noise(self, input_latent): + latent_image = input_latent["samples"] + return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + + +class Noise_RandomNoise: + def __init__(self, seed): + self.seed = seed + + def generate_noise(self, input_latent): + latent_image = input_latent["samples"] + batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None + return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds) + +class SamplerCustom: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": ("BOOLEAN", {"default": True}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "sampler": ("SAMPLER", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT","LATENT") + RETURN_NAMES = ("output", "denoised_output") + + FUNCTION = "sample" + + CATEGORY = "sampling/custom_sampling" + + def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): + latent = latent_image + latent_image = latent["samples"] + latent = latent.copy() + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + latent["samples"] = latent_image + + if not add_noise: + noise = Noise_EmptyNoise().generate_noise(latent) + else: + noise = Noise_RandomNoise(noise_seed).generate_noise(latent) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + x0_output = {} + callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) + + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed) + + out = latent.copy() + out["samples"] = samples + if "x0" in x0_output: + out_denoised = latent.copy() + out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) + else: + out_denoised = out + return (out, out_denoised) + +class Guider_Basic(comfy.samplers.CFGGuider): + def set_conds(self, positive): + self.inner_set_conds({"positive": positive}) + +class BasicGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "conditioning": ("CONDITIONING", ), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, conditioning): + guider = Guider_Basic(model) + guider.set_conds(conditioning) + return (guider,) + +class CFGGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, positive, negative, cfg): + guider = comfy.samplers.CFGGuider(model) + guider.set_conds(positive, negative) + guider.set_cfg(cfg) + return (guider,) + +class Guider_DualCFG(comfy.samplers.CFGGuider): + def set_cfg(self, cfg1, cfg2): + self.cfg1 = cfg1 + self.cfg2 = cfg2 + + def set_conds(self, positive, middle, negative): + middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"}) + self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative}) + + def predict_noise(self, x, timestep, model_options={}, seed=None): + negative_cond = self.conds.get("negative", None) + middle_cond = self.conds.get("middle", None) + + out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options) + return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1 + +class DualCFGGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "cond1": ("CONDITIONING", ), + "cond2": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative): + guider = Guider_DualCFG(model) + guider.set_conds(cond1, cond2, negative) + guider.set_cfg(cfg_conds, cfg_cond2_negative) + return (guider,) + +class DisableNoise: + @classmethod + def INPUT_TYPES(s): + return {"required":{ + } + } + + RETURN_TYPES = ("NOISE",) + FUNCTION = "get_noise" + CATEGORY = "sampling/custom_sampling/noise" + + def get_noise(self): + return (Noise_EmptyNoise(),) + + +class RandomNoise(DisableNoise): + @classmethod + def INPUT_TYPES(s): + return {"required":{ + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + } + } + + def get_noise(self, noise_seed): + return (Noise_RandomNoise(noise_seed),) + + +class SamplerCustomAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"noise": ("NOISE", ), + "guider": ("GUIDER", ), + "sampler": ("SAMPLER", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT","LATENT") + RETURN_NAMES = ("output", "denoised_output") + + FUNCTION = "sample" + + CATEGORY = "sampling/custom_sampling" + + def sample(self, noise, guider, sampler, sigmas, latent_image): + latent = latent_image + latent_image = latent["samples"] + latent = latent.copy() + latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image) + latent["samples"] = latent_image + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + x0_output = {} + callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output) + + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed) + samples = samples.to(comfy.model_management.intermediate_device()) + + out = latent.copy() + out["samples"] = samples + if "x0" in x0_output: + out_denoised = latent.copy() + out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu()) + else: + out_denoised = out + return (out, out_denoised) + +class AddNoise: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "noise": ("NOISE", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT",) + + FUNCTION = "add_noise" + + CATEGORY = "_for_testing/custom_sampling/noise" + + def add_noise(self, model, noise, sigmas, latent_image): + if len(sigmas) == 0: + return latent_image + + latent = latent_image + latent_image = latent["samples"] + + noisy = noise.generate_noise(latent) + + model_sampling = model.get_model_object("model_sampling") + process_latent_out = model.get_model_object("process_latent_out") + process_latent_in = model.get_model_object("process_latent_in") + + if len(sigmas) > 1: + scale = torch.abs(sigmas[0] - sigmas[-1]) + else: + scale = sigmas[0] + + if torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. + latent_image = process_latent_in(latent_image) + noisy = model_sampling.noise_scaling(scale, noisy, latent_image) + noisy = process_latent_out(noisy) + noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0) + + out = latent.copy() + out["samples"] = noisy + return (out,) + + +NODE_CLASS_MAPPINGS = { + "SamplerCustom": SamplerCustom, + "BasicScheduler": BasicScheduler, + "KarrasScheduler": KarrasScheduler, + "ExponentialScheduler": ExponentialScheduler, + "PolyexponentialScheduler": PolyexponentialScheduler, + "LaplaceScheduler": LaplaceScheduler, + "VPScheduler": VPScheduler, + "BetaSamplingScheduler": BetaSamplingScheduler, + "SDTurboScheduler": SDTurboScheduler, + "KSamplerSelect": KSamplerSelect, + "SamplerEulerAncestral": SamplerEulerAncestral, + "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP, + "SamplerLMS": SamplerLMS, + "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE, + "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, + "SamplerDPMPP_SDE": SamplerDPMPP_SDE, + "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral, + "SamplerDPMAdaptative": SamplerDPMAdaptative, + "SplitSigmas": SplitSigmas, + "SplitSigmasDenoise": SplitSigmasDenoise, + "FlipSigmas": FlipSigmas, + + "CFGGuider": CFGGuider, + "DualCFGGuider": DualCFGGuider, + "BasicGuider": BasicGuider, + "RandomNoise": RandomNoise, + "DisableNoise": DisableNoise, + "AddNoise": AddNoise, + "SamplerCustomAdvanced": SamplerCustomAdvanced, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++", +} diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py new file mode 100644 index 0000000..98dbbf1 --- /dev/null +++ b/comfy_extras/nodes_differential_diffusion.py @@ -0,0 +1,42 @@ +# code adapted from https://github.com/exx8/differential-diffusion + +import torch + +class DifferentialDiffusion(): + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "apply" + CATEGORY = "_for_testing" + INIT = False + + def apply(self, model): + model = model.clone() + model.set_model_denoise_mask_function(self.forward) + return (model,) + + def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): + model = extra_options["model"] + step_sigmas = extra_options["sigmas"] + sigma_to = model.inner_model.model_sampling.sigma_min + if step_sigmas[-1] > sigma_to: + sigma_to = step_sigmas[-1] + sigma_from = step_sigmas[0] + + ts_from = model.inner_model.model_sampling.timestep(sigma_from) + ts_to = model.inner_model.model_sampling.timestep(sigma_to) + current_ts = model.inner_model.model_sampling.timestep(sigma[0]) + + threshold = (current_ts - ts_to) / (ts_from - ts_to) + + return (denoise_mask >= threshold).to(denoise_mask.dtype) + + +NODE_CLASS_MAPPINGS = { + "DifferentialDiffusion": DifferentialDiffusion, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DifferentialDiffusion": "Differential Diffusion", +} diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py new file mode 100644 index 0000000..b690432 --- /dev/null +++ b/comfy_extras/nodes_flux.py @@ -0,0 +1,47 @@ +import node_helpers + +class CLIPTextEncodeFlux: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning/flux" + + def encode(self, clip, clip_l, t5xxl, guidance): + tokens = clip.tokenize(clip_l) + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + + output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True) + cond = output.pop("cond") + output["guidance"] = guidance + return ([[cond, output]], ) + +class FluxGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning": ("CONDITIONING", ), + "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "advanced/conditioning/flux" + + def append(self, conditioning, guidance): + c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance}) + return (c, ) + + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeFlux": CLIPTextEncodeFlux, + "FluxGuidance": FluxGuidance, +} diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py new file mode 100644 index 0000000..e3ac584 --- /dev/null +++ b/comfy_extras/nodes_freelunch.py @@ -0,0 +1,113 @@ +#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) + +import torch +import logging + +def Fourier_filter(x, threshold, scale): + # FFT + x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) + x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) + + B, C, H, W = x_freq.shape + mask = torch.ones((B, C, H, W), device=x.device) + + crow, ccol = H // 2, W //2 + mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale + x_freq = x_freq * mask + + # IFFT + x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real + + return x_filtered.to(x.dtype) + + +class FreeU: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), + "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), + "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), + "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + on_cpu_devices = {} + + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(int(h.shape[1]), None) + if scale is not None: + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] + if hsp.device not in on_cpu_devices: + try: + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + except: + logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) + on_cpu_devices[hsp.device] = True + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + else: + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +class FreeU_V2: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), + "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), + "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), + "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + on_cpu_devices = {} + + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(int(h.shape[1]), None) + if scale is not None: + hidden_mean = h.mean(1).unsqueeze(1) + B = hidden_mean.shape[0] + hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) + hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) + hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) + + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1) + + if hsp.device not in on_cpu_devices: + try: + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + except: + logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) + on_cpu_devices[hsp.device] = True + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + else: + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "FreeU": FreeU, + "FreeU_V2": FreeU_V2, +} diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py new file mode 100644 index 0000000..7bfae4c --- /dev/null +++ b/comfy_extras/nodes_gits.py @@ -0,0 +1,369 @@ +# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main +import numpy as np +import torch + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + +NOISE_LEVELS = { + 0.80: [ + [14.61464119, 7.49001646, 0.02916753], + [14.61464119, 11.54541874, 6.77309084, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 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0.02916753], + [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + ], +} + +class GITSScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}), + "steps": ("INT", {"default": 10, "min": 2, "max": 1000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, coeff, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + + if steps <= 20: + sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:] + else: + sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:] + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas), ) + +NODE_CLASS_MAPPINGS = { + "GITSScheduler": GITSScheduler, +} diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py new file mode 100644 index 0000000..b03eaf6 --- /dev/null +++ b/comfy_extras/nodes_hunyuan.py @@ -0,0 +1,25 @@ +class CLIPTextEncodeHunyuanDiT: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "bert": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, bert, mt5xl): + tokens = clip.tokenize(bert) + tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"] + + output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True) + cond = output.pop("cond") + return ([[cond, output]], ) + + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT, +} diff --git a/comfy_extras/nodes_hypernetwork.py b/comfy_extras/nodes_hypernetwork.py new file mode 100644 index 0000000..6656322 --- /dev/null +++ b/comfy_extras/nodes_hypernetwork.py @@ -0,0 +1,120 @@ +import comfy.utils +import folder_paths +import torch +import logging + +def load_hypernetwork_patch(path, strength): + sd = comfy.utils.load_torch_file(path, safe_load=True) + activation_func = sd.get('activation_func', 'linear') + is_layer_norm = sd.get('is_layer_norm', False) + use_dropout = sd.get('use_dropout', False) + activate_output = sd.get('activate_output', False) + last_layer_dropout = sd.get('last_layer_dropout', False) + + valid_activation = { + "linear": torch.nn.Identity, + "relu": torch.nn.ReLU, + "leakyrelu": torch.nn.LeakyReLU, + "elu": torch.nn.ELU, + "swish": torch.nn.Hardswish, + "tanh": torch.nn.Tanh, + "sigmoid": torch.nn.Sigmoid, + "softsign": torch.nn.Softsign, + "mish": torch.nn.Mish, + } + + if activation_func not in valid_activation: + logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {} {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)) + return None + + out = {} + + for d in sd: + try: + dim = int(d) + except: + continue + + output = [] + for index in [0, 1]: + attn_weights = sd[dim][index] + keys = attn_weights.keys() + + linears = filter(lambda a: a.endswith(".weight"), keys) + linears = list(map(lambda a: a[:-len(".weight")], linears)) + layers = [] + + i = 0 + while i < len(linears): + lin_name = linears[i] + last_layer = (i == (len(linears) - 1)) + penultimate_layer = (i == (len(linears) - 2)) + + lin_weight = attn_weights['{}.weight'.format(lin_name)] + lin_bias = attn_weights['{}.bias'.format(lin_name)] + layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0]) + layer.load_state_dict({"weight": lin_weight, "bias": lin_bias}) + layers.append(layer) + if activation_func != "linear": + if (not last_layer) or (activate_output): + layers.append(valid_activation[activation_func]()) + if is_layer_norm: + i += 1 + ln_name = linears[i] + ln_weight = attn_weights['{}.weight'.format(ln_name)] + ln_bias = attn_weights['{}.bias'.format(ln_name)] + ln = torch.nn.LayerNorm(ln_weight.shape[0]) + ln.load_state_dict({"weight": ln_weight, "bias": ln_bias}) + layers.append(ln) + if use_dropout: + if (not last_layer) and (not penultimate_layer or last_layer_dropout): + layers.append(torch.nn.Dropout(p=0.3)) + i += 1 + + output.append(torch.nn.Sequential(*layers)) + out[dim] = torch.nn.ModuleList(output) + + class hypernetwork_patch: + def __init__(self, hypernet, strength): + self.hypernet = hypernet + self.strength = strength + def __call__(self, q, k, v, extra_options): + dim = k.shape[-1] + if dim in self.hypernet: + hn = self.hypernet[dim] + k = k + hn[0](k) * self.strength + v = v + hn[1](v) * self.strength + + return q, k, v + + def to(self, device): + for d in self.hypernet.keys(): + self.hypernet[d] = self.hypernet[d].to(device) + return self + + return hypernetwork_patch(out, strength) + +class HypernetworkLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_hypernetwork" + + CATEGORY = "loaders" + + def load_hypernetwork(self, model, hypernetwork_name, strength): + hypernetwork_path = folder_paths.get_full_path_or_raise("hypernetworks", hypernetwork_name) + model_hypernetwork = model.clone() + patch = load_hypernetwork_patch(hypernetwork_path, strength) + if patch is not None: + model_hypernetwork.set_model_attn1_patch(patch) + model_hypernetwork.set_model_attn2_patch(patch) + return (model_hypernetwork,) + +NODE_CLASS_MAPPINGS = { + "HypernetworkLoader": HypernetworkLoader +} diff --git a/comfy_extras/nodes_hypertile.py b/comfy_extras/nodes_hypertile.py new file mode 100644 index 0000000..227133f --- /dev/null +++ b/comfy_extras/nodes_hypertile.py @@ -0,0 +1,83 @@ +#Taken from: https://github.com/tfernd/HyperTile/ + +import math +from einops import rearrange +# Use torch rng for consistency across generations +from torch import randint + +def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: + min_value = min(min_value, value) + + # All big divisors of value (inclusive) + divisors = [i for i in range(min_value, value + 1) if value % i == 0] + + ns = [value // i for i in divisors[:max_options]] # has at least 1 element + + if len(ns) - 1 > 0: + idx = randint(low=0, high=len(ns) - 1, size=(1,)).item() + else: + idx = 0 + + return ns[idx] + +class HyperTile: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), + "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), + "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), + "scale_depth": ("BOOLEAN", {"default": False}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, tile_size, swap_size, max_depth, scale_depth): + model_channels = model.model.model_config.unet_config["model_channels"] + + latent_tile_size = max(32, tile_size) // 8 + self.temp = None + + def hypertile_in(q, k, v, extra_options): + model_chans = q.shape[-2] + orig_shape = extra_options['original_shape'] + apply_to = [] + for i in range(max_depth + 1): + apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i))) + + if model_chans in apply_to: + shape = extra_options["original_shape"] + aspect_ratio = shape[-1] / shape[-2] + + hw = q.size(1) + h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) + + factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1 + nh = random_divisor(h, latent_tile_size * factor, swap_size) + nw = random_divisor(w, latent_tile_size * factor, swap_size) + + if nh * nw > 1: + q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) + self.temp = (nh, nw, h, w) + return q, k, v + + return q, k, v + def hypertile_out(out, extra_options): + if self.temp is not None: + nh, nw, h, w = self.temp + self.temp = None + out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) + out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) + return out + + + m = model.clone() + m.set_model_attn1_patch(hypertile_in) + m.set_model_attn1_output_patch(hypertile_out) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "HyperTile": HyperTile, +} diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py new file mode 100644 index 0000000..af37666 --- /dev/null +++ b/comfy_extras/nodes_images.py @@ -0,0 +1,195 @@ +import nodes +import folder_paths +from comfy.cli_args import args + +from PIL import Image +from PIL.PngImagePlugin import PngInfo + +import numpy as np +import json +import os + +MAX_RESOLUTION = nodes.MAX_RESOLUTION + +class ImageCrop: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "crop" + + CATEGORY = "image/transform" + + def crop(self, image, width, height, x, y): + x = min(x, image.shape[2] - 1) + y = min(y, image.shape[1] - 1) + to_x = width + x + to_y = height + y + img = image[:,y:to_y, x:to_x, :] + return (img,) + +class RepeatImageBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "amount": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "repeat" + + CATEGORY = "image/batch" + + def repeat(self, image, amount): + s = image.repeat((amount, 1,1,1)) + return (s,) + +class ImageFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}), + "length": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "frombatch" + + CATEGORY = "image/batch" + + def frombatch(self, image, batch_index, length): + s_in = image + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s = s_in[batch_index:batch_index + length].clone() + return (s,) + +class SaveAnimatedWEBP: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + methods = {"default": 4, "fastest": 0, "slowest": 6} + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + "lossless": ("BOOLEAN", {"default": True}), + "quality": ("INT", {"default": 80, "min": 0, "max": 100}), + "method": (list(s.methods.keys()),), + # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image/animation" + + def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None): + method = self.methods.get(method) + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + pil_images = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + + metadata = pil_images[0].getexif() + if not args.disable_metadata: + if prompt is not None: + metadata[0x0110] = "prompt:{}".format(json.dumps(prompt)) + if extra_pnginfo is not None: + inital_exif = 0x010f + for x in extra_pnginfo: + metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x])) + inital_exif -= 1 + + if num_frames == 0: + num_frames = len(pil_images) + + c = len(pil_images) + for i in range(0, c, num_frames): + file = f"{filename}_{counter:05}_.webp" + pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + animated = num_frames != 1 + return { "ui": { "images": results, "animated": (animated,) } } + +class SaveAnimatedPNG: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + "compress_level": ("INT", {"default": 4, "min": 0, "max": 9}) + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image/animation" + + def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + pil_images = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True) + + file = f"{filename}_{counter:05}_.png" + pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:]) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + return { "ui": { "images": results, "animated": (True,)} } + +NODE_CLASS_MAPPINGS = { + "ImageCrop": ImageCrop, + "RepeatImageBatch": RepeatImageBatch, + "ImageFromBatch": ImageFromBatch, + "SaveAnimatedWEBP": SaveAnimatedWEBP, + "SaveAnimatedPNG": SaveAnimatedPNG, +} diff --git a/comfy_extras/nodes_ip2p.py b/comfy_extras/nodes_ip2p.py new file mode 100644 index 0000000..c2e70a8 --- /dev/null +++ b/comfy_extras/nodes_ip2p.py @@ -0,0 +1,45 @@ +import torch + +class InstructPixToPixConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "pixels": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/instructpix2pix" + + def encode(self, positive, negative, pixels, vae): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + + concat_latent = vae.encode(pixels) + + out_latent = {} + out_latent["samples"] = torch.zeros_like(concat_latent) + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + d["concat_latent_image"] = concat_latent + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1], out_latent) + +NODE_CLASS_MAPPINGS = { + "InstructPixToPixConditioning": InstructPixToPixConditioning, +} diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py new file mode 100644 index 0000000..af27368 --- /dev/null +++ b/comfy_extras/nodes_latent.py @@ -0,0 +1,285 @@ +import comfy.utils +import comfy_extras.nodes_post_processing +import torch + +def reshape_latent_to(target_shape, latent): + if latent.shape[1:] != target_shape[1:]: + latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") + return comfy.utils.repeat_to_batch_size(latent, target_shape[0]) + + +class LatentAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + samples_out["samples"] = s1 + s2 + return (samples_out,) + +class LatentSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + samples_out["samples"] = s1 - s2 + return (samples_out,) + +class LatentMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples, multiplier): + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = s1 * multiplier + return (samples_out,) + +class LatentInterpolate: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), + "samples2": ("LATENT",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2, ratio): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + + m1 = torch.linalg.vector_norm(s1, dim=(1)) + m2 = torch.linalg.vector_norm(s2, dim=(1)) + + s1 = torch.nan_to_num(s1 / m1) + s2 = torch.nan_to_num(s2 / m2) + + t = (s1 * ratio + s2 * (1.0 - ratio)) + mt = torch.linalg.vector_norm(t, dim=(1)) + st = torch.nan_to_num(t / mt) + + samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio)) + return (samples_out,) + +class LatentBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "batch" + + CATEGORY = "latent/batch" + + def batch(self, samples1, samples2): + samples_out = samples1.copy() + s1 = samples1["samples"] + s2 = samples2["samples"] + + if s1.shape[1:] != s2.shape[1:]: + s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center") + s = torch.cat((s1, s2), dim=0) + samples_out["samples"] = s + samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])]) + return (samples_out,) + +class LatentBatchSeedBehavior: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "seed_behavior": (["random", "fixed"],{"default": "fixed"}),}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples, seed_behavior): + samples_out = samples.copy() + latent = samples["samples"] + if seed_behavior == "random": + if 'batch_index' in samples_out: + samples_out.pop('batch_index') + elif seed_behavior == "fixed": + batch_number = samples_out.get("batch_index", [0])[0] + samples_out["batch_index"] = [batch_number] * latent.shape[0] + + return (samples_out,) + +class LatentApplyOperation: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "operation": ("LATENT_OPERATION",), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced/operations" + EXPERIMENTAL = True + + def op(self, samples, operation): + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = operation(latent=s1) + return (samples_out,) + +class LatentApplyOperationCFG: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "operation": ("LATENT_OPERATION",), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "latent/advanced/operations" + EXPERIMENTAL = True + + def patch(self, model, operation): + m = model.clone() + + def pre_cfg_function(args): + conds_out = args["conds_out"] + if len(conds_out) == 2: + conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1] + else: + conds_out[0] = operation(latent=conds_out[0]) + return conds_out + + m.set_model_sampler_pre_cfg_function(pre_cfg_function) + return (m, ) + +class LatentOperationTonemapReinhard: + @classmethod + def INPUT_TYPES(s): + return {"required": { "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT_OPERATION",) + FUNCTION = "op" + + CATEGORY = "latent/advanced/operations" + EXPERIMENTAL = True + + def op(self, multiplier): + def tonemap_reinhard(latent, **kwargs): + latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None] + normalized_latent = latent / latent_vector_magnitude + + mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True) + std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True) + + top = (std * 5 + mean) * multiplier + + #reinhard + latent_vector_magnitude *= (1.0 / top) + new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0) + new_magnitude *= top + + return normalized_latent * new_magnitude + return (tonemap_reinhard,) + +class LatentOperationSharpen: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "sharpen_radius": ("INT", { + "default": 9, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.1 + }), + "alpha": ("FLOAT", { + "default": 0.1, + "min": 0.0, + "max": 5.0, + "step": 0.01 + }), + }} + + RETURN_TYPES = ("LATENT_OPERATION",) + FUNCTION = "op" + + CATEGORY = "latent/advanced/operations" + EXPERIMENTAL = True + + def op(self, sharpen_radius, sigma, alpha): + def sharpen(latent, **kwargs): + luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None] + normalized_latent = latent / luminance + channels = latent.shape[1] + + kernel_size = sharpen_radius * 2 + 1 + kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device) + center = kernel_size // 2 + + kernel *= alpha * -10 + kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 + + padded_image = torch.nn.functional.pad(normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') + sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] + + return luminance * sharpened + return (sharpen,) + +NODE_CLASS_MAPPINGS = { + "LatentAdd": LatentAdd, + "LatentSubtract": LatentSubtract, + "LatentMultiply": LatentMultiply, + "LatentInterpolate": LatentInterpolate, + "LatentBatch": LatentBatch, + "LatentBatchSeedBehavior": LatentBatchSeedBehavior, + "LatentApplyOperation": LatentApplyOperation, + "LatentApplyOperationCFG": LatentApplyOperationCFG, + "LatentOperationTonemapReinhard": LatentOperationTonemapReinhard, + "LatentOperationSharpen": LatentOperationSharpen, +} diff --git a/comfy_extras/nodes_lora_extract.py b/comfy_extras/nodes_lora_extract.py new file mode 100644 index 0000000..dfd4fe9 --- /dev/null +++ b/comfy_extras/nodes_lora_extract.py @@ -0,0 +1,119 @@ +import torch +import comfy.model_management +import comfy.utils +import folder_paths +import os +import logging +from enum import Enum + +CLAMP_QUANTILE = 0.99 + +def extract_lora(diff, rank): + conv2d = (len(diff.shape) == 4) + kernel_size = None if not conv2d else diff.size()[2:4] + conv2d_3x3 = conv2d and kernel_size != (1, 1) + out_dim, in_dim = diff.size()[0:2] + rank = min(rank, in_dim, out_dim) + + if conv2d: + if conv2d_3x3: + diff = diff.flatten(start_dim=1) + else: + diff = diff.squeeze() + + + U, S, Vh = torch.linalg.svd(diff.float()) + U = U[:, :rank] + S = S[:rank] + U = U @ torch.diag(S) + Vh = Vh[:rank, :] + + dist = torch.cat([U.flatten(), Vh.flatten()]) + hi_val = torch.quantile(dist, CLAMP_QUANTILE) + low_val = -hi_val + + U = U.clamp(low_val, hi_val) + Vh = Vh.clamp(low_val, hi_val) + if conv2d: + U = U.reshape(out_dim, rank, 1, 1) + Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1]) + return (U, Vh) + +class LORAType(Enum): + STANDARD = 0 + FULL_DIFF = 1 + +LORA_TYPES = {"standard": LORAType.STANDARD, + "full_diff": LORAType.FULL_DIFF} + +def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, bias_diff=False): + comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True) + sd = model_diff.model_state_dict(filter_prefix=prefix_model) + + for k in sd: + if k.endswith(".weight"): + weight_diff = sd[k] + if lora_type == LORAType.STANDARD: + if weight_diff.ndim < 2: + if bias_diff: + output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu() + continue + try: + out = extract_lora(weight_diff, rank) + output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu() + output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu() + except: + logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k)) + elif lora_type == LORAType.FULL_DIFF: + output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu() + + elif bias_diff and k.endswith(".bias"): + output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu() + return output_sd + +class LoraSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}), + "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}), + "lora_type": (tuple(LORA_TYPES.keys()),), + "bias_diff": ("BOOLEAN", {"default": True}), + }, + "optional": {"model_diff": ("MODEL", {"tooltip": "The ModelSubtract output to be converted to a lora."}), + "text_encoder_diff": ("CLIP", {"tooltip": "The CLIPSubtract output to be converted to a lora."})}, + } + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "_for_testing" + + def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None): + if model_diff is None and text_encoder_diff is None: + return {} + + lora_type = LORA_TYPES.get(lora_type) + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + output_sd = {} + if model_diff is not None: + output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, bias_diff=bias_diff) + if text_encoder_diff is not None: + output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None) + return {} + +NODE_CLASS_MAPPINGS = { + "LoraSave": LoraSave +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LoraSave": "Extract and Save Lora" +} diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py new file mode 100644 index 0000000..29589b4 --- /dev/null +++ b/comfy_extras/nodes_mask.py @@ -0,0 +1,382 @@ +import numpy as np +import scipy.ndimage +import torch +import comfy.utils + +from nodes import MAX_RESOLUTION + +def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): + source = source.to(destination.device) + if resize_source: + source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") + + source = comfy.utils.repeat_to_batch_size(source, destination.shape[0]) + + x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) + y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) + + left, top = (x // multiplier, y // multiplier) + right, bottom = (left + source.shape[3], top + source.shape[2],) + + if mask is None: + mask = torch.ones_like(source) + else: + mask = mask.to(destination.device, copy=True) + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") + mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0]) + + # calculate the bounds of the source that will be overlapping the destination + # this prevents the source trying to overwrite latent pixels that are out of bounds + # of the destination + visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) + + mask = mask[:, :, :visible_height, :visible_width] + inverse_mask = torch.ones_like(mask) - mask + + source_portion = mask * source[:, :, :visible_height, :visible_width] + destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] + + destination[:, :, top:bottom, left:right] = source_portion + destination_portion + return destination + +class LatentCompositeMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("LATENT",), + "source": ("LATENT",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("LATENT",) + FUNCTION = "composite" + + CATEGORY = "latent" + + def composite(self, destination, source, x, y, resize_source, mask = None): + output = destination.copy() + destination = destination["samples"].clone() + source = source["samples"] + output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) + return (output,) + +class ImageCompositeMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("IMAGE",), + "source": ("IMAGE",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "composite" + + CATEGORY = "image" + + def composite(self, destination, source, x, y, resize_source, mask = None): + destination = destination.clone().movedim(-1, 1) + output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) + return (output,) + +class MaskToImage: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "mask_to_image" + + def mask_to_image(self, mask): + result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + return (result,) + +class ImageToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "channel": (["red", "green", "blue", "alpha"],), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, channel): + channels = ["red", "green", "blue", "alpha"] + mask = image[:, :, :, channels.index(channel)] + return (mask,) + +class ImageColorToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, color): + temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) + temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] + mask = torch.where(temp == color, 255, 0).float() + return (mask,) + +class SolidMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "solid" + + def solid(self, value, width, height): + out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") + return (out,) + +class InvertMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "invert" + + def invert(self, mask): + out = 1.0 - mask + return (out,) + +class CropMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "crop" + + def crop(self, mask, x, y, width, height): + mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) + out = mask[:, y:y + height, x:x + width] + return (out,) + +class MaskComposite: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "destination": ("MASK",), + "source": ("MASK",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "operation": (["multiply", "add", "subtract", "and", "or", "xor"],), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "combine" + + def combine(self, destination, source, x, y, operation): + output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() + source = source.reshape((-1, source.shape[-2], source.shape[-1])) + + left, top = (x, y,) + right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) + visible_width, visible_height = (right - left, bottom - top,) + + source_portion = source[:, :visible_height, :visible_width] + destination_portion = destination[:, top:bottom, left:right] + + if operation == "multiply": + output[:, top:bottom, left:right] = destination_portion * source_portion + elif operation == "add": + output[:, top:bottom, left:right] = destination_portion + source_portion + elif operation == "subtract": + output[:, top:bottom, left:right] = destination_portion - source_portion + elif operation == "and": + output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() + elif operation == "or": + output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() + elif operation == "xor": + output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() + + output = torch.clamp(output, 0.0, 1.0) + + return (output,) + +class FeatherMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "feather" + + def feather(self, mask, left, top, right, bottom): + output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() + + left = min(left, output.shape[-1]) + right = min(right, output.shape[-1]) + top = min(top, output.shape[-2]) + bottom = min(bottom, output.shape[-2]) + + for x in range(left): + feather_rate = (x + 1.0) / left + output[:, :, x] *= feather_rate + + for x in range(right): + feather_rate = (x + 1) / right + output[:, :, -x] *= feather_rate + + for y in range(top): + feather_rate = (y + 1) / top + output[:, y, :] *= feather_rate + + for y in range(bottom): + feather_rate = (y + 1) / bottom + output[:, -y, :] *= feather_rate + + return (output,) + +class GrowMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), + "tapered_corners": ("BOOLEAN", {"default": True}), + }, + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "expand_mask" + + def expand_mask(self, mask, expand, tapered_corners): + c = 0 if tapered_corners else 1 + kernel = np.array([[c, 1, c], + [1, 1, 1], + [c, 1, c]]) + mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) + out = [] + for m in mask: + output = m.numpy() + for _ in range(abs(expand)): + if expand < 0: + output = scipy.ndimage.grey_erosion(output, footprint=kernel) + else: + output = scipy.ndimage.grey_dilation(output, footprint=kernel) + output = torch.from_numpy(output) + out.append(output) + return (torch.stack(out, dim=0),) + +class ThresholdMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, mask, value): + mask = (mask > value).float() + return (mask,) + + +NODE_CLASS_MAPPINGS = { + "LatentCompositeMasked": LatentCompositeMasked, + "ImageCompositeMasked": ImageCompositeMasked, + "MaskToImage": MaskToImage, + "ImageToMask": ImageToMask, + "ImageColorToMask": ImageColorToMask, + "SolidMask": SolidMask, + "InvertMask": InvertMask, + "CropMask": CropMask, + "MaskComposite": MaskComposite, + "FeatherMask": FeatherMask, + "GrowMask": GrowMask, + "ThresholdMask": ThresholdMask, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageToMask": "Convert Image to Mask", + "MaskToImage": "Convert Mask to Image", +} diff --git a/comfy_extras/nodes_mochi.py b/comfy_extras/nodes_mochi.py new file mode 100644 index 0000000..1c474fa --- /dev/null +++ b/comfy_extras/nodes_mochi.py @@ -0,0 +1,23 @@ +import nodes +import torch +import comfy.model_management + +class EmptyMochiLatentVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 25, "min": 7, "max": nodes.MAX_RESOLUTION, "step": 6}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/video" + + def generate(self, width, height, length, batch_size=1): + latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + return ({"samples":latent}, ) + +NODE_CLASS_MAPPINGS = { + "EmptyMochiLatentVideo": EmptyMochiLatentVideo, +} diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py new file mode 100644 index 0000000..918e608 --- /dev/null +++ b/comfy_extras/nodes_model_advanced.py @@ -0,0 +1,326 @@ +import folder_paths +import comfy.sd +import comfy.model_sampling +import comfy.latent_formats +import nodes +import torch + +class LCM(comfy.model_sampling.EPS): + def calculate_denoised(self, sigma, model_output, model_input): + timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + x0 = model_input - model_output * sigma + + sigma_data = 0.5 + scaled_timestep = timestep * 10.0 #timestep_scaling + + c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) + c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 + + return c_out * x0 + c_skip * model_input + +class X0(comfy.model_sampling.EPS): + def calculate_denoised(self, sigma, model_output, model_input): + return model_output + +class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete): + original_timesteps = 50 + + def __init__(self, model_config=None): + super().__init__(model_config) + + self.skip_steps = self.num_timesteps // self.original_timesteps + + sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32) + for x in range(self.original_timesteps): + sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps] + + self.set_sigmas(sigmas_valid) + + def timestep(self, sigma): + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device) + + def sigma(self, timestep): + t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp().to(timestep.device) + + +def rescale_zero_terminal_snr_sigmas(sigmas): + alphas_cumprod = 1 / ((sigmas * sigmas) + 1) + alphas_bar_sqrt = alphas_cumprod.sqrt() + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + + # Shift so the last timestep is zero. + alphas_bar_sqrt -= (alphas_bar_sqrt_T) + + # Scale so the first timestep is back to the old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt**2 # Revert sqrt + alphas_bar[-1] = 4.8973451890853435e-08 + return ((1 - alphas_bar) / alphas_bar) ** 0.5 + +class ModelSamplingDiscrete: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["eps", "v_prediction", "lcm", "x0"],), + "zsnr": ("BOOLEAN", {"default": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, zsnr): + m = model.clone() + + sampling_base = comfy.model_sampling.ModelSamplingDiscrete + if sampling == "eps": + sampling_type = comfy.model_sampling.EPS + elif sampling == "v_prediction": + sampling_type = comfy.model_sampling.V_PREDICTION + elif sampling == "lcm": + sampling_type = LCM + sampling_base = ModelSamplingDiscreteDistilled + elif sampling == "x0": + sampling_type = X0 + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + if zsnr: + model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas)) + + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class ModelSamplingStableCascade: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, shift): + m = model.clone() + + sampling_base = comfy.model_sampling.StableCascadeSampling + sampling_type = comfy.model_sampling.EPS + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class ModelSamplingSD3: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, shift, multiplier=1000): + m = model.clone() + + sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow + sampling_type = comfy.model_sampling.CONST + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift=shift, multiplier=multiplier) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class ModelSamplingAuraFlow(ModelSamplingSD3): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "shift": ("FLOAT", {"default": 1.73, "min": 0.0, "max": 100.0, "step":0.01}), + }} + + FUNCTION = "patch_aura" + + def patch_aura(self, model, shift): + return self.patch(model, shift, multiplier=1.0) + +class ModelSamplingFlux: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}), + "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}), + "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, max_shift, base_shift, width, height): + m = model.clone() + + x1 = 256 + x2 = 4096 + mm = (max_shift - base_shift) / (x2 - x1) + b = base_shift - mm * x1 + shift = (width * height / (8 * 8 * 2 * 2)) * mm + b + + sampling_base = comfy.model_sampling.ModelSamplingFlux + sampling_type = comfy.model_sampling.CONST + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift=shift) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + + +class ModelSamplingContinuousEDM: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],), + "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, sigma_max, sigma_min): + m = model.clone() + + latent_format = None + sigma_data = 1.0 + if sampling == "eps": + sampling_type = comfy.model_sampling.EPS + elif sampling == "v_prediction": + sampling_type = comfy.model_sampling.V_PREDICTION + elif sampling == "edm_playground_v2.5": + sampling_type = comfy.model_sampling.EDM + sigma_data = 0.5 + latent_format = comfy.latent_formats.SDXL_Playground_2_5() + + class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) + m.add_object_patch("model_sampling", model_sampling) + if latent_format is not None: + m.add_object_patch("latent_format", latent_format) + return (m, ) + +class ModelSamplingContinuousV: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["v_prediction"],), + "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, sigma_max, sigma_min): + m = model.clone() + + latent_format = None + sigma_data = 1.0 + if sampling == "v_prediction": + sampling_type = comfy.model_sampling.V_PREDICTION + + class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class RescaleCFG: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, multiplier): + def rescale_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + sigma = args["sigma"] + sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1)) + x_orig = args["input"] + + #rescale cfg has to be done on v-pred model output + x = x_orig / (sigma * sigma + 1.0) + cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) + uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) + + #rescalecfg + x_cfg = uncond + cond_scale * (cond - uncond) + ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) + ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) + + x_rescaled = x_cfg * (ro_pos / ro_cfg) + x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg + + return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) + + m = model.clone() + m.set_model_sampler_cfg_function(rescale_cfg) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "ModelSamplingDiscrete": ModelSamplingDiscrete, + "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, + "ModelSamplingContinuousV": ModelSamplingContinuousV, + "ModelSamplingStableCascade": ModelSamplingStableCascade, + "ModelSamplingSD3": ModelSamplingSD3, + "ModelSamplingAuraFlow": ModelSamplingAuraFlow, + "ModelSamplingFlux": ModelSamplingFlux, + "RescaleCFG": RescaleCFG, +} diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py new file mode 100644 index 0000000..15ffc4c --- /dev/null +++ b/comfy_extras/nodes_model_downscale.py @@ -0,0 +1,54 @@ +import torch +import comfy.utils + +class PatchModelAddDownscale: + upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), + "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), + "downscale_after_skip": ("BOOLEAN", {"default": True}), + "downscale_method": (s.upscale_methods,), + "upscale_method": (s.upscale_methods,), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): + model_sampling = model.get_model_object("model_sampling") + sigma_start = model_sampling.percent_to_sigma(start_percent) + sigma_end = model_sampling.percent_to_sigma(end_percent) + + def input_block_patch(h, transformer_options): + if transformer_options["block"][1] == block_number: + sigma = transformer_options["sigmas"][0].item() + if sigma <= sigma_start and sigma >= sigma_end: + h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") + return h + + def output_block_patch(h, hsp, transformer_options): + if h.shape[2] != hsp.shape[2]: + h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") + return h, hsp + + m = model.clone() + if downscale_after_skip: + m.set_model_input_block_patch_after_skip(input_block_patch) + else: + m.set_model_input_block_patch(input_block_patch) + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "PatchModelAddDownscale": PatchModelAddDownscale, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)", +} diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py new file mode 100644 index 0000000..ccf6011 --- /dev/null +++ b/comfy_extras/nodes_model_merging.py @@ -0,0 +1,371 @@ +import comfy.sd +import comfy.utils +import comfy.model_base +import comfy.model_management +import comfy.model_sampling + +import torch +import folder_paths +import json +import os + +from comfy.cli_args import args + +class ModelMergeSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, ratio): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +class ModelSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, multiplier): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, - multiplier, multiplier) + return (m, ) + +class ModelAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, 1.0, 1.0) + return (m, ) + + +class CLIPMergeSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2, ratio): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + + +class CLIPSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2, multiplier): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, - multiplier, multiplier) + return (m, ) + + +class CLIPAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, 1.0, 1.0) + return (m, ) + + +class ModelMergeBlocks: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, **kwargs): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + default_ratio = next(iter(kwargs.values())) + + for k in kp: + ratio = default_ratio + k_unet = k[len("diffusion_model."):] + + last_arg_size = 0 + for arg in kwargs: + if k_unet.startswith(arg) and last_arg_size < len(arg): + ratio = kwargs[arg] + last_arg_size = len(arg) + + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir) + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + + enable_modelspec = True + if isinstance(model.model, comfy.model_base.SDXL): + if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit" + else: + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" + elif isinstance(model.model, comfy.model_base.SDXLRefiner): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" + elif isinstance(model.model, comfy.model_base.SVD_img2vid): + metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1" + elif isinstance(model.model, comfy.model_base.SD3): + metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants + else: + enable_modelspec = False + + if enable_modelspec: + metadata["modelspec.sai_model_spec"] = "1.0.0" + metadata["modelspec.implementation"] = "sgm" + metadata["modelspec.title"] = "{} {}".format(filename, counter) + + #TODO: + # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512", + # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h", + # "v2-inpainting" + + extra_keys = {} + model_sampling = model.get_model_object("model_sampling") + if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM): + if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION): + extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float() + extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float() + + if model.model.model_type == comfy.model_base.ModelType.EPS: + metadata["modelspec.predict_key"] = "epsilon" + elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: + metadata["modelspec.predict_key"] = "v" + + if not args.disable_metadata: + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys) + +class CheckpointSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): + save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) + return {} + +class CLIPSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP",), + "filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + if not args.disable_metadata: + metadata["format"] = "pt" + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True) + clip_sd = clip.get_sd() + + for prefix in ["clip_l.", "clip_g.", ""]: + k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) + current_clip_sd = {} + for x in k: + current_clip_sd[x] = clip_sd.pop(x) + if len(current_clip_sd) == 0: + continue + + p = prefix[:-1] + replace_prefix = {} + filename_prefix_ = filename_prefix + if len(p) > 0: + filename_prefix_ = "{}_{}".format(filename_prefix_, p) + replace_prefix[prefix] = "" + replace_prefix["transformer."] = "" + + full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) + + comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) + return {} + +class VAESave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + if not args.disable_metadata: + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) + return {} + +class ModelSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None): + save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) + return {} + +NODE_CLASS_MAPPINGS = { + "ModelMergeSimple": ModelMergeSimple, + "ModelMergeBlocks": ModelMergeBlocks, + "ModelMergeSubtract": ModelSubtract, + "ModelMergeAdd": ModelAdd, + "CheckpointSave": CheckpointSave, + "CLIPMergeSimple": CLIPMergeSimple, + "CLIPMergeSubtract": CLIPSubtract, + "CLIPMergeAdd": CLIPAdd, + "CLIPSave": CLIPSave, + "VAESave": VAESave, + "ModelSave": ModelSave, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "CheckpointSave": "Save Checkpoint", +} diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py new file mode 100644 index 0000000..0e84719 --- /dev/null +++ b/comfy_extras/nodes_model_merging_model_specific.py @@ -0,0 +1,134 @@ +import comfy_extras.nodes_model_merging + +class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["time_embed."] = argument + arg_dict["label_emb."] = argument + + for i in range(12): + arg_dict["input_blocks.{}.".format(i)] = argument + + for i in range(3): + arg_dict["middle_block.{}.".format(i)] = argument + + for i in range(12): + arg_dict["output_blocks.{}.".format(i)] = argument + + arg_dict["out."] = argument + + return {"required": arg_dict} + + +class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["time_embed."] = argument + arg_dict["label_emb."] = argument + + for i in range(9): + arg_dict["input_blocks.{}".format(i)] = argument + + for i in range(3): + arg_dict["middle_block.{}".format(i)] = argument + + for i in range(9): + arg_dict["output_blocks.{}".format(i)] = argument + + arg_dict["out."] = argument + + return {"required": arg_dict} + +class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embed."] = argument + arg_dict["x_embedder."] = argument + arg_dict["context_embedder."] = argument + arg_dict["y_embedder."] = argument + arg_dict["t_embedder."] = argument + + for i in range(24): + arg_dict["joint_blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["img_in."] = argument + arg_dict["time_in."] = argument + arg_dict["guidance_in"] = argument + arg_dict["vector_in."] = argument + arg_dict["txt_in."] = argument + + for i in range(19): + arg_dict["double_blocks.{}.".format(i)] = argument + + for i in range(38): + arg_dict["single_blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embed."] = argument + arg_dict["x_embedder."] = argument + arg_dict["context_embedder."] = argument + arg_dict["y_embedder."] = argument + arg_dict["t_embedder."] = argument + + for i in range(38): + arg_dict["joint_blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +NODE_CLASS_MAPPINGS = { + "ModelMergeSD1": ModelMergeSD1, + "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks + "ModelMergeSDXL": ModelMergeSDXL, + "ModelMergeSD3_2B": ModelMergeSD3_2B, + "ModelMergeFlux1": ModelMergeFlux1, + "ModelMergeSD35_Large": ModelMergeSD35_Large, +} diff --git a/comfy_extras/nodes_morphology.py b/comfy_extras/nodes_morphology.py new file mode 100644 index 0000000..071521d --- /dev/null +++ b/comfy_extras/nodes_morphology.py @@ -0,0 +1,49 @@ +import torch +import comfy.model_management + +from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat + + +class Morphology: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), + "operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],), + "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "process" + + CATEGORY = "image/postprocessing" + + def process(self, image, operation, kernel_size): + device = comfy.model_management.get_torch_device() + kernel = torch.ones(kernel_size, kernel_size, device=device) + image_k = image.to(device).movedim(-1, 1) + if operation == "erode": + output = erosion(image_k, kernel) + elif operation == "dilate": + output = dilation(image_k, kernel) + elif operation == "open": + output = opening(image_k, kernel) + elif operation == "close": + output = closing(image_k, kernel) + elif operation == "gradient": + output = gradient(image_k, kernel) + elif operation == "top_hat": + output = top_hat(image_k, kernel) + elif operation == "bottom_hat": + output = bottom_hat(image_k, kernel) + else: + raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'") + img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1) + return (img_out,) + +NODE_CLASS_MAPPINGS = { + "Morphology": Morphology, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "Morphology": "ImageMorphology", +} \ No newline at end of file diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py new file mode 100644 index 0000000..eb28196 --- /dev/null +++ b/comfy_extras/nodes_pag.py @@ -0,0 +1,56 @@ +#Modified/simplified version of the node from: https://github.com/pamparamm/sd-perturbed-attention +#If you want the one with more options see the above repo. + +#My modified one here is more basic but has less chances of breaking with ComfyUI updates. + +import comfy.model_patcher +import comfy.samplers + +class PerturbedAttentionGuidance: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.01}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, scale): + unet_block = "middle" + unet_block_id = 0 + m = model.clone() + + def perturbed_attention(q, k, v, extra_options, mask=None): + return v + + def post_cfg_function(args): + model = args["model"] + cond_pred = args["cond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"].copy() + x = args["input"] + + if scale == 0: + return cfg_result + + # Replace Self-attention with PAG + model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, perturbed_attention, "attn1", unet_block, unet_block_id) + (pag,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) + + return cfg_result + (cond_pred - pag) * scale + + m.set_model_sampler_post_cfg_function(post_cfg_function) + + return (m,) + +NODE_CLASS_MAPPINGS = { + "PerturbedAttentionGuidance": PerturbedAttentionGuidance, +} diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py new file mode 100644 index 0000000..762c402 --- /dev/null +++ b/comfy_extras/nodes_perpneg.py @@ -0,0 +1,129 @@ +import torch +import comfy.model_management +import comfy.sampler_helpers +import comfy.samplers +import comfy.utils +import node_helpers + +def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale): + pos = noise_pred_pos - noise_pred_nocond + neg = noise_pred_neg - noise_pred_nocond + + perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos + perp_neg = perp * neg_scale + cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) + return cfg_result + +#TODO: This node should be removed, it has been replaced with PerpNegGuider +class PerpNeg: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + "empty_conditioning": ("CONDITIONING", ), + "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + DEPRECATED = True + + def patch(self, model, empty_conditioning, neg_scale): + m = model.clone() + nocond = comfy.sampler_helpers.convert_cond(empty_conditioning) + + def cfg_function(args): + model = args["model"] + noise_pred_pos = args["cond_denoised"] + noise_pred_neg = args["uncond_denoised"] + cond_scale = args["cond_scale"] + x = args["input"] + sigma = args["sigma"] + model_options = args["model_options"] + nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") + + (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options) + + cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale) + return cfg_result + + m.set_model_sampler_cfg_function(cfg_function) + + return (m, ) + + +class Guider_PerpNeg(comfy.samplers.CFGGuider): + def set_conds(self, positive, negative, empty_negative_prompt): + empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"}) + self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative}) + + def set_cfg(self, cfg, neg_scale): + self.cfg = cfg + self.neg_scale = neg_scale + + def predict_noise(self, x, timestep, model_options={}, seed=None): + # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg + # but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly + + positive_cond = self.conds.get("positive", None) + negative_cond = self.conds.get("negative", None) + empty_cond = self.conds.get("empty_negative_prompt", None) + + (noise_pred_pos, noise_pred_neg, noise_pred_empty) = \ + comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options) + cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg) + + # normally this would be done in cfg_function, but we skipped + # that for efficiency: we can compute the noise predictions in + # a single call to calc_cond_batch() (rather than two) + # so we replicate the hook here + for fn in model_options.get("sampler_post_cfg_function", []): + args = { + "denoised": cfg_result, + "cond": positive_cond, + "uncond": negative_cond, + "model": self.inner_model, + "uncond_denoised": noise_pred_neg, + "cond_denoised": noise_pred_pos, + "sigma": timestep, + "model_options": model_options, + "input": x, + # not in the original call in samplers.py:cfg_function, but made available for future hooks + "empty_cond": empty_cond, + "empty_cond_denoised": noise_pred_empty,} + cfg_result = fn(args) + + return cfg_result + +class PerpNegGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "empty_conditioning": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "_for_testing" + + def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale): + guider = Guider_PerpNeg(model) + guider.set_conds(positive, negative, empty_conditioning) + guider.set_cfg(cfg, neg_scale) + return (guider,) + +NODE_CLASS_MAPPINGS = { + "PerpNeg": PerpNeg, + "PerpNegGuider": PerpNegGuider, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)", +} diff --git a/comfy_extras/nodes_photomaker.py b/comfy_extras/nodes_photomaker.py new file mode 100644 index 0000000..95d24dd --- /dev/null +++ b/comfy_extras/nodes_photomaker.py @@ -0,0 +1,187 @@ +import torch +import torch.nn as nn +import folder_paths +import comfy.clip_model +import comfy.clip_vision +import comfy.ops + +# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0 +VISION_CONFIG_DICT = { + "hidden_size": 1024, + "image_size": 224, + "intermediate_size": 4096, + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "hidden_act": "quick_gelu", +} + +class MLP(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops): + super().__init__() + if use_residual: + assert in_dim == out_dim + self.layernorm = operations.LayerNorm(in_dim) + self.fc1 = operations.Linear(in_dim, hidden_dim) + self.fc2 = operations.Linear(hidden_dim, out_dim) + self.use_residual = use_residual + self.act_fn = nn.GELU() + + def forward(self, x): + residual = x + x = self.layernorm(x) + x = self.fc1(x) + x = self.act_fn(x) + x = self.fc2(x) + if self.use_residual: + x = x + residual + return x + + +class FuseModule(nn.Module): + def __init__(self, embed_dim, operations): + super().__init__() + self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations) + self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations) + self.layer_norm = operations.LayerNorm(embed_dim) + + def fuse_fn(self, prompt_embeds, id_embeds): + stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) + stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds + stacked_id_embeds = self.mlp2(stacked_id_embeds) + stacked_id_embeds = self.layer_norm(stacked_id_embeds) + return stacked_id_embeds + + def forward( + self, + prompt_embeds, + id_embeds, + class_tokens_mask, + ) -> torch.Tensor: + # id_embeds shape: [b, max_num_inputs, 1, 2048] + id_embeds = id_embeds.to(prompt_embeds.dtype) + num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case + batch_size, max_num_inputs = id_embeds.shape[:2] + # seq_length: 77 + seq_length = prompt_embeds.shape[1] + # flat_id_embeds shape: [b*max_num_inputs, 1, 2048] + flat_id_embeds = id_embeds.view( + -1, id_embeds.shape[-2], id_embeds.shape[-1] + ) + # valid_id_mask [b*max_num_inputs] + valid_id_mask = ( + torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] + < num_inputs[:, None] + ) + valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] + + prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) + class_tokens_mask = class_tokens_mask.view(-1) + valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) + # slice out the image token embeddings + image_token_embeds = prompt_embeds[class_tokens_mask] + stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) + assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" + prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) + updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) + return updated_prompt_embeds + +class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection): + def __init__(self): + self.load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + dtype = comfy.model_management.text_encoder_dtype(self.load_device) + + super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast) + self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False) + self.fuse_module = FuseModule(2048, comfy.ops.manual_cast) + + def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): + b, num_inputs, c, h, w = id_pixel_values.shape + id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) + + shared_id_embeds = self.vision_model(id_pixel_values)[2] + id_embeds = self.visual_projection(shared_id_embeds) + id_embeds_2 = self.visual_projection_2(shared_id_embeds) + + id_embeds = id_embeds.view(b, num_inputs, 1, -1) + id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) + + id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) + updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) + + return updated_prompt_embeds + + +class PhotoMakerLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}} + + RETURN_TYPES = ("PHOTOMAKER",) + FUNCTION = "load_photomaker_model" + + CATEGORY = "_for_testing/photomaker" + + def load_photomaker_model(self, photomaker_model_name): + photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name) + photomaker_model = PhotoMakerIDEncoder() + data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True) + if "id_encoder" in data: + data = data["id_encoder"] + photomaker_model.load_state_dict(data) + return (photomaker_model,) + + +class PhotoMakerEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "photomaker": ("PHOTOMAKER",), + "image": ("IMAGE",), + "clip": ("CLIP", ), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_photomaker" + + CATEGORY = "_for_testing/photomaker" + + def apply_photomaker(self, photomaker, image, clip, text): + special_token = "photomaker" + pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float() + try: + index = text.split(" ").index(special_token) + 1 + except ValueError: + index = -1 + tokens = clip.tokenize(text, return_word_ids=True) + out_tokens = {} + for k in tokens: + out_tokens[k] = [] + for t in tokens[k]: + f = list(filter(lambda x: x[2] != index, t)) + while len(f) < len(t): + f.append(t[-1]) + out_tokens[k].append(f) + + cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True) + + if index > 0: + token_index = index - 1 + num_id_images = 1 + class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)] + out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device), + class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0)) + else: + out = cond + + return ([[out, {"pooled_output": pooled}]], ) + + +NODE_CLASS_MAPPINGS = { + "PhotoMakerLoader": PhotoMakerLoader, + "PhotoMakerEncode": PhotoMakerEncode, +} + diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py new file mode 100644 index 0000000..68f6ef5 --- /dev/null +++ b/comfy_extras/nodes_post_processing.py @@ -0,0 +1,279 @@ +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image +import math + +import comfy.utils +import comfy.model_management + + +class Blend: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01 + }), + "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blend_images" + + CATEGORY = "image/postprocessing" + + def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): + image2 = image2.to(image1.device) + if image1.shape != image2.shape: + image2 = image2.permute(0, 3, 1, 2) + image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') + image2 = image2.permute(0, 2, 3, 1) + + blended_image = self.blend_mode(image1, image2, blend_mode) + blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor + blended_image = torch.clamp(blended_image, 0, 1) + return (blended_image,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + elif mode == "multiply": + return img1 * img2 + elif mode == "screen": + return 1 - (1 - img1) * (1 - img2) + elif mode == "overlay": + return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) + elif mode == "soft_light": + return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) + elif mode == "difference": + return img1 - img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + + def g(self, x): + return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) + +def gaussian_kernel(kernel_size: int, sigma: float, device=None): + x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") + d = torch.sqrt(x * x + y * y) + g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) + return g / g.sum() + +class Blur: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "blur_radius": ("INT", { + "default": 1, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.1 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blur" + + CATEGORY = "image/postprocessing" + + def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): + if blur_radius == 0: + return (image,) + + image = image.to(comfy.model_management.get_torch_device()) + batch_size, height, width, channels = image.shape + + kernel_size = blur_radius * 2 + 1 + kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) + + image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) + padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') + blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius] + blurred = blurred.permute(0, 2, 3, 1) + + return (blurred.to(comfy.model_management.intermediate_device()),) + +class Quantize: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "colors": ("INT", { + "default": 256, + "min": 1, + "max": 256, + "step": 1 + }), + "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "quantize" + + CATEGORY = "image/postprocessing" + + def bayer(im, pal_im, order): + def normalized_bayer_matrix(n): + if n == 0: + return np.zeros((1,1), "float32") + else: + q = 4 ** n + m = q * normalized_bayer_matrix(n - 1) + return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q + + num_colors = len(pal_im.getpalette()) // 3 + spread = 2 * 256 / num_colors + bayer_n = int(math.log2(order)) + bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5) + + result = torch.from_numpy(np.array(im).astype(np.float32)) + tw = math.ceil(result.shape[0] / bayer_matrix.shape[0]) + th = math.ceil(result.shape[1] / bayer_matrix.shape[1]) + tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1) + result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255) + result = result.to(dtype=torch.uint8) + + im = Image.fromarray(result.cpu().numpy()) + im = im.quantize(palette=pal_im, dither=Image.Dither.NONE) + return im + + def quantize(self, image: torch.Tensor, colors: int, dither: str): + batch_size, height, width, _ = image.shape + result = torch.zeros_like(image) + + for b in range(batch_size): + im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB') + + pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836 + + if dither == "none": + quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE) + elif dither == "floyd-steinberg": + quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG) + elif dither.startswith("bayer"): + order = int(dither.split('-')[-1]) + quantized_image = Quantize.bayer(im, pal_im, order) + + quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 + result[b] = quantized_array + + return (result,) + +class Sharpen: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "sharpen_radius": ("INT", { + "default": 1, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.01 + }), + "alpha": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 5.0, + "step": 0.01 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "sharpen" + + CATEGORY = "image/postprocessing" + + def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): + if sharpen_radius == 0: + return (image,) + + batch_size, height, width, channels = image.shape + image = image.to(comfy.model_management.get_torch_device()) + + kernel_size = sharpen_radius * 2 + 1 + kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) + center = kernel_size // 2 + kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 + kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) + + tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) + tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') + sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] + sharpened = sharpened.permute(0, 2, 3, 1) + + result = torch.clamp(sharpened, 0, 1) + + return (result.to(comfy.model_management.intermediate_device()),) + +class ImageScaleToTotalPixels: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, megapixels): + samples = image.movedim(-1,1) + total = int(megapixels * 1024 * 1024) + + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + +NODE_CLASS_MAPPINGS = { + "ImageBlend": Blend, + "ImageBlur": Blur, + "ImageQuantize": Quantize, + "ImageSharpen": Sharpen, + "ImageScaleToTotalPixels": ImageScaleToTotalPixels, +} diff --git a/comfy_extras/nodes_rebatch.py b/comfy_extras/nodes_rebatch.py new file mode 100644 index 0000000..3010fbd --- /dev/null +++ b/comfy_extras/nodes_rebatch.py @@ -0,0 +1,138 @@ +import torch + +class LatentRebatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "latents": ("LATENT",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("LATENT",) + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + FUNCTION = "rebatch" + + CATEGORY = "latent/batch" + + @staticmethod + def get_batch(latents, list_ind, offset): + '''prepare a batch out of the list of latents''' + samples = latents[list_ind]['samples'] + shape = samples.shape + mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu') + if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]: + torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear") + if mask.shape[0] < samples.shape[0]: + mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] + if 'batch_index' in latents[list_ind]: + batch_inds = latents[list_ind]['batch_index'] + else: + batch_inds = [x+offset for x in range(shape[0])] + return samples, mask, batch_inds + + @staticmethod + def get_slices(indexable, num, batch_size): + '''divides an indexable object into num slices of length batch_size, and a remainder''' + slices = [] + for i in range(num): + slices.append(indexable[i*batch_size:(i+1)*batch_size]) + if num * batch_size < len(indexable): + return slices, indexable[num * batch_size:] + else: + return slices, None + + @staticmethod + def slice_batch(batch, num, batch_size): + result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] + return list(zip(*result)) + + @staticmethod + def cat_batch(batch1, batch2): + if batch1[0] is None: + return batch2 + result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] + return result + + def rebatch(self, latents, batch_size): + batch_size = batch_size[0] + + output_list = [] + current_batch = (None, None, None) + processed = 0 + + for i in range(len(latents)): + # fetch new entry of list + #samples, masks, indices = self.get_batch(latents, i) + next_batch = self.get_batch(latents, i, processed) + processed += len(next_batch[2]) + # set to current if current is None + if current_batch[0] is None: + current_batch = next_batch + # add previous to list if dimensions do not match + elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: + sliced, _ = self.slice_batch(current_batch, 1, batch_size) + output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) + current_batch = next_batch + # cat if everything checks out + else: + current_batch = self.cat_batch(current_batch, next_batch) + + # add to list if dimensions gone above target batch size + if current_batch[0].shape[0] > batch_size: + num = current_batch[0].shape[0] // batch_size + sliced, remainder = self.slice_batch(current_batch, num, batch_size) + + for i in range(num): + output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) + + current_batch = remainder + + #add remainder + if current_batch[0] is not None: + sliced, _ = self.slice_batch(current_batch, 1, batch_size) + output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) + + #get rid of empty masks + for s in output_list: + if s['noise_mask'].mean() == 1.0: + del s['noise_mask'] + + return (output_list,) + +class ImageRebatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "images": ("IMAGE",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("IMAGE",) + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + FUNCTION = "rebatch" + + CATEGORY = "image/batch" + + def rebatch(self, images, batch_size): + batch_size = batch_size[0] + + output_list = [] + all_images = [] + for img in images: + for i in range(img.shape[0]): + all_images.append(img[i:i+1]) + + for i in range(0, len(all_images), batch_size): + output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) + + return (output_list,) + +NODE_CLASS_MAPPINGS = { + "RebatchLatents": LatentRebatch, + "RebatchImages": ImageRebatch, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RebatchLatents": "Rebatch Latents", + "RebatchImages": "Rebatch Images", +} diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py new file mode 100644 index 0000000..3f03533 --- /dev/null +++ b/comfy_extras/nodes_sag.py @@ -0,0 +1,174 @@ +import torch +from torch import einsum +import torch.nn.functional as F +import math + +from einops import rearrange, repeat +from comfy.ldm.modules.attention import optimized_attention +import comfy.samplers + +# from comfy/ldm/modules/attention.py +# but modified to return attention scores as well as output +def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None): + b, _, dim_head = q.shape + dim_head //= heads + scale = dim_head ** -0.5 + + h = heads + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + # force cast to fp32 to avoid overflowing + if attn_precision == torch.float32: + sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * scale + + del q, k + + if mask is not None: + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return (out, sim) + +def create_blur_map(x0, attn, sigma=3.0, threshold=1.0): + # reshape and GAP the attention map + _, hw1, hw2 = attn.shape + b, _, lh, lw = x0.shape + attn = attn.reshape(b, -1, hw1, hw2) + # Global Average Pool + mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold + + f = float(lh) / float(lw) + fh = f ** 0.5 + fw = (1/f) ** 0.5 + S = mask.size(1) ** 0.5 + w = int(0.5 + S * fw) + h = int(0.5 + S * fh) + + # Reshape + mask = ( + mask.reshape(b, h, w) + .unsqueeze(1) + .type(attn.dtype) + ) + # Upsample + mask = F.interpolate(mask, (lh, lw)) + + blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma) + blurred = blurred * mask + x0 * (1 - mask) + return blurred + +def gaussian_blur_2d(img, kernel_size, sigma): + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + + x_kernel = pdf / pdf.sum() + x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) + + kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) + kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) + + padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] + + img = F.pad(img, padding, mode="reflect") + img = F.conv2d(img, kernel2d, groups=img.shape[-3]) + return img + +class SelfAttentionGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.01}), + "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, scale, blur_sigma): + m = model.clone() + + attn_scores = None + + # TODO: make this work properly with chunked batches + # currently, we can only save the attn from one UNet call + def attn_and_record(q, k, v, extra_options): + nonlocal attn_scores + # if uncond, save the attention scores + heads = extra_options["n_heads"] + cond_or_uncond = extra_options["cond_or_uncond"] + b = q.shape[0] // len(cond_or_uncond) + if 1 in cond_or_uncond: + uncond_index = cond_or_uncond.index(1) + # do the entire attention operation, but save the attention scores to attn_scores + (out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) + # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn] + n_slices = heads * b + attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] + return out + else: + return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) + + def post_cfg_function(args): + nonlocal attn_scores + uncond_attn = attn_scores + + sag_scale = scale + sag_sigma = blur_sigma + sag_threshold = 1.0 + model = args["model"] + uncond_pred = args["uncond_denoised"] + uncond = args["uncond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"] + x = args["input"] + if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding + return cfg_result + + # create the adversarially blurred image + degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) + degraded_noised = degraded + x - uncond_pred + # call into the UNet + (sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options) + return cfg_result + (degraded - sag) * sag_scale + + m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) + + # from diffusers: + # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch + m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) + + return (m, ) + +NODE_CLASS_MAPPINGS = { + "SelfAttentionGuidance": SelfAttentionGuidance, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SelfAttentionGuidance": "Self-Attention Guidance", +} diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py new file mode 100644 index 0000000..91c60de --- /dev/null +++ b/comfy_extras/nodes_sd3.py @@ -0,0 +1,167 @@ +import folder_paths +import comfy.sd +import comfy.model_management +import nodes +import torch +import re +class TripleCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), ) + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name1, clip_name2, clip_name3): + clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) + clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) + clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (clip,) + +class EmptySD3LatentImage: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/sd3" + + def generate(self, width, height, batch_size=1): + latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device) + return ({"samples":latent}, ) + +class CLIPTextEncodeSD3: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "empty_padding": (["none", "empty_prompt"], ) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding): + no_padding = empty_padding == "none" + + tokens = clip.tokenize(clip_g) + if len(clip_g) == 0 and no_padding: + tokens["g"] = [] + + if len(clip_l) == 0 and no_padding: + tokens["l"] = [] + else: + tokens["l"] = clip.tokenize(clip_l)["l"] + + if len(t5xxl) == 0 and no_padding: + tokens["t5xxl"] = [] + else: + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + if len(tokens["l"]) != len(tokens["g"]): + empty = clip.tokenize("") + while len(tokens["l"]) < len(tokens["g"]): + tokens["l"] += empty["l"] + while len(tokens["l"]) > len(tokens["g"]): + tokens["g"] += empty["g"] + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled}]], ) + + +class ControlNetApplySD3(nodes.ControlNetApplyAdvanced): + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "vae": ("VAE", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + CATEGORY = "conditioning/controlnet" + DEPRECATED = True + +class SkipLayerGuidanceSD3: + ''' + Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers. + Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377) + Experimental implementation by Dango233@StabilityAI. + ''' + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + "layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}), + "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "skip_guidance" + + CATEGORY = "advanced/guidance" + + + def skip_guidance(self, model, layers, scale, start_percent, end_percent): + if layers == "" or layers == None: + return (model, ) + # check if layer is comma separated integers + def skip(args, extra_args): + return args + + model_sampling = model.get_model_object("model_sampling") + sigma_start = model_sampling.percent_to_sigma(start_percent) + sigma_end = model_sampling.percent_to_sigma(end_percent) + + def post_cfg_function(args): + model = args["model"] + cond_pred = args["cond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + x = args["input"] + model_options = args["model_options"].copy() + + for layer in layers: + model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer) + model_sampling.percent_to_sigma(start_percent) + + sigma_ = sigma[0].item() + if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start: + (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) + cfg_result = cfg_result + (cond_pred - slg) * scale + return cfg_result + + layers = re.findall(r'\d+', layers) + layers = [int(i) for i in layers] + m = model.clone() + m.set_model_sampler_post_cfg_function(post_cfg_function) + + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "TripleCLIPLoader": TripleCLIPLoader, + "EmptySD3LatentImage": EmptySD3LatentImage, + "CLIPTextEncodeSD3": CLIPTextEncodeSD3, + "ControlNetApplySD3": ControlNetApplySD3, + "SkipLayerGuidanceSD3": SkipLayerGuidanceSD3, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "ControlNetApplySD3": "Apply Controlnet with VAE", +} diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py new file mode 100644 index 0000000..bba67e8 --- /dev/null +++ b/comfy_extras/nodes_sdupscale.py @@ -0,0 +1,46 @@ +import torch +import comfy.utils + +class SD_4XUpscale_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "images": ("IMAGE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/upscale_diffusion" + + def encode(self, images, positive, negative, scale_ratio, noise_augmentation): + width = max(1, round(images.shape[-2] * scale_ratio)) + height = max(1, round(images.shape[-3] * scale_ratio)) + + pixels = comfy.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center") + + out_cp = [] + out_cn = [] + + for t in positive: + n = [t[0], t[1].copy()] + n[1]['concat_image'] = pixels + n[1]['noise_augmentation'] = noise_augmentation + out_cp.append(n) + + for t in negative: + n = [t[0], t[1].copy()] + n[1]['concat_image'] = pixels + n[1]['noise_augmentation'] = noise_augmentation + out_cn.append(n) + + latent = torch.zeros([images.shape[0], 4, height // 4, width // 4]) + return (out_cp, out_cn, {"samples":latent}) + +NODE_CLASS_MAPPINGS = { + "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, +} diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py new file mode 100644 index 0000000..be2e34c --- /dev/null +++ b/comfy_extras/nodes_stable3d.py @@ -0,0 +1,143 @@ +import torch +import nodes +import comfy.utils + +def camera_embeddings(elevation, azimuth): + elevation = torch.as_tensor([elevation]) + azimuth = torch.as_tensor([azimuth]) + embeddings = torch.stack( + [ + torch.deg2rad( + (90 - elevation) - (90) + ), # Zero123 polar is 90-elevation + torch.sin(torch.deg2rad(azimuth)), + torch.cos(torch.deg2rad(azimuth)), + torch.deg2rad( + 90 - torch.full_like(elevation, 0) + ), + ], dim=-1).unsqueeze(1) + + return embeddings + + +class StableZero123_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + cam_embeds = camera_embeddings(elevation, azimuth) + cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1) + + positive = [[cond, {"concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + +class StableZero123_Conditioning_Batched: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + + cam_embeds = [] + for i in range(batch_size): + cam_embeds.append(camera_embeddings(elevation, azimuth)) + elevation += elevation_batch_increment + azimuth += azimuth_batch_increment + + cam_embeds = torch.cat(cam_embeds, dim=0) + cond = torch.cat([comfy.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1) + + positive = [[cond, {"concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) + +class SV3D_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + + azimuth = 0 + azimuth_increment = 360 / (max(video_frames, 2) - 1) + + elevations = [] + azimuths = [] + for i in range(video_frames): + elevations.append(elevation) + azimuths.append(azimuth) + azimuth += azimuth_increment + + positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]] + latent = torch.zeros([video_frames, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + + +NODE_CLASS_MAPPINGS = { + "StableZero123_Conditioning": StableZero123_Conditioning, + "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, + "SV3D_Conditioning": SV3D_Conditioning, +} diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py new file mode 100644 index 0000000..0034032 --- /dev/null +++ b/comfy_extras/nodes_stable_cascade.py @@ -0,0 +1,141 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import nodes +import comfy.utils + + +class StableCascade_EmptyLatentImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), + "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}) + }} + RETURN_TYPES = ("LATENT", "LATENT") + RETURN_NAMES = ("stage_c", "stage_b") + FUNCTION = "generate" + + CATEGORY = "latent/stable_cascade" + + def generate(self, width, height, compression, batch_size=1): + c_latent = torch.zeros([batch_size, 16, height // compression, width // compression]) + b_latent = torch.zeros([batch_size, 4, height // 4, width // 4]) + return ({ + "samples": c_latent, + }, { + "samples": b_latent, + }) + +class StableCascade_StageC_VAEEncode: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "vae": ("VAE", ), + "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), + }} + RETURN_TYPES = ("LATENT", "LATENT") + RETURN_NAMES = ("stage_c", "stage_b") + FUNCTION = "generate" + + CATEGORY = "latent/stable_cascade" + + def generate(self, image, vae, compression): + width = image.shape[-2] + height = image.shape[-3] + out_width = (width // compression) * vae.downscale_ratio + out_height = (height // compression) * vae.downscale_ratio + + s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1) + + c_latent = vae.encode(s[:,:,:,:3]) + b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2]) + return ({ + "samples": c_latent, + }, { + "samples": b_latent, + }) + +class StableCascade_StageB_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "conditioning": ("CONDITIONING",), + "stage_c": ("LATENT",), + }} + RETURN_TYPES = ("CONDITIONING",) + + FUNCTION = "set_prior" + + CATEGORY = "conditioning/stable_cascade" + + def set_prior(self, conditioning, stage_c): + c = [] + for t in conditioning: + d = t[1].copy() + d['stable_cascade_prior'] = stage_c['samples'] + n = [t[0], d] + c.append(n) + return (c, ) + +class StableCascade_SuperResolutionControlnet: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "vae": ("VAE", ), + }} + RETURN_TYPES = ("IMAGE", "LATENT", "LATENT") + RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b") + FUNCTION = "generate" + + EXPERIMENTAL = True + CATEGORY = "_for_testing/stable_cascade" + + def generate(self, image, vae): + width = image.shape[-2] + height = image.shape[-3] + batch_size = image.shape[0] + controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1) + + c_latent = torch.zeros([batch_size, 16, height // 16, width // 16]) + b_latent = torch.zeros([batch_size, 4, height // 2, width // 2]) + return (controlnet_input, { + "samples": c_latent, + }, { + "samples": b_latent, + }) + +NODE_CLASS_MAPPINGS = { + "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, + "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, + "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode, + "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet, +} diff --git a/comfy_extras/nodes_tomesd.py b/comfy_extras/nodes_tomesd.py new file mode 100644 index 0000000..ce7b32c --- /dev/null +++ b/comfy_extras/nodes_tomesd.py @@ -0,0 +1,177 @@ +#Taken from: https://github.com/dbolya/tomesd + +import torch +from typing import Tuple, Callable +import math + +def do_nothing(x: torch.Tensor, mode:str=None): + return x + + +def mps_gather_workaround(input, dim, index): + if input.shape[-1] == 1: + return torch.gather( + input.unsqueeze(-1), + dim - 1 if dim < 0 else dim, + index.unsqueeze(-1) + ).squeeze(-1) + else: + return torch.gather(input, dim, index) + + +def bipartite_soft_matching_random2d(metric: torch.Tensor, + w: int, h: int, sx: int, sy: int, r: int, + no_rand: bool = False) -> Tuple[Callable, Callable]: + """ + Partitions the tokens into src and dst and merges r tokens from src to dst. + Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. + Args: + - metric [B, N, C]: metric to use for similarity + - w: image width in tokens + - h: image height in tokens + - sx: stride in the x dimension for dst, must divide w + - sy: stride in the y dimension for dst, must divide h + - r: number of tokens to remove (by merging) + - no_rand: if true, disable randomness (use top left corner only) + """ + B, N, _ = metric.shape + + if r <= 0 or w == 1 or h == 1: + return do_nothing, do_nothing + + gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather + + with torch.no_grad(): + + hsy, wsx = h // sy, w // sx + + # For each sy by sx kernel, randomly assign one token to be dst and the rest src + if no_rand: + rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) + else: + rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) + + # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead + idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) + idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) + idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) + + # Image is not divisible by sx or sy so we need to move it into a new buffer + if (hsy * sy) < h or (wsx * sx) < w: + idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) + idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view + else: + idx_buffer = idx_buffer_view + + # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices + rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) + + # We're finished with these + del idx_buffer, idx_buffer_view + + # rand_idx is currently dst|src, so split them + num_dst = hsy * wsx + a_idx = rand_idx[:, num_dst:, :] # src + b_idx = rand_idx[:, :num_dst, :] # dst + + def split(x): + C = x.shape[-1] + src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) + dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) + return src, dst + + # Cosine similarity between A and B + metric = metric / metric.norm(dim=-1, keepdim=True) + a, b = split(metric) + scores = a @ b.transpose(-1, -2) + + # Can't reduce more than the # tokens in src + r = min(a.shape[1], r) + + # Find the most similar greedily + node_max, node_idx = scores.max(dim=-1) + edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] + + unm_idx = edge_idx[..., r:, :] # Unmerged Tokens + src_idx = edge_idx[..., :r, :] # Merged Tokens + dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx) + + def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: + src, dst = split(x) + n, t1, c = src.shape + + unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) + src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) + dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) + + return torch.cat([unm, dst], dim=1) + + def unmerge(x: torch.Tensor) -> torch.Tensor: + unm_len = unm_idx.shape[1] + unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] + _, _, c = unm.shape + + src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) + + # Combine back to the original shape + out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) + out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) + out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm) + out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src) + + return out + + return merge, unmerge + + +def get_functions(x, ratio, original_shape): + b, c, original_h, original_w = original_shape + original_tokens = original_h * original_w + downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1]))) + stride_x = 2 + stride_y = 2 + max_downsample = 1 + + if downsample <= max_downsample: + w = int(math.ceil(original_w / downsample)) + h = int(math.ceil(original_h / downsample)) + r = int(x.shape[1] * ratio) + no_rand = False + m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand) + return m, u + + nothing = lambda y: y + return nothing, nothing + + + +class TomePatchModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, ratio): + self.u = None + def tomesd_m(q, k, v, extra_options): + #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q + #however from my basic testing it seems that using q instead gives better results + m, self.u = get_functions(q, ratio, extra_options["original_shape"]) + return m(q), k, v + def tomesd_u(n, extra_options): + return self.u(n) + + m = model.clone() + m.set_model_attn1_patch(tomesd_m) + m.set_model_attn1_output_patch(tomesd_u) + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "TomePatchModel": TomePatchModel, +} diff --git a/comfy_extras/nodes_torch_compile.py b/comfy_extras/nodes_torch_compile.py new file mode 100644 index 0000000..1fe6f42 --- /dev/null +++ b/comfy_extras/nodes_torch_compile.py @@ -0,0 +1,22 @@ +import torch + +class TorchCompileModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "backend": (["inductor", "cudagraphs"],), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + EXPERIMENTAL = True + + def patch(self, model, backend): + m = model.clone() + m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model"), backend=backend)) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "TorchCompileModel": TorchCompileModel, +} diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py new file mode 100644 index 0000000..6ba3e40 --- /dev/null +++ b/comfy_extras/nodes_upscale_model.py @@ -0,0 +1,84 @@ +import os +import logging +from spandrel import ModelLoader, ImageModelDescriptor +from comfy import model_management +import torch +import comfy.utils +import folder_paths + +try: + from spandrel_extra_arches import EXTRA_REGISTRY + from spandrel import MAIN_REGISTRY + MAIN_REGISTRY.add(*EXTRA_REGISTRY) + logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.") +except: + pass + +class UpscaleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ), + }} + RETURN_TYPES = ("UPSCALE_MODEL",) + FUNCTION = "load_model" + + CATEGORY = "loaders" + + def load_model(self, model_name): + model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name) + sd = comfy.utils.load_torch_file(model_path, safe_load=True) + if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""}) + out = ModelLoader().load_from_state_dict(sd).eval() + + if not isinstance(out, ImageModelDescriptor): + raise Exception("Upscale model must be a single-image model.") + + return (out, ) + + +class ImageUpscaleWithModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "upscale_model": ("UPSCALE_MODEL",), + "image": ("IMAGE",), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, upscale_model, image): + device = model_management.get_torch_device() + + memory_required = model_management.module_size(upscale_model.model) + memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate + memory_required += image.nelement() * image.element_size() + model_management.free_memory(memory_required, device) + + upscale_model.to(device) + in_img = image.movedim(-1,-3).to(device) + + tile = 512 + overlap = 32 + + oom = True + while oom: + try: + steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) + pbar = comfy.utils.ProgressBar(steps) + s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) + oom = False + except model_management.OOM_EXCEPTION as e: + tile //= 2 + if tile < 128: + raise e + + upscale_model.to("cpu") + s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) + return (s,) + +NODE_CLASS_MAPPINGS = { + "UpscaleModelLoader": UpscaleModelLoader, + "ImageUpscaleWithModel": ImageUpscaleWithModel +} diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py new file mode 100644 index 0000000..e7a7ec1 --- /dev/null +++ b/comfy_extras/nodes_video_model.py @@ -0,0 +1,134 @@ +import nodes +import torch +import comfy.utils +import comfy.sd +import folder_paths +import comfy_extras.nodes_model_merging + + +class ImageOnlyCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders/video_models" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (out[0], out[3], out[2]) + + +class SVD_img2vid_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), + "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), + "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), + "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + if augmentation_level > 0: + encode_pixels += torch.randn_like(pixels) * augmentation_level + t = vae.encode(encode_pixels) + positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([video_frames, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + +class VideoLinearCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "sampling/video_models" + + def patch(self, model, min_cfg): + def linear_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + + scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1)) + return uncond + scale * (cond - uncond) + + m = model.clone() + m.set_model_sampler_cfg_function(linear_cfg) + return (m, ) + +class VideoTriangleCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "sampling/video_models" + + def patch(self, model, min_cfg): + def linear_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + period = 1.0 + values = torch.linspace(0, 1, cond.shape[0], device=cond.device) + values = 2 * (values / period - torch.floor(values / period + 0.5)).abs() + scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1)) + + return uncond + scale * (cond - uncond) + + m = model.clone() + m.set_model_sampler_cfg_function(linear_cfg) + return (m, ) + +class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): + CATEGORY = "advanced/model_merging" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip_vision": ("CLIP_VISION",), + "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + + def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None): + comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) + return {} + +NODE_CLASS_MAPPINGS = { + "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, + "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, + "VideoLinearCFGGuidance": VideoLinearCFGGuidance, + "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance, + "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)", +} diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py new file mode 100644 index 0000000..32a0ba2 --- /dev/null +++ b/comfy_extras/nodes_webcam.py @@ -0,0 +1,33 @@ +import nodes +import folder_paths + +MAX_RESOLUTION = nodes.MAX_RESOLUTION + + +class WebcamCapture(nodes.LoadImage): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("WEBCAM", {}), + "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "capture_on_queue": ("BOOLEAN", {"default": True}), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "load_capture" + + CATEGORY = "image" + + def load_capture(s, image, **kwargs): + return super().load_image(folder_paths.get_annotated_filepath(image)) + + +NODE_CLASS_MAPPINGS = { + "WebcamCapture": WebcamCapture, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "WebcamCapture": "Webcam Capture", +} \ No newline at end of file diff --git a/comfyui_screenshot.png b/comfyui_screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..73272eae69339f92c88422c45ad166cbc5900adf GIT binary patch literal 118577 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z?-`%Io-H{F1DrNag3}H&6$I@^XGl5_mg*>$pgBSkq(ON&<-gXQznPf?m=N zdRvhX-w1<%GlElc?-h4>-t&oHxcd5FU2gu3;})S?YlhiS=$+N|shK2;I{E~VnS&qo zRf!($PNUC?_tD}6n=wKY+9|Tj{c5HmPB5NpQaZh5WXSR+a-@Fzmq4=yheUPnEamM> z6EH8oZZgbl>o8Yntk^hS^V7`u-;dOrQz%3j-F3qU9m&2_X<^22*DqsM^Q+UAOHCGY Wyn7vbfzlwia_y=OE3f)~dHBC$=2n~l literal 0 HcmV?d00001 diff --git a/cuda_malloc.py b/cuda_malloc.py new file mode 100644 index 0000000..eb2857c --- /dev/null +++ b/cuda_malloc.py @@ -0,0 +1,90 @@ +import os +import importlib.util +from comfy.cli_args import args +import subprocess + +#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import. +def get_gpu_names(): + if os.name == 'nt': + import ctypes + + # Define necessary C structures and types + class DISPLAY_DEVICEA(ctypes.Structure): + _fields_ = [ + ('cb', ctypes.c_ulong), + ('DeviceName', ctypes.c_char * 32), + ('DeviceString', ctypes.c_char * 128), + ('StateFlags', ctypes.c_ulong), + ('DeviceID', ctypes.c_char * 128), + ('DeviceKey', ctypes.c_char * 128) + ] + + # Load user32.dll + user32 = ctypes.windll.user32 + + # Call EnumDisplayDevicesA + def enum_display_devices(): + device_info = DISPLAY_DEVICEA() + device_info.cb = ctypes.sizeof(device_info) + device_index = 0 + gpu_names = set() + + while user32.EnumDisplayDevicesA(None, device_index, ctypes.byref(device_info), 0): + device_index += 1 + gpu_names.add(device_info.DeviceString.decode('utf-8')) + return gpu_names + return enum_display_devices() + else: + gpu_names = set() + out = subprocess.check_output(['nvidia-smi', '-L']) + for l in out.split(b'\n'): + if len(l) > 0: + gpu_names.add(l.decode('utf-8').split(' (UUID')[0]) + return gpu_names + +blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M", + "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620", + "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000", + "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000", + "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M", + "GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60" + } + +def cuda_malloc_supported(): + try: + names = get_gpu_names() + except: + names = set() + for x in names: + if "NVIDIA" in x: + for b in blacklist: + if b in x: + return False + return True + + +if not args.cuda_malloc: + try: + version = "" + torch_spec = importlib.util.find_spec("torch") + for folder in torch_spec.submodule_search_locations: + ver_file = os.path.join(folder, "version.py") + if os.path.isfile(ver_file): + spec = importlib.util.spec_from_file_location("torch_version_import", ver_file) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + version = module.__version__ + if int(version[0]) >= 2: #enable by default for torch version 2.0 and up + args.cuda_malloc = cuda_malloc_supported() + except: + pass + + +if args.cuda_malloc and not args.disable_cuda_malloc: + env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None) + if env_var is None: + env_var = "backend:cudaMallocAsync" + else: + env_var += ",backend:cudaMallocAsync" + + os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example new file mode 100644 index 0000000..29ab2aa --- /dev/null +++ b/custom_nodes/example_node.py.example @@ -0,0 +1,155 @@ +class Example: + """ + A example node + + Class methods + ------------- + INPUT_TYPES (dict): + Tell the main program input parameters of nodes. + IS_CHANGED: + optional method to control when the node is re executed. + + Attributes + ---------- + RETURN_TYPES (`tuple`): + The type of each element in the output tuple. + RETURN_NAMES (`tuple`): + Optional: The name of each output in the output tuple. + FUNCTION (`str`): + The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute() + OUTPUT_NODE ([`bool`]): + If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example. + The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected. + Assumed to be False if not present. + CATEGORY (`str`): + The category the node should appear in the UI. + DEPRECATED (`bool`): + Indicates whether the node is deprecated. Deprecated nodes are hidden by default in the UI, but remain + functional in existing workflows that use them. + EXPERIMENTAL (`bool`): + Indicates whether the node is experimental. Experimental nodes are marked as such in the UI and may be subject to + significant changes or removal in future versions. Use with caution in production workflows. + execute(s) -> tuple || None: + The entry point method. The name of this method must be the same as the value of property `FUNCTION`. + For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`. + """ + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + """ + Return a dictionary which contains config for all input fields. + Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT". + Input types "INT", "STRING" or "FLOAT" are special values for fields on the node. + The type can be a list for selection. + + Returns: `dict`: + - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required` + - Value input_fields (`dict`): Contains input fields config: + * Key field_name (`string`): Name of a entry-point method's argument + * Value field_config (`tuple`): + + First value is a string indicate the type of field or a list for selection. + + Second value is a config for type "INT", "STRING" or "FLOAT". + """ + return { + "required": { + "image": ("IMAGE",), + "int_field": ("INT", { + "default": 0, + "min": 0, #Minimum value + "max": 4096, #Maximum value + "step": 64, #Slider's step + "display": "number", # Cosmetic only: display as "number" or "slider" + "lazy": True # Will only be evaluated if check_lazy_status requires it + }), + "float_field": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 10.0, + "step": 0.01, + "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. + "display": "number", + "lazy": True + }), + "print_to_screen": (["enable", "disable"],), + "string_field": ("STRING", { + "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node + "default": "Hello World!", + "lazy": True + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + #RETURN_NAMES = ("image_output_name",) + + FUNCTION = "test" + + #OUTPUT_NODE = False + + CATEGORY = "Example" + + def check_lazy_status(self, image, string_field, int_field, float_field, print_to_screen): + """ + Return a list of input names that need to be evaluated. + + This function will be called if there are any lazy inputs which have not yet been + evaluated. As long as you return at least one field which has not yet been evaluated + (and more exist), this function will be called again once the value of the requested + field is available. + + Any evaluated inputs will be passed as arguments to this function. Any unevaluated + inputs will have the value None. + """ + if print_to_screen == "enable": + return ["int_field", "float_field", "string_field"] + else: + return [] + + def test(self, image, string_field, int_field, float_field, print_to_screen): + if print_to_screen == "enable": + print(f"""Your input contains: + string_field aka input text: {string_field} + int_field: {int_field} + float_field: {float_field} + """) + #do some processing on the image, in this example I just invert it + image = 1.0 - image + return (image,) + + """ + The node will always be re executed if any of the inputs change but + this method can be used to force the node to execute again even when the inputs don't change. + You can make this node return a number or a string. This value will be compared to the one returned the last time the node was + executed, if it is different the node will be executed again. + This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash + changes between executions the LoadImage node is executed again. + """ + #@classmethod + #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen): + # return "" + +# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension +# WEB_DIRECTORY = "./somejs" + + +# Add custom API routes, using router +from aiohttp import web +from server import PromptServer + +@PromptServer.instance.routes.get("/hello") +async def get_hello(request): + return web.json_response("hello") + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "Example": Example +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "Example": "Example Node" +} diff --git a/custom_nodes/websocket_image_save.py b/custom_nodes/websocket_image_save.py new file mode 100644 index 0000000..09fe1bd --- /dev/null +++ b/custom_nodes/websocket_image_save.py @@ -0,0 +1,46 @@ +from PIL import Image, ImageOps +from io import BytesIO +import numpy as np +import struct +import comfy.utils +import time + +#You can use this node to save full size images through the websocket, the +#images will be sent in exactly the same format as the image previews: as +#binary images on the websocket with a 8 byte header indicating the type +#of binary message (first 4 bytes) and the image format (next 4 bytes). + +#Note that no metadata will be put in the images saved with this node. + +class SaveImageWebsocket: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ),} + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "api/image" + + def save_images(self, images): + pbar = comfy.utils.ProgressBar(images.shape[0]) + step = 0 + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pbar.update_absolute(step, images.shape[0], ("PNG", img, None)) + step += 1 + + return {} + + @classmethod + def IS_CHANGED(s, images): + return time.time() + +NODE_CLASS_MAPPINGS = { + "SaveImageWebsocket": SaveImageWebsocket, +} diff --git a/execution.py b/execution.py new file mode 100644 index 0000000..6c38634 --- /dev/null +++ b/execution.py @@ -0,0 +1,991 @@ +import sys +import copy +import logging +import threading +import heapq +import time +import traceback +from enum import Enum +import inspect +from typing import List, Literal, NamedTuple, Optional + +import torch +import nodes + +import comfy.model_management +from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker +from comfy_execution.graph_utils import is_link, GraphBuilder +from comfy_execution.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID +from comfy.cli_args import args + +class ExecutionResult(Enum): + SUCCESS = 0 + FAILURE = 1 + PENDING = 2 + +class DuplicateNodeError(Exception): + pass + +class IsChangedCache: + def __init__(self, dynprompt, outputs_cache): + self.dynprompt = dynprompt + self.outputs_cache = outputs_cache + self.is_changed = {} + + def get(self, node_id): + if node_id in self.is_changed: + return self.is_changed[node_id] + + node = self.dynprompt.get_node(node_id) + class_type = node["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if not hasattr(class_def, "IS_CHANGED"): + self.is_changed[node_id] = False + return self.is_changed[node_id] + + if "is_changed" in node: + self.is_changed[node_id] = node["is_changed"] + return self.is_changed[node_id] + + # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED + input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None) + try: + is_changed = _map_node_over_list(class_def, input_data_all, "IS_CHANGED") + node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed] + except Exception as e: + logging.warning("WARNING: {}".format(e)) + node["is_changed"] = float("NaN") + finally: + self.is_changed[node_id] = node["is_changed"] + return self.is_changed[node_id] + +class CacheSet: + def __init__(self, lru_size=None): + if lru_size is None or lru_size == 0: + self.init_classic_cache() + else: + self.init_lru_cache(lru_size) + self.all = [self.outputs, self.ui, self.objects] + + # Useful for those with ample RAM/VRAM -- allows experimenting without + # blowing away the cache every time + def init_lru_cache(self, cache_size): + self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size) + self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size) + self.objects = HierarchicalCache(CacheKeySetID) + + # Performs like the old cache -- dump data ASAP + def init_classic_cache(self): + self.outputs = HierarchicalCache(CacheKeySetInputSignature) + self.ui = HierarchicalCache(CacheKeySetInputSignature) + self.objects = HierarchicalCache(CacheKeySetID) + + def recursive_debug_dump(self): + result = { + "outputs": self.outputs.recursive_debug_dump(), + "ui": self.ui.recursive_debug_dump(), + } + return result + +def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}): + valid_inputs = class_def.INPUT_TYPES() + input_data_all = {} + missing_keys = {} + for x in inputs: + input_data = inputs[x] + input_type, input_category, input_info = get_input_info(class_def, x) + def mark_missing(): + missing_keys[x] = True + input_data_all[x] = (None,) + if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)): + input_unique_id = input_data[0] + output_index = input_data[1] + if outputs is None: + mark_missing() + continue # This might be a lazily-evaluated input + cached_output = outputs.get(input_unique_id) + if cached_output is None: + mark_missing() + continue + if output_index >= len(cached_output): + mark_missing() + continue + obj = cached_output[output_index] + input_data_all[x] = obj + elif input_category is not None: + input_data_all[x] = [input_data] + + if "hidden" in valid_inputs: + h = valid_inputs["hidden"] + for x in h: + if h[x] == "PROMPT": + input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}] + if h[x] == "DYNPROMPT": + input_data_all[x] = [dynprompt] + if h[x] == "EXTRA_PNGINFO": + input_data_all[x] = [extra_data.get('extra_pnginfo', None)] + if h[x] == "UNIQUE_ID": + input_data_all[x] = [unique_id] + return input_data_all, missing_keys + +map_node_over_list = None #Don't hook this please + +def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None): + # check if node wants the lists + input_is_list = getattr(obj, "INPUT_IS_LIST", False) + + if len(input_data_all) == 0: + max_len_input = 0 + else: + max_len_input = max(len(x) for x in input_data_all.values()) + + # get a slice of inputs, repeat last input when list isn't long enough + def slice_dict(d, i): + return {k: v[i if len(v) > i else -1] for k, v in d.items()} + + results = [] + def process_inputs(inputs, index=None): + if allow_interrupt: + nodes.before_node_execution() + execution_block = None + for k, v in inputs.items(): + if isinstance(v, ExecutionBlocker): + execution_block = execution_block_cb(v) if execution_block_cb else v + break + if execution_block is None: + if pre_execute_cb is not None and index is not None: + pre_execute_cb(index) + results.append(getattr(obj, func)(**inputs)) + else: + results.append(execution_block) + + if input_is_list: + process_inputs(input_data_all, 0) + elif max_len_input == 0: + process_inputs({}) + else: + for i in range(max_len_input): + input_dict = slice_dict(input_data_all, i) + process_inputs(input_dict, i) + return results + +def merge_result_data(results, obj): + # check which outputs need concatenating + output = [] + output_is_list = [False] * len(results[0]) + if hasattr(obj, "OUTPUT_IS_LIST"): + output_is_list = obj.OUTPUT_IS_LIST + + # merge node execution results + for i, is_list in zip(range(len(results[0])), output_is_list): + if is_list: + value = [] + for o in results: + if isinstance(o[i], ExecutionBlocker): + value.append(o[i]) + else: + value.extend(o[i]) + output.append(value) + else: + output.append([o[i] for o in results]) + return output + +def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb=None): + + results = [] + uis = [] + subgraph_results = [] + return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb) + has_subgraph = False + for i in range(len(return_values)): + r = return_values[i] + if isinstance(r, dict): + if 'ui' in r: + uis.append(r['ui']) + if 'expand' in r: + # Perform an expansion, but do not append results + has_subgraph = True + new_graph = r['expand'] + result = r.get("result", None) + if isinstance(result, ExecutionBlocker): + result = tuple([result] * len(obj.RETURN_TYPES)) + subgraph_results.append((new_graph, result)) + elif 'result' in r: + result = r.get("result", None) + if isinstance(result, ExecutionBlocker): + result = tuple([result] * len(obj.RETURN_TYPES)) + results.append(result) + subgraph_results.append((None, result)) + else: + if isinstance(r, ExecutionBlocker): + r = tuple([r] * len(obj.RETURN_TYPES)) + results.append(r) + subgraph_results.append((None, r)) + + if has_subgraph: + output = subgraph_results + elif len(results) > 0: + output = merge_result_data(results, obj) + else: + output = [] + ui = dict() + if len(uis) > 0: + ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()} + return output, ui, has_subgraph + +def format_value(x): + if x is None: + return None + elif isinstance(x, (int, float, bool, str)): + return x + else: + return str(x) + +def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results): + unique_id = current_item + real_node_id = dynprompt.get_real_node_id(unique_id) + display_node_id = dynprompt.get_display_node_id(unique_id) + parent_node_id = dynprompt.get_parent_node_id(unique_id) + inputs = dynprompt.get_node(unique_id)['inputs'] + class_type = dynprompt.get_node(unique_id)['class_type'] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if caches.outputs.get(unique_id) is not None: + if server.client_id is not None: + cached_output = caches.ui.get(unique_id) or {} + server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id) + return (ExecutionResult.SUCCESS, None, None) + + input_data_all = None + try: + if unique_id in pending_subgraph_results: + cached_results = pending_subgraph_results[unique_id] + resolved_outputs = [] + for is_subgraph, result in cached_results: + if not is_subgraph: + resolved_outputs.append(result) + else: + resolved_output = [] + for r in result: + if is_link(r): + source_node, source_output = r[0], r[1] + node_output = caches.outputs.get(source_node)[source_output] + for o in node_output: + resolved_output.append(o) + + else: + resolved_output.append(r) + resolved_outputs.append(tuple(resolved_output)) + output_data = merge_result_data(resolved_outputs, class_def) + output_ui = [] + has_subgraph = False + else: + input_data_all, missing_keys = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data) + if server.client_id is not None: + server.last_node_id = display_node_id + server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id) + + obj = caches.objects.get(unique_id) + if obj is None: + obj = class_def() + caches.objects.set(unique_id, obj) + + if hasattr(obj, "check_lazy_status"): + required_inputs = _map_node_over_list(obj, input_data_all, "check_lazy_status", allow_interrupt=True) + required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], [])) + required_inputs = [x for x in required_inputs if isinstance(x,str) and ( + x not in input_data_all or x in missing_keys + )] + if len(required_inputs) > 0: + for i in required_inputs: + execution_list.make_input_strong_link(unique_id, i) + return (ExecutionResult.PENDING, None, None) + + def execution_block_cb(block): + if block.message is not None: + mes = { + "prompt_id": prompt_id, + "node_id": unique_id, + "node_type": class_type, + "executed": list(executed), + + "exception_message": f"Execution Blocked: {block.message}", + "exception_type": "ExecutionBlocked", + "traceback": [], + "current_inputs": [], + "current_outputs": [], + } + server.send_sync("execution_error", mes, server.client_id) + return ExecutionBlocker(None) + else: + return block + def pre_execute_cb(call_index): + GraphBuilder.set_default_prefix(unique_id, call_index, 0) + output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb) + if len(output_ui) > 0: + caches.ui.set(unique_id, { + "meta": { + "node_id": unique_id, + "display_node": display_node_id, + "parent_node": parent_node_id, + "real_node_id": real_node_id, + }, + "output": output_ui + }) + if server.client_id is not None: + server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id) + if has_subgraph: + cached_outputs = [] + new_node_ids = [] + new_output_ids = [] + new_output_links = [] + for i in range(len(output_data)): + new_graph, node_outputs = output_data[i] + if new_graph is None: + cached_outputs.append((False, node_outputs)) + else: + # Check for conflicts + for node_id in new_graph.keys(): + if dynprompt.has_node(node_id): + raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.") + for node_id, node_info in new_graph.items(): + new_node_ids.append(node_id) + display_id = node_info.get("override_display_id", unique_id) + dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id) + # Figure out if the newly created node is an output node + class_type = node_info["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: + new_output_ids.append(node_id) + for i in range(len(node_outputs)): + if is_link(node_outputs[i]): + from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1] + new_output_links.append((from_node_id, from_socket)) + cached_outputs.append((True, node_outputs)) + new_node_ids = set(new_node_ids) + for cache in caches.all: + cache.ensure_subcache_for(unique_id, new_node_ids).clean_unused() + for node_id in new_output_ids: + execution_list.add_node(node_id) + for link in new_output_links: + execution_list.add_strong_link(link[0], link[1], unique_id) + pending_subgraph_results[unique_id] = cached_outputs + return (ExecutionResult.PENDING, None, None) + caches.outputs.set(unique_id, output_data) + except comfy.model_management.InterruptProcessingException as iex: + logging.info("Processing interrupted") + + # skip formatting inputs/outputs + error_details = { + "node_id": real_node_id, + } + + return (ExecutionResult.FAILURE, error_details, iex) + except Exception as ex: + typ, _, tb = sys.exc_info() + exception_type = full_type_name(typ) + input_data_formatted = {} + if input_data_all is not None: + input_data_formatted = {} + for name, inputs in input_data_all.items(): + input_data_formatted[name] = [format_value(x) for x in inputs] + + logging.error(f"!!! Exception during processing !!! {ex}") + logging.error(traceback.format_exc()) + + error_details = { + "node_id": real_node_id, + "exception_message": str(ex), + "exception_type": exception_type, + "traceback": traceback.format_tb(tb), + "current_inputs": input_data_formatted + } + if isinstance(ex, comfy.model_management.OOM_EXCEPTION): + logging.error("Got an OOM, unloading all loaded models.") + comfy.model_management.unload_all_models() + + return (ExecutionResult.FAILURE, error_details, ex) + + executed.add(unique_id) + + return (ExecutionResult.SUCCESS, None, None) + +class PromptExecutor: + def __init__(self, server, lru_size=None): + self.lru_size = lru_size + self.server = server + self.reset() + + def reset(self): + self.caches = CacheSet(self.lru_size) + self.status_messages = [] + self.success = True + + def add_message(self, event, data: dict, broadcast: bool): + data = { + **data, + "timestamp": int(time.time() * 1000), + } + self.status_messages.append((event, data)) + if self.server.client_id is not None or broadcast: + self.server.send_sync(event, data, self.server.client_id) + + def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex): + node_id = error["node_id"] + class_type = prompt[node_id]["class_type"] + + # First, send back the status to the frontend depending + # on the exception type + if isinstance(ex, comfy.model_management.InterruptProcessingException): + mes = { + "prompt_id": prompt_id, + "node_id": node_id, + "node_type": class_type, + "executed": list(executed), + } + self.add_message("execution_interrupted", mes, broadcast=True) + else: + mes = { + "prompt_id": prompt_id, + "node_id": node_id, + "node_type": class_type, + "executed": list(executed), + "exception_message": error["exception_message"], + "exception_type": error["exception_type"], + "traceback": error["traceback"], + "current_inputs": error["current_inputs"], + "current_outputs": list(current_outputs), + } + self.add_message("execution_error", mes, broadcast=False) + + def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): + nodes.interrupt_processing(False) + + if "client_id" in extra_data: + self.server.client_id = extra_data["client_id"] + else: + self.server.client_id = None + + self.status_messages = [] + self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False) + + with torch.inference_mode(): + dynamic_prompt = DynamicPrompt(prompt) + is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs) + for cache in self.caches.all: + cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache) + cache.clean_unused() + + cached_nodes = [] + for node_id in prompt: + if self.caches.outputs.get(node_id) is not None: + cached_nodes.append(node_id) + + comfy.model_management.cleanup_models(keep_clone_weights_loaded=True) + self.add_message("execution_cached", + { "nodes": cached_nodes, "prompt_id": prompt_id}, + broadcast=False) + pending_subgraph_results = {} + executed = set() + execution_list = ExecutionList(dynamic_prompt, self.caches.outputs) + current_outputs = self.caches.outputs.all_node_ids() + for node_id in list(execute_outputs): + execution_list.add_node(node_id) + + while not execution_list.is_empty(): + node_id, error, ex = execution_list.stage_node_execution() + if error is not None: + self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) + break + + result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results) + self.success = result != ExecutionResult.FAILURE + if result == ExecutionResult.FAILURE: + self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) + break + elif result == ExecutionResult.PENDING: + execution_list.unstage_node_execution() + else: # result == ExecutionResult.SUCCESS: + execution_list.complete_node_execution() + else: + # Only execute when the while-loop ends without break + self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False) + + ui_outputs = {} + meta_outputs = {} + all_node_ids = self.caches.ui.all_node_ids() + for node_id in all_node_ids: + ui_info = self.caches.ui.get(node_id) + if ui_info is not None: + ui_outputs[node_id] = ui_info["output"] + meta_outputs[node_id] = ui_info["meta"] + self.history_result = { + "outputs": ui_outputs, + "meta": meta_outputs, + } + self.server.last_node_id = None + if comfy.model_management.DISABLE_SMART_MEMORY: + comfy.model_management.unload_all_models() + + + +def validate_inputs(prompt, item, validated): + unique_id = item + if unique_id in validated: + return validated[unique_id] + + inputs = prompt[unique_id]['inputs'] + class_type = prompt[unique_id]['class_type'] + obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] + + class_inputs = obj_class.INPUT_TYPES() + valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{}))) + + errors = [] + valid = True + + validate_function_inputs = [] + validate_has_kwargs = False + if hasattr(obj_class, "VALIDATE_INPUTS"): + argspec = inspect.getfullargspec(obj_class.VALIDATE_INPUTS) + validate_function_inputs = argspec.args + validate_has_kwargs = argspec.varkw is not None + received_types = {} + + for x in valid_inputs: + type_input, input_category, extra_info = get_input_info(obj_class, x) + assert extra_info is not None + if x not in inputs: + if input_category == "required": + error = { + "type": "required_input_missing", + "message": "Required input is missing", + "details": f"{x}", + "extra_info": { + "input_name": x + } + } + errors.append(error) + continue + + val = inputs[x] + info = (type_input, extra_info) + if isinstance(val, list): + if len(val) != 2: + error = { + "type": "bad_linked_input", + "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]", + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val + } + } + errors.append(error) + continue + + o_id = val[0] + o_class_type = prompt[o_id]['class_type'] + r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES + received_type = r[val[1]] + received_types[x] = received_type + if 'input_types' not in validate_function_inputs and received_type != type_input: + details = f"{x}, {received_type} != {type_input}" + error = { + "type": "return_type_mismatch", + "message": "Return type mismatch between linked nodes", + "details": details, + "extra_info": { + "input_name": x, + "input_config": info, + "received_type": received_type, + "linked_node": val + } + } + errors.append(error) + continue + try: + r = validate_inputs(prompt, o_id, validated) + if r[0] is False: + # `r` will be set in `validated[o_id]` already + valid = False + continue + except Exception as ex: + typ, _, tb = sys.exc_info() + valid = False + exception_type = full_type_name(typ) + reasons = [{ + "type": "exception_during_inner_validation", + "message": "Exception when validating inner node", + "details": str(ex), + "extra_info": { + "input_name": x, + "input_config": info, + "exception_message": str(ex), + "exception_type": exception_type, + "traceback": traceback.format_tb(tb), + "linked_node": val + } + }] + validated[o_id] = (False, reasons, o_id) + continue + else: + try: + if type_input == "INT": + val = int(val) + inputs[x] = val + if type_input == "FLOAT": + val = float(val) + inputs[x] = val + if type_input == "STRING": + val = str(val) + inputs[x] = val + if type_input == "BOOLEAN": + val = bool(val) + inputs[x] = val + except Exception as ex: + error = { + "type": "invalid_input_type", + "message": f"Failed to convert an input value to a {type_input} value", + "details": f"{x}, {val}, {ex}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + "exception_message": str(ex) + } + } + errors.append(error) + continue + + if x not in validate_function_inputs and not validate_has_kwargs: + if "min" in extra_info and val < extra_info["min"]: + error = { + "type": "value_smaller_than_min", + "message": "Value {} smaller than min of {}".format(val, extra_info["min"]), + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + } + } + errors.append(error) + continue + if "max" in extra_info and val > extra_info["max"]: + error = { + "type": "value_bigger_than_max", + "message": "Value {} bigger than max of {}".format(val, extra_info["max"]), + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + } + } + errors.append(error) + continue + + if isinstance(type_input, list): + if val not in type_input: + input_config = info + list_info = "" + + # Don't send back gigantic lists like if they're lots of + # scanned model filepaths + if len(type_input) > 20: + list_info = f"(list of length {len(type_input)})" + input_config = None + else: + list_info = str(type_input) + + error = { + "type": "value_not_in_list", + "message": "Value not in list", + "details": f"{x}: '{val}' not in {list_info}", + "extra_info": { + "input_name": x, + "input_config": input_config, + "received_value": val, + } + } + errors.append(error) + continue + + if len(validate_function_inputs) > 0 or validate_has_kwargs: + input_data_all, _ = get_input_data(inputs, obj_class, unique_id) + input_filtered = {} + for x in input_data_all: + if x in validate_function_inputs or validate_has_kwargs: + input_filtered[x] = input_data_all[x] + if 'input_types' in validate_function_inputs: + input_filtered['input_types'] = [received_types] + + #ret = obj_class.VALIDATE_INPUTS(**input_filtered) + ret = _map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS") + for x in input_filtered: + for i, r in enumerate(ret): + if r is not True and not isinstance(r, ExecutionBlocker): + details = f"{x}" + if r is not False: + details += f" - {str(r)}" + + error = { + "type": "custom_validation_failed", + "message": "Custom validation failed for node", + "details": details, + "extra_info": { + "input_name": x, + } + } + errors.append(error) + continue + + if len(errors) > 0 or valid is not True: + ret = (False, errors, unique_id) + else: + ret = (True, [], unique_id) + + validated[unique_id] = ret + return ret + +def full_type_name(klass): + module = klass.__module__ + if module == 'builtins': + return klass.__qualname__ + return module + '.' + klass.__qualname__ + +def validate_prompt(prompt): + outputs = set() + for x in prompt: + if 'class_type' not in prompt[x]: + error = { + "type": "invalid_prompt", + "message": f"Cannot execute because a node is missing the class_type property.", + "details": f"Node ID '#{x}'", + "extra_info": {} + } + return (False, error, [], []) + + class_type = prompt[x]['class_type'] + class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) + if class_ is None: + error = { + "type": "invalid_prompt", + "message": f"Cannot execute because node {class_type} does not exist.", + "details": f"Node ID '#{x}'", + "extra_info": {} + } + return (False, error, [], []) + + if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: + outputs.add(x) + + if len(outputs) == 0: + error = { + "type": "prompt_no_outputs", + "message": "Prompt has no outputs", + "details": "", + "extra_info": {} + } + return (False, error, [], []) + + good_outputs = set() + errors = [] + node_errors = {} + validated = {} + for o in outputs: + valid = False + reasons = [] + try: + m = validate_inputs(prompt, o, validated) + valid = m[0] + reasons = m[1] + except Exception as ex: + typ, _, tb = sys.exc_info() + valid = False + exception_type = full_type_name(typ) + reasons = [{ + "type": "exception_during_validation", + "message": "Exception when validating node", + "details": str(ex), + "extra_info": { + "exception_type": exception_type, + "traceback": traceback.format_tb(tb) + } + }] + validated[o] = (False, reasons, o) + + if valid is True: + good_outputs.add(o) + else: + logging.error(f"Failed to validate prompt for output {o}:") + if len(reasons) > 0: + logging.error("* (prompt):") + for reason in reasons: + logging.error(f" - {reason['message']}: {reason['details']}") + errors += [(o, reasons)] + for node_id, result in validated.items(): + valid = result[0] + reasons = result[1] + # If a node upstream has errors, the nodes downstream will also + # be reported as invalid, but there will be no errors attached. + # So don't return those nodes as having errors in the response. + if valid is not True and len(reasons) > 0: + if node_id not in node_errors: + class_type = prompt[node_id]['class_type'] + node_errors[node_id] = { + "errors": reasons, + "dependent_outputs": [], + "class_type": class_type + } + logging.error(f"* {class_type} {node_id}:") + for reason in reasons: + logging.error(f" - {reason['message']}: {reason['details']}") + node_errors[node_id]["dependent_outputs"].append(o) + logging.error("Output will be ignored") + + if len(good_outputs) == 0: + errors_list = [] + for o, errors in errors: + for error in errors: + errors_list.append(f"{error['message']}: {error['details']}") + errors_list = "\n".join(errors_list) + + error = { + "type": "prompt_outputs_failed_validation", + "message": "Prompt outputs failed validation", + "details": errors_list, + "extra_info": {} + } + + return (False, error, list(good_outputs), node_errors) + + return (True, None, list(good_outputs), node_errors) + +MAXIMUM_HISTORY_SIZE = 10000 + +class PromptQueue: + def __init__(self, server): + self.server = server + self.mutex = threading.RLock() + self.not_empty = threading.Condition(self.mutex) + self.task_counter = 0 + self.queue = [] + self.currently_running = {} + self.history = {} + self.flags = {} + server.prompt_queue = self + + def put(self, item): + with self.mutex: + heapq.heappush(self.queue, item) + self.server.queue_updated() + self.not_empty.notify() + + def get(self, timeout=None): + with self.not_empty: + while len(self.queue) == 0: + self.not_empty.wait(timeout=timeout) + if timeout is not None and len(self.queue) == 0: + return None + item = heapq.heappop(self.queue) + i = self.task_counter + self.currently_running[i] = copy.deepcopy(item) + self.task_counter += 1 + self.server.queue_updated() + return (item, i) + + class ExecutionStatus(NamedTuple): + status_str: Literal['success', 'error'] + completed: bool + messages: List[str] + + def task_done(self, item_id, history_result, + status: Optional['PromptQueue.ExecutionStatus']): + with self.mutex: + prompt = self.currently_running.pop(item_id) + if len(self.history) > MAXIMUM_HISTORY_SIZE: + self.history.pop(next(iter(self.history))) + + status_dict: Optional[dict] = None + if status is not None: + status_dict = copy.deepcopy(status._asdict()) + + self.history[prompt[1]] = { + "prompt": prompt, + "outputs": {}, + 'status': status_dict, + } + self.history[prompt[1]].update(history_result) + self.server.queue_updated() + + def get_current_queue(self): + with self.mutex: + out = [] + for x in self.currently_running.values(): + out += [x] + return (out, copy.deepcopy(self.queue)) + + def get_tasks_remaining(self): + with self.mutex: + return len(self.queue) + len(self.currently_running) + + def wipe_queue(self): + with self.mutex: + self.queue = [] + self.server.queue_updated() + + def delete_queue_item(self, function): + with self.mutex: + for x in range(len(self.queue)): + if function(self.queue[x]): + if len(self.queue) == 1: + self.wipe_queue() + else: + self.queue.pop(x) + heapq.heapify(self.queue) + self.server.queue_updated() + return True + return False + + def get_history(self, prompt_id=None, max_items=None, offset=-1): + with self.mutex: + if prompt_id is None: + out = {} + i = 0 + if offset < 0 and max_items is not None: + offset = len(self.history) - max_items + for k in self.history: + if i >= offset: + out[k] = self.history[k] + if max_items is not None and len(out) >= max_items: + break + i += 1 + return out + elif prompt_id in self.history: + return {prompt_id: copy.deepcopy(self.history[prompt_id])} + else: + return {} + + def wipe_history(self): + with self.mutex: + self.history = {} + + def delete_history_item(self, id_to_delete): + with self.mutex: + self.history.pop(id_to_delete, None) + + def set_flag(self, name, data): + with self.mutex: + self.flags[name] = data + self.not_empty.notify() + + def get_flags(self, reset=True): + with self.mutex: + if reset: + ret = self.flags + self.flags = {} + return ret + else: + return self.flags.copy() diff --git a/extra_model_paths.yaml.example b/extra_model_paths.yaml.example new file mode 100644 index 0000000..b55913a --- /dev/null +++ b/extra_model_paths.yaml.example @@ -0,0 +1,47 @@ +#Rename this to extra_model_paths.yaml and ComfyUI will load it + + +#config for a1111 ui +#all you have to do is change the base_path to where yours is installed +a111: + base_path: path/to/stable-diffusion-webui/ + + checkpoints: models/Stable-diffusion + configs: models/Stable-diffusion + vae: models/VAE + loras: | + models/Lora + models/LyCORIS + upscale_models: | + models/ESRGAN + models/RealESRGAN + models/SwinIR + embeddings: embeddings + hypernetworks: models/hypernetworks + controlnet: models/ControlNet + +#config for comfyui +#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc. + +#comfyui: +# base_path: path/to/comfyui/ +# # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads +# #is_default: true +# checkpoints: models/checkpoints/ +# clip: models/clip/ +# clip_vision: models/clip_vision/ +# configs: models/configs/ +# controlnet: models/controlnet/ +# diffusion_models: | +# models/diffusion_models +# models/unet +# embeddings: models/embeddings/ +# loras: models/loras/ +# upscale_models: models/upscale_models/ +# vae: models/vae/ + +#other_ui: +# base_path: path/to/ui +# checkpoints: models/checkpoints +# gligen: models/gligen +# custom_nodes: path/custom_nodes diff --git a/fix_torch.py b/fix_torch.py new file mode 100644 index 0000000..e350f5c --- /dev/null +++ b/fix_torch.py @@ -0,0 +1,24 @@ +import importlib.util +import shutil +import os +import ctypes +import logging + + +torch_spec = importlib.util.find_spec("torch") +for folder in torch_spec.submodule_search_locations: + lib_folder = os.path.join(folder, "lib") + test_file = os.path.join(lib_folder, "fbgemm.dll") + dest = os.path.join(lib_folder, "libomp140.x86_64.dll") + if os.path.exists(dest): + break + + with open(test_file, 'rb') as f: + contents = f.read() + if b"libomp140.x86_64.dll" not in contents: + break + try: + mydll = ctypes.cdll.LoadLibrary(test_file) + except FileNotFoundError as e: + logging.warning("Detected pytorch version with libomp issue, patching.") + shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), dest) diff --git a/folder_paths.py b/folder_paths.py new file mode 100644 index 0000000..0c9e9f1 --- /dev/null +++ b/folder_paths.py @@ -0,0 +1,378 @@ +from __future__ import annotations + +import os +import time +import mimetypes +import logging +from typing import Set, List, Dict, Tuple, Literal +from collections.abc import Collection + +supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'} + +folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {} + +base_path = os.path.dirname(os.path.realpath(__file__)) +models_dir = os.path.join(base_path, "models") +folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) +folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) + +folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions) +folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions) +folder_names_and_paths["text_encoders"] = ([os.path.join(models_dir, "text_encoders"), os.path.join(models_dir, "clip")], supported_pt_extensions) +folder_names_and_paths["diffusion_models"] = ([os.path.join(models_dir, "unet"), os.path.join(models_dir, "diffusion_models")], supported_pt_extensions) +folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions) +folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions) +folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions) +folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"]) +folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions) + +folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions) +folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions) + +folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) + +folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set()) + +folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) + +folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions) + +folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) + +output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output") +temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp") +input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input") +user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user") + +filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {} + +class CacheHelper: + """ + Helper class for managing file list cache data. + """ + def __init__(self): + self.cache: dict[str, tuple[list[str], dict[str, float], float]] = {} + self.active = False + + def get(self, key: str, default=None) -> tuple[list[str], dict[str, float], float]: + if not self.active: + return default + return self.cache.get(key, default) + + def set(self, key: str, value: tuple[list[str], dict[str, float], float]) -> None: + if self.active: + self.cache[key] = value + + def clear(self): + self.cache.clear() + + def __enter__(self): + self.active = True + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.active = False + self.clear() + +cache_helper = CacheHelper() + +extension_mimetypes_cache = { + "webp" : "image", +} + +def map_legacy(folder_name: str) -> str: + legacy = {"unet": "diffusion_models", + "clip": "text_encoders"} + return legacy.get(folder_name, folder_name) + +if not os.path.exists(input_directory): + try: + os.makedirs(input_directory) + except: + logging.error("Failed to create input directory") + +def set_output_directory(output_dir: str) -> None: + global output_directory + output_directory = output_dir + +def set_temp_directory(temp_dir: str) -> None: + global temp_directory + temp_directory = temp_dir + +def set_input_directory(input_dir: str) -> None: + global input_directory + input_directory = input_dir + +def get_output_directory() -> str: + global output_directory + return output_directory + +def get_temp_directory() -> str: + global temp_directory + return temp_directory + +def get_input_directory() -> str: + global input_directory + return input_directory + +def get_user_directory() -> str: + return user_directory + +def set_user_directory(user_dir: str) -> None: + global user_directory + user_directory = user_dir + + +#NOTE: used in http server so don't put folders that should not be accessed remotely +def get_directory_by_type(type_name: str) -> str | None: + if type_name == "output": + return get_output_directory() + if type_name == "temp": + return get_temp_directory() + if type_name == "input": + return get_input_directory() + return None + +def filter_files_content_types(files: List[str], content_types: Literal["image", "video", "audio"]) -> List[str]: + """ + Example: + files = os.listdir(folder_paths.get_input_directory()) + filter_files_content_types(files, ["image", "audio", "video"]) + """ + global extension_mimetypes_cache + result = [] + for file in files: + extension = file.split('.')[-1] + if extension not in extension_mimetypes_cache: + mime_type, _ = mimetypes.guess_type(file, strict=False) + if not mime_type: + continue + content_type = mime_type.split('/')[0] + extension_mimetypes_cache[extension] = content_type + else: + content_type = extension_mimetypes_cache[extension] + + if content_type in content_types: + result.append(file) + return result + +# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format +# otherwise use default_path as base_dir +def annotated_filepath(name: str) -> tuple[str, str | None]: + if name.endswith("[output]"): + base_dir = get_output_directory() + name = name[:-9] + elif name.endswith("[input]"): + base_dir = get_input_directory() + name = name[:-8] + elif name.endswith("[temp]"): + base_dir = get_temp_directory() + name = name[:-7] + else: + return name, None + + return name, base_dir + + +def get_annotated_filepath(name: str, default_dir: str | None=None) -> str: + name, base_dir = annotated_filepath(name) + + if base_dir is None: + if default_dir is not None: + base_dir = default_dir + else: + base_dir = get_input_directory() # fallback path + + return os.path.join(base_dir, name) + + +def exists_annotated_filepath(name) -> bool: + name, base_dir = annotated_filepath(name) + + if base_dir is None: + base_dir = get_input_directory() # fallback path + + filepath = os.path.join(base_dir, name) + return os.path.exists(filepath) + + +def add_model_folder_path(folder_name: str, full_folder_path: str, is_default: bool = False) -> None: + global folder_names_and_paths + folder_name = map_legacy(folder_name) + if folder_name in folder_names_and_paths: + if is_default: + folder_names_and_paths[folder_name][0].insert(0, full_folder_path) + else: + folder_names_and_paths[folder_name][0].append(full_folder_path) + else: + folder_names_and_paths[folder_name] = ([full_folder_path], set()) + +def get_folder_paths(folder_name: str) -> list[str]: + folder_name = map_legacy(folder_name) + return folder_names_and_paths[folder_name][0][:] + +def recursive_search(directory: str, excluded_dir_names: list[str] | None=None) -> tuple[list[str], dict[str, float]]: + if not os.path.isdir(directory): + return [], {} + + if excluded_dir_names is None: + excluded_dir_names = [] + + result = [] + dirs = {} + + # Attempt to add the initial directory to dirs with error handling + try: + dirs[directory] = os.path.getmtime(directory) + except FileNotFoundError: + logging.warning(f"Warning: Unable to access {directory}. Skipping this path.") + + logging.debug("recursive file list on directory {}".format(directory)) + dirpath: str + subdirs: list[str] + filenames: list[str] + + for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): + subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] + for file_name in filenames: + try: + relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) + result.append(relative_path) + except: + logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.") + continue + + for d in subdirs: + path: str = os.path.join(dirpath, d) + try: + dirs[path] = os.path.getmtime(path) + except FileNotFoundError: + logging.warning(f"Warning: Unable to access {path}. Skipping this path.") + continue + logging.debug("found {} files".format(len(result))) + return result, dirs + +def filter_files_extensions(files: Collection[str], extensions: Collection[str]) -> list[str]: + return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files))) + + + +def get_full_path(folder_name: str, filename: str) -> str | None: + global folder_names_and_paths + folder_name = map_legacy(folder_name) + if folder_name not in folder_names_and_paths: + return None + folders = folder_names_and_paths[folder_name] + filename = os.path.relpath(os.path.join("/", filename), "/") + for x in folders[0]: + full_path = os.path.join(x, filename) + if os.path.isfile(full_path): + return full_path + elif os.path.islink(full_path): + logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path)) + + return None + + +def get_full_path_or_raise(folder_name: str, filename: str) -> str: + full_path = get_full_path(folder_name, filename) + if full_path is None: + raise FileNotFoundError(f"Model in folder '{folder_name}' with filename '{filename}' not found.") + return full_path + + +def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float]: + folder_name = map_legacy(folder_name) + global folder_names_and_paths + output_list = set() + folders = folder_names_and_paths[folder_name] + output_folders = {} + for x in folders[0]: + files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) + output_list.update(filter_files_extensions(files, folders[1])) + output_folders = {**output_folders, **folders_all} + + return sorted(list(output_list)), output_folders, time.perf_counter() + +def cached_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float] | None: + strong_cache = cache_helper.get(folder_name) + if strong_cache is not None: + return strong_cache + + global filename_list_cache + global folder_names_and_paths + folder_name = map_legacy(folder_name) + if folder_name not in filename_list_cache: + return None + out = filename_list_cache[folder_name] + + for x in out[1]: + time_modified = out[1][x] + folder = x + if os.path.getmtime(folder) != time_modified: + return None + + folders = folder_names_and_paths[folder_name] + for x in folders[0]: + if os.path.isdir(x): + if x not in out[1]: + return None + + return out + +def get_filename_list(folder_name: str) -> list[str]: + folder_name = map_legacy(folder_name) + out = cached_filename_list_(folder_name) + if out is None: + out = get_filename_list_(folder_name) + global filename_list_cache + filename_list_cache[folder_name] = out + cache_helper.set(folder_name, out) + return list(out[0]) + +def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, image_height=0) -> tuple[str, str, int, str, str]: + def map_filename(filename: str) -> tuple[int, str]: + prefix_len = len(os.path.basename(filename_prefix)) + prefix = filename[:prefix_len + 1] + try: + digits = int(filename[prefix_len + 1:].split('_')[0]) + except: + digits = 0 + return digits, prefix + + def compute_vars(input: str, image_width: int, image_height: int) -> str: + input = input.replace("%width%", str(image_width)) + input = input.replace("%height%", str(image_height)) + now = time.localtime() + input = input.replace("%year%", str(now.tm_year)) + input = input.replace("%month%", str(now.tm_mon).zfill(2)) + input = input.replace("%day%", str(now.tm_mday).zfill(2)) + input = input.replace("%hour%", str(now.tm_hour).zfill(2)) + input = input.replace("%minute%", str(now.tm_min).zfill(2)) + input = input.replace("%second%", str(now.tm_sec).zfill(2)) + return input + + if "%" in filename_prefix: + filename_prefix = compute_vars(filename_prefix, image_width, image_height) + + subfolder = os.path.dirname(os.path.normpath(filename_prefix)) + filename = os.path.basename(os.path.normpath(filename_prefix)) + + full_output_folder = os.path.join(output_dir, subfolder) + + if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: + err = "**** ERROR: Saving image outside the output folder is not allowed." + \ + "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ + "\n output_dir: " + output_dir + \ + "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) + logging.error(err) + raise Exception(err) + + try: + counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 + except ValueError: + counter = 1 + except FileNotFoundError: + os.makedirs(full_output_folder, exist_ok=True) + counter = 1 + return full_output_folder, filename, counter, subfolder, filename_prefix diff --git a/input/example.png b/input/example.png new file mode 100644 index 0000000000000000000000000000000000000000..7b7f3c9cbbe6d8750c4a9eaf65d6ae4d2f108f79 GIT binary patch literal 8589 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z0dV#5xv2`4_`)&1z{dklG=XMnrGH}Z;74XNWiH?u0Gko2X@mWNd97W@HFwNgkQpeGg$G*kf!Z-ZFTi?e ztq26tE8+hF2x;FY^m+^$B%5pmM|;hxheL86SQuc7Eu9h))WHPAJN@b}ZM9plM+eW@ z*Lx&{MM%e6uv8a!mH-y?Rl%?l+!j`=;g0v<|kom 0: + print("\nPrestartup times for custom nodes:") + for n in sorted(node_prestartup_times): + if n[2]: + import_message = "" + else: + import_message = " (PRESTARTUP FAILED)" + print("{:6.1f} seconds{}:".format(n[0], import_message), n[1]) + print() + +execute_prestartup_script() + + +# Main code +import asyncio +import itertools +import shutil +import threading +import gc + +import logging +import utils.extra_config + +if os.name == "nt": + logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) + +if __name__ == "__main__": + if args.cuda_device is not None: + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device) + logging.info("Set cuda device to: {}".format(args.cuda_device)) + + if args.deterministic: + if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ: + os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8" + + import cuda_malloc + +if args.windows_standalone_build: + try: + import fix_torch + except: + pass + +import comfy.utils + +import execution +import server +from server import BinaryEventTypes +import nodes +import comfy.model_management + +def cuda_malloc_warning(): + device = comfy.model_management.get_torch_device() + device_name = comfy.model_management.get_torch_device_name(device) + cuda_malloc_warning = False + if "cudaMallocAsync" in device_name: + for b in cuda_malloc.blacklist: + if b in device_name: + cuda_malloc_warning = True + if cuda_malloc_warning: + logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n") + +def prompt_worker(q, server): + e = execution.PromptExecutor(server, lru_size=args.cache_lru) + last_gc_collect = 0 + need_gc = False + gc_collect_interval = 10.0 + + while True: + timeout = 1000.0 + if need_gc: + timeout = max(gc_collect_interval - (current_time - last_gc_collect), 0.0) + + queue_item = q.get(timeout=timeout) + if queue_item is not None: + item, item_id = queue_item + execution_start_time = time.perf_counter() + prompt_id = item[1] + server.last_prompt_id = prompt_id + + e.execute(item[2], prompt_id, item[3], item[4]) + need_gc = True + q.task_done(item_id, + e.history_result, + status=execution.PromptQueue.ExecutionStatus( + status_str='success' if e.success else 'error', + completed=e.success, + messages=e.status_messages)) + if server.client_id is not None: + server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, server.client_id) + + current_time = time.perf_counter() + execution_time = current_time - execution_start_time + logging.info("Prompt executed in {:.2f} seconds".format(execution_time)) + + flags = q.get_flags() + free_memory = flags.get("free_memory", False) + + if flags.get("unload_models", free_memory): + comfy.model_management.unload_all_models() + need_gc = True + last_gc_collect = 0 + + if free_memory: + e.reset() + need_gc = True + last_gc_collect = 0 + + if need_gc: + current_time = time.perf_counter() + if (current_time - last_gc_collect) > gc_collect_interval: + comfy.model_management.cleanup_models() + gc.collect() + comfy.model_management.soft_empty_cache() + last_gc_collect = current_time + need_gc = False + +async def run(server, address='', port=8188, verbose=True, call_on_start=None): + addresses = [] + for addr in address.split(","): + addresses.append((addr, port)) + await asyncio.gather(server.start_multi_address(addresses, call_on_start), server.publish_loop()) + + +def hijack_progress(server): + def hook(value, total, preview_image): + comfy.model_management.throw_exception_if_processing_interrupted() + progress = {"value": value, "max": total, "prompt_id": server.last_prompt_id, "node": server.last_node_id} + + server.send_sync("progress", progress, server.client_id) + if preview_image is not None: + server.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server.client_id) + comfy.utils.set_progress_bar_global_hook(hook) + + +def cleanup_temp(): + temp_dir = folder_paths.get_temp_directory() + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir, ignore_errors=True) + + +if __name__ == "__main__": + if args.temp_directory: + temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp") + logging.info(f"Setting temp directory to: {temp_dir}") + folder_paths.set_temp_directory(temp_dir) + cleanup_temp() + + if args.windows_standalone_build: + try: + import new_updater + new_updater.update_windows_updater() + except: + pass + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + server = server.PromptServer(loop) + q = execution.PromptQueue(server) + + extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml") + if os.path.isfile(extra_model_paths_config_path): + utils.extra_config.load_extra_path_config(extra_model_paths_config_path) + + if args.extra_model_paths_config: + for config_path in itertools.chain(*args.extra_model_paths_config): + utils.extra_config.load_extra_path_config(config_path) + + nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes) + + cuda_malloc_warning() + + server.add_routes() + hijack_progress(server) + + threading.Thread(target=prompt_worker, daemon=True, args=(q, server,)).start() + + if args.output_directory: + output_dir = os.path.abspath(args.output_directory) + logging.info(f"Setting output directory to: {output_dir}") + folder_paths.set_output_directory(output_dir) + + #These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes + folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints")) + folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip")) + folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae")) + folder_paths.add_model_folder_path("diffusion_models", os.path.join(folder_paths.get_output_directory(), "diffusion_models")) + folder_paths.add_model_folder_path("loras", os.path.join(folder_paths.get_output_directory(), "loras")) + + if args.input_directory: + input_dir = os.path.abspath(args.input_directory) + logging.info(f"Setting input directory to: {input_dir}") + folder_paths.set_input_directory(input_dir) + + if args.user_directory: + user_dir = os.path.abspath(args.user_directory) + logging.info(f"Setting user directory to: {user_dir}") + folder_paths.set_user_directory(user_dir) + + if args.quick_test_for_ci: + exit(0) + + os.makedirs(folder_paths.get_temp_directory(), exist_ok=True) + call_on_start = None + if args.auto_launch: + def startup_server(scheme, address, port): + import webbrowser + if os.name == 'nt' and address == '0.0.0.0': + address = '127.0.0.1' + if ':' in address: + address = "[{}]".format(address) + webbrowser.open(f"{scheme}://{address}:{port}") + call_on_start = startup_server + + try: + loop.run_until_complete(server.setup()) + loop.run_until_complete(run(server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start)) + except KeyboardInterrupt: + logging.info("\nStopped server") + + cleanup_temp() diff --git a/model_filemanager/__init__.py b/model_filemanager/__init__.py new file mode 100644 index 0000000..b7ac162 --- /dev/null +++ b/model_filemanager/__init__.py @@ -0,0 +1,2 @@ +# model_manager/__init__.py +from .download_models import download_model, DownloadModelStatus, DownloadStatusType, create_model_path, check_file_exists, track_download_progress, validate_filename diff --git a/model_filemanager/download_models.py b/model_filemanager/download_models.py new file mode 100644 index 0000000..6722b6e --- /dev/null +++ b/model_filemanager/download_models.py @@ -0,0 +1,234 @@ +#NOTE: This was an experiment and WILL BE REMOVED +from __future__ import annotations +import aiohttp +import os +import traceback +import logging +from folder_paths import folder_names_and_paths, get_folder_paths +import re +from typing import Callable, Any, Optional, Awaitable, Dict +from enum import Enum +import time +from dataclasses import dataclass + + +class DownloadStatusType(Enum): + PENDING = "pending" + IN_PROGRESS = "in_progress" + COMPLETED = "completed" + ERROR = "error" + + +@dataclass +class DownloadModelStatus(): + status: str + progress_percentage: float + message: str + already_existed: bool = False + + def __init__(self, status: DownloadStatusType, progress_percentage: float, message: str, already_existed: bool): + self.status = status.value # Store the string value of the Enum + self.progress_percentage = progress_percentage + self.message = message + self.already_existed = already_existed + + def to_dict(self) -> Dict[str, Any]: + return { + "status": self.status, + "progress_percentage": self.progress_percentage, + "message": self.message, + "already_existed": self.already_existed + } + + +async def download_model(model_download_request: Callable[[str], Awaitable[aiohttp.ClientResponse]], + model_name: str, + model_url: str, + model_directory: str, + folder_path: str, + progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]], + progress_interval: float = 1.0) -> DownloadModelStatus: + """ + Download a model file from a given URL into the models directory. + + Args: + model_download_request (Callable[[str], Awaitable[aiohttp.ClientResponse]]): + A function that makes an HTTP request. This makes it easier to mock in unit tests. + model_name (str): + The name of the model file to be downloaded. This will be the filename on disk. + model_url (str): + The URL from which to download the model. + model_directory (str): + The subdirectory within the main models directory where the model + should be saved (e.g., 'checkpoints', 'loras', etc.). + progress_callback (Callable[[str, DownloadModelStatus], Awaitable[Any]]): + An asynchronous function to call with progress updates. + folder_path (str); + Path to which model folder should be used as the root. + + Returns: + DownloadModelStatus: The result of the download operation. + """ + if not validate_filename(model_name): + return DownloadModelStatus( + DownloadStatusType.ERROR, + 0, + "Invalid model name", + False + ) + + if not model_directory in folder_names_and_paths: + return DownloadModelStatus( + DownloadStatusType.ERROR, + 0, + "Invalid or unrecognized model directory. model_directory must be a known model type (eg 'checkpoints'). If you are seeing this error for a custom model type, ensure the relevant custom nodes are installed and working.", + False + ) + + if not folder_path in get_folder_paths(model_directory): + return DownloadModelStatus( + DownloadStatusType.ERROR, + 0, + f"Invalid folder path '{folder_path}', does not match the list of known directories ({get_folder_paths(model_directory)}). If you're seeing this in the downloader UI, you may need to refresh the page.", + False + ) + + file_path = create_model_path(model_name, folder_path) + existing_file = await check_file_exists(file_path, model_name, progress_callback) + if existing_file: + return existing_file + + try: + logging.info(f"Downloading {model_name} from {model_url}") + status = DownloadModelStatus(DownloadStatusType.PENDING, 0, f"Starting download of {model_name}", False) + await progress_callback(model_name, status) + + response = await model_download_request(model_url) + if response.status != 200: + error_message = f"Failed to download {model_name}. Status code: {response.status}" + logging.error(error_message) + status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False) + await progress_callback(model_name, status) + return DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False) + + return await track_download_progress(response, file_path, model_name, progress_callback, progress_interval) + + except Exception as e: + logging.error(f"Error in downloading model: {e}") + return await handle_download_error(e, model_name, progress_callback) + + +def create_model_path(model_name: str, folder_path: str) -> tuple[str, str]: + os.makedirs(folder_path, exist_ok=True) + file_path = os.path.join(folder_path, model_name) + + # Ensure the resulting path is still within the base directory + abs_file_path = os.path.abspath(file_path) + abs_base_dir = os.path.abspath(folder_path) + if os.path.commonprefix([abs_file_path, abs_base_dir]) != abs_base_dir: + raise Exception(f"Invalid model directory: {folder_path}/{model_name}") + + return file_path + + +async def check_file_exists(file_path: str, + model_name: str, + progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]] + ) -> Optional[DownloadModelStatus]: + if os.path.exists(file_path): + status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"{model_name} already exists", True) + await progress_callback(model_name, status) + return status + return None + + +async def track_download_progress(response: aiohttp.ClientResponse, + file_path: str, + model_name: str, + progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]], + interval: float = 1.0) -> DownloadModelStatus: + try: + total_size = int(response.headers.get('Content-Length', 0)) + downloaded = 0 + last_update_time = time.time() + + async def update_progress(): + nonlocal last_update_time + progress = (downloaded / total_size) * 100 if total_size > 0 else 0 + status = DownloadModelStatus(DownloadStatusType.IN_PROGRESS, progress, f"Downloading {model_name}", False) + await progress_callback(model_name, status) + last_update_time = time.time() + + temp_file_path = file_path + '.tmp' + with open(temp_file_path, 'wb') as f: + chunk_iterator = response.content.iter_chunked(8192) + while True: + try: + chunk = await chunk_iterator.__anext__() + except StopAsyncIteration: + break + f.write(chunk) + downloaded += len(chunk) + + if time.time() - last_update_time >= interval: + await update_progress() + + os.rename(temp_file_path, file_path) + + await update_progress() + + logging.info(f"Successfully downloaded {model_name}. Total downloaded: {downloaded}") + status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"Successfully downloaded {model_name}", False) + await progress_callback(model_name, status) + + return status + except Exception as e: + logging.error(f"Error in track_download_progress: {e}") + logging.error(traceback.format_exc()) + return await handle_download_error(e, model_name, progress_callback) + + +async def handle_download_error(e: Exception, + model_name: str, + progress_callback: Callable[[str, DownloadModelStatus], Any] + ) -> DownloadModelStatus: + error_message = f"Error downloading {model_name}: {str(e)}" + status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False) + await progress_callback(model_name, status) + return status + + +def validate_filename(filename: str)-> bool: + """ + Validate a filename to ensure it's safe and doesn't contain any path traversal attempts. + + Args: + filename (str): The filename to validate + + Returns: + bool: True if the filename is valid, False otherwise + """ + if not filename.lower().endswith(('.sft', '.safetensors')): + return False + + # Check if the filename is empty, None, or just whitespace + if not filename or not filename.strip(): + return False + + # Check for any directory traversal attempts or invalid characters + if any(char in filename for char in ['..', '/', '\\', '\n', '\r', '\t', '\0']): + return False + + # Check if the filename starts with a dot (hidden file) + if filename.startswith('.'): + return False + + # Use a whitelist of allowed characters + if not re.match(r'^[a-zA-Z0-9_\-. ]+$', filename): + return False + + # Ensure the filename isn't too long + if len(filename) > 255: + return False + + return True diff --git a/models/checkpoints/put_checkpoints_here b/models/checkpoints/put_checkpoints_here new file mode 100644 index 0000000..e69de29 diff --git a/models/clip/put_clip_or_text_encoder_models_here b/models/clip/put_clip_or_text_encoder_models_here new file mode 100644 index 0000000..e69de29 diff --git a/models/clip_vision/put_clip_vision_models_here b/models/clip_vision/put_clip_vision_models_here new file mode 100644 index 0000000..e69de29 diff --git a/models/configs/anything_v3.yaml b/models/configs/anything_v3.yaml new file mode 100644 index 0000000..8bcfe58 --- /dev/null +++ b/models/configs/anything_v3.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference.yaml b/models/configs/v1-inference.yaml new file mode 100644 index 0000000..d4effe5 --- /dev/null +++ b/models/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inference_clip_skip_2.yaml b/models/configs/v1-inference_clip_skip_2.yaml new file mode 100644 index 0000000..8bcfe58 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_clip_skip_2_fp16.yaml b/models/configs/v1-inference_clip_skip_2_fp16.yaml new file mode 100644 index 0000000..7eca31c --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2_fp16.yaml @@ -0,0 +1,74 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_fp16.yaml b/models/configs/v1-inference_fp16.yaml new file mode 100644 index 0000000..147f42b --- /dev/null +++ b/models/configs/v1-inference_fp16.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inpainting-inference.yaml b/models/configs/v1-inpainting-inference.yaml new file mode 100644 index 0000000..45f3f82 --- /dev/null +++ b/models/configs/v1-inpainting-inference.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 7.5e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid # important + monitor: val/loss_simple_ema + scale_factor: 0.18215 + finetune_keys: null + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 9 # 4 data + 4 downscaled image + 1 mask + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + diff --git a/models/configs/v2-inference-v.yaml b/models/configs/v2-inference-v.yaml new file mode 100644 index 0000000..8ec8dfb --- /dev/null +++ b/models/configs/v2-inference-v.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference-v_fp32.yaml b/models/configs/v2-inference-v_fp32.yaml new file mode 100644 index 0000000..d5c9b9c --- /dev/null +++ b/models/configs/v2-inference-v_fp32.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference.yaml b/models/configs/v2-inference.yaml new file mode 100644 index 0000000..152c4f3 --- /dev/null +++ b/models/configs/v2-inference.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference_fp32.yaml b/models/configs/v2-inference_fp32.yaml new file mode 100644 index 0000000..0d03231 --- /dev/null +++ b/models/configs/v2-inference_fp32.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inpainting-inference.yaml b/models/configs/v2-inpainting-inference.yaml new file mode 100644 index 0000000..32a9471 --- /dev/null +++ b/models/configs/v2-inpainting-inference.yaml @@ -0,0 +1,158 @@ +model: + base_learning_rate: 5.0e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: hybrid + scale_factor: 0.18215 + monitor: val/loss_simple_ema + finetune_keys: null + use_ema: False + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: null # for concat as in LAION-A + p_unsafe_threshold: 0.1 + filter_word_list: "data/filters.yaml" + max_pwatermark: 0.45 + batch_size: 8 + num_workers: 6 + multinode: True + min_size: 512 + train: + shards: + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: + - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + +lightning: + find_unused_parameters: True + modelcheckpoint: + params: + every_n_train_steps: 5000 + + callbacks: + metrics_over_trainsteps_checkpoint: + params: + every_n_train_steps: 10000 + + image_logger: + target: main.ImageLogger + params: + enable_autocast: False + disabled: False + batch_frequency: 1000 + max_images: 4 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 4 + unconditional_guidance_scale: 5.0 + unconditional_guidance_label: [""] + ddim_steps: 50 # todo check these out for depth2img, + ddim_eta: 0.0 # todo check these out for depth2img, + + trainer: + benchmark: True + val_check_interval: 5000000 + num_sanity_val_steps: 0 + accumulate_grad_batches: 1 diff --git a/models/controlnet/put_controlnets_and_t2i_here b/models/controlnet/put_controlnets_and_t2i_here new file mode 100644 index 0000000..e69de29 diff --git a/models/diffusers/put_diffusers_models_here b/models/diffusers/put_diffusers_models_here new file mode 100644 index 0000000..e69de29 diff --git a/models/diffusion_models/put_diffusion_model_files_here b/models/diffusion_models/put_diffusion_model_files_here new file mode 100644 index 0000000..e69de29 diff --git a/models/embeddings/put_embeddings_or_textual_inversion_concepts_here b/models/embeddings/put_embeddings_or_textual_inversion_concepts_here new file mode 100644 index 0000000..e69de29 diff --git a/models/gligen/put_gligen_models_here b/models/gligen/put_gligen_models_here new file mode 100644 index 0000000..e69de29 diff --git a/models/hypernetworks/put_hypernetworks_here b/models/hypernetworks/put_hypernetworks_here new file mode 100644 index 0000000..e69de29 diff --git a/models/loras/put_loras_here b/models/loras/put_loras_here new file mode 100644 index 0000000..e69de29 diff --git a/models/photomaker/put_photomaker_models_here b/models/photomaker/put_photomaker_models_here new file mode 100644 index 0000000..e69de29 diff --git a/models/style_models/put_t2i_style_model_here b/models/style_models/put_t2i_style_model_here new file mode 100644 index 0000000..e69de29 diff --git a/models/text_encoders/put_text_encoder_files_here b/models/text_encoders/put_text_encoder_files_here new file mode 100644 index 0000000..e69de29 diff --git a/models/unet/put_unet_files_here b/models/unet/put_unet_files_here new file mode 100644 index 0000000..e69de29 diff --git a/models/upscale_models/put_esrgan_and_other_upscale_models_here b/models/upscale_models/put_esrgan_and_other_upscale_models_here new file mode 100644 index 0000000..e69de29 diff --git a/models/vae/put_vae_here b/models/vae/put_vae_here new file mode 100644 index 0000000..e69de29 diff --git a/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here b/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here new file mode 100644 index 0000000..e69de29 diff --git a/new_updater.py b/new_updater.py new file mode 100644 index 0000000..a49e087 --- /dev/null +++ b/new_updater.py @@ -0,0 +1,35 @@ +import os +import shutil + +base_path = os.path.dirname(os.path.realpath(__file__)) + + +def update_windows_updater(): + top_path = os.path.dirname(base_path) + updater_path = os.path.join(base_path, ".ci/update_windows/update.py") + bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat") + + dest_updater_path = os.path.join(top_path, "update/update.py") + dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat") + dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat") + + try: + with open(dest_bat_path, 'rb') as f: + contents = f.read() + except: + return + + if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"): + return + + shutil.copy(updater_path, dest_updater_path) + try: + with open(dest_bat_deps_path, 'rb') as f: + contents = f.read() + contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause') + with open(dest_bat_deps_path, 'wb') as f: + f.write(contents) + except: + pass + shutil.copy(bat_path, dest_bat_path) + print("Updated the windows standalone package updater.") diff --git a/node_helpers.py b/node_helpers.py new file mode 100644 index 0000000..4b38bff --- /dev/null +++ b/node_helpers.py @@ -0,0 +1,37 @@ +import hashlib + +from comfy.cli_args import args + +from PIL import ImageFile, UnidentifiedImageError + +def conditioning_set_values(conditioning, values={}): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + for k in values: + n[1][k] = values[k] + c.append(n) + + return c + +def pillow(fn, arg): + prev_value = None + try: + x = fn(arg) + except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416 + prev_value = ImageFile.LOAD_TRUNCATED_IMAGES + ImageFile.LOAD_TRUNCATED_IMAGES = True + x = fn(arg) + finally: + if prev_value is not None: + ImageFile.LOAD_TRUNCATED_IMAGES = prev_value + return x + +def hasher(): + hashfuncs = { + "md5": hashlib.md5, + "sha1": hashlib.sha1, + "sha256": hashlib.sha256, + "sha512": hashlib.sha512 + } + return hashfuncs[args.default_hashing_function] diff --git a/nodes.py b/nodes.py new file mode 100644 index 0000000..d48bbc2 --- /dev/null +++ b/nodes.py @@ -0,0 +1,2161 @@ +import torch + +import os +import sys +import json +import hashlib +import traceback +import math +import time +import random +import logging + +from PIL import Image, ImageOps, ImageSequence, ImageFile +from PIL.PngImagePlugin import PngInfo + +import numpy as np +import safetensors.torch + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) + +import comfy.diffusers_load +import comfy.samplers +import comfy.sample +import comfy.sd +import comfy.utils +import comfy.controlnet + +import comfy.clip_vision + +import comfy.model_management +from comfy.cli_args import args + +import importlib + +import folder_paths +import latent_preview +import node_helpers + +def before_node_execution(): + comfy.model_management.throw_exception_if_processing_interrupted() + +def interrupt_processing(value=True): + comfy.model_management.interrupt_current_processing(value) + +MAX_RESOLUTION=16384 + +class CLIPTextEncode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}), + "clip": ("CLIP", {"tooltip": "The CLIP model used for encoding the text."}) + } + } + RETURN_TYPES = ("CONDITIONING",) + OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images." + + def encode(self, clip, text): + tokens = clip.tokenize(text) + output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True) + cond = output.pop("cond") + return ([[cond, output]], ) + +class ConditioningCombine: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "combine" + + CATEGORY = "conditioning" + + def combine(self, conditioning_1, conditioning_2): + return (conditioning_1 + conditioning_2, ) + +class ConditioningAverage : + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ), + "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "addWeighted" + + CATEGORY = "conditioning" + + def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): + out = [] + + if len(conditioning_from) > 1: + logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + pooled_output_from = conditioning_from[0][1].get("pooled_output", None) + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) + t0 = cond_from[:,:t1.shape[1]] + if t0.shape[1] < t1.shape[1]: + t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1) + + tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) + t_to = conditioning_to[i][1].copy() + if pooled_output_from is not None and pooled_output_to is not None: + t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength)) + elif pooled_output_from is not None: + t_to["pooled_output"] = pooled_output_from + + n = [tw, t_to] + out.append(n) + return (out, ) + +class ConditioningConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning_to": ("CONDITIONING",), + "conditioning_from": ("CONDITIONING",), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "concat" + + CATEGORY = "conditioning" + + def concat(self, conditioning_to, conditioning_from): + out = [] + + if len(conditioning_from) > 1: + logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + tw = torch.cat((t1, cond_from),1) + n = [tw, conditioning_to[i][1].copy()] + out.append(n) + + return (out, ) + +class ConditioningSetArea: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8), + "strength": strength, + "set_area_to_bounds": False}) + return (c, ) + +class ConditioningSetAreaPercentage: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x), + "strength": strength, + "set_area_to_bounds": False}) + return (c, ) + +class ConditioningSetAreaStrength: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, strength): + c = node_helpers.conditioning_set_values(conditioning, {"strength": strength}) + return (c, ) + + +class ConditioningSetMask: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "mask": ("MASK", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, mask, set_cond_area, strength): + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask.shape) < 3: + mask = mask.unsqueeze(0) + + c = node_helpers.conditioning_set_values(conditioning, {"mask": mask, + "set_area_to_bounds": set_area_to_bounds, + "mask_strength": strength}) + return (c, ) + +class ConditioningZeroOut: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "zero_out" + + CATEGORY = "advanced/conditioning" + + def zero_out(self, conditioning): + c = [] + for t in conditioning: + d = t[1].copy() + pooled_output = d.get("pooled_output", None) + if pooled_output is not None: + d["pooled_output"] = torch.zeros_like(pooled_output) + n = [torch.zeros_like(t[0]), d] + c.append(n) + return (c, ) + +class ConditioningSetTimestepRange: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "set_range" + + CATEGORY = "advanced/conditioning" + + def set_range(self, conditioning, start, end): + c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, + "end_percent": end}) + return (c, ) + +class VAEDecode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "samples": ("LATENT", {"tooltip": "The latent to be decoded."}), + "vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."}) + } + } + RETURN_TYPES = ("IMAGE",) + OUTPUT_TOOLTIPS = ("The decoded image.",) + FUNCTION = "decode" + + CATEGORY = "latent" + DESCRIPTION = "Decodes latent images back into pixel space images." + + def decode(self, vae, samples): + images = vae.decode(samples["samples"]) + if len(images.shape) == 5: #Combine batches + images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) + return (images, ) + +class VAEDecodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 128, "max": 4096, "step": 32}), + "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + CATEGORY = "_for_testing" + + def decode(self, vae, samples, tile_size, overlap): + if tile_size < overlap * 4: + overlap = tile_size // 4 + images = vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, overlap=overlap // 8) + if len(images.shape) == 5: #Combine batches + images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) + return (images, ) + +class VAEEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent" + + def encode(self, vae, pixels): + t = vae.encode(pixels[:,:,:,:3]) + return ({"samples":t}, ) + +class VAEEncodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "_for_testing" + + def encode(self, vae, pixels, tile_size): + t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, ) + return ({"samples":t}, ) + +class VAEEncodeForInpaint: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent/inpaint" + + def encode(self, vae, pixels, mask, grow_mask_by=6): + x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio + y = (pixels.shape[2] // vae.downscale_ratio) * vae.downscale_ratio + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + pixels = pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % vae.downscale_ratio) // 2 + y_offset = (pixels.shape[2] % vae.downscale_ratio) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + #grow mask by a few pixels to keep things seamless in latent space + if grow_mask_by == 0: + mask_erosion = mask + else: + kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) + padding = math.ceil((grow_mask_by - 1) / 2) + + mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1) + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:,:,:,i] -= 0.5 + pixels[:,:,:,i] *= m + pixels[:,:,:,i] += 0.5 + t = vae.encode(pixels) + + return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, ) + + +class InpaintModelConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "pixels": ("IMAGE", ), + "mask": ("MASK", ), + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/inpaint" + + def encode(self, positive, negative, pixels, vae, mask): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + orig_pixels = pixels + pixels = orig_pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:,:,:,i] -= 0.5 + pixels[:,:,:,i] *= m + pixels[:,:,:,i] += 0.5 + concat_latent = vae.encode(pixels) + orig_latent = vae.encode(orig_pixels) + + out_latent = {} + + out_latent["samples"] = orig_latent + out_latent["noise_mask"] = mask + + out = [] + for conditioning in [positive, negative]: + c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent, + "concat_mask": mask}) + out.append(c) + return (out[0], out[1], out_latent) + + +class SaveLatent: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), + "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + RETURN_TYPES = () + FUNCTION = "save" + + OUTPUT_NODE = True + + CATEGORY = "_for_testing" + + def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + # support save metadata for latent sharing + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = None + if not args.disable_metadata: + metadata = {"prompt": prompt_info} + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + file = f"{filename}_{counter:05}_.latent" + + results = list() + results.append({ + "filename": file, + "subfolder": subfolder, + "type": "output" + }) + + file = os.path.join(full_output_folder, file) + + output = {} + output["latent_tensor"] = samples["samples"] + output["latent_format_version_0"] = torch.tensor([]) + + comfy.utils.save_torch_file(output, file, metadata=metadata) + return { "ui": { "latents": results } } + + +class LoadLatent: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] + return {"required": {"latent": [sorted(files), ]}, } + + CATEGORY = "_for_testing" + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "load" + + def load(self, latent): + latent_path = folder_paths.get_annotated_filepath(latent) + latent = safetensors.torch.load_file(latent_path, device="cpu") + multiplier = 1.0 + if "latent_format_version_0" not in latent: + multiplier = 1.0 / 0.18215 + samples = {"samples": latent["latent_tensor"].float() * multiplier} + return (samples, ) + + @classmethod + def IS_CHANGED(s, latent): + image_path = folder_paths.get_annotated_filepath(latent) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, latent): + if not folder_paths.exists_annotated_filepath(latent): + return "Invalid latent file: {}".format(latent) + return True + + +class CheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ), + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders" + DEPRECATED = True + + def load_checkpoint(self, config_name, ckpt_name): + config_path = folder_paths.get_full_path("configs", config_name) + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + +class CheckpointLoaderSimple: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}), + } + } + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + OUTPUT_TOOLTIPS = ("The model used for denoising latents.", + "The CLIP model used for encoding text prompts.", + "The VAE model used for encoding and decoding images to and from latent space.") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents." + + def load_checkpoint(self, ckpt_name): + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return out[:3] + +class DiffusersLoader: + @classmethod + def INPUT_TYPES(cls): + paths = [] + for search_path in folder_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + for root, subdir, files in os.walk(search_path, followlinks=True): + if "model_index.json" in files: + paths.append(os.path.relpath(root, start=search_path)) + + return {"required": {"model_path": (paths,), }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders/deprecated" + + def load_checkpoint(self, model_path, output_vae=True, output_clip=True): + for search_path in folder_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + path = os.path.join(search_path, model_path) + if os.path.exists(path): + model_path = path + break + + return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) + + +class unCLIPCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return out + +class CLIPSetLastLayer: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP", ), + "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "set_last_layer" + + CATEGORY = "conditioning" + + def set_last_layer(self, clip, stop_at_clip_layer): + clip = clip.clone() + clip.clip_layer(stop_at_clip_layer) + return (clip,) + +class LoraLoader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), + "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}), + "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}), + } + } + + RETURN_TYPES = ("MODEL", "CLIP") + OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.") + FUNCTION = "load_lora" + + CATEGORY = "loaders" + DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together." + + def load_lora(self, model, clip, lora_name, strength_model, strength_clip): + if strength_model == 0 and strength_clip == 0: + return (model, clip) + + lora_path = folder_paths.get_full_path_or_raise("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + temp = self.loaded_lora + self.loaded_lora = None + del temp + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) + return (model_lora, clip_lora) + +class LoraLoaderModelOnly(LoraLoader): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "lora_name": (folder_paths.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_lora_model_only" + + def load_lora_model_only(self, model, lora_name, strength_model): + return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) + +class VAELoader: + @staticmethod + def vae_list(): + vaes = folder_paths.get_filename_list("vae") + approx_vaes = folder_paths.get_filename_list("vae_approx") + sdxl_taesd_enc = False + sdxl_taesd_dec = False + sd1_taesd_enc = False + sd1_taesd_dec = False + sd3_taesd_enc = False + sd3_taesd_dec = False + f1_taesd_enc = False + f1_taesd_dec = False + + for v in approx_vaes: + if v.startswith("taesd_decoder."): + sd1_taesd_dec = True + elif v.startswith("taesd_encoder."): + sd1_taesd_enc = True + elif v.startswith("taesdxl_decoder."): + sdxl_taesd_dec = True + elif v.startswith("taesdxl_encoder."): + sdxl_taesd_enc = True + elif v.startswith("taesd3_decoder."): + sd3_taesd_dec = True + elif v.startswith("taesd3_encoder."): + sd3_taesd_enc = True + elif v.startswith("taef1_encoder."): + f1_taesd_dec = True + elif v.startswith("taef1_decoder."): + f1_taesd_enc = True + if sd1_taesd_dec and sd1_taesd_enc: + vaes.append("taesd") + if sdxl_taesd_dec and sdxl_taesd_enc: + vaes.append("taesdxl") + if sd3_taesd_dec and sd3_taesd_enc: + vaes.append("taesd3") + if f1_taesd_dec and f1_taesd_enc: + vaes.append("taef1") + return vaes + + @staticmethod + def load_taesd(name): + sd = {} + approx_vaes = folder_paths.get_filename_list("vae_approx") + + encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) + decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) + + enc = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder)) + for k in enc: + sd["taesd_encoder.{}".format(k)] = enc[k] + + dec = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder)) + for k in dec: + sd["taesd_decoder.{}".format(k)] = dec[k] + + if name == "taesd": + sd["vae_scale"] = torch.tensor(0.18215) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesdxl": + sd["vae_scale"] = torch.tensor(0.13025) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesd3": + sd["vae_scale"] = torch.tensor(1.5305) + sd["vae_shift"] = torch.tensor(0.0609) + elif name == "taef1": + sd["vae_scale"] = torch.tensor(0.3611) + sd["vae_shift"] = torch.tensor(0.1159) + return sd + + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae_name": (s.vae_list(), )}} + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + + CATEGORY = "loaders" + + #TODO: scale factor? + def load_vae(self, vae_name): + if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: + sd = self.load_taesd(vae_name) + else: + vae_path = folder_paths.get_full_path_or_raise("vae", vae_name) + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + return (vae,) + +class ControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, control_net_name): + controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name) + controlnet = comfy.controlnet.load_controlnet(controlnet_path) + return (controlnet,) + +class DiffControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, model, control_net_name): + controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name) + controlnet = comfy.controlnet.load_controlnet(controlnet_path, model) + return (controlnet,) + + +class ControlNetApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_controlnet" + + DEPRECATED = True + CATEGORY = "conditioning/controlnet" + + def apply_controlnet(self, conditioning, control_net, image, strength): + if strength == 0: + return (conditioning, ) + + c = [] + control_hint = image.movedim(-1,1) + for t in conditioning: + n = [t[0], t[1].copy()] + c_net = control_net.copy().set_cond_hint(control_hint, strength) + if 'control' in t[1]: + c_net.set_previous_controlnet(t[1]['control']) + n[1]['control'] = c_net + n[1]['control_apply_to_uncond'] = True + c.append(n) + return (c, ) + + +class ControlNetApplyAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }, + "optional": {"vae": ("VAE", ), + } + } + + RETURN_TYPES = ("CONDITIONING","CONDITIONING") + RETURN_NAMES = ("positive", "negative") + FUNCTION = "apply_controlnet" + + CATEGORY = "conditioning/controlnet" + + def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]): + if strength == 0: + return (positive, negative) + + control_hint = image.movedim(-1,1) + cnets = {} + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + + prev_cnet = d.get('control', None) + if prev_cnet in cnets: + c_net = cnets[prev_cnet] + else: + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat) + c_net.set_previous_controlnet(prev_cnet) + cnets[prev_cnet] = c_net + + d['control'] = c_net + d['control_apply_to_uncond'] = False + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1]) + + +class UNETLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ), + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + + CATEGORY = "advanced/loaders" + + def load_unet(self, unet_name, weight_dtype): + model_options = {} + if weight_dtype == "fp8_e4m3fn": + model_options["dtype"] = torch.float8_e4m3fn + elif weight_dtype == "fp8_e4m3fn_fast": + model_options["dtype"] = torch.float8_e4m3fn + model_options["fp8_optimizations"] = True + elif weight_dtype == "fp8_e5m2": + model_options["dtype"] = torch.float8_e5m2 + + unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name) + model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options) + return (model,) + +class CLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi"], ), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name, type="stable_diffusion"): + if type == "stable_cascade": + clip_type = comfy.sd.CLIPType.STABLE_CASCADE + elif type == "sd3": + clip_type = comfy.sd.CLIPType.SD3 + elif type == "stable_audio": + clip_type = comfy.sd.CLIPType.STABLE_AUDIO + elif type == "mochi": + clip_type = comfy.sd.CLIPType.MOCHI + else: + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + + clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) + return (clip,) + +class DualCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), + "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), + "type": (["sdxl", "sd3", "flux"], ), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name1, clip_name2, type): + clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) + clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) + if type == "sdxl": + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + elif type == "sd3": + clip_type = comfy.sd.CLIPType.SD3 + elif type == "flux": + clip_type = comfy.sd.CLIPType.FLUX + + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) + return (clip,) + +class CLIPVisionLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ), + }} + RETURN_TYPES = ("CLIP_VISION",) + FUNCTION = "load_clip" + + CATEGORY = "loaders" + + def load_clip(self, clip_name): + clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name) + clip_vision = comfy.clip_vision.load(clip_path) + return (clip_vision,) + +class CLIPVisionEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "image": ("IMAGE",) + }} + RETURN_TYPES = ("CLIP_VISION_OUTPUT",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip_vision, image): + output = clip_vision.encode_image(image) + return (output,) + +class StyleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}} + + RETURN_TYPES = ("STYLE_MODEL",) + FUNCTION = "load_style_model" + + CATEGORY = "loaders" + + def load_style_model(self, style_model_name): + style_model_path = folder_paths.get_full_path_or_raise("style_models", style_model_name) + style_model = comfy.sd.load_style_model(style_model_path) + return (style_model,) + + +class StyleModelApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "style_model": ("STYLE_MODEL", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_stylemodel" + + CATEGORY = "conditioning/style_model" + + def apply_stylemodel(self, clip_vision_output, style_model, conditioning): + cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) + c = [] + for t in conditioning: + n = [torch.cat((t[0], cond), dim=1), t[1].copy()] + c.append(n) + return (c, ) + +class unCLIPConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_adm" + + CATEGORY = "conditioning" + + def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): + if strength == 0: + return (conditioning, ) + + c = [] + for t in conditioning: + o = t[1].copy() + x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation} + if "unclip_conditioning" in o: + o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x] + else: + o["unclip_conditioning"] = [x] + n = [t[0], o] + c.append(n) + return (c, ) + +class GLIGENLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}} + + RETURN_TYPES = ("GLIGEN",) + FUNCTION = "load_gligen" + + CATEGORY = "loaders" + + def load_gligen(self, gligen_name): + gligen_path = folder_paths.get_full_path_or_raise("gligen", gligen_name) + gligen = comfy.sd.load_gligen(gligen_path) + return (gligen,) + +class GLIGENTextBoxApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), + "clip": ("CLIP", ), + "gligen_textbox_model": ("GLIGEN", ), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning/gligen" + + def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): + c = [] + cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected") + for t in conditioning_to: + n = [t[0], t[1].copy()] + position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)] + prev = [] + if "gligen" in n[1]: + prev = n[1]['gligen'][2] + + n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params) + c.append(n) + return (c, ) + +class EmptyLatentImage: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of the latent images in pixels."}), + "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The height of the latent images in pixels."}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}) + } + } + RETURN_TYPES = ("LATENT",) + OUTPUT_TOOLTIPS = ("The empty latent image batch.",) + FUNCTION = "generate" + + CATEGORY = "latent" + DESCRIPTION = "Create a new batch of empty latent images to be denoised via sampling." + + def generate(self, width, height, batch_size=1): + latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device) + return ({"samples":latent}, ) + + +class LatentFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), + "length": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "frombatch" + + CATEGORY = "latent/batch" + + def frombatch(self, samples, batch_index, length): + s = samples.copy() + s_in = samples["samples"] + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s["samples"] = s_in[batch_index:batch_index + length].clone() + if "noise_mask" in samples: + masks = samples["noise_mask"] + if masks.shape[0] == 1: + s["noise_mask"] = masks.clone() + else: + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = masks[batch_index:batch_index + length].clone() + if "batch_index" not in s: + s["batch_index"] = [x for x in range(batch_index, batch_index+length)] + else: + s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] + return (s,) + +class RepeatLatentBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "amount": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "repeat" + + CATEGORY = "latent/batch" + + def repeat(self, samples, amount): + s = samples.copy() + s_in = samples["samples"] + + s["samples"] = s_in.repeat((amount, 1,1,1)) + if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: + masks = samples["noise_mask"] + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1)) + if "batch_index" in s: + offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 + s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] + return (s,) + +class LatentUpscale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = samples + else: + s = samples.copy() + + if width == 0: + height = max(64, height) + width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2])) + elif height == 0: + width = max(64, width) + height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1])) + else: + width = max(64, width) + height = max(64, height) + + s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) + return (s,) + +class LatentUpscaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, scale_by): + s = samples.copy() + width = round(samples["samples"].shape[-1] * scale_by) + height = round(samples["samples"].shape[-2] * scale_by) + s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") + return (s,) + +class LatentRotate: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "rotate" + + CATEGORY = "latent/transform" + + def rotate(self, samples, rotation): + s = samples.copy() + rotate_by = 0 + if rotation.startswith("90"): + rotate_by = 1 + elif rotation.startswith("180"): + rotate_by = 2 + elif rotation.startswith("270"): + rotate_by = 3 + + s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2]) + return (s,) + +class LatentFlip: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "flip" + + CATEGORY = "latent/transform" + + def flip(self, samples, flip_method): + s = samples.copy() + if flip_method.startswith("x"): + s["samples"] = torch.flip(samples["samples"], dims=[2]) + elif flip_method.startswith("y"): + s["samples"] = torch.flip(samples["samples"], dims=[3]) + + return (s,) + +class LatentComposite: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples_to": ("LATENT",), + "samples_from": ("LATENT",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "composite" + + CATEGORY = "latent" + + def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0): + x = x // 8 + y = y // 8 + feather = feather // 8 + samples_out = samples_to.copy() + s = samples_to["samples"].clone() + samples_to = samples_to["samples"] + samples_from = samples_from["samples"] + if feather == 0: + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] + else: + samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] + mask = torch.ones_like(samples_from) + for t in range(feather): + if y != 0: + mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) + + if y + samples_from.shape[2] < samples_to.shape[2]: + mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) + if x != 0: + mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) + if x + samples_from.shape[3] < samples_to.shape[3]: + mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) + rev_mask = torch.ones_like(mask) - mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask + samples_out["samples"] = s + return (samples_out,) + +class LatentBlend: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples1": ("LATENT",), + "samples2": ("LATENT",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0, + "max": 1, + "step": 0.01 + }), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "blend" + + CATEGORY = "_for_testing" + + def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"): + + samples_out = samples1.copy() + samples1 = samples1["samples"] + samples2 = samples2["samples"] + + if samples1.shape != samples2.shape: + samples2.permute(0, 3, 1, 2) + samples2 = comfy.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') + samples2.permute(0, 2, 3, 1) + + samples_blended = self.blend_mode(samples1, samples2, blend_mode) + samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor) + samples_out["samples"] = samples_blended + return (samples_out,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + +class LatentCrop: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "crop" + + CATEGORY = "latent/transform" + + def crop(self, samples, width, height, x, y): + s = samples.copy() + samples = samples['samples'] + x = x // 8 + y = y // 8 + + #enfonce minimum size of 64 + if x > (samples.shape[3] - 8): + x = samples.shape[3] - 8 + if y > (samples.shape[2] - 8): + y = samples.shape[2] - 8 + + new_height = height // 8 + new_width = width // 8 + to_x = new_width + x + to_y = new_height + y + s['samples'] = samples[:,:,y:to_y, x:to_x] + return (s,) + +class SetLatentNoiseMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "mask": ("MASK",), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "set_mask" + + CATEGORY = "latent/inpaint" + + def set_mask(self, samples, mask): + s = samples.copy() + s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + return (s,) + +def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): + latent_image = latent["samples"] + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + callback = latent_preview.prepare_callback(model, steps) + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + out = latent.copy() + out["samples"] = samples + return (out, ) + +class KSampler: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The random seed used for creating the noise."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}), + "positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}), + "negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}), + "latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}), + } + } + + RETURN_TYPES = ("LATENT",) + OUTPUT_TOOLTIPS = ("The denoised latent.",) + FUNCTION = "sample" + + CATEGORY = "sampling" + DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image." + + def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): + return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) + +class KSamplerAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": (["enable", "disable"], ), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "return_with_leftover_noise": (["disable", "enable"], ), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "sampling" + + def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): + force_full_denoise = True + if return_with_leftover_noise == "enable": + force_full_denoise = False + disable_noise = False + if add_noise == "disable": + disable_noise = True + return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) + +class SaveImage: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE", {"tooltip": "The images to save."}), + "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO" + }, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image" + DESCRIPTION = "Saves the input images to your ComfyUI output directory." + + def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + for (batch_number, image) in enumerate(images): + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "images": results } } + +class PreviewImage(SaveImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 1 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + +class LoadImage: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True})}, + } + + CATEGORY = "image" + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "load_image" + def load_image(self, image): + image_path = folder_paths.get_annotated_filepath(image) + + img = node_helpers.pillow(Image.open, image_path) + + output_images = [] + output_masks = [] + w, h = None, None + + excluded_formats = ['MPO'] + + for i in ImageSequence.Iterator(img): + i = node_helpers.pillow(ImageOps.exif_transpose, i) + + if i.mode == 'I': + i = i.point(lambda i: i * (1 / 255)) + image = i.convert("RGB") + + if len(output_images) == 0: + w = image.size[0] + h = image.size[1] + + if image.size[0] != w or image.size[1] != h: + continue + + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + output_images.append(image) + output_masks.append(mask.unsqueeze(0)) + + if len(output_images) > 1 and img.format not in excluded_formats: + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) + else: + output_image = output_images[0] + output_mask = output_masks[0] + + return (output_image, output_mask) + + @classmethod + def IS_CHANGED(s, image): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class LoadImageMask: + _color_channels = ["alpha", "red", "green", "blue"] + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True}), + "channel": (s._color_channels, ), } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "load_image" + def load_image(self, image, channel): + image_path = folder_paths.get_annotated_filepath(image) + i = node_helpers.pillow(Image.open, image_path) + i = node_helpers.pillow(ImageOps.exif_transpose, i) + if i.getbands() != ("R", "G", "B", "A"): + if i.mode == 'I': + i = i.point(lambda i: i * (1 / 255)) + i = i.convert("RGBA") + mask = None + c = channel[0].upper() + if c in i.getbands(): + mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0 + mask = torch.from_numpy(mask) + if c == 'A': + mask = 1. - mask + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + return (mask.unsqueeze(0),) + + @classmethod + def IS_CHANGED(s, image, channel): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class ImageScale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = image + else: + samples = image.movedim(-1,1) + + if width == 0: + width = max(1, round(samples.shape[3] * height / samples.shape[2])) + elif height == 0: + height = max(1, round(samples.shape[2] * width / samples.shape[3])) + + s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop) + s = s.movedim(1,-1) + return (s,) + +class ImageScaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, scale_by): + samples = image.movedim(-1,1) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + +class ImageInvert: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "invert" + + CATEGORY = "image" + + def invert(self, image): + s = 1.0 - image + return (s,) + +class ImageBatch: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "batch" + + CATEGORY = "image" + + def batch(self, image1, image2): + if image1.shape[1:] != image2.shape[1:]: + image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) + s = torch.cat((image1, image2), dim=0) + return (s,) + +class EmptyImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generate" + + CATEGORY = "image" + + def generate(self, width, height, batch_size=1, color=0): + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + return (torch.cat((r, g, b), dim=-1), ) + +class ImagePadForOutpaint: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "expand_image" + + CATEGORY = "image" + + def expand_image(self, image, left, top, right, bottom, feathering): + d1, d2, d3, d4 = image.size() + + new_image = torch.ones( + (d1, d2 + top + bottom, d3 + left + right, d4), + dtype=torch.float32, + ) * 0.5 + + new_image[:, top:top + d2, left:left + d3, :] = image + + mask = torch.ones( + (d2 + top + bottom, d3 + left + right), + dtype=torch.float32, + ) + + t = torch.zeros( + (d2, d3), + dtype=torch.float32 + ) + + if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3: + + for i in range(d2): + for j in range(d3): + dt = i if top != 0 else d2 + db = d2 - i if bottom != 0 else d2 + + dl = j if left != 0 else d3 + dr = d3 - j if right != 0 else d3 + + d = min(dt, db, dl, dr) + + if d >= feathering: + continue + + v = (feathering - d) / feathering + + t[i, j] = v * v + + mask[top:top + d2, left:left + d3] = t + + return (new_image, mask) + + +NODE_CLASS_MAPPINGS = { + "KSampler": KSampler, + "CheckpointLoaderSimple": CheckpointLoaderSimple, + "CLIPTextEncode": CLIPTextEncode, + "CLIPSetLastLayer": CLIPSetLastLayer, + "VAEDecode": VAEDecode, + "VAEEncode": VAEEncode, + "VAEEncodeForInpaint": VAEEncodeForInpaint, + "VAELoader": VAELoader, + "EmptyLatentImage": EmptyLatentImage, + "LatentUpscale": LatentUpscale, + "LatentUpscaleBy": LatentUpscaleBy, + "LatentFromBatch": LatentFromBatch, + "RepeatLatentBatch": RepeatLatentBatch, + "SaveImage": SaveImage, + "PreviewImage": PreviewImage, + "LoadImage": LoadImage, + "LoadImageMask": LoadImageMask, + "ImageScale": ImageScale, + "ImageScaleBy": ImageScaleBy, + "ImageInvert": ImageInvert, + "ImageBatch": ImageBatch, + "ImagePadForOutpaint": ImagePadForOutpaint, + "EmptyImage": EmptyImage, + "ConditioningAverage": ConditioningAverage , + "ConditioningCombine": ConditioningCombine, + "ConditioningConcat": ConditioningConcat, + "ConditioningSetArea": ConditioningSetArea, + "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, + "ConditioningSetAreaStrength": ConditioningSetAreaStrength, + "ConditioningSetMask": ConditioningSetMask, + "KSamplerAdvanced": KSamplerAdvanced, + "SetLatentNoiseMask": SetLatentNoiseMask, + "LatentComposite": LatentComposite, + "LatentBlend": LatentBlend, + "LatentRotate": LatentRotate, + "LatentFlip": LatentFlip, + "LatentCrop": LatentCrop, + "LoraLoader": LoraLoader, + "CLIPLoader": CLIPLoader, + "UNETLoader": UNETLoader, + "DualCLIPLoader": DualCLIPLoader, + "CLIPVisionEncode": CLIPVisionEncode, + "StyleModelApply": StyleModelApply, + "unCLIPConditioning": unCLIPConditioning, + "ControlNetApply": ControlNetApply, + "ControlNetApplyAdvanced": ControlNetApplyAdvanced, + "ControlNetLoader": ControlNetLoader, + "DiffControlNetLoader": DiffControlNetLoader, + "StyleModelLoader": StyleModelLoader, + "CLIPVisionLoader": CLIPVisionLoader, + "VAEDecodeTiled": VAEDecodeTiled, + "VAEEncodeTiled": VAEEncodeTiled, + "unCLIPCheckpointLoader": unCLIPCheckpointLoader, + "GLIGENLoader": GLIGENLoader, + "GLIGENTextBoxApply": GLIGENTextBoxApply, + "InpaintModelConditioning": InpaintModelConditioning, + + "CheckpointLoader": CheckpointLoader, + "DiffusersLoader": DiffusersLoader, + + "LoadLatent": LoadLatent, + "SaveLatent": SaveLatent, + + "ConditioningZeroOut": ConditioningZeroOut, + "ConditioningSetTimestepRange": ConditioningSetTimestepRange, + "LoraLoaderModelOnly": LoraLoaderModelOnly, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "KSampler": "KSampler", + "KSamplerAdvanced": "KSampler (Advanced)", + # Loaders + "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)", + "CheckpointLoaderSimple": "Load Checkpoint", + "VAELoader": "Load VAE", + "LoraLoader": "Load LoRA", + "CLIPLoader": "Load CLIP", + "ControlNetLoader": "Load ControlNet Model", + "DiffControlNetLoader": "Load ControlNet Model (diff)", + "StyleModelLoader": "Load Style Model", + "CLIPVisionLoader": "Load CLIP Vision", + "UpscaleModelLoader": "Load Upscale Model", + "UNETLoader": "Load Diffusion Model", + # Conditioning + "CLIPVisionEncode": "CLIP Vision Encode", + "StyleModelApply": "Apply Style Model", + "CLIPTextEncode": "CLIP Text Encode (Prompt)", + "CLIPSetLastLayer": "CLIP Set Last Layer", + "ConditioningCombine": "Conditioning (Combine)", + "ConditioningAverage ": "Conditioning (Average)", + "ConditioningConcat": "Conditioning (Concat)", + "ConditioningSetArea": "Conditioning (Set Area)", + "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", + "ConditioningSetMask": "Conditioning (Set Mask)", + "ControlNetApply": "Apply ControlNet (OLD)", + "ControlNetApplyAdvanced": "Apply ControlNet", + # Latent + "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", + "SetLatentNoiseMask": "Set Latent Noise Mask", + "VAEDecode": "VAE Decode", + "VAEEncode": "VAE Encode", + "LatentRotate": "Rotate Latent", + "LatentFlip": "Flip Latent", + "LatentCrop": "Crop Latent", + "EmptyLatentImage": "Empty Latent Image", + "LatentUpscale": "Upscale Latent", + "LatentUpscaleBy": "Upscale Latent By", + "LatentComposite": "Latent Composite", + "LatentBlend": "Latent Blend", + "LatentFromBatch" : "Latent From Batch", + "RepeatLatentBatch": "Repeat Latent Batch", + # Image + "SaveImage": "Save Image", + "PreviewImage": "Preview Image", + "LoadImage": "Load Image", + "LoadImageMask": "Load Image (as Mask)", + "ImageScale": "Upscale Image", + "ImageScaleBy": "Upscale Image By", + "ImageUpscaleWithModel": "Upscale Image (using Model)", + "ImageInvert": "Invert Image", + "ImagePadForOutpaint": "Pad Image for Outpainting", + "ImageBatch": "Batch Images", + "ImageCrop": "Image Crop", + "ImageBlend": "Image Blend", + "ImageBlur": "Image Blur", + "ImageQuantize": "Image Quantize", + "ImageSharpen": "Image Sharpen", + "ImageScaleToTotalPixels": "Scale Image to Total Pixels", + # _for_testing + "VAEDecodeTiled": "VAE Decode (Tiled)", + "VAEEncodeTiled": "VAE Encode (Tiled)", +} + +EXTENSION_WEB_DIRS = {} + + +def get_module_name(module_path: str) -> str: + """ + Returns the module name based on the given module path. + Examples: + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes + Args: + module_path (str): The path of the module. + Returns: + str: The module name. + """ + base_path = os.path.basename(module_path) + if os.path.isfile(module_path): + base_path = os.path.splitext(base_path)[0] + return base_path + + +def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool: + module_name = os.path.basename(module_path) + if os.path.isfile(module_path): + sp = os.path.splitext(module_path) + module_name = sp[0] + try: + logging.debug("Trying to load custom node {}".format(module_path)) + if os.path.isfile(module_path): + module_spec = importlib.util.spec_from_file_location(module_name, module_path) + module_dir = os.path.split(module_path)[0] + else: + module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py")) + module_dir = module_path + + module = importlib.util.module_from_spec(module_spec) + sys.modules[module_name] = module + module_spec.loader.exec_module(module) + + if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: + web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) + if os.path.isdir(web_dir): + EXTENSION_WEB_DIRS[module_name] = web_dir + + if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: + for name, node_cls in module.NODE_CLASS_MAPPINGS.items(): + if name not in ignore: + NODE_CLASS_MAPPINGS[name] = node_cls + node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path)) + if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: + NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) + return True + else: + logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") + return False + except Exception as e: + logging.warning(traceback.format_exc()) + logging.warning(f"Cannot import {module_path} module for custom nodes: {e}") + return False + +def init_external_custom_nodes(): + """ + Initializes the external custom nodes. + + This function loads custom nodes from the specified folder paths and imports them into the application. + It measures the import times for each custom node and logs the results. + + Returns: + None + """ + base_node_names = set(NODE_CLASS_MAPPINGS.keys()) + node_paths = folder_paths.get_folder_paths("custom_nodes") + node_import_times = [] + for custom_node_path in node_paths: + possible_modules = os.listdir(os.path.realpath(custom_node_path)) + if "__pycache__" in possible_modules: + possible_modules.remove("__pycache__") + + for possible_module in possible_modules: + module_path = os.path.join(custom_node_path, possible_module) + if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue + if module_path.endswith(".disabled"): continue + time_before = time.perf_counter() + success = load_custom_node(module_path, base_node_names, module_parent="custom_nodes") + node_import_times.append((time.perf_counter() - time_before, module_path, success)) + + if len(node_import_times) > 0: + logging.info("\nImport times for custom nodes:") + for n in sorted(node_import_times): + if n[2]: + import_message = "" + else: + import_message = " (IMPORT FAILED)" + logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) + logging.info("") + +def init_builtin_extra_nodes(): + """ + Initializes the built-in extra nodes in ComfyUI. + + This function loads the extra node files located in the "comfy_extras" directory and imports them into ComfyUI. + If any of the extra node files fail to import, a warning message is logged. + + Returns: + None + """ + extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") + extras_files = [ + "nodes_latent.py", + "nodes_hypernetwork.py", + "nodes_upscale_model.py", + "nodes_post_processing.py", + "nodes_mask.py", + "nodes_compositing.py", + "nodes_rebatch.py", + "nodes_model_merging.py", + "nodes_tomesd.py", + "nodes_clip_sdxl.py", + "nodes_canny.py", + "nodes_freelunch.py", + "nodes_custom_sampler.py", + "nodes_hypertile.py", + "nodes_model_advanced.py", + "nodes_model_downscale.py", + "nodes_images.py", + "nodes_video_model.py", + "nodes_sag.py", + "nodes_perpneg.py", + "nodes_stable3d.py", + "nodes_sdupscale.py", + "nodes_photomaker.py", + "nodes_cond.py", + "nodes_morphology.py", + "nodes_stable_cascade.py", + "nodes_differential_diffusion.py", + "nodes_ip2p.py", + "nodes_model_merging_model_specific.py", + "nodes_pag.py", + "nodes_align_your_steps.py", + "nodes_attention_multiply.py", + "nodes_advanced_samplers.py", + "nodes_webcam.py", + "nodes_audio.py", + "nodes_sd3.py", + "nodes_gits.py", + "nodes_controlnet.py", + "nodes_hunyuan.py", + "nodes_flux.py", + "nodes_lora_extract.py", + "nodes_torch_compile.py", + "nodes_mochi.py", + ] + + import_failed = [] + for node_file in extras_files: + if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"): + import_failed.append(node_file) + + return import_failed + + +def init_extra_nodes(init_custom_nodes=True): + import_failed = init_builtin_extra_nodes() + + if init_custom_nodes: + init_external_custom_nodes() + else: + logging.info("Skipping loading of custom nodes") + + if len(import_failed) > 0: + logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") + for node in import_failed: + logging.warning("IMPORT FAILED: {}".format(node)) + logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") + if args.windows_standalone_build: + logging.warning("Please run the update script: update/update_comfyui.bat") + else: + logging.warning("Please do a: pip install -r requirements.txt") + logging.warning("") + + return import_failed diff --git a/notebooks/comfyui_colab.ipynb b/notebooks/comfyui_colab.ipynb new file mode 100644 index 0000000..b1ed4ac --- /dev/null +++ b/notebooks/comfyui_colab.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aaaaaaaaaa" + }, + "source": [ + "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbbbbbbbbb" + }, + "outputs": [], + "source": [ + "#@title Environment Setup\n", + "\n", + "from pathlib import Path\n", + "\n", + "OPTIONS = {}\n", + "\n", + "USE_GOOGLE_DRIVE = False #@param {type:\"boolean\"}\n", + "UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n", + "WORKSPACE = 'ComfyUI'\n", + "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n", + "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n", + "\n", + "if OPTIONS['USE_GOOGLE_DRIVE']:\n", + " !echo \"Mounting Google Drive...\"\n", + " %cd /\n", + " \n", + " from google.colab import drive\n", + " drive.mount('/content/drive')\n", + "\n", + " WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n", + " %cd /content/drive/MyDrive\n", + "\n", + "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n", + "%cd $WORKSPACE\n", + "\n", + "if OPTIONS['UPDATE_COMFY_UI']:\n", + " !echo -= Updating ComfyUI =-\n", + " !git pull\n", + "\n", + "!echo -= Install dependencies =-\n", + "!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cccccccccc" + }, + "source": [ + "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dddddddddd" + }, + "outputs": [], + "source": [ + "# Checkpoints\n", + "\n", + "### SDXL\n", + "### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n", + "\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n", + "\n", + "# SDXL ReVision\n", + "#!wget -c https://huggingface.co/comfyanonymous/clip_vision_g/resolve/main/clip_vision_g.safetensors -P ./models/clip_vision/\n", + "\n", + "# SD1.5\n", + "!wget -c https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "# SD2\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Some SD1.5 anime style\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n", + "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# unCLIP models\n", + "#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# VAE\n", + "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n", + "#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n", + "\n", + "\n", + "# Loras\n", + "#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n", + "#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n", + "\n", + "\n", + "# T2I-Adapter\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n", + "\n", + "# T2I Styles Model\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n", + "\n", + "# CLIPVision model (needed for styles model)\n", + "#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n", + "\n", + "\n", + "# ControlNet\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n", + "\n", + "# ControlNet SDXL\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/\n", + "\n", + "# Controlnet Preprocessor nodes by Fannovel16\n", + "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n", + "\n", + "\n", + "# GLIGEN\n", + "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n", + "\n", + "\n", + "# ESRGAN upscale model\n", + "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with cloudflared (Recommended Way)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n", + "!dpkg -i cloudflared-linux-amd64.deb\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n", + "\n", + " p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " for line in p.stderr:\n", + " l = line.decode()\n", + " if \"trycloudflare.com \" in l:\n", + " print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n", + " #print(l, end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with localtunnel\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!npm install -g localtunnel\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n", + "\n", + " print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", + " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", + " for line in p.stdout:\n", + " print(line.decode(), end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gggggggggg" + }, + "source": [ + "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n", + "\n", + "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n", + "\n", + "If you want to open it in another window use the link.\n", + "\n", + "Note that some UI features like live image previews won't work because the colab iframe blocks websockets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hhhhhhhhhh" + }, + "outputs": [], + "source": [ + "import threading\n", + "import time\n", + "import socket\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " from google.colab import output\n", + " output.serve_kernel_port_as_iframe(port, height=1024)\n", + " print(\"to open it in a window you can open this link here:\")\n", + " output.serve_kernel_port_as_window(port)\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/output/_output_images_will_be_put_here b/output/_output_images_will_be_put_here new file mode 100644 index 0000000..e69de29 diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..a224d8c --- /dev/null +++ b/pytest.ini @@ -0,0 +1,9 @@ +[pytest] +markers = + inference: mark as inference test (deselect with '-m "not inference"') + execution: mark as execution test (deselect with '-m "not execution"') +testpaths = + tests + tests-unit +addopts = -s +pythonpath = . diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..4c2c0b2 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,20 @@ +torch +torchsde +torchvision +torchaudio +einops +transformers>=4.28.1 +tokenizers>=0.13.3 +sentencepiece +safetensors>=0.4.2 +aiohttp +pyyaml +Pillow +scipy +tqdm +psutil + +#non essential dependencies: +kornia>=0.7.1 +spandrel +soundfile diff --git a/script_examples/basic_api_example.py b/script_examples/basic_api_example.py new file mode 100644 index 0000000..bc8ad71 --- /dev/null +++ b/script_examples/basic_api_example.py @@ -0,0 +1,120 @@ +import json +from urllib import request, parse +import random + +#This is the ComfyUI api prompt format. + +#If you want it for a specific workflow you can "enable dev mode options" +#in the settings of the UI (gear beside the "Queue Size: ") this will enable +#a button on the UI to save workflows in api format. + +#keep in mind ComfyUI is pre alpha software so this format will change a bit. + +#this is the one for the default workflow +prompt_text = """ +{ + "3": { + "class_type": "KSampler", + "inputs": { + "cfg": 8, + "denoise": 1, + "latent_image": [ + "5", + 0 + ], + "model": [ + "4", + 0 + ], + "negative": [ + "7", + 0 + ], + "positive": [ + "6", + 0 + ], + "sampler_name": "euler", + "scheduler": "normal", + "seed": 8566257, + "steps": 20 + } + }, + "4": { + "class_type": "CheckpointLoaderSimple", + "inputs": { + "ckpt_name": "v1-5-pruned-emaonly.safetensors" + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "batch_size": 1, + "height": 512, + "width": 512 + } + }, + "6": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "masterpiece best quality girl" + } + }, + "7": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "bad hands" + } + }, + "8": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "3", + 0 + ], + "vae": [ + "4", + 2 + ] + } + }, + "9": { + "class_type": "SaveImage", + "inputs": { + "filename_prefix": "ComfyUI", + "images": [ + "8", + 0 + ] + } + } +} +""" + +def queue_prompt(prompt): + p = {"prompt": prompt} + data = json.dumps(p).encode('utf-8') + req = request.Request("http://127.0.0.1:8188/prompt", data=data) + request.urlopen(req) + + +prompt = json.loads(prompt_text) +#set the text prompt for our positive CLIPTextEncode +prompt["6"]["inputs"]["text"] = "masterpiece best quality man" + +#set the seed for our KSampler node +prompt["3"]["inputs"]["seed"] = 5 + + +queue_prompt(prompt) + + diff --git a/script_examples/websockets_api_example.py b/script_examples/websockets_api_example.py new file mode 100644 index 0000000..d696d2b --- /dev/null +++ b/script_examples/websockets_api_example.py @@ -0,0 +1,166 @@ +#This is an example that uses the websockets api to know when a prompt execution is done +#Once the prompt execution is done it downloads the images using the /history endpoint + +import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) +import uuid +import json +import urllib.request +import urllib.parse + +server_address = "127.0.0.1:8188" +client_id = str(uuid.uuid4()) + +def queue_prompt(prompt): + p = {"prompt": prompt, "client_id": client_id} + data = json.dumps(p).encode('utf-8') + req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) + return json.loads(urllib.request.urlopen(req).read()) + +def get_image(filename, subfolder, folder_type): + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response: + return response.read() + +def get_history(prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: + return json.loads(response.read()) + +def get_images(ws, prompt): + prompt_id = queue_prompt(prompt)['prompt_id'] + output_images = {} + while True: + out = ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message['type'] == 'executing': + data = message['data'] + if data['node'] is None and data['prompt_id'] == prompt_id: + break #Execution is done + else: + # If you want to be able to decode the binary stream for latent previews, here is how you can do it: + # bytesIO = BytesIO(out[8:]) + # preview_image = Image.open(bytesIO) # This is your preview in PIL image format, store it in a global + continue #previews are binary data + + history = get_history(prompt_id)[prompt_id] + for node_id in history['outputs']: + node_output = history['outputs'][node_id] + images_output = [] + if 'images' in node_output: + for image in node_output['images']: + image_data = get_image(image['filename'], image['subfolder'], image['type']) + images_output.append(image_data) + output_images[node_id] = images_output + + return output_images + +prompt_text = """ +{ + "3": { + "class_type": "KSampler", + "inputs": { + "cfg": 8, + "denoise": 1, + "latent_image": [ + "5", + 0 + ], + "model": [ + "4", + 0 + ], + "negative": [ + "7", + 0 + ], + "positive": [ + "6", + 0 + ], + "sampler_name": "euler", + "scheduler": "normal", + "seed": 8566257, + "steps": 20 + } + }, + "4": { + "class_type": "CheckpointLoaderSimple", + "inputs": { + "ckpt_name": "v1-5-pruned-emaonly.safetensors" + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "batch_size": 1, + "height": 512, + "width": 512 + } + }, + "6": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "masterpiece best quality girl" + } + }, + "7": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "bad hands" + } + }, + "8": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "3", + 0 + ], + "vae": [ + "4", + 2 + ] + } + }, + "9": { + "class_type": "SaveImage", + "inputs": { + "filename_prefix": "ComfyUI", + "images": [ + "8", + 0 + ] + } + } +} +""" + +prompt = json.loads(prompt_text) +#set the text prompt for our positive CLIPTextEncode +prompt["6"]["inputs"]["text"] = "masterpiece best quality man" + +#set the seed for our KSampler node +prompt["3"]["inputs"]["seed"] = 5 + +ws = websocket.WebSocket() +ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) +images = get_images(ws, prompt) +ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts +#Commented out code to display the output images: + +# for node_id in images: +# for image_data in images[node_id]: +# from PIL import Image +# import io +# image = Image.open(io.BytesIO(image_data)) +# image.show() + diff --git a/script_examples/websockets_api_example_ws_images.py b/script_examples/websockets_api_example_ws_images.py new file mode 100644 index 0000000..6508ecc --- /dev/null +++ b/script_examples/websockets_api_example_ws_images.py @@ -0,0 +1,159 @@ +#This is an example that uses the websockets api and the SaveImageWebsocket node to get images directly without +#them being saved to disk + +import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) +import uuid +import json +import urllib.request +import urllib.parse + +server_address = "127.0.0.1:8188" +client_id = str(uuid.uuid4()) + +def queue_prompt(prompt): + p = {"prompt": prompt, "client_id": client_id} + data = json.dumps(p).encode('utf-8') + req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) + return json.loads(urllib.request.urlopen(req).read()) + +def get_image(filename, subfolder, folder_type): + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response: + return response.read() + +def get_history(prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: + return json.loads(response.read()) + +def get_images(ws, prompt): + prompt_id = queue_prompt(prompt)['prompt_id'] + output_images = {} + current_node = "" + while True: + out = ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message['type'] == 'executing': + data = message['data'] + if data['prompt_id'] == prompt_id: + if data['node'] is None: + break #Execution is done + else: + current_node = data['node'] + else: + if current_node == 'save_image_websocket_node': + images_output = output_images.get(current_node, []) + images_output.append(out[8:]) + output_images[current_node] = images_output + + return output_images + +prompt_text = """ +{ + "3": { + "class_type": "KSampler", + "inputs": { + "cfg": 8, + "denoise": 1, + "latent_image": [ + "5", + 0 + ], + "model": [ + "4", + 0 + ], + "negative": [ + "7", + 0 + ], + "positive": [ + "6", + 0 + ], + "sampler_name": "euler", + "scheduler": "normal", + "seed": 8566257, + "steps": 20 + } + }, + "4": { + "class_type": "CheckpointLoaderSimple", + "inputs": { + "ckpt_name": "v1-5-pruned-emaonly.safetensors" + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "batch_size": 1, + "height": 512, + "width": 512 + } + }, + "6": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "masterpiece best quality girl" + } + }, + "7": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "bad hands" + } + }, + "8": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "3", + 0 + ], + "vae": [ + "4", + 2 + ] + } + }, + "save_image_websocket_node": { + "class_type": "SaveImageWebsocket", + "inputs": { + "images": [ + "8", + 0 + ] + } + } +} +""" + +prompt = json.loads(prompt_text) +#set the text prompt for our positive CLIPTextEncode +prompt["6"]["inputs"]["text"] = "masterpiece best quality man" + +#set the seed for our KSampler node +prompt["3"]["inputs"]["seed"] = 5 + +ws = websocket.WebSocket() +ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) +images = get_images(ws, prompt) +ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts +#Commented out code to display the output images: + +# for node_id in images: +# for image_data in images[node_id]: +# from PIL import Image +# import io +# image = Image.open(io.BytesIO(image_data)) +# image.show() + diff --git a/server.py b/server.py new file mode 100644 index 0000000..e663095 --- /dev/null +++ b/server.py @@ -0,0 +1,867 @@ +import os +import sys +import asyncio +import traceback + +import nodes +import folder_paths +import execution +import uuid +import urllib +import json +import glob +import struct +import ssl +import socket +import ipaddress +from PIL import Image, ImageOps +from PIL.PngImagePlugin import PngInfo +from io import BytesIO + +import aiohttp +from aiohttp import web +import logging + +import mimetypes +from comfy.cli_args import args +import comfy.utils +import comfy.model_management +import node_helpers +from app.frontend_management import FrontendManager +from app.user_manager import UserManager +from model_filemanager import download_model, DownloadModelStatus +from typing import Optional +from api_server.routes.internal.internal_routes import InternalRoutes + +class BinaryEventTypes: + PREVIEW_IMAGE = 1 + UNENCODED_PREVIEW_IMAGE = 2 + +async def send_socket_catch_exception(function, message): + try: + await function(message) + except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError, BrokenPipeError, ConnectionError) as err: + logging.warning("send error: {}".format(err)) + +def get_comfyui_version(): + comfyui_version = "unknown" + repo_path = os.path.dirname(os.path.realpath(__file__)) + try: + import pygit2 + repo = pygit2.Repository(repo_path) + comfyui_version = repo.describe(describe_strategy=pygit2.GIT_DESCRIBE_TAGS) + except Exception: + try: + import subprocess + comfyui_version = subprocess.check_output(["git", "describe", "--tags"], cwd=repo_path).decode('utf-8') + except Exception as e: + logging.warning(f"Failed to get ComfyUI version: {e}") + return comfyui_version.strip() + +@web.middleware +async def cache_control(request: web.Request, handler): + response: web.Response = await handler(request) + if request.path.endswith('.js') or request.path.endswith('.css'): + response.headers.setdefault('Cache-Control', 'no-cache') + return response + +def create_cors_middleware(allowed_origin: str): + @web.middleware + async def cors_middleware(request: web.Request, handler): + if request.method == "OPTIONS": + # Pre-flight request. Reply successfully: + response = web.Response() + else: + response = await handler(request) + + response.headers['Access-Control-Allow-Origin'] = allowed_origin + response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS' + response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization' + response.headers['Access-Control-Allow-Credentials'] = 'true' + return response + + return cors_middleware + +def is_loopback(host): + if host is None: + return False + try: + if ipaddress.ip_address(host).is_loopback: + return True + else: + return False + except: + pass + + loopback = False + for family in (socket.AF_INET, socket.AF_INET6): + try: + r = socket.getaddrinfo(host, None, family, socket.SOCK_STREAM) + for family, _, _, _, sockaddr in r: + if not ipaddress.ip_address(sockaddr[0]).is_loopback: + return loopback + else: + loopback = True + except socket.gaierror: + pass + + return loopback + + +def create_origin_only_middleware(): + @web.middleware + async def origin_only_middleware(request: web.Request, handler): + #this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason. + #in that case the Host and Origin hostnames won't match + #I know the proper fix would be to add a cookie but this should take care of the problem in the meantime + if 'Host' in request.headers and 'Origin' in request.headers: + host = request.headers['Host'] + origin = request.headers['Origin'] + host_domain = host.lower() + parsed = urllib.parse.urlparse(origin) + origin_domain = parsed.netloc.lower() + host_domain_parsed = urllib.parse.urlsplit('//' + host_domain) + + #limit the check to when the host domain is localhost, this makes it slightly less safe but should still prevent the exploit + loopback = is_loopback(host_domain_parsed.hostname) + + if parsed.port is None: #if origin doesn't have a port strip it from the host to handle weird browsers, same for host + host_domain = host_domain_parsed.hostname + if host_domain_parsed.port is None: + origin_domain = parsed.hostname + + if loopback and host_domain is not None and origin_domain is not None and len(host_domain) > 0 and len(origin_domain) > 0: + if host_domain != origin_domain: + logging.warning("WARNING: request with non matching host and origin {} != {}, returning 403".format(host_domain, origin_domain)) + return web.Response(status=403) + + if request.method == "OPTIONS": + response = web.Response() + else: + response = await handler(request) + + return response + + return origin_only_middleware + +class PromptServer(): + def __init__(self, loop): + PromptServer.instance = self + + mimetypes.init() + mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8' + + self.user_manager = UserManager() + self.internal_routes = InternalRoutes(self) + self.supports = ["custom_nodes_from_web"] + self.prompt_queue = None + self.loop = loop + self.messages = asyncio.Queue() + self.client_session:Optional[aiohttp.ClientSession] = None + self.number = 0 + + middlewares = [cache_control] + if args.enable_cors_header: + middlewares.append(create_cors_middleware(args.enable_cors_header)) + else: + middlewares.append(create_origin_only_middleware()) + + max_upload_size = round(args.max_upload_size * 1024 * 1024) + self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares) + self.sockets = dict() + self.web_root = ( + FrontendManager.init_frontend(args.front_end_version) + if args.front_end_root is None + else args.front_end_root + ) + logging.info(f"[Prompt Server] web root: {self.web_root}") + routes = web.RouteTableDef() + self.routes = routes + self.last_node_id = None + self.client_id = None + + self.on_prompt_handlers = [] + + @routes.get('/ws') + async def websocket_handler(request): + ws = web.WebSocketResponse() + await ws.prepare(request) + sid = request.rel_url.query.get('clientId', '') + if sid: + # Reusing existing session, remove old + self.sockets.pop(sid, None) + else: + sid = uuid.uuid4().hex + + self.sockets[sid] = ws + + try: + # Send initial state to the new client + await self.send("status", { "status": self.get_queue_info(), 'sid': sid }, sid) + # On reconnect if we are the currently executing client send the current node + if self.client_id == sid and self.last_node_id is not None: + await self.send("executing", { "node": self.last_node_id }, sid) + + async for msg in ws: + if msg.type == aiohttp.WSMsgType.ERROR: + logging.warning('ws connection closed with exception %s' % ws.exception()) + finally: + self.sockets.pop(sid, None) + return ws + + @routes.get("/") + async def get_root(request): + response = web.FileResponse(os.path.join(self.web_root, "index.html")) + response.headers['Cache-Control'] = 'no-cache' + response.headers["Pragma"] = "no-cache" + response.headers["Expires"] = "0" + return response + + @routes.get("/embeddings") + def get_embeddings(self): + embeddings = folder_paths.get_filename_list("embeddings") + return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings))) + + @routes.get("/models") + def list_model_types(request): + model_types = list(folder_paths.folder_names_and_paths.keys()) + + return web.json_response(model_types) + + @routes.get("/models/{folder}") + async def get_models(request): + folder = request.match_info.get("folder", None) + if not folder in folder_paths.folder_names_and_paths: + return web.Response(status=404) + files = folder_paths.get_filename_list(folder) + return web.json_response(files) + + @routes.get("/extensions") + async def get_extensions(request): + files = glob.glob(os.path.join( + glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True) + + extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files)) + + for name, dir in nodes.EXTENSION_WEB_DIRS.items(): + files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True) + extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote( + name) + "/" + os.path.relpath(f, dir).replace("\\", "/"), files))) + + return web.json_response(extensions) + + def get_dir_by_type(dir_type): + if dir_type is None: + dir_type = "input" + + if dir_type == "input": + type_dir = folder_paths.get_input_directory() + elif dir_type == "temp": + type_dir = folder_paths.get_temp_directory() + elif dir_type == "output": + type_dir = folder_paths.get_output_directory() + + return type_dir, dir_type + + def compare_image_hash(filepath, image): + hasher = node_helpers.hasher() + + # function to compare hashes of two images to see if it already exists, fix to #3465 + if os.path.exists(filepath): + a = hasher() + b = hasher() + with open(filepath, "rb") as f: + a.update(f.read()) + b.update(image.file.read()) + image.file.seek(0) + f.close() + return a.hexdigest() == b.hexdigest() + return False + + def image_upload(post, image_save_function=None): + image = post.get("image") + overwrite = post.get("overwrite") + image_is_duplicate = False + + image_upload_type = post.get("type") + upload_dir, image_upload_type = get_dir_by_type(image_upload_type) + + if image and image.file: + filename = image.filename + if not filename: + return web.Response(status=400) + + subfolder = post.get("subfolder", "") + full_output_folder = os.path.join(upload_dir, os.path.normpath(subfolder)) + filepath = os.path.abspath(os.path.join(full_output_folder, filename)) + + if os.path.commonpath((upload_dir, filepath)) != upload_dir: + return web.Response(status=400) + + if not os.path.exists(full_output_folder): + os.makedirs(full_output_folder) + + split = os.path.splitext(filename) + + if overwrite is not None and (overwrite == "true" or overwrite == "1"): + pass + else: + i = 1 + while os.path.exists(filepath): + if compare_image_hash(filepath, image): #compare hash to prevent saving of duplicates with same name, fix for #3465 + image_is_duplicate = True + break + filename = f"{split[0]} ({i}){split[1]}" + filepath = os.path.join(full_output_folder, filename) + i += 1 + + if not image_is_duplicate: + if image_save_function is not None: + image_save_function(image, post, filepath) + else: + with open(filepath, "wb") as f: + f.write(image.file.read()) + + return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type}) + else: + return web.Response(status=400) + + @routes.post("/upload/image") + async def upload_image(request): + post = await request.post() + return image_upload(post) + + + @routes.post("/upload/mask") + async def upload_mask(request): + post = await request.post() + + def image_save_function(image, post, filepath): + original_ref = json.loads(post.get("original_ref")) + filename, output_dir = folder_paths.annotated_filepath(original_ref['filename']) + + # validation for security: prevent accessing arbitrary path + if filename[0] == '/' or '..' in filename: + return web.Response(status=400) + + if output_dir is None: + type = original_ref.get("type", "output") + output_dir = folder_paths.get_directory_by_type(type) + + if output_dir is None: + return web.Response(status=400) + + if original_ref.get("subfolder", "") != "": + full_output_dir = os.path.join(output_dir, original_ref["subfolder"]) + if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: + return web.Response(status=403) + output_dir = full_output_dir + + file = os.path.join(output_dir, filename) + + if os.path.isfile(file): + with Image.open(file) as original_pil: + metadata = PngInfo() + if hasattr(original_pil,'text'): + for key in original_pil.text: + metadata.add_text(key, original_pil.text[key]) + original_pil = original_pil.convert('RGBA') + mask_pil = Image.open(image.file).convert('RGBA') + + # alpha copy + new_alpha = mask_pil.getchannel('A') + original_pil.putalpha(new_alpha) + original_pil.save(filepath, compress_level=4, pnginfo=metadata) + + return image_upload(post, image_save_function) + + @routes.get("/view") + async def view_image(request): + if "filename" in request.rel_url.query: + filename = request.rel_url.query["filename"] + filename,output_dir = folder_paths.annotated_filepath(filename) + + # validation for security: prevent accessing arbitrary path + if filename[0] == '/' or '..' in filename: + return web.Response(status=400) + + if output_dir is None: + type = request.rel_url.query.get("type", "output") + output_dir = folder_paths.get_directory_by_type(type) + + if output_dir is None: + return web.Response(status=400) + + if "subfolder" in request.rel_url.query: + full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"]) + if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: + return web.Response(status=403) + output_dir = full_output_dir + + filename = os.path.basename(filename) + file = os.path.join(output_dir, filename) + + if os.path.isfile(file): + if 'preview' in request.rel_url.query: + with Image.open(file) as img: + preview_info = request.rel_url.query['preview'].split(';') + image_format = preview_info[0] + if image_format not in ['webp', 'jpeg'] or 'a' in request.rel_url.query.get('channel', ''): + image_format = 'webp' + + quality = 90 + if preview_info[-1].isdigit(): + quality = int(preview_info[-1]) + + buffer = BytesIO() + if image_format in ['jpeg'] or request.rel_url.query.get('channel', '') == 'rgb': + img = img.convert("RGB") + img.save(buffer, format=image_format, quality=quality) + buffer.seek(0) + + return web.Response(body=buffer.read(), content_type=f'image/{image_format}', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + + if 'channel' not in request.rel_url.query: + channel = 'rgba' + else: + channel = request.rel_url.query["channel"] + + if channel == 'rgb': + with Image.open(file) as img: + if img.mode == "RGBA": + r, g, b, a = img.split() + new_img = Image.merge('RGB', (r, g, b)) + else: + new_img = img.convert("RGB") + + buffer = BytesIO() + new_img.save(buffer, format='PNG') + buffer.seek(0) + + return web.Response(body=buffer.read(), content_type='image/png', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + + elif channel == 'a': + with Image.open(file) as img: + if img.mode == "RGBA": + _, _, _, a = img.split() + else: + a = Image.new('L', img.size, 255) + + # alpha img + alpha_img = Image.new('RGBA', img.size) + alpha_img.putalpha(a) + alpha_buffer = BytesIO() + alpha_img.save(alpha_buffer, format='PNG') + alpha_buffer.seek(0) + + return web.Response(body=alpha_buffer.read(), content_type='image/png', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + else: + return web.FileResponse(file, headers={"Content-Disposition": f"filename=\"{filename}\""}) + + return web.Response(status=404) + + @routes.get("/view_metadata/{folder_name}") + async def view_metadata(request): + folder_name = request.match_info.get("folder_name", None) + if folder_name is None: + return web.Response(status=404) + if not "filename" in request.rel_url.query: + return web.Response(status=404) + + filename = request.rel_url.query["filename"] + if not filename.endswith(".safetensors"): + return web.Response(status=404) + + safetensors_path = folder_paths.get_full_path(folder_name, filename) + if safetensors_path is None: + return web.Response(status=404) + out = comfy.utils.safetensors_header(safetensors_path, max_size=1024*1024) + if out is None: + return web.Response(status=404) + dt = json.loads(out) + if not "__metadata__" in dt: + return web.Response(status=404) + return web.json_response(dt["__metadata__"]) + + @routes.get("/system_stats") + async def system_stats(request): + device = comfy.model_management.get_torch_device() + device_name = comfy.model_management.get_torch_device_name(device) + cpu_device = comfy.model_management.torch.device("cpu") + ram_total = comfy.model_management.get_total_memory(cpu_device) + ram_free = comfy.model_management.get_free_memory(cpu_device) + vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True) + vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True) + + system_stats = { + "system": { + "os": os.name, + "ram_total": ram_total, + "ram_free": ram_free, + "comfyui_version": get_comfyui_version(), + "python_version": sys.version, + "pytorch_version": comfy.model_management.torch_version, + "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded", + "argv": sys.argv + }, + "devices": [ + { + "name": device_name, + "type": device.type, + "index": device.index, + "vram_total": vram_total, + "vram_free": vram_free, + "torch_vram_total": torch_vram_total, + "torch_vram_free": torch_vram_free, + } + ] + } + return web.json_response(system_stats) + + @routes.get("/prompt") + async def get_prompt(request): + return web.json_response(self.get_queue_info()) + + def node_info(node_class): + obj_class = nodes.NODE_CLASS_MAPPINGS[node_class] + info = {} + info['input'] = obj_class.INPUT_TYPES() + info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()} + info['output'] = obj_class.RETURN_TYPES + info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES) + info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output'] + info['name'] = node_class + info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class + info['description'] = obj_class.DESCRIPTION if hasattr(obj_class,'DESCRIPTION') else '' + info['python_module'] = getattr(obj_class, "RELATIVE_PYTHON_MODULE", "nodes") + info['category'] = 'sd' + if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True: + info['output_node'] = True + else: + info['output_node'] = False + + if hasattr(obj_class, 'CATEGORY'): + info['category'] = obj_class.CATEGORY + + if hasattr(obj_class, 'OUTPUT_TOOLTIPS'): + info['output_tooltips'] = obj_class.OUTPUT_TOOLTIPS + + if getattr(obj_class, "DEPRECATED", False): + info['deprecated'] = True + if getattr(obj_class, "EXPERIMENTAL", False): + info['experimental'] = True + return info + + @routes.get("/object_info") + async def get_object_info(request): + with folder_paths.cache_helper: + out = {} + for x in nodes.NODE_CLASS_MAPPINGS: + try: + out[x] = node_info(x) + except Exception as e: + logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.") + logging.error(traceback.format_exc()) + return web.json_response(out) + + @routes.get("/object_info/{node_class}") + async def get_object_info_node(request): + node_class = request.match_info.get("node_class", None) + out = {} + if (node_class is not None) and (node_class in nodes.NODE_CLASS_MAPPINGS): + out[node_class] = node_info(node_class) + return web.json_response(out) + + @routes.get("/history") + async def get_history(request): + max_items = request.rel_url.query.get("max_items", None) + if max_items is not None: + max_items = int(max_items) + return web.json_response(self.prompt_queue.get_history(max_items=max_items)) + + @routes.get("/history/{prompt_id}") + async def get_history(request): + prompt_id = request.match_info.get("prompt_id", None) + return web.json_response(self.prompt_queue.get_history(prompt_id=prompt_id)) + + @routes.get("/queue") + async def get_queue(request): + queue_info = {} + current_queue = self.prompt_queue.get_current_queue() + queue_info['queue_running'] = current_queue[0] + queue_info['queue_pending'] = current_queue[1] + return web.json_response(queue_info) + + @routes.post("/prompt") + async def post_prompt(request): + logging.info("got prompt") + resp_code = 200 + out_string = "" + json_data = await request.json() + json_data = self.trigger_on_prompt(json_data) + + if "number" in json_data: + number = float(json_data['number']) + else: + number = self.number + if "front" in json_data: + if json_data['front']: + number = -number + + self.number += 1 + + if "prompt" in json_data: + prompt = json_data["prompt"] + valid = execution.validate_prompt(prompt) + extra_data = {} + if "extra_data" in json_data: + extra_data = json_data["extra_data"] + + if "client_id" in json_data: + extra_data["client_id"] = json_data["client_id"] + if valid[0]: + prompt_id = str(uuid.uuid4()) + outputs_to_execute = valid[2] + self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute)) + response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]} + return web.json_response(response) + else: + logging.warning("invalid prompt: {}".format(valid[1])) + return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400) + else: + return web.json_response({"error": "no prompt", "node_errors": []}, status=400) + + @routes.post("/queue") + async def post_queue(request): + json_data = await request.json() + if "clear" in json_data: + if json_data["clear"]: + self.prompt_queue.wipe_queue() + if "delete" in json_data: + to_delete = json_data['delete'] + for id_to_delete in to_delete: + delete_func = lambda a: a[1] == id_to_delete + self.prompt_queue.delete_queue_item(delete_func) + + return web.Response(status=200) + + @routes.post("/interrupt") + async def post_interrupt(request): + nodes.interrupt_processing() + return web.Response(status=200) + + @routes.post("/free") + async def post_free(request): + json_data = await request.json() + unload_models = json_data.get("unload_models", False) + free_memory = json_data.get("free_memory", False) + if unload_models: + self.prompt_queue.set_flag("unload_models", unload_models) + if free_memory: + self.prompt_queue.set_flag("free_memory", free_memory) + return web.Response(status=200) + + @routes.post("/history") + async def post_history(request): + json_data = await request.json() + if "clear" in json_data: + if json_data["clear"]: + self.prompt_queue.wipe_history() + if "delete" in json_data: + to_delete = json_data['delete'] + for id_to_delete in to_delete: + self.prompt_queue.delete_history_item(id_to_delete) + + return web.Response(status=200) + + # Internal route. Should not be depended upon and is subject to change at any time. + # TODO(robinhuang): Move to internal route table class once we refactor PromptServer to pass around Websocket. + # NOTE: This was an experiment and WILL BE REMOVED + @routes.post("/internal/models/download") + async def download_handler(request): + async def report_progress(filename: str, status: DownloadModelStatus): + payload = status.to_dict() + payload['download_path'] = filename + await self.send_json("download_progress", payload) + + data = await request.json() + url = data.get('url') + model_directory = data.get('model_directory') + folder_path = data.get('folder_path') + model_filename = data.get('model_filename') + progress_interval = data.get('progress_interval', 1.0) # In seconds, how often to report download progress. + + if not url or not model_directory or not model_filename or not folder_path: + return web.json_response({"status": "error", "message": "Missing URL or folder path or filename"}, status=400) + + session = self.client_session + if session is None: + logging.error("Client session is not initialized") + return web.Response(status=500) + + task = asyncio.create_task(download_model(lambda url: session.get(url), model_filename, url, model_directory, folder_path, report_progress, progress_interval)) + await task + + return web.json_response(task.result().to_dict()) + + async def setup(self): + timeout = aiohttp.ClientTimeout(total=None) # no timeout + self.client_session = aiohttp.ClientSession(timeout=timeout) + + def add_routes(self): + self.user_manager.add_routes(self.routes) + self.app.add_subapp('/internal', self.internal_routes.get_app()) + + # Prefix every route with /api for easier matching for delegation. + # This is very useful for frontend dev server, which need to forward + # everything except serving of static files. + # Currently both the old endpoints without prefix and new endpoints with + # prefix are supported. + api_routes = web.RouteTableDef() + for route in self.routes: + # Custom nodes might add extra static routes. Only process non-static + # routes to add /api prefix. + if isinstance(route, web.RouteDef): + api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs) + self.app.add_routes(api_routes) + self.app.add_routes(self.routes) + + for name, dir in nodes.EXTENSION_WEB_DIRS.items(): + self.app.add_routes([ + web.static('/extensions/' + urllib.parse.quote(name), dir), + ]) + + self.app.add_routes([ + web.static('/', self.web_root), + ]) + + def get_queue_info(self): + prompt_info = {} + exec_info = {} + exec_info['queue_remaining'] = self.prompt_queue.get_tasks_remaining() + prompt_info['exec_info'] = exec_info + return prompt_info + + async def send(self, event, data, sid=None): + if event == BinaryEventTypes.UNENCODED_PREVIEW_IMAGE: + await self.send_image(data, sid=sid) + elif isinstance(data, (bytes, bytearray)): + await self.send_bytes(event, data, sid) + else: + await self.send_json(event, data, sid) + + def encode_bytes(self, event, data): + if not isinstance(event, int): + raise RuntimeError(f"Binary event types must be integers, got {event}") + + packed = struct.pack(">I", event) + message = bytearray(packed) + message.extend(data) + return message + + async def send_image(self, image_data, sid=None): + image_type = image_data[0] + image = image_data[1] + max_size = image_data[2] + if max_size is not None: + if hasattr(Image, 'Resampling'): + resampling = Image.Resampling.BILINEAR + else: + resampling = Image.ANTIALIAS + + image = ImageOps.contain(image, (max_size, max_size), resampling) + type_num = 1 + if image_type == "JPEG": + type_num = 1 + elif image_type == "PNG": + type_num = 2 + + bytesIO = BytesIO() + header = struct.pack(">I", type_num) + bytesIO.write(header) + image.save(bytesIO, format=image_type, quality=95, compress_level=1) + preview_bytes = bytesIO.getvalue() + await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid) + + async def send_bytes(self, event, data, sid=None): + message = self.encode_bytes(event, data) + + if sid is None: + sockets = list(self.sockets.values()) + for ws in sockets: + await send_socket_catch_exception(ws.send_bytes, message) + elif sid in self.sockets: + await send_socket_catch_exception(self.sockets[sid].send_bytes, message) + + async def send_json(self, event, data, sid=None): + message = {"type": event, "data": data} + + if sid is None: + sockets = list(self.sockets.values()) + for ws in sockets: + await send_socket_catch_exception(ws.send_json, message) + elif sid in self.sockets: + await send_socket_catch_exception(self.sockets[sid].send_json, message) + + def send_sync(self, event, data, sid=None): + self.loop.call_soon_threadsafe( + self.messages.put_nowait, (event, data, sid)) + + def queue_updated(self): + self.send_sync("status", { "status": self.get_queue_info() }) + + async def publish_loop(self): + while True: + msg = await self.messages.get() + await self.send(*msg) + + async def start(self, address, port, verbose=True, call_on_start=None): + await self.start_multi_address([(address, port)], call_on_start=call_on_start) + + async def start_multi_address(self, addresses, call_on_start=None): + runner = web.AppRunner(self.app, access_log=None) + await runner.setup() + ssl_ctx = None + scheme = "http" + if args.tls_keyfile and args.tls_certfile: + ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE) + ssl_ctx.load_cert_chain(certfile=args.tls_certfile, + keyfile=args.tls_keyfile) + scheme = "https" + + logging.info("Starting server\n") + for addr in addresses: + address = addr[0] + port = addr[1] + site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx) + await site.start() + + if not hasattr(self, 'address'): + self.address = address #TODO: remove this + self.port = port + + if ':' in address: + address_print = "[{}]".format(address) + else: + address_print = address + + logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address_print, port)) + + if call_on_start is not None: + call_on_start(scheme, self.address, self.port) + + def add_on_prompt_handler(self, handler): + self.on_prompt_handlers.append(handler) + + def trigger_on_prompt(self, json_data): + for handler in self.on_prompt_handlers: + try: + json_data = handler(json_data) + except Exception as e: + logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing") + logging.warning(traceback.format_exc()) + + return json_data diff --git a/tests-unit/README.md b/tests-unit/README.md new file mode 100644 index 0000000..81692b8 --- /dev/null +++ b/tests-unit/README.md @@ -0,0 +1,8 @@ +# Pytest Unit Tests + +## Install test dependencies + +`pip install -r tests-unit/requirements.txt` + +## Run tests +`pytest tests-unit/` diff --git a/tests-unit/app_test/__init__.py b/tests-unit/app_test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests-unit/app_test/frontend_manager_test.py b/tests-unit/app_test/frontend_manager_test.py new file mode 100644 index 0000000..a8df524 --- /dev/null +++ b/tests-unit/app_test/frontend_manager_test.py @@ -0,0 +1,130 @@ +import argparse +import pytest +from requests.exceptions import HTTPError +from unittest.mock import patch + +from app.frontend_management import ( + FrontendManager, + FrontEndProvider, + Release, +) +from comfy.cli_args import DEFAULT_VERSION_STRING + + +@pytest.fixture +def mock_releases(): + return [ + Release( + id=1, + tag_name="1.0.0", + name="Release 1.0.0", + prerelease=False, + created_at="2022-01-01T00:00:00Z", + published_at="2022-01-01T00:00:00Z", + body="Release notes for 1.0.0", + assets=[{"name": "dist.zip", "url": "https://example.com/dist.zip"}], + ), + Release( + id=2, + tag_name="2.0.0", + name="Release 2.0.0", + prerelease=False, + created_at="2022-02-01T00:00:00Z", + published_at="2022-02-01T00:00:00Z", + body="Release notes for 2.0.0", + assets=[{"name": "dist.zip", "url": "https://example.com/dist.zip"}], + ), + ] + + +@pytest.fixture +def mock_provider(mock_releases): + provider = FrontEndProvider( + owner="test-owner", + repo="test-repo", + ) + provider.all_releases = mock_releases + provider.latest_release = mock_releases[1] + FrontendManager.PROVIDERS = [provider] + return provider + + +def test_get_release(mock_provider, mock_releases): + version = "1.0.0" + release = mock_provider.get_release(version) + assert release == mock_releases[0] + + +def test_get_release_latest(mock_provider, mock_releases): + version = "latest" + release = mock_provider.get_release(version) + assert release == mock_releases[1] + + +def test_get_release_invalid_version(mock_provider): + version = "invalid" + with pytest.raises(ValueError): + mock_provider.get_release(version) + + +def test_init_frontend_default(): + version_string = DEFAULT_VERSION_STRING + frontend_path = FrontendManager.init_frontend(version_string) + assert frontend_path == FrontendManager.DEFAULT_FRONTEND_PATH + + +def test_init_frontend_invalid_version(): + version_string = "test-owner/test-repo@1.100.99" + with pytest.raises(HTTPError): + FrontendManager.init_frontend_unsafe(version_string) + + +def test_init_frontend_invalid_provider(): + version_string = "invalid/invalid@latest" + with pytest.raises(HTTPError): + FrontendManager.init_frontend_unsafe(version_string) + +@pytest.fixture +def mock_os_functions(): + with patch('app.frontend_management.os.makedirs') as mock_makedirs, \ + patch('app.frontend_management.os.listdir') as mock_listdir, \ + patch('app.frontend_management.os.rmdir') as mock_rmdir: + mock_listdir.return_value = [] # Simulate empty directory + yield mock_makedirs, mock_listdir, mock_rmdir + +@pytest.fixture +def mock_download(): + with patch('app.frontend_management.download_release_asset_zip') as mock: + mock.side_effect = Exception("Download failed") # Simulate download failure + yield mock + +def test_finally_block(mock_os_functions, mock_download, mock_provider): + # Arrange + mock_makedirs, mock_listdir, mock_rmdir = mock_os_functions + version_string = 'test-owner/test-repo@1.0.0' + + # Act & Assert + with pytest.raises(Exception): + FrontendManager.init_frontend_unsafe(version_string, mock_provider) + + # Assert + mock_makedirs.assert_called_once() + mock_download.assert_called_once() + mock_listdir.assert_called_once() + mock_rmdir.assert_called_once() + + +def test_parse_version_string(): + version_string = "owner/repo@1.0.0" + repo_owner, repo_name, version = FrontendManager.parse_version_string( + version_string + ) + assert repo_owner == "owner" + assert repo_name == "repo" + assert version == "1.0.0" + + +def test_parse_version_string_invalid(): + version_string = "invalid" + with pytest.raises(argparse.ArgumentTypeError): + FrontendManager.parse_version_string(version_string) diff --git a/tests-unit/comfy_test/folder_path_test.py b/tests-unit/comfy_test/folder_path_test.py new file mode 100644 index 0000000..0bbec59 --- /dev/null +++ b/tests-unit/comfy_test/folder_path_test.py @@ -0,0 +1,66 @@ +### 🗻 This file is created through the spirit of Mount Fuji at its peak +# TODO(yoland): clean up this after I get back down +import pytest +import os +import tempfile +from unittest.mock import patch + +import folder_paths + +@pytest.fixture +def temp_dir(): + with tempfile.TemporaryDirectory() as tmpdirname: + yield tmpdirname + + +def test_get_directory_by_type(): + test_dir = "/test/dir" + folder_paths.set_output_directory(test_dir) + assert folder_paths.get_directory_by_type("output") == test_dir + assert folder_paths.get_directory_by_type("invalid") is None + +def test_annotated_filepath(): + assert folder_paths.annotated_filepath("test.txt") == ("test.txt", None) + assert folder_paths.annotated_filepath("test.txt [output]") == ("test.txt", folder_paths.get_output_directory()) + assert folder_paths.annotated_filepath("test.txt [input]") == ("test.txt", folder_paths.get_input_directory()) + assert folder_paths.annotated_filepath("test.txt [temp]") == ("test.txt", folder_paths.get_temp_directory()) + +def test_get_annotated_filepath(): + default_dir = "/default/dir" + assert folder_paths.get_annotated_filepath("test.txt", default_dir) == os.path.join(default_dir, "test.txt") + assert folder_paths.get_annotated_filepath("test.txt [output]") == os.path.join(folder_paths.get_output_directory(), "test.txt") + +def test_add_model_folder_path(): + folder_paths.add_model_folder_path("test_folder", "/test/path") + assert "/test/path" in folder_paths.get_folder_paths("test_folder") + +def test_recursive_search(temp_dir): + os.makedirs(os.path.join(temp_dir, "subdir")) + open(os.path.join(temp_dir, "file1.txt"), "w").close() + open(os.path.join(temp_dir, "subdir", "file2.txt"), "w").close() + + files, dirs = folder_paths.recursive_search(temp_dir) + assert set(files) == {"file1.txt", os.path.join("subdir", "file2.txt")} + assert len(dirs) == 2 # temp_dir and subdir + +def test_filter_files_extensions(): + files = ["file1.txt", "file2.jpg", "file3.png", "file4.txt"] + assert folder_paths.filter_files_extensions(files, [".txt"]) == ["file1.txt", "file4.txt"] + assert folder_paths.filter_files_extensions(files, [".jpg", ".png"]) == ["file2.jpg", "file3.png"] + assert folder_paths.filter_files_extensions(files, []) == files + +@patch("folder_paths.recursive_search") +@patch("folder_paths.folder_names_and_paths") +def test_get_filename_list(mock_folder_names_and_paths, mock_recursive_search): + mock_folder_names_and_paths.__getitem__.return_value = (["/test/path"], {".txt"}) + mock_recursive_search.return_value = (["file1.txt", "file2.jpg"], {}) + assert folder_paths.get_filename_list("test_folder") == ["file1.txt"] + +def test_get_save_image_path(temp_dir): + with patch("folder_paths.output_directory", temp_dir): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("test", temp_dir, 100, 100) + assert os.path.samefile(full_output_folder, temp_dir) + assert filename == "test" + assert counter == 1 + assert subfolder == "" + assert filename_prefix == "test" \ No newline at end of file diff --git a/tests-unit/folder_paths_test/__init__.py b/tests-unit/folder_paths_test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests-unit/folder_paths_test/filter_by_content_types_test.py b/tests-unit/folder_paths_test/filter_by_content_types_test.py new file mode 100644 index 0000000..5574789 --- /dev/null +++ b/tests-unit/folder_paths_test/filter_by_content_types_test.py @@ -0,0 +1,52 @@ +import pytest +import os +import tempfile +from folder_paths import filter_files_content_types + +@pytest.fixture(scope="module") +def file_extensions(): + return { + 'image': ['gif', 'heif', 'ico', 'jpeg', 'jpg', 'png', 'pnm', 'ppm', 'svg', 'tiff', 'webp', 'xbm', 'xpm'], + 'audio': ['aif', 'aifc', 'aiff', 'au', 'flac', 'm4a', 'mp2', 'mp3', 'ogg', 'snd', 'wav'], + 'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv'] + } + + +@pytest.fixture(scope="module") +def mock_dir(file_extensions): + with tempfile.TemporaryDirectory() as directory: + for content_type, extensions in file_extensions.items(): + for extension in extensions: + with open(f"{directory}/sample_{content_type}.{extension}", "w") as f: + f.write(f"Sample {content_type} file in {extension} format") + yield directory + + +def test_categorizes_all_correctly(mock_dir, file_extensions): + files = os.listdir(mock_dir) + for content_type, extensions in file_extensions.items(): + filtered_files = filter_files_content_types(files, [content_type]) + for extension in extensions: + assert f"sample_{content_type}.{extension}" in filtered_files + + +def test_categorizes_all_uniquely(mock_dir, file_extensions): + files = os.listdir(mock_dir) + for content_type, extensions in file_extensions.items(): + filtered_files = filter_files_content_types(files, [content_type]) + assert len(filtered_files) == len(extensions) + + +def test_handles_bad_extensions(): + files = ["file1.txt", "file2.py", "file3.example", "file4.pdf", "file5.ini", "file6.doc", "file7.md"] + assert filter_files_content_types(files, ["image", "audio", "video"]) == [] + + +def test_handles_no_extension(): + files = ["file1", "file2", "file3", "file4", "file5", "file6", "file7"] + assert filter_files_content_types(files, ["image", "audio", "video"]) == [] + + +def test_handles_no_files(): + files = [] + assert filter_files_content_types(files, ["image", "audio", "video"]) == [] \ No newline at end of file diff --git a/tests-unit/prompt_server_test/__init__.py b/tests-unit/prompt_server_test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests-unit/prompt_server_test/download_models_test.py b/tests-unit/prompt_server_test/download_models_test.py new file mode 100644 index 0000000..128dfeb --- /dev/null +++ b/tests-unit/prompt_server_test/download_models_test.py @@ -0,0 +1,337 @@ +import pytest +import tempfile +import aiohttp +from aiohttp import ClientResponse +import itertools +import os +from unittest.mock import AsyncMock, patch, MagicMock +from model_filemanager import download_model, track_download_progress, create_model_path, check_file_exists, DownloadStatusType, DownloadModelStatus, validate_filename +import folder_paths + +@pytest.fixture +def temp_dir(): + with tempfile.TemporaryDirectory() as tmpdirname: + yield tmpdirname + +class AsyncIteratorMock: + """ + A mock class that simulates an asynchronous iterator. + This is used to mimic the behavior of aiohttp's content iterator. + """ + def __init__(self, seq): + # Convert the input sequence into an iterator + self.iter = iter(seq) + + def __aiter__(self): + # This method is called when 'async for' is used + return self + + async def __anext__(self): + # This method is called for each iteration in an 'async for' loop + try: + return next(self.iter) + except StopIteration: + # This is the asynchronous equivalent of StopIteration + raise StopAsyncIteration + +class ContentMock: + """ + A mock class that simulates the content attribute of an aiohttp ClientResponse. + This class provides the iter_chunked method which returns an async iterator of chunks. + """ + def __init__(self, chunks): + # Store the chunks that will be returned by the iterator + self.chunks = chunks + + def iter_chunked(self, chunk_size): + # This method mimics aiohttp's content.iter_chunked() + # For simplicity in testing, we ignore chunk_size and just return our predefined chunks + return AsyncIteratorMock(self.chunks) + +@pytest.mark.asyncio +async def test_download_model_success(temp_dir): + mock_response = AsyncMock(spec=aiohttp.ClientResponse) + mock_response.status = 200 + mock_response.headers = {'Content-Length': '1000'} + # Create a mock for content that returns an async iterator directly + chunks = [b'a' * 500, b'b' * 300, b'c' * 200] + mock_response.content = ContentMock(chunks) + + mock_make_request = AsyncMock(return_value=mock_response) + mock_progress_callback = AsyncMock() + + time_values = itertools.count(0, 0.1) + + fake_paths = {'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)} + + with patch('model_filemanager.create_model_path', return_value=('models/checkpoints/model.sft', 'model.sft')), \ + patch('model_filemanager.check_file_exists', return_value=None), \ + patch('folder_paths.folder_names_and_paths', fake_paths), \ + patch('time.time', side_effect=time_values): # Simulate time passing + + result = await download_model( + mock_make_request, + 'model.sft', + 'http://example.com/model.sft', + 'checkpoints', + temp_dir, + mock_progress_callback + ) + + # Assert the result + assert isinstance(result, DownloadModelStatus) + assert result.message == 'Successfully downloaded model.sft' + assert result.status == 'completed' + assert result.already_existed is False + + # Check progress callback calls + assert mock_progress_callback.call_count >= 3 # At least start, one progress update, and completion + + # Check initial call + mock_progress_callback.assert_any_call( + 'model.sft', + DownloadModelStatus(DownloadStatusType.PENDING, 0, "Starting download of model.sft", False) + ) + + # Check final call + mock_progress_callback.assert_any_call( + 'model.sft', + DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "Successfully downloaded model.sft", False) + ) + + mock_file_path = os.path.join(temp_dir, 'model.sft') + assert os.path.exists(mock_file_path) + with open(mock_file_path, 'rb') as mock_file: + assert mock_file.read() == b''.join(chunks) + os.remove(mock_file_path) + + # Verify request was made + mock_make_request.assert_called_once_with('http://example.com/model.sft') + +@pytest.mark.asyncio +async def test_download_model_url_request_failure(temp_dir): + # Mock dependencies + mock_response = AsyncMock(spec=ClientResponse) + mock_response.status = 404 # Simulate a "Not Found" error + mock_get = AsyncMock(return_value=mock_response) + mock_progress_callback = AsyncMock() + + fake_paths = {'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)} + + # Mock the create_model_path function + with patch('model_filemanager.create_model_path', return_value='/mock/path/model.safetensors'), \ + patch('model_filemanager.check_file_exists', return_value=None), \ + patch('folder_paths.folder_names_and_paths', fake_paths): + # Call the function + result = await download_model( + mock_get, + 'model.safetensors', + 'http://example.com/model.safetensors', + 'checkpoints', + temp_dir, + mock_progress_callback + ) + + # Assert the expected behavior + assert isinstance(result, DownloadModelStatus) + assert result.status == 'error' + assert result.message == 'Failed to download model.safetensors. Status code: 404' + assert result.already_existed is False + + # Check that progress_callback was called with the correct arguments + mock_progress_callback.assert_any_call( + 'model.safetensors', + DownloadModelStatus( + status=DownloadStatusType.PENDING, + progress_percentage=0, + message='Starting download of model.safetensors', + already_existed=False + ) + ) + mock_progress_callback.assert_called_with( + 'model.safetensors', + DownloadModelStatus( + status=DownloadStatusType.ERROR, + progress_percentage=0, + message='Failed to download model.safetensors. Status code: 404', + already_existed=False + ) + ) + + # Verify that the get method was called with the correct URL + mock_get.assert_called_once_with('http://example.com/model.safetensors') + +@pytest.mark.asyncio +async def test_download_model_invalid_model_subdirectory(): + mock_make_request = AsyncMock() + mock_progress_callback = AsyncMock() + + result = await download_model( + mock_make_request, + 'model.sft', + 'http://example.com/model.sft', + '../bad_path', + '../bad_path', + mock_progress_callback + ) + + # Assert the result + assert isinstance(result, DownloadModelStatus) + assert result.message.startswith('Invalid or unrecognized model directory') + assert result.status == 'error' + assert result.already_existed is False + +@pytest.mark.asyncio +async def test_download_model_invalid_folder_path(): + mock_make_request = AsyncMock() + mock_progress_callback = AsyncMock() + + result = await download_model( + mock_make_request, + 'model.sft', + 'http://example.com/model.sft', + 'checkpoints', + 'invalid_path', + mock_progress_callback + ) + + # Assert the result + assert isinstance(result, DownloadModelStatus) + assert result.message.startswith("Invalid folder path") + assert result.status == 'error' + assert result.already_existed is False + +def test_create_model_path(tmp_path, monkeypatch): + model_name = "model.safetensors" + folder_path = os.path.join(tmp_path, "mock_dir") + + file_path = create_model_path(model_name, folder_path) + + assert file_path == os.path.join(folder_path, "model.safetensors") + assert os.path.exists(os.path.dirname(file_path)) + + with pytest.raises(Exception, match="Invalid model directory"): + create_model_path("../path_traversal.safetensors", folder_path) + + with pytest.raises(Exception, match="Invalid model directory"): + create_model_path("/etc/some_root_path", folder_path) + + +@pytest.mark.asyncio +async def test_check_file_exists_when_file_exists(tmp_path): + file_path = tmp_path / "existing_model.sft" + file_path.touch() # Create an empty file + + mock_callback = AsyncMock() + + result = await check_file_exists(str(file_path), "existing_model.sft", mock_callback) + + assert result is not None + assert result.status == "completed" + assert result.message == "existing_model.sft already exists" + assert result.already_existed is True + + mock_callback.assert_called_once_with( + "existing_model.sft", + DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "existing_model.sft already exists", already_existed=True) + ) + +@pytest.mark.asyncio +async def test_check_file_exists_when_file_does_not_exist(tmp_path): + file_path = tmp_path / "non_existing_model.sft" + + mock_callback = AsyncMock() + + result = await check_file_exists(str(file_path), "non_existing_model.sft", mock_callback) + + assert result is None + mock_callback.assert_not_called() + +@pytest.mark.asyncio +async def test_track_download_progress_no_content_length(temp_dir): + mock_response = AsyncMock(spec=aiohttp.ClientResponse) + mock_response.headers = {} # No Content-Length header + chunks = [b'a' * 500, b'b' * 500] + mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks) + + mock_callback = AsyncMock() + + full_path = os.path.join(temp_dir, 'model.sft') + + result = await track_download_progress( + mock_response, full_path, 'model.sft', + mock_callback, interval=0.1 + ) + + assert result.status == "completed" + + assert os.path.exists(full_path) + with open(full_path, 'rb') as f: + assert f.read() == b''.join(chunks) + os.remove(full_path) + + # Check that progress was reported even without knowing the total size + mock_callback.assert_any_call( + 'model.sft', + DownloadModelStatus(DownloadStatusType.IN_PROGRESS, 0, "Downloading model.sft", already_existed=False) + ) + +@pytest.mark.asyncio +async def test_track_download_progress_interval(temp_dir): + mock_response = AsyncMock(spec=aiohttp.ClientResponse) + mock_response.headers = {'Content-Length': '1000'} + chunks = [b'a' * 100] * 10 + mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks) + + mock_callback = AsyncMock() + mock_open = MagicMock(return_value=MagicMock()) + + # Create a mock time function that returns incremental float values + mock_time = MagicMock() + mock_time.side_effect = [i * 0.5 for i in range(30)] # This should be enough for 10 chunks + + full_path = os.path.join(temp_dir, 'model.sft') + + with patch('time.time', mock_time): + await track_download_progress( + mock_response, full_path, 'model.sft', + mock_callback, interval=1.0 + ) + + assert os.path.exists(full_path) + with open(full_path, 'rb') as f: + assert f.read() == b''.join(chunks) + os.remove(full_path) + + # Assert that progress was updated at least 3 times (start, at least one interval, and end) + assert mock_callback.call_count >= 3, f"Expected at least 3 calls, but got {mock_callback.call_count}" + + # Verify the first and last calls + first_call = mock_callback.call_args_list[0] + assert first_call[0][1].status == "in_progress" + # Allow for some initial progress, but it should be less than 50% + assert 0 <= first_call[0][1].progress_percentage < 50, f"First call progress was {first_call[0][1].progress_percentage}%" + + last_call = mock_callback.call_args_list[-1] + assert last_call[0][1].status == "completed" + assert last_call[0][1].progress_percentage == 100 + +@pytest.mark.parametrize("filename, expected", [ + ("valid_model.safetensors", True), + ("valid_model.sft", True), + ("valid model.safetensors", True), # Test with space + ("UPPERCASE_MODEL.SAFETENSORS", True), + ("model_with.multiple.dots.pt", False), + ("", False), # Empty string + ("../../../etc/passwd", False), # Path traversal attempt + ("/etc/passwd", False), # Absolute path + ("\\windows\\system32\\config\\sam", False), # Windows path + (".hidden_file.pt", False), # Hidden file + ("invalid.ckpt", False), # Invalid character + ("invalid?.ckpt", False), # Another invalid character + ("very" * 100 + ".safetensors", False), # Too long filename + ("\nmodel_with_newline.pt", False), # Newline character + ("model_with_emoji😊.pt", False), # Emoji in filename +]) +def test_validate_filename(filename, expected): + assert validate_filename(filename) == expected diff --git a/tests-unit/prompt_server_test/user_manager_test.py b/tests-unit/prompt_server_test/user_manager_test.py new file mode 100644 index 0000000..7e523cb --- /dev/null +++ b/tests-unit/prompt_server_test/user_manager_test.py @@ -0,0 +1,231 @@ +import pytest +import os +from aiohttp import web +from app.user_manager import UserManager +from unittest.mock import patch + +pytestmark = ( + pytest.mark.asyncio +) # This applies the asyncio mark to all test functions in the module + + +@pytest.fixture +def user_manager(tmp_path): + um = UserManager() + um.get_request_user_filepath = lambda req, file, **kwargs: os.path.join( + tmp_path, file + ) if file else tmp_path + return um + + +@pytest.fixture +def app(user_manager): + app = web.Application() + routes = web.RouteTableDef() + user_manager.add_routes(routes) + app.add_routes(routes) + return app + + +async def test_listuserdata_empty_directory(aiohttp_client, app, tmp_path): + client = await aiohttp_client(app) + resp = await client.get("/userdata?dir=test_dir") + assert resp.status == 404 + + +async def test_listuserdata_with_files(aiohttp_client, app, tmp_path): + os.makedirs(tmp_path / "test_dir") + with open(tmp_path / "test_dir" / "file1.txt", "w") as f: + f.write("test content") + + client = await aiohttp_client(app) + resp = await client.get("/userdata?dir=test_dir") + assert resp.status == 200 + assert await resp.json() == ["file1.txt"] + + +async def test_listuserdata_recursive(aiohttp_client, app, tmp_path): + os.makedirs(tmp_path / "test_dir" / "subdir") + with open(tmp_path / "test_dir" / "file1.txt", "w") as f: + f.write("test content") + with open(tmp_path / "test_dir" / "subdir" / "file2.txt", "w") as f: + f.write("test content") + + client = await aiohttp_client(app) + resp = await client.get("/userdata?dir=test_dir&recurse=true") + assert resp.status == 200 + assert set(await resp.json()) == {"file1.txt", "subdir/file2.txt"} + + +async def test_listuserdata_full_info(aiohttp_client, app, tmp_path): + os.makedirs(tmp_path / "test_dir") + with open(tmp_path / "test_dir" / "file1.txt", "w") as f: + f.write("test content") + + client = await aiohttp_client(app) + resp = await client.get("/userdata?dir=test_dir&full_info=true") + assert resp.status == 200 + result = await resp.json() + assert len(result) == 1 + assert result[0]["path"] == "file1.txt" + assert "size" in result[0] + assert "modified" in result[0] + + +async def test_listuserdata_split_path(aiohttp_client, app, tmp_path): + os.makedirs(tmp_path / "test_dir" / "subdir") + with open(tmp_path / "test_dir" / "subdir" / "file1.txt", "w") as f: + f.write("test content") + + client = await aiohttp_client(app) + resp = await client.get("/userdata?dir=test_dir&recurse=true&split=true") + assert resp.status == 200 + assert await resp.json() == [["subdir/file1.txt", "subdir", "file1.txt"]] + + +async def test_listuserdata_invalid_directory(aiohttp_client, app): + client = await aiohttp_client(app) + resp = await client.get("/userdata?dir=") + assert resp.status == 400 + + +async def test_listuserdata_normalized_separator(aiohttp_client, app, tmp_path): + os_sep = "\\" + with patch("os.sep", os_sep): + with patch("os.path.sep", os_sep): + os.makedirs(tmp_path / "test_dir" / "subdir") + with open(tmp_path / "test_dir" / "subdir" / "file1.txt", "w") as f: + f.write("test content") + + client = await aiohttp_client(app) + resp = await client.get("/userdata?dir=test_dir&recurse=true") + assert resp.status == 200 + result = await resp.json() + assert len(result) == 1 + assert "/" in result[0] # Ensure forward slash is used + assert "\\" not in result[0] # Ensure backslash is not present + assert result[0] == "subdir/file1.txt" + + # Test with full_info + resp = await client.get( + "/userdata?dir=test_dir&recurse=true&full_info=true" + ) + assert resp.status == 200 + result = await resp.json() + assert len(result) == 1 + assert "/" in result[0]["path"] # Ensure forward slash is used + assert "\\" not in result[0]["path"] # Ensure backslash is not present + assert result[0]["path"] == "subdir/file1.txt" + + +async def test_post_userdata_new_file(aiohttp_client, app, tmp_path): + client = await aiohttp_client(app) + content = b"test content" + resp = await client.post("/userdata/test.txt", data=content) + + assert resp.status == 200 + assert await resp.text() == '"test.txt"' + + # Verify file was created with correct content + with open(tmp_path / "test.txt", "rb") as f: + assert f.read() == content + + +async def test_post_userdata_overwrite_existing(aiohttp_client, app, tmp_path): + # Create initial file + with open(tmp_path / "test.txt", "w") as f: + f.write("initial content") + + client = await aiohttp_client(app) + new_content = b"updated content" + resp = await client.post("/userdata/test.txt", data=new_content) + + assert resp.status == 200 + assert await resp.text() == '"test.txt"' + + # Verify file was overwritten + with open(tmp_path / "test.txt", "rb") as f: + assert f.read() == new_content + + +async def test_post_userdata_no_overwrite(aiohttp_client, app, tmp_path): + # Create initial file + with open(tmp_path / "test.txt", "w") as f: + f.write("initial content") + + client = await aiohttp_client(app) + resp = await client.post("/userdata/test.txt?overwrite=false", data=b"new content") + + assert resp.status == 409 + + # Verify original content unchanged + with open(tmp_path / "test.txt", "r") as f: + assert f.read() == "initial content" + + +async def test_post_userdata_full_info(aiohttp_client, app, tmp_path): + client = await aiohttp_client(app) + content = b"test content" + resp = await client.post("/userdata/test.txt?full_info=true", data=content) + + assert resp.status == 200 + result = await resp.json() + assert result["path"] == "test.txt" + assert result["size"] == len(content) + assert "modified" in result + + +async def test_move_userdata(aiohttp_client, app, tmp_path): + # Create initial file + with open(tmp_path / "source.txt", "w") as f: + f.write("test content") + + client = await aiohttp_client(app) + resp = await client.post("/userdata/source.txt/move/dest.txt") + + assert resp.status == 200 + assert await resp.text() == '"dest.txt"' + + # Verify file was moved + assert not os.path.exists(tmp_path / "source.txt") + with open(tmp_path / "dest.txt", "r") as f: + assert f.read() == "test content" + + +async def test_move_userdata_no_overwrite(aiohttp_client, app, tmp_path): + # Create source and destination files + with open(tmp_path / "source.txt", "w") as f: + f.write("source content") + with open(tmp_path / "dest.txt", "w") as f: + f.write("destination content") + + client = await aiohttp_client(app) + resp = await client.post("/userdata/source.txt/move/dest.txt?overwrite=false") + + assert resp.status == 409 + + # Verify files remain unchanged + with open(tmp_path / "source.txt", "r") as f: + assert f.read() == "source content" + with open(tmp_path / "dest.txt", "r") as f: + assert f.read() == "destination content" + + +async def test_move_userdata_full_info(aiohttp_client, app, tmp_path): + # Create initial file + with open(tmp_path / "source.txt", "w") as f: + f.write("test content") + + client = await aiohttp_client(app) + resp = await client.post("/userdata/source.txt/move/dest.txt?full_info=true") + + assert resp.status == 200 + result = await resp.json() + assert result["path"] == "dest.txt" + assert result["size"] == len("test content") + assert "modified" in result + + # Verify file was moved + assert not os.path.exists(tmp_path / "source.txt") + with open(tmp_path / "dest.txt", "r") as f: + assert f.read() == "test content" diff --git a/tests-unit/requirements.txt b/tests-unit/requirements.txt new file mode 100644 index 0000000..d70d00f --- /dev/null +++ b/tests-unit/requirements.txt @@ -0,0 +1,3 @@ +pytest>=7.8.0 +pytest-aiohttp +pytest-asyncio diff --git a/tests-unit/server/routes/internal_routes_test.py b/tests-unit/server/routes/internal_routes_test.py new file mode 100644 index 0000000..4fe5442 --- /dev/null +++ b/tests-unit/server/routes/internal_routes_test.py @@ -0,0 +1,115 @@ +import pytest +from aiohttp import web +from unittest.mock import MagicMock, patch +from api_server.routes.internal.internal_routes import InternalRoutes +from api_server.services.file_service import FileService +from folder_paths import models_dir, user_directory, output_directory + + +@pytest.fixture +def internal_routes(): + return InternalRoutes(None) + +@pytest.fixture +def aiohttp_client_factory(aiohttp_client, internal_routes): + async def _get_client(): + app = internal_routes.get_app() + return await aiohttp_client(app) + return _get_client + +@pytest.mark.asyncio +async def test_list_files_valid_directory(aiohttp_client_factory, internal_routes): + mock_file_list = [ + {"name": "file1.txt", "path": "file1.txt", "type": "file", "size": 100}, + {"name": "dir1", "path": "dir1", "type": "directory"} + ] + internal_routes.file_service.list_files = MagicMock(return_value=mock_file_list) + client = await aiohttp_client_factory() + resp = await client.get('/files?directory=models') + assert resp.status == 200 + data = await resp.json() + assert 'files' in data + assert len(data['files']) == 2 + assert data['files'] == mock_file_list + + # Check other valid directories + resp = await client.get('/files?directory=user') + assert resp.status == 200 + resp = await client.get('/files?directory=output') + assert resp.status == 200 + +@pytest.mark.asyncio +async def test_list_files_invalid_directory(aiohttp_client_factory, internal_routes): + internal_routes.file_service.list_files = MagicMock(side_effect=ValueError("Invalid directory key")) + client = await aiohttp_client_factory() + resp = await client.get('/files?directory=invalid') + assert resp.status == 400 + data = await resp.json() + assert 'error' in data + assert data['error'] == "Invalid directory key" + +@pytest.mark.asyncio +async def test_list_files_exception(aiohttp_client_factory, internal_routes): + internal_routes.file_service.list_files = MagicMock(side_effect=Exception("Unexpected error")) + client = await aiohttp_client_factory() + resp = await client.get('/files?directory=models') + assert resp.status == 500 + data = await resp.json() + assert 'error' in data + assert data['error'] == "Unexpected error" + +@pytest.mark.asyncio +async def test_list_files_no_directory_param(aiohttp_client_factory, internal_routes): + mock_file_list = [] + internal_routes.file_service.list_files = MagicMock(return_value=mock_file_list) + client = await aiohttp_client_factory() + resp = await client.get('/files') + assert resp.status == 200 + data = await resp.json() + assert 'files' in data + assert len(data['files']) == 0 + +def test_setup_routes(internal_routes): + internal_routes.setup_routes() + routes = internal_routes.routes + assert any(route.method == 'GET' and str(route.path) == '/files' for route in routes) + +def test_get_app(internal_routes): + app = internal_routes.get_app() + assert isinstance(app, web.Application) + assert internal_routes._app is not None + +def test_get_app_reuse(internal_routes): + app1 = internal_routes.get_app() + app2 = internal_routes.get_app() + assert app1 is app2 + +@pytest.mark.asyncio +async def test_routes_added_to_app(aiohttp_client_factory, internal_routes): + client = await aiohttp_client_factory() + try: + resp = await client.get('/files') + print(f"Response received: status {resp.status}") + except Exception as e: + print(f"Exception occurred during GET request: {e}") + raise + + assert resp.status != 404, "Route /files does not exist" + +@pytest.mark.asyncio +async def test_file_service_initialization(): + with patch('api_server.routes.internal.internal_routes.FileService') as MockFileService: + # Create a mock instance + mock_file_service_instance = MagicMock(spec=FileService) + MockFileService.return_value = mock_file_service_instance + internal_routes = InternalRoutes(None) + + # Check if FileService was initialized with the correct parameters + MockFileService.assert_called_once_with({ + "models": models_dir, + "user": user_directory, + "output": output_directory + }) + + # Verify that the file_service attribute of InternalRoutes is set + assert internal_routes.file_service == mock_file_service_instance diff --git a/tests-unit/server/services/file_service_test.py b/tests-unit/server/services/file_service_test.py new file mode 100644 index 0000000..5650452 --- /dev/null +++ b/tests-unit/server/services/file_service_test.py @@ -0,0 +1,54 @@ +import pytest +from unittest.mock import MagicMock +from api_server.services.file_service import FileService + +@pytest.fixture +def mock_file_system_ops(): + return MagicMock() + +@pytest.fixture +def file_service(mock_file_system_ops): + allowed_directories = { + "models": "/path/to/models", + "user": "/path/to/user", + "output": "/path/to/output" + } + return FileService(allowed_directories, file_system_ops=mock_file_system_ops) + +def test_list_files_valid_directory(file_service, mock_file_system_ops): + mock_file_system_ops.walk_directory.return_value = [ + {"name": "file1.txt", "path": "file1.txt", "type": "file", "size": 100}, + {"name": "dir1", "path": "dir1", "type": "directory"} + ] + + result = file_service.list_files("models") + + assert len(result) == 2 + assert result[0]["name"] == "file1.txt" + assert result[1]["name"] == "dir1" + mock_file_system_ops.walk_directory.assert_called_once_with("/path/to/models") + +def test_list_files_invalid_directory(file_service): + # Does not support walking directories outside of the allowed directories + with pytest.raises(ValueError, match="Invalid directory key"): + file_service.list_files("invalid_key") + +def test_list_files_empty_directory(file_service, mock_file_system_ops): + mock_file_system_ops.walk_directory.return_value = [] + + result = file_service.list_files("models") + + assert len(result) == 0 + mock_file_system_ops.walk_directory.assert_called_once_with("/path/to/models") + +@pytest.mark.parametrize("directory_key", ["models", "user", "output"]) +def test_list_files_all_allowed_directories(file_service, mock_file_system_ops, directory_key): + mock_file_system_ops.walk_directory.return_value = [ + {"name": f"file_{directory_key}.txt", "path": f"file_{directory_key}.txt", "type": "file", "size": 100} + ] + + result = file_service.list_files(directory_key) + + assert len(result) == 1 + assert result[0]["name"] == f"file_{directory_key}.txt" + mock_file_system_ops.walk_directory.assert_called_once_with(f"/path/to/{directory_key}") \ No newline at end of file diff --git a/tests-unit/server/utils/file_operations_test.py b/tests-unit/server/utils/file_operations_test.py new file mode 100644 index 0000000..5a2a837 --- /dev/null +++ b/tests-unit/server/utils/file_operations_test.py @@ -0,0 +1,42 @@ +import pytest +from typing import List +from api_server.utils.file_operations import FileSystemOperations, FileSystemItem, is_file_info + +@pytest.fixture +def temp_directory(tmp_path): + # Create a temporary directory structure + dir1 = tmp_path / "dir1" + dir2 = tmp_path / "dir2" + dir1.mkdir() + dir2.mkdir() + (dir1 / "file1.txt").write_text("content1") + (dir2 / "file2.txt").write_text("content2") + (tmp_path / "file3.txt").write_text("content3") + return tmp_path + +def test_walk_directory(temp_directory): + result: List[FileSystemItem] = FileSystemOperations.walk_directory(str(temp_directory)) + + assert len(result) == 5 # 2 directories and 3 files + + files = [item for item in result if item['type'] == 'file'] + dirs = [item for item in result if item['type'] == 'directory'] + + assert len(files) == 3 + assert len(dirs) == 2 + + file_names = {file['name'] for file in files} + assert file_names == {'file1.txt', 'file2.txt', 'file3.txt'} + + dir_names = {dir['name'] for dir in dirs} + assert dir_names == {'dir1', 'dir2'} + +def test_walk_directory_empty(tmp_path): + result = FileSystemOperations.walk_directory(str(tmp_path)) + assert len(result) == 0 + +def test_walk_directory_file_size(temp_directory): + result: List[FileSystemItem] = FileSystemOperations.walk_directory(str(temp_directory)) + files = [item for item in result if is_file_info(item)] + for file in files: + assert file['size'] > 0 # Assuming all files have some content diff --git a/tests-unit/utils/extra_config_test.py b/tests-unit/utils/extra_config_test.py new file mode 100644 index 0000000..ef772d4 --- /dev/null +++ b/tests-unit/utils/extra_config_test.py @@ -0,0 +1,126 @@ +import pytest +import yaml +import os +from unittest.mock import Mock, patch, mock_open + +from utils.extra_config import load_extra_path_config +import folder_paths + +@pytest.fixture +def mock_yaml_content(): + return { + 'test_config': { + 'base_path': '~/App/', + 'checkpoints': 'subfolder1', + } + } + +@pytest.fixture +def mock_expanded_home(): + return '/home/user' + +@pytest.fixture +def yaml_config_with_appdata(): + return """ + test_config: + base_path: '%APPDATA%/ComfyUI' + checkpoints: 'models/checkpoints' + """ + +@pytest.fixture +def mock_yaml_content_appdata(yaml_config_with_appdata): + return yaml.safe_load(yaml_config_with_appdata) + +@pytest.fixture +def mock_expandvars_appdata(): + mock = Mock() + mock.side_effect = lambda path: path.replace('%APPDATA%', 'C:/Users/TestUser/AppData/Roaming') + return mock + +@pytest.fixture +def mock_add_model_folder_path(): + return Mock() + +@pytest.fixture +def mock_expanduser(mock_expanded_home): + def _expanduser(path): + if path.startswith('~/'): + return os.path.join(mock_expanded_home, path[2:]) + return path + return _expanduser + +@pytest.fixture +def mock_yaml_safe_load(mock_yaml_content): + return Mock(return_value=mock_yaml_content) + +@patch('builtins.open', new_callable=mock_open, read_data="dummy file content") +def test_load_extra_model_paths_expands_userpath( + mock_file, + monkeypatch, + mock_add_model_folder_path, + mock_expanduser, + mock_yaml_safe_load, + mock_expanded_home +): + # Attach mocks used by load_extra_path_config + monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path) + monkeypatch.setattr(os.path, 'expanduser', mock_expanduser) + monkeypatch.setattr(yaml, 'safe_load', mock_yaml_safe_load) + + dummy_yaml_file_name = 'dummy_path.yaml' + load_extra_path_config(dummy_yaml_file_name) + + expected_calls = [ + ('checkpoints', os.path.join(mock_expanded_home, 'App', 'subfolder1'), False), + ] + + assert mock_add_model_folder_path.call_count == len(expected_calls) + + # Check if add_model_folder_path was called with the correct arguments + for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls): + assert actual_call.args[0] == expected_call[0] + assert os.path.normpath(actual_call.args[1]) == os.path.normpath(expected_call[1]) # Normalize and check the path to check on multiple OS. + assert actual_call.args[2] == expected_call[2] + + # Check if yaml.safe_load was called + mock_yaml_safe_load.assert_called_once() + + # Check if open was called with the correct file path + mock_file.assert_called_once_with(dummy_yaml_file_name, 'r') + +@patch('builtins.open', new_callable=mock_open) +def test_load_extra_model_paths_expands_appdata( + mock_file, + monkeypatch, + mock_add_model_folder_path, + mock_expandvars_appdata, + yaml_config_with_appdata, + mock_yaml_content_appdata +): + # Set the mock_file to return yaml with appdata as a variable + mock_file.return_value.read.return_value = yaml_config_with_appdata + + # Attach mocks + monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path) + monkeypatch.setattr(os.path, 'expandvars', mock_expandvars_appdata) + monkeypatch.setattr(yaml, 'safe_load', Mock(return_value=mock_yaml_content_appdata)) + + # Mock expanduser to do nothing (since we're not testing it here) + monkeypatch.setattr(os.path, 'expanduser', lambda x: x) + + dummy_yaml_file_name = 'dummy_path.yaml' + load_extra_path_config(dummy_yaml_file_name) + + expected_base_path = 'C:/Users/TestUser/AppData/Roaming/ComfyUI' + expected_calls = [ + ('checkpoints', os.path.join(expected_base_path, 'models/checkpoints'), False), + ] + + assert mock_add_model_folder_path.call_count == len(expected_calls) + + # Check the base path variable was expanded + for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls): + assert actual_call.args == expected_call + + # Verify that expandvars was called + assert mock_expandvars_appdata.called diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 0000000..2005fd4 --- /dev/null +++ b/tests/README.md @@ -0,0 +1,29 @@ +# Automated Testing + +## Running tests locally + +Additional requirements for running tests: +``` +pip install pytest +pip install websocket-client==1.6.1 +opencv-python==4.6.0.66 +scikit-image==0.21.0 +``` +Run inference tests: +``` +pytest tests/inference +``` + +## Quality regression test +Compares images in 2 directories to ensure they are the same + +1) Run an inference test to save a directory of "ground truth" images +``` + pytest tests/inference --output_dir tests/inference/baseline +``` +2) Make code edits + +3) Run inference and quality comparison tests +``` +pytest +``` \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/compare/conftest.py b/tests/compare/conftest.py new file mode 100644 index 0000000..dd5078c --- /dev/null +++ b/tests/compare/conftest.py @@ -0,0 +1,41 @@ +import os +import pytest + +# Command line arguments for pytest +def pytest_addoption(parser): + parser.addoption('--baseline_dir', action="store", default='tests/inference/baseline', help='Directory for ground-truth images') + parser.addoption('--test_dir', action="store", default='tests/inference/samples', help='Directory for images to test') + parser.addoption('--metrics_file', action="store", default='tests/metrics.md', help='Output file for metrics') + parser.addoption('--img_output_dir', action="store", default='tests/compare/samples', help='Output directory for diff metric images') + +# This initializes args at the beginning of the test session +@pytest.fixture(scope="session", autouse=True) +def args_pytest(pytestconfig): + args = {} + args['baseline_dir'] = pytestconfig.getoption('baseline_dir') + args['test_dir'] = pytestconfig.getoption('test_dir') + args['metrics_file'] = pytestconfig.getoption('metrics_file') + args['img_output_dir'] = pytestconfig.getoption('img_output_dir') + + # Initialize metrics file + with open(args['metrics_file'], 'a') as f: + # if file is empty, write header + if os.stat(args['metrics_file']).st_size == 0: + f.write("| date | run | file | status | value | \n") + f.write("| --- | --- | --- | --- | --- | \n") + + return args + + +def gather_file_basenames(directory: str): + files = [] + for file in os.listdir(directory): + if file.endswith(".png"): + files.append(file) + return files + +# Creates the list of baseline file names to use as a fixture +def pytest_generate_tests(metafunc): + if "baseline_fname" in metafunc.fixturenames: + baseline_fnames = gather_file_basenames(metafunc.config.getoption("baseline_dir")) + metafunc.parametrize("baseline_fname", baseline_fnames) diff --git a/tests/compare/test_quality.py b/tests/compare/test_quality.py new file mode 100644 index 0000000..92a2d5a --- /dev/null +++ b/tests/compare/test_quality.py @@ -0,0 +1,195 @@ +import datetime +import numpy as np +import os +from PIL import Image +import pytest +from pytest import fixture +from typing import Tuple, List + +from cv2 import imread, cvtColor, COLOR_BGR2RGB +from skimage.metrics import structural_similarity as ssim + + +""" +This test suite compares images in 2 directories by file name +The directories are specified by the command line arguments --baseline_dir and --test_dir + +""" +# ssim: Structural Similarity Index +# Returns a tuple of (ssim, diff_image) +def ssim_score(img0: np.ndarray, img1: np.ndarray) -> Tuple[float, np.ndarray]: + score, diff = ssim(img0, img1, channel_axis=-1, full=True) + # rescale the difference image to 0-255 range + diff = (diff * 255).astype("uint8") + return score, diff + +# Metrics must return a tuple of (score, diff_image) +METRICS = {"ssim": ssim_score} +METRICS_PASS_THRESHOLD = {"ssim": 0.95} + + +class TestCompareImageMetrics: + @fixture(scope="class") + def test_file_names(self, args_pytest): + test_dir = args_pytest['test_dir'] + fnames = self.gather_file_basenames(test_dir) + yield fnames + del fnames + + @fixture(scope="class", autouse=True) + def teardown(self, args_pytest): + yield + # Runs after all tests are complete + # Aggregate output files into a grid of images + baseline_dir = args_pytest['baseline_dir'] + test_dir = args_pytest['test_dir'] + img_output_dir = args_pytest['img_output_dir'] + metrics_file = args_pytest['metrics_file'] + + grid_dir = os.path.join(img_output_dir, "grid") + os.makedirs(grid_dir, exist_ok=True) + + for metric_dir in METRICS.keys(): + metric_path = os.path.join(img_output_dir, metric_dir) + for file in os.listdir(metric_path): + if file.endswith(".png"): + score = self.lookup_score_from_fname(file, metrics_file) + image_file_list = [] + image_file_list.append([ + os.path.join(baseline_dir, file), + os.path.join(test_dir, file), + os.path.join(metric_path, file) + ]) + # Create grid + image_list = [[Image.open(file) for file in files] for files in image_file_list] + grid = self.image_grid(image_list) + grid.save(os.path.join(grid_dir, f"{metric_dir}_{score:.3f}_{file}")) + + # Tests run for each baseline file name + @fixture() + def fname(self, baseline_fname): + yield baseline_fname + del baseline_fname + + def test_directories_not_empty(self, args_pytest): + baseline_dir = args_pytest['baseline_dir'] + test_dir = args_pytest['test_dir'] + assert len(os.listdir(baseline_dir)) != 0, f"Baseline directory {baseline_dir} is empty" + assert len(os.listdir(test_dir)) != 0, f"Test directory {test_dir} is empty" + + def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest): + # Check that all files in baseline_dir have a file in test_dir with matching metadata + baseline_file_path = os.path.join(args_pytest['baseline_dir'], fname) + file_paths = [os.path.join(args_pytest['test_dir'], f) for f in test_file_names] + file_match = self.find_file_match(baseline_file_path, file_paths) + assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}" + + # For a baseline image file, finds the corresponding file name in test_dir and + # compares the images using the metrics in METRICS + @pytest.mark.parametrize("metric", METRICS.keys()) + def test_pipeline_compare( + self, + args_pytest, + fname, + test_file_names, + metric, + ): + baseline_dir = args_pytest['baseline_dir'] + test_dir = args_pytest['test_dir'] + metrics_output_file = args_pytest['metrics_file'] + img_output_dir = args_pytest['img_output_dir'] + + baseline_file_path = os.path.join(baseline_dir, fname) + + # Find file match + file_paths = [os.path.join(test_dir, f) for f in test_file_names] + test_file = self.find_file_match(baseline_file_path, file_paths) + + # Run metrics + sample_baseline = self.read_img(baseline_file_path) + sample_secondary = self.read_img(test_file) + + score, metric_img = METRICS[metric](sample_baseline, sample_secondary) + metric_status = score > METRICS_PASS_THRESHOLD[metric] + + # Save metric values + with open(metrics_output_file, 'a') as f: + run_info = os.path.splitext(fname)[0] + metric_status_str = "PASS ✅" if metric_status else "FAIL ❌" + date_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + f.write(f"| {date_str} | {run_info} | {metric} | {metric_status_str} | {score} | \n") + + # Save metric image + metric_img_dir = os.path.join(img_output_dir, metric) + os.makedirs(metric_img_dir, exist_ok=True) + output_filename = f'{fname}' + Image.fromarray(metric_img).save(os.path.join(metric_img_dir, output_filename)) + + assert score > METRICS_PASS_THRESHOLD[metric] + + def read_img(self, filename: str) -> np.ndarray: + cvImg = imread(filename) + cvImg = cvtColor(cvImg, COLOR_BGR2RGB) + return cvImg + + def image_grid(self, img_list: list[list[Image.Image]]): + # imgs is a 2D list of images + # Assumes the input images are a rectangular grid of equal sized images + rows = len(img_list) + cols = len(img_list[0]) + + w, h = img_list[0][0].size + grid = Image.new('RGB', size=(cols*w, rows*h)) + + for i, row in enumerate(img_list): + for j, img in enumerate(row): + grid.paste(img, box=(j*w, i*h)) + return grid + + def lookup_score_from_fname(self, + fname: str, + metrics_output_file: str + ) -> float: + fname_basestr = os.path.splitext(fname)[0] + with open(metrics_output_file, 'r') as f: + for line in f: + if fname_basestr in line: + score = float(line.split('|')[5]) + return score + raise ValueError(f"Could not find score for {fname} in {metrics_output_file}") + + def gather_file_basenames(self, directory: str): + files = [] + for file in os.listdir(directory): + if file.endswith(".png"): + files.append(file) + return files + + def read_file_prompt(self, fname:str) -> str: + # Read prompt from image file metadata + img = Image.open(fname) + img.load() + return img.info['prompt'] + + def find_file_match(self, baseline_file: str, file_paths: List[str]): + # Find a file in file_paths with matching metadata to baseline_file + baseline_prompt = self.read_file_prompt(baseline_file) + + # Do not match empty prompts + if baseline_prompt is None or baseline_prompt == "": + return None + + # Find file match + # Reorder test_file_names so that the file with matching name is first + # This is an optimization because matching file names are more likely + # to have matching metadata if they were generated with the same script + basename = os.path.basename(baseline_file) + file_path_basenames = [os.path.basename(f) for f in file_paths] + if basename in file_path_basenames: + match_index = file_path_basenames.index(basename) + file_paths.insert(0, file_paths.pop(match_index)) + + for f in file_paths: + test_file_prompt = self.read_file_prompt(f) + if baseline_prompt == test_file_prompt: + return f \ No newline at end of file diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..1a35880 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,36 @@ +import os +import pytest + +# Command line arguments for pytest +def pytest_addoption(parser): + parser.addoption('--output_dir', action="store", default='tests/inference/samples', help='Output directory for generated images') + parser.addoption("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") + parser.addoption("--port", type=int, default=8188, help="Set the listen port.") + +# This initializes args at the beginning of the test session +@pytest.fixture(scope="session", autouse=True) +def args_pytest(pytestconfig): + args = {} + args['output_dir'] = pytestconfig.getoption('output_dir') + args['listen'] = pytestconfig.getoption('listen') + args['port'] = pytestconfig.getoption('port') + + os.makedirs(args['output_dir'], exist_ok=True) + + return args + +def pytest_collection_modifyitems(items): + # Modifies items so tests run in the correct order + + LAST_TESTS = ['test_quality'] + + # Move the last items to the end + last_items = [] + for test_name in LAST_TESTS: + for item in items.copy(): + print(item.module.__name__, item) + if item.module.__name__ == test_name: + last_items.append(item) + items.remove(item) + + items.extend(last_items) diff --git a/tests/inference/__init__.py b/tests/inference/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/inference/extra_model_paths.yaml b/tests/inference/extra_model_paths.yaml new file mode 100644 index 0000000..75b2e1a --- /dev/null +++ b/tests/inference/extra_model_paths.yaml @@ -0,0 +1,4 @@ +# Config for testing nodes +testing: + custom_nodes: tests/inference/testing_nodes + diff --git a/tests/inference/graphs/default_graph_sdxl1_0.json b/tests/inference/graphs/default_graph_sdxl1_0.json new file mode 100644 index 0000000..c06c682 --- /dev/null +++ b/tests/inference/graphs/default_graph_sdxl1_0.json @@ -0,0 +1,144 @@ +{ + "4": { + "inputs": { + "ckpt_name": "sd_xl_base_1.0.safetensors" + }, + "class_type": "CheckpointLoaderSimple" + }, + "5": { + "inputs": { + "width": 1024, + "height": 1024, + "batch_size": 1 + }, + "class_type": "EmptyLatentImage" + }, + "6": { + "inputs": { + "text": "a photo of a cat", + "clip": [ + "4", + 1 + ] + }, + "class_type": "CLIPTextEncode" + }, + "10": { + "inputs": { + "add_noise": "enable", + "noise_seed": 42, + "steps": 20, + "cfg": 7.5, + "sampler_name": "euler", + "scheduler": "normal", + "start_at_step": 0, + "end_at_step": 32, + "return_with_leftover_noise": "enable", + "model": [ + "4", + 0 + ], + "positive": [ + "6", + 0 + ], + "negative": [ + "15", + 0 + ], + "latent_image": [ + "5", + 0 + ] + }, + "class_type": "KSamplerAdvanced" + }, + "12": { + "inputs": { + "samples": [ + "14", + 0 + ], + "vae": [ + "4", + 2 + ] + }, + "class_type": "VAEDecode" + }, + "13": { + "inputs": { + "filename_prefix": "test_inference", + "images": [ + "12", + 0 + ] + }, + "class_type": "SaveImage" + }, + "14": { + "inputs": { + "add_noise": "disable", + "noise_seed": 42, + "steps": 20, + "cfg": 7.5, + "sampler_name": "euler", + "scheduler": "normal", + "start_at_step": 32, + "end_at_step": 10000, + "return_with_leftover_noise": "disable", + "model": [ + "16", + 0 + ], + "positive": [ + "17", + 0 + ], + "negative": [ + "20", + 0 + ], + "latent_image": [ + "10", + 0 + ] + }, + "class_type": "KSamplerAdvanced" + }, + "15": { + "inputs": { + "conditioning": [ + "6", + 0 + ] + }, + "class_type": "ConditioningZeroOut" + }, + "16": { + "inputs": { + "ckpt_name": "sd_xl_refiner_1.0.safetensors" + }, + "class_type": "CheckpointLoaderSimple" + }, + "17": { + "inputs": { + "text": "a photo of a cat", + "clip": [ + "16", + 1 + ] + }, + "class_type": "CLIPTextEncode" + }, + "20": { + "inputs": { + "text": "", + "clip": [ + "16", + 1 + ] + }, + "class_type": "CLIPTextEncode" + } + } \ No newline at end of file diff --git a/tests/inference/test_execution.py b/tests/inference/test_execution.py new file mode 100644 index 0000000..3909ca6 --- /dev/null +++ b/tests/inference/test_execution.py @@ -0,0 +1,524 @@ +from io import BytesIO +import numpy +from PIL import Image +import pytest +from pytest import fixture +import time +import torch +from typing import Union, Dict +import json +import subprocess +import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) +import uuid +import urllib.request +import urllib.parse +import urllib.error +from comfy_execution.graph_utils import GraphBuilder, Node + +class RunResult: + def __init__(self, prompt_id: str): + self.outputs: Dict[str,Dict] = {} + self.runs: Dict[str,bool] = {} + self.prompt_id: str = prompt_id + + def get_output(self, node: Node): + return self.outputs.get(node.id, None) + + def did_run(self, node: Node): + return self.runs.get(node.id, False) + + def get_images(self, node: Node): + output = self.get_output(node) + if output is None: + return [] + return output.get('image_objects', []) + + def get_prompt_id(self): + return self.prompt_id + +class ComfyClient: + def __init__(self): + self.test_name = "" + + def connect(self, + listen:str = '127.0.0.1', + port:Union[str,int] = 8188, + client_id: str = str(uuid.uuid4()) + ): + self.client_id = client_id + self.server_address = f"{listen}:{port}" + ws = websocket.WebSocket() + ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id)) + self.ws = ws + + def queue_prompt(self, prompt): + p = {"prompt": prompt, "client_id": self.client_id} + data = json.dumps(p).encode('utf-8') + req = urllib.request.Request("http://{}/prompt".format(self.server_address), data=data) + return json.loads(urllib.request.urlopen(req).read()) + + def get_image(self, filename, subfolder, folder_type): + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response: + return response.read() + + def get_history(self, prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response: + return json.loads(response.read()) + + def set_test_name(self, name): + self.test_name = name + + def run(self, graph): + prompt = graph.finalize() + for node in graph.nodes.values(): + if node.class_type == 'SaveImage': + node.inputs['filename_prefix'] = self.test_name + + prompt_id = self.queue_prompt(prompt)['prompt_id'] + result = RunResult(prompt_id) + while True: + out = self.ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message['type'] == 'executing': + data = message['data'] + if data['prompt_id'] != prompt_id: + continue + if data['node'] is None: + break + result.runs[data['node']] = True + elif message['type'] == 'execution_error': + raise Exception(message['data']) + elif message['type'] == 'execution_cached': + pass # Probably want to store this off for testing + + history = self.get_history(prompt_id)[prompt_id] + for node_id in history['outputs']: + node_output = history['outputs'][node_id] + result.outputs[node_id] = node_output + images_output = [] + if 'images' in node_output: + for image in node_output['images']: + image_data = self.get_image(image['filename'], image['subfolder'], image['type']) + image_obj = Image.open(BytesIO(image_data)) + images_output.append(image_obj) + node_output['image_objects'] = images_output + + return result + +# +# Loop through these variables +# +@pytest.mark.execution +class TestExecution: + # + # Initialize server and client + # + @fixture(scope="class", autouse=True, params=[ + # (use_lru, lru_size) + (False, 0), + (True, 0), + (True, 100), + ]) + def _server(self, args_pytest, request): + # Start server + pargs = [ + 'python','main.py', + '--output-directory', args_pytest["output_dir"], + '--listen', args_pytest["listen"], + '--port', str(args_pytest["port"]), + '--extra-model-paths-config', 'tests/inference/extra_model_paths.yaml', + ] + use_lru, lru_size = request.param + if use_lru: + pargs += ['--cache-lru', str(lru_size)] + print("Running server with args:", pargs) + p = subprocess.Popen(pargs) + yield + p.kill() + torch.cuda.empty_cache() + + def start_client(self, listen:str, port:int): + # Start client + comfy_client = ComfyClient() + # Connect to server (with retries) + n_tries = 5 + for i in range(n_tries): + time.sleep(4) + try: + comfy_client.connect(listen=listen, port=port) + except ConnectionRefusedError as e: + print(e) + print(f"({i+1}/{n_tries}) Retrying...") + else: + break + return comfy_client + + @fixture(scope="class", autouse=True) + def shared_client(self, args_pytest, _server): + client = self.start_client(args_pytest["listen"], args_pytest["port"]) + yield client + del client + torch.cuda.empty_cache() + + @fixture + def client(self, shared_client, request): + shared_client.set_test_name(f"execution[{request.node.name}]") + yield shared_client + + @fixture + def builder(self, request): + yield GraphBuilder(prefix=request.node.name) + + def test_lazy_input(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + mask = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1) + + lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) + output = g.node("SaveImage", images=lazy_mix.out(0)) + result = client.run(g) + + result_image = result.get_images(output)[0] + assert numpy.array(result_image).any() == 0, "Image should be black" + assert result.did_run(input1) + assert not result.did_run(input2) + assert result.did_run(mask) + assert result.did_run(lazy_mix) + + def test_full_cache(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) + mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) + + lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) + g.node("SaveImage", images=lazy_mix.out(0)) + + client.run(g) + result2 = client.run(g) + for node_id, node in g.nodes.items(): + assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached" + + def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) + mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) + + lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) + g.node("SaveImage", images=lazy_mix.out(0)) + + client.run(g) + mask.inputs['value'] = 0.4 + result2 = client.run(g) + assert not result2.did_run(input1), "Input1 should have been cached" + assert not result2.did_run(input2), "Input2 should have been cached" + + def test_error(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + # Different size of the two images + input2 = g.node("StubImage", content="NOISE", height=256, width=256, batch_size=1) + mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) + + lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) + g.node("SaveImage", images=lazy_mix.out(0)) + + try: + client.run(g) + assert False, "Should have raised an error" + except Exception as e: + assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}" + + @pytest.mark.parametrize("test_value, expect_error", [ + (5, True), + ("foo", True), + (5.0, False), + ]) + def test_validation_error_literal(self, test_value, expect_error, client: ComfyClient, builder: GraphBuilder): + g = builder + validation1 = g.node("TestCustomValidation1", input1=test_value, input2=3.0) + g.node("SaveImage", images=validation1.out(0)) + + if expect_error: + with pytest.raises(urllib.error.HTTPError): + client.run(g) + else: + client.run(g) + + @pytest.mark.parametrize("test_type, test_value", [ + ("StubInt", 5), + ("StubFloat", 5.0) + ]) + def test_validation_error_edge1(self, test_type, test_value, client: ComfyClient, builder: GraphBuilder): + g = builder + stub = g.node(test_type, value=test_value) + validation1 = g.node("TestCustomValidation1", input1=stub.out(0), input2=3.0) + g.node("SaveImage", images=validation1.out(0)) + + with pytest.raises(urllib.error.HTTPError): + client.run(g) + + @pytest.mark.parametrize("test_type, test_value, expect_error", [ + ("StubInt", 5, True), + ("StubFloat", 5.0, False) + ]) + def test_validation_error_edge2(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder): + g = builder + stub = g.node(test_type, value=test_value) + validation2 = g.node("TestCustomValidation2", input1=stub.out(0), input2=3.0) + g.node("SaveImage", images=validation2.out(0)) + + if expect_error: + with pytest.raises(urllib.error.HTTPError): + client.run(g) + else: + client.run(g) + + @pytest.mark.parametrize("test_type, test_value, expect_error", [ + ("StubInt", 5, True), + ("StubFloat", 5.0, False) + ]) + def test_validation_error_edge3(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder): + g = builder + stub = g.node(test_type, value=test_value) + validation3 = g.node("TestCustomValidation3", input1=stub.out(0), input2=3.0) + g.node("SaveImage", images=validation3.out(0)) + + if expect_error: + with pytest.raises(urllib.error.HTTPError): + client.run(g) + else: + client.run(g) + + @pytest.mark.parametrize("test_type, test_value, expect_error", [ + ("StubInt", 5, True), + ("StubFloat", 5.0, False) + ]) + def test_validation_error_edge4(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder): + g = builder + stub = g.node(test_type, value=test_value) + validation4 = g.node("TestCustomValidation4", input1=stub.out(0), input2=3.0) + g.node("SaveImage", images=validation4.out(0)) + + if expect_error: + with pytest.raises(urllib.error.HTTPError): + client.run(g) + else: + client.run(g) + + @pytest.mark.parametrize("test_value1, test_value2, expect_error", [ + (0.0, 0.5, False), + (0.0, 5.0, False), + (0.0, 7.0, True) + ]) + def test_validation_error_kwargs(self, test_value1, test_value2, expect_error, client: ComfyClient, builder: GraphBuilder): + g = builder + validation5 = g.node("TestCustomValidation5", input1=test_value1, input2=test_value2) + g.node("SaveImage", images=validation5.out(0)) + + if expect_error: + with pytest.raises(urllib.error.HTTPError): + client.run(g) + else: + client.run(g) + + def test_cycle_error(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) + + lazy_mix1 = g.node("TestLazyMixImages", image1=input1.out(0), mask=mask.out(0)) + lazy_mix2 = g.node("TestLazyMixImages", image1=lazy_mix1.out(0), image2=input2.out(0), mask=mask.out(0)) + g.node("SaveImage", images=lazy_mix2.out(0)) + + # When the cycle exists on initial submission, it should raise a validation error + with pytest.raises(urllib.error.HTTPError): + client.run(g) + + def test_dynamic_cycle_error(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + generator = g.node("TestDynamicDependencyCycle", input1=input1.out(0), input2=input2.out(0)) + g.node("SaveImage", images=generator.out(0)) + + # When the cycle is in a graph that is generated dynamically, it should raise a runtime error + try: + client.run(g) + assert False, "Should have raised an error" + except Exception as e: + assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}" + assert e.args[0]['node_id'] == generator.id, "Error should have been on the generator node" + + def test_missing_node_error(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1) + input3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) + mix1 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) + mix2 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input3.out(0), mask=mask.out(0)) + # We have multiple outputs. The first is invalid, but the second is valid + g.node("SaveImage", images=mix1.out(0)) + g.node("SaveImage", images=mix2.out(0)) + g.remove_node("removeme") + + client.run(g) + + # Add back in the missing node to make sure the error doesn't break the server + input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1) + client.run(g) + + def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder): + g = builder + # Creating the nodes in this specific order previously caused a bug + save = g.node("SaveImage") + is_changed = g.node("TestCustomIsChanged", should_change=False) + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + + save.set_input('images', is_changed.out(0)) + is_changed.set_input('image', input1.out(0)) + + result1 = client.run(g) + result2 = client.run(g) + is_changed.set_input('should_change', True) + result3 = client.run(g) + result4 = client.run(g) + assert result1.did_run(is_changed), "is_changed should have been run" + assert not result2.did_run(is_changed), "is_changed should have been cached" + assert result3.did_run(is_changed), "is_changed should have been re-run" + assert result4.did_run(is_changed), "is_changed should not have been cached" + + def test_undeclared_inputs(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + input3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input4 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + average = g.node("TestVariadicAverage", input1=input1.out(0), input2=input2.out(0), input3=input3.out(0), input4=input4.out(0)) + output = g.node("SaveImage", images=average.out(0)) + + result = client.run(g) + result_image = result.get_images(output)[0] + expected = 255 // 4 + assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey" + + def test_for_loop(self, client: ComfyClient, builder: GraphBuilder): + g = builder + iterations = 4 + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + is_changed = g.node("TestCustomIsChanged", should_change=True, image=input2.out(0)) + for_open = g.node("TestForLoopOpen", remaining=iterations, initial_value1=is_changed.out(0)) + average = g.node("TestVariadicAverage", input1=input1.out(0), input2=for_open.out(2)) + for_close = g.node("TestForLoopClose", flow_control=for_open.out(0), initial_value1=average.out(0)) + output = g.node("SaveImage", images=for_close.out(0)) + + for iterations in range(1, 5): + for_open.set_input('remaining', iterations) + result = client.run(g) + result_image = result.get_images(output)[0] + expected = 255 // (2 ** iterations) + assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey" + assert result.did_run(is_changed) + + def test_mixed_expansion_returns(self, client: ComfyClient, builder: GraphBuilder): + g = builder + val_list = g.node("TestMakeListNode", value1=0.1, value2=0.2, value3=0.3) + mixed = g.node("TestMixedExpansionReturns", input1=val_list.out(0)) + output_dynamic = g.node("SaveImage", images=mixed.out(0)) + output_literal = g.node("SaveImage", images=mixed.out(1)) + + result = client.run(g) + images_dynamic = result.get_images(output_dynamic) + assert len(images_dynamic) == 3, "Should have 2 images" + assert numpy.array(images_dynamic[0]).min() == 25 and numpy.array(images_dynamic[0]).max() == 25, "First image should be 0.1" + assert numpy.array(images_dynamic[1]).min() == 51 and numpy.array(images_dynamic[1]).max() == 51, "Second image should be 0.2" + assert numpy.array(images_dynamic[2]).min() == 76 and numpy.array(images_dynamic[2]).max() == 76, "Third image should be 0.3" + + images_literal = result.get_images(output_literal) + assert len(images_literal) == 3, "Should have 2 images" + for i in range(3): + assert numpy.array(images_literal[i]).min() == 255 and numpy.array(images_literal[i]).max() == 255, "All images should be white" + + def test_mixed_lazy_results(self, client: ComfyClient, builder: GraphBuilder): + g = builder + val_list = g.node("TestMakeListNode", value1=0.0, value2=0.5, value3=1.0) + mask = g.node("StubMask", value=val_list.out(0), height=512, width=512, batch_size=1) + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) + rebatch = g.node("RebatchImages", images=mix.out(0), batch_size=3) + output = g.node("SaveImage", images=rebatch.out(0)) + + result = client.run(g) + images = result.get_images(output) + assert len(images) == 3, "Should have 3 image" + assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be 0.0" + assert numpy.array(images[1]).min() == 127 and numpy.array(images[1]).max() == 127, "Second image should be 0.5" + assert numpy.array(images[2]).min() == 255 and numpy.array(images[2]).max() == 255, "Third image should be 1.0" + + def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + + output1 = g.node("SaveImage", images=input1.out(0)) + output2 = g.node("SaveImage", images=input1.out(0)) + + result = client.run(g) + images1 = result.get_images(output1) + images2 = result.get_images(output2) + assert len(images1) == 1, "Should have 1 image" + assert len(images2) == 1, "Should have 1 image" + + + # This tests that only constant outputs are used in the call to `IS_CHANGED` + def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1) + test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5) + + output = g.node("PreviewImage", images=test_node.out(0)) + + result = client.run(g) + images = result.get_images(output) + assert len(images) == 1, "Should have 1 image" + assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25" + + result = client.run(g) + images = result.get_images(output) + assert len(images) == 1, "Should have 1 image" + assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25" + assert not result.did_run(test_node), "The execution should have been cached" + + # This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker + # as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node, + # only that one entry in the list is blocked. + def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder): + g = builder + image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + image3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + image_list = g.node("TestMakeListNode", value1=image1.out(0), value2=image2.out(0), value3=image3.out(0)) + int1 = g.node("StubInt", value=1) + int2 = g.node("StubInt", value=2) + int3 = g.node("StubInt", value=3) + int_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0), value3=int3.out(0)) + compare = g.node("TestIntConditions", a=int_list.out(0), b=2, operation="==") + blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False) + + list_output = g.node("TestMakeListNode", value1=blocker.out(0)) + output = g.node("PreviewImage", images=list_output.out(0)) + + result = client.run(g) + assert result.did_run(output), "The execution should have run" + images = result.get_images(output) + assert len(images) == 2, "Should have 2 images" + assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be black" + assert numpy.array(images[1]).min() == 0 and numpy.array(images[1]).max() == 0, "Second image should also be black" diff --git a/tests/inference/test_inference.py b/tests/inference/test_inference.py new file mode 100644 index 0000000..2e11778 --- /dev/null +++ b/tests/inference/test_inference.py @@ -0,0 +1,238 @@ +from copy import deepcopy +from io import BytesIO +from urllib import request +import numpy +import os +from PIL import Image +import pytest +from pytest import fixture +import time +import torch +from typing import Union +import json +import subprocess +import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) +import uuid +import urllib.request +import urllib.parse + + +from comfy.samplers import KSampler + +""" +These tests generate and save images through a range of parameters +""" + +class ComfyGraph: + def __init__(self, + graph: dict, + sampler_nodes: list[str], + ): + self.graph = graph + self.sampler_nodes = sampler_nodes + + def set_prompt(self, prompt, negative_prompt=None): + # Sets the prompt for the sampler nodes (eg. base and refiner) + for node in self.sampler_nodes: + prompt_node = self.graph[node]['inputs']['positive'][0] + self.graph[prompt_node]['inputs']['text'] = prompt + if negative_prompt: + negative_prompt_node = self.graph[node]['inputs']['negative'][0] + self.graph[negative_prompt_node]['inputs']['text'] = negative_prompt + + def set_sampler_name(self, sampler_name:str, ): + # sets the sampler name for the sampler nodes (eg. base and refiner) + for node in self.sampler_nodes: + self.graph[node]['inputs']['sampler_name'] = sampler_name + + def set_scheduler(self, scheduler:str): + # sets the sampler name for the sampler nodes (eg. base and refiner) + for node in self.sampler_nodes: + self.graph[node]['inputs']['scheduler'] = scheduler + + def set_filename_prefix(self, prefix:str): + # sets the filename prefix for the save nodes + for node in self.graph: + if self.graph[node]['class_type'] == 'SaveImage': + self.graph[node]['inputs']['filename_prefix'] = prefix + + +class ComfyClient: + # From examples/websockets_api_example.py + + def connect(self, + listen:str = '127.0.0.1', + port:Union[str,int] = 8188, + client_id: str = str(uuid.uuid4()) + ): + self.client_id = client_id + self.server_address = f"{listen}:{port}" + ws = websocket.WebSocket() + ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id)) + self.ws = ws + + def queue_prompt(self, prompt): + p = {"prompt": prompt, "client_id": self.client_id} + data = json.dumps(p).encode('utf-8') + req = urllib.request.Request("http://{}/prompt".format(self.server_address), data=data) + return json.loads(urllib.request.urlopen(req).read()) + + def get_image(self, filename, subfolder, folder_type): + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response: + return response.read() + + def get_history(self, prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response: + return json.loads(response.read()) + + def get_images(self, graph, save=True): + prompt = graph + if not save: + # Replace save nodes with preview nodes + prompt_str = json.dumps(prompt) + prompt_str = prompt_str.replace('SaveImage', 'PreviewImage') + prompt = json.loads(prompt_str) + + prompt_id = self.queue_prompt(prompt)['prompt_id'] + output_images = {} + while True: + out = self.ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message['type'] == 'executing': + data = message['data'] + if data['node'] is None and data['prompt_id'] == prompt_id: + break #Execution is done + else: + continue #previews are binary data + + history = self.get_history(prompt_id)[prompt_id] + for node_id in history['outputs']: + node_output = history['outputs'][node_id] + images_output = [] + if 'images' in node_output: + for image in node_output['images']: + image_data = self.get_image(image['filename'], image['subfolder'], image['type']) + images_output.append(image_data) + output_images[node_id] = images_output + + return output_images + +# +# Initialize graphs +# +default_graph_file = 'tests/inference/graphs/default_graph_sdxl1_0.json' +with open(default_graph_file, 'r') as file: + default_graph = json.loads(file.read()) +DEFAULT_COMFY_GRAPH = ComfyGraph(graph=default_graph, sampler_nodes=['10','14']) +DEFAULT_COMFY_GRAPH_ID = os.path.splitext(os.path.basename(default_graph_file))[0] + +# +# Loop through these variables +# +comfy_graph_list = [DEFAULT_COMFY_GRAPH] +comfy_graph_ids = [DEFAULT_COMFY_GRAPH_ID] +prompt_list = [ + 'a painting of a cat', +] + +sampler_list = KSampler.SAMPLERS +scheduler_list = KSampler.SCHEDULERS + +@pytest.mark.inference +@pytest.mark.parametrize("sampler", sampler_list) +@pytest.mark.parametrize("scheduler", scheduler_list) +@pytest.mark.parametrize("prompt", prompt_list) +class TestInference: + # + # Initialize server and client + # + @fixture(scope="class", autouse=True) + def _server(self, args_pytest): + # Start server + p = subprocess.Popen([ + 'python','main.py', + '--output-directory', args_pytest["output_dir"], + '--listen', args_pytest["listen"], + '--port', str(args_pytest["port"]), + ]) + yield + p.kill() + torch.cuda.empty_cache() + + def start_client(self, listen:str, port:int): + # Start client + comfy_client = ComfyClient() + # Connect to server (with retries) + n_tries = 5 + for i in range(n_tries): + time.sleep(4) + try: + comfy_client.connect(listen=listen, port=port) + except ConnectionRefusedError as e: + print(e) + print(f"({i+1}/{n_tries}) Retrying...") + else: + break + return comfy_client + + # + # Client and graph fixtures with server warmup + # + # Returns a "_client_graph", which is client-graph pair corresponding to an initialized server + # The "graph" is the default graph + @fixture(scope="class", params=comfy_graph_list, ids=comfy_graph_ids, autouse=True) + def _client_graph(self, request, args_pytest, _server) -> (ComfyClient, ComfyGraph): + comfy_graph = request.param + + # Start client + comfy_client = self.start_client(args_pytest["listen"], args_pytest["port"]) + + # Warm up pipeline + comfy_client.get_images(graph=comfy_graph.graph, save=False) + + yield comfy_client, comfy_graph + del comfy_client + del comfy_graph + torch.cuda.empty_cache() + + @fixture + def client(self, _client_graph): + client = _client_graph[0] + yield client + + @fixture + def comfy_graph(self, _client_graph): + # avoid mutating the graph + graph = deepcopy(_client_graph[1]) + yield graph + + def test_comfy( + self, + client, + comfy_graph, + sampler, + scheduler, + prompt, + request + ): + test_info = request.node.name + comfy_graph.set_filename_prefix(test_info) + # Settings for comfy graph + comfy_graph.set_sampler_name(sampler) + comfy_graph.set_scheduler(scheduler) + comfy_graph.set_prompt(prompt) + + # Generate + images = client.get_images(comfy_graph.graph) + + assert len(images) != 0, "No images generated" + # assert all images are not blank + for images_output in images.values(): + for image_data in images_output: + pil_image = Image.open(BytesIO(image_data)) + assert numpy.array(pil_image).any() != 0, "Image is blank" + + diff --git a/tests/inference/testing_nodes/testing-pack/__init__.py b/tests/inference/testing_nodes/testing-pack/__init__.py new file mode 100644 index 0000000..dcc7165 --- /dev/null +++ b/tests/inference/testing_nodes/testing-pack/__init__.py @@ -0,0 +1,23 @@ +from .specific_tests import TEST_NODE_CLASS_MAPPINGS, TEST_NODE_DISPLAY_NAME_MAPPINGS +from .flow_control import FLOW_CONTROL_NODE_CLASS_MAPPINGS, FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS +from .util import UTILITY_NODE_CLASS_MAPPINGS, UTILITY_NODE_DISPLAY_NAME_MAPPINGS +from .conditions import CONDITION_NODE_CLASS_MAPPINGS, CONDITION_NODE_DISPLAY_NAME_MAPPINGS +from .stubs import TEST_STUB_NODE_CLASS_MAPPINGS, TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS + +# NODE_CLASS_MAPPINGS = GENERAL_NODE_CLASS_MAPPINGS.update(COMPONENT_NODE_CLASS_MAPPINGS) +# NODE_DISPLAY_NAME_MAPPINGS = GENERAL_NODE_DISPLAY_NAME_MAPPINGS.update(COMPONENT_NODE_DISPLAY_NAME_MAPPINGS) + +NODE_CLASS_MAPPINGS = {} +NODE_CLASS_MAPPINGS.update(TEST_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(FLOW_CONTROL_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(UTILITY_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(CONDITION_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(TEST_STUB_NODE_CLASS_MAPPINGS) + +NODE_DISPLAY_NAME_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS.update(TEST_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(UTILITY_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(CONDITION_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS) + diff --git a/tests/inference/testing_nodes/testing-pack/conditions.py b/tests/inference/testing_nodes/testing-pack/conditions.py new file mode 100644 index 0000000..0c200ee --- /dev/null +++ b/tests/inference/testing_nodes/testing-pack/conditions.py @@ -0,0 +1,194 @@ +import re +import torch + +class TestIntConditions: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "a": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), + "b": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), + "operation": (["==", "!=", "<", ">", "<=", ">="],), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "int_condition" + + CATEGORY = "Testing/Logic" + + def int_condition(self, a, b, operation): + if operation == "==": + return (a == b,) + elif operation == "!=": + return (a != b,) + elif operation == "<": + return (a < b,) + elif operation == ">": + return (a > b,) + elif operation == "<=": + return (a <= b,) + elif operation == ">=": + return (a >= b,) + + +class TestFloatConditions: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "a": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 1}), + "b": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 1}), + "operation": (["==", "!=", "<", ">", "<=", ">="],), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "float_condition" + + CATEGORY = "Testing/Logic" + + def float_condition(self, a, b, operation): + if operation == "==": + return (a == b,) + elif operation == "!=": + return (a != b,) + elif operation == "<": + return (a < b,) + elif operation == ">": + return (a > b,) + elif operation == "<=": + return (a <= b,) + elif operation == ">=": + return (a >= b,) + +class TestStringConditions: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "a": ("STRING", {"multiline": False}), + "b": ("STRING", {"multiline": False}), + "operation": (["a == b", "a != b", "a IN b", "a MATCH REGEX(b)", "a BEGINSWITH b", "a ENDSWITH b"],), + "case_sensitive": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "string_condition" + + CATEGORY = "Testing/Logic" + + def string_condition(self, a, b, operation, case_sensitive): + if not case_sensitive: + a = a.lower() + b = b.lower() + + if operation == "a == b": + return (a == b,) + elif operation == "a != b": + return (a != b,) + elif operation == "a IN b": + return (a in b,) + elif operation == "a MATCH REGEX(b)": + try: + return (re.match(b, a) is not None,) + except: + return (False,) + elif operation == "a BEGINSWITH b": + return (a.startswith(b),) + elif operation == "a ENDSWITH b": + return (a.endswith(b),) + +class TestToBoolNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("*",), + }, + "optional": { + "invert": ("BOOLEAN", {"default": False}), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "to_bool" + + CATEGORY = "Testing/Logic" + + def to_bool(self, value, invert = False): + if isinstance(value, torch.Tensor): + if value.max().item() == 0 and value.min().item() == 0: + result = False + else: + result = True + else: + try: + result = bool(value) + except: + # Can't convert it? Well then it's something or other. I dunno, I'm not a Python programmer. + result = True + + if invert: + result = not result + + return (result,) + +class TestBoolOperationNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "a": ("BOOLEAN",), + "b": ("BOOLEAN",), + "op": (["a AND b", "a OR b", "a XOR b", "NOT a"],), + }, + } + + RETURN_TYPES = ("BOOLEAN",) + FUNCTION = "bool_operation" + + CATEGORY = "Testing/Logic" + + def bool_operation(self, a, b, op): + if op == "a AND b": + return (a and b,) + elif op == "a OR b": + return (a or b,) + elif op == "a XOR b": + return (a ^ b,) + elif op == "NOT a": + return (not a,) + + +CONDITION_NODE_CLASS_MAPPINGS = { + "TestIntConditions": TestIntConditions, + "TestFloatConditions": TestFloatConditions, + "TestStringConditions": TestStringConditions, + "TestToBoolNode": TestToBoolNode, + "TestBoolOperationNode": TestBoolOperationNode, +} + +CONDITION_NODE_DISPLAY_NAME_MAPPINGS = { + "TestIntConditions": "Int Condition", + "TestFloatConditions": "Float Condition", + "TestStringConditions": "String Condition", + "TestToBoolNode": "To Bool", + "TestBoolOperationNode": "Bool Operation", +} diff --git a/tests/inference/testing_nodes/testing-pack/flow_control.py b/tests/inference/testing_nodes/testing-pack/flow_control.py new file mode 100644 index 0000000..ba943be --- /dev/null +++ b/tests/inference/testing_nodes/testing-pack/flow_control.py @@ -0,0 +1,173 @@ +from comfy_execution.graph_utils import GraphBuilder, is_link +from comfy_execution.graph import ExecutionBlocker +from .tools import VariantSupport + +NUM_FLOW_SOCKETS = 5 +@VariantSupport() +class TestWhileLoopOpen: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "condition": ("BOOLEAN", {"default": True}), + }, + "optional": { + }, + } + for i in range(NUM_FLOW_SOCKETS): + inputs["optional"][f"initial_value{i}"] = ("*",) + return inputs + + RETURN_TYPES = tuple(["FLOW_CONTROL"] + ["*"] * NUM_FLOW_SOCKETS) + RETURN_NAMES = tuple(["FLOW_CONTROL"] + [f"value{i}" for i in range(NUM_FLOW_SOCKETS)]) + FUNCTION = "while_loop_open" + + CATEGORY = "Testing/Flow" + + def while_loop_open(self, condition, **kwargs): + values = [] + for i in range(NUM_FLOW_SOCKETS): + values.append(kwargs.get(f"initial_value{i}", None)) + return tuple(["stub"] + values) + +@VariantSupport() +class TestWhileLoopClose: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "flow_control": ("FLOW_CONTROL", {"rawLink": True}), + "condition": ("BOOLEAN", {"forceInput": True}), + }, + "optional": { + }, + "hidden": { + "dynprompt": "DYNPROMPT", + "unique_id": "UNIQUE_ID", + } + } + for i in range(NUM_FLOW_SOCKETS): + inputs["optional"][f"initial_value{i}"] = ("*",) + return inputs + + RETURN_TYPES = tuple(["*"] * NUM_FLOW_SOCKETS) + RETURN_NAMES = tuple([f"value{i}" for i in range(NUM_FLOW_SOCKETS)]) + FUNCTION = "while_loop_close" + + CATEGORY = "Testing/Flow" + + def explore_dependencies(self, node_id, dynprompt, upstream): + node_info = dynprompt.get_node(node_id) + if "inputs" not in node_info: + return + for k, v in node_info["inputs"].items(): + if is_link(v): + parent_id = v[0] + if parent_id not in upstream: + upstream[parent_id] = [] + self.explore_dependencies(parent_id, dynprompt, upstream) + upstream[parent_id].append(node_id) + + def collect_contained(self, node_id, upstream, contained): + if node_id not in upstream: + return + for child_id in upstream[node_id]: + if child_id not in contained: + contained[child_id] = True + self.collect_contained(child_id, upstream, contained) + + + def while_loop_close(self, flow_control, condition, dynprompt=None, unique_id=None, **kwargs): + assert dynprompt is not None + if not condition: + # We're done with the loop + values = [] + for i in range(NUM_FLOW_SOCKETS): + values.append(kwargs.get(f"initial_value{i}", None)) + return tuple(values) + + # We want to loop + upstream = {} + # Get the list of all nodes between the open and close nodes + self.explore_dependencies(unique_id, dynprompt, upstream) + + contained = {} + open_node = flow_control[0] + self.collect_contained(open_node, upstream, contained) + contained[unique_id] = True + contained[open_node] = True + + # We'll use the default prefix, but to avoid having node names grow exponentially in size, + # we'll use "Recurse" for the name of the recursively-generated copy of this node. + graph = GraphBuilder() + for node_id in contained: + original_node = dynprompt.get_node(node_id) + node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id) + node.set_override_display_id(node_id) + for node_id in contained: + original_node = dynprompt.get_node(node_id) + node = graph.lookup_node("Recurse" if node_id == unique_id else node_id) + assert node is not None + for k, v in original_node["inputs"].items(): + if is_link(v) and v[0] in contained: + parent = graph.lookup_node(v[0]) + assert parent is not None + node.set_input(k, parent.out(v[1])) + else: + node.set_input(k, v) + new_open = graph.lookup_node(open_node) + assert new_open is not None + for i in range(NUM_FLOW_SOCKETS): + key = f"initial_value{i}" + new_open.set_input(key, kwargs.get(key, None)) + my_clone = graph.lookup_node("Recurse") + assert my_clone is not None + result = map(lambda x: my_clone.out(x), range(NUM_FLOW_SOCKETS)) + return { + "result": tuple(result), + "expand": graph.finalize(), + } + +@VariantSupport() +class TestExecutionBlockerNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + inputs = { + "required": { + "input": ("*",), + "block": ("BOOLEAN",), + "verbose": ("BOOLEAN", {"default": False}), + }, + } + return inputs + + RETURN_TYPES = ("*",) + RETURN_NAMES = ("output",) + FUNCTION = "execution_blocker" + + CATEGORY = "Testing/Flow" + + def execution_blocker(self, input, block, verbose): + if block: + return (ExecutionBlocker("Blocked Execution" if verbose else None),) + return (input,) + +FLOW_CONTROL_NODE_CLASS_MAPPINGS = { + "TestWhileLoopOpen": TestWhileLoopOpen, + "TestWhileLoopClose": TestWhileLoopClose, + "TestExecutionBlocker": TestExecutionBlockerNode, +} +FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS = { + "TestWhileLoopOpen": "While Loop Open", + "TestWhileLoopClose": "While Loop Close", + "TestExecutionBlocker": "Execution Blocker", +} diff --git a/tests/inference/testing_nodes/testing-pack/specific_tests.py b/tests/inference/testing_nodes/testing-pack/specific_tests.py new file mode 100644 index 0000000..dd81002 --- /dev/null +++ b/tests/inference/testing_nodes/testing-pack/specific_tests.py @@ -0,0 +1,362 @@ +import torch +from .tools import VariantSupport +from comfy_execution.graph_utils import GraphBuilder + +class TestLazyMixImages: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image1": ("IMAGE",{"lazy": True}), + "image2": ("IMAGE",{"lazy": True}), + "mask": ("MASK",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "mix" + + CATEGORY = "Testing/Nodes" + + def check_lazy_status(self, mask, image1, image2): + mask_min = mask.min() + mask_max = mask.max() + needed = [] + if image1 is None and (mask_min != 1.0 or mask_max != 1.0): + needed.append("image1") + if image2 is None and (mask_min != 0.0 or mask_max != 0.0): + needed.append("image2") + return needed + + # Not trying to handle different batch sizes here just to keep the demo simple + def mix(self, mask, image1, image2): + mask_min = mask.min() + mask_max = mask.max() + if mask_min == 0.0 and mask_max == 0.0: + return (image1,) + elif mask_min == 1.0 and mask_max == 1.0: + return (image2,) + + if len(mask.shape) == 2: + mask = mask.unsqueeze(0) + if len(mask.shape) == 3: + mask = mask.unsqueeze(3) + if mask.shape[3] < image1.shape[3]: + mask = mask.repeat(1, 1, 1, image1.shape[3]) + + result = image1 * (1. - mask) + image2 * mask, + return (result[0],) + +class TestVariadicAverage: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "variadic_average" + + CATEGORY = "Testing/Nodes" + + def variadic_average(self, input1, **kwargs): + inputs = [input1] + while 'input' + str(len(inputs) + 1) in kwargs: + inputs.append(kwargs['input' + str(len(inputs) + 1)]) + return (torch.stack(inputs).mean(dim=0),) + + +class TestCustomIsChanged: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + }, + "optional": { + "should_change": ("BOOL", {"default": False}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "custom_is_changed" + + CATEGORY = "Testing/Nodes" + + def custom_is_changed(self, image, should_change=False): + return (image,) + + @classmethod + def IS_CHANGED(cls, should_change=False, *args, **kwargs): + if should_change: + return float("NaN") + else: + return False + +class TestIsChangedWithConstants: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "custom_is_changed" + + CATEGORY = "Testing/Nodes" + + def custom_is_changed(self, image, value): + return (image * value,) + + @classmethod + def IS_CHANGED(cls, image, value): + if image is None: + return value + else: + return image.mean().item() * value + +class TestCustomValidation1: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": ("IMAGE,FLOAT",), + "input2": ("IMAGE,FLOAT",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "custom_validation1" + + CATEGORY = "Testing/Nodes" + + def custom_validation1(self, input1, input2): + if isinstance(input1, float) and isinstance(input2, float): + result = torch.ones([1, 512, 512, 3]) * input1 * input2 + else: + result = input1 * input2 + return (result,) + + @classmethod + def VALIDATE_INPUTS(cls, input1=None, input2=None): + if input1 is not None: + if not isinstance(input1, (torch.Tensor, float)): + return f"Invalid type of input1: {type(input1)}" + if input2 is not None: + if not isinstance(input2, (torch.Tensor, float)): + return f"Invalid type of input2: {type(input2)}" + + return True + +class TestCustomValidation2: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": ("IMAGE,FLOAT",), + "input2": ("IMAGE,FLOAT",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "custom_validation2" + + CATEGORY = "Testing/Nodes" + + def custom_validation2(self, input1, input2): + if isinstance(input1, float) and isinstance(input2, float): + result = torch.ones([1, 512, 512, 3]) * input1 * input2 + else: + result = input1 * input2 + return (result,) + + @classmethod + def VALIDATE_INPUTS(cls, input_types, input1=None, input2=None): + if input1 is not None: + if not isinstance(input1, (torch.Tensor, float)): + return f"Invalid type of input1: {type(input1)}" + if input2 is not None: + if not isinstance(input2, (torch.Tensor, float)): + return f"Invalid type of input2: {type(input2)}" + + if 'input1' in input_types: + if input_types['input1'] not in ["IMAGE", "FLOAT"]: + return f"Invalid type of input1: {input_types['input1']}" + if 'input2' in input_types: + if input_types['input2'] not in ["IMAGE", "FLOAT"]: + return f"Invalid type of input2: {input_types['input2']}" + + return True + +@VariantSupport() +class TestCustomValidation3: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": ("IMAGE,FLOAT",), + "input2": ("IMAGE,FLOAT",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "custom_validation3" + + CATEGORY = "Testing/Nodes" + + def custom_validation3(self, input1, input2): + if isinstance(input1, float) and isinstance(input2, float): + result = torch.ones([1, 512, 512, 3]) * input1 * input2 + else: + result = input1 * input2 + return (result,) + +class TestCustomValidation4: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": ("FLOAT",), + "input2": ("FLOAT",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "custom_validation4" + + CATEGORY = "Testing/Nodes" + + def custom_validation4(self, input1, input2): + result = torch.ones([1, 512, 512, 3]) * input1 * input2 + return (result,) + + @classmethod + def VALIDATE_INPUTS(cls, input1, input2): + if input1 is not None: + if not isinstance(input1, float): + return f"Invalid type of input1: {type(input1)}" + if input2 is not None: + if not isinstance(input2, float): + return f"Invalid type of input2: {type(input2)}" + + return True + +class TestCustomValidation5: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": ("FLOAT", {"min": 0.0, "max": 1.0}), + "input2": ("FLOAT", {"min": 0.0, "max": 1.0}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "custom_validation5" + + CATEGORY = "Testing/Nodes" + + def custom_validation5(self, input1, input2): + value = input1 * input2 + return (torch.ones([1, 512, 512, 3]) * value,) + + @classmethod + def VALIDATE_INPUTS(cls, **kwargs): + if kwargs['input2'] == 7.0: + return "7s are not allowed. I've never liked 7s." + return True + +class TestDynamicDependencyCycle: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": ("IMAGE",), + "input2": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "dynamic_dependency_cycle" + + CATEGORY = "Testing/Nodes" + + def dynamic_dependency_cycle(self, input1, input2): + g = GraphBuilder() + mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) + mix1 = g.node("TestLazyMixImages", image1=input1, mask=mask.out(0)) + mix2 = g.node("TestLazyMixImages", image1=mix1.out(0), image2=input2, mask=mask.out(0)) + + # Create the cyle + mix1.set_input("image2", mix2.out(0)) + + return { + "result": (mix2.out(0),), + "expand": g.finalize(), + } + +class TestMixedExpansionReturns: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": ("FLOAT",), + }, + } + + RETURN_TYPES = ("IMAGE","IMAGE") + FUNCTION = "mixed_expansion_returns" + + CATEGORY = "Testing/Nodes" + + def mixed_expansion_returns(self, input1): + white_image = torch.ones([1, 512, 512, 3]) + if input1 <= 0.1: + return (torch.ones([1, 512, 512, 3]) * 0.1, white_image) + elif input1 <= 0.2: + return { + "result": (torch.ones([1, 512, 512, 3]) * 0.2, white_image), + } + else: + g = GraphBuilder() + mask = g.node("StubMask", value=0.3, height=512, width=512, batch_size=1) + black = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + white = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + mix = g.node("TestLazyMixImages", image1=black.out(0), image2=white.out(0), mask=mask.out(0)) + return { + "result": (mix.out(0), white_image), + "expand": g.finalize(), + } + +TEST_NODE_CLASS_MAPPINGS = { + "TestLazyMixImages": TestLazyMixImages, + "TestVariadicAverage": TestVariadicAverage, + "TestCustomIsChanged": TestCustomIsChanged, + "TestIsChangedWithConstants": TestIsChangedWithConstants, + "TestCustomValidation1": TestCustomValidation1, + "TestCustomValidation2": TestCustomValidation2, + "TestCustomValidation3": TestCustomValidation3, + "TestCustomValidation4": TestCustomValidation4, + "TestCustomValidation5": TestCustomValidation5, + "TestDynamicDependencyCycle": TestDynamicDependencyCycle, + "TestMixedExpansionReturns": TestMixedExpansionReturns, +} + +TEST_NODE_DISPLAY_NAME_MAPPINGS = { + "TestLazyMixImages": "Lazy Mix Images", + "TestVariadicAverage": "Variadic Average", + "TestCustomIsChanged": "Custom IsChanged", + "TestIsChangedWithConstants": "IsChanged With Constants", + "TestCustomValidation1": "Custom Validation 1", + "TestCustomValidation2": "Custom Validation 2", + "TestCustomValidation3": "Custom Validation 3", + "TestCustomValidation4": "Custom Validation 4", + "TestCustomValidation5": "Custom Validation 5", + "TestDynamicDependencyCycle": "Dynamic Dependency Cycle", + "TestMixedExpansionReturns": "Mixed Expansion Returns", +} diff --git a/tests/inference/testing_nodes/testing-pack/stubs.py b/tests/inference/testing_nodes/testing-pack/stubs.py new file mode 100644 index 0000000..a1df875 --- /dev/null +++ b/tests/inference/testing_nodes/testing-pack/stubs.py @@ -0,0 +1,129 @@ +import torch + +class StubImage: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "content": (['WHITE', 'BLACK', 'NOISE'],), + "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}), + "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "stub_image" + + CATEGORY = "Testing/Stub Nodes" + + def stub_image(self, content, height, width, batch_size): + if content == "WHITE": + return (torch.ones(batch_size, height, width, 3),) + elif content == "BLACK": + return (torch.zeros(batch_size, height, width, 3),) + elif content == "NOISE": + return (torch.rand(batch_size, height, width, 3),) + +class StubConstantImage: + def __init__(self): + pass + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}), + "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "stub_constant_image" + + CATEGORY = "Testing/Stub Nodes" + + def stub_constant_image(self, value, height, width, batch_size): + return (torch.ones(batch_size, height, width, 3) * value,) + +class StubMask: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}), + "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}), + }, + } + + RETURN_TYPES = ("MASK",) + FUNCTION = "stub_mask" + + CATEGORY = "Testing/Stub Nodes" + + def stub_mask(self, value, height, width, batch_size): + return (torch.ones(batch_size, height, width) * value,) + +class StubInt: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("INT", {"default": 0, "min": -0xffffffff, "max": 0xffffffff, "step": 1}), + }, + } + + RETURN_TYPES = ("INT",) + FUNCTION = "stub_int" + + CATEGORY = "Testing/Stub Nodes" + + def stub_int(self, value): + return (value,) + +class StubFloat: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 0.0, "min": -1.0e38, "max": 1.0e38, "step": 0.01}), + }, + } + + RETURN_TYPES = ("FLOAT",) + FUNCTION = "stub_float" + + CATEGORY = "Testing/Stub Nodes" + + def stub_float(self, value): + return (value,) + +TEST_STUB_NODE_CLASS_MAPPINGS = { + "StubImage": StubImage, + "StubConstantImage": StubConstantImage, + "StubMask": StubMask, + "StubInt": StubInt, + "StubFloat": StubFloat, +} +TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS = { + "StubImage": "Stub Image", + "StubConstantImage": "Stub Constant Image", + "StubMask": "Stub Mask", + "StubInt": "Stub Int", + "StubFloat": "Stub Float", +} diff --git a/tests/inference/testing_nodes/testing-pack/tools.py b/tests/inference/testing_nodes/testing-pack/tools.py new file mode 100644 index 0000000..34b28c0 --- /dev/null +++ b/tests/inference/testing_nodes/testing-pack/tools.py @@ -0,0 +1,53 @@ + +def MakeSmartType(t): + if isinstance(t, str): + return SmartType(t) + return t + +class SmartType(str): + def __ne__(self, other): + if self == "*" or other == "*": + return False + selfset = set(self.split(',')) + otherset = set(other.split(',')) + return not selfset.issubset(otherset) + +def VariantSupport(): + def decorator(cls): + if hasattr(cls, "INPUT_TYPES"): + old_input_types = getattr(cls, "INPUT_TYPES") + def new_input_types(*args, **kwargs): + types = old_input_types(*args, **kwargs) + for category in ["required", "optional"]: + if category not in types: + continue + for key, value in types[category].items(): + if isinstance(value, tuple): + types[category][key] = (MakeSmartType(value[0]),) + value[1:] + return types + setattr(cls, "INPUT_TYPES", new_input_types) + if hasattr(cls, "RETURN_TYPES"): + old_return_types = cls.RETURN_TYPES + setattr(cls, "RETURN_TYPES", tuple(MakeSmartType(x) for x in old_return_types)) + if hasattr(cls, "VALIDATE_INPUTS"): + # Reflection is used to determine what the function signature is, so we can't just change the function signature + raise NotImplementedError("VariantSupport does not support VALIDATE_INPUTS yet") + else: + def validate_inputs(input_types): + inputs = cls.INPUT_TYPES() + for key, value in input_types.items(): + if isinstance(value, SmartType): + continue + if "required" in inputs and key in inputs["required"]: + expected_type = inputs["required"][key][0] + elif "optional" in inputs and key in inputs["optional"]: + expected_type = inputs["optional"][key][0] + else: + expected_type = None + if expected_type is not None and MakeSmartType(value) != expected_type: + return f"Invalid type of {key}: {value} (expected {expected_type})" + return True + setattr(cls, "VALIDATE_INPUTS", validate_inputs) + return cls + return decorator + diff --git a/tests/inference/testing_nodes/testing-pack/util.py b/tests/inference/testing_nodes/testing-pack/util.py new file mode 100644 index 0000000..ca116c1 --- /dev/null +++ b/tests/inference/testing_nodes/testing-pack/util.py @@ -0,0 +1,364 @@ +from comfy_execution.graph_utils import GraphBuilder +from .tools import VariantSupport + +@VariantSupport() +class TestAccumulateNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "to_add": ("*",), + }, + "optional": { + "accumulation": ("ACCUMULATION",), + }, + } + + RETURN_TYPES = ("ACCUMULATION",) + FUNCTION = "accumulate" + + CATEGORY = "Testing/Lists" + + def accumulate(self, to_add, accumulation = None): + if accumulation is None: + value = [to_add] + else: + value = accumulation["accum"] + [to_add] + return ({"accum": value},) + +@VariantSupport() +class TestAccumulationHeadNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "accumulation": ("ACCUMULATION",), + }, + } + + RETURN_TYPES = ("ACCUMULATION", "*",) + FUNCTION = "accumulation_head" + + CATEGORY = "Testing/Lists" + + def accumulation_head(self, accumulation): + accum = accumulation["accum"] + if len(accum) == 0: + return (accumulation, None) + else: + return ({"accum": accum[1:]}, accum[0]) + +class TestAccumulationTailNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "accumulation": ("ACCUMULATION",), + }, + } + + RETURN_TYPES = ("ACCUMULATION", "*",) + FUNCTION = "accumulation_tail" + + CATEGORY = "Testing/Lists" + + def accumulation_tail(self, accumulation): + accum = accumulation["accum"] + if len(accum) == 0: + return (None, accumulation) + else: + return ({"accum": accum[:-1]}, accum[-1]) + +@VariantSupport() +class TestAccumulationToListNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "accumulation": ("ACCUMULATION",), + }, + } + + RETURN_TYPES = ("*",) + OUTPUT_IS_LIST = (True,) + + FUNCTION = "accumulation_to_list" + + CATEGORY = "Testing/Lists" + + def accumulation_to_list(self, accumulation): + return (accumulation["accum"],) + +@VariantSupport() +class TestListToAccumulationNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "list": ("*",), + }, + } + + RETURN_TYPES = ("ACCUMULATION",) + INPUT_IS_LIST = (True,) + + FUNCTION = "list_to_accumulation" + + CATEGORY = "Testing/Lists" + + def list_to_accumulation(self, list): + return ({"accum": list},) + +@VariantSupport() +class TestAccumulationGetLengthNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "accumulation": ("ACCUMULATION",), + }, + } + + RETURN_TYPES = ("INT",) + + FUNCTION = "accumlength" + + CATEGORY = "Testing/Lists" + + def accumlength(self, accumulation): + return (len(accumulation['accum']),) + +@VariantSupport() +class TestAccumulationGetItemNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "accumulation": ("ACCUMULATION",), + "index": ("INT", {"default":0, "step":1}) + }, + } + + RETURN_TYPES = ("*",) + + FUNCTION = "get_item" + + CATEGORY = "Testing/Lists" + + def get_item(self, accumulation, index): + return (accumulation['accum'][index],) + +@VariantSupport() +class TestAccumulationSetItemNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "accumulation": ("ACCUMULATION",), + "index": ("INT", {"default":0, "step":1}), + "value": ("*",), + }, + } + + RETURN_TYPES = ("ACCUMULATION",) + + FUNCTION = "set_item" + + CATEGORY = "Testing/Lists" + + def set_item(self, accumulation, index, value): + new_accum = accumulation['accum'][:] + new_accum[index] = value + return ({"accum": new_accum},) + +class TestIntMathOperation: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "a": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), + "b": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), + "operation": (["add", "subtract", "multiply", "divide", "modulo", "power"],), + }, + } + + RETURN_TYPES = ("INT",) + FUNCTION = "int_math_operation" + + CATEGORY = "Testing/Logic" + + def int_math_operation(self, a, b, operation): + if operation == "add": + return (a + b,) + elif operation == "subtract": + return (a - b,) + elif operation == "multiply": + return (a * b,) + elif operation == "divide": + return (a // b,) + elif operation == "modulo": + return (a % b,) + elif operation == "power": + return (a ** b,) + + +from .flow_control import NUM_FLOW_SOCKETS +@VariantSupport() +class TestForLoopOpen: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "remaining": ("INT", {"default": 1, "min": 0, "max": 100000, "step": 1}), + }, + "optional": { + f"initial_value{i}": ("*",) for i in range(1, NUM_FLOW_SOCKETS) + }, + "hidden": { + "initial_value0": ("*",) + } + } + + RETURN_TYPES = tuple(["FLOW_CONTROL", "INT",] + ["*"] * (NUM_FLOW_SOCKETS-1)) + RETURN_NAMES = tuple(["flow_control", "remaining"] + [f"value{i}" for i in range(1, NUM_FLOW_SOCKETS)]) + FUNCTION = "for_loop_open" + + CATEGORY = "Testing/Flow" + + def for_loop_open(self, remaining, **kwargs): + graph = GraphBuilder() + if "initial_value0" in kwargs: + remaining = kwargs["initial_value0"] + while_open = graph.node("TestWhileLoopOpen", condition=remaining, initial_value0=remaining, **{(f"initial_value{i}"): kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)}) + outputs = [kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)] + return { + "result": tuple(["stub", remaining] + outputs), + "expand": graph.finalize(), + } + +@VariantSupport() +class TestForLoopClose: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "flow_control": ("FLOW_CONTROL", {"rawLink": True}), + }, + "optional": { + f"initial_value{i}": ("*",{"rawLink": True}) for i in range(1, NUM_FLOW_SOCKETS) + }, + } + + RETURN_TYPES = tuple(["*"] * (NUM_FLOW_SOCKETS-1)) + RETURN_NAMES = tuple([f"value{i}" for i in range(1, NUM_FLOW_SOCKETS)]) + FUNCTION = "for_loop_close" + + CATEGORY = "Testing/Flow" + + def for_loop_close(self, flow_control, **kwargs): + graph = GraphBuilder() + while_open = flow_control[0] + sub = graph.node("TestIntMathOperation", operation="subtract", a=[while_open,1], b=1) + cond = graph.node("TestToBoolNode", value=sub.out(0)) + input_values = {f"initial_value{i}": kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)} + while_close = graph.node("TestWhileLoopClose", + flow_control=flow_control, + condition=cond.out(0), + initial_value0=sub.out(0), + **input_values) + return { + "result": tuple([while_close.out(i) for i in range(1, NUM_FLOW_SOCKETS)]), + "expand": graph.finalize(), + } + +NUM_LIST_SOCKETS = 10 +@VariantSupport() +class TestMakeListNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value1": ("*",), + }, + "optional": { + f"value{i}": ("*",) for i in range(1, NUM_LIST_SOCKETS) + }, + } + + RETURN_TYPES = ("*",) + FUNCTION = "make_list" + OUTPUT_IS_LIST = (True,) + + CATEGORY = "Testing/Lists" + + def make_list(self, **kwargs): + result = [] + for i in range(NUM_LIST_SOCKETS): + if f"value{i}" in kwargs: + result.append(kwargs[f"value{i}"]) + return (result,) + +UTILITY_NODE_CLASS_MAPPINGS = { + "TestAccumulateNode": TestAccumulateNode, + "TestAccumulationHeadNode": TestAccumulationHeadNode, + "TestAccumulationTailNode": TestAccumulationTailNode, + "TestAccumulationToListNode": TestAccumulationToListNode, + "TestListToAccumulationNode": TestListToAccumulationNode, + "TestAccumulationGetLengthNode": TestAccumulationGetLengthNode, + "TestAccumulationGetItemNode": TestAccumulationGetItemNode, + "TestAccumulationSetItemNode": TestAccumulationSetItemNode, + "TestForLoopOpen": TestForLoopOpen, + "TestForLoopClose": TestForLoopClose, + "TestIntMathOperation": TestIntMathOperation, + "TestMakeListNode": TestMakeListNode, +} +UTILITY_NODE_DISPLAY_NAME_MAPPINGS = { + "TestAccumulateNode": "Accumulate", + "TestAccumulationHeadNode": "Accumulation Head", + "TestAccumulationTailNode": "Accumulation Tail", + "TestAccumulationToListNode": "Accumulation to List", + "TestListToAccumulationNode": "List to Accumulation", + "TestAccumulationGetLengthNode": "Accumulation Get Length", + "TestAccumulationGetItemNode": "Accumulation Get Item", + "TestAccumulationSetItemNode": "Accumulation Set Item", + "TestForLoopOpen": "For Loop Open", + "TestForLoopClose": "For Loop Close", + "TestIntMathOperation": "Int Math Operation", + "TestMakeListNode": "Make List", +} diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/extra_config.py b/utils/extra_config.py new file mode 100644 index 0000000..9087659 --- /dev/null +++ b/utils/extra_config.py @@ -0,0 +1,28 @@ +import os +import yaml +import folder_paths +import logging + +def load_extra_path_config(yaml_path): + with open(yaml_path, 'r') as stream: + config = yaml.safe_load(stream) + for c in config: + conf = config[c] + if conf is None: + continue + base_path = None + if "base_path" in conf: + base_path = conf.pop("base_path") + base_path = os.path.expandvars(os.path.expanduser(base_path)) + is_default = False + if "is_default" in conf: + is_default = conf.pop("is_default") + for x in conf: + for y in conf[x].split("\n"): + if len(y) == 0: + continue + full_path = y + if base_path is not None: + full_path = os.path.join(base_path, full_path) + logging.info("Adding extra search path {} {}".format(x, full_path)) + folder_paths.add_model_folder_path(x, full_path, is_default) diff --git a/web/assets/CREDIT.txt b/web/assets/CREDIT.txt new file mode 100644 index 0000000..b3a9bc9 --- /dev/null +++ b/web/assets/CREDIT.txt @@ -0,0 +1 @@ +Thanks to OpenArt (https://openart.ai) for providing the sorted-custom-node-map data, captured in September 2024. \ No newline at end of file diff --git a/web/assets/ExtensionPanel-BmKi_NKS.js b/web/assets/ExtensionPanel-BmKi_NKS.js new file mode 100644 index 0000000..6d83df5 --- /dev/null +++ b/web/assets/ExtensionPanel-BmKi_NKS.js @@ -0,0 +1,103 @@ +var __defProp = Object.defineProperty; +var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); +import { d as defineComponent, bQ as useExtensionStore, u as useSettingStore, r as ref, o as onMounted, q as computed, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, z as unref, bR as script$1, A as createBaseVNode, x as createBlock, N as Fragment, O as renderList, a6 as toDisplayString, aw as createTextVNode, j as createCommentVNode, D as script$4 } from "./index-BHayQCxv.js"; +import { s as script, a as script$2, b as script$3 } from "./index-CwRXxFdA.js"; +import "./index-C_wOqB0f.js"; +const _hoisted_1 = { class: "extension-panel" }; +const _hoisted_2 = { class: "mt-4" }; +const _sfc_main = /* @__PURE__ */ defineComponent({ + __name: "ExtensionPanel", + setup(__props) { + const extensionStore = useExtensionStore(); + const settingStore = useSettingStore(); + const editingEnabledExtensions = ref({}); + onMounted(() => { + extensionStore.extensions.forEach((ext) => { + editingEnabledExtensions.value[ext.name] = extensionStore.isExtensionEnabled(ext.name); + }); + }); + const changedExtensions = computed(() => { + return extensionStore.extensions.filter( + (ext) => editingEnabledExtensions.value[ext.name] !== extensionStore.isExtensionEnabled(ext.name) + ); + }); + const hasChanges = computed(() => { + return changedExtensions.value.length > 0; + }); + const updateExtensionStatus = /* @__PURE__ */ __name(() => { + const editingDisabledExtensionNames = Object.entries( + editingEnabledExtensions.value + ).filter(([_, enabled]) => !enabled).map(([name]) => name); + settingStore.set("Comfy.Extension.Disabled", [ + ...extensionStore.inactiveDisabledExtensionNames, + ...editingDisabledExtensionNames + ]); + }, "updateExtensionStatus"); + const applyChanges = /* @__PURE__ */ __name(() => { + window.location.reload(); + }, "applyChanges"); + return (_ctx, _cache) => { + return openBlock(), createElementBlock("div", _hoisted_1, [ + createVNode(unref(script$2), { + value: unref(extensionStore).extensions, + stripedRows: "", + size: "small" + }, { + default: withCtx(() => [ + createVNode(unref(script), { + field: "name", + header: _ctx.$t("extensionName"), + sortable: "" + }, null, 8, ["header"]), + createVNode(unref(script), { pt: { + bodyCell: "flex items-center justify-end" + } }, { + body: withCtx((slotProps) => [ + createVNode(unref(script$1), { + modelValue: editingEnabledExtensions.value[slotProps.data.name], + "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => editingEnabledExtensions.value[slotProps.data.name] = $event, "onUpdate:modelValue"), + onChange: updateExtensionStatus + }, null, 8, ["modelValue", "onUpdate:modelValue"]) + ]), + _: 1 + }) + ]), + _: 1 + }, 8, ["value"]), + createBaseVNode("div", _hoisted_2, [ + hasChanges.value ? (openBlock(), createBlock(unref(script$3), { + key: 0, + severity: "info" + }, { + default: withCtx(() => [ + createBaseVNode("ul", null, [ + (openBlock(true), createElementBlock(Fragment, null, renderList(changedExtensions.value, (ext) => { + return openBlock(), createElementBlock("li", { + key: ext.name + }, [ + createBaseVNode("span", null, toDisplayString(unref(extensionStore).isExtensionEnabled(ext.name) ? "[-]" : "[+]"), 1), + createTextVNode(" " + toDisplayString(ext.name), 1) + ]); + }), 128)) + ]) + ]), + _: 1 + })) : createCommentVNode("", true), + createVNode(unref(script$4), { + label: _ctx.$t("reloadToApplyChanges"), + icon: "pi pi-refresh", + onClick: applyChanges, + disabled: !hasChanges.value, + text: "", + fluid: "", + severity: "danger" + }, null, 8, ["label", "disabled"]) + ]) + ]); + }; + } +}); +export { + _sfc_main as default +}; +//# sourceMappingURL=ExtensionPanel-BmKi_NKS.js.map diff --git a/web/assets/ExtensionPanel-BmKi_NKS.js.map b/web/assets/ExtensionPanel-BmKi_NKS.js.map new file mode 100644 index 0000000..e45b90e --- /dev/null +++ b/web/assets/ExtensionPanel-BmKi_NKS.js.map @@ -0,0 +1 @@ +{"version":3,"file":"ExtensionPanel-BmKi_NKS.js","sources":["../../src/components/dialog/content/setting/ExtensionPanel.vue"],"sourcesContent":["\n\n\n"],"names":[],"mappings":";;;;;;;;;;AAmDA,UAAM,iBAAiB;AACvB,UAAM,eAAe;AAEf,UAAA,2BAA2B,IAA6B,CAAA,CAAE;AAEhE,cAAU,MAAM;AACC,qBAAA,WAAW,QAAQ,CAAC,QAAQ;AACzC,iCAAyB,MAAM,IAAI,IAAI,IACrC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA,CAC7C;AAAA,IAAA,CACF;AAEK,UAAA,oBAAoB,SAAS,MAAM;AACvC,aAAO,eAAe,WAAW;AAAA,QAC/B,CAAC,QACC,yBAAyB,MAAM,IAAI,IAAI,MACvC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA;AAAA,IAC9C,CACD;AAEK,UAAA,aAAa,SAAS,MAAM;AACzB,aAAA,kBAAkB,MAAM,SAAS;AAAA,IAAA,CACzC;AAED,UAAM,wBAAwB,6BAAM;AAClC,YAAM,gCAAgC,OAAO;AAAA,QAC3C,yBAAyB;AAAA,MAExB,EAAA,OAAO,CAAC,CAAC,GAAG,OAAO,MAAM,CAAC,OAAO,EACjC,IAAI,CAAC,CAAC,IAAI,MAAM,IAAI;AAEvB,mBAAa,IAAI,4BAA4B;AAAA,QAC3C,GAAG,eAAe;AAAA,QAClB,GAAG;AAAA,MAAA,CACJ;AAAA,IAAA,GAV2B;AAa9B,UAAM,eAAe,6BAAM;AAEzB,aAAO,SAAS;IAAO,GAFJ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"} \ No newline at end of file diff --git a/web/assets/GraphView-C4blCugc.js b/web/assets/GraphView-C4blCugc.js new file mode 100644 index 0000000..af0124a --- /dev/null +++ b/web/assets/GraphView-C4blCugc.js @@ -0,0 +1,7986 @@ +var __defProp = Object.defineProperty; +var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); +import { d as defineComponent, u as useSettingStore, r as ref, a as useTitleEditorStore, b as useCanvasStore, w as watch, L as LGraphGroup, c as app, e as LGraphNode, o as onMounted, f as onUnmounted, g as openBlock, h as createElementBlock, i as createVNode, E as EditableText, n as normalizeStyle, j as createCommentVNode, k as LiteGraph, _ as _export_sfc, B as BaseStyle, s as script$e, l as resolveComponent, m as mergeProps, p as renderSlot, q as computed, t as resolveDirective, v as withDirectives, x as createBlock, y as withCtx, z as unref, A as createBaseVNode, C as normalizeClass, D as script$f, F as useCommandStore, G as useDialogStore, S as SettingDialogHeader, H as SettingDialogContent, I as onBeforeUnmount, J as resolveDynamicComponent, K as useWorkspaceStore, M as useKeybindingStore, N as Fragment, O as renderList, T as Teleport, P as pushScopeId, Q as popScopeId, R as script$g, U as getWidth, V as findSingle, W as getOuterHeight, X as getOffset, Y as getOuterWidth, Z as getHeight, $ as script$h, a0 as script$i, a1 as Ripple, a2 as getAttribute, a3 as focus, a4 as equals, a5 as useBottomPanelStore, a6 as toDisplayString, a7 as script$j, a8 as getVNodeProp, a9 as isArray, aa as useSidebarTabStore, ab as vShow, ac as isNotEmpty, ad as UniqueComponentId, ae as ZIndex, af as resolveFieldData, ag as OverlayEventBus, ah as isEmpty, ai as addStyle, aj as relativePosition, ak as absolutePosition, al as ConnectedOverlayScrollHandler, am as isTouchDevice, an as findLastIndex, ao as script$k, ap as script$l, aq as script$m, ar as script$n, as as script$o, at as script$p, au as Transition, av as createSlots, aw as createTextVNode, ax as useNodeFrequencyStore, ay as useNodeBookmarkStore, az as highlightQuery, aA as script$q, aB as formatNumberWithSuffix, aC as NodeSourceType, aD as useI18n, aE as useNodeDefStore, aF as NodePreview, aG as NodeSearchFilter, aH as script$r, aI as SearchFilterChip, aJ as watchEffect, aK as toRaw, aL as LinkReleaseTriggerAction, aM as useEventListener, aN as nextTick, aO as getColorPalette, aP as BadgePosition, aQ as LGraphBadge, aR as _, aS as defaultColorPalette, aT as NodeBadgeMode, aU as markRaw, aV as useModelToNodeStore, aW as usePragmaticDroppable, aX as ComfyNodeDefImpl, aY as ComfyModelDef, aZ as LGraph, a_ as LLink, a$ as DragAndScale, b0 as LGraphCanvas, b1 as ContextMenu, b2 as script$t, b3 as script$v, b4 as script$w, b5 as normalizeProps, b6 as ToastEventBus, b7 as setAttribute, b8 as TransitionGroup, b9 as useToast, ba as useToastStore, bb as useExecutionStore, bc as useWorkflowStore, bd as useTitle, be as withModifiers, bf as script$x, bg as resolve, bh as script$y, bi as script$z, bj as isPrintableCharacter, bk as guardReactiveProps, bl as useMenuItemStore, bm as script$C, bn as nestedPosition, bo as useQueueSettingsStore, bp as storeToRefs, bq as isRef, br as script$D, bs as useQueuePendingTaskCountStore, bt as useLocalStorage, bu as useDraggable, bv as watchDebounced, bw as inject, bx as useElementBounding, by as script$E, bz as lodashExports, bA as useEventBus, bB as provide, bC as api, bD as i18n, bE as useWorkflowBookmarkStore } from "./index-BHayQCxv.js"; +import { s as script$s, a as script$u, b as script$A, c as script$B } from "./index-C_wOqB0f.js"; +const _sfc_main$q = /* @__PURE__ */ defineComponent({ + __name: "TitleEditor", + setup(__props) { + const settingStore = useSettingStore(); + const showInput = ref(false); + const editedTitle = ref(""); + const inputStyle = ref({ + position: "fixed", + left: "0px", + top: "0px", + width: "200px", + height: "20px", + fontSize: "12px" + }); + const titleEditorStore = useTitleEditorStore(); + const canvasStore = useCanvasStore(); + const previousCanvasDraggable = ref(true); + const onEdit = /* @__PURE__ */ __name((newValue) => { + if (titleEditorStore.titleEditorTarget && newValue.trim() !== "") { + titleEditorStore.titleEditorTarget.title = newValue.trim(); + app.graph.setDirtyCanvas(true, true); + } + showInput.value = false; + titleEditorStore.titleEditorTarget = null; + canvasStore.canvas.allow_dragcanvas = previousCanvasDraggable.value; + }, "onEdit"); + watch( + () => titleEditorStore.titleEditorTarget, + (target) => { + if (target === null) { + return; + } + editedTitle.value = target.title; + showInput.value = true; + previousCanvasDraggable.value = canvasStore.canvas.allow_dragcanvas; + canvasStore.canvas.allow_dragcanvas = false; + if (target instanceof LGraphGroup) { + const group = target; + const [x, y] = group.pos; + const [w, h] = group.size; + const [left, top] = app.canvasPosToClientPos([x, y]); + inputStyle.value.left = `${left}px`; + inputStyle.value.top = `${top}px`; + const width = w * app.canvas.ds.scale; + const height = group.titleHeight * app.canvas.ds.scale; + inputStyle.value.width = `${width}px`; + inputStyle.value.height = `${height}px`; + const fontSize = group.font_size * app.canvas.ds.scale; + inputStyle.value.fontSize = `${fontSize}px`; + } else if (target instanceof LGraphNode) { + const node = target; + const [x, y] = node.getBounding(); + const canvasWidth = node.width; + const canvasHeight = LiteGraph.NODE_TITLE_HEIGHT; + const [left, top] = app.canvasPosToClientPos([x, y]); + inputStyle.value.left = `${left}px`; + inputStyle.value.top = `${top}px`; + const width = canvasWidth * app.canvas.ds.scale; + const height = canvasHeight * app.canvas.ds.scale; + inputStyle.value.width = `${width}px`; + inputStyle.value.height = `${height}px`; + const fontSize = 12 * app.canvas.ds.scale; + inputStyle.value.fontSize = `${fontSize}px`; + } + } + ); + const canvasEventHandler = /* @__PURE__ */ __name((event) => { + if (!settingStore.get("Comfy.Group.DoubleClickTitleToEdit")) { + return; + } + if (event.detail.subType === "group-double-click") { + const group = event.detail.group; + const [x, y] = group.pos; + const e = event.detail.originalEvent; + const relativeY = e.canvasY - y; + if (relativeY > group.titleHeight) { + return; + } + titleEditorStore.titleEditorTarget = group; + } + }, "canvasEventHandler"); + const extension = { + name: "Comfy.NodeTitleEditor", + nodeCreated(node) { + const originalCallback = node.onNodeTitleDblClick; + node.onNodeTitleDblClick = function(e, ...args) { + if (!settingStore.get("Comfy.Node.DoubleClickTitleToEdit")) { + return; + } + titleEditorStore.titleEditorTarget = this; + if (typeof originalCallback === "function") { + originalCallback.call(this, e, ...args); + } + }; + } + }; + onMounted(() => { + document.addEventListener("litegraph:canvas", canvasEventHandler); + app.registerExtension(extension); + }); + onUnmounted(() => { + document.removeEventListener("litegraph:canvas", canvasEventHandler); + }); + return (_ctx, _cache) => { + return showInput.value ? (openBlock(), createElementBlock("div", { + key: 0, + class: "group-title-editor node-title-editor", + style: normalizeStyle(inputStyle.value) + }, [ + createVNode(EditableText, { + isEditing: showInput.value, + modelValue: editedTitle.value, + onEdit + }, null, 8, ["isEditing", "modelValue"]) + ], 4)) : createCommentVNode("", true); + }; + } +}); +const TitleEditor = /* @__PURE__ */ _export_sfc(_sfc_main$q, [["__scopeId", "data-v-8a100d5a"]]); +var theme$8 = /* @__PURE__ */ __name(function theme(_ref) { + var dt = _ref.dt; + return "\n.p-overlaybadge {\n position: relative;\n}\n\n.p-overlaybadge .p-badge {\n position: absolute;\n top: 0;\n right: 0;\n transform: translate(50%, -50%);\n transform-origin: 100% 0;\n margin: 0;\n outline-width: ".concat(dt("overlaybadge.outline.width"), ";\n outline-style: solid;\n outline-color: ").concat(dt("overlaybadge.outline.color"), ";\n}\n"); +}, "theme"); +var classes$b = { + root: "p-overlaybadge" +}; +var OverlayBadgeStyle = BaseStyle.extend({ + name: "overlaybadge", + theme: theme$8, + classes: classes$b +}); +var script$1$b = { + name: "OverlayBadge", + "extends": script$e, + style: OverlayBadgeStyle, + provide: /* @__PURE__ */ __name(function provide2() { + return { + $pcOverlayBadge: this, + $parentInstance: this + }; + }, "provide") +}; +var script$d = { + name: "OverlayBadge", + "extends": script$1$b, + inheritAttrs: false, + components: { + Badge: script$e + } +}; +function render$g(_ctx, _cache, $props, $setup, $data, $options) { + var _component_Badge = resolveComponent("Badge"); + return openBlock(), createElementBlock("div", mergeProps({ + "class": _ctx.cx("root") + }, _ctx.ptmi("root")), [renderSlot(_ctx.$slots, "default"), createVNode(_component_Badge, mergeProps(_ctx.$props, { + pt: _ctx.ptm("pcBadge") + }), null, 16, ["pt"])], 16); +} +__name(render$g, "render$g"); +script$d.render = render$g; +const _sfc_main$p = /* @__PURE__ */ defineComponent({ + __name: "SidebarIcon", + props: { + icon: String, + selected: Boolean, + tooltip: { + type: String, + default: "" + }, + class: { + type: String, + default: "" + }, + iconBadge: { + type: [String, Function], + default: "" + } + }, + emits: ["click"], + setup(__props, { emit: __emit }) { + const props = __props; + const emit = __emit; + const overlayValue = computed( + () => typeof props.iconBadge === "function" ? props.iconBadge() || "" : props.iconBadge + ); + const shouldShowBadge = computed(() => !!overlayValue.value); + return (_ctx, _cache) => { + const _directive_tooltip = resolveDirective("tooltip"); + return withDirectives((openBlock(), createBlock(unref(script$f), { + class: normalizeClass(props.class), + text: "", + pt: { + root: { + class: `side-bar-button ${props.selected ? "p-button-primary side-bar-button-selected" : "p-button-secondary"}`, + "aria-label": props.tooltip + } + }, + onClick: _cache[0] || (_cache[0] = ($event) => emit("click", $event)) + }, { + icon: withCtx(() => [ + shouldShowBadge.value ? (openBlock(), createBlock(unref(script$d), { + key: 0, + value: overlayValue.value + }, { + default: withCtx(() => [ + createBaseVNode("i", { + class: normalizeClass(props.icon + " side-bar-button-icon") + }, null, 2) + ]), + _: 1 + }, 8, ["value"])) : (openBlock(), createElementBlock("i", { + key: 1, + class: normalizeClass(props.icon + " side-bar-button-icon") + }, null, 2)) + ]), + _: 1 + }, 8, ["class", "pt"])), [ + [_directive_tooltip, { value: props.tooltip, showDelay: 300, hideDelay: 300 }] + ]); + }; + } +}); +const SidebarIcon = /* @__PURE__ */ _export_sfc(_sfc_main$p, [["__scopeId", "data-v-caa3ee9c"]]); +const _sfc_main$o = /* @__PURE__ */ defineComponent({ + __name: "SidebarThemeToggleIcon", + setup(__props) { + const settingStore = useSettingStore(); + const currentTheme = computed(() => settingStore.get("Comfy.ColorPalette")); + const icon = computed( + () => currentTheme.value !== "light" ? "pi pi-moon" : "pi pi-sun" + ); + const commandStore = useCommandStore(); + const toggleTheme = /* @__PURE__ */ __name(() => { + commandStore.execute("Comfy.ToggleTheme"); + }, "toggleTheme"); + return (_ctx, _cache) => { + return openBlock(), createBlock(SidebarIcon, { + icon: icon.value, + onClick: toggleTheme, + tooltip: _ctx.$t("sideToolbar.themeToggle"), + class: "comfy-vue-theme-toggle" + }, null, 8, ["icon", "tooltip"]); + }; + } +}); +const _sfc_main$n = /* @__PURE__ */ defineComponent({ + __name: "SidebarSettingsToggleIcon", + setup(__props) { + const dialogStore = useDialogStore(); + const showSetting = /* @__PURE__ */ __name(() => { + dialogStore.showDialog({ + headerComponent: SettingDialogHeader, + component: SettingDialogContent + }); + }, "showSetting"); + return (_ctx, _cache) => { + return openBlock(), createBlock(SidebarIcon, { + icon: "pi pi-cog", + class: "comfy-settings-btn", + onClick: showSetting, + tooltip: _ctx.$t("settings") + }, null, 8, ["tooltip"]); + }; + } +}); +const _sfc_main$m = /* @__PURE__ */ defineComponent({ + __name: "ExtensionSlot", + props: { + extension: {} + }, + setup(__props) { + const props = __props; + const mountCustomExtension = /* @__PURE__ */ __name((extension, el) => { + extension.render(el); + }, "mountCustomExtension"); + onBeforeUnmount(() => { + if (props.extension.type === "custom" && props.extension.destroy) { + props.extension.destroy(); + } + }); + return (_ctx, _cache) => { + return _ctx.extension.type === "vue" ? (openBlock(), createBlock(resolveDynamicComponent(_ctx.extension.component), { key: 0 })) : (openBlock(), createElementBlock("div", { + key: 1, + ref: /* @__PURE__ */ __name((el) => { + if (el) + mountCustomExtension( + props.extension, + el + ); + }, "ref") + }, null, 512)); + }; + } +}); +const _withScopeId$6 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-37fd2fa4"), n = n(), popScopeId(), n), "_withScopeId$6"); +const _hoisted_1$k = { class: "side-tool-bar-end" }; +const _hoisted_2$h = { + key: 0, + class: "sidebar-content-container h-full overflow-y-auto overflow-x-hidden" +}; +const _sfc_main$l = /* @__PURE__ */ defineComponent({ + __name: "SideToolbar", + setup(__props) { + const workspaceStore = useWorkspaceStore(); + const settingStore = useSettingStore(); + const teleportTarget = computed( + () => settingStore.get("Comfy.Sidebar.Location") === "left" ? ".comfyui-body-left" : ".comfyui-body-right" + ); + const isSmall = computed( + () => settingStore.get("Comfy.Sidebar.Size") === "small" + ); + const tabs = computed(() => workspaceStore.getSidebarTabs()); + const selectedTab = computed(() => workspaceStore.sidebarTab.activeSidebarTab); + const onTabClick = /* @__PURE__ */ __name((item3) => { + workspaceStore.sidebarTab.toggleSidebarTab(item3.id); + }, "onTabClick"); + const keybindingStore = useKeybindingStore(); + const getTabTooltipSuffix = /* @__PURE__ */ __name((tab) => { + const keybinding = keybindingStore.getKeybindingByCommandId( + `Workspace.ToggleSidebarTab.${tab.id}` + ); + return keybinding ? ` (${keybinding.combo.toString()})` : ""; + }, "getTabTooltipSuffix"); + return (_ctx, _cache) => { + return openBlock(), createElementBlock(Fragment, null, [ + (openBlock(), createBlock(Teleport, { to: teleportTarget.value }, [ + createBaseVNode("nav", { + class: normalizeClass("side-tool-bar-container" + (isSmall.value ? " small-sidebar" : "")) + }, [ + (openBlock(true), createElementBlock(Fragment, null, renderList(tabs.value, (tab) => { + return openBlock(), createBlock(SidebarIcon, { + key: tab.id, + icon: tab.icon, + iconBadge: tab.iconBadge, + tooltip: tab.tooltip + getTabTooltipSuffix(tab), + selected: tab.id === selectedTab.value?.id, + class: normalizeClass(tab.id + "-tab-button"), + onClick: /* @__PURE__ */ __name(($event) => onTabClick(tab), "onClick") + }, null, 8, ["icon", "iconBadge", "tooltip", "selected", "class", "onClick"]); + }), 128)), + createBaseVNode("div", _hoisted_1$k, [ + createVNode(_sfc_main$o), + createVNode(_sfc_main$n) + ]) + ], 2) + ], 8, ["to"])), + selectedTab.value ? (openBlock(), createElementBlock("div", _hoisted_2$h, [ + createVNode(_sfc_main$m, { extension: selectedTab.value }, null, 8, ["extension"]) + ])) : createCommentVNode("", true) + ], 64); + }; + } +}); +const SideToolbar = /* @__PURE__ */ _export_sfc(_sfc_main$l, [["__scopeId", "data-v-37fd2fa4"]]); +var classes$a = { + root: "p-tablist", + content: /* @__PURE__ */ __name(function content(_ref) { + var instance = _ref.instance; + return ["p-tablist-content", { + "p-tablist-viewport": instance.$pcTabs.scrollable + }]; + }, "content"), + tabList: "p-tablist-tab-list", + activeBar: "p-tablist-active-bar", + prevButton: "p-tablist-prev-button p-tablist-nav-button", + nextButton: "p-tablist-next-button p-tablist-nav-button" +}; +var TabListStyle = BaseStyle.extend({ + name: "tablist", + classes: classes$a +}); +var script$1$a = { + name: "BaseTabList", + "extends": script$g, + props: {}, + style: TabListStyle, + provide: /* @__PURE__ */ __name(function provide3() { + return { + $pcTabList: this, + $parentInstance: this + }; + }, "provide") +}; +var script$c = { + name: "TabList", + "extends": script$1$a, + inheritAttrs: false, + inject: ["$pcTabs"], + data: /* @__PURE__ */ __name(function data() { + return { + isPrevButtonEnabled: false, + isNextButtonEnabled: true + }; + }, "data"), + resizeObserver: void 0, + watch: { + showNavigators: /* @__PURE__ */ __name(function showNavigators(newValue) { + newValue ? this.bindResizeObserver() : this.unbindResizeObserver(); + }, "showNavigators"), + activeValue: { + flush: "post", + handler: /* @__PURE__ */ __name(function handler() { + this.updateInkBar(); + }, "handler") + } + }, + mounted: /* @__PURE__ */ __name(function mounted() { + var _this = this; + this.$nextTick(function() { + _this.updateInkBar(); + }); + if (this.showNavigators) { + this.updateButtonState(); + this.bindResizeObserver(); + } + }, "mounted"), + updated: /* @__PURE__ */ __name(function updated() { + this.showNavigators && this.updateButtonState(); + }, "updated"), + beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount() { + this.unbindResizeObserver(); + }, "beforeUnmount"), + methods: { + onScroll: /* @__PURE__ */ __name(function onScroll(event) { + this.showNavigators && this.updateButtonState(); + event.preventDefault(); + }, "onScroll"), + onPrevButtonClick: /* @__PURE__ */ __name(function onPrevButtonClick() { + var content2 = this.$refs.content; + var width = getWidth(content2); + var pos = content2.scrollLeft - width; + content2.scrollLeft = pos <= 0 ? 0 : pos; + }, "onPrevButtonClick"), + onNextButtonClick: /* @__PURE__ */ __name(function onNextButtonClick() { + var content2 = this.$refs.content; + var width = getWidth(content2) - this.getVisibleButtonWidths(); + var pos = content2.scrollLeft + width; + var lastPos = content2.scrollWidth - width; + content2.scrollLeft = pos >= lastPos ? lastPos : pos; + }, "onNextButtonClick"), + bindResizeObserver: /* @__PURE__ */ __name(function bindResizeObserver() { + var _this2 = this; + this.resizeObserver = new ResizeObserver(function() { + return _this2.updateButtonState(); + }); + this.resizeObserver.observe(this.$refs.list); + }, "bindResizeObserver"), + unbindResizeObserver: /* @__PURE__ */ __name(function unbindResizeObserver() { + var _this$resizeObserver; + (_this$resizeObserver = this.resizeObserver) === null || _this$resizeObserver === void 0 || _this$resizeObserver.unobserve(this.$refs.list); + this.resizeObserver = void 0; + }, "unbindResizeObserver"), + updateInkBar: /* @__PURE__ */ __name(function updateInkBar() { + var _this$$refs = this.$refs, content2 = _this$$refs.content, inkbar = _this$$refs.inkbar, tabs = _this$$refs.tabs; + var activeTab = findSingle(content2, '[data-pc-name="tab"][data-p-active="true"]'); + if (this.$pcTabs.isVertical()) { + inkbar.style.height = getOuterHeight(activeTab) + "px"; + inkbar.style.top = getOffset(activeTab).top - getOffset(tabs).top + "px"; + } else { + inkbar.style.width = getOuterWidth(activeTab) + "px"; + inkbar.style.left = getOffset(activeTab).left - getOffset(tabs).left + "px"; + } + }, "updateInkBar"), + updateButtonState: /* @__PURE__ */ __name(function updateButtonState() { + var _this$$refs2 = this.$refs, list = _this$$refs2.list, content2 = _this$$refs2.content; + var scrollLeft = content2.scrollLeft, scrollTop = content2.scrollTop, scrollWidth = content2.scrollWidth, scrollHeight = content2.scrollHeight, offsetWidth = content2.offsetWidth, offsetHeight = content2.offsetHeight; + var _ref = [getWidth(content2), getHeight(content2)], width = _ref[0], height = _ref[1]; + if (this.$pcTabs.isVertical()) { + this.isPrevButtonEnabled = scrollTop !== 0; + this.isNextButtonEnabled = list.offsetHeight >= offsetHeight && parseInt(scrollTop) !== scrollHeight - height; + } else { + this.isPrevButtonEnabled = scrollLeft !== 0; + this.isNextButtonEnabled = list.offsetWidth >= offsetWidth && parseInt(scrollLeft) !== scrollWidth - width; + } + }, "updateButtonState"), + getVisibleButtonWidths: /* @__PURE__ */ __name(function getVisibleButtonWidths() { + var _this$$refs3 = this.$refs, prevBtn = _this$$refs3.prevBtn, nextBtn = _this$$refs3.nextBtn; + return [prevBtn, nextBtn].reduce(function(acc, el) { + return el ? acc + getWidth(el) : acc; + }, 0); + }, "getVisibleButtonWidths") + }, + computed: { + templates: /* @__PURE__ */ __name(function templates() { + return this.$pcTabs.$slots; + }, "templates"), + activeValue: /* @__PURE__ */ __name(function activeValue() { + return this.$pcTabs.d_value; + }, "activeValue"), + showNavigators: /* @__PURE__ */ __name(function showNavigators2() { + return this.$pcTabs.scrollable && this.$pcTabs.showNavigators; + }, "showNavigators"), + prevButtonAriaLabel: /* @__PURE__ */ __name(function prevButtonAriaLabel() { + return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.previous : void 0; + }, "prevButtonAriaLabel"), + nextButtonAriaLabel: /* @__PURE__ */ __name(function nextButtonAriaLabel() { + return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.next : void 0; + }, "nextButtonAriaLabel") + }, + components: { + ChevronLeftIcon: script$h, + ChevronRightIcon: script$i + }, + directives: { + ripple: Ripple + } +}; +var _hoisted_1$j = ["aria-label", "tabindex"]; +var _hoisted_2$g = ["aria-orientation"]; +var _hoisted_3$d = ["aria-label", "tabindex"]; +function render$f(_ctx, _cache, $props, $setup, $data, $options) { + var _directive_ripple = resolveDirective("ripple"); + return openBlock(), createElementBlock("div", mergeProps({ + ref: "list", + "class": _ctx.cx("root") + }, _ctx.ptmi("root")), [$options.showNavigators && $data.isPrevButtonEnabled ? withDirectives((openBlock(), createElementBlock("button", mergeProps({ + key: 0, + ref: "prevButton", + "class": _ctx.cx("prevButton"), + "aria-label": $options.prevButtonAriaLabel, + tabindex: $options.$pcTabs.tabindex, + onClick: _cache[0] || (_cache[0] = function() { + return $options.onPrevButtonClick && $options.onPrevButtonClick.apply($options, arguments); + }) + }, _ctx.ptm("prevButton"), { + "data-pc-group-section": "navigator" + }), [(openBlock(), createBlock(resolveDynamicComponent($options.templates.previcon || "ChevronLeftIcon"), mergeProps({ + "aria-hidden": "true" + }, _ctx.ptm("prevIcon")), null, 16))], 16, _hoisted_1$j)), [[_directive_ripple]]) : createCommentVNode("", true), createBaseVNode("div", mergeProps({ + ref: "content", + "class": _ctx.cx("content"), + onScroll: _cache[1] || (_cache[1] = function() { + return $options.onScroll && $options.onScroll.apply($options, arguments); + }) + }, _ctx.ptm("content")), [createBaseVNode("div", mergeProps({ + ref: "tabs", + "class": _ctx.cx("tabList"), + role: "tablist", + "aria-orientation": $options.$pcTabs.orientation || "horizontal" + }, _ctx.ptm("tabList")), [renderSlot(_ctx.$slots, "default"), createBaseVNode("span", mergeProps({ + ref: "inkbar", + "class": _ctx.cx("activeBar"), + role: "presentation", + "aria-hidden": "true" + }, _ctx.ptm("activeBar")), null, 16)], 16, _hoisted_2$g)], 16), $options.showNavigators && $data.isNextButtonEnabled ? withDirectives((openBlock(), createElementBlock("button", mergeProps({ + key: 1, + ref: "nextButton", + "class": _ctx.cx("nextButton"), + "aria-label": $options.nextButtonAriaLabel, + tabindex: $options.$pcTabs.tabindex, + onClick: _cache[2] || (_cache[2] = function() { + return $options.onNextButtonClick && $options.onNextButtonClick.apply($options, arguments); + }) + }, _ctx.ptm("nextButton"), { + "data-pc-group-section": "navigator" + }), [(openBlock(), createBlock(resolveDynamicComponent($options.templates.nexticon || "ChevronRightIcon"), mergeProps({ + "aria-hidden": "true" + }, _ctx.ptm("nextIcon")), null, 16))], 16, _hoisted_3$d)), [[_directive_ripple]]) : createCommentVNode("", true)], 16); +} +__name(render$f, "render$f"); +script$c.render = render$f; +var classes$9 = { + root: /* @__PURE__ */ __name(function root(_ref) { + var instance = _ref.instance, props = _ref.props; + return ["p-tab", { + "p-tab-active": instance.active, + "p-disabled": props.disabled + }]; + }, "root") +}; +var TabStyle = BaseStyle.extend({ + name: "tab", + classes: classes$9 +}); +var script$1$9 = { + name: "BaseTab", + "extends": script$g, + props: { + value: { + type: [String, Number], + "default": void 0 + }, + disabled: { + type: Boolean, + "default": false + }, + as: { + type: [String, Object], + "default": "BUTTON" + }, + asChild: { + type: Boolean, + "default": false + } + }, + style: TabStyle, + provide: /* @__PURE__ */ __name(function provide4() { + return { + $pcTab: this, + $parentInstance: this + }; + }, "provide") +}; +var script$b = { + name: "Tab", + "extends": script$1$9, + inheritAttrs: false, + inject: ["$pcTabs", "$pcTabList"], + methods: { + onFocus: /* @__PURE__ */ __name(function onFocus() { + this.$pcTabs.selectOnFocus && this.changeActiveValue(); + }, "onFocus"), + onClick: /* @__PURE__ */ __name(function onClick() { + this.changeActiveValue(); + }, "onClick"), + onKeydown: /* @__PURE__ */ __name(function onKeydown(event) { + switch (event.code) { + case "ArrowRight": + this.onArrowRightKey(event); + break; + case "ArrowLeft": + this.onArrowLeftKey(event); + break; + case "Home": + this.onHomeKey(event); + break; + case "End": + this.onEndKey(event); + break; + case "PageDown": + this.onPageDownKey(event); + break; + case "PageUp": + this.onPageUpKey(event); + break; + case "Enter": + case "NumpadEnter": + case "Space": + this.onEnterKey(event); + break; + } + }, "onKeydown"), + onArrowRightKey: /* @__PURE__ */ __name(function onArrowRightKey(event) { + var nextTab = this.findNextTab(event.currentTarget); + nextTab ? this.changeFocusedTab(event, nextTab) : this.onHomeKey(event); + event.preventDefault(); + }, "onArrowRightKey"), + onArrowLeftKey: /* @__PURE__ */ __name(function onArrowLeftKey(event) { + var prevTab = this.findPrevTab(event.currentTarget); + prevTab ? this.changeFocusedTab(event, prevTab) : this.onEndKey(event); + event.preventDefault(); + }, "onArrowLeftKey"), + onHomeKey: /* @__PURE__ */ __name(function onHomeKey(event) { + var firstTab = this.findFirstTab(); + this.changeFocusedTab(event, firstTab); + event.preventDefault(); + }, "onHomeKey"), + onEndKey: /* @__PURE__ */ __name(function onEndKey(event) { + var lastTab = this.findLastTab(); + this.changeFocusedTab(event, lastTab); + event.preventDefault(); + }, "onEndKey"), + onPageDownKey: /* @__PURE__ */ __name(function onPageDownKey(event) { + this.scrollInView(this.findLastTab()); + event.preventDefault(); + }, "onPageDownKey"), + onPageUpKey: /* @__PURE__ */ __name(function onPageUpKey(event) { + this.scrollInView(this.findFirstTab()); + event.preventDefault(); + }, "onPageUpKey"), + onEnterKey: /* @__PURE__ */ __name(function onEnterKey(event) { + this.changeActiveValue(); + event.preventDefault(); + }, "onEnterKey"), + findNextTab: /* @__PURE__ */ __name(function findNextTab(tabElement) { + var selfCheck = arguments.length > 1 && arguments[1] !== void 0 ? arguments[1] : false; + var element = selfCheck ? tabElement : tabElement.nextElementSibling; + return element ? getAttribute(element, "data-p-disabled") || getAttribute(element, "data-pc-section") === "inkbar" ? this.findNextTab(element) : findSingle(element, '[data-pc-name="tab"]') : null; + }, "findNextTab"), + findPrevTab: /* @__PURE__ */ __name(function findPrevTab(tabElement) { + var selfCheck = arguments.length > 1 && arguments[1] !== void 0 ? arguments[1] : false; + var element = selfCheck ? tabElement : tabElement.previousElementSibling; + return element ? getAttribute(element, "data-p-disabled") || getAttribute(element, "data-pc-section") === "inkbar" ? this.findPrevTab(element) : findSingle(element, '[data-pc-name="tab"]') : null; + }, "findPrevTab"), + findFirstTab: /* @__PURE__ */ __name(function findFirstTab() { + return this.findNextTab(this.$pcTabList.$refs.content.firstElementChild, true); + }, "findFirstTab"), + findLastTab: /* @__PURE__ */ __name(function findLastTab() { + return this.findPrevTab(this.$pcTabList.$refs.content.lastElementChild, true); + }, "findLastTab"), + changeActiveValue: /* @__PURE__ */ __name(function changeActiveValue() { + this.$pcTabs.updateValue(this.value); + }, "changeActiveValue"), + changeFocusedTab: /* @__PURE__ */ __name(function changeFocusedTab(event, element) { + focus(element); + this.scrollInView(element); + }, "changeFocusedTab"), + scrollInView: /* @__PURE__ */ __name(function scrollInView(element) { + var _element$scrollIntoVi; + element === null || element === void 0 || (_element$scrollIntoVi = element.scrollIntoView) === null || _element$scrollIntoVi === void 0 || _element$scrollIntoVi.call(element, { + block: "nearest" + }); + }, "scrollInView") + }, + computed: { + active: /* @__PURE__ */ __name(function active() { + var _this$$pcTabs; + return equals((_this$$pcTabs = this.$pcTabs) === null || _this$$pcTabs === void 0 ? void 0 : _this$$pcTabs.d_value, this.value); + }, "active"), + id: /* @__PURE__ */ __name(function id() { + var _this$$pcTabs2; + return "".concat((_this$$pcTabs2 = this.$pcTabs) === null || _this$$pcTabs2 === void 0 ? void 0 : _this$$pcTabs2.id, "_tab_").concat(this.value); + }, "id"), + ariaControls: /* @__PURE__ */ __name(function ariaControls() { + var _this$$pcTabs3; + return "".concat((_this$$pcTabs3 = this.$pcTabs) === null || _this$$pcTabs3 === void 0 ? void 0 : _this$$pcTabs3.id, "_tabpanel_").concat(this.value); + }, "ariaControls"), + attrs: /* @__PURE__ */ __name(function attrs() { + return mergeProps(this.asAttrs, this.a11yAttrs, this.ptmi("root", this.ptParams)); + }, "attrs"), + asAttrs: /* @__PURE__ */ __name(function asAttrs() { + return this.as === "BUTTON" ? { + type: "button", + disabled: this.disabled + } : void 0; + }, "asAttrs"), + a11yAttrs: /* @__PURE__ */ __name(function a11yAttrs() { + return { + id: this.id, + tabindex: this.active ? this.$pcTabs.tabindex : -1, + role: "tab", + "aria-selected": this.active, + "aria-controls": this.ariaControls, + "data-pc-name": "tab", + "data-p-disabled": this.disabled, + "data-p-active": this.active, + onFocus: this.onFocus, + onKeydown: this.onKeydown + }; + }, "a11yAttrs"), + ptParams: /* @__PURE__ */ __name(function ptParams() { + return { + context: { + active: this.active + } + }; + }, "ptParams") + }, + directives: { + ripple: Ripple + } +}; +function render$e(_ctx, _cache, $props, $setup, $data, $options) { + var _directive_ripple = resolveDirective("ripple"); + return !_ctx.asChild ? withDirectives((openBlock(), createBlock(resolveDynamicComponent(_ctx.as), mergeProps({ + key: 0, + "class": _ctx.cx("root"), + onClick: $options.onClick + }, $options.attrs), { + "default": withCtx(function() { + return [renderSlot(_ctx.$slots, "default")]; + }), + _: 3 + }, 16, ["class", "onClick"])), [[_directive_ripple]]) : renderSlot(_ctx.$slots, "default", { + key: 1, + "class": normalizeClass(_ctx.cx("root")), + active: $options.active, + a11yAttrs: $options.a11yAttrs, + onClick: $options.onClick + }); +} +__name(render$e, "render$e"); +script$b.render = render$e; +const _hoisted_1$i = { class: "flex flex-col h-full" }; +const _hoisted_2$f = { class: "w-full flex justify-between" }; +const _hoisted_3$c = { class: "tabs-container" }; +const _hoisted_4$5 = { class: "font-bold" }; +const _hoisted_5$4 = { class: "flex-grow h-0" }; +const _sfc_main$k = /* @__PURE__ */ defineComponent({ + __name: "BottomPanel", + setup(__props) { + const bottomPanelStore = useBottomPanelStore(); + return (_ctx, _cache) => { + return openBlock(), createElementBlock("div", _hoisted_1$i, [ + createVNode(unref(script$j), { + value: unref(bottomPanelStore).activeBottomPanelTabId, + "onUpdate:value": _cache[1] || (_cache[1] = ($event) => unref(bottomPanelStore).activeBottomPanelTabId = $event) + }, { + default: withCtx(() => [ + createVNode(unref(script$c), { "pt:tabList": "border-none" }, { + default: withCtx(() => [ + createBaseVNode("div", _hoisted_2$f, [ + createBaseVNode("div", _hoisted_3$c, [ + (openBlock(true), createElementBlock(Fragment, null, renderList(unref(bottomPanelStore).bottomPanelTabs, (tab) => { + return openBlock(), createBlock(unref(script$b), { + key: tab.id, + value: tab.id, + class: "p-3 border-none" + }, { + default: withCtx(() => [ + createBaseVNode("span", _hoisted_4$5, toDisplayString(tab.title.toUpperCase()), 1) + ]), + _: 2 + }, 1032, ["value"]); + }), 128)) + ]), + createVNode(unref(script$f), { + class: "justify-self-end", + icon: "pi pi-times", + severity: "secondary", + size: "small", + text: "", + onClick: _cache[0] || (_cache[0] = ($event) => unref(bottomPanelStore).bottomPanelVisible = false) + }) + ]) + ]), + _: 1 + }) + ]), + _: 1 + }, 8, ["value"]), + createBaseVNode("div", _hoisted_5$4, [ + unref(bottomPanelStore).bottomPanelVisible && unref(bottomPanelStore).activeBottomPanelTab ? (openBlock(), createBlock(_sfc_main$m, { + key: 0, + extension: unref(bottomPanelStore).activeBottomPanelTab + }, null, 8, ["extension"])) : createCommentVNode("", true) + ]) + ]); + }; + } +}); +var theme$7 = /* @__PURE__ */ __name(function theme2(_ref) { + var dt = _ref.dt; + return "\n.p-splitter {\n display: flex;\n flex-wrap: nowrap;\n border: 1px solid ".concat(dt("splitter.border.color"), ";\n background: ").concat(dt("splitter.background"), ";\n border-radius: ").concat(dt("border.radius.md"), ";\n color: ").concat(dt("splitter.color"), ";\n}\n\n.p-splitter-vertical {\n flex-direction: column;\n}\n\n.p-splitter-gutter {\n flex-grow: 0;\n flex-shrink: 0;\n display: flex;\n align-items: center;\n justify-content: center;\n z-index: 1;\n background: ").concat(dt("splitter.gutter.background"), ";\n}\n\n.p-splitter-gutter-handle {\n border-radius: ").concat(dt("splitter.handle.border.radius"), ";\n background: ").concat(dt("splitter.handle.background"), ";\n transition: outline-color ").concat(dt("splitter.transition.duration"), ", box-shadow ").concat(dt("splitter.transition.duration"), ";\n outline-color: transparent;\n}\n\n.p-splitter-gutter-handle:focus-visible {\n box-shadow: ").concat(dt("splitter.handle.focus.ring.shadow"), ";\n outline: ").concat(dt("splitter.handle.focus.ring.width"), " ").concat(dt("splitter.handle.focus.ring.style"), " ").concat(dt("splitter.handle.focus.ring.color"), ";\n outline-offset: ").concat(dt("splitter.handle.focus.ring.offset"), ";\n}\n\n.p-splitter-horizontal.p-splitter-resizing {\n cursor: col-resize;\n user-select: none;\n}\n\n.p-splitter-vertical.p-splitter-resizing {\n cursor: row-resize;\n user-select: none;\n}\n\n.p-splitter-horizontal > .p-splitter-gutter > .p-splitter-gutter-handle {\n height: ").concat(dt("splitter.handle.size"), ";\n width: 100%;\n}\n\n.p-splitter-vertical > .p-splitter-gutter > .p-splitter-gutter-handle {\n width: ").concat(dt("splitter.handle.size"), ";\n height: 100%;\n}\n\n.p-splitter-horizontal > .p-splitter-gutter {\n cursor: col-resize;\n}\n\n.p-splitter-vertical > .p-splitter-gutter {\n cursor: row-resize;\n}\n\n.p-splitterpanel {\n flex-grow: 1;\n overflow: hidden;\n}\n\n.p-splitterpanel-nested {\n display: flex;\n}\n\n.p-splitterpanel .p-splitter {\n flex-grow: 1;\n border: 0 none;\n}\n"); +}, "theme"); +var classes$8 = { + root: /* @__PURE__ */ __name(function root2(_ref2) { + var props = _ref2.props; + return ["p-splitter p-component", "p-splitter-" + props.layout]; + }, "root"), + gutter: "p-splitter-gutter", + gutterHandle: "p-splitter-gutter-handle" +}; +var inlineStyles$4 = { + root: /* @__PURE__ */ __name(function root3(_ref3) { + var props = _ref3.props; + return [{ + display: "flex", + "flex-wrap": "nowrap" + }, props.layout === "vertical" ? { + "flex-direction": "column" + } : ""]; + }, "root") +}; +var SplitterStyle = BaseStyle.extend({ + name: "splitter", + theme: theme$7, + classes: classes$8, + inlineStyles: inlineStyles$4 +}); +var script$1$8 = { + name: "BaseSplitter", + "extends": script$g, + props: { + layout: { + type: String, + "default": "horizontal" + }, + gutterSize: { + type: Number, + "default": 4 + }, + stateKey: { + type: String, + "default": null + }, + stateStorage: { + type: String, + "default": "session" + }, + step: { + type: Number, + "default": 5 + } + }, + style: SplitterStyle, + provide: /* @__PURE__ */ __name(function provide5() { + return { + $pcSplitter: this, + $parentInstance: this + }; + }, "provide") +}; +function _toConsumableArray$2(r) { + return _arrayWithoutHoles$2(r) || _iterableToArray$2(r) || _unsupportedIterableToArray$2(r) || _nonIterableSpread$2(); +} +__name(_toConsumableArray$2, "_toConsumableArray$2"); +function _nonIterableSpread$2() { + throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method."); +} +__name(_nonIterableSpread$2, "_nonIterableSpread$2"); +function _unsupportedIterableToArray$2(r, a) { + if (r) { + if ("string" == typeof r) return _arrayLikeToArray$2(r, a); + var t = {}.toString.call(r).slice(8, -1); + return "Object" === t && r.constructor && (t = r.constructor.name), "Map" === t || "Set" === t ? Array.from(r) : "Arguments" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray$2(r, a) : void 0; + } +} +__name(_unsupportedIterableToArray$2, "_unsupportedIterableToArray$2"); +function _iterableToArray$2(r) { + if ("undefined" != typeof Symbol && null != r[Symbol.iterator] || null != r["@@iterator"]) return Array.from(r); +} +__name(_iterableToArray$2, "_iterableToArray$2"); +function _arrayWithoutHoles$2(r) { + if (Array.isArray(r)) return _arrayLikeToArray$2(r); +} +__name(_arrayWithoutHoles$2, "_arrayWithoutHoles$2"); +function _arrayLikeToArray$2(r, a) { + (null == a || a > r.length) && (a = r.length); + for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; + return n; +} +__name(_arrayLikeToArray$2, "_arrayLikeToArray$2"); +var script$a = { + name: "Splitter", + "extends": script$1$8, + inheritAttrs: false, + emits: ["resizestart", "resizeend", "resize"], + dragging: false, + mouseMoveListener: null, + mouseUpListener: null, + touchMoveListener: null, + touchEndListener: null, + size: null, + gutterElement: null, + startPos: null, + prevPanelElement: null, + nextPanelElement: null, + nextPanelSize: null, + prevPanelSize: null, + panelSizes: null, + prevPanelIndex: null, + timer: null, + data: /* @__PURE__ */ __name(function data2() { + return { + prevSize: null + }; + }, "data"), + mounted: /* @__PURE__ */ __name(function mounted2() { + var _this = this; + if (this.panels && this.panels.length) { + var initialized = false; + if (this.isStateful()) { + initialized = this.restoreState(); + } + if (!initialized) { + var children = _toConsumableArray$2(this.$el.children).filter(function(child) { + return child.getAttribute("data-pc-name") === "splitterpanel"; + }); + var _panelSizes = []; + this.panels.map(function(panel, i) { + var panelInitialSize = panel.props && panel.props.size ? panel.props.size : null; + var panelSize = panelInitialSize || 100 / _this.panels.length; + _panelSizes[i] = panelSize; + children[i].style.flexBasis = "calc(" + panelSize + "% - " + (_this.panels.length - 1) * _this.gutterSize + "px)"; + }); + this.panelSizes = _panelSizes; + this.prevSize = parseFloat(_panelSizes[0]).toFixed(4); + } + } + }, "mounted"), + beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount2() { + this.clear(); + this.unbindMouseListeners(); + }, "beforeUnmount"), + methods: { + isSplitterPanel: /* @__PURE__ */ __name(function isSplitterPanel(child) { + return child.type.name === "SplitterPanel"; + }, "isSplitterPanel"), + onResizeStart: /* @__PURE__ */ __name(function onResizeStart(event, index, isKeyDown) { + this.gutterElement = event.currentTarget || event.target.parentElement; + this.size = this.horizontal ? getWidth(this.$el) : getHeight(this.$el); + if (!isKeyDown) { + this.dragging = true; + this.startPos = this.layout === "horizontal" ? event.pageX || event.changedTouches[0].pageX : event.pageY || event.changedTouches[0].pageY; + } + this.prevPanelElement = this.gutterElement.previousElementSibling; + this.nextPanelElement = this.gutterElement.nextElementSibling; + if (isKeyDown) { + this.prevPanelSize = this.horizontal ? getOuterWidth(this.prevPanelElement, true) : getOuterHeight(this.prevPanelElement, true); + this.nextPanelSize = this.horizontal ? getOuterWidth(this.nextPanelElement, true) : getOuterHeight(this.nextPanelElement, true); + } else { + this.prevPanelSize = 100 * (this.horizontal ? getOuterWidth(this.prevPanelElement, true) : getOuterHeight(this.prevPanelElement, true)) / this.size; + this.nextPanelSize = 100 * (this.horizontal ? getOuterWidth(this.nextPanelElement, true) : getOuterHeight(this.nextPanelElement, true)) / this.size; + } + this.prevPanelIndex = index; + this.$emit("resizestart", { + originalEvent: event, + sizes: this.panelSizes + }); + this.$refs.gutter[index].setAttribute("data-p-gutter-resizing", true); + this.$el.setAttribute("data-p-resizing", true); + }, "onResizeStart"), + onResize: /* @__PURE__ */ __name(function onResize(event, step, isKeyDown) { + var newPos, newPrevPanelSize, newNextPanelSize; + if (isKeyDown) { + if (this.horizontal) { + newPrevPanelSize = 100 * (this.prevPanelSize + step) / this.size; + newNextPanelSize = 100 * (this.nextPanelSize - step) / this.size; + } else { + newPrevPanelSize = 100 * (this.prevPanelSize - step) / this.size; + newNextPanelSize = 100 * (this.nextPanelSize + step) / this.size; + } + } else { + if (this.horizontal) newPos = event.pageX * 100 / this.size - this.startPos * 100 / this.size; + else newPos = event.pageY * 100 / this.size - this.startPos * 100 / this.size; + newPrevPanelSize = this.prevPanelSize + newPos; + newNextPanelSize = this.nextPanelSize - newPos; + } + if (this.validateResize(newPrevPanelSize, newNextPanelSize)) { + this.prevPanelElement.style.flexBasis = "calc(" + newPrevPanelSize + "% - " + (this.panels.length - 1) * this.gutterSize + "px)"; + this.nextPanelElement.style.flexBasis = "calc(" + newNextPanelSize + "% - " + (this.panels.length - 1) * this.gutterSize + "px)"; + this.panelSizes[this.prevPanelIndex] = newPrevPanelSize; + this.panelSizes[this.prevPanelIndex + 1] = newNextPanelSize; + this.prevSize = parseFloat(newPrevPanelSize).toFixed(4); + } + this.$emit("resize", { + originalEvent: event, + sizes: this.panelSizes + }); + }, "onResize"), + onResizeEnd: /* @__PURE__ */ __name(function onResizeEnd(event) { + if (this.isStateful()) { + this.saveState(); + } + this.$emit("resizeend", { + originalEvent: event, + sizes: this.panelSizes + }); + this.$refs.gutter.forEach(function(gutter) { + return gutter.setAttribute("data-p-gutter-resizing", false); + }); + this.$el.setAttribute("data-p-resizing", false); + this.clear(); + }, "onResizeEnd"), + repeat: /* @__PURE__ */ __name(function repeat(event, index, step) { + this.onResizeStart(event, index, true); + this.onResize(event, step, true); + }, "repeat"), + setTimer: /* @__PURE__ */ __name(function setTimer(event, index, step) { + var _this2 = this; + if (!this.timer) { + this.timer = setInterval(function() { + _this2.repeat(event, index, step); + }, 40); + } + }, "setTimer"), + clearTimer: /* @__PURE__ */ __name(function clearTimer() { + if (this.timer) { + clearInterval(this.timer); + this.timer = null; + } + }, "clearTimer"), + onGutterKeyUp: /* @__PURE__ */ __name(function onGutterKeyUp() { + this.clearTimer(); + this.onResizeEnd(); + }, "onGutterKeyUp"), + onGutterKeyDown: /* @__PURE__ */ __name(function onGutterKeyDown(event, index) { + switch (event.code) { + case "ArrowLeft": { + if (this.layout === "horizontal") { + this.setTimer(event, index, this.step * -1); + } + event.preventDefault(); + break; + } + case "ArrowRight": { + if (this.layout === "horizontal") { + this.setTimer(event, index, this.step); + } + event.preventDefault(); + break; + } + case "ArrowDown": { + if (this.layout === "vertical") { + this.setTimer(event, index, this.step * -1); + } + event.preventDefault(); + break; + } + case "ArrowUp": { + if (this.layout === "vertical") { + this.setTimer(event, index, this.step); + } + event.preventDefault(); + break; + } + } + }, "onGutterKeyDown"), + onGutterMouseDown: /* @__PURE__ */ __name(function onGutterMouseDown(event, index) { + this.onResizeStart(event, index); + this.bindMouseListeners(); + }, "onGutterMouseDown"), + onGutterTouchStart: /* @__PURE__ */ __name(function onGutterTouchStart(event, index) { + this.onResizeStart(event, index); + this.bindTouchListeners(); + event.preventDefault(); + }, "onGutterTouchStart"), + onGutterTouchMove: /* @__PURE__ */ __name(function onGutterTouchMove(event) { + this.onResize(event); + event.preventDefault(); + }, "onGutterTouchMove"), + onGutterTouchEnd: /* @__PURE__ */ __name(function onGutterTouchEnd(event) { + this.onResizeEnd(event); + this.unbindTouchListeners(); + event.preventDefault(); + }, "onGutterTouchEnd"), + bindMouseListeners: /* @__PURE__ */ __name(function bindMouseListeners() { + var _this3 = this; + if (!this.mouseMoveListener) { + this.mouseMoveListener = function(event) { + return _this3.onResize(event); + }; + document.addEventListener("mousemove", this.mouseMoveListener); + } + if (!this.mouseUpListener) { + this.mouseUpListener = function(event) { + _this3.onResizeEnd(event); + _this3.unbindMouseListeners(); + }; + document.addEventListener("mouseup", this.mouseUpListener); + } + }, "bindMouseListeners"), + bindTouchListeners: /* @__PURE__ */ __name(function bindTouchListeners() { + var _this4 = this; + if (!this.touchMoveListener) { + this.touchMoveListener = function(event) { + return _this4.onResize(event.changedTouches[0]); + }; + document.addEventListener("touchmove", this.touchMoveListener); + } + if (!this.touchEndListener) { + this.touchEndListener = function(event) { + _this4.resizeEnd(event); + _this4.unbindTouchListeners(); + }; + document.addEventListener("touchend", this.touchEndListener); + } + }, "bindTouchListeners"), + validateResize: /* @__PURE__ */ __name(function validateResize(newPrevPanelSize, newNextPanelSize) { + if (newPrevPanelSize > 100 || newPrevPanelSize < 0) return false; + if (newNextPanelSize > 100 || newNextPanelSize < 0) return false; + var prevPanelMinSize = getVNodeProp(this.panels[this.prevPanelIndex], "minSize"); + if (this.panels[this.prevPanelIndex].props && prevPanelMinSize && prevPanelMinSize > newPrevPanelSize) { + return false; + } + var newPanelMinSize = getVNodeProp(this.panels[this.prevPanelIndex + 1], "minSize"); + if (this.panels[this.prevPanelIndex + 1].props && newPanelMinSize && newPanelMinSize > newNextPanelSize) { + return false; + } + return true; + }, "validateResize"), + unbindMouseListeners: /* @__PURE__ */ __name(function unbindMouseListeners() { + if (this.mouseMoveListener) { + document.removeEventListener("mousemove", this.mouseMoveListener); + this.mouseMoveListener = null; + } + if (this.mouseUpListener) { + document.removeEventListener("mouseup", this.mouseUpListener); + this.mouseUpListener = null; + } + }, "unbindMouseListeners"), + unbindTouchListeners: /* @__PURE__ */ __name(function unbindTouchListeners() { + if (this.touchMoveListener) { + document.removeEventListener("touchmove", this.touchMoveListener); + this.touchMoveListener = null; + } + if (this.touchEndListener) { + document.removeEventListener("touchend", this.touchEndListener); + this.touchEndListener = null; + } + }, "unbindTouchListeners"), + clear: /* @__PURE__ */ __name(function clear() { + this.dragging = false; + this.size = null; + this.startPos = null; + this.prevPanelElement = null; + this.nextPanelElement = null; + this.prevPanelSize = null; + this.nextPanelSize = null; + this.gutterElement = null; + this.prevPanelIndex = null; + }, "clear"), + isStateful: /* @__PURE__ */ __name(function isStateful() { + return this.stateKey != null; + }, "isStateful"), + getStorage: /* @__PURE__ */ __name(function getStorage() { + switch (this.stateStorage) { + case "local": + return window.localStorage; + case "session": + return window.sessionStorage; + default: + throw new Error(this.stateStorage + ' is not a valid value for the state storage, supported values are "local" and "session".'); + } + }, "getStorage"), + saveState: /* @__PURE__ */ __name(function saveState() { + if (isArray(this.panelSizes)) { + this.getStorage().setItem(this.stateKey, JSON.stringify(this.panelSizes)); + } + }, "saveState"), + restoreState: /* @__PURE__ */ __name(function restoreState() { + var _this5 = this; + var storage = this.getStorage(); + var stateString = storage.getItem(this.stateKey); + if (stateString) { + this.panelSizes = JSON.parse(stateString); + var children = _toConsumableArray$2(this.$el.children).filter(function(child) { + return child.getAttribute("data-pc-name") === "splitterpanel"; + }); + children.forEach(function(child, i) { + child.style.flexBasis = "calc(" + _this5.panelSizes[i] + "% - " + (_this5.panels.length - 1) * _this5.gutterSize + "px)"; + }); + return true; + } + return false; + }, "restoreState") + }, + computed: { + panels: /* @__PURE__ */ __name(function panels() { + var _this6 = this; + var panels2 = []; + this.$slots["default"]().forEach(function(child) { + if (_this6.isSplitterPanel(child)) { + panels2.push(child); + } else if (child.children instanceof Array) { + child.children.forEach(function(nestedChild) { + if (_this6.isSplitterPanel(nestedChild)) { + panels2.push(nestedChild); + } + }); + } + }); + return panels2; + }, "panels"), + gutterStyle: /* @__PURE__ */ __name(function gutterStyle() { + if (this.horizontal) return { + width: this.gutterSize + "px" + }; + else return { + height: this.gutterSize + "px" + }; + }, "gutterStyle"), + horizontal: /* @__PURE__ */ __name(function horizontal() { + return this.layout === "horizontal"; + }, "horizontal"), + getPTOptions: /* @__PURE__ */ __name(function getPTOptions() { + var _this$$parentInstance; + return { + context: { + nested: (_this$$parentInstance = this.$parentInstance) === null || _this$$parentInstance === void 0 ? void 0 : _this$$parentInstance.nestedState + } + }; + }, "getPTOptions") + } +}; +var _hoisted_1$h = ["onMousedown", "onTouchstart", "onTouchmove", "onTouchend"]; +var _hoisted_2$e = ["aria-orientation", "aria-valuenow", "onKeydown"]; +function render$d(_ctx, _cache, $props, $setup, $data, $options) { + return openBlock(), createElementBlock("div", mergeProps({ + "class": _ctx.cx("root"), + style: _ctx.sx("root"), + "data-p-resizing": false + }, _ctx.ptmi("root", $options.getPTOptions)), [(openBlock(true), createElementBlock(Fragment, null, renderList($options.panels, function(panel, i) { + return openBlock(), createElementBlock(Fragment, { + key: i + }, [(openBlock(), createBlock(resolveDynamicComponent(panel), { + tabindex: "-1" + })), i !== $options.panels.length - 1 ? (openBlock(), createElementBlock("div", mergeProps({ + key: 0, + ref_for: true, + ref: "gutter", + "class": _ctx.cx("gutter"), + role: "separator", + tabindex: "-1", + onMousedown: /* @__PURE__ */ __name(function onMousedown($event) { + return $options.onGutterMouseDown($event, i); + }, "onMousedown"), + onTouchstart: /* @__PURE__ */ __name(function onTouchstart($event) { + return $options.onGutterTouchStart($event, i); + }, "onTouchstart"), + onTouchmove: /* @__PURE__ */ __name(function onTouchmove($event) { + return $options.onGutterTouchMove($event, i); + }, "onTouchmove"), + onTouchend: /* @__PURE__ */ __name(function onTouchend($event) { + return $options.onGutterTouchEnd($event, i); + }, "onTouchend"), + "data-p-gutter-resizing": false + }, _ctx.ptm("gutter")), [createBaseVNode("div", mergeProps({ + "class": _ctx.cx("gutterHandle"), + tabindex: "0", + style: [$options.gutterStyle], + "aria-orientation": _ctx.layout, + "aria-valuenow": $data.prevSize, + onKeyup: _cache[0] || (_cache[0] = function() { + return $options.onGutterKeyUp && $options.onGutterKeyUp.apply($options, arguments); + }), + onKeydown: /* @__PURE__ */ __name(function onKeydown2($event) { + return $options.onGutterKeyDown($event, i); + }, "onKeydown"), + ref_for: true + }, _ctx.ptm("gutterHandle")), null, 16, _hoisted_2$e)], 16, _hoisted_1$h)) : createCommentVNode("", true)], 64); + }), 128))], 16); +} +__name(render$d, "render$d"); +script$a.render = render$d; +var classes$7 = { + root: /* @__PURE__ */ __name(function root4(_ref) { + var instance = _ref.instance; + return ["p-splitterpanel", { + "p-splitterpanel-nested": instance.isNested + }]; + }, "root") +}; +var SplitterPanelStyle = BaseStyle.extend({ + name: "splitterpanel", + classes: classes$7 +}); +var script$1$7 = { + name: "BaseSplitterPanel", + "extends": script$g, + props: { + size: { + type: Number, + "default": null + }, + minSize: { + type: Number, + "default": null + } + }, + style: SplitterPanelStyle, + provide: /* @__PURE__ */ __name(function provide6() { + return { + $pcSplitterPanel: this, + $parentInstance: this + }; + }, "provide") +}; +var script$9 = { + name: "SplitterPanel", + "extends": script$1$7, + inheritAttrs: false, + data: /* @__PURE__ */ __name(function data3() { + return { + nestedState: null + }; + }, "data"), + computed: { + isNested: /* @__PURE__ */ __name(function isNested() { + var _this = this; + return this.$slots["default"]().some(function(child) { + _this.nestedState = child.type.name === "Splitter" ? true : null; + return _this.nestedState; + }); + }, "isNested"), + getPTOptions: /* @__PURE__ */ __name(function getPTOptions2() { + return { + context: { + nested: this.isNested + } + }; + }, "getPTOptions") + } +}; +function render$c(_ctx, _cache, $props, $setup, $data, $options) { + return openBlock(), createElementBlock("div", mergeProps({ + ref: "container", + "class": _ctx.cx("root") + }, _ctx.ptmi("root", $options.getPTOptions)), [renderSlot(_ctx.$slots, "default")], 16); +} +__name(render$c, "render$c"); +script$9.render = render$c; +const _sfc_main$j = /* @__PURE__ */ defineComponent({ + __name: "LiteGraphCanvasSplitterOverlay", + setup(__props) { + const settingStore = useSettingStore(); + const sidebarLocation = computed( + () => settingStore.get("Comfy.Sidebar.Location") + ); + const sidebarPanelVisible = computed( + () => useSidebarTabStore().activeSidebarTab !== null + ); + const bottomPanelVisible = computed( + () => useBottomPanelStore().bottomPanelVisible + ); + return (_ctx, _cache) => { + return openBlock(), createBlock(unref(script$a), { + class: "splitter-overlay-root splitter-overlay", + "pt:gutter": sidebarPanelVisible.value ? "" : "hidden" + }, { + default: withCtx(() => [ + sidebarLocation.value === "left" ? withDirectives((openBlock(), createBlock(unref(script$9), { + key: 0, + class: "side-bar-panel", + minSize: 10, + size: 20 + }, { + default: withCtx(() => [ + renderSlot(_ctx.$slots, "side-bar-panel", {}, void 0, true) + ]), + _: 3 + }, 512)), [ + [vShow, sidebarPanelVisible.value] + ]) : createCommentVNode("", true), + createVNode(unref(script$9), { size: 100 }, { + default: withCtx(() => [ + createVNode(unref(script$a), { + class: "splitter-overlay", + layout: "vertical", + "pt:gutter": bottomPanelVisible.value ? "" : "hidden" + }, { + default: withCtx(() => [ + createVNode(unref(script$9), { class: "graph-canvas-panel relative" }, { + default: withCtx(() => [ + renderSlot(_ctx.$slots, "graph-canvas-panel", {}, void 0, true) + ]), + _: 3 + }), + withDirectives(createVNode(unref(script$9), { class: "bottom-panel" }, { + default: withCtx(() => [ + renderSlot(_ctx.$slots, "bottom-panel", {}, void 0, true) + ]), + _: 3 + }, 512), [ + [vShow, bottomPanelVisible.value] + ]) + ]), + _: 3 + }, 8, ["pt:gutter"]) + ]), + _: 3 + }), + sidebarLocation.value === "right" ? withDirectives((openBlock(), createBlock(unref(script$9), { + key: 1, + class: "side-bar-panel", + minSize: 10, + size: 20 + }, { + default: withCtx(() => [ + renderSlot(_ctx.$slots, "side-bar-panel", {}, void 0, true) + ]), + _: 3 + }, 512)), [ + [vShow, sidebarPanelVisible.value] + ]) : createCommentVNode("", true) + ]), + _: 3 + }, 8, ["pt:gutter"]); + }; + } +}); +const LiteGraphCanvasSplitterOverlay = /* @__PURE__ */ _export_sfc(_sfc_main$j, [["__scopeId", "data-v-b49f20b1"]]); +var theme$6 = /* @__PURE__ */ __name(function theme3(_ref) { + var dt = _ref.dt; + return "\n.p-autocomplete {\n display: inline-flex;\n}\n\n.p-autocomplete-loader {\n position: absolute;\n top: 50%;\n margin-top: -0.5rem;\n right: ".concat(dt("autocomplete.padding.x"), ";\n}\n\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-loader {\n right: calc(").concat(dt("autocomplete.dropdown.width"), " + ").concat(dt("autocomplete.padding.x"), ");\n}\n\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input {\n flex: 1 1 auto;\n width: 1%;\n}\n\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input,\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input-multiple {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n\n.p-autocomplete-dropdown {\n cursor: pointer;\n display: inline-flex;\n cursor: pointer;\n user-select: none;\n align-items: center;\n justify-content: center;\n overflow: hidden;\n position: relative;\n width: ").concat(dt("autocomplete.dropdown.width"), ";\n border-top-right-radius: ").concat(dt("autocomplete.dropdown.border.radius"), ";\n border-bottom-right-radius: ").concat(dt("autocomplete.dropdown.border.radius"), ";\n background: ").concat(dt("autocomplete.dropdown.background"), ";\n border: 1px solid ").concat(dt("autocomplete.dropdown.border.color"), ";\n border-left: 0 none;\n color: ").concat(dt("autocomplete.dropdown.color"), ";\n transition: background ").concat(dt("autocomplete.transition.duration"), ", color ").concat(dt("autocomplete.transition.duration"), ", border-color ").concat(dt("autocomplete.transition.duration"), ", outline-color ").concat(dt("autocomplete.transition.duration"), ", box-shadow ").concat(dt("autocomplete.transition.duration"), ";\n outline-color: transparent;\n}\n\n.p-autocomplete-dropdown:not(:disabled):hover {\n background: ").concat(dt("autocomplete.dropdown.hover.background"), ";\n border-color: ").concat(dt("autocomplete.dropdown.hover.border.color"), ";\n color: ").concat(dt("autocomplete.dropdown.hover.color"), ";\n}\n\n.p-autocomplete-dropdown:not(:disabled):active {\n background: ").concat(dt("autocomplete.dropdown.active.background"), ";\n border-color: ").concat(dt("autocomplete.dropdown.active.border.color"), ";\n color: ").concat(dt("autocomplete.dropdown.active.color"), ";\n}\n\n.p-autocomplete-dropdown:focus-visible {\n box-shadow: ").concat(dt("autocomplete.dropdown.focus.ring.shadow"), ";\n outline: ").concat(dt("autocomplete.dropdown.focus.ring.width"), " ").concat(dt("autocomplete.dropdown.focus.ring.style"), " ").concat(dt("autocomplete.dropdown.focus.ring.color"), ";\n outline-offset: ").concat(dt("autocomplete.dropdown.focus.ring.offset"), ";\n}\n\n.p-autocomplete .p-autocomplete-overlay {\n min-width: 100%;\n}\n\n.p-autocomplete-overlay {\n position: absolute;\n overflow: auto;\n top: 0;\n left: 0;\n background: ").concat(dt("autocomplete.overlay.background"), ";\n color: ").concat(dt("autocomplete.overlay.color"), ";\n border: 1px solid ").concat(dt("autocomplete.overlay.border.color"), ";\n border-radius: ").concat(dt("autocomplete.overlay.border.radius"), ";\n box-shadow: ").concat(dt("autocomplete.overlay.shadow"), ";\n}\n\n.p-autocomplete-list {\n margin: 0;\n padding: 0;\n list-style-type: none;\n display: flex;\n flex-direction: column;\n gap: ").concat(dt("autocomplete.list.gap"), ";\n padding: ").concat(dt("autocomplete.list.padding"), ";\n}\n\n.p-autocomplete-option {\n cursor: pointer;\n white-space: nowrap;\n position: relative;\n overflow: hidden;\n display: flex;\n align-items: center;\n padding: ").concat(dt("autocomplete.option.padding"), ";\n border: 0 none;\n color: ").concat(dt("autocomplete.option.color"), ";\n background: transparent;\n transition: background ").concat(dt("autocomplete.transition.duration"), ", color ").concat(dt("autocomplete.transition.duration"), ", border-color ").concat(dt("autocomplete.transition.duration"), ";\n border-radius: ").concat(dt("autocomplete.option.border.radius"), ";\n}\n\n.p-autocomplete-option:not(.p-autocomplete-option-selected):not(.p-disabled).p-focus {\n background: ").concat(dt("autocomplete.option.focus.background"), ";\n color: ").concat(dt("autocomplete.option.focus.color"), ";\n}\n\n.p-autocomplete-option-selected {\n background: ").concat(dt("autocomplete.option.selected.background"), ";\n color: ").concat(dt("autocomplete.option.selected.color"), ";\n}\n\n.p-autocomplete-option-selected.p-focus {\n background: ").concat(dt("autocomplete.option.selected.focus.background"), ";\n color: ").concat(dt("autocomplete.option.selected.focus.color"), ";\n}\n\n.p-autocomplete-option-group {\n margin: 0;\n padding: ").concat(dt("autocomplete.option.group.padding"), ";\n color: ").concat(dt("autocomplete.option.group.color"), ";\n background: ").concat(dt("autocomplete.option.group.background"), ";\n font-weight: ").concat(dt("autocomplete.option.group.font.weight"), ";\n}\n\n.p-autocomplete-input-multiple {\n margin: 0;\n list-style-type: none;\n cursor: text;\n overflow: hidden;\n display: flex;\n align-items: center;\n flex-wrap: wrap;\n padding: calc(").concat(dt("autocomplete.padding.y"), " / 2) ").concat(dt("autocomplete.padding.x"), ";\n gap: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n color: ").concat(dt("autocomplete.color"), ";\n background: ").concat(dt("autocomplete.background"), ";\n border: 1px solid ").concat(dt("autocomplete.border.color"), ";\n border-radius: ").concat(dt("autocomplete.border.radius"), ";\n width: 100%;\n transition: background ").concat(dt("autocomplete.transition.duration"), ", color ").concat(dt("autocomplete.transition.duration"), ", border-color ").concat(dt("autocomplete.transition.duration"), ", outline-color ").concat(dt("autocomplete.transition.duration"), ", box-shadow ").concat(dt("autocomplete.transition.duration"), ";\n outline-color: transparent;\n box-shadow: ").concat(dt("autocomplete.shadow"), ";\n}\n\n.p-autocomplete:not(.p-disabled):hover .p-autocomplete-input-multiple {\n border-color: ").concat(dt("autocomplete.hover.border.color"), ";\n}\n\n.p-autocomplete:not(.p-disabled).p-focus .p-autocomplete-input-multiple {\n border-color: ").concat(dt("autocomplete.focus.border.color"), ";\n box-shadow: ").concat(dt("autocomplete.focus.ring.shadow"), ";\n outline: ").concat(dt("autocomplete.focus.ring.width"), " ").concat(dt("autocomplete.focus.ring.style"), " ").concat(dt("autocomplete.focus.ring.color"), ";\n outline-offset: ").concat(dt("autocomplete.focus.ring.offset"), ";\n}\n\n.p-autocomplete.p-invalid .p-autocomplete-input-multiple {\n border-color: ").concat(dt("autocomplete.invalid.border.color"), ";\n}\n\n.p-variant-filled.p-autocomplete-input-multiple {\n background: ").concat(dt("autocomplete.filled.background"), ";\n}\n\n.p-autocomplete:not(.p-disabled).p-focus .p-variant-filled.p-autocomplete-input-multiple {\n background: ").concat(dt("autocomplete.filled.focus.background"), ";\n}\n\n.p-autocomplete.p-disabled .p-autocomplete-input-multiple {\n opacity: 1;\n background: ").concat(dt("autocomplete.disabled.background"), ";\n color: ").concat(dt("autocomplete.disabled.color"), ";\n}\n\n.p-autocomplete-chip.p-chip {\n padding-top: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n padding-bottom: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n border-radius: ").concat(dt("autocomplete.chip.border.radius"), ";\n}\n\n.p-autocomplete-input-multiple:has(.p-autocomplete-chip) {\n padding-left: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n padding-right: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n}\n\n.p-autocomplete-chip-item.p-focus .p-autocomplete-chip {\n background: ").concat(dt("inputchips.chip.focus.background"), ";\n color: ").concat(dt("inputchips.chip.focus.color"), ";\n}\n\n.p-autocomplete-input-chip {\n flex: 1 1 auto;\n display: inline-flex;\n padding-top: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n padding-bottom: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n}\n\n.p-autocomplete-input-chip input {\n border: 0 none;\n outline: 0 none;\n background: transparent;\n margin: 0;\n padding: 0;\n box-shadow: none;\n border-radius: 0;\n width: 100%;\n font-family: inherit;\n font-feature-settings: inherit;\n font-size: 1rem;\n color: inherit;\n}\n\n.p-autocomplete-input-chip input::placeholder {\n color: ").concat(dt("autocomplete.placeholder.color"), ";\n}\n\n.p-autocomplete-empty-message {\n padding: ").concat(dt("autocomplete.empty.message.padding"), ";\n}\n\n.p-autocomplete-fluid {\n display: flex;\n}\n\n.p-autocomplete-fluid:has(.p-autocomplete-dropdown) .p-autocomplete-input {\n width: 1%;\n}\n"); +}, "theme"); +var inlineStyles$3 = { + root: { + position: "relative" + } +}; +var classes$6 = { + root: /* @__PURE__ */ __name(function root5(_ref2) { + var instance = _ref2.instance, props = _ref2.props; + return ["p-autocomplete p-component p-inputwrapper", { + "p-disabled": props.disabled, + "p-invalid": props.invalid, + "p-focus": instance.focused, + "p-inputwrapper-filled": props.modelValue || isNotEmpty(instance.inputValue), + "p-inputwrapper-focus": instance.focused, + "p-autocomplete-open": instance.overlayVisible, + "p-autocomplete-fluid": instance.hasFluid + }]; + }, "root"), + pcInput: "p-autocomplete-input", + inputMultiple: /* @__PURE__ */ __name(function inputMultiple(_ref3) { + var props = _ref3.props, instance = _ref3.instance; + return ["p-autocomplete-input-multiple", { + "p-variant-filled": props.variant ? props.variant === "filled" : instance.$primevue.config.inputStyle === "filled" || instance.$primevue.config.inputVariant === "filled" + }]; + }, "inputMultiple"), + chipItem: /* @__PURE__ */ __name(function chipItem(_ref4) { + var instance = _ref4.instance, i = _ref4.i; + return ["p-autocomplete-chip-item", { + "p-focus": instance.focusedMultipleOptionIndex === i + }]; + }, "chipItem"), + pcChip: "p-autocomplete-chip", + chipIcon: "p-autocomplete-chip-icon", + inputChip: "p-autocomplete-input-chip", + loader: "p-autocomplete-loader", + dropdown: "p-autocomplete-dropdown", + overlay: "p-autocomplete-overlay p-component", + list: "p-autocomplete-list", + optionGroup: "p-autocomplete-option-group", + option: /* @__PURE__ */ __name(function option(_ref5) { + var instance = _ref5.instance, _option = _ref5.option, i = _ref5.i, getItemOptions = _ref5.getItemOptions; + return ["p-autocomplete-option", { + "p-autocomplete-option-selected": instance.isSelected(_option), + "p-focus": instance.focusedOptionIndex === instance.getOptionIndex(i, getItemOptions), + "p-disabled": instance.isOptionDisabled(_option) + }]; + }, "option"), + emptyMessage: "p-autocomplete-empty-message" +}; +var AutoCompleteStyle = BaseStyle.extend({ + name: "autocomplete", + theme: theme$6, + classes: classes$6, + inlineStyles: inlineStyles$3 +}); +var script$1$6 = { + name: "BaseAutoComplete", + "extends": script$g, + props: { + modelValue: null, + suggestions: { + type: Array, + "default": null + }, + optionLabel: null, + optionDisabled: null, + optionGroupLabel: null, + optionGroupChildren: null, + scrollHeight: { + type: String, + "default": "14rem" + }, + dropdown: { + type: Boolean, + "default": false + }, + dropdownMode: { + type: String, + "default": "blank" + }, + multiple: { + type: Boolean, + "default": false + }, + loading: { + type: Boolean, + "default": false + }, + variant: { + type: String, + "default": null + }, + invalid: { + type: Boolean, + "default": false + }, + disabled: { + type: Boolean, + "default": false + }, + placeholder: { + type: String, + "default": null + }, + dataKey: { + type: String, + "default": null + }, + minLength: { + type: Number, + "default": 1 + }, + delay: { + type: Number, + "default": 300 + }, + appendTo: { + type: [String, Object], + "default": "body" + }, + forceSelection: { + type: Boolean, + "default": false + }, + completeOnFocus: { + type: Boolean, + "default": false + }, + inputId: { + type: String, + "default": null + }, + inputStyle: { + type: Object, + "default": null + }, + inputClass: { + type: [String, Object], + "default": null + }, + panelStyle: { + type: Object, + "default": null + }, + panelClass: { + type: [String, Object], + "default": null + }, + overlayStyle: { + type: Object, + "default": null + }, + overlayClass: { + type: [String, Object], + "default": null + }, + dropdownIcon: { + type: String, + "default": null + }, + dropdownClass: { + type: [String, Object], + "default": null + }, + loader: { + type: String, + "default": null + }, + loadingIcon: { + type: String, + "default": null + }, + removeTokenIcon: { + type: String, + "default": null + }, + chipIcon: { + type: String, + "default": null + }, + virtualScrollerOptions: { + type: Object, + "default": null + }, + autoOptionFocus: { + type: Boolean, + "default": false + }, + selectOnFocus: { + type: Boolean, + "default": false + }, + focusOnHover: { + type: Boolean, + "default": true + }, + searchLocale: { + type: String, + "default": void 0 + }, + searchMessage: { + type: String, + "default": null + }, + selectionMessage: { + type: String, + "default": null + }, + emptySelectionMessage: { + type: String, + "default": null + }, + emptySearchMessage: { + type: String, + "default": null + }, + tabindex: { + type: Number, + "default": 0 + }, + typeahead: { + type: Boolean, + "default": true + }, + ariaLabel: { + type: String, + "default": null + }, + ariaLabelledby: { + type: String, + "default": null + }, + fluid: { + type: Boolean, + "default": null + } + }, + style: AutoCompleteStyle, + provide: /* @__PURE__ */ __name(function provide7() { + return { + $pcAutoComplete: this, + $parentInstance: this + }; + }, "provide") +}; +function _typeof$1$1(o) { + "@babel/helpers - typeof"; + return _typeof$1$1 = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { + return typeof o2; + } : function(o2) { + return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; + }, _typeof$1$1(o); +} +__name(_typeof$1$1, "_typeof$1$1"); +function _toConsumableArray$1(r) { + return _arrayWithoutHoles$1(r) || _iterableToArray$1(r) || _unsupportedIterableToArray$1(r) || _nonIterableSpread$1(); +} +__name(_toConsumableArray$1, "_toConsumableArray$1"); +function _nonIterableSpread$1() { + throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method."); +} +__name(_nonIterableSpread$1, "_nonIterableSpread$1"); +function _unsupportedIterableToArray$1(r, a) { + if (r) { + if ("string" == typeof r) return _arrayLikeToArray$1(r, a); + var t = {}.toString.call(r).slice(8, -1); + return "Object" === t && r.constructor && (t = r.constructor.name), "Map" === t || "Set" === t ? Array.from(r) : "Arguments" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray$1(r, a) : void 0; + } +} +__name(_unsupportedIterableToArray$1, "_unsupportedIterableToArray$1"); +function _iterableToArray$1(r) { + if ("undefined" != typeof Symbol && null != r[Symbol.iterator] || null != r["@@iterator"]) return Array.from(r); +} +__name(_iterableToArray$1, "_iterableToArray$1"); +function _arrayWithoutHoles$1(r) { + if (Array.isArray(r)) return _arrayLikeToArray$1(r); +} +__name(_arrayWithoutHoles$1, "_arrayWithoutHoles$1"); +function _arrayLikeToArray$1(r, a) { + (null == a || a > r.length) && (a = r.length); + for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; + return n; +} +__name(_arrayLikeToArray$1, "_arrayLikeToArray$1"); +var script$8 = { + name: "AutoComplete", + "extends": script$1$6, + inheritAttrs: false, + emits: ["update:modelValue", "change", "focus", "blur", "item-select", "item-unselect", "option-select", "option-unselect", "dropdown-click", "clear", "complete", "before-show", "before-hide", "show", "hide"], + inject: { + $pcFluid: { + "default": null + } + }, + outsideClickListener: null, + resizeListener: null, + scrollHandler: null, + overlay: null, + virtualScroller: null, + searchTimeout: null, + dirty: false, + data: /* @__PURE__ */ __name(function data4() { + return { + id: this.$attrs.id, + clicked: false, + focused: false, + focusedOptionIndex: -1, + focusedMultipleOptionIndex: -1, + overlayVisible: false, + searching: false + }; + }, "data"), + watch: { + "$attrs.id": /* @__PURE__ */ __name(function $attrsId(newValue) { + this.id = newValue || UniqueComponentId(); + }, "$attrsId"), + suggestions: /* @__PURE__ */ __name(function suggestions() { + if (this.searching) { + this.show(); + this.focusedOptionIndex = this.overlayVisible && this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1; + this.searching = false; + } + this.autoUpdateModel(); + }, "suggestions") + }, + mounted: /* @__PURE__ */ __name(function mounted3() { + this.id = this.id || UniqueComponentId(); + this.autoUpdateModel(); + }, "mounted"), + updated: /* @__PURE__ */ __name(function updated2() { + if (this.overlayVisible) { + this.alignOverlay(); + } + }, "updated"), + beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount3() { + this.unbindOutsideClickListener(); + this.unbindResizeListener(); + if (this.scrollHandler) { + this.scrollHandler.destroy(); + this.scrollHandler = null; + } + if (this.overlay) { + ZIndex.clear(this.overlay); + this.overlay = null; + } + }, "beforeUnmount"), + methods: { + getOptionIndex: /* @__PURE__ */ __name(function getOptionIndex(index, fn) { + return this.virtualScrollerDisabled ? index : fn && fn(index)["index"]; + }, "getOptionIndex"), + getOptionLabel: /* @__PURE__ */ __name(function getOptionLabel(option2) { + return this.optionLabel ? resolveFieldData(option2, this.optionLabel) : option2; + }, "getOptionLabel"), + getOptionValue: /* @__PURE__ */ __name(function getOptionValue(option2) { + return option2; + }, "getOptionValue"), + getOptionRenderKey: /* @__PURE__ */ __name(function getOptionRenderKey(option2, index) { + return (this.dataKey ? resolveFieldData(option2, this.dataKey) : this.getOptionLabel(option2)) + "_" + index; + }, "getOptionRenderKey"), + getPTOptions: /* @__PURE__ */ __name(function getPTOptions3(option2, itemOptions, index, key) { + return this.ptm(key, { + context: { + selected: this.isSelected(option2), + focused: this.focusedOptionIndex === this.getOptionIndex(index, itemOptions), + disabled: this.isOptionDisabled(option2) + } + }); + }, "getPTOptions"), + isOptionDisabled: /* @__PURE__ */ __name(function isOptionDisabled(option2) { + return this.optionDisabled ? resolveFieldData(option2, this.optionDisabled) : false; + }, "isOptionDisabled"), + isOptionGroup: /* @__PURE__ */ __name(function isOptionGroup(option2) { + return this.optionGroupLabel && option2.optionGroup && option2.group; + }, "isOptionGroup"), + getOptionGroupLabel: /* @__PURE__ */ __name(function getOptionGroupLabel(optionGroup) { + return resolveFieldData(optionGroup, this.optionGroupLabel); + }, "getOptionGroupLabel"), + getOptionGroupChildren: /* @__PURE__ */ __name(function getOptionGroupChildren(optionGroup) { + return resolveFieldData(optionGroup, this.optionGroupChildren); + }, "getOptionGroupChildren"), + getAriaPosInset: /* @__PURE__ */ __name(function getAriaPosInset(index) { + var _this = this; + return (this.optionGroupLabel ? index - this.visibleOptions.slice(0, index).filter(function(option2) { + return _this.isOptionGroup(option2); + }).length : index) + 1; + }, "getAriaPosInset"), + show: /* @__PURE__ */ __name(function show(isFocus) { + this.$emit("before-show"); + this.dirty = true; + this.overlayVisible = true; + this.focusedOptionIndex = this.focusedOptionIndex !== -1 ? this.focusedOptionIndex : this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1; + isFocus && focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el); + }, "show"), + hide: /* @__PURE__ */ __name(function hide(isFocus) { + var _this2 = this; + var _hide = /* @__PURE__ */ __name(function _hide2() { + _this2.$emit("before-hide"); + _this2.dirty = isFocus; + _this2.overlayVisible = false; + _this2.clicked = false; + _this2.focusedOptionIndex = -1; + isFocus && focus(_this2.multiple ? _this2.$refs.focusInput : _this2.$refs.focusInput.$el); + }, "_hide"); + setTimeout(function() { + _hide(); + }, 0); + }, "hide"), + onFocus: /* @__PURE__ */ __name(function onFocus2(event) { + if (this.disabled) { + return; + } + if (!this.dirty && this.completeOnFocus) { + this.search(event, event.target.value, "focus"); + } + this.dirty = true; + this.focused = true; + if (this.overlayVisible) { + this.focusedOptionIndex = this.focusedOptionIndex !== -1 ? this.focusedOptionIndex : this.overlayVisible && this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1; + this.scrollInView(this.focusedOptionIndex); + } + this.$emit("focus", event); + }, "onFocus"), + onBlur: /* @__PURE__ */ __name(function onBlur(event) { + this.dirty = false; + this.focused = false; + this.focusedOptionIndex = -1; + this.$emit("blur", event); + }, "onBlur"), + onKeyDown: /* @__PURE__ */ __name(function onKeyDown(event) { + if (this.disabled) { + event.preventDefault(); + return; + } + switch (event.code) { + case "ArrowDown": + this.onArrowDownKey(event); + break; + case "ArrowUp": + this.onArrowUpKey(event); + break; + case "ArrowLeft": + this.onArrowLeftKey(event); + break; + case "ArrowRight": + this.onArrowRightKey(event); + break; + case "Home": + this.onHomeKey(event); + break; + case "End": + this.onEndKey(event); + break; + case "PageDown": + this.onPageDownKey(event); + break; + case "PageUp": + this.onPageUpKey(event); + break; + case "Enter": + case "NumpadEnter": + this.onEnterKey(event); + break; + case "Escape": + this.onEscapeKey(event); + break; + case "Tab": + this.onTabKey(event); + break; + case "Backspace": + this.onBackspaceKey(event); + break; + } + this.clicked = false; + }, "onKeyDown"), + onInput: /* @__PURE__ */ __name(function onInput(event) { + var _this3 = this; + if (this.typeahead) { + if (this.searchTimeout) { + clearTimeout(this.searchTimeout); + } + var query = event.target.value; + if (!this.multiple) { + this.updateModel(event, query); + } + if (query.length === 0) { + this.hide(); + this.$emit("clear"); + } else { + if (query.length >= this.minLength) { + this.focusedOptionIndex = -1; + this.searchTimeout = setTimeout(function() { + _this3.search(event, query, "input"); + }, this.delay); + } else { + this.hide(); + } + } + } + }, "onInput"), + onChange: /* @__PURE__ */ __name(function onChange(event) { + var _this4 = this; + if (this.forceSelection) { + var valid = false; + if (this.visibleOptions && !this.multiple) { + var value = this.multiple ? this.$refs.focusInput.value : this.$refs.focusInput.$el.value; + var matchedValue = this.visibleOptions.find(function(option2) { + return _this4.isOptionMatched(option2, value || ""); + }); + if (matchedValue !== void 0) { + valid = true; + !this.isSelected(matchedValue) && this.onOptionSelect(event, matchedValue); + } + } + if (!valid) { + if (this.multiple) this.$refs.focusInput.value = ""; + else this.$refs.focusInput.$el.value = ""; + this.$emit("clear"); + !this.multiple && this.updateModel(event, null); + } + } + }, "onChange"), + onMultipleContainerFocus: /* @__PURE__ */ __name(function onMultipleContainerFocus() { + if (this.disabled) { + return; + } + this.focused = true; + }, "onMultipleContainerFocus"), + onMultipleContainerBlur: /* @__PURE__ */ __name(function onMultipleContainerBlur() { + this.focusedMultipleOptionIndex = -1; + this.focused = false; + }, "onMultipleContainerBlur"), + onMultipleContainerKeyDown: /* @__PURE__ */ __name(function onMultipleContainerKeyDown(event) { + if (this.disabled) { + event.preventDefault(); + return; + } + switch (event.code) { + case "ArrowLeft": + this.onArrowLeftKeyOnMultiple(event); + break; + case "ArrowRight": + this.onArrowRightKeyOnMultiple(event); + break; + case "Backspace": + this.onBackspaceKeyOnMultiple(event); + break; + } + }, "onMultipleContainerKeyDown"), + onContainerClick: /* @__PURE__ */ __name(function onContainerClick(event) { + this.clicked = true; + if (this.disabled || this.searching || this.loading || this.isInputClicked(event) || this.isDropdownClicked(event)) { + return; + } + if (!this.overlay || !this.overlay.contains(event.target)) { + focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el); + } + }, "onContainerClick"), + onDropdownClick: /* @__PURE__ */ __name(function onDropdownClick(event) { + var query = void 0; + if (this.overlayVisible) { + this.hide(true); + } else { + var target = this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el; + focus(target); + query = target.value; + if (this.dropdownMode === "blank") this.search(event, "", "dropdown"); + else if (this.dropdownMode === "current") this.search(event, query, "dropdown"); + } + this.$emit("dropdown-click", { + originalEvent: event, + query + }); + }, "onDropdownClick"), + onOptionSelect: /* @__PURE__ */ __name(function onOptionSelect(event, option2) { + var isHide = arguments.length > 2 && arguments[2] !== void 0 ? arguments[2] : true; + var value = this.getOptionValue(option2); + if (this.multiple) { + this.$refs.focusInput.value = ""; + if (!this.isSelected(option2)) { + this.updateModel(event, [].concat(_toConsumableArray$1(this.modelValue || []), [value])); + } + } else { + this.updateModel(event, value); + } + this.$emit("item-select", { + originalEvent: event, + value: option2 + }); + this.$emit("option-select", { + originalEvent: event, + value: option2 + }); + isHide && this.hide(true); + }, "onOptionSelect"), + onOptionMouseMove: /* @__PURE__ */ __name(function onOptionMouseMove(event, index) { + if (this.focusOnHover) { + this.changeFocusedOptionIndex(event, index); + } + }, "onOptionMouseMove"), + onOverlayClick: /* @__PURE__ */ __name(function onOverlayClick(event) { + OverlayEventBus.emit("overlay-click", { + originalEvent: event, + target: this.$el + }); + }, "onOverlayClick"), + onOverlayKeyDown: /* @__PURE__ */ __name(function onOverlayKeyDown(event) { + switch (event.code) { + case "Escape": + this.onEscapeKey(event); + break; + } + }, "onOverlayKeyDown"), + onArrowDownKey: /* @__PURE__ */ __name(function onArrowDownKey(event) { + if (!this.overlayVisible) { + return; + } + var optionIndex = this.focusedOptionIndex !== -1 ? this.findNextOptionIndex(this.focusedOptionIndex) : this.clicked ? this.findFirstOptionIndex() : this.findFirstFocusedOptionIndex(); + this.changeFocusedOptionIndex(event, optionIndex); + event.preventDefault(); + }, "onArrowDownKey"), + onArrowUpKey: /* @__PURE__ */ __name(function onArrowUpKey(event) { + if (!this.overlayVisible) { + return; + } + if (event.altKey) { + if (this.focusedOptionIndex !== -1) { + this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]); + } + this.overlayVisible && this.hide(); + event.preventDefault(); + } else { + var optionIndex = this.focusedOptionIndex !== -1 ? this.findPrevOptionIndex(this.focusedOptionIndex) : this.clicked ? this.findLastOptionIndex() : this.findLastFocusedOptionIndex(); + this.changeFocusedOptionIndex(event, optionIndex); + event.preventDefault(); + } + }, "onArrowUpKey"), + onArrowLeftKey: /* @__PURE__ */ __name(function onArrowLeftKey2(event) { + var target = event.currentTarget; + this.focusedOptionIndex = -1; + if (this.multiple) { + if (isEmpty(target.value) && this.hasSelectedOption) { + focus(this.$refs.multiContainer); + this.focusedMultipleOptionIndex = this.modelValue.length; + } else { + event.stopPropagation(); + } + } + }, "onArrowLeftKey"), + onArrowRightKey: /* @__PURE__ */ __name(function onArrowRightKey2(event) { + this.focusedOptionIndex = -1; + this.multiple && event.stopPropagation(); + }, "onArrowRightKey"), + onHomeKey: /* @__PURE__ */ __name(function onHomeKey2(event) { + var currentTarget = event.currentTarget; + var len = currentTarget.value.length; + currentTarget.setSelectionRange(0, event.shiftKey ? len : 0); + this.focusedOptionIndex = -1; + event.preventDefault(); + }, "onHomeKey"), + onEndKey: /* @__PURE__ */ __name(function onEndKey2(event) { + var currentTarget = event.currentTarget; + var len = currentTarget.value.length; + currentTarget.setSelectionRange(event.shiftKey ? 0 : len, len); + this.focusedOptionIndex = -1; + event.preventDefault(); + }, "onEndKey"), + onPageUpKey: /* @__PURE__ */ __name(function onPageUpKey2(event) { + this.scrollInView(0); + event.preventDefault(); + }, "onPageUpKey"), + onPageDownKey: /* @__PURE__ */ __name(function onPageDownKey2(event) { + this.scrollInView(this.visibleOptions.length - 1); + event.preventDefault(); + }, "onPageDownKey"), + onEnterKey: /* @__PURE__ */ __name(function onEnterKey2(event) { + if (!this.typeahead) { + if (this.multiple) { + this.updateModel(event, [].concat(_toConsumableArray$1(this.modelValue || []), [event.target.value])); + this.$refs.focusInput.value = ""; + } + } else { + if (!this.overlayVisible) { + this.focusedOptionIndex = -1; + this.onArrowDownKey(event); + } else { + if (this.focusedOptionIndex !== -1) { + this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]); + } + this.hide(); + } + } + }, "onEnterKey"), + onEscapeKey: /* @__PURE__ */ __name(function onEscapeKey(event) { + this.overlayVisible && this.hide(true); + event.preventDefault(); + }, "onEscapeKey"), + onTabKey: /* @__PURE__ */ __name(function onTabKey(event) { + if (this.focusedOptionIndex !== -1) { + this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]); + } + this.overlayVisible && this.hide(); + }, "onTabKey"), + onBackspaceKey: /* @__PURE__ */ __name(function onBackspaceKey(event) { + if (this.multiple) { + if (isNotEmpty(this.modelValue) && !this.$refs.focusInput.value) { + var removedValue = this.modelValue[this.modelValue.length - 1]; + var newValue = this.modelValue.slice(0, -1); + this.$emit("update:modelValue", newValue); + this.$emit("item-unselect", { + originalEvent: event, + value: removedValue + }); + this.$emit("option-unselect", { + originalEvent: event, + value: removedValue + }); + } + event.stopPropagation(); + } + }, "onBackspaceKey"), + onArrowLeftKeyOnMultiple: /* @__PURE__ */ __name(function onArrowLeftKeyOnMultiple() { + this.focusedMultipleOptionIndex = this.focusedMultipleOptionIndex < 1 ? 0 : this.focusedMultipleOptionIndex - 1; + }, "onArrowLeftKeyOnMultiple"), + onArrowRightKeyOnMultiple: /* @__PURE__ */ __name(function onArrowRightKeyOnMultiple() { + this.focusedMultipleOptionIndex++; + if (this.focusedMultipleOptionIndex > this.modelValue.length - 1) { + this.focusedMultipleOptionIndex = -1; + focus(this.$refs.focusInput); + } + }, "onArrowRightKeyOnMultiple"), + onBackspaceKeyOnMultiple: /* @__PURE__ */ __name(function onBackspaceKeyOnMultiple(event) { + if (this.focusedMultipleOptionIndex !== -1) { + this.removeOption(event, this.focusedMultipleOptionIndex); + } + }, "onBackspaceKeyOnMultiple"), + onOverlayEnter: /* @__PURE__ */ __name(function onOverlayEnter(el) { + ZIndex.set("overlay", el, this.$primevue.config.zIndex.overlay); + addStyle(el, { + position: "absolute", + top: "0", + left: "0" + }); + this.alignOverlay(); + }, "onOverlayEnter"), + onOverlayAfterEnter: /* @__PURE__ */ __name(function onOverlayAfterEnter() { + this.bindOutsideClickListener(); + this.bindScrollListener(); + this.bindResizeListener(); + this.$emit("show"); + }, "onOverlayAfterEnter"), + onOverlayLeave: /* @__PURE__ */ __name(function onOverlayLeave() { + this.unbindOutsideClickListener(); + this.unbindScrollListener(); + this.unbindResizeListener(); + this.$emit("hide"); + this.overlay = null; + }, "onOverlayLeave"), + onOverlayAfterLeave: /* @__PURE__ */ __name(function onOverlayAfterLeave(el) { + ZIndex.clear(el); + }, "onOverlayAfterLeave"), + alignOverlay: /* @__PURE__ */ __name(function alignOverlay() { + var target = this.multiple ? this.$refs.multiContainer : this.$refs.focusInput.$el; + if (this.appendTo === "self") { + relativePosition(this.overlay, target); + } else { + this.overlay.style.minWidth = getOuterWidth(target) + "px"; + absolutePosition(this.overlay, target); + } + }, "alignOverlay"), + bindOutsideClickListener: /* @__PURE__ */ __name(function bindOutsideClickListener() { + var _this5 = this; + if (!this.outsideClickListener) { + this.outsideClickListener = function(event) { + if (_this5.overlayVisible && _this5.overlay && _this5.isOutsideClicked(event)) { + _this5.hide(); + } + }; + document.addEventListener("click", this.outsideClickListener); + } + }, "bindOutsideClickListener"), + unbindOutsideClickListener: /* @__PURE__ */ __name(function unbindOutsideClickListener() { + if (this.outsideClickListener) { + document.removeEventListener("click", this.outsideClickListener); + this.outsideClickListener = null; + } + }, "unbindOutsideClickListener"), + bindScrollListener: /* @__PURE__ */ __name(function bindScrollListener() { + var _this6 = this; + if (!this.scrollHandler) { + this.scrollHandler = new ConnectedOverlayScrollHandler(this.$refs.container, function() { + if (_this6.overlayVisible) { + _this6.hide(); + } + }); + } + this.scrollHandler.bindScrollListener(); + }, "bindScrollListener"), + unbindScrollListener: /* @__PURE__ */ __name(function unbindScrollListener() { + if (this.scrollHandler) { + this.scrollHandler.unbindScrollListener(); + } + }, "unbindScrollListener"), + bindResizeListener: /* @__PURE__ */ __name(function bindResizeListener() { + var _this7 = this; + if (!this.resizeListener) { + this.resizeListener = function() { + if (_this7.overlayVisible && !isTouchDevice()) { + _this7.hide(); + } + }; + window.addEventListener("resize", this.resizeListener); + } + }, "bindResizeListener"), + unbindResizeListener: /* @__PURE__ */ __name(function unbindResizeListener() { + if (this.resizeListener) { + window.removeEventListener("resize", this.resizeListener); + this.resizeListener = null; + } + }, "unbindResizeListener"), + isOutsideClicked: /* @__PURE__ */ __name(function isOutsideClicked(event) { + return !this.overlay.contains(event.target) && !this.isInputClicked(event) && !this.isDropdownClicked(event); + }, "isOutsideClicked"), + isInputClicked: /* @__PURE__ */ __name(function isInputClicked(event) { + if (this.multiple) return event.target === this.$refs.multiContainer || this.$refs.multiContainer.contains(event.target); + else return event.target === this.$refs.focusInput.$el; + }, "isInputClicked"), + isDropdownClicked: /* @__PURE__ */ __name(function isDropdownClicked(event) { + return this.$refs.dropdownButton ? event.target === this.$refs.dropdownButton || this.$refs.dropdownButton.contains(event.target) : false; + }, "isDropdownClicked"), + isOptionMatched: /* @__PURE__ */ __name(function isOptionMatched(option2, value) { + var _this$getOptionLabel; + return this.isValidOption(option2) && ((_this$getOptionLabel = this.getOptionLabel(option2)) === null || _this$getOptionLabel === void 0 ? void 0 : _this$getOptionLabel.toLocaleLowerCase(this.searchLocale)) === value.toLocaleLowerCase(this.searchLocale); + }, "isOptionMatched"), + isValidOption: /* @__PURE__ */ __name(function isValidOption(option2) { + return isNotEmpty(option2) && !(this.isOptionDisabled(option2) || this.isOptionGroup(option2)); + }, "isValidOption"), + isValidSelectedOption: /* @__PURE__ */ __name(function isValidSelectedOption(option2) { + return this.isValidOption(option2) && this.isSelected(option2); + }, "isValidSelectedOption"), + isEquals: /* @__PURE__ */ __name(function isEquals(value1, value2) { + return equals(value1, value2, this.equalityKey); + }, "isEquals"), + isSelected: /* @__PURE__ */ __name(function isSelected(option2) { + var _this8 = this; + var optionValue = this.getOptionValue(option2); + return this.multiple ? (this.modelValue || []).some(function(value) { + return _this8.isEquals(value, optionValue); + }) : this.isEquals(this.modelValue, this.getOptionValue(option2)); + }, "isSelected"), + findFirstOptionIndex: /* @__PURE__ */ __name(function findFirstOptionIndex() { + var _this9 = this; + return this.visibleOptions.findIndex(function(option2) { + return _this9.isValidOption(option2); + }); + }, "findFirstOptionIndex"), + findLastOptionIndex: /* @__PURE__ */ __name(function findLastOptionIndex() { + var _this10 = this; + return findLastIndex(this.visibleOptions, function(option2) { + return _this10.isValidOption(option2); + }); + }, "findLastOptionIndex"), + findNextOptionIndex: /* @__PURE__ */ __name(function findNextOptionIndex(index) { + var _this11 = this; + var matchedOptionIndex = index < this.visibleOptions.length - 1 ? this.visibleOptions.slice(index + 1).findIndex(function(option2) { + return _this11.isValidOption(option2); + }) : -1; + return matchedOptionIndex > -1 ? matchedOptionIndex + index + 1 : index; + }, "findNextOptionIndex"), + findPrevOptionIndex: /* @__PURE__ */ __name(function findPrevOptionIndex(index) { + var _this12 = this; + var matchedOptionIndex = index > 0 ? findLastIndex(this.visibleOptions.slice(0, index), function(option2) { + return _this12.isValidOption(option2); + }) : -1; + return matchedOptionIndex > -1 ? matchedOptionIndex : index; + }, "findPrevOptionIndex"), + findSelectedOptionIndex: /* @__PURE__ */ __name(function findSelectedOptionIndex() { + var _this13 = this; + return this.hasSelectedOption ? this.visibleOptions.findIndex(function(option2) { + return _this13.isValidSelectedOption(option2); + }) : -1; + }, "findSelectedOptionIndex"), + findFirstFocusedOptionIndex: /* @__PURE__ */ __name(function findFirstFocusedOptionIndex() { + var selectedIndex = this.findSelectedOptionIndex(); + return selectedIndex < 0 ? this.findFirstOptionIndex() : selectedIndex; + }, "findFirstFocusedOptionIndex"), + findLastFocusedOptionIndex: /* @__PURE__ */ __name(function findLastFocusedOptionIndex() { + var selectedIndex = this.findSelectedOptionIndex(); + return selectedIndex < 0 ? this.findLastOptionIndex() : selectedIndex; + }, "findLastFocusedOptionIndex"), + search: /* @__PURE__ */ __name(function search(event, query, source) { + if (query === void 0 || query === null) { + return; + } + if (source === "input" && query.trim().length === 0) { + return; + } + this.searching = true; + this.$emit("complete", { + originalEvent: event, + query + }); + }, "search"), + removeOption: /* @__PURE__ */ __name(function removeOption(event, index) { + var _this14 = this; + var removedOption = this.modelValue[index]; + var value = this.modelValue.filter(function(_2, i) { + return i !== index; + }).map(function(option2) { + return _this14.getOptionValue(option2); + }); + this.updateModel(event, value); + this.$emit("item-unselect", { + originalEvent: event, + value: removedOption + }); + this.$emit("option-unselect", { + originalEvent: event, + value: removedOption + }); + this.dirty = true; + focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el); + }, "removeOption"), + changeFocusedOptionIndex: /* @__PURE__ */ __name(function changeFocusedOptionIndex(event, index) { + if (this.focusedOptionIndex !== index) { + this.focusedOptionIndex = index; + this.scrollInView(); + if (this.selectOnFocus) { + this.onOptionSelect(event, this.visibleOptions[index], false); + } + } + }, "changeFocusedOptionIndex"), + scrollInView: /* @__PURE__ */ __name(function scrollInView2() { + var _this15 = this; + var index = arguments.length > 0 && arguments[0] !== void 0 ? arguments[0] : -1; + this.$nextTick(function() { + var id2 = index !== -1 ? "".concat(_this15.id, "_").concat(index) : _this15.focusedOptionId; + var element = findSingle(_this15.list, 'li[id="'.concat(id2, '"]')); + if (element) { + element.scrollIntoView && element.scrollIntoView({ + block: "nearest", + inline: "start" + }); + } else if (!_this15.virtualScrollerDisabled) { + _this15.virtualScroller && _this15.virtualScroller.scrollToIndex(index !== -1 ? index : _this15.focusedOptionIndex); + } + }); + }, "scrollInView"), + autoUpdateModel: /* @__PURE__ */ __name(function autoUpdateModel() { + if (this.selectOnFocus && this.autoOptionFocus && !this.hasSelectedOption) { + this.focusedOptionIndex = this.findFirstFocusedOptionIndex(); + this.onOptionSelect(null, this.visibleOptions[this.focusedOptionIndex], false); + } + }, "autoUpdateModel"), + updateModel: /* @__PURE__ */ __name(function updateModel(event, value) { + this.$emit("update:modelValue", value); + this.$emit("change", { + originalEvent: event, + value + }); + }, "updateModel"), + flatOptions: /* @__PURE__ */ __name(function flatOptions(options) { + var _this16 = this; + return (options || []).reduce(function(result, option2, index) { + result.push({ + optionGroup: option2, + group: true, + index + }); + var optionGroupChildren = _this16.getOptionGroupChildren(option2); + optionGroupChildren && optionGroupChildren.forEach(function(o) { + return result.push(o); + }); + return result; + }, []); + }, "flatOptions"), + overlayRef: /* @__PURE__ */ __name(function overlayRef(el) { + this.overlay = el; + }, "overlayRef"), + listRef: /* @__PURE__ */ __name(function listRef(el, contentRef) { + this.list = el; + contentRef && contentRef(el); + }, "listRef"), + virtualScrollerRef: /* @__PURE__ */ __name(function virtualScrollerRef(el) { + this.virtualScroller = el; + }, "virtualScrollerRef") + }, + computed: { + visibleOptions: /* @__PURE__ */ __name(function visibleOptions() { + return this.optionGroupLabel ? this.flatOptions(this.suggestions) : this.suggestions || []; + }, "visibleOptions"), + inputValue: /* @__PURE__ */ __name(function inputValue() { + if (isNotEmpty(this.modelValue)) { + if (_typeof$1$1(this.modelValue) === "object") { + var label = this.getOptionLabel(this.modelValue); + return label != null ? label : this.modelValue; + } else { + return this.modelValue; + } + } else { + return ""; + } + }, "inputValue"), + hasSelectedOption: /* @__PURE__ */ __name(function hasSelectedOption() { + return isNotEmpty(this.modelValue); + }, "hasSelectedOption"), + equalityKey: /* @__PURE__ */ __name(function equalityKey() { + return this.dataKey; + }, "equalityKey"), + searchResultMessageText: /* @__PURE__ */ __name(function searchResultMessageText() { + return isNotEmpty(this.visibleOptions) && this.overlayVisible ? this.searchMessageText.replaceAll("{0}", this.visibleOptions.length) : this.emptySearchMessageText; + }, "searchResultMessageText"), + searchMessageText: /* @__PURE__ */ __name(function searchMessageText() { + return this.searchMessage || this.$primevue.config.locale.searchMessage || ""; + }, "searchMessageText"), + emptySearchMessageText: /* @__PURE__ */ __name(function emptySearchMessageText() { + return this.emptySearchMessage || this.$primevue.config.locale.emptySearchMessage || ""; + }, "emptySearchMessageText"), + selectionMessageText: /* @__PURE__ */ __name(function selectionMessageText() { + return this.selectionMessage || this.$primevue.config.locale.selectionMessage || ""; + }, "selectionMessageText"), + emptySelectionMessageText: /* @__PURE__ */ __name(function emptySelectionMessageText() { + return this.emptySelectionMessage || this.$primevue.config.locale.emptySelectionMessage || ""; + }, "emptySelectionMessageText"), + selectedMessageText: /* @__PURE__ */ __name(function selectedMessageText() { + return this.hasSelectedOption ? this.selectionMessageText.replaceAll("{0}", this.multiple ? this.modelValue.length : "1") : this.emptySelectionMessageText; + }, "selectedMessageText"), + listAriaLabel: /* @__PURE__ */ __name(function listAriaLabel() { + return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.listLabel : void 0; + }, "listAriaLabel"), + focusedOptionId: /* @__PURE__ */ __name(function focusedOptionId() { + return this.focusedOptionIndex !== -1 ? "".concat(this.id, "_").concat(this.focusedOptionIndex) : null; + }, "focusedOptionId"), + focusedMultipleOptionId: /* @__PURE__ */ __name(function focusedMultipleOptionId() { + return this.focusedMultipleOptionIndex !== -1 ? "".concat(this.id, "_multiple_option_").concat(this.focusedMultipleOptionIndex) : null; + }, "focusedMultipleOptionId"), + ariaSetSize: /* @__PURE__ */ __name(function ariaSetSize() { + var _this17 = this; + return this.visibleOptions.filter(function(option2) { + return !_this17.isOptionGroup(option2); + }).length; + }, "ariaSetSize"), + virtualScrollerDisabled: /* @__PURE__ */ __name(function virtualScrollerDisabled() { + return !this.virtualScrollerOptions; + }, "virtualScrollerDisabled"), + panelId: /* @__PURE__ */ __name(function panelId() { + return this.id + "_panel"; + }, "panelId"), + hasFluid: /* @__PURE__ */ __name(function hasFluid() { + return isEmpty(this.fluid) ? !!this.$pcFluid : this.fluid; + }, "hasFluid") + }, + components: { + InputText: script$k, + VirtualScroller: script$l, + Portal: script$m, + ChevronDownIcon: script$n, + SpinnerIcon: script$o, + Chip: script$p + }, + directives: { + ripple: Ripple + } +}; +function _typeof$4(o) { + "@babel/helpers - typeof"; + return _typeof$4 = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { + return typeof o2; + } : function(o2) { + return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; + }, _typeof$4(o); +} +__name(_typeof$4, "_typeof$4"); +function ownKeys$3(e, r) { + var t = Object.keys(e); + if (Object.getOwnPropertySymbols) { + var o = Object.getOwnPropertySymbols(e); + r && (o = o.filter(function(r2) { + return Object.getOwnPropertyDescriptor(e, r2).enumerable; + })), t.push.apply(t, o); + } + return t; +} +__name(ownKeys$3, "ownKeys$3"); +function _objectSpread$3(e) { + for (var r = 1; r < arguments.length; r++) { + var t = null != arguments[r] ? arguments[r] : {}; + r % 2 ? ownKeys$3(Object(t), true).forEach(function(r2) { + _defineProperty$4(e, r2, t[r2]); + }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys$3(Object(t)).forEach(function(r2) { + Object.defineProperty(e, r2, Object.getOwnPropertyDescriptor(t, r2)); + }); + } + return e; +} +__name(_objectSpread$3, "_objectSpread$3"); +function _defineProperty$4(e, r, t) { + return (r = _toPropertyKey$4(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: true, configurable: true, writable: true }) : e[r] = t, e; +} +__name(_defineProperty$4, "_defineProperty$4"); +function _toPropertyKey$4(t) { + var i = _toPrimitive$4(t, "string"); + return "symbol" == _typeof$4(i) ? i : i + ""; +} +__name(_toPropertyKey$4, "_toPropertyKey$4"); +function _toPrimitive$4(t, r) { + if ("object" != _typeof$4(t) || !t) return t; + var e = t[Symbol.toPrimitive]; + if (void 0 !== e) { + var i = e.call(t, r || "default"); + if ("object" != _typeof$4(i)) return i; + throw new TypeError("@@toPrimitive must return a primitive value."); + } + return ("string" === r ? String : Number)(t); +} +__name(_toPrimitive$4, "_toPrimitive$4"); +var _hoisted_1$g = ["aria-activedescendant"]; +var _hoisted_2$d = ["id", "aria-label", "aria-setsize", "aria-posinset"]; +var _hoisted_3$b = ["id", "placeholder", "tabindex", "disabled", "aria-label", "aria-labelledby", "aria-expanded", "aria-controls", "aria-activedescendant", "aria-invalid"]; +var _hoisted_4$4 = ["disabled", "aria-expanded", "aria-controls"]; +var _hoisted_5$3 = ["id"]; +var _hoisted_6$2 = ["id", "aria-label"]; +var _hoisted_7$1 = ["id"]; +var _hoisted_8$1 = ["id", "aria-label", "aria-selected", "aria-disabled", "aria-setsize", "aria-posinset", "onClick", "onMousemove", "data-p-selected", "data-p-focus", "data-p-disabled"]; +function render$b(_ctx, _cache, $props, $setup, $data, $options) { + var _component_InputText = resolveComponent("InputText"); + var _component_Chip = resolveComponent("Chip"); + var _component_SpinnerIcon = resolveComponent("SpinnerIcon"); + var _component_VirtualScroller = resolveComponent("VirtualScroller"); + var _component_Portal = resolveComponent("Portal"); + var _directive_ripple = resolveDirective("ripple"); + return openBlock(), createElementBlock("div", mergeProps({ + ref: "container", + "class": _ctx.cx("root"), + style: _ctx.sx("root"), + onClick: _cache[11] || (_cache[11] = function() { + return $options.onContainerClick && $options.onContainerClick.apply($options, arguments); + }) + }, _ctx.ptmi("root")), [!_ctx.multiple ? (openBlock(), createBlock(_component_InputText, { + key: 0, + ref: "focusInput", + id: _ctx.inputId, + type: "text", + "class": normalizeClass([_ctx.cx("pcInput"), _ctx.inputClass]), + style: normalizeStyle(_ctx.inputStyle), + value: $options.inputValue, + placeholder: _ctx.placeholder, + tabindex: !_ctx.disabled ? _ctx.tabindex : -1, + fluid: $options.hasFluid, + disabled: _ctx.disabled, + invalid: _ctx.invalid, + variant: _ctx.variant, + autocomplete: "off", + role: "combobox", + "aria-label": _ctx.ariaLabel, + "aria-labelledby": _ctx.ariaLabelledby, + "aria-haspopup": "listbox", + "aria-autocomplete": "list", + "aria-expanded": $data.overlayVisible, + "aria-controls": $options.panelId, + "aria-activedescendant": $data.focused ? $options.focusedOptionId : void 0, + onFocus: $options.onFocus, + onBlur: $options.onBlur, + onKeydown: $options.onKeyDown, + onInput: $options.onInput, + onChange: $options.onChange, + unstyled: _ctx.unstyled, + pt: _ctx.ptm("pcInput") + }, null, 8, ["id", "class", "style", "value", "placeholder", "tabindex", "fluid", "disabled", "invalid", "variant", "aria-label", "aria-labelledby", "aria-expanded", "aria-controls", "aria-activedescendant", "onFocus", "onBlur", "onKeydown", "onInput", "onChange", "unstyled", "pt"])) : createCommentVNode("", true), _ctx.multiple ? (openBlock(), createElementBlock("ul", mergeProps({ + key: 1, + ref: "multiContainer", + "class": _ctx.cx("inputMultiple"), + tabindex: "-1", + role: "listbox", + "aria-orientation": "horizontal", + "aria-activedescendant": $data.focused ? $options.focusedMultipleOptionId : void 0, + onFocus: _cache[5] || (_cache[5] = function() { + return $options.onMultipleContainerFocus && $options.onMultipleContainerFocus.apply($options, arguments); + }), + onBlur: _cache[6] || (_cache[6] = function() { + return $options.onMultipleContainerBlur && $options.onMultipleContainerBlur.apply($options, arguments); + }), + onKeydown: _cache[7] || (_cache[7] = function() { + return $options.onMultipleContainerKeyDown && $options.onMultipleContainerKeyDown.apply($options, arguments); + }) + }, _ctx.ptm("inputMultiple")), [(openBlock(true), createElementBlock(Fragment, null, renderList(_ctx.modelValue, function(option2, i) { + return openBlock(), createElementBlock("li", mergeProps({ + key: "".concat(i, "_").concat($options.getOptionLabel(option2)), + id: $data.id + "_multiple_option_" + i, + "class": _ctx.cx("chipItem", { + i + }), + role: "option", + "aria-label": $options.getOptionLabel(option2), + "aria-selected": true, + "aria-setsize": _ctx.modelValue.length, + "aria-posinset": i + 1, + ref_for: true + }, _ctx.ptm("chipItem")), [renderSlot(_ctx.$slots, "chip", mergeProps({ + "class": _ctx.cx("pcChip"), + value: option2, + index: i, + removeCallback: /* @__PURE__ */ __name(function removeCallback(event) { + return $options.removeOption(event, i); + }, "removeCallback"), + ref_for: true + }, _ctx.ptm("pcChip")), function() { + return [createVNode(_component_Chip, { + "class": normalizeClass(_ctx.cx("pcChip")), + label: $options.getOptionLabel(option2), + removeIcon: _ctx.chipIcon || _ctx.removeTokenIcon, + removable: "", + unstyled: _ctx.unstyled, + onRemove: /* @__PURE__ */ __name(function onRemove2($event) { + return $options.removeOption($event, i); + }, "onRemove"), + pt: _ctx.ptm("pcChip") + }, { + removeicon: withCtx(function() { + return [renderSlot(_ctx.$slots, _ctx.$slots.chipicon ? "chipicon" : "removetokenicon", { + "class": normalizeClass(_ctx.cx("chipIcon")), + index: i, + removeCallback: /* @__PURE__ */ __name(function removeCallback(event) { + return $options.removeOption(event, i); + }, "removeCallback") + })]; + }), + _: 2 + }, 1032, ["class", "label", "removeIcon", "unstyled", "onRemove", "pt"])]; + })], 16, _hoisted_2$d); + }), 128)), createBaseVNode("li", mergeProps({ + "class": _ctx.cx("inputChip"), + role: "option" + }, _ctx.ptm("inputChip")), [createBaseVNode("input", mergeProps({ + ref: "focusInput", + id: _ctx.inputId, + type: "text", + style: _ctx.inputStyle, + "class": _ctx.inputClass, + placeholder: _ctx.placeholder, + tabindex: !_ctx.disabled ? _ctx.tabindex : -1, + disabled: _ctx.disabled, + autocomplete: "off", + role: "combobox", + "aria-label": _ctx.ariaLabel, + "aria-labelledby": _ctx.ariaLabelledby, + "aria-haspopup": "listbox", + "aria-autocomplete": "list", + "aria-expanded": $data.overlayVisible, + "aria-controls": $data.id + "_list", + "aria-activedescendant": $data.focused ? $options.focusedOptionId : void 0, + "aria-invalid": _ctx.invalid || void 0, + onFocus: _cache[0] || (_cache[0] = function() { + return $options.onFocus && $options.onFocus.apply($options, arguments); + }), + onBlur: _cache[1] || (_cache[1] = function() { + return $options.onBlur && $options.onBlur.apply($options, arguments); + }), + onKeydown: _cache[2] || (_cache[2] = function() { + return $options.onKeyDown && $options.onKeyDown.apply($options, arguments); + }), + onInput: _cache[3] || (_cache[3] = function() { + return $options.onInput && $options.onInput.apply($options, arguments); + }), + onChange: _cache[4] || (_cache[4] = function() { + return $options.onChange && $options.onChange.apply($options, arguments); + }) + }, _ctx.ptm("input")), null, 16, _hoisted_3$b)], 16)], 16, _hoisted_1$g)) : createCommentVNode("", true), $data.searching || _ctx.loading ? renderSlot(_ctx.$slots, _ctx.$slots.loader ? "loader" : "loadingicon", { + key: 2, + "class": normalizeClass(_ctx.cx("loader")) + }, function() { + return [_ctx.loader || _ctx.loadingIcon ? (openBlock(), createElementBlock("i", mergeProps({ + key: 0, + "class": ["pi-spin", _ctx.cx("loader"), _ctx.loader, _ctx.loadingIcon], + "aria-hidden": "true" + }, _ctx.ptm("loader")), null, 16)) : (openBlock(), createBlock(_component_SpinnerIcon, mergeProps({ + key: 1, + "class": _ctx.cx("loader"), + spin: "", + "aria-hidden": "true" + }, _ctx.ptm("loader")), null, 16, ["class"]))]; + }) : createCommentVNode("", true), renderSlot(_ctx.$slots, _ctx.$slots.dropdown ? "dropdown" : "dropdownbutton", { + toggleCallback: /* @__PURE__ */ __name(function toggleCallback(event) { + return $options.onDropdownClick(event); + }, "toggleCallback") + }, function() { + return [_ctx.dropdown ? (openBlock(), createElementBlock("button", mergeProps({ + key: 0, + ref: "dropdownButton", + type: "button", + "class": [_ctx.cx("dropdown"), _ctx.dropdownClass], + disabled: _ctx.disabled, + "aria-haspopup": "listbox", + "aria-expanded": $data.overlayVisible, + "aria-controls": $options.panelId, + onClick: _cache[8] || (_cache[8] = function() { + return $options.onDropdownClick && $options.onDropdownClick.apply($options, arguments); + }) + }, _ctx.ptm("dropdown")), [renderSlot(_ctx.$slots, "dropdownicon", { + "class": normalizeClass(_ctx.dropdownIcon) + }, function() { + return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.dropdownIcon ? "span" : "ChevronDownIcon"), mergeProps({ + "class": _ctx.dropdownIcon + }, _ctx.ptm("dropdownIcon")), null, 16, ["class"]))]; + })], 16, _hoisted_4$4)) : createCommentVNode("", true)]; + }), createBaseVNode("span", mergeProps({ + role: "status", + "aria-live": "polite", + "class": "p-hidden-accessible" + }, _ctx.ptm("hiddenSearchResult"), { + "data-p-hidden-accessible": true + }), toDisplayString($options.searchResultMessageText), 17), createVNode(_component_Portal, { + appendTo: _ctx.appendTo + }, { + "default": withCtx(function() { + return [createVNode(Transition, mergeProps({ + name: "p-connected-overlay", + onEnter: $options.onOverlayEnter, + onAfterEnter: $options.onOverlayAfterEnter, + onLeave: $options.onOverlayLeave, + onAfterLeave: $options.onOverlayAfterLeave + }, _ctx.ptm("transition")), { + "default": withCtx(function() { + return [$data.overlayVisible ? (openBlock(), createElementBlock("div", mergeProps({ + key: 0, + ref: $options.overlayRef, + id: $options.panelId, + "class": [_ctx.cx("overlay"), _ctx.panelClass, _ctx.overlayClass], + style: _objectSpread$3(_objectSpread$3(_objectSpread$3({}, _ctx.panelStyle), _ctx.overlayStyle), {}, { + "max-height": $options.virtualScrollerDisabled ? _ctx.scrollHeight : "" + }), + onClick: _cache[9] || (_cache[9] = function() { + return $options.onOverlayClick && $options.onOverlayClick.apply($options, arguments); + }), + onKeydown: _cache[10] || (_cache[10] = function() { + return $options.onOverlayKeyDown && $options.onOverlayKeyDown.apply($options, arguments); + }) + }, _ctx.ptm("overlay")), [renderSlot(_ctx.$slots, "header", { + value: _ctx.modelValue, + suggestions: $options.visibleOptions + }), createVNode(_component_VirtualScroller, mergeProps({ + ref: $options.virtualScrollerRef + }, _ctx.virtualScrollerOptions, { + style: { + height: _ctx.scrollHeight + }, + items: $options.visibleOptions, + tabindex: -1, + disabled: $options.virtualScrollerDisabled, + pt: _ctx.ptm("virtualScroller") + }), createSlots({ + content: withCtx(function(_ref) { + var styleClass = _ref.styleClass, contentRef = _ref.contentRef, items = _ref.items, getItemOptions = _ref.getItemOptions, contentStyle = _ref.contentStyle, itemSize = _ref.itemSize; + return [createBaseVNode("ul", mergeProps({ + ref: /* @__PURE__ */ __name(function ref2(el) { + return $options.listRef(el, contentRef); + }, "ref"), + id: $data.id + "_list", + "class": [_ctx.cx("list"), styleClass], + style: contentStyle, + role: "listbox", + "aria-label": $options.listAriaLabel + }, _ctx.ptm("list")), [(openBlock(true), createElementBlock(Fragment, null, renderList(items, function(option2, i) { + return openBlock(), createElementBlock(Fragment, { + key: $options.getOptionRenderKey(option2, $options.getOptionIndex(i, getItemOptions)) + }, [$options.isOptionGroup(option2) ? (openBlock(), createElementBlock("li", mergeProps({ + key: 0, + id: $data.id + "_" + $options.getOptionIndex(i, getItemOptions), + style: { + height: itemSize ? itemSize + "px" : void 0 + }, + "class": _ctx.cx("optionGroup"), + role: "option", + ref_for: true + }, _ctx.ptm("optionGroup")), [renderSlot(_ctx.$slots, "optiongroup", { + option: option2.optionGroup, + index: $options.getOptionIndex(i, getItemOptions) + }, function() { + return [createTextVNode(toDisplayString($options.getOptionGroupLabel(option2.optionGroup)), 1)]; + })], 16, _hoisted_7$1)) : withDirectives((openBlock(), createElementBlock("li", mergeProps({ + key: 1, + id: $data.id + "_" + $options.getOptionIndex(i, getItemOptions), + style: { + height: itemSize ? itemSize + "px" : void 0 + }, + "class": _ctx.cx("option", { + option: option2, + i, + getItemOptions + }), + role: "option", + "aria-label": $options.getOptionLabel(option2), + "aria-selected": $options.isSelected(option2), + "aria-disabled": $options.isOptionDisabled(option2), + "aria-setsize": $options.ariaSetSize, + "aria-posinset": $options.getAriaPosInset($options.getOptionIndex(i, getItemOptions)), + onClick: /* @__PURE__ */ __name(function onClick2($event) { + return $options.onOptionSelect($event, option2); + }, "onClick"), + onMousemove: /* @__PURE__ */ __name(function onMousemove($event) { + return $options.onOptionMouseMove($event, $options.getOptionIndex(i, getItemOptions)); + }, "onMousemove"), + "data-p-selected": $options.isSelected(option2), + "data-p-focus": $data.focusedOptionIndex === $options.getOptionIndex(i, getItemOptions), + "data-p-disabled": $options.isOptionDisabled(option2), + ref_for: true + }, $options.getPTOptions(option2, getItemOptions, i, "option")), [renderSlot(_ctx.$slots, "option", { + option: option2, + index: $options.getOptionIndex(i, getItemOptions) + }, function() { + return [createTextVNode(toDisplayString($options.getOptionLabel(option2)), 1)]; + })], 16, _hoisted_8$1)), [[_directive_ripple]])], 64); + }), 128)), !items || items && items.length === 0 ? (openBlock(), createElementBlock("li", mergeProps({ + key: 0, + "class": _ctx.cx("emptyMessage"), + role: "option" + }, _ctx.ptm("emptyMessage")), [renderSlot(_ctx.$slots, "empty", {}, function() { + return [createTextVNode(toDisplayString($options.searchResultMessageText), 1)]; + })], 16)) : createCommentVNode("", true)], 16, _hoisted_6$2)]; + }), + _: 2 + }, [_ctx.$slots.loader ? { + name: "loader", + fn: withCtx(function(_ref2) { + var options = _ref2.options; + return [renderSlot(_ctx.$slots, "loader", { + options + })]; + }), + key: "0" + } : void 0]), 1040, ["style", "items", "disabled", "pt"]), renderSlot(_ctx.$slots, "footer", { + value: _ctx.modelValue, + suggestions: $options.visibleOptions + }), createBaseVNode("span", mergeProps({ + role: "status", + "aria-live": "polite", + "class": "p-hidden-accessible" + }, _ctx.ptm("hiddenSelectedMessage"), { + "data-p-hidden-accessible": true + }), toDisplayString($options.selectedMessageText), 17)], 16, _hoisted_5$3)) : createCommentVNode("", true)]; + }), + _: 3 + }, 16, ["onEnter", "onAfterEnter", "onLeave", "onAfterLeave"])]; + }), + _: 3 + }, 8, ["appendTo"])], 16); +} +__name(render$b, "render$b"); +script$8.render = render$b; +const _sfc_main$i = { + name: "AutoCompletePlus", + extends: script$8, + emits: ["focused-option-changed"], + mounted() { + if (typeof script$8.mounted === "function") { + script$8.mounted.call(this); + } + this.$watch( + () => this.focusedOptionIndex, + (newVal, oldVal) => { + this.$emit("focused-option-changed", newVal); + } + ); + } +}; +const _withScopeId$5 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-37f672ab"), n = n(), popScopeId(), n), "_withScopeId$5"); +const _hoisted_1$f = { class: "option-container flex justify-between items-center px-2 py-0 cursor-pointer overflow-hidden w-full" }; +const _hoisted_2$c = { class: "option-display-name font-semibold flex flex-col" }; +const _hoisted_3$a = { key: 0 }; +const _hoisted_4$3 = /* @__PURE__ */ _withScopeId$5(() => /* @__PURE__ */ createBaseVNode("i", { class: "pi pi-bookmark-fill text-sm mr-1" }, null, -1)); +const _hoisted_5$2 = [ + _hoisted_4$3 +]; +const _hoisted_6$1 = ["innerHTML"]; +const _hoisted_7 = /* @__PURE__ */ _withScopeId$5(() => /* @__PURE__ */ createBaseVNode("span", null, " ", -1)); +const _hoisted_8 = ["innerHTML"]; +const _hoisted_9 = { + key: 0, + class: "option-category font-light text-sm text-gray-400 overflow-hidden text-ellipsis whitespace-nowrap" +}; +const _hoisted_10 = { class: "option-badges" }; +const _sfc_main$h = /* @__PURE__ */ defineComponent({ + __name: "NodeSearchItem", + props: { + nodeDef: {}, + currentQuery: {} + }, + setup(__props) { + const settingStore = useSettingStore(); + const showCategory = computed( + () => settingStore.get("Comfy.NodeSearchBoxImpl.ShowCategory") + ); + const showIdName = computed( + () => settingStore.get("Comfy.NodeSearchBoxImpl.ShowIdName") + ); + const showNodeFrequency = computed( + () => settingStore.get("Comfy.NodeSearchBoxImpl.ShowNodeFrequency") + ); + const nodeFrequencyStore = useNodeFrequencyStore(); + const nodeFrequency = computed( + () => nodeFrequencyStore.getNodeFrequency(props.nodeDef) + ); + const nodeBookmarkStore = useNodeBookmarkStore(); + const isBookmarked = computed( + () => nodeBookmarkStore.isBookmarked(props.nodeDef) + ); + const props = __props; + return (_ctx, _cache) => { + return openBlock(), createElementBlock("div", _hoisted_1$f, [ + createBaseVNode("div", _hoisted_2$c, [ + createBaseVNode("div", null, [ + isBookmarked.value ? (openBlock(), createElementBlock("span", _hoisted_3$a, _hoisted_5$2)) : createCommentVNode("", true), + createBaseVNode("span", { + innerHTML: unref(highlightQuery)(_ctx.nodeDef.display_name, _ctx.currentQuery) + }, null, 8, _hoisted_6$1), + _hoisted_7, + showIdName.value ? (openBlock(), createBlock(unref(script$q), { + key: 1, + severity: "secondary" + }, { + default: withCtx(() => [ + createBaseVNode("span", { + innerHTML: unref(highlightQuery)(_ctx.nodeDef.name, _ctx.currentQuery) + }, null, 8, _hoisted_8) + ]), + _: 1 + })) : createCommentVNode("", true) + ]), + showCategory.value ? (openBlock(), createElementBlock("div", _hoisted_9, toDisplayString(_ctx.nodeDef.category.replaceAll("/", " > ")), 1)) : createCommentVNode("", true) + ]), + createBaseVNode("div", _hoisted_10, [ + _ctx.nodeDef.experimental ? (openBlock(), createBlock(unref(script$q), { + key: 0, + value: _ctx.$t("experimental"), + severity: "primary" + }, null, 8, ["value"])) : createCommentVNode("", true), + _ctx.nodeDef.deprecated ? (openBlock(), createBlock(unref(script$q), { + key: 1, + value: _ctx.$t("deprecated"), + severity: "danger" + }, null, 8, ["value"])) : createCommentVNode("", true), + showNodeFrequency.value && nodeFrequency.value > 0 ? (openBlock(), createBlock(unref(script$q), { + key: 2, + value: unref(formatNumberWithSuffix)(nodeFrequency.value, { roundToInt: true }), + severity: "secondary" + }, null, 8, ["value"])) : createCommentVNode("", true), + _ctx.nodeDef.nodeSource.type !== unref(NodeSourceType).Unknown ? (openBlock(), createBlock(unref(script$p), { + key: 3, + class: "text-sm font-light" + }, { + default: withCtx(() => [ + createTextVNode(toDisplayString(_ctx.nodeDef.nodeSource.displayText), 1) + ]), + _: 1 + })) : createCommentVNode("", true) + ]) + ]); + }; + } +}); +const NodeSearchItem = /* @__PURE__ */ _export_sfc(_sfc_main$h, [["__scopeId", "data-v-37f672ab"]]); +const _withScopeId$4 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2d409367"), n = n(), popScopeId(), n), "_withScopeId$4"); +const _hoisted_1$e = { class: "comfy-vue-node-search-container" }; +const _hoisted_2$b = { + key: 0, + class: "comfy-vue-node-preview-container" +}; +const _hoisted_3$9 = /* @__PURE__ */ _withScopeId$4(() => /* @__PURE__ */ createBaseVNode("h3", null, "Add node filter condition", -1)); +const _hoisted_4$2 = { class: "_dialog-body" }; +const _sfc_main$g = /* @__PURE__ */ defineComponent({ + __name: "NodeSearchBox", + props: { + filters: {}, + searchLimit: { default: 64 } + }, + emits: ["addFilter", "removeFilter", "addNode"], + setup(__props, { emit: __emit }) { + const settingStore = useSettingStore(); + const { t } = useI18n(); + const enableNodePreview = computed( + () => settingStore.get("Comfy.NodeSearchBoxImpl.NodePreview") + ); + const props = __props; + const nodeSearchFilterVisible = ref(false); + const inputId = `comfy-vue-node-search-box-input-${Math.random()}`; + const suggestions2 = ref([]); + const hoveredSuggestion = ref(null); + const currentQuery = ref(""); + const placeholder = computed(() => { + return props.filters.length === 0 ? t("searchNodes") + "..." : ""; + }); + const nodeDefStore = useNodeDefStore(); + const nodeFrequencyStore = useNodeFrequencyStore(); + const search2 = /* @__PURE__ */ __name((query) => { + const queryIsEmpty = query === "" && props.filters.length === 0; + currentQuery.value = query; + suggestions2.value = queryIsEmpty ? nodeFrequencyStore.topNodeDefs : [ + ...nodeDefStore.nodeSearchService.searchNode(query, props.filters, { + limit: props.searchLimit + }) + ]; + }, "search"); + const emit = __emit; + const reFocusInput = /* @__PURE__ */ __name(() => { + const inputElement = document.getElementById(inputId); + if (inputElement) { + inputElement.blur(); + inputElement.focus(); + } + }, "reFocusInput"); + onMounted(reFocusInput); + const onAddFilter = /* @__PURE__ */ __name((filterAndValue) => { + nodeSearchFilterVisible.value = false; + emit("addFilter", filterAndValue); + reFocusInput(); + }, "onAddFilter"); + const onRemoveFilter = /* @__PURE__ */ __name((event, filterAndValue) => { + event.stopPropagation(); + event.preventDefault(); + emit("removeFilter", filterAndValue); + reFocusInput(); + }, "onRemoveFilter"); + const setHoverSuggestion = /* @__PURE__ */ __name((index) => { + if (index === -1) { + hoveredSuggestion.value = null; + return; + } + const value = suggestions2.value[index]; + hoveredSuggestion.value = value; + }, "setHoverSuggestion"); + return (_ctx, _cache) => { + return openBlock(), createElementBlock("div", _hoisted_1$e, [ + enableNodePreview.value ? (openBlock(), createElementBlock("div", _hoisted_2$b, [ + hoveredSuggestion.value ? (openBlock(), createBlock(NodePreview, { + nodeDef: hoveredSuggestion.value, + key: hoveredSuggestion.value?.name || "" + }, null, 8, ["nodeDef"])) : createCommentVNode("", true) + ])) : createCommentVNode("", true), + createVNode(unref(script$f), { + icon: "pi pi-filter", + severity: "secondary", + class: "_filter-button", + onClick: _cache[0] || (_cache[0] = ($event) => nodeSearchFilterVisible.value = true) + }), + createVNode(unref(script$r), { + visible: nodeSearchFilterVisible.value, + "onUpdate:visible": _cache[1] || (_cache[1] = ($event) => nodeSearchFilterVisible.value = $event), + class: "_dialog" + }, { + header: withCtx(() => [ + _hoisted_3$9 + ]), + default: withCtx(() => [ + createBaseVNode("div", _hoisted_4$2, [ + createVNode(NodeSearchFilter, { onAddFilter }) + ]) + ]), + _: 1 + }, 8, ["visible"]), + createVNode(_sfc_main$i, { + "model-value": props.filters, + class: "comfy-vue-node-search-box", + scrollHeight: "40vh", + placeholder: placeholder.value, + "input-id": inputId, + "append-to": "self", + suggestions: suggestions2.value, + "min-length": 0, + delay: 100, + loading: !unref(nodeFrequencyStore).isLoaded, + onComplete: _cache[2] || (_cache[2] = ($event) => search2($event.query)), + onOptionSelect: _cache[3] || (_cache[3] = ($event) => emit("addNode", $event.value)), + onFocusedOptionChanged: _cache[4] || (_cache[4] = ($event) => setHoverSuggestion($event)), + "complete-on-focus": "", + "auto-option-focus": "", + "force-selection": "", + multiple: "", + optionLabel: "display_name" + }, { + option: withCtx(({ option: option2 }) => [ + createVNode(NodeSearchItem, { + nodeDef: option2, + currentQuery: currentQuery.value + }, null, 8, ["nodeDef", "currentQuery"]) + ]), + chip: withCtx(({ value }) => [ + createVNode(SearchFilterChip, { + onRemove: /* @__PURE__ */ __name(($event) => onRemoveFilter($event, value), "onRemove"), + text: value[1], + badge: value[0].invokeSequence.toUpperCase(), + "badge-class": value[0].invokeSequence + "-badge" + }, null, 8, ["onRemove", "text", "badge", "badge-class"]) + ]), + _: 1 + }, 8, ["model-value", "placeholder", "suggestions", "loading"]) + ]); + }; + } +}); +const NodeSearchBox = /* @__PURE__ */ _export_sfc(_sfc_main$g, [["__scopeId", "data-v-2d409367"]]); +class ConnectingLinkImpl { + static { + __name(this, "ConnectingLinkImpl"); + } + node; + slot; + input; + output; + pos; + constructor(node, slot, input, output, pos) { + this.node = node; + this.slot = slot; + this.input = input; + this.output = output; + this.pos = pos; + } + static createFromPlainObject(obj) { + return new ConnectingLinkImpl( + obj.node, + obj.slot, + obj.input, + obj.output, + obj.pos + ); + } + get type() { + const result = this.input ? this.input.type : this.output.type; + return result === -1 ? null : result; + } + /** + * Which slot type is release and need to be reconnected. + * - 'output' means we need a new node's outputs slot to connect with this link + */ + get releaseSlotType() { + return this.output ? "input" : "output"; + } + connectTo(newNode) { + const newNodeSlots = this.releaseSlotType === "output" ? newNode.outputs : newNode.inputs; + if (!newNodeSlots) return; + const newNodeSlot = newNodeSlots.findIndex( + (slot) => LiteGraph.isValidConnection(slot.type, this.type) + ); + if (newNodeSlot === -1) { + console.warn( + `Could not find slot with type ${this.type} on node ${newNode.title}. This should never happen` + ); + return; + } + if (this.releaseSlotType === "input") { + this.node.connect(this.slot, newNode, newNodeSlot); + } else { + newNode.connect(newNodeSlot, this.node, this.slot); + } + } +} +const _sfc_main$f = /* @__PURE__ */ defineComponent({ + __name: "NodeSearchBoxPopover", + setup(__props) { + const settingStore = useSettingStore(); + const visible = ref(false); + const dismissable = ref(true); + const triggerEvent = ref(null); + const getNewNodeLocation = /* @__PURE__ */ __name(() => { + if (triggerEvent.value === null) { + return [100, 100]; + } + const originalEvent = triggerEvent.value.detail.originalEvent; + return [originalEvent.canvasX, originalEvent.canvasY]; + }, "getNewNodeLocation"); + const nodeFilters = ref([]); + const addFilter = /* @__PURE__ */ __name((filter) => { + nodeFilters.value.push(filter); + }, "addFilter"); + const removeFilter = /* @__PURE__ */ __name((filter) => { + nodeFilters.value = nodeFilters.value.filter( + (f) => toRaw(f) !== toRaw(filter) + ); + }, "removeFilter"); + const clearFilters = /* @__PURE__ */ __name(() => { + nodeFilters.value = []; + }, "clearFilters"); + const closeDialog = /* @__PURE__ */ __name(() => { + visible.value = false; + }, "closeDialog"); + const addNode = /* @__PURE__ */ __name((nodeDef) => { + const node = app.addNodeOnGraph(nodeDef, { pos: getNewNodeLocation() }); + const eventDetail = triggerEvent.value.detail; + if (eventDetail.subType === "empty-release") { + eventDetail.linkReleaseContext.links.forEach((link) => { + ConnectingLinkImpl.createFromPlainObject(link).connectTo(node); + }); + } + window.setTimeout(() => { + closeDialog(); + }, 100); + }, "addNode"); + const newSearchBoxEnabled = computed( + () => settingStore.get("Comfy.NodeSearchBoxImpl") === "default" + ); + const showSearchBox = /* @__PURE__ */ __name((e) => { + if (newSearchBoxEnabled.value) { + if (e.detail.originalEvent?.pointerType === "touch") { + setTimeout(() => { + showNewSearchBox(e); + }, 128); + } else { + showNewSearchBox(e); + } + } else { + canvasStore.canvas.showSearchBox(e.detail.originalEvent); + } + }, "showSearchBox"); + const nodeDefStore = useNodeDefStore(); + const showNewSearchBox = /* @__PURE__ */ __name((e) => { + if (e.detail.linkReleaseContext) { + const links = e.detail.linkReleaseContext.links; + if (links.length === 0) { + console.warn("Empty release with no links! This should never happen"); + return; + } + const firstLink = ConnectingLinkImpl.createFromPlainObject(links[0]); + const filter = nodeDefStore.nodeSearchService.getFilterById( + firstLink.releaseSlotType + ); + const dataType = firstLink.type; + addFilter([filter, dataType]); + } + visible.value = true; + triggerEvent.value = e; + dismissable.value = false; + setTimeout(() => { + dismissable.value = true; + }, 300); + }, "showNewSearchBox"); + const showContextMenu = /* @__PURE__ */ __name((e) => { + const links = e.detail.linkReleaseContext.links; + if (links.length === 0) { + console.warn("Empty release with no links! This should never happen"); + return; + } + const firstLink = ConnectingLinkImpl.createFromPlainObject(links[0]); + const mouseEvent = e.detail.originalEvent; + const commonOptions = { + e: mouseEvent, + allow_searchbox: true, + showSearchBox: /* @__PURE__ */ __name(() => showSearchBox(e), "showSearchBox") + }; + const connectionOptions = firstLink.output ? { nodeFrom: firstLink.node, slotFrom: firstLink.output } : { nodeTo: firstLink.node, slotTo: firstLink.input }; + canvasStore.canvas.showConnectionMenu({ + ...connectionOptions, + ...commonOptions + }); + }, "showContextMenu"); + const canvasStore = useCanvasStore(); + watchEffect(() => { + if (canvasStore.canvas) { + LiteGraph.release_link_on_empty_shows_menu = false; + canvasStore.canvas.allow_searchbox = false; + } + }); + const canvasEventHandler = /* @__PURE__ */ __name((e) => { + if (e.detail.subType === "empty-double-click") { + showSearchBox(e); + } else if (e.detail.subType === "empty-release") { + handleCanvasEmptyRelease(e); + } else if (e.detail.subType === "group-double-click") { + const group = e.detail.group; + const [x, y] = group.pos; + const relativeY = e.detail.originalEvent.canvasY - y; + if (relativeY > group.titleHeight) { + showSearchBox(e); + } + } + }, "canvasEventHandler"); + const linkReleaseAction = computed(() => { + return settingStore.get("Comfy.LinkRelease.Action"); + }); + const linkReleaseActionShift = computed(() => { + return settingStore.get("Comfy.LinkRelease.ActionShift"); + }); + const handleCanvasEmptyRelease = /* @__PURE__ */ __name((e) => { + const originalEvent = e.detail.originalEvent; + const shiftPressed = originalEvent.shiftKey; + const action = shiftPressed ? linkReleaseActionShift.value : linkReleaseAction.value; + switch (action) { + case LinkReleaseTriggerAction.SEARCH_BOX: + showSearchBox(e); + break; + case LinkReleaseTriggerAction.CONTEXT_MENU: + showContextMenu(e); + break; + case LinkReleaseTriggerAction.NO_ACTION: + default: + break; + } + }, "handleCanvasEmptyRelease"); + onMounted(() => { + document.addEventListener("litegraph:canvas", canvasEventHandler); + }); + onUnmounted(() => { + document.removeEventListener("litegraph:canvas", canvasEventHandler); + }); + return (_ctx, _cache) => { + return openBlock(), createElementBlock("div", null, [ + createVNode(unref(script$r), { + visible: visible.value, + "onUpdate:visible": _cache[0] || (_cache[0] = ($event) => visible.value = $event), + modal: "", + "dismissable-mask": dismissable.value, + onHide: clearFilters, + pt: { + root: { + class: "invisible-dialog-root", + role: "search" + }, + mask: { class: "node-search-box-dialog-mask" }, + transition: { + enterFromClass: "opacity-0 scale-75", + // 100ms is the duration of the transition in the dialog component + enterActiveClass: "transition-all duration-100 ease-out", + leaveActiveClass: "transition-all duration-100 ease-in", + leaveToClass: "opacity-0 scale-75" + } + } + }, { + container: withCtx(() => [ + createVNode(NodeSearchBox, { + filters: nodeFilters.value, + onAddFilter: addFilter, + onRemoveFilter: removeFilter, + onAddNode: addNode + }, null, 8, ["filters"]) + ]), + _: 1 + }, 8, ["visible", "dismissable-mask"]) + ]); + }; + } +}); +const _sfc_main$e = /* @__PURE__ */ defineComponent({ + __name: "NodeTooltip", + setup(__props) { + let idleTimeout; + const nodeDefStore = useNodeDefStore(); + const tooltipRef = ref(); + const tooltipText = ref(""); + const left = ref(); + const top = ref(); + const getHoveredWidget = /* @__PURE__ */ __name(() => { + const node = app.canvas.node_over; + if (!node.widgets) return; + const graphPos = app.canvas.graph_mouse; + const x = graphPos[0] - node.pos[0]; + const y = graphPos[1] - node.pos[1]; + for (const w of node.widgets) { + let widgetWidth, widgetHeight; + if (w.computeSize) { + ; + [widgetWidth, widgetHeight] = w.computeSize(node.size[0]); + } else { + widgetWidth = w.width || node.size[0]; + widgetHeight = LiteGraph.NODE_WIDGET_HEIGHT; + } + if (w.last_y !== void 0 && x >= 6 && x <= widgetWidth - 12 && y >= w.last_y && y <= w.last_y + widgetHeight) { + return w; + } + } + }, "getHoveredWidget"); + const hideTooltip = /* @__PURE__ */ __name(() => tooltipText.value = null, "hideTooltip"); + const showTooltip = /* @__PURE__ */ __name(async (tooltip) => { + if (!tooltip) return; + left.value = app.canvas.mouse[0] + "px"; + top.value = app.canvas.mouse[1] + "px"; + tooltipText.value = tooltip; + await nextTick(); + const rect = tooltipRef.value.getBoundingClientRect(); + if (rect.right > window.innerWidth) { + left.value = app.canvas.mouse[0] - rect.width + "px"; + } + if (rect.top < 0) { + top.value = app.canvas.mouse[1] + rect.height + "px"; + } + }, "showTooltip"); + const onIdle = /* @__PURE__ */ __name(() => { + const { canvas } = app; + const node = canvas.node_over; + if (!node) return; + const ctor = node.constructor; + const nodeDef = nodeDefStore.nodeDefsByName[node.type]; + if (ctor.title_mode !== LiteGraph.NO_TITLE && canvas.graph_mouse[1] < node.pos[1]) { + return showTooltip(nodeDef.description); + } + if (node.flags?.collapsed) return; + const inputSlot = canvas.isOverNodeInput( + node, + canvas.graph_mouse[0], + canvas.graph_mouse[1], + [0, 0] + ); + if (inputSlot !== -1) { + const inputName = node.inputs[inputSlot].name; + return showTooltip(nodeDef.input.getInput(inputName)?.tooltip); + } + const outputSlot = canvas.isOverNodeOutput( + node, + canvas.graph_mouse[0], + canvas.graph_mouse[1], + [0, 0] + ); + if (outputSlot !== -1) { + return showTooltip(nodeDef.output.all?.[outputSlot]?.tooltip); + } + const widget = getHoveredWidget(); + if (widget && !widget.element) { + return showTooltip( + widget.tooltip ?? nodeDef.input.getInput(widget.name)?.tooltip + ); + } + }, "onIdle"); + const onMouseMove = /* @__PURE__ */ __name((e) => { + hideTooltip(); + clearTimeout(idleTimeout); + if (e.target.nodeName !== "CANVAS") return; + idleTimeout = window.setTimeout(onIdle, 500); + }, "onMouseMove"); + useEventListener(window, "mousemove", onMouseMove); + useEventListener(window, "click", hideTooltip); + return (_ctx, _cache) => { + return tooltipText.value ? (openBlock(), createElementBlock("div", { + key: 0, + ref_key: "tooltipRef", + ref: tooltipRef, + class: "node-tooltip", + style: normalizeStyle({ left: left.value, top: top.value }) + }, toDisplayString(tooltipText.value), 5)) : createCommentVNode("", true); + }; + } +}); +const NodeTooltip = /* @__PURE__ */ _export_sfc(_sfc_main$e, [["__scopeId", "data-v-79ec8c53"]]); +const _sfc_main$d = /* @__PURE__ */ defineComponent({ + __name: "NodeBadge", + setup(__props) { + const settingStore = useSettingStore(); + const nodeSourceBadgeMode = computed( + () => settingStore.get("Comfy.NodeBadge.NodeSourceBadgeMode") + ); + const nodeIdBadgeMode = computed( + () => settingStore.get("Comfy.NodeBadge.NodeIdBadgeMode") + ); + const nodeLifeCycleBadgeMode = computed( + () => settingStore.get("Comfy.NodeBadge.NodeLifeCycleBadgeMode") + ); + watch([nodeSourceBadgeMode, nodeIdBadgeMode, nodeLifeCycleBadgeMode], () => { + app.graph?.setDirtyCanvas(true, true); + }); + const colorPalette = computed( + () => getColorPalette(settingStore.get("Comfy.ColorPalette")) + ); + const nodeDefStore = useNodeDefStore(); + function badgeTextVisible(nodeDef, badgeMode) { + return !(badgeMode === NodeBadgeMode.None || nodeDef?.isCoreNode && badgeMode === NodeBadgeMode.HideBuiltIn); + } + __name(badgeTextVisible, "badgeTextVisible"); + onMounted(() => { + app.registerExtension({ + name: "Comfy.NodeBadge", + nodeCreated(node) { + node.badgePosition = BadgePosition.TopRight; + const badge = computed(() => { + const nodeDef = nodeDefStore.fromLGraphNode(node); + return new LGraphBadge({ + text: _.truncate( + [ + badgeTextVisible(nodeDef, nodeIdBadgeMode.value) ? `#${node.id}` : "", + badgeTextVisible(nodeDef, nodeLifeCycleBadgeMode.value) ? nodeDef?.nodeLifeCycleBadgeText ?? "" : "", + badgeTextVisible(nodeDef, nodeSourceBadgeMode.value) ? nodeDef?.nodeSource?.badgeText ?? "" : "" + ].filter((s) => s.length > 0).join(" "), + { + length: 31 + } + ), + fgColor: colorPalette.value.colors.litegraph_base?.BADGE_FG_COLOR || defaultColorPalette.colors.litegraph_base.BADGE_FG_COLOR, + bgColor: colorPalette.value.colors.litegraph_base?.BADGE_BG_COLOR || defaultColorPalette.colors.litegraph_base.BADGE_BG_COLOR + }); + }); + node.badges.push(() => badge.value); + } + }); + }); + return (_ctx, _cache) => { + return openBlock(), createElementBlock("div"); + }; + } +}); +const _hoisted_1$d = { + viewBox: "0 0 1024 1024", + width: "1.2em", + height: "1.2em" +}; +const _hoisted_2$a = /* @__PURE__ */ createBaseVNode("path", { + fill: "currentColor", + d: "M921.088 103.232L584.832 889.024L465.52 544.512L121.328 440.48zM1004.46.769c-6.096 0-13.52 1.728-22.096 5.36L27.708 411.2c-34.383 14.592-36.56 42.704-4.847 62.464l395.296 123.584l129.36 403.264c9.28 15.184 20.496 22.72 31.263 22.72c11.936 0 23.296-9.152 31.04-27.248l408.272-953.728C1029.148 16.368 1022.86.769 1004.46.769" +}, null, -1); +const _hoisted_3$8 = [ + _hoisted_2$a +]; +function render$a(_ctx, _cache) { + return openBlock(), createElementBlock("svg", _hoisted_1$d, [..._hoisted_3$8]); +} +__name(render$a, "render$a"); +const __unplugin_components_1$1 = markRaw({ name: "simple-line-icons-cursor", render: render$a }); +const _hoisted_1$c = { + viewBox: "0 0 24 24", + width: "1.2em", + height: "1.2em" +}; +const _hoisted_2$9 = /* @__PURE__ */ createBaseVNode("path", { + fill: "currentColor", + d: "M10.05 23q-.75 0-1.4-.337T7.575 21.7L1.2 12.375l.6-.575q.475-.475 1.125-.55t1.175.3L7 13.575V4q0-.425.288-.712T8 3t.713.288T9 4v13.425l-3.7-2.6l3.925 5.725q.125.2.35.325t.475.125H17q.825 0 1.413-.587T19 19V5q0-.425.288-.712T20 4t.713.288T21 5v14q0 1.65-1.175 2.825T17 23zM11 12V2q0-.425.288-.712T12 1t.713.288T13 2v10zm4 0V3q0-.425.288-.712T16 2t.713.288T17 3v9zm-2.85 4.5" +}, null, -1); +const _hoisted_3$7 = [ + _hoisted_2$9 +]; +function render$9(_ctx, _cache) { + return openBlock(), createElementBlock("svg", _hoisted_1$c, [..._hoisted_3$7]); +} +__name(render$9, "render$9"); +const __unplugin_components_0$2 = markRaw({ name: "material-symbols-pan-tool-outline", render: render$9 }); +var theme$5 = /* @__PURE__ */ __name(function theme4(_ref) { + _ref.dt; + return "\n.p-buttongroup .p-button {\n margin: 0;\n}\n\n.p-buttongroup .p-button:not(:last-child),\n.p-buttongroup .p-button:not(:last-child):hover {\n border-right: 0 none;\n}\n\n.p-buttongroup .p-button:not(:first-of-type):not(:last-of-type) {\n border-radius: 0;\n}\n\n.p-buttongroup .p-button:first-of-type:not(:only-of-type) {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n\n.p-buttongroup .p-button:last-of-type:not(:only-of-type) {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n\n.p-buttongroup .p-button:focus {\n position: relative;\n z-index: 1;\n}\n"; +}, "theme"); +var classes$5 = { + root: "p-buttongroup p-component" +}; +var ButtonGroupStyle = BaseStyle.extend({ + name: "buttongroup", + theme: theme$5, + classes: classes$5 +}); +var script$1$5 = { + name: "BaseButtonGroup", + "extends": script$g, + style: ButtonGroupStyle, + provide: /* @__PURE__ */ __name(function provide8() { + return { + $pcButtonGroup: this, + $parentInstance: this + }; + }, "provide") +}; +var script$7 = { + name: "ButtonGroup", + "extends": script$1$5, + inheritAttrs: false +}; +function render$8(_ctx, _cache, $props, $setup, $data, $options) { + return openBlock(), createElementBlock("span", mergeProps({ + "class": _ctx.cx("root"), + role: "group" + }, _ctx.ptmi("root")), [renderSlot(_ctx.$slots, "default")], 16); +} +__name(render$8, "render$8"); +script$7.render = render$8; +const _sfc_main$c = /* @__PURE__ */ defineComponent({ + __name: "GraphCanvasMenu", + setup(__props) { + const { t } = useI18n(); + const commandStore = useCommandStore(); + const canvasStore = useCanvasStore(); + const settingStore = useSettingStore(); + const linkHidden = computed( + () => settingStore.get("Comfy.LinkRenderMode") === LiteGraph.HIDDEN_LINK + ); + let interval = null; + const repeat2 = /* @__PURE__ */ __name((command) => { + if (interval) return; + const cmd = /* @__PURE__ */ __name(() => commandStore.execute(command), "cmd"); + cmd(); + interval = window.setInterval(cmd, 100); + }, "repeat"); + const stopRepeat = /* @__PURE__ */ __name(() => { + if (interval) { + clearInterval(interval); + interval = null; + } + }, "stopRepeat"); + return (_ctx, _cache) => { + const _component_i_material_symbols58pan_tool_outline = __unplugin_components_0$2; + const _component_i_simple_line_icons58cursor = __unplugin_components_1$1; + const _directive_tooltip = resolveDirective("tooltip"); + return openBlock(), createBlock(unref(script$7), { class: "p-buttongroup-vertical absolute bottom-[10px] right-[10px] z-[1000] pointer-events-auto" }, { + default: withCtx(() => [ + withDirectives(createVNode(unref(script$f), { + severity: "secondary", + icon: "pi pi-plus", + onMousedown: _cache[0] || (_cache[0] = ($event) => repeat2("Comfy.Canvas.ZoomIn")), + onMouseup: stopRepeat + }, null, 512), [ + [ + _directive_tooltip, + unref(t)("graphCanvasMenu.zoomIn"), + void 0, + { left: true } + ] + ]), + withDirectives(createVNode(unref(script$f), { + severity: "secondary", + icon: "pi pi-minus", + onMousedown: _cache[1] || (_cache[1] = ($event) => repeat2("Comfy.Canvas.ZoomOut")), + onMouseup: stopRepeat + }, null, 512), [ + [ + _directive_tooltip, + unref(t)("graphCanvasMenu.zoomOut"), + void 0, + { left: true } + ] + ]), + withDirectives(createVNode(unref(script$f), { + severity: "secondary", + icon: "pi pi-expand", + onClick: _cache[2] || (_cache[2] = () => unref(commandStore).execute("Comfy.Canvas.ResetView")) + }, null, 512), [ + [ + _directive_tooltip, + unref(t)("graphCanvasMenu.resetView"), + void 0, + { left: true } + ] + ]), + withDirectives((openBlock(), createBlock(unref(script$f), { + severity: "secondary", + onClick: _cache[3] || (_cache[3] = () => unref(commandStore).execute("Comfy.Canvas.ToggleLock")) + }, { + icon: withCtx(() => [ + unref(canvasStore).canvas?.read_only ? (openBlock(), createBlock(_component_i_material_symbols58pan_tool_outline, { key: 0 })) : (openBlock(), createBlock(_component_i_simple_line_icons58cursor, { key: 1 })) + ]), + _: 1 + })), [ + [ + _directive_tooltip, + unref(t)( + "graphCanvasMenu." + (unref(canvasStore).canvas?.read_only ? "panMode" : "selectMode") + ) + " (Space)", + void 0, + { left: true } + ] + ]), + withDirectives(createVNode(unref(script$f), { + severity: "secondary", + icon: linkHidden.value ? "pi pi-eye-slash" : "pi pi-eye", + onClick: _cache[4] || (_cache[4] = () => unref(commandStore).execute("Comfy.Canvas.ToggleLinkVisibility")), + "data-testid": "toggle-link-visibility-button" + }, null, 8, ["icon"]), [ + [ + _directive_tooltip, + unref(t)("graphCanvasMenu.toggleLinkVisibility"), + void 0, + { left: true } + ] + ]) + ]), + _: 1 + }); + }; + } +}); +const GraphCanvasMenu = /* @__PURE__ */ _export_sfc(_sfc_main$c, [["__scopeId", "data-v-444d3768"]]); +const _sfc_main$b = /* @__PURE__ */ defineComponent({ + __name: "GraphCanvas", + emits: ["ready"], + setup(__props, { emit: __emit }) { + const emit = __emit; + const canvasRef = ref(null); + const settingStore = useSettingStore(); + const nodeDefStore = useNodeDefStore(); + const workspaceStore = useWorkspaceStore(); + const canvasStore = useCanvasStore(); + const modelToNodeStore = useModelToNodeStore(); + const betaMenuEnabled = computed( + () => settingStore.get("Comfy.UseNewMenu") !== "Disabled" + ); + const canvasMenuEnabled = computed( + () => settingStore.get("Comfy.Graph.CanvasMenu") + ); + const tooltipEnabled = computed(() => settingStore.get("Comfy.EnableTooltips")); + watchEffect(() => { + const canvasInfoEnabled = settingStore.get("Comfy.Graph.CanvasInfo"); + if (canvasStore.canvas) { + canvasStore.canvas.show_info = canvasInfoEnabled; + } + }); + watchEffect(() => { + const zoomSpeed = settingStore.get("Comfy.Graph.ZoomSpeed"); + if (canvasStore.canvas) { + canvasStore.canvas.zoom_speed = zoomSpeed; + } + }); + watchEffect(() => { + nodeDefStore.showDeprecated = settingStore.get("Comfy.Node.ShowDeprecated"); + }); + watchEffect(() => { + nodeDefStore.showExperimental = settingStore.get( + "Comfy.Node.ShowExperimental" + ); + }); + watchEffect(() => { + const spellcheckEnabled = settingStore.get("Comfy.TextareaWidget.Spellcheck"); + const textareas = document.querySelectorAll("textarea.comfy-multiline-input"); + textareas.forEach((textarea) => { + textarea.spellcheck = spellcheckEnabled; + textarea.focus(); + textarea.blur(); + }); + }); + watchEffect(() => { + if (!canvasStore.canvas) return; + if (canvasStore.canvas.dragging_canvas) { + canvasStore.canvas.canvas.style.cursor = "grabbing"; + return; + } + if (canvasStore.canvas.read_only) { + canvasStore.canvas.canvas.style.cursor = "grab"; + return; + } + canvasStore.canvas.canvas.style.cursor = "default"; + }); + usePragmaticDroppable(() => canvasRef.value, { + onDrop: /* @__PURE__ */ __name((event) => { + const loc = event.location.current.input; + const dndData = event.source.data; + if (dndData.type === "tree-explorer-node") { + const node = dndData.data; + if (node.data instanceof ComfyNodeDefImpl) { + const nodeDef = node.data; + const pos = app.clientPosToCanvasPos([ + loc.clientX - 20, + loc.clientY + ]); + app.addNodeOnGraph(nodeDef, { pos }); + } else if (node.data instanceof ComfyModelDef) { + const model = node.data; + const pos = app.clientPosToCanvasPos([loc.clientX, loc.clientY]); + const nodeAtPos = app.graph.getNodeOnPos(pos[0], pos[1]); + let targetProvider = null; + let targetGraphNode = null; + if (nodeAtPos) { + const providers = modelToNodeStore.getAllNodeProviders( + model.directory + ); + for (const provider of providers) { + if (provider.nodeDef.name === nodeAtPos.comfyClass) { + targetGraphNode = nodeAtPos; + targetProvider = provider; + } + } + } + if (!targetGraphNode) { + const provider = modelToNodeStore.getNodeProvider(model.directory); + if (provider) { + targetGraphNode = app.addNodeOnGraph(provider.nodeDef, { + pos + }); + targetProvider = provider; + } + } + if (targetGraphNode) { + const widget = targetGraphNode.widgets.find( + (widget2) => widget2.name === targetProvider.key + ); + if (widget) { + widget.value = model.file_name; + } + } + } + } + }, "onDrop") + }); + onMounted(async () => { + window["LiteGraph"] = LiteGraph; + window["LGraph"] = LGraph; + window["LLink"] = LLink; + window["LGraphNode"] = LGraphNode; + window["LGraphGroup"] = LGraphGroup; + window["DragAndScale"] = DragAndScale; + window["LGraphCanvas"] = LGraphCanvas; + window["ContextMenu"] = ContextMenu; + window["LGraphBadge"] = LGraphBadge; + app.vueAppReady = true; + workspaceStore.spinner = true; + await app.setup(canvasRef.value); + canvasStore.canvas = app.canvas; + workspaceStore.spinner = false; + window["app"] = app; + window["graph"] = app.graph; + emit("ready"); + }); + return (_ctx, _cache) => { + return openBlock(), createElementBlock(Fragment, null, [ + (openBlock(), createBlock(Teleport, { to: ".graph-canvas-container" }, [ + betaMenuEnabled.value ? (openBlock(), createBlock(LiteGraphCanvasSplitterOverlay, { key: 0 }, { + "side-bar-panel": withCtx(() => [ + createVNode(SideToolbar) + ]), + "bottom-panel": withCtx(() => [ + createVNode(_sfc_main$k) + ]), + "graph-canvas-panel": withCtx(() => [ + canvasMenuEnabled.value ? (openBlock(), createBlock(GraphCanvasMenu, { key: 0 })) : createCommentVNode("", true) + ]), + _: 1 + })) : createCommentVNode("", true), + createVNode(TitleEditor), + !betaMenuEnabled.value && canvasMenuEnabled.value ? (openBlock(), createBlock(GraphCanvasMenu, { key: 1 })) : createCommentVNode("", true), + createBaseVNode("canvas", { + ref_key: "canvasRef", + ref: canvasRef, + id: "graph-canvas", + tabindex: "1" + }, null, 512) + ])), + createVNode(_sfc_main$f), + tooltipEnabled.value ? (openBlock(), createBlock(NodeTooltip, { key: 0 })) : createCommentVNode("", true), + createVNode(_sfc_main$d) + ], 64); + }; + } +}); +function _typeof$3(o) { + "@babel/helpers - typeof"; + return _typeof$3 = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { + return typeof o2; + } : function(o2) { + return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; + }, _typeof$3(o); +} +__name(_typeof$3, "_typeof$3"); +function _defineProperty$3(e, r, t) { + return (r = _toPropertyKey$3(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: true, configurable: true, writable: true }) : e[r] = t, e; +} +__name(_defineProperty$3, "_defineProperty$3"); +function _toPropertyKey$3(t) { + var i = _toPrimitive$3(t, "string"); + return "symbol" == _typeof$3(i) ? i : i + ""; +} +__name(_toPropertyKey$3, "_toPropertyKey$3"); +function _toPrimitive$3(t, r) { + if ("object" != _typeof$3(t) || !t) return t; + var e = t[Symbol.toPrimitive]; + if (void 0 !== e) { + var i = e.call(t, r || "default"); + if ("object" != _typeof$3(i)) return i; + throw new TypeError("@@toPrimitive must return a primitive value."); + } + return ("string" === r ? String : Number)(t); +} +__name(_toPrimitive$3, "_toPrimitive$3"); +var theme$4 = /* @__PURE__ */ __name(function theme5(_ref) { + var dt = _ref.dt; + return "\n.p-toast {\n width: ".concat(dt("toast.width"), ";\n white-space: pre-line;\n word-break: break-word;\n}\n\n.p-toast-message {\n margin: 0 0 1rem 0;\n}\n\n.p-toast-message-icon {\n flex-shrink: 0;\n font-size: ").concat(dt("toast.icon.size"), ";\n width: ").concat(dt("toast.icon.size"), ";\n height: ").concat(dt("toast.icon.size"), ";\n}\n\n.p-toast-message-content {\n display: flex;\n align-items: flex-start;\n padding: ").concat(dt("toast.content.padding"), ";\n gap: ").concat(dt("toast.content.gap"), ";\n}\n\n.p-toast-message-text {\n flex: 1 1 auto;\n display: flex;\n flex-direction: column;\n gap: ").concat(dt("toast.text.gap"), ";\n}\n\n.p-toast-summary {\n font-weight: ").concat(dt("toast.summary.font.weight"), ";\n font-size: ").concat(dt("toast.summary.font.size"), ";\n}\n\n.p-toast-detail {\n font-weight: 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none;\n}\n\n.p-toast-message-info,\n.p-toast-message-success,\n.p-toast-message-warn,\n.p-toast-message-error,\n.p-toast-message-secondary,\n.p-toast-message-contrast {\n border-width: ").concat(dt("toast.border.width"), ";\n border-style: solid;\n backdrop-filter: blur(").concat(dt("toast.blur"), ");\n border-radius: ").concat(dt("toast.border.radius"), ";\n}\n\n.p-toast-close-icon {\n font-size: ").concat(dt("toast.close.icon.size"), ";\n width: ").concat(dt("toast.close.icon.size"), ";\n height: ").concat(dt("toast.close.icon.size"), ";\n}\n\n.p-toast-close-button:focus-visible {\n outline-width: ").concat(dt("focus.ring.width"), ";\n outline-style: ").concat(dt("focus.ring.style"), ";\n outline-offset: ").concat(dt("focus.ring.offset"), ";\n}\n\n.p-toast-message-info {\n background: ").concat(dt("toast.info.background"), ";\n border-color: ").concat(dt("toast.info.border.color"), ";\n color: ").concat(dt("toast.info.color"), ";\n box-shadow: ").concat(dt("toast.info.shadow"), ";\n}\n\n.p-toast-message-info .p-toast-detail {\n color: ").concat(dt("toast.info.detail.color"), ";\n}\n\n.p-toast-message-info .p-toast-close-button:focus-visible {\n outline-color: ").concat(dt("toast.info.close.button.focus.ring.color"), ";\n box-shadow: ").concat(dt("toast.info.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-info .p-toast-close-button:hover {\n background: ").concat(dt("toast.info.close.button.hover.background"), ";\n}\n\n.p-toast-message-success {\n background: ").concat(dt("toast.success.background"), ";\n border-color: ").concat(dt("toast.success.border.color"), ";\n color: ").concat(dt("toast.success.color"), ";\n box-shadow: ").concat(dt("toast.success.shadow"), ";\n}\n\n.p-toast-message-success .p-toast-detail {\n color: ").concat(dt("toast.success.detail.color"), ";\n}\n\n.p-toast-message-success .p-toast-close-button:focus-visible {\n outline-color: ").concat(dt("toast.success.close.button.focus.ring.color"), ";\n box-shadow: ").concat(dt("toast.success.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-success .p-toast-close-button:hover {\n background: ").concat(dt("toast.success.close.button.hover.background"), ";\n}\n\n.p-toast-message-warn {\n background: ").concat(dt("toast.warn.background"), ";\n border-color: ").concat(dt("toast.warn.border.color"), ";\n color: ").concat(dt("toast.warn.color"), ";\n box-shadow: ").concat(dt("toast.warn.shadow"), ";\n}\n\n.p-toast-message-warn .p-toast-detail {\n color: ").concat(dt("toast.warn.detail.color"), ";\n}\n\n.p-toast-message-warn .p-toast-close-button:focus-visible {\n outline-color: ").concat(dt("toast.warn.close.button.focus.ring.color"), ";\n box-shadow: ").concat(dt("toast.warn.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-warn .p-toast-close-button:hover {\n background: ").concat(dt("toast.warn.close.button.hover.background"), ";\n}\n\n.p-toast-message-error {\n background: ").concat(dt("toast.error.background"), ";\n border-color: ").concat(dt("toast.error.border.color"), ";\n color: ").concat(dt("toast.error.color"), ";\n box-shadow: ").concat(dt("toast.error.shadow"), ";\n}\n\n.p-toast-message-error .p-toast-detail {\n color: ").concat(dt("toast.error.detail.color"), ";\n}\n\n.p-toast-message-error .p-toast-close-button:focus-visible {\n outline-color: ").concat(dt("toast.error.close.button.focus.ring.color"), ";\n box-shadow: ").concat(dt("toast.error.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-error .p-toast-close-button:hover {\n background: ").concat(dt("toast.error.close.button.hover.background"), ";\n}\n\n.p-toast-message-secondary {\n background: ").concat(dt("toast.secondary.background"), ";\n border-color: ").concat(dt("toast.secondary.border.color"), ";\n color: ").concat(dt("toast.secondary.color"), ";\n box-shadow: ").concat(dt("toast.secondary.shadow"), ";\n}\n\n.p-toast-message-secondary .p-toast-detail {\n color: ").concat(dt("toast.secondary.detail.color"), ";\n}\n\n.p-toast-message-secondary .p-toast-close-button:focus-visible {\n outline-color: ").concat(dt("toast.secondary.close.button.focus.ring.color"), ";\n box-shadow: ").concat(dt("toast.secondary.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-secondary .p-toast-close-button:hover {\n background: ").concat(dt("toast.secondary.close.button.hover.background"), ";\n}\n\n.p-toast-message-contrast {\n background: ").concat(dt("toast.contrast.background"), ";\n border-color: ").concat(dt("toast.contrast.border.color"), ";\n color: ").concat(dt("toast.contrast.color"), ";\n box-shadow: ").concat(dt("toast.contrast.shadow"), ";\n}\n\n.p-toast-message-contrast .p-toast-detail {\n color: ").concat(dt("toast.contrast.detail.color"), ";\n}\n\n.p-toast-message-contrast .p-toast-close-button:focus-visible {\n outline-color: ").concat(dt("toast.contrast.close.button.focus.ring.color"), ";\n box-shadow: ").concat(dt("toast.contrast.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-contrast .p-toast-close-button:hover {\n background: ").concat(dt("toast.contrast.close.button.hover.background"), ";\n}\n\n.p-toast-top-center {\n transform: translateX(-50%);\n}\n\n.p-toast-bottom-center {\n transform: translateX(-50%);\n}\n\n.p-toast-center {\n min-width: 20vw;\n transform: translate(-50%, -50%);\n}\n\n.p-toast-message-enter-from {\n opacity: 0;\n transform: translateY(50%);\n}\n\n.p-toast-message-leave-from {\n max-height: 1000px;\n}\n\n.p-toast .p-toast-message.p-toast-message-leave-to {\n max-height: 0;\n opacity: 0;\n margin-bottom: 0;\n overflow: hidden;\n}\n\n.p-toast-message-enter-active {\n transition: transform 0.3s, opacity 0.3s;\n}\n\n.p-toast-message-leave-active {\n transition: max-height 0.45s cubic-bezier(0, 1, 0, 1), opacity 0.3s, margin-bottom 0.3s;\n}\n"); +}, "theme"); +var inlineStyles$2 = { + root: /* @__PURE__ */ __name(function root6(_ref2) { + var position = _ref2.position; + return { + position: "fixed", + top: position === "top-right" || position === "top-left" || position === "top-center" ? "20px" : position === "center" ? "50%" : null, + right: (position === "top-right" || position === "bottom-right") && "20px", + bottom: (position === "bottom-left" || position === "bottom-right" || position === "bottom-center") && "20px", + left: position === "top-left" || position === "bottom-left" ? "20px" : position === "center" || position === "top-center" || position === "bottom-center" ? "50%" : null + }; + }, "root") +}; +var classes$4 = { + root: /* @__PURE__ */ __name(function root7(_ref3) { + var props = _ref3.props; + return ["p-toast p-component p-toast-" + props.position]; + }, "root"), + message: /* @__PURE__ */ __name(function message(_ref4) { + var props = _ref4.props; + return ["p-toast-message", { + "p-toast-message-info": props.message.severity === "info" || props.message.severity === void 0, + "p-toast-message-warn": props.message.severity === "warn", + "p-toast-message-error": props.message.severity === "error", + "p-toast-message-success": props.message.severity === "success", + "p-toast-message-secondary": props.message.severity === "secondary", + "p-toast-message-contrast": props.message.severity === "contrast" + }]; + }, "message"), + messageContent: "p-toast-message-content", + messageIcon: /* @__PURE__ */ __name(function messageIcon(_ref5) { + var props = _ref5.props; + return ["p-toast-message-icon", _defineProperty$3(_defineProperty$3(_defineProperty$3(_defineProperty$3({}, props.infoIcon, props.message.severity === "info"), props.warnIcon, props.message.severity === "warn"), props.errorIcon, props.message.severity === "error"), props.successIcon, props.message.severity === "success")]; + }, "messageIcon"), + messageText: "p-toast-message-text", + summary: "p-toast-summary", + detail: "p-toast-detail", + closeButton: "p-toast-close-button", + closeIcon: "p-toast-close-icon" +}; +var ToastStyle = BaseStyle.extend({ + name: "toast", + theme: theme$4, + classes: classes$4, + inlineStyles: inlineStyles$2 +}); +var script$2$2 = { + name: "BaseToast", + "extends": script$g, + props: { + group: { + type: String, + "default": null + }, + position: { + type: String, + "default": "top-right" + }, + autoZIndex: { + type: Boolean, + "default": true + }, + baseZIndex: { + type: Number, + "default": 0 + }, + breakpoints: { + type: Object, + "default": null + }, + closeIcon: { + type: String, + "default": void 0 + }, + infoIcon: { + type: String, + "default": void 0 + }, + warnIcon: { + type: String, + "default": void 0 + }, + errorIcon: { + type: String, + "default": void 0 + }, + successIcon: { + type: String, + "default": void 0 + }, + closeButtonProps: { + type: null, + "default": null + } + }, + style: ToastStyle, + provide: /* @__PURE__ */ __name(function provide9() { + return { + $pcToast: this, + $parentInstance: this + }; + }, "provide") +}; +var script$1$4 = { + name: "ToastMessage", + hostName: "Toast", + "extends": script$g, + emits: ["close"], + closeTimeout: null, + props: { + message: { + type: null, + "default": null + }, + templates: { + type: Object, + "default": null + }, + closeIcon: { + type: String, + "default": null + }, + infoIcon: { + type: String, + "default": null + }, + warnIcon: { + type: String, + "default": null + }, + errorIcon: { + type: String, + "default": null + }, + successIcon: { + type: String, + "default": null + }, + closeButtonProps: { + type: null, + "default": null + } + }, + mounted: /* @__PURE__ */ __name(function mounted4() { + var _this = this; + if (this.message.life) { + this.closeTimeout = setTimeout(function() { + _this.close({ + message: _this.message, + type: "life-end" + }); + }, this.message.life); + } + }, "mounted"), + beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount4() { + this.clearCloseTimeout(); + }, "beforeUnmount"), + methods: { + close: /* @__PURE__ */ __name(function close(params) { + this.$emit("close", params); + }, "close"), + onCloseClick: /* @__PURE__ */ __name(function onCloseClick() { + this.clearCloseTimeout(); + this.close({ + message: this.message, + type: "close" + }); + }, "onCloseClick"), + clearCloseTimeout: /* @__PURE__ */ __name(function clearCloseTimeout() { + if (this.closeTimeout) { + clearTimeout(this.closeTimeout); + this.closeTimeout = null; + } + }, "clearCloseTimeout") + }, + computed: { + iconComponent: /* @__PURE__ */ __name(function iconComponent() { + return { + info: !this.infoIcon && script$s, + success: !this.successIcon && script$t, + warn: !this.warnIcon && script$u, + error: !this.errorIcon && script$v + }[this.message.severity]; + }, "iconComponent"), + closeAriaLabel: /* @__PURE__ */ __name(function closeAriaLabel() { + return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.close : void 0; + }, "closeAriaLabel") + }, + components: { + TimesIcon: script$w, + InfoCircleIcon: script$s, + CheckIcon: script$t, + ExclamationTriangleIcon: script$u, + TimesCircleIcon: script$v + }, + directives: { + ripple: Ripple + } +}; +function _typeof$1(o) { + "@babel/helpers - typeof"; + return _typeof$1 = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { + return typeof o2; + } : function(o2) { + return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; + }, _typeof$1(o); +} +__name(_typeof$1, "_typeof$1"); +function ownKeys$1(e, r) { + var t = Object.keys(e); + if (Object.getOwnPropertySymbols) { + var o = Object.getOwnPropertySymbols(e); + r && (o = o.filter(function(r2) { + return Object.getOwnPropertyDescriptor(e, r2).enumerable; + })), t.push.apply(t, o); + } + return t; +} +__name(ownKeys$1, "ownKeys$1"); +function _objectSpread$1(e) { + for (var r = 1; r < arguments.length; r++) { + var t = null != arguments[r] ? arguments[r] : {}; + r % 2 ? ownKeys$1(Object(t), true).forEach(function(r2) { + _defineProperty$1(e, r2, t[r2]); + }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys$1(Object(t)).forEach(function(r2) { + Object.defineProperty(e, r2, Object.getOwnPropertyDescriptor(t, r2)); + }); + } + return e; +} +__name(_objectSpread$1, "_objectSpread$1"); +function _defineProperty$1(e, r, t) { + return (r = _toPropertyKey$1(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: true, configurable: true, writable: true }) : e[r] = t, e; +} +__name(_defineProperty$1, "_defineProperty$1"); +function _toPropertyKey$1(t) { + var i = _toPrimitive$1(t, "string"); + return "symbol" == _typeof$1(i) ? i : i + ""; +} +__name(_toPropertyKey$1, "_toPropertyKey$1"); +function _toPrimitive$1(t, r) { + if ("object" != _typeof$1(t) || !t) return t; + var e = t[Symbol.toPrimitive]; + if (void 0 !== e) { + var i = e.call(t, r || "default"); + if ("object" != _typeof$1(i)) return i; + throw new TypeError("@@toPrimitive must return a primitive value."); + } + return ("string" === r ? String : Number)(t); +} +__name(_toPrimitive$1, "_toPrimitive$1"); +var _hoisted_1$b = ["aria-label"]; +function render$1$3(_ctx, _cache, $props, $setup, $data, $options) { + var _directive_ripple = resolveDirective("ripple"); + return openBlock(), createElementBlock("div", mergeProps({ + "class": [_ctx.cx("message"), $props.message.styleClass], + role: "alert", + "aria-live": "assertive", + "aria-atomic": "true" + }, _ctx.ptm("message")), [$props.templates.container ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.container), { + key: 0, + message: $props.message, + closeCallback: $options.onCloseClick + }, null, 8, ["message", "closeCallback"])) : (openBlock(), createElementBlock("div", mergeProps({ + key: 1, + "class": [_ctx.cx("messageContent"), $props.message.contentStyleClass] + }, _ctx.ptm("messageContent")), [!$props.templates.message ? (openBlock(), createElementBlock(Fragment, { + key: 0 + }, [(openBlock(), createBlock(resolveDynamicComponent($props.templates.messageicon ? $props.templates.messageicon : $props.templates.icon ? $props.templates.icon : $options.iconComponent && $options.iconComponent.name ? $options.iconComponent : "span"), mergeProps({ + "class": _ctx.cx("messageIcon") + }, _ctx.ptm("messageIcon")), null, 16, ["class"])), createBaseVNode("div", mergeProps({ + "class": _ctx.cx("messageText") + }, _ctx.ptm("messageText")), [createBaseVNode("span", mergeProps({ + "class": _ctx.cx("summary") + }, _ctx.ptm("summary")), toDisplayString($props.message.summary), 17), createBaseVNode("div", mergeProps({ + "class": _ctx.cx("detail") + }, _ctx.ptm("detail")), toDisplayString($props.message.detail), 17)], 16)], 64)) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.message), { + key: 1, + message: $props.message + }, null, 8, ["message"])), $props.message.closable !== false ? (openBlock(), createElementBlock("div", normalizeProps(mergeProps({ + key: 2 + }, _ctx.ptm("buttonContainer"))), [withDirectives((openBlock(), createElementBlock("button", mergeProps({ + "class": _ctx.cx("closeButton"), + type: "button", + "aria-label": $options.closeAriaLabel, + onClick: _cache[0] || (_cache[0] = function() { + return $options.onCloseClick && $options.onCloseClick.apply($options, arguments); + }), + autofocus: "" + }, _objectSpread$1(_objectSpread$1({}, $props.closeButtonProps), _ctx.ptm("closeButton"))), [(openBlock(), createBlock(resolveDynamicComponent($props.templates.closeicon || "TimesIcon"), mergeProps({ + "class": [_ctx.cx("closeIcon"), $props.closeIcon] + }, _ctx.ptm("closeIcon")), null, 16, ["class"]))], 16, _hoisted_1$b)), [[_directive_ripple]])], 16)) : createCommentVNode("", true)], 16))], 16); +} +__name(render$1$3, "render$1$3"); +script$1$4.render = render$1$3; +function _toConsumableArray(r) { + return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); +} +__name(_toConsumableArray, "_toConsumableArray"); +function _nonIterableSpread() { + throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method."); +} +__name(_nonIterableSpread, "_nonIterableSpread"); +function _unsupportedIterableToArray(r, a) { + if (r) { + if ("string" == typeof r) return _arrayLikeToArray(r, a); + var t = {}.toString.call(r).slice(8, -1); + return "Object" === t && r.constructor && (t = r.constructor.name), "Map" === t || "Set" === t ? Array.from(r) : "Arguments" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; + } +} +__name(_unsupportedIterableToArray, "_unsupportedIterableToArray"); +function _iterableToArray(r) { + if ("undefined" != typeof Symbol && null != r[Symbol.iterator] || null != r["@@iterator"]) return Array.from(r); +} +__name(_iterableToArray, "_iterableToArray"); +function _arrayWithoutHoles(r) { + if (Array.isArray(r)) return _arrayLikeToArray(r); +} +__name(_arrayWithoutHoles, "_arrayWithoutHoles"); +function _arrayLikeToArray(r, a) { + (null == a || a > r.length) && (a = r.length); + for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; + return n; +} +__name(_arrayLikeToArray, "_arrayLikeToArray"); +var messageIdx = 0; +var script$6 = { + name: "Toast", + "extends": script$2$2, + inheritAttrs: false, + emits: ["close", "life-end"], + data: /* @__PURE__ */ __name(function data5() { + return { + messages: [] + }; + }, "data"), + styleElement: null, + mounted: /* @__PURE__ */ __name(function mounted5() { + ToastEventBus.on("add", this.onAdd); + ToastEventBus.on("remove", this.onRemove); + ToastEventBus.on("remove-group", this.onRemoveGroup); + ToastEventBus.on("remove-all-groups", this.onRemoveAllGroups); + if (this.breakpoints) { + this.createStyle(); + } + }, "mounted"), + beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount5() { + this.destroyStyle(); + if (this.$refs.container && this.autoZIndex) { + ZIndex.clear(this.$refs.container); + } + ToastEventBus.off("add", this.onAdd); + ToastEventBus.off("remove", this.onRemove); + ToastEventBus.off("remove-group", this.onRemoveGroup); + ToastEventBus.off("remove-all-groups", this.onRemoveAllGroups); + }, "beforeUnmount"), + methods: { + add: /* @__PURE__ */ __name(function add(message2) { + if (message2.id == null) { + message2.id = messageIdx++; + } + this.messages = [].concat(_toConsumableArray(this.messages), [message2]); + }, "add"), + remove: /* @__PURE__ */ __name(function remove(params) { + var index = this.messages.findIndex(function(m) { + return m.id === params.message.id; + }); + if (index !== -1) { + this.messages.splice(index, 1); + this.$emit(params.type, { + message: params.message + }); + } + }, "remove"), + onAdd: /* @__PURE__ */ __name(function onAdd(message2) { + if (this.group == message2.group) { + this.add(message2); + } + }, "onAdd"), + onRemove: /* @__PURE__ */ __name(function onRemove(message2) { + this.remove({ + message: message2, + type: "close" + }); + }, "onRemove"), + onRemoveGroup: /* @__PURE__ */ __name(function onRemoveGroup(group) { + if (this.group === group) { + this.messages = []; + } + }, "onRemoveGroup"), + onRemoveAllGroups: /* @__PURE__ */ __name(function onRemoveAllGroups() { + this.messages = []; + }, "onRemoveAllGroups"), + onEnter: /* @__PURE__ */ __name(function onEnter() { + this.$refs.container.setAttribute(this.attributeSelector, ""); + if (this.autoZIndex) { + ZIndex.set("modal", this.$refs.container, this.baseZIndex || this.$primevue.config.zIndex.modal); + } + }, "onEnter"), + onLeave: /* @__PURE__ */ __name(function onLeave() { + var _this = this; + if (this.$refs.container && this.autoZIndex && isEmpty(this.messages)) { + setTimeout(function() { + ZIndex.clear(_this.$refs.container); + }, 200); + } + }, "onLeave"), + createStyle: /* @__PURE__ */ __name(function createStyle() { + if (!this.styleElement && !this.isUnstyled) { + var _this$$primevue; + this.styleElement = document.createElement("style"); + this.styleElement.type = "text/css"; + setAttribute(this.styleElement, "nonce", (_this$$primevue = this.$primevue) === null || _this$$primevue === void 0 || (_this$$primevue = _this$$primevue.config) === null || _this$$primevue === void 0 || (_this$$primevue = _this$$primevue.csp) === null || _this$$primevue === void 0 ? void 0 : _this$$primevue.nonce); + document.head.appendChild(this.styleElement); + var innerHTML = ""; + for (var breakpoint in this.breakpoints) { + var breakpointStyle = ""; + for (var styleProp in this.breakpoints[breakpoint]) { + breakpointStyle += styleProp + ":" + this.breakpoints[breakpoint][styleProp] + "!important;"; + } + innerHTML += "\n @media screen and (max-width: ".concat(breakpoint, ") {\n .p-toast[").concat(this.attributeSelector, "] {\n ").concat(breakpointStyle, "\n }\n }\n "); + } + this.styleElement.innerHTML = innerHTML; + } + }, "createStyle"), + destroyStyle: /* @__PURE__ */ __name(function destroyStyle() { + if (this.styleElement) { + document.head.removeChild(this.styleElement); + this.styleElement = null; + } + }, "destroyStyle") + }, + computed: { + attributeSelector: /* @__PURE__ */ __name(function attributeSelector() { + return UniqueComponentId(); + }, "attributeSelector") + }, + components: { + ToastMessage: script$1$4, + Portal: script$m + } +}; +function _typeof$2(o) { + "@babel/helpers - typeof"; + return _typeof$2 = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { + return typeof o2; + } : function(o2) { + return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; + }, _typeof$2(o); +} +__name(_typeof$2, "_typeof$2"); +function ownKeys$2(e, r) { + var t = Object.keys(e); + if (Object.getOwnPropertySymbols) { + var o = Object.getOwnPropertySymbols(e); + r && (o = o.filter(function(r2) { + return Object.getOwnPropertyDescriptor(e, r2).enumerable; + })), t.push.apply(t, o); + } + return t; +} +__name(ownKeys$2, "ownKeys$2"); +function _objectSpread$2(e) { + for (var r = 1; r < arguments.length; r++) { + var t = null != arguments[r] ? arguments[r] : {}; + r % 2 ? ownKeys$2(Object(t), true).forEach(function(r2) { + _defineProperty$2(e, r2, t[r2]); + }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys$2(Object(t)).forEach(function(r2) { + Object.defineProperty(e, r2, Object.getOwnPropertyDescriptor(t, r2)); + }); + } + return e; +} +__name(_objectSpread$2, "_objectSpread$2"); +function _defineProperty$2(e, r, t) { + return (r = _toPropertyKey$2(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: true, configurable: true, writable: true }) : e[r] = t, e; +} +__name(_defineProperty$2, "_defineProperty$2"); +function _toPropertyKey$2(t) { + var i = _toPrimitive$2(t, "string"); + return "symbol" == _typeof$2(i) ? i : i + ""; +} +__name(_toPropertyKey$2, "_toPropertyKey$2"); +function _toPrimitive$2(t, r) { + if ("object" != _typeof$2(t) || !t) return t; + var e = t[Symbol.toPrimitive]; + if (void 0 !== e) { + var i = e.call(t, r || "default"); + if ("object" != _typeof$2(i)) return i; + throw new TypeError("@@toPrimitive must return a primitive value."); + } + return ("string" === r ? String : Number)(t); +} +__name(_toPrimitive$2, "_toPrimitive$2"); +function render$7(_ctx, _cache, $props, $setup, $data, $options) { + var _component_ToastMessage = resolveComponent("ToastMessage"); + var _component_Portal = resolveComponent("Portal"); + return openBlock(), createBlock(_component_Portal, null, { + "default": withCtx(function() { + return [createBaseVNode("div", mergeProps({ + ref: "container", + "class": _ctx.cx("root"), + style: _ctx.sx("root", true, { + position: _ctx.position + }) + }, _ctx.ptmi("root")), [createVNode(TransitionGroup, mergeProps({ + name: "p-toast-message", + tag: "div", + onEnter: $options.onEnter, + onLeave: $options.onLeave + }, _objectSpread$2({}, _ctx.ptm("transition"))), { + "default": withCtx(function() { + return [(openBlock(true), createElementBlock(Fragment, null, renderList($data.messages, function(msg) { + return openBlock(), createBlock(_component_ToastMessage, { + key: msg.id, + message: msg, + templates: _ctx.$slots, + closeIcon: _ctx.closeIcon, + infoIcon: _ctx.infoIcon, + warnIcon: _ctx.warnIcon, + errorIcon: _ctx.errorIcon, + successIcon: _ctx.successIcon, + closeButtonProps: _ctx.closeButtonProps, + unstyled: _ctx.unstyled, + onClose: _cache[0] || (_cache[0] = function($event) { + return $options.remove($event); + }), + pt: _ctx.pt + }, null, 8, ["message", "templates", "closeIcon", "infoIcon", "warnIcon", "errorIcon", "successIcon", "closeButtonProps", "unstyled", "pt"]); + }), 128))]; + }), + _: 1 + }, 16, ["onEnter", "onLeave"])], 16)]; + }), + _: 1 + }); +} +__name(render$7, "render$7"); +script$6.render = render$7; +const _sfc_main$a = /* @__PURE__ */ defineComponent({ + __name: "GlobalToast", + setup(__props) { + const toast = useToast(); + const toastStore = useToastStore(); + const settingStore = useSettingStore(); + watch( + () => toastStore.messagesToAdd, + (newMessages) => { + if (newMessages.length === 0) { + return; + } + newMessages.forEach((message2) => { + toast.add(message2); + }); + toastStore.messagesToAdd = []; + }, + { deep: true } + ); + watch( + () => toastStore.messagesToRemove, + (messagesToRemove) => { + if (messagesToRemove.length === 0) { + return; + } + messagesToRemove.forEach((message2) => { + toast.remove(message2); + }); + toastStore.messagesToRemove = []; + }, + { deep: true } + ); + watch( + () => toastStore.removeAllRequested, + (requested) => { + if (requested) { + toast.removeAllGroups(); + toastStore.removeAllRequested = false; + } + } + ); + function updateToastPosition() { + const styleElement = document.getElementById("dynamic-toast-style") || createStyleElement(); + const rect = document.querySelector(".graph-canvas-container").getBoundingClientRect(); + styleElement.textContent = ` + .p-toast.p-component.p-toast-top-right { + top: ${rect.top + 20}px !important; + right: ${window.innerWidth - (rect.left + rect.width) + 20}px !important; + } + `; + } + __name(updateToastPosition, "updateToastPosition"); + function createStyleElement() { + const style = document.createElement("style"); + style.id = "dynamic-toast-style"; + document.head.appendChild(style); + return style; + } + __name(createStyleElement, "createStyleElement"); + watch( + () => settingStore.get("Comfy.UseNewMenu"), + () => nextTick(updateToastPosition), + { immediate: true } + ); + watch( + () => settingStore.get("Comfy.Sidebar.Location"), + () => nextTick(updateToastPosition), + { immediate: true } + ); + return (_ctx, _cache) => { + return openBlock(), createBlock(unref(script$6)); + }; + } +}); +const _sfc_main$9 = /* @__PURE__ */ defineComponent({ + __name: "UnloadWindowConfirmDialog", + setup(__props) { + const settingStore = useSettingStore(); + const handleBeforeUnload = /* @__PURE__ */ __name((event) => { + if (settingStore.get("Comfy.Window.UnloadConfirmation")) { + event.preventDefault(); + return true; + } + return void 0; + }, "handleBeforeUnload"); + onMounted(() => { + window.addEventListener("beforeunload", handleBeforeUnload); + }); + onBeforeUnmount(() => { + window.removeEventListener("beforeunload", handleBeforeUnload); + }); + return (_ctx, _cache) => { + return openBlock(), createElementBlock("div"); + }; + } +}); +const DEFAULT_TITLE = "ComfyUI"; +const TITLE_SUFFIX = " - ComfyUI"; +const _sfc_main$8 = /* @__PURE__ */ defineComponent({ + __name: "BrowserTabTitle", + setup(__props) { + const executionStore = useExecutionStore(); + const executionText = computed( + () => executionStore.isIdle ? "" : `[${executionStore.executionProgress}%]` + ); + const settingStore = useSettingStore(); + const betaMenuEnabled = computed( + () => settingStore.get("Comfy.UseNewMenu") !== "Disabled" + ); + const workflowStore = useWorkflowStore(); + const isUnsavedText = computed( + () => workflowStore.activeWorkflow?.unsaved ? " *" : "" + ); + const workflowNameText = computed(() => { + const workflowName = workflowStore.activeWorkflow?.name; + return workflowName ? isUnsavedText.value + workflowName + TITLE_SUFFIX : DEFAULT_TITLE; + }); + const nodeExecutionTitle = computed( + () => executionStore.executingNode && executionStore.executingNodeProgress ? `${executionText.value}[${executionStore.executingNodeProgress}%] ${executionStore.executingNode.type}` : "" + ); + const workflowTitle = computed( + () => executionText.value + (betaMenuEnabled.value ? workflowNameText.value : DEFAULT_TITLE) + ); + const title = computed(() => nodeExecutionTitle.value || workflowTitle.value); + useTitle(title); + return (_ctx, _cache) => { + return openBlock(), createElementBlock("div"); + }; + } +}); +const _withScopeId$3 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-84e785b8"), n = n(), popScopeId(), n), "_withScopeId$3"); +const _hoisted_1$a = { class: "workflow-label text-sm max-w-[150px] truncate inline-block" }; +const _hoisted_2$8 = { class: "relative" }; +const _hoisted_3$6 = { + key: 0, + class: "status-indicator" +}; +const _sfc_main$7 = /* @__PURE__ */ defineComponent({ + __name: "WorkflowTabs", + props: { + class: {} + }, + setup(__props) { + const props = __props; + const workflowStore = useWorkflowStore(); + const workflowToOption = /* @__PURE__ */ __name((workflow) => ({ + label: workflow.name, + tooltip: workflow.path, + value: workflow.key, + unsaved: workflow.unsaved + }), "workflowToOption"); + const optionToWorkflow = /* @__PURE__ */ __name((option2) => workflowStore.workflowLookup[option2.value], "optionToWorkflow"); + const options = computed( + () => workflowStore.openWorkflows.map(workflowToOption) + ); + const selectedWorkflow = computed( + () => workflowStore.activeWorkflow ? workflowToOption(workflowStore.activeWorkflow) : null + ); + const onWorkflowChange = /* @__PURE__ */ __name((option2) => { + if (!option2) { + return; + } + if (selectedWorkflow.value?.value === option2.value) { + return; + } + const workflow = optionToWorkflow(option2); + workflow.load(); + }, "onWorkflowChange"); + const onCloseWorkflow = /* @__PURE__ */ __name((option2) => { + const workflow = optionToWorkflow(option2); + app.workflowManager.closeWorkflow(workflow); + }, "onCloseWorkflow"); + return (_ctx, _cache) => { + const _directive_tooltip = resolveDirective("tooltip"); + return openBlock(), createBlock(unref(script$x), { + class: normalizeClass(["workflow-tabs bg-transparent flex flex-wrap", props.class]), + modelValue: selectedWorkflow.value, + "onUpdate:modelValue": onWorkflowChange, + options: options.value, + optionLabel: "label", + dataKey: "value" + }, { + option: withCtx(({ option: option2 }) => [ + withDirectives((openBlock(), createElementBlock("span", _hoisted_1$a, [ + createTextVNode(toDisplayString(option2.label), 1) + ])), [ + [_directive_tooltip, option2.tooltip] + ]), + createBaseVNode("div", _hoisted_2$8, [ + option2.unsaved ? (openBlock(), createElementBlock("span", _hoisted_3$6, "•")) : createCommentVNode("", true), + createVNode(unref(script$f), { + class: "close-button p-0 w-auto", + icon: "pi pi-times", + text: "", + severity: "secondary", + size: "small", + onClick: withModifiers(($event) => onCloseWorkflow(option2), ["stop"]) + }, null, 8, ["onClick"]) + ]) + ]), + _: 1 + }, 8, ["class", "modelValue", "options"]); + }; + } +}); +const WorkflowTabs = /* @__PURE__ */ _export_sfc(_sfc_main$7, [["__scopeId", "data-v-84e785b8"]]); +var theme$3 = /* @__PURE__ */ __name(function theme6(_ref) { + var dt = _ref.dt; + return "\n.p-menubar {\n display: flex;\n align-items: center;\n background: ".concat(dt("menubar.background"), ";\n border: 1px solid ").concat(dt("menubar.border.color"), ";\n border-radius: ").concat(dt("menubar.border.radius"), ";\n color: ").concat(dt("menubar.color"), ";\n padding: ").concat(dt("menubar.padding"), ";\n gap: ").concat(dt("menubar.gap"), ";\n}\n\n.p-menubar-start,\n.p-megamenu-end {\n display: flex;\n align-items: center;\n}\n\n.p-menubar-root-list,\n.p-menubar-submenu {\n display: flex;\n margin: 0;\n padding: 0;\n list-style: none;\n outline: 0 none;\n}\n\n.p-menubar-root-list {\n align-items: center;\n flex-wrap: wrap;\n gap: ").concat(dt("menubar.gap"), ";\n}\n\n.p-menubar-root-list > .p-menubar-item > .p-menubar-item-content {\n border-radius: ").concat(dt("menubar.base.item.border.radius"), ";\n}\n\n.p-menubar-root-list > .p-menubar-item > .p-menubar-item-content > .p-menubar-item-link {\n padding: ").concat(dt("menubar.base.item.padding"), ";\n}\n\n.p-menubar-item-content {\n transition: background ").concat(dt("menubar.transition.duration"), ", color ").concat(dt("menubar.transition.duration"), ";\n border-radius: ").concat(dt("menubar.item.border.radius"), ";\n color: ").concat(dt("menubar.item.color"), ";\n}\n\n.p-menubar-item-link {\n cursor: pointer;\n display: flex;\n align-items: center;\n text-decoration: none;\n overflow: hidden;\n position: relative;\n color: inherit;\n padding: ").concat(dt("menubar.item.padding"), ";\n gap: ").concat(dt("menubar.item.gap"), ";\n user-select: none;\n outline: 0 none;\n}\n\n.p-menubar-item-label {\n line-height: 1;\n}\n\n.p-menubar-item-icon {\n color: ").concat(dt("menubar.item.icon.color"), ";\n}\n\n.p-menubar-submenu-icon {\n color: ").concat(dt("menubar.submenu.icon.color"), ";\n margin-left: auto;\n font-size: ").concat(dt("menubar.submenu.icon.size"), ";\n width: ").concat(dt("menubar.submenu.icon.size"), ";\n height: ").concat(dt("menubar.submenu.icon.size"), ";\n}\n\n.p-menubar-item.p-focus > .p-menubar-item-content {\n color: ").concat(dt("menubar.item.focus.color"), ";\n background: ").concat(dt("menubar.item.focus.background"), ";\n}\n\n.p-menubar-item.p-focus > .p-menubar-item-content .p-menubar-item-icon {\n color: ").concat(dt("menubar.item.icon.focus.color"), ";\n}\n\n.p-menubar-item.p-focus > .p-menubar-item-content .p-menubar-submenu-icon {\n color: ").concat(dt("menubar.submenu.icon.focus.color"), ";\n}\n\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover {\n color: ").concat(dt("menubar.item.focus.color"), ";\n background: ").concat(dt("menubar.item.focus.background"), ";\n}\n\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover .p-menubar-item-icon {\n color: ").concat(dt("menubar.item.icon.focus.color"), ";\n}\n\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover .p-menubar-submenu-icon {\n color: ").concat(dt("menubar.submenu.icon.focus.color"), ";\n}\n\n.p-menubar-item-active > .p-menubar-item-content {\n color: ").concat(dt("menubar.item.active.color"), ";\n background: ").concat(dt("menubar.item.active.background"), ";\n}\n\n.p-menubar-item-active > .p-menubar-item-content .p-menubar-item-icon {\n color: ").concat(dt("menubar.item.icon.active.color"), ";\n}\n\n.p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\n color: ").concat(dt("menubar.submenu.icon.active.color"), ";\n}\n\n.p-menubar-submenu {\n display: none;\n position: absolute;\n min-width: 12.5rem;\n z-index: 1;\n background: ").concat(dt("menubar.submenu.background"), ";\n border: 1px solid ").concat(dt("menubar.submenu.border.color"), ";\n border-radius: ").concat(dt("menubar.border.radius"), ";\n box-shadow: ").concat(dt("menubar.submenu.shadow"), ";\n color: ").concat(dt("menubar.submenu.color"), ";\n flex-direction: column;\n padding: ").concat(dt("menubar.submenu.padding"), ";\n gap: ").concat(dt("menubar.submenu.gap"), ";\n}\n\n.p-menubar-submenu .p-menubar-separator {\n border-top: 1px solid ").concat(dt("menubar.separator.border.color"), ";\n}\n\n.p-menubar-submenu .p-menubar-item {\n position: relative;\n}\n\n .p-menubar-submenu > .p-menubar-item-active > .p-menubar-submenu {\n display: block;\n left: 100%;\n top: 0;\n}\n\n.p-menubar-end {\n margin-left: auto;\n align-self: center;\n}\n\n.p-menubar-button {\n display: none;\n justify-content: center;\n align-items: center;\n cursor: pointer;\n width: ").concat(dt("menubar.mobile.button.size"), ";\n height: ").concat(dt("menubar.mobile.button.size"), ";\n position: relative;\n color: ").concat(dt("menubar.mobile.button.color"), ";\n border: 0 none;\n background: transparent;\n border-radius: ").concat(dt("menubar.mobile.button.border.radius"), ";\n transition: background ").concat(dt("menubar.transition.duration"), ", color ").concat(dt("menubar.transition.duration"), ", outline-color ").concat(dt("menubar.transition.duration"), ";\n outline-color: transparent;\n}\n\n.p-menubar-button:hover {\n color: ").concat(dt("menubar.mobile.button.hover.color"), ";\n background: ").concat(dt("menubar.mobile.button.hover.background"), ";\n}\n\n.p-menubar-button:focus-visible {\n box-shadow: ").concat(dt("menubar.mobile.button.focus.ring.shadow"), ";\n outline: ").concat(dt("menubar.mobile.button.focus.ring.width"), " ").concat(dt("menubar.mobile.button.focus.ring.style"), " ").concat(dt("menubar.mobile.button.focus.ring.color"), ";\n outline-offset: ").concat(dt("menubar.mobile.button.focus.ring.offset"), ";\n}\n\n.p-menubar-mobile {\n position: relative;\n}\n\n.p-menubar-mobile .p-menubar-button {\n display: flex;\n}\n\n.p-menubar-mobile .p-menubar-root-list {\n position: absolute;\n display: none;\n width: 100%;\n padding: ").concat(dt("menubar.submenu.padding"), ";\n background: ").concat(dt("menubar.submenu.background"), ";\n border: 1px solid ").concat(dt("menubar.submenu.border.color"), ";\n box-shadow: ").concat(dt("menubar.submenu.shadow"), ";\n}\n\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content {\n border-radius: ").concat(dt("menubar.item.border.radius"), ";\n}\n\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content > .p-menubar-item-link {\n padding: ").concat(dt("menubar.item.padding"), ";\n}\n\n.p-menubar-mobile-active .p-menubar-root-list {\n display: flex;\n flex-direction: column;\n top: 100%;\n left: 0;\n z-index: 1;\n}\n\n.p-menubar-mobile .p-menubar-root-list .p-menubar-item {\n width: 100%;\n position: static;\n}\n\n.p-menubar-mobile .p-menubar-root-list .p-menubar-separator {\n border-top: 1px solid ").concat(dt("menubar.separator.border.color"), ";\n}\n\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content .p-menubar-submenu-icon {\n margin-left: auto;\n transition: transform 0.2s;\n}\n\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\n transform: rotate(-180deg);\n}\n\n.p-menubar-mobile .p-menubar-submenu .p-menubar-submenu-icon {\n transition: transform 0.2s;\n transform: rotate(90deg);\n}\n\n.p-menubar-mobile .p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\n transform: rotate(-90deg);\n}\n\n.p-menubar-mobile .p-menubar-submenu {\n width: 100%;\n position: static;\n box-shadow: none;\n border: 0 none;\n padding-left: ").concat(dt("menubar.submenu.mobile.indent"), ";\n}\n"); +}, "theme"); +var inlineStyles$1 = { + submenu: /* @__PURE__ */ __name(function submenu(_ref2) { + var instance = _ref2.instance, processedItem = _ref2.processedItem; + return { + display: instance.isItemActive(processedItem) ? "flex" : "none" + }; + }, "submenu") +}; +var classes$3 = { + root: /* @__PURE__ */ __name(function root8(_ref3) { + var instance = _ref3.instance; + return ["p-menubar p-component", { + "p-menubar-mobile": instance.queryMatches, + "p-menubar-mobile-active": instance.mobileActive + }]; + }, "root"), + start: "p-menubar-start", + button: "p-menubar-button", + rootList: "p-menubar-root-list", + item: /* @__PURE__ */ __name(function item(_ref4) { + var instance = _ref4.instance, processedItem = _ref4.processedItem; + return ["p-menubar-item", { + "p-menubar-item-active": instance.isItemActive(processedItem), + "p-focus": instance.isItemFocused(processedItem), + "p-disabled": instance.isItemDisabled(processedItem) + }]; + }, "item"), + itemContent: "p-menubar-item-content", + itemLink: "p-menubar-item-link", + itemIcon: "p-menubar-item-icon", + itemLabel: "p-menubar-item-label", + submenuIcon: "p-menubar-submenu-icon", + submenu: "p-menubar-submenu", + separator: "p-menubar-separator", + end: "p-menubar-end" +}; +var MenubarStyle = BaseStyle.extend({ + name: "menubar", + theme: theme$3, + classes: classes$3, + inlineStyles: inlineStyles$1 +}); +var script$2$1 = { + name: "BaseMenubar", + "extends": script$g, + props: { + model: { + type: Array, + "default": null + }, + buttonProps: { + type: null, + "default": null + }, + breakpoint: { + type: String, + "default": "960px" + }, + ariaLabelledby: { + type: String, + "default": null + }, + ariaLabel: { + type: String, + "default": null + } + }, + style: MenubarStyle, + provide: /* @__PURE__ */ __name(function provide10() { + return { + $pcMenubar: this, + $parentInstance: this + }; + }, "provide") +}; +var script$1$3 = { + name: "MenubarSub", + hostName: "Menubar", + "extends": script$g, + emits: ["item-mouseenter", "item-click", "item-mousemove"], + props: { + items: { + type: Array, + "default": null + }, + root: { + type: Boolean, + "default": false + }, + popup: { + type: Boolean, + "default": false + }, + mobileActive: { + type: Boolean, + "default": false + }, + templates: { + type: Object, + "default": null + }, + level: { + type: Number, + "default": 0 + }, + menuId: { + type: String, + "default": null + }, + focusedItemId: { + type: String, + "default": null + }, + activeItemPath: { + type: Object, + "default": null + } + }, + list: null, + methods: { + getItemId: /* @__PURE__ */ __name(function getItemId(processedItem) { + return "".concat(this.menuId, "_").concat(processedItem.key); + }, "getItemId"), + getItemKey: /* @__PURE__ */ __name(function getItemKey(processedItem) { + return this.getItemId(processedItem); + }, "getItemKey"), + getItemProp: /* @__PURE__ */ __name(function getItemProp(processedItem, name, params) { + return processedItem && processedItem.item ? resolve(processedItem.item[name], params) : void 0; + }, "getItemProp"), + getItemLabel: /* @__PURE__ */ __name(function getItemLabel(processedItem) { + return this.getItemProp(processedItem, "label"); + }, "getItemLabel"), + getItemLabelId: /* @__PURE__ */ __name(function getItemLabelId(processedItem) { + return "".concat(this.menuId, "_").concat(processedItem.key, "_label"); + }, "getItemLabelId"), + getPTOptions: /* @__PURE__ */ __name(function getPTOptions4(processedItem, index, key) { + return this.ptm(key, { + context: { + item: processedItem.item, + index, + active: this.isItemActive(processedItem), + focused: this.isItemFocused(processedItem), + disabled: this.isItemDisabled(processedItem), + level: this.level + } + }); + }, "getPTOptions"), + isItemActive: /* @__PURE__ */ __name(function isItemActive(processedItem) { + return this.activeItemPath.some(function(path) { + return path.key === processedItem.key; + }); + }, "isItemActive"), + isItemVisible: /* @__PURE__ */ __name(function isItemVisible(processedItem) { + return this.getItemProp(processedItem, "visible") !== false; + }, "isItemVisible"), + isItemDisabled: /* @__PURE__ */ __name(function isItemDisabled(processedItem) { + return this.getItemProp(processedItem, "disabled"); + }, "isItemDisabled"), + isItemFocused: /* @__PURE__ */ __name(function isItemFocused(processedItem) { + return this.focusedItemId === this.getItemId(processedItem); + }, "isItemFocused"), + isItemGroup: /* @__PURE__ */ __name(function isItemGroup(processedItem) { + return isNotEmpty(processedItem.items); + }, "isItemGroup"), + onItemClick: /* @__PURE__ */ __name(function onItemClick(event, processedItem) { + this.getItemProp(processedItem, "command", { + originalEvent: event, + item: processedItem.item + }); + this.$emit("item-click", { + originalEvent: event, + processedItem, + isFocus: true + }); + }, "onItemClick"), + onItemMouseEnter: /* @__PURE__ */ __name(function onItemMouseEnter(event, processedItem) { + this.$emit("item-mouseenter", { + originalEvent: event, + processedItem + }); + }, "onItemMouseEnter"), + onItemMouseMove: /* @__PURE__ */ __name(function onItemMouseMove(event, processedItem) { + this.$emit("item-mousemove", { + originalEvent: event, + processedItem + }); + }, "onItemMouseMove"), + getAriaPosInset: /* @__PURE__ */ __name(function getAriaPosInset2(index) { + return index - this.calculateAriaSetSize.slice(0, index).length + 1; + }, "getAriaPosInset"), + getMenuItemProps: /* @__PURE__ */ __name(function getMenuItemProps(processedItem, index) { + return { + action: mergeProps({ + "class": this.cx("itemLink"), + tabindex: -1, + "aria-hidden": true + }, this.getPTOptions(processedItem, index, "itemLink")), + icon: mergeProps({ + "class": [this.cx("itemIcon"), this.getItemProp(processedItem, "icon")] + }, this.getPTOptions(processedItem, index, "itemIcon")), + label: mergeProps({ + "class": this.cx("itemLabel") + }, this.getPTOptions(processedItem, index, "itemLabel")), + submenuicon: mergeProps({ + "class": this.cx("submenuIcon") + }, this.getPTOptions(processedItem, index, "submenuIcon")) + }; + }, "getMenuItemProps") + }, + computed: { + calculateAriaSetSize: /* @__PURE__ */ __name(function calculateAriaSetSize() { + var _this = this; + return this.items.filter(function(processedItem) { + return _this.isItemVisible(processedItem) && _this.getItemProp(processedItem, "separator"); + }); + }, "calculateAriaSetSize"), + getAriaSetSize: /* @__PURE__ */ __name(function getAriaSetSize() { + var _this2 = this; + return this.items.filter(function(processedItem) { + return _this2.isItemVisible(processedItem) && !_this2.getItemProp(processedItem, "separator"); + }).length; + }, "getAriaSetSize") + }, + components: { + AngleRightIcon: script$y, + AngleDownIcon: script$z + }, + directives: { + ripple: Ripple + } +}; +var _hoisted_1$1$2 = ["id", "aria-label", "aria-disabled", "aria-expanded", "aria-haspopup", "aria-level", "aria-setsize", "aria-posinset", "data-p-active", "data-p-focused", "data-p-disabled"]; +var _hoisted_2$7 = ["onClick", "onMouseenter", "onMousemove"]; +var _hoisted_3$5 = ["href", "target"]; +var _hoisted_4$1 = ["id"]; +var _hoisted_5$1 = ["id"]; +function render$1$2(_ctx, _cache, $props, $setup, $data, $options) { + var _component_MenubarSub = resolveComponent("MenubarSub", true); + var _directive_ripple = resolveDirective("ripple"); + return openBlock(), createElementBlock("ul", mergeProps({ + "class": $props.level === 0 ? _ctx.cx("rootList") : _ctx.cx("submenu") + }, $props.level === 0 ? _ctx.ptm("rootList") : _ctx.ptm("submenu")), [(openBlock(true), createElementBlock(Fragment, null, renderList($props.items, function(processedItem, index) { + return openBlock(), createElementBlock(Fragment, { + key: $options.getItemKey(processedItem) + }, [$options.isItemVisible(processedItem) && !$options.getItemProp(processedItem, "separator") ? (openBlock(), createElementBlock("li", mergeProps({ + key: 0, + id: $options.getItemId(processedItem), + style: $options.getItemProp(processedItem, "style"), + "class": [_ctx.cx("item", { + processedItem + }), $options.getItemProp(processedItem, "class")], + role: "menuitem", + "aria-label": $options.getItemLabel(processedItem), + "aria-disabled": $options.isItemDisabled(processedItem) || void 0, + "aria-expanded": $options.isItemGroup(processedItem) ? $options.isItemActive(processedItem) : void 0, + "aria-haspopup": $options.isItemGroup(processedItem) && !$options.getItemProp(processedItem, "to") ? "menu" : void 0, + "aria-level": $props.level + 1, + "aria-setsize": $options.getAriaSetSize, + "aria-posinset": $options.getAriaPosInset(index), + ref_for: true + }, $options.getPTOptions(processedItem, index, "item"), { + "data-p-active": $options.isItemActive(processedItem), + "data-p-focused": $options.isItemFocused(processedItem), + "data-p-disabled": $options.isItemDisabled(processedItem) + }), [createBaseVNode("div", mergeProps({ + "class": _ctx.cx("itemContent"), + onClick: /* @__PURE__ */ __name(function onClick2($event) { + return $options.onItemClick($event, processedItem); + }, "onClick"), + onMouseenter: /* @__PURE__ */ __name(function onMouseenter($event) { + return $options.onItemMouseEnter($event, processedItem); + }, "onMouseenter"), + onMousemove: /* @__PURE__ */ __name(function onMousemove($event) { + return $options.onItemMouseMove($event, processedItem); + }, "onMousemove"), + ref_for: true + }, $options.getPTOptions(processedItem, index, "itemContent")), [!$props.templates.item ? withDirectives((openBlock(), createElementBlock("a", mergeProps({ + key: 0, + href: $options.getItemProp(processedItem, "url"), + "class": _ctx.cx("itemLink"), + target: $options.getItemProp(processedItem, "target"), + tabindex: "-1", + ref_for: true + }, $options.getPTOptions(processedItem, index, "itemLink")), [$props.templates.itemicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.itemicon), { + key: 0, + item: processedItem.item, + "class": normalizeClass(_ctx.cx("itemIcon")) + }, null, 8, ["item", "class"])) : $options.getItemProp(processedItem, "icon") ? (openBlock(), createElementBlock("span", mergeProps({ + key: 1, + "class": [_ctx.cx("itemIcon"), $options.getItemProp(processedItem, "icon")], + ref_for: true + }, $options.getPTOptions(processedItem, index, "itemIcon")), null, 16)) : createCommentVNode("", true), createBaseVNode("span", mergeProps({ + id: $options.getItemLabelId(processedItem), + "class": _ctx.cx("itemLabel"), + ref_for: true + }, $options.getPTOptions(processedItem, index, "itemLabel")), toDisplayString($options.getItemLabel(processedItem)), 17, _hoisted_4$1), $options.getItemProp(processedItem, "items") ? (openBlock(), createElementBlock(Fragment, { + key: 2 + }, [$props.templates.submenuicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.submenuicon), { + key: 0, + root: $props.root, + active: $options.isItemActive(processedItem), + "class": normalizeClass(_ctx.cx("submenuIcon")) + }, null, 8, ["root", "active", "class"])) : (openBlock(), createBlock(resolveDynamicComponent($props.root ? "AngleDownIcon" : "AngleRightIcon"), mergeProps({ + key: 1, + "class": _ctx.cx("submenuIcon"), + ref_for: true + }, $options.getPTOptions(processedItem, index, "submenuIcon")), null, 16, ["class"]))], 64)) : createCommentVNode("", true)], 16, _hoisted_3$5)), [[_directive_ripple]]) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.item), { + key: 1, + item: processedItem.item, + root: $props.root, + hasSubmenu: $options.getItemProp(processedItem, "items"), + label: $options.getItemLabel(processedItem), + props: $options.getMenuItemProps(processedItem, index) + }, null, 8, ["item", "root", "hasSubmenu", "label", "props"]))], 16, _hoisted_2$7), $options.isItemVisible(processedItem) && $options.isItemGroup(processedItem) ? (openBlock(), createBlock(_component_MenubarSub, { + key: 0, + id: $options.getItemId(processedItem) + "_list", + menuId: $props.menuId, + role: "menu", + style: normalizeStyle(_ctx.sx("submenu", true, { + processedItem + })), + focusedItemId: $props.focusedItemId, + items: processedItem.items, + mobileActive: $props.mobileActive, + activeItemPath: $props.activeItemPath, + templates: $props.templates, + level: $props.level + 1, + "aria-labelledby": $options.getItemLabelId(processedItem), + pt: _ctx.pt, + unstyled: _ctx.unstyled, + onItemClick: _cache[0] || (_cache[0] = function($event) { + return _ctx.$emit("item-click", $event); + }), + onItemMouseenter: _cache[1] || (_cache[1] = function($event) { + return _ctx.$emit("item-mouseenter", $event); + }), + onItemMousemove: _cache[2] || (_cache[2] = function($event) { + return _ctx.$emit("item-mousemove", $event); + }) + }, null, 8, ["id", "menuId", "style", "focusedItemId", "items", "mobileActive", "activeItemPath", "templates", "level", "aria-labelledby", "pt", "unstyled"])) : createCommentVNode("", true)], 16, _hoisted_1$1$2)) : createCommentVNode("", true), $options.isItemVisible(processedItem) && $options.getItemProp(processedItem, "separator") ? (openBlock(), createElementBlock("li", mergeProps({ + key: 1, + id: $options.getItemId(processedItem), + "class": [_ctx.cx("separator"), $options.getItemProp(processedItem, "class")], + style: $options.getItemProp(processedItem, "style"), + role: "separator", + ref_for: true + }, _ctx.ptm("separator")), null, 16, _hoisted_5$1)) : createCommentVNode("", true)], 64); + }), 128))], 16); +} +__name(render$1$2, "render$1$2"); +script$1$3.render = render$1$2; +var script$5 = { + name: "Menubar", + "extends": script$2$1, + inheritAttrs: false, + emits: ["focus", "blur"], + matchMediaListener: null, + data: /* @__PURE__ */ __name(function data6() { + return { + id: this.$attrs.id, + mobileActive: false, + focused: false, + focusedItemInfo: { + index: -1, + level: 0, + parentKey: "" + }, + activeItemPath: [], + dirty: false, + query: null, + queryMatches: false + }; + }, "data"), + watch: { + "$attrs.id": /* @__PURE__ */ __name(function $attrsId2(newValue) { + this.id = newValue || UniqueComponentId(); + }, "$attrsId"), + activeItemPath: /* @__PURE__ */ __name(function activeItemPath(newPath) { + if (isNotEmpty(newPath)) { + this.bindOutsideClickListener(); + this.bindResizeListener(); + } else { + this.unbindOutsideClickListener(); + this.unbindResizeListener(); + } + }, "activeItemPath") + }, + outsideClickListener: null, + container: null, + menubar: null, + mounted: /* @__PURE__ */ __name(function mounted6() { + this.id = this.id || UniqueComponentId(); + this.bindMatchMediaListener(); + }, "mounted"), + beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount6() { + this.mobileActive = false; + this.unbindOutsideClickListener(); + this.unbindResizeListener(); + this.unbindMatchMediaListener(); + if (this.container) { + ZIndex.clear(this.container); + } + this.container = null; + }, "beforeUnmount"), + methods: { + getItemProp: /* @__PURE__ */ __name(function getItemProp2(item3, name) { + return item3 ? resolve(item3[name]) : void 0; + }, "getItemProp"), + getItemLabel: /* @__PURE__ */ __name(function getItemLabel2(item3) { + return this.getItemProp(item3, "label"); + }, "getItemLabel"), + isItemDisabled: /* @__PURE__ */ __name(function isItemDisabled2(item3) { + return this.getItemProp(item3, "disabled"); + }, "isItemDisabled"), + isItemVisible: /* @__PURE__ */ __name(function isItemVisible2(item3) { + return this.getItemProp(item3, "visible") !== false; + }, "isItemVisible"), + isItemGroup: /* @__PURE__ */ __name(function isItemGroup2(item3) { + return isNotEmpty(this.getItemProp(item3, "items")); + }, "isItemGroup"), + isItemSeparator: /* @__PURE__ */ __name(function isItemSeparator(item3) { + return this.getItemProp(item3, "separator"); + }, "isItemSeparator"), + getProccessedItemLabel: /* @__PURE__ */ __name(function getProccessedItemLabel(processedItem) { + return processedItem ? this.getItemLabel(processedItem.item) : void 0; + }, "getProccessedItemLabel"), + isProccessedItemGroup: /* @__PURE__ */ __name(function isProccessedItemGroup(processedItem) { + return processedItem && isNotEmpty(processedItem.items); + }, "isProccessedItemGroup"), + toggle: /* @__PURE__ */ __name(function toggle(event) { + var _this = this; + if (this.mobileActive) { + this.mobileActive = false; + ZIndex.clear(this.menubar); + this.hide(); + } else { + this.mobileActive = true; + ZIndex.set("menu", this.menubar, this.$primevue.config.zIndex.menu); + setTimeout(function() { + _this.show(); + }, 1); + } + this.bindOutsideClickListener(); + event.preventDefault(); + }, "toggle"), + show: /* @__PURE__ */ __name(function show2() { + focus(this.menubar); + }, "show"), + hide: /* @__PURE__ */ __name(function hide2(event, isFocus) { + var _this2 = this; + if (this.mobileActive) { + this.mobileActive = false; + setTimeout(function() { + focus(_this2.$refs.menubutton); + }, 0); + } + this.activeItemPath = []; + this.focusedItemInfo = { + index: -1, + level: 0, + parentKey: "" + }; + isFocus && focus(this.menubar); + this.dirty = false; + }, "hide"), + onFocus: /* @__PURE__ */ __name(function onFocus3(event) { + this.focused = true; + this.focusedItemInfo = this.focusedItemInfo.index !== -1 ? this.focusedItemInfo : { + index: this.findFirstFocusedItemIndex(), + level: 0, + parentKey: "" + }; + this.$emit("focus", event); + }, "onFocus"), + onBlur: /* @__PURE__ */ __name(function onBlur2(event) { + this.focused = false; + this.focusedItemInfo = { + index: -1, + level: 0, + parentKey: "" + }; + this.searchValue = ""; + this.dirty = false; + this.$emit("blur", event); + }, "onBlur"), + onKeyDown: /* @__PURE__ */ __name(function onKeyDown2(event) { + var metaKey = event.metaKey || event.ctrlKey; + switch (event.code) { + case "ArrowDown": + this.onArrowDownKey(event); + break; + case "ArrowUp": + this.onArrowUpKey(event); + break; + case "ArrowLeft": + this.onArrowLeftKey(event); + break; + case "ArrowRight": + this.onArrowRightKey(event); + break; + case "Home": + this.onHomeKey(event); + break; + case "End": + this.onEndKey(event); + break; + case "Space": + this.onSpaceKey(event); + break; + case "Enter": + case "NumpadEnter": + this.onEnterKey(event); + break; + case "Escape": + this.onEscapeKey(event); + break; + case "Tab": + this.onTabKey(event); + break; + case "PageDown": + case "PageUp": + case "Backspace": + case "ShiftLeft": + case "ShiftRight": + break; + default: + if (!metaKey && isPrintableCharacter(event.key)) { + this.searchItems(event, event.key); + } + break; + } + }, "onKeyDown"), + onItemChange: /* @__PURE__ */ __name(function onItemChange(event) { + var processedItem = event.processedItem, isFocus = event.isFocus; + if (isEmpty(processedItem)) return; + var index = processedItem.index, key = processedItem.key, level = processedItem.level, parentKey = processedItem.parentKey, items = processedItem.items; + var grouped = isNotEmpty(items); + var activeItemPath3 = this.activeItemPath.filter(function(p) { + return p.parentKey !== parentKey && p.parentKey !== key; + }); + grouped && activeItemPath3.push(processedItem); + this.focusedItemInfo = { + index, + level, + parentKey + }; + this.activeItemPath = activeItemPath3; + grouped && (this.dirty = true); + isFocus && focus(this.menubar); + }, "onItemChange"), + onItemClick: /* @__PURE__ */ __name(function onItemClick2(event) { + var originalEvent = event.originalEvent, processedItem = event.processedItem; + var grouped = this.isProccessedItemGroup(processedItem); + var root12 = isEmpty(processedItem.parent); + var selected = this.isSelected(processedItem); + if (selected) { + var index = processedItem.index, key = processedItem.key, level = processedItem.level, parentKey = processedItem.parentKey; + this.activeItemPath = this.activeItemPath.filter(function(p) { + return key !== p.key && key.startsWith(p.key); + }); + this.focusedItemInfo = { + index, + level, + parentKey + }; + this.dirty = !root12; + focus(this.menubar); + } else { + if (grouped) { + this.onItemChange(event); + } else { + var rootProcessedItem = root12 ? processedItem : this.activeItemPath.find(function(p) { + return p.parentKey === ""; + }); + this.hide(originalEvent); + this.changeFocusedItemIndex(originalEvent, rootProcessedItem ? rootProcessedItem.index : -1); + this.mobileActive = false; + focus(this.menubar); + } + } + }, "onItemClick"), + onItemMouseEnter: /* @__PURE__ */ __name(function onItemMouseEnter2(event) { + if (this.dirty) { + this.onItemChange(event); + } + }, "onItemMouseEnter"), + onItemMouseMove: /* @__PURE__ */ __name(function onItemMouseMove2(event) { + if (this.focused) { + this.changeFocusedItemIndex(event, event.processedItem.index); + } + }, "onItemMouseMove"), + menuButtonClick: /* @__PURE__ */ __name(function menuButtonClick(event) { + this.toggle(event); + }, "menuButtonClick"), + menuButtonKeydown: /* @__PURE__ */ __name(function menuButtonKeydown(event) { + (event.code === "Enter" || event.code === "NumpadEnter" || event.code === "Space") && this.menuButtonClick(event); + }, "menuButtonKeydown"), + onArrowDownKey: /* @__PURE__ */ __name(function onArrowDownKey2(event) { + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var root12 = processedItem ? isEmpty(processedItem.parent) : null; + if (root12) { + var grouped = this.isProccessedItemGroup(processedItem); + if (grouped) { + this.onItemChange({ + originalEvent: event, + processedItem + }); + this.focusedItemInfo = { + index: -1, + parentKey: processedItem.key + }; + this.onArrowRightKey(event); + } + } else { + var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex(); + this.changeFocusedItemIndex(event, itemIndex); + } + event.preventDefault(); + }, "onArrowDownKey"), + onArrowUpKey: /* @__PURE__ */ __name(function onArrowUpKey2(event) { + var _this3 = this; + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var root12 = isEmpty(processedItem.parent); + if (root12) { + var grouped = this.isProccessedItemGroup(processedItem); + if (grouped) { + this.onItemChange({ + originalEvent: event, + processedItem + }); + this.focusedItemInfo = { + index: -1, + parentKey: processedItem.key + }; + var itemIndex = this.findLastItemIndex(); + this.changeFocusedItemIndex(event, itemIndex); + } + } else { + var parentItem = this.activeItemPath.find(function(p) { + return p.key === processedItem.parentKey; + }); + if (this.focusedItemInfo.index === 0) { + this.focusedItemInfo = { + index: -1, + parentKey: parentItem ? parentItem.parentKey : "" + }; + this.searchValue = ""; + this.onArrowLeftKey(event); + this.activeItemPath = this.activeItemPath.filter(function(p) { + return p.parentKey !== _this3.focusedItemInfo.parentKey; + }); + } else { + var _itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex(); + this.changeFocusedItemIndex(event, _itemIndex); + } + } + event.preventDefault(); + }, "onArrowUpKey"), + onArrowLeftKey: /* @__PURE__ */ __name(function onArrowLeftKey3(event) { + var _this4 = this; + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var parentItem = processedItem ? this.activeItemPath.find(function(p) { + return p.key === processedItem.parentKey; + }) : null; + if (parentItem) { + this.onItemChange({ + originalEvent: event, + processedItem: parentItem + }); + this.activeItemPath = this.activeItemPath.filter(function(p) { + return p.parentKey !== _this4.focusedItemInfo.parentKey; + }); + event.preventDefault(); + } else { + var itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex(); + this.changeFocusedItemIndex(event, itemIndex); + event.preventDefault(); + } + }, "onArrowLeftKey"), + onArrowRightKey: /* @__PURE__ */ __name(function onArrowRightKey3(event) { + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var parentItem = processedItem ? this.activeItemPath.find(function(p) { + return p.key === processedItem.parentKey; + }) : null; + if (parentItem) { + var grouped = this.isProccessedItemGroup(processedItem); + if (grouped) { + this.onItemChange({ + originalEvent: event, + processedItem + }); + this.focusedItemInfo = { + index: -1, + parentKey: processedItem.key + }; + this.onArrowDownKey(event); + } + } else { + var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex(); + this.changeFocusedItemIndex(event, itemIndex); + event.preventDefault(); + } + }, "onArrowRightKey"), + onHomeKey: /* @__PURE__ */ __name(function onHomeKey3(event) { + this.changeFocusedItemIndex(event, this.findFirstItemIndex()); + event.preventDefault(); + }, "onHomeKey"), + onEndKey: /* @__PURE__ */ __name(function onEndKey3(event) { + this.changeFocusedItemIndex(event, this.findLastItemIndex()); + event.preventDefault(); + }, "onEndKey"), + onEnterKey: /* @__PURE__ */ __name(function onEnterKey3(event) { + if (this.focusedItemInfo.index !== -1) { + var element = findSingle(this.menubar, 'li[id="'.concat("".concat(this.focusedItemId), '"]')); + var anchorElement = element && findSingle(element, 'a[data-pc-section="itemlink"]'); + anchorElement ? anchorElement.click() : element && element.click(); + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var grouped = this.isProccessedItemGroup(processedItem); + !grouped && (this.focusedItemInfo.index = this.findFirstFocusedItemIndex()); + } + event.preventDefault(); + }, "onEnterKey"), + onSpaceKey: /* @__PURE__ */ __name(function onSpaceKey(event) { + this.onEnterKey(event); + }, "onSpaceKey"), + onEscapeKey: /* @__PURE__ */ __name(function onEscapeKey2(event) { + if (this.focusedItemInfo.level !== 0) { + var _focusedItemInfo = this.focusedItemInfo; + this.hide(event, false); + this.focusedItemInfo = { + index: Number(_focusedItemInfo.parentKey.split("_")[0]), + level: 0, + parentKey: "" + }; + } + event.preventDefault(); + }, "onEscapeKey"), + onTabKey: /* @__PURE__ */ __name(function onTabKey2(event) { + if (this.focusedItemInfo.index !== -1) { + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var grouped = this.isProccessedItemGroup(processedItem); + !grouped && this.onItemChange({ + originalEvent: event, + processedItem + }); + } + this.hide(); + }, "onTabKey"), + bindOutsideClickListener: /* @__PURE__ */ __name(function bindOutsideClickListener2() { + var _this5 = this; + if (!this.outsideClickListener) { + this.outsideClickListener = function(event) { + var isOutsideContainer = _this5.container && !_this5.container.contains(event.target); + var isOutsideTarget = !(_this5.target && (_this5.target === event.target || _this5.target.contains(event.target))); + if (isOutsideContainer && isOutsideTarget) { + _this5.hide(); + } + }; + document.addEventListener("click", this.outsideClickListener); + } + }, "bindOutsideClickListener"), + unbindOutsideClickListener: /* @__PURE__ */ __name(function unbindOutsideClickListener2() { + if (this.outsideClickListener) { + document.removeEventListener("click", this.outsideClickListener); + this.outsideClickListener = null; + } + }, "unbindOutsideClickListener"), + bindResizeListener: /* @__PURE__ */ __name(function bindResizeListener2() { + var _this6 = this; + if (!this.resizeListener) { + this.resizeListener = function(event) { + if (!isTouchDevice()) { + _this6.hide(event, true); + } + _this6.mobileActive = false; + }; + window.addEventListener("resize", this.resizeListener); + } + }, "bindResizeListener"), + unbindResizeListener: /* @__PURE__ */ __name(function unbindResizeListener2() { + if (this.resizeListener) { + window.removeEventListener("resize", this.resizeListener); + this.resizeListener = null; + } + }, "unbindResizeListener"), + bindMatchMediaListener: /* @__PURE__ */ __name(function bindMatchMediaListener() { + var _this7 = this; + if (!this.matchMediaListener) { + var query = matchMedia("(max-width: ".concat(this.breakpoint, ")")); + this.query = query; + this.queryMatches = query.matches; + this.matchMediaListener = function() { + _this7.queryMatches = query.matches; + _this7.mobileActive = false; + }; + this.query.addEventListener("change", this.matchMediaListener); + } + }, "bindMatchMediaListener"), + unbindMatchMediaListener: /* @__PURE__ */ __name(function unbindMatchMediaListener() { + if (this.matchMediaListener) { + this.query.removeEventListener("change", this.matchMediaListener); + this.matchMediaListener = null; + } + }, "unbindMatchMediaListener"), + isItemMatched: /* @__PURE__ */ __name(function isItemMatched(processedItem) { + var _this$getProccessedIt; + return this.isValidItem(processedItem) && ((_this$getProccessedIt = this.getProccessedItemLabel(processedItem)) === null || _this$getProccessedIt === void 0 ? void 0 : _this$getProccessedIt.toLocaleLowerCase().startsWith(this.searchValue.toLocaleLowerCase())); + }, "isItemMatched"), + isValidItem: /* @__PURE__ */ __name(function isValidItem(processedItem) { + return !!processedItem && !this.isItemDisabled(processedItem.item) && !this.isItemSeparator(processedItem.item) && this.isItemVisible(processedItem.item); + }, "isValidItem"), + isValidSelectedItem: /* @__PURE__ */ __name(function isValidSelectedItem(processedItem) { + return this.isValidItem(processedItem) && this.isSelected(processedItem); + }, "isValidSelectedItem"), + isSelected: /* @__PURE__ */ __name(function isSelected2(processedItem) { + return this.activeItemPath.some(function(p) { + return p.key === processedItem.key; + }); + }, "isSelected"), + findFirstItemIndex: /* @__PURE__ */ __name(function findFirstItemIndex() { + var _this8 = this; + return this.visibleItems.findIndex(function(processedItem) { + return _this8.isValidItem(processedItem); + }); + }, "findFirstItemIndex"), + findLastItemIndex: /* @__PURE__ */ __name(function findLastItemIndex() { + var _this9 = this; + return findLastIndex(this.visibleItems, function(processedItem) { + return _this9.isValidItem(processedItem); + }); + }, "findLastItemIndex"), + findNextItemIndex: /* @__PURE__ */ __name(function findNextItemIndex(index) { + var _this10 = this; + var matchedItemIndex = index < this.visibleItems.length - 1 ? this.visibleItems.slice(index + 1).findIndex(function(processedItem) { + return _this10.isValidItem(processedItem); + }) : -1; + return matchedItemIndex > -1 ? matchedItemIndex + index + 1 : index; + }, "findNextItemIndex"), + findPrevItemIndex: /* @__PURE__ */ __name(function findPrevItemIndex(index) { + var _this11 = this; + var matchedItemIndex = index > 0 ? findLastIndex(this.visibleItems.slice(0, index), function(processedItem) { + return _this11.isValidItem(processedItem); + }) : -1; + return matchedItemIndex > -1 ? matchedItemIndex : index; + }, "findPrevItemIndex"), + findSelectedItemIndex: /* @__PURE__ */ __name(function findSelectedItemIndex() { + var _this12 = this; + return this.visibleItems.findIndex(function(processedItem) { + return _this12.isValidSelectedItem(processedItem); + }); + }, "findSelectedItemIndex"), + findFirstFocusedItemIndex: /* @__PURE__ */ __name(function findFirstFocusedItemIndex() { + var selectedIndex = this.findSelectedItemIndex(); + return selectedIndex < 0 ? this.findFirstItemIndex() : selectedIndex; + }, "findFirstFocusedItemIndex"), + findLastFocusedItemIndex: /* @__PURE__ */ __name(function findLastFocusedItemIndex() { + var selectedIndex = this.findSelectedItemIndex(); + return selectedIndex < 0 ? this.findLastItemIndex() : selectedIndex; + }, "findLastFocusedItemIndex"), + searchItems: /* @__PURE__ */ __name(function searchItems(event, _char) { + var _this13 = this; + this.searchValue = (this.searchValue || "") + _char; + var itemIndex = -1; + var matched = false; + if (this.focusedItemInfo.index !== -1) { + itemIndex = this.visibleItems.slice(this.focusedItemInfo.index).findIndex(function(processedItem) { + return _this13.isItemMatched(processedItem); + }); + itemIndex = itemIndex === -1 ? this.visibleItems.slice(0, this.focusedItemInfo.index).findIndex(function(processedItem) { + return _this13.isItemMatched(processedItem); + }) : itemIndex + this.focusedItemInfo.index; + } else { + itemIndex = this.visibleItems.findIndex(function(processedItem) { + return _this13.isItemMatched(processedItem); + }); + } + if (itemIndex !== -1) { + matched = true; + } + if (itemIndex === -1 && this.focusedItemInfo.index === -1) { + itemIndex = this.findFirstFocusedItemIndex(); + } + if (itemIndex !== -1) { + this.changeFocusedItemIndex(event, itemIndex); + } + if (this.searchTimeout) { + clearTimeout(this.searchTimeout); + } + this.searchTimeout = setTimeout(function() { + _this13.searchValue = ""; + _this13.searchTimeout = null; + }, 500); + return matched; + }, "searchItems"), + changeFocusedItemIndex: /* @__PURE__ */ __name(function changeFocusedItemIndex(event, index) { + if (this.focusedItemInfo.index !== index) { + this.focusedItemInfo.index = index; + this.scrollInView(); + } + }, "changeFocusedItemIndex"), + scrollInView: /* @__PURE__ */ __name(function scrollInView3() { + var index = arguments.length > 0 && arguments[0] !== void 0 ? arguments[0] : -1; + var id2 = index !== -1 ? "".concat(this.id, "_").concat(index) : this.focusedItemId; + var element = findSingle(this.menubar, 'li[id="'.concat(id2, '"]')); + if (element) { + element.scrollIntoView && element.scrollIntoView({ + block: "nearest", + inline: "start" + }); + } + }, "scrollInView"), + createProcessedItems: /* @__PURE__ */ __name(function createProcessedItems(items) { + var _this14 = this; + var level = arguments.length > 1 && arguments[1] !== void 0 ? arguments[1] : 0; + var parent = arguments.length > 2 && arguments[2] !== void 0 ? arguments[2] : {}; + var parentKey = arguments.length > 3 && arguments[3] !== void 0 ? arguments[3] : ""; + var processedItems3 = []; + items && items.forEach(function(item3, index) { + var key = (parentKey !== "" ? parentKey + "_" : "") + index; + var newItem = { + item: item3, + index, + level, + key, + parent, + parentKey + }; + newItem["items"] = _this14.createProcessedItems(item3.items, level + 1, newItem, key); + processedItems3.push(newItem); + }); + return processedItems3; + }, "createProcessedItems"), + containerRef: /* @__PURE__ */ __name(function containerRef(el) { + this.container = el; + }, "containerRef"), + menubarRef: /* @__PURE__ */ __name(function menubarRef(el) { + this.menubar = el ? el.$el : void 0; + }, "menubarRef") + }, + computed: { + processedItems: /* @__PURE__ */ __name(function processedItems() { + return this.createProcessedItems(this.model || []); + }, "processedItems"), + visibleItems: /* @__PURE__ */ __name(function visibleItems() { + var _this15 = this; + var processedItem = this.activeItemPath.find(function(p) { + return p.key === _this15.focusedItemInfo.parentKey; + }); + return processedItem ? processedItem.items : this.processedItems; + }, "visibleItems"), + focusedItemId: /* @__PURE__ */ __name(function focusedItemId() { + return this.focusedItemInfo.index !== -1 ? "".concat(this.id).concat(isNotEmpty(this.focusedItemInfo.parentKey) ? "_" + this.focusedItemInfo.parentKey : "", "_").concat(this.focusedItemInfo.index) : null; + }, "focusedItemId") + }, + components: { + MenubarSub: script$1$3, + BarsIcon: script$A + } +}; +function _typeof(o) { + "@babel/helpers - typeof"; + return _typeof = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { + return typeof o2; + } : function(o2) { + return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; + }, _typeof(o); +} +__name(_typeof, "_typeof"); +function ownKeys(e, r) { + var t = Object.keys(e); + if (Object.getOwnPropertySymbols) { + var o = Object.getOwnPropertySymbols(e); + r && (o = o.filter(function(r2) { + return Object.getOwnPropertyDescriptor(e, r2).enumerable; + })), t.push.apply(t, o); + } + return t; +} +__name(ownKeys, "ownKeys"); +function _objectSpread(e) { + for (var r = 1; r < arguments.length; r++) { + var t = null != arguments[r] ? arguments[r] : {}; + r % 2 ? ownKeys(Object(t), true).forEach(function(r2) { + _defineProperty(e, r2, t[r2]); + }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function(r2) { + Object.defineProperty(e, r2, Object.getOwnPropertyDescriptor(t, r2)); + }); + } + return e; +} +__name(_objectSpread, "_objectSpread"); +function _defineProperty(e, r, t) { + return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: true, configurable: true, writable: true }) : e[r] = t, e; +} +__name(_defineProperty, "_defineProperty"); +function _toPropertyKey(t) { + var i = _toPrimitive(t, "string"); + return "symbol" == _typeof(i) ? i : i + ""; +} +__name(_toPropertyKey, "_toPropertyKey"); +function _toPrimitive(t, r) { + if ("object" != _typeof(t) || !t) return t; + var e = t[Symbol.toPrimitive]; + if (void 0 !== e) { + var i = e.call(t, r || "default"); + if ("object" != _typeof(i)) return i; + throw new TypeError("@@toPrimitive must return a primitive value."); + } + return ("string" === r ? String : Number)(t); +} +__name(_toPrimitive, "_toPrimitive"); +var _hoisted_1$9 = ["aria-haspopup", "aria-expanded", "aria-controls", "aria-label"]; +function render$6(_ctx, _cache, $props, $setup, $data, $options) { + var _component_BarsIcon = resolveComponent("BarsIcon"); + var _component_MenubarSub = resolveComponent("MenubarSub"); + return openBlock(), createElementBlock("div", mergeProps({ + ref: $options.containerRef, + "class": _ctx.cx("root") + }, _ctx.ptmi("root")), [_ctx.$slots.start ? (openBlock(), createElementBlock("div", mergeProps({ + key: 0, + "class": _ctx.cx("start") + }, _ctx.ptm("start")), [renderSlot(_ctx.$slots, "start")], 16)) : createCommentVNode("", true), renderSlot(_ctx.$slots, _ctx.$slots.button ? "button" : "menubutton", { + id: $data.id, + "class": normalizeClass(_ctx.cx("button")), + toggleCallback: /* @__PURE__ */ __name(function toggleCallback(event) { + return $options.menuButtonClick(event); + }, "toggleCallback") + }, function() { + var _ctx$$primevue$config; + return [_ctx.model && _ctx.model.length > 0 ? (openBlock(), createElementBlock("a", mergeProps({ + key: 0, + ref: "menubutton", + role: "button", + tabindex: "0", + "class": _ctx.cx("button"), + "aria-haspopup": _ctx.model.length && _ctx.model.length > 0 ? true : false, + "aria-expanded": $data.mobileActive, + "aria-controls": $data.id, + "aria-label": (_ctx$$primevue$config = _ctx.$primevue.config.locale.aria) === null || _ctx$$primevue$config === void 0 ? void 0 : _ctx$$primevue$config.navigation, + onClick: _cache[0] || (_cache[0] = function($event) { + return $options.menuButtonClick($event); + }), + onKeydown: _cache[1] || (_cache[1] = function($event) { + return $options.menuButtonKeydown($event); + }) + }, _objectSpread(_objectSpread({}, _ctx.buttonProps), _ctx.ptm("button"))), [renderSlot(_ctx.$slots, _ctx.$slots.buttonicon ? "buttonicon" : "menubuttonicon", {}, function() { + return [createVNode(_component_BarsIcon, normalizeProps(guardReactiveProps(_ctx.ptm("buttonicon"))), null, 16)]; + })], 16, _hoisted_1$9)) : createCommentVNode("", true)]; + }), createVNode(_component_MenubarSub, { + ref: $options.menubarRef, + id: $data.id + "_list", + role: "menubar", + items: $options.processedItems, + templates: _ctx.$slots, + root: true, + mobileActive: $data.mobileActive, + tabindex: "0", + "aria-activedescendant": $data.focused ? $options.focusedItemId : void 0, + menuId: $data.id, + focusedItemId: $data.focused ? $options.focusedItemId : void 0, + activeItemPath: $data.activeItemPath, + level: 0, + "aria-labelledby": _ctx.ariaLabelledby, + "aria-label": _ctx.ariaLabel, + pt: _ctx.pt, + unstyled: _ctx.unstyled, + onFocus: $options.onFocus, + onBlur: $options.onBlur, + onKeydown: $options.onKeyDown, + onItemClick: $options.onItemClick, + onItemMouseenter: $options.onItemMouseEnter, + onItemMousemove: $options.onItemMouseMove + }, null, 8, ["id", "items", "templates", "mobileActive", "aria-activedescendant", "menuId", "focusedItemId", "activeItemPath", "aria-labelledby", "aria-label", "pt", "unstyled", "onFocus", "onBlur", "onKeydown", "onItemClick", "onItemMouseenter", "onItemMousemove"]), _ctx.$slots.end ? (openBlock(), createElementBlock("div", mergeProps({ + key: 1, + "class": _ctx.cx("end") + }, _ctx.ptm("end")), [renderSlot(_ctx.$slots, "end")], 16)) : createCommentVNode("", true)], 16); +} +__name(render$6, "render$6"); +script$5.render = render$6; +const _withScopeId$2 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-9646ca0a"), n = n(), popScopeId(), n), "_withScopeId$2"); +const _hoisted_1$8 = ["href"]; +const _hoisted_2$6 = { class: "p-menubar-item-label" }; +const _hoisted_3$4 = { + key: 1, + class: "ml-auto border border-surface rounded text-muted text-xs p-1 keybinding-tag" +}; +const _sfc_main$6 = /* @__PURE__ */ defineComponent({ + __name: "CommandMenubar", + setup(__props) { + const settingStore = useSettingStore(); + const dropdownDirection = computed( + () => settingStore.get("Comfy.UseNewMenu") === "Top" ? "down" : "up" + ); + const menuItemsStore = useMenuItemStore(); + const items = menuItemsStore.menuItems; + return (_ctx, _cache) => { + return openBlock(), createBlock(unref(script$5), { + model: unref(items), + class: "top-menubar border-none p-0 bg-transparent", + pt: { + rootList: "gap-0 flex-nowrap w-auto", + submenu: `dropdown-direction-${dropdownDirection.value}`, + item: "relative" + } + }, { + item: withCtx(({ item: item3, props }) => [ + createBaseVNode("a", mergeProps({ class: "p-menubar-item-link" }, props.action, { + href: item3.url, + target: "_blank" + }), [ + item3.icon ? (openBlock(), createElementBlock("span", { + key: 0, + class: normalizeClass(["p-menubar-item-icon", item3.icon]) + }, null, 2)) : createCommentVNode("", true), + createBaseVNode("span", _hoisted_2$6, toDisplayString(item3.label), 1), + item3?.comfyCommand?.keybinding ? (openBlock(), createElementBlock("span", _hoisted_3$4, toDisplayString(item3.comfyCommand.keybinding.combo.toString()), 1)) : createCommentVNode("", true) + ], 16, _hoisted_1$8) + ]), + _: 1 + }, 8, ["model", "pt"]); + }; + } +}); +const CommandMenubar = /* @__PURE__ */ _export_sfc(_sfc_main$6, [["__scopeId", "data-v-9646ca0a"]]); +var theme$2 = /* @__PURE__ */ __name(function theme7(_ref) { + var dt = _ref.dt; + return "\n.p-panel {\n border: 1px solid ".concat(dt("panel.border.color"), ";\n border-radius: ").concat(dt("panel.border.radius"), ";\n background: ").concat(dt("panel.background"), ";\n color: ").concat(dt("panel.color"), ";\n}\n\n.p-panel-header {\n display: flex;\n justify-content: space-between;\n align-items: center;\n padding: ").concat(dt("panel.header.padding"), ";\n background: ").concat(dt("panel.header.background"), ";\n color: ").concat(dt("panel.header.color"), ";\n border-style: solid;\n border-width: ").concat(dt("panel.header.border.width"), ";\n border-color: ").concat(dt("panel.header.border.color"), ";\n border-radius: ").concat(dt("panel.header.border.radius"), ";\n}\n\n.p-panel-toggleable .p-panel-header {\n padding: ").concat(dt("panel.toggleable.header.padding"), ";\n}\n\n.p-panel-title {\n line-height: 1;\n font-weight: ").concat(dt("panel.title.font.weight"), ";\n}\n\n.p-panel-content {\n padding: ").concat(dt("panel.content.padding"), ";\n}\n\n.p-panel-footer {\n padding: ").concat(dt("panel.footer.padding"), ";\n}\n"); +}, "theme"); +var classes$2 = { + root: /* @__PURE__ */ __name(function root9(_ref2) { + var props = _ref2.props; + return ["p-panel p-component", { + "p-panel-toggleable": props.toggleable + }]; + }, "root"), + header: "p-panel-header", + title: "p-panel-title", + headerActions: "p-panel-header-actions", + pcToggleButton: "p-panel-toggle-button", + contentContainer: "p-panel-content-container", + content: "p-panel-content", + footer: "p-panel-footer" +}; +var PanelStyle = BaseStyle.extend({ + name: "panel", + theme: theme$2, + classes: classes$2 +}); +var script$1$2 = { + name: "BasePanel", + "extends": script$g, + props: { + header: String, + toggleable: Boolean, + collapsed: Boolean, + toggleButtonProps: { + type: Object, + "default": /* @__PURE__ */ __name(function _default() { + return { + severity: "secondary", + text: true, + rounded: true + }; + }, "_default") + } + }, + style: PanelStyle, + provide: /* @__PURE__ */ __name(function provide11() { + return { + $pcPanel: this, + $parentInstance: this + }; + }, "provide") +}; +var script$4 = { + name: "Panel", + "extends": script$1$2, + inheritAttrs: false, + emits: ["update:collapsed", "toggle"], + data: /* @__PURE__ */ __name(function data7() { + return { + id: this.$attrs.id, + d_collapsed: this.collapsed + }; + }, "data"), + watch: { + "$attrs.id": /* @__PURE__ */ __name(function $attrsId3(newValue) { + this.id = newValue || UniqueComponentId(); + }, "$attrsId"), + collapsed: /* @__PURE__ */ __name(function collapsed(newValue) { + this.d_collapsed = newValue; + }, "collapsed") + }, + mounted: /* @__PURE__ */ __name(function mounted7() { + this.id = this.id || UniqueComponentId(); + }, "mounted"), + methods: { + toggle: /* @__PURE__ */ __name(function toggle2(event) { + this.d_collapsed = !this.d_collapsed; + this.$emit("update:collapsed", this.d_collapsed); + this.$emit("toggle", { + originalEvent: event, + value: this.d_collapsed + }); + }, "toggle"), + onKeyDown: /* @__PURE__ */ __name(function onKeyDown3(event) { + if (event.code === "Enter" || event.code === "NumpadEnter" || event.code === "Space") { + this.toggle(event); + event.preventDefault(); + } + }, "onKeyDown") + }, + computed: { + buttonAriaLabel: /* @__PURE__ */ __name(function buttonAriaLabel() { + return this.toggleButtonProps && this.toggleButtonProps.ariaLabel ? this.toggleButtonProps.ariaLabel : this.header; + }, "buttonAriaLabel") + }, + components: { + PlusIcon: script$B, + MinusIcon: script$C, + Button: script$f + }, + directives: { + ripple: Ripple + } +}; +var _hoisted_1$7 = ["id"]; +var _hoisted_2$5 = ["id", "aria-labelledby"]; +function render$5(_ctx, _cache, $props, $setup, $data, $options) { + var _component_Button = resolveComponent("Button"); + return openBlock(), createElementBlock("div", mergeProps({ + "class": _ctx.cx("root") + }, _ctx.ptmi("root")), [createBaseVNode("div", mergeProps({ + "class": _ctx.cx("header") + }, _ctx.ptm("header")), [renderSlot(_ctx.$slots, "header", { + id: $data.id + "_header", + "class": normalizeClass(_ctx.cx("title")) + }, function() { + return [_ctx.header ? (openBlock(), createElementBlock("span", mergeProps({ + key: 0, + id: $data.id + "_header", + "class": _ctx.cx("title") + }, _ctx.ptm("title")), toDisplayString(_ctx.header), 17, _hoisted_1$7)) : createCommentVNode("", true)]; + }), createBaseVNode("div", mergeProps({ + "class": _ctx.cx("headerActions") + }, _ctx.ptm("headerActions")), [renderSlot(_ctx.$slots, "icons"), _ctx.toggleable ? (openBlock(), createBlock(_component_Button, mergeProps({ + key: 0, + id: $data.id + "_header", + "class": _ctx.cx("pcToggleButton"), + "aria-label": $options.buttonAriaLabel, + "aria-controls": $data.id + "_content", + "aria-expanded": !$data.d_collapsed, + unstyled: _ctx.unstyled, + onClick: $options.toggle, + onKeydown: $options.onKeyDown + }, _ctx.toggleButtonProps, { + pt: _ctx.ptm("pcToggleButton") + }), { + icon: withCtx(function(slotProps) { + return [renderSlot(_ctx.$slots, _ctx.$slots.toggleicon ? "toggleicon" : "togglericon", { + collapsed: $data.d_collapsed + }, function() { + return [(openBlock(), createBlock(resolveDynamicComponent($data.d_collapsed ? "PlusIcon" : "MinusIcon"), mergeProps({ + "class": slotProps["class"] + }, _ctx.ptm("pcToggleButton")["icon"]), null, 16, ["class"]))]; + })]; + }), + _: 3 + }, 16, ["id", "class", "aria-label", "aria-controls", "aria-expanded", "unstyled", "onClick", "onKeydown", "pt"])) : createCommentVNode("", true)], 16)], 16), createVNode(Transition, mergeProps({ + name: "p-toggleable-content" + }, _ctx.ptm("transition")), { + "default": withCtx(function() { + return [withDirectives(createBaseVNode("div", mergeProps({ + id: $data.id + "_content", + "class": _ctx.cx("contentContainer"), + role: "region", + "aria-labelledby": $data.id + "_header" + }, _ctx.ptm("contentContainer")), [createBaseVNode("div", mergeProps({ + "class": _ctx.cx("content") + }, _ctx.ptm("content")), [renderSlot(_ctx.$slots, "default")], 16), _ctx.$slots.footer ? (openBlock(), createElementBlock("div", mergeProps({ + key: 0, + "class": _ctx.cx("footer") + }, _ctx.ptm("footer")), [renderSlot(_ctx.$slots, "footer")], 16)) : createCommentVNode("", true)], 16, _hoisted_2$5), [[vShow, !$data.d_collapsed]])]; + }), + _: 3 + }, 16)], 16); +} +__name(render$5, "render$5"); +script$4.render = render$5; +const _hoisted_1$6 = { + viewBox: "0 0 24 24", + width: "1.2em", + height: "1.2em" +}; +const _hoisted_2$4 = /* @__PURE__ */ createBaseVNode("g", { + fill: "none", + stroke: "currentColor", + "stroke-linecap": "round", + "stroke-linejoin": "round", + "stroke-width": "2" +}, [ + /* @__PURE__ */ createBaseVNode("path", { d: "M16 12H3m13 6H3m7-12H3m18 12V8a2 2 0 0 0-2-2h-5" }), + /* @__PURE__ */ createBaseVNode("path", { d: "m16 8l-2-2l2-2" }) +], -1); +const _hoisted_3$3 = [ + _hoisted_2$4 +]; +function render$4(_ctx, _cache) { + return openBlock(), createElementBlock("svg", _hoisted_1$6, [..._hoisted_3$3]); +} +__name(render$4, "render$4"); +const __unplugin_components_0$1 = markRaw({ name: "lucide-list-start", render: render$4 }); +var theme$1 = /* @__PURE__ */ __name(function theme8(_ref) { + var dt = _ref.dt; + return "\n.p-tieredmenu {\n background: ".concat(dt("tieredmenu.background"), ";\n color: ").concat(dt("tieredmenu.color"), ";\n border: 1px solid ").concat(dt("tieredmenu.border.color"), ";\n border-radius: ").concat(dt("tieredmenu.border.radius"), ";\n min-width: 12.5rem;\n}\n\n.p-tieredmenu-root-list,\n.p-tieredmenu-submenu {\n margin: 0;\n padding: ").concat(dt("tieredmenu.list.padding"), ";\n list-style: none;\n outline: 0 none;\n display: flex;\n flex-direction: column;\n gap: ").concat(dt("tieredmenu.list.gap"), ";\n}\n\n.p-tieredmenu-submenu {\n position: absolute;\n min-width: 100%;\n z-index: 1;\n background: ").concat(dt("tieredmenu.background"), ";\n color: ").concat(dt("tieredmenu.color"), ";\n border: 1px solid ").concat(dt("tieredmenu.border.color"), ";\n border-radius: ").concat(dt("tieredmenu.border.radius"), ";\n box-shadow: ").concat(dt("tieredmenu.shadow"), ";\n}\n\n.p-tieredmenu-item {\n position: relative;\n}\n\n.p-tieredmenu-item-content {\n transition: background ").concat(dt("tieredmenu.transition.duration"), ", color ").concat(dt("tieredmenu.transition.duration"), ";\n border-radius: ").concat(dt("tieredmenu.item.border.radius"), ";\n color: ").concat(dt("tieredmenu.item.color"), ";\n}\n\n.p-tieredmenu-item-link {\n cursor: pointer;\n display: flex;\n align-items: center;\n text-decoration: none;\n overflow: hidden;\n position: relative;\n color: inherit;\n padding: ").concat(dt("tieredmenu.item.padding"), ";\n gap: ").concat(dt("tieredmenu.item.gap"), ";\n user-select: none;\n outline: 0 none;\n}\n\n.p-tieredmenu-item-label {\n line-height: 1;\n}\n\n.p-tieredmenu-item-icon {\n color: ").concat(dt("tieredmenu.item.icon.color"), ";\n}\n\n.p-tieredmenu-submenu-icon {\n color: ").concat(dt("tieredmenu.submenu.icon.color"), ";\n margin-left: auto;\n font-size: ").concat(dt("tieredmenu.submenu.icon.size"), ";\n width: ").concat(dt("tieredmenu.submenu.icon.size"), ";\n height: ").concat(dt("tieredmenu.submenu.icon.size"), ";\n}\n\n.p-tieredmenu-item.p-focus > .p-tieredmenu-item-content {\n color: ").concat(dt("tieredmenu.item.focus.color"), ";\n background: ").concat(dt("tieredmenu.item.focus.background"), ";\n}\n\n.p-tieredmenu-item.p-focus > .p-tieredmenu-item-content .p-tieredmenu-item-icon {\n color: ").concat(dt("tieredmenu.item.icon.focus.color"), ";\n}\n\n.p-tieredmenu-item.p-focus > .p-tieredmenu-item-content .p-tieredmenu-submenu-icon {\n color: ").concat(dt("tieredmenu.submenu.icon.focus.color"), ";\n}\n\n.p-tieredmenu-item:not(.p-disabled) > .p-tieredmenu-item-content:hover {\n color: ").concat(dt("tieredmenu.item.focus.color"), ";\n background: ").concat(dt("tieredmenu.item.focus.background"), ";\n}\n\n.p-tieredmenu-item:not(.p-disabled) > .p-tieredmenu-item-content:hover .p-tieredmenu-item-icon {\n color: ").concat(dt("tieredmenu.item.icon.focus.color"), ";\n}\n\n.p-tieredmenu-item:not(.p-disabled) > .p-tieredmenu-item-content:hover .p-tieredmenu-submenu-icon {\n color: ").concat(dt("tieredmenu.submenu.icon.focus.color"), ";\n}\n\n.p-tieredmenu-item-active > .p-tieredmenu-item-content {\n color: ").concat(dt("tieredmenu.item.active.color"), ";\n background: ").concat(dt("tieredmenu.item.active.background"), ";\n}\n\n.p-tieredmenu-item-active > .p-tieredmenu-item-content .p-tieredmenu-item-icon {\n color: ").concat(dt("tieredmenu.item.icon.active.color"), ";\n}\n\n.p-tieredmenu-item-active > .p-tieredmenu-item-content .p-tieredmenu-submenu-icon {\n color: ").concat(dt("tieredmenu.submenu.icon.active.color"), ";\n}\n\n.p-tieredmenu-separator {\n border-top: 1px solid ").concat(dt("tieredmenu.separator.border.color"), ";\n}\n\n.p-tieredmenu-overlay {\n box-shadow: ").concat(dt("tieredmenu.shadow"), ";\n}\n\n.p-tieredmenu-enter-from,\n.p-tieredmenu-leave-active {\n opacity: 0;\n}\n\n.p-tieredmenu-enter-active {\n transition: opacity 250ms;\n}\n"); +}, "theme"); +var inlineStyles = { + submenu: /* @__PURE__ */ __name(function submenu2(_ref2) { + var instance = _ref2.instance, processedItem = _ref2.processedItem; + return { + display: instance.isItemActive(processedItem) ? "flex" : "none" + }; + }, "submenu") +}; +var classes$1 = { + root: /* @__PURE__ */ __name(function root10(_ref3) { + _ref3.instance; + var props = _ref3.props; + return ["p-tieredmenu p-component", { + "p-tieredmenu-overlay": props.popup + }]; + }, "root"), + start: "p-tieredmenu-start", + rootList: "p-tieredmenu-root-list", + item: /* @__PURE__ */ __name(function item2(_ref4) { + var instance = _ref4.instance, processedItem = _ref4.processedItem; + return ["p-tieredmenu-item", { + "p-tieredmenu-item-active": instance.isItemActive(processedItem), + "p-focus": instance.isItemFocused(processedItem), + "p-disabled": instance.isItemDisabled(processedItem) + }]; + }, "item"), + itemContent: "p-tieredmenu-item-content", + itemLink: "p-tieredmenu-item-link", + itemIcon: "p-tieredmenu-item-icon", + itemLabel: "p-tieredmenu-item-label", + submenuIcon: "p-tieredmenu-submenu-icon", + submenu: "p-tieredmenu-submenu", + separator: "p-tieredmenu-separator", + end: "p-tieredmenu-end" +}; +var TieredMenuStyle = BaseStyle.extend({ + name: "tieredmenu", + theme: theme$1, + classes: classes$1, + inlineStyles +}); +var script$2 = { + name: "BaseTieredMenu", + "extends": script$g, + props: { + popup: { + type: Boolean, + "default": false + }, + model: { + type: Array, + "default": null + }, + appendTo: { + type: [String, Object], + "default": "body" + }, + autoZIndex: { + type: Boolean, + "default": true + }, + baseZIndex: { + type: Number, + "default": 0 + }, + disabled: { + type: Boolean, + "default": false + }, + tabindex: { + type: Number, + "default": 0 + }, + ariaLabelledby: { + type: String, + "default": null + }, + ariaLabel: { + type: String, + "default": null + } + }, + style: TieredMenuStyle, + provide: /* @__PURE__ */ __name(function provide12() { + return { + $pcTieredMenu: this, + $parentInstance: this + }; + }, "provide") +}; +var script$1$1 = { + name: "TieredMenuSub", + hostName: "TieredMenu", + "extends": script$g, + emits: ["item-click", "item-mouseenter", "item-mousemove"], + container: null, + props: { + menuId: { + type: String, + "default": null + }, + focusedItemId: { + type: String, + "default": null + }, + items: { + type: Array, + "default": null + }, + visible: { + type: Boolean, + "default": false + }, + level: { + type: Number, + "default": 0 + }, + templates: { + type: Object, + "default": null + }, + activeItemPath: { + type: Object, + "default": null + }, + tabindex: { + type: Number, + "default": 0 + } + }, + methods: { + getItemId: /* @__PURE__ */ __name(function getItemId2(processedItem) { + return "".concat(this.menuId, "_").concat(processedItem.key); + }, "getItemId"), + getItemKey: /* @__PURE__ */ __name(function getItemKey2(processedItem) { + return this.getItemId(processedItem); + }, "getItemKey"), + getItemProp: /* @__PURE__ */ __name(function getItemProp3(processedItem, name, params) { + return processedItem && processedItem.item ? resolve(processedItem.item[name], params) : void 0; + }, "getItemProp"), + getItemLabel: /* @__PURE__ */ __name(function getItemLabel3(processedItem) { + return this.getItemProp(processedItem, "label"); + }, "getItemLabel"), + getItemLabelId: /* @__PURE__ */ __name(function getItemLabelId2(processedItem) { + return "".concat(this.menuId, "_").concat(processedItem.key, "_label"); + }, "getItemLabelId"), + getPTOptions: /* @__PURE__ */ __name(function getPTOptions5(processedItem, index, key) { + return this.ptm(key, { + context: { + item: processedItem.item, + index, + active: this.isItemActive(processedItem), + focused: this.isItemFocused(processedItem), + disabled: this.isItemDisabled(processedItem) + } + }); + }, "getPTOptions"), + isItemActive: /* @__PURE__ */ __name(function isItemActive2(processedItem) { + return this.activeItemPath.some(function(path) { + return path.key === processedItem.key; + }); + }, "isItemActive"), + isItemVisible: /* @__PURE__ */ __name(function isItemVisible3(processedItem) { + return this.getItemProp(processedItem, "visible") !== false; + }, "isItemVisible"), + isItemDisabled: /* @__PURE__ */ __name(function isItemDisabled3(processedItem) { + return this.getItemProp(processedItem, "disabled"); + }, "isItemDisabled"), + isItemFocused: /* @__PURE__ */ __name(function isItemFocused2(processedItem) { + return this.focusedItemId === this.getItemId(processedItem); + }, "isItemFocused"), + isItemGroup: /* @__PURE__ */ __name(function isItemGroup3(processedItem) { + return isNotEmpty(processedItem.items); + }, "isItemGroup"), + onEnter: /* @__PURE__ */ __name(function onEnter2() { + nestedPosition(this.container, this.level); + }, "onEnter"), + onItemClick: /* @__PURE__ */ __name(function onItemClick3(event, processedItem) { + this.getItemProp(processedItem, "command", { + originalEvent: event, + item: processedItem.item + }); + this.$emit("item-click", { + originalEvent: event, + processedItem, + isFocus: true + }); + }, "onItemClick"), + onItemMouseEnter: /* @__PURE__ */ __name(function onItemMouseEnter3(event, processedItem) { + this.$emit("item-mouseenter", { + originalEvent: event, + processedItem + }); + }, "onItemMouseEnter"), + onItemMouseMove: /* @__PURE__ */ __name(function onItemMouseMove3(event, processedItem) { + this.$emit("item-mousemove", { + originalEvent: event, + processedItem + }); + }, "onItemMouseMove"), + getAriaSetSize: /* @__PURE__ */ __name(function getAriaSetSize2() { + var _this = this; + return this.items.filter(function(processedItem) { + return _this.isItemVisible(processedItem) && !_this.getItemProp(processedItem, "separator"); + }).length; + }, "getAriaSetSize"), + getAriaPosInset: /* @__PURE__ */ __name(function getAriaPosInset3(index) { + var _this2 = this; + return index - this.items.slice(0, index).filter(function(processedItem) { + return _this2.isItemVisible(processedItem) && _this2.getItemProp(processedItem, "separator"); + }).length + 1; + }, "getAriaPosInset"), + getMenuItemProps: /* @__PURE__ */ __name(function getMenuItemProps2(processedItem, index) { + return { + action: mergeProps({ + "class": this.cx("itemLink"), + tabindex: -1, + "aria-hidden": true + }, this.getPTOptions(processedItem, index, "itemLink")), + icon: mergeProps({ + "class": [this.cx("itemIcon"), this.getItemProp(processedItem, "icon")] + }, this.getPTOptions(processedItem, index, "itemIcon")), + label: mergeProps({ + "class": this.cx("itemLabel") + }, this.getPTOptions(processedItem, index, "itemLabel")), + submenuicon: mergeProps({ + "class": this.cx("submenuIcon") + }, this.getPTOptions(processedItem, index, "submenuIcon")) + }; + }, "getMenuItemProps"), + containerRef: /* @__PURE__ */ __name(function containerRef2(el) { + this.container = el; + }, "containerRef") + }, + components: { + AngleRightIcon: script$y + }, + directives: { + ripple: Ripple + } +}; +var _hoisted_1$1$1 = ["tabindex"]; +var _hoisted_2$3 = ["id", "aria-label", "aria-disabled", "aria-expanded", "aria-haspopup", "aria-level", "aria-setsize", "aria-posinset", "data-p-active", "data-p-focused", "data-p-disabled"]; +var _hoisted_3$2 = ["onClick", "onMouseenter", "onMousemove"]; +var _hoisted_4 = ["href", "target"]; +var _hoisted_5 = ["id"]; +var _hoisted_6 = ["id"]; +function render$1$1(_ctx, _cache, $props, $setup, $data, $options) { + var _component_AngleRightIcon = resolveComponent("AngleRightIcon"); + var _component_TieredMenuSub = resolveComponent("TieredMenuSub", true); + var _directive_ripple = resolveDirective("ripple"); + return openBlock(), createBlock(Transition, mergeProps({ + name: "p-tieredmenu", + onEnter: $options.onEnter + }, _ctx.ptm("menu.transition")), { + "default": withCtx(function() { + return [($props.level === 0 ? true : $props.visible) ? (openBlock(), createElementBlock("ul", mergeProps({ + key: 0, + ref: $options.containerRef, + "class": $props.level === 0 ? _ctx.cx("rootList") : _ctx.cx("submenu"), + tabindex: $props.tabindex + }, $props.level === 0 ? _ctx.ptm("rootList") : _ctx.ptm("submenu")), [(openBlock(true), createElementBlock(Fragment, null, renderList($props.items, function(processedItem, index) { + return openBlock(), createElementBlock(Fragment, { + key: $options.getItemKey(processedItem) + }, [$options.isItemVisible(processedItem) && !$options.getItemProp(processedItem, "separator") ? (openBlock(), createElementBlock("li", mergeProps({ + key: 0, + id: $options.getItemId(processedItem), + style: $options.getItemProp(processedItem, "style"), + "class": [_ctx.cx("item", { + processedItem + }), $options.getItemProp(processedItem, "class")], + role: "menuitem", + "aria-label": $options.getItemLabel(processedItem), + "aria-disabled": $options.isItemDisabled(processedItem) || void 0, + "aria-expanded": $options.isItemGroup(processedItem) ? $options.isItemActive(processedItem) : void 0, + "aria-haspopup": $options.isItemGroup(processedItem) && !$options.getItemProp(processedItem, "to") ? "menu" : void 0, + "aria-level": $props.level + 1, + "aria-setsize": $options.getAriaSetSize(), + "aria-posinset": $options.getAriaPosInset(index), + ref_for: true + }, $options.getPTOptions(processedItem, index, "item"), { + "data-p-active": $options.isItemActive(processedItem), + "data-p-focused": $options.isItemFocused(processedItem), + "data-p-disabled": $options.isItemDisabled(processedItem) + }), [createBaseVNode("div", mergeProps({ + "class": _ctx.cx("itemContent"), + onClick: /* @__PURE__ */ __name(function onClick2($event) { + return $options.onItemClick($event, processedItem); + }, "onClick"), + onMouseenter: /* @__PURE__ */ __name(function onMouseenter($event) { + return $options.onItemMouseEnter($event, processedItem); + }, "onMouseenter"), + onMousemove: /* @__PURE__ */ __name(function onMousemove($event) { + return $options.onItemMouseMove($event, processedItem); + }, "onMousemove"), + ref_for: true + }, $options.getPTOptions(processedItem, index, "itemContent")), [!$props.templates.item ? withDirectives((openBlock(), createElementBlock("a", mergeProps({ + key: 0, + href: $options.getItemProp(processedItem, "url"), + "class": _ctx.cx("itemLink"), + target: $options.getItemProp(processedItem, "target"), + tabindex: "-1", + ref_for: true + }, $options.getPTOptions(processedItem, index, "itemLink")), [$props.templates.itemicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.itemicon), { + key: 0, + item: processedItem.item, + "class": normalizeClass(_ctx.cx("itemIcon")) + }, null, 8, ["item", "class"])) : $options.getItemProp(processedItem, "icon") ? (openBlock(), createElementBlock("span", mergeProps({ + key: 1, + "class": [_ctx.cx("itemIcon"), $options.getItemProp(processedItem, "icon")], + ref_for: true + }, $options.getPTOptions(processedItem, index, "itemIcon")), null, 16)) : createCommentVNode("", true), createBaseVNode("span", mergeProps({ + id: $options.getItemLabelId(processedItem), + "class": _ctx.cx("itemLabel"), + ref_for: true + }, $options.getPTOptions(processedItem, index, "itemLabel")), toDisplayString($options.getItemLabel(processedItem)), 17, _hoisted_5), $options.getItemProp(processedItem, "items") ? (openBlock(), createElementBlock(Fragment, { + key: 2 + }, [$props.templates.submenuicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.submenuicon), mergeProps({ + key: 0, + "class": _ctx.cx("submenuIcon"), + active: $options.isItemActive(processedItem), + ref_for: true + }, $options.getPTOptions(processedItem, index, "submenuIcon")), null, 16, ["class", "active"])) : (openBlock(), createBlock(_component_AngleRightIcon, mergeProps({ + key: 1, + "class": _ctx.cx("submenuIcon"), + ref_for: true + }, $options.getPTOptions(processedItem, index, "submenuIcon")), null, 16, ["class"]))], 64)) : createCommentVNode("", true)], 16, _hoisted_4)), [[_directive_ripple]]) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.item), { + key: 1, + item: processedItem.item, + hasSubmenu: $options.getItemProp(processedItem, "items"), + label: $options.getItemLabel(processedItem), + props: $options.getMenuItemProps(processedItem, index) + }, null, 8, ["item", "hasSubmenu", "label", "props"]))], 16, _hoisted_3$2), $options.isItemVisible(processedItem) && $options.isItemGroup(processedItem) ? (openBlock(), createBlock(_component_TieredMenuSub, { + key: 0, + id: $options.getItemId(processedItem) + "_list", + style: normalizeStyle(_ctx.sx("submenu", true, { + processedItem + })), + "aria-labelledby": $options.getItemLabelId(processedItem), + role: "menu", + menuId: $props.menuId, + focusedItemId: $props.focusedItemId, + items: processedItem.items, + templates: $props.templates, + activeItemPath: $props.activeItemPath, + level: $props.level + 1, + visible: $options.isItemActive(processedItem) && $options.isItemGroup(processedItem), + pt: _ctx.pt, + unstyled: _ctx.unstyled, + onItemClick: _cache[0] || (_cache[0] = function($event) { + return _ctx.$emit("item-click", $event); + }), + onItemMouseenter: _cache[1] || (_cache[1] = function($event) { + return _ctx.$emit("item-mouseenter", $event); + }), + onItemMousemove: _cache[2] || (_cache[2] = function($event) { + return _ctx.$emit("item-mousemove", $event); + }) + }, null, 8, ["id", "style", "aria-labelledby", "menuId", "focusedItemId", "items", "templates", "activeItemPath", "level", "visible", "pt", "unstyled"])) : createCommentVNode("", true)], 16, _hoisted_2$3)) : createCommentVNode("", true), $options.isItemVisible(processedItem) && $options.getItemProp(processedItem, "separator") ? (openBlock(), createElementBlock("li", mergeProps({ + key: 1, + id: $options.getItemId(processedItem), + style: $options.getItemProp(processedItem, "style"), + "class": [_ctx.cx("separator"), $options.getItemProp(processedItem, "class")], + role: "separator", + ref_for: true + }, _ctx.ptm("separator")), null, 16, _hoisted_6)) : createCommentVNode("", true)], 64); + }), 128))], 16, _hoisted_1$1$1)) : createCommentVNode("", true)]; + }), + _: 1 + }, 16, ["onEnter"]); +} +__name(render$1$1, "render$1$1"); +script$1$1.render = render$1$1; +var script$3 = { + name: "TieredMenu", + "extends": script$2, + inheritAttrs: false, + emits: ["focus", "blur", "before-show", "before-hide", "hide", "show"], + outsideClickListener: null, + scrollHandler: null, + resizeListener: null, + target: null, + container: null, + menubar: null, + searchTimeout: null, + searchValue: null, + data: /* @__PURE__ */ __name(function data8() { + return { + id: this.$attrs.id, + focused: false, + focusedItemInfo: { + index: -1, + level: 0, + parentKey: "" + }, + activeItemPath: [], + visible: !this.popup, + submenuVisible: false, + dirty: false + }; + }, "data"), + watch: { + "$attrs.id": /* @__PURE__ */ __name(function $attrsId4(newValue) { + this.id = newValue || UniqueComponentId(); + }, "$attrsId"), + activeItemPath: /* @__PURE__ */ __name(function activeItemPath2(newPath) { + if (!this.popup) { + if (isNotEmpty(newPath)) { + this.bindOutsideClickListener(); + this.bindResizeListener(); + } else { + this.unbindOutsideClickListener(); + this.unbindResizeListener(); + } + } + }, "activeItemPath") + }, + mounted: /* @__PURE__ */ __name(function mounted8() { + this.id = this.id || UniqueComponentId(); + }, "mounted"), + beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount7() { + this.unbindOutsideClickListener(); + this.unbindResizeListener(); + if (this.scrollHandler) { + this.scrollHandler.destroy(); + this.scrollHandler = null; + } + if (this.container && this.autoZIndex) { + ZIndex.clear(this.container); + } + this.target = null; + this.container = null; + }, "beforeUnmount"), + methods: { + getItemProp: /* @__PURE__ */ __name(function getItemProp4(item3, name) { + return item3 ? resolve(item3[name]) : void 0; + }, "getItemProp"), + getItemLabel: /* @__PURE__ */ __name(function getItemLabel4(item3) { + return this.getItemProp(item3, "label"); + }, "getItemLabel"), + isItemDisabled: /* @__PURE__ */ __name(function isItemDisabled4(item3) { + return this.getItemProp(item3, "disabled"); + }, "isItemDisabled"), + isItemVisible: /* @__PURE__ */ __name(function isItemVisible4(item3) { + return this.getItemProp(item3, "visible") !== false; + }, "isItemVisible"), + isItemGroup: /* @__PURE__ */ __name(function isItemGroup4(item3) { + return isNotEmpty(this.getItemProp(item3, "items")); + }, "isItemGroup"), + isItemSeparator: /* @__PURE__ */ __name(function isItemSeparator2(item3) { + return this.getItemProp(item3, "separator"); + }, "isItemSeparator"), + getProccessedItemLabel: /* @__PURE__ */ __name(function getProccessedItemLabel2(processedItem) { + return processedItem ? this.getItemLabel(processedItem.item) : void 0; + }, "getProccessedItemLabel"), + isProccessedItemGroup: /* @__PURE__ */ __name(function isProccessedItemGroup2(processedItem) { + return processedItem && isNotEmpty(processedItem.items); + }, "isProccessedItemGroup"), + toggle: /* @__PURE__ */ __name(function toggle3(event) { + this.visible ? this.hide(event, true) : this.show(event); + }, "toggle"), + show: /* @__PURE__ */ __name(function show3(event, isFocus) { + if (this.popup) { + this.$emit("before-show"); + this.visible = true; + this.target = this.target || event.currentTarget; + this.relatedTarget = event.relatedTarget || null; + } + isFocus && focus(this.menubar); + }, "show"), + hide: /* @__PURE__ */ __name(function hide3(event, isFocus) { + if (this.popup) { + this.$emit("before-hide"); + this.visible = false; + } + this.activeItemPath = []; + this.focusedItemInfo = { + index: -1, + level: 0, + parentKey: "" + }; + isFocus && focus(this.relatedTarget || this.target || this.menubar); + this.dirty = false; + }, "hide"), + onFocus: /* @__PURE__ */ __name(function onFocus4(event) { + this.focused = true; + if (!this.popup) { + this.focusedItemInfo = this.focusedItemInfo.index !== -1 ? this.focusedItemInfo : { + index: this.findFirstFocusedItemIndex(), + level: 0, + parentKey: "" + }; + } + this.$emit("focus", event); + }, "onFocus"), + onBlur: /* @__PURE__ */ __name(function onBlur3(event) { + this.focused = false; + this.focusedItemInfo = { + index: -1, + level: 0, + parentKey: "" + }; + this.searchValue = ""; + this.dirty = false; + this.$emit("blur", event); + }, "onBlur"), + onKeyDown: /* @__PURE__ */ __name(function onKeyDown4(event) { + if (this.disabled) { + event.preventDefault(); + return; + } + var metaKey = event.metaKey || event.ctrlKey; + switch (event.code) { + case "ArrowDown": + this.onArrowDownKey(event); + break; + case "ArrowUp": + this.onArrowUpKey(event); + break; + case "ArrowLeft": + this.onArrowLeftKey(event); + break; + case "ArrowRight": + this.onArrowRightKey(event); + break; + case "Home": + this.onHomeKey(event); + break; + case "End": + this.onEndKey(event); + break; + case "Space": + this.onSpaceKey(event); + break; + case "Enter": + case "NumpadEnter": + this.onEnterKey(event); + break; + case "Escape": + this.onEscapeKey(event); + break; + case "Tab": + this.onTabKey(event); + break; + case "PageDown": + case "PageUp": + case "Backspace": + case "ShiftLeft": + case "ShiftRight": + break; + default: + if (!metaKey && isPrintableCharacter(event.key)) { + this.searchItems(event, event.key); + } + break; + } + }, "onKeyDown"), + onItemChange: /* @__PURE__ */ __name(function onItemChange2(event) { + var processedItem = event.processedItem, isFocus = event.isFocus; + if (isEmpty(processedItem)) return; + var index = processedItem.index, key = processedItem.key, level = processedItem.level, parentKey = processedItem.parentKey, items = processedItem.items; + var grouped = isNotEmpty(items); + var activeItemPath3 = this.activeItemPath.filter(function(p) { + return p.parentKey !== parentKey && p.parentKey !== key; + }); + if (grouped) { + activeItemPath3.push(processedItem); + this.submenuVisible = true; + } + this.focusedItemInfo = { + index, + level, + parentKey + }; + this.activeItemPath = activeItemPath3; + grouped && (this.dirty = true); + isFocus && focus(this.menubar); + }, "onItemChange"), + onOverlayClick: /* @__PURE__ */ __name(function onOverlayClick2(event) { + OverlayEventBus.emit("overlay-click", { + originalEvent: event, + target: this.target + }); + }, "onOverlayClick"), + onItemClick: /* @__PURE__ */ __name(function onItemClick4(event) { + var originalEvent = event.originalEvent, processedItem = event.processedItem; + var grouped = this.isProccessedItemGroup(processedItem); + var root12 = isEmpty(processedItem.parent); + var selected = this.isSelected(processedItem); + if (selected) { + var index = processedItem.index, key = processedItem.key, level = processedItem.level, parentKey = processedItem.parentKey; + this.activeItemPath = this.activeItemPath.filter(function(p) { + return key !== p.key && key.startsWith(p.key); + }); + this.focusedItemInfo = { + index, + level, + parentKey + }; + this.dirty = !root12; + focus(this.menubar); + } else { + if (grouped) { + this.onItemChange(event); + } else { + var rootProcessedItem = root12 ? processedItem : this.activeItemPath.find(function(p) { + return p.parentKey === ""; + }); + this.hide(originalEvent); + this.changeFocusedItemIndex(originalEvent, rootProcessedItem ? rootProcessedItem.index : -1); + focus(this.menubar); + } + } + }, "onItemClick"), + onItemMouseEnter: /* @__PURE__ */ __name(function onItemMouseEnter4(event) { + if (this.dirty) { + this.onItemChange(event); + } + }, "onItemMouseEnter"), + onItemMouseMove: /* @__PURE__ */ __name(function onItemMouseMove4(event) { + if (this.focused) { + this.changeFocusedItemIndex(event, event.processedItem.index); + } + }, "onItemMouseMove"), + onArrowDownKey: /* @__PURE__ */ __name(function onArrowDownKey3(event) { + var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex(); + this.changeFocusedItemIndex(event, itemIndex); + event.preventDefault(); + }, "onArrowDownKey"), + onArrowUpKey: /* @__PURE__ */ __name(function onArrowUpKey3(event) { + if (event.altKey) { + if (this.focusedItemInfo.index !== -1) { + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var grouped = this.isProccessedItemGroup(processedItem); + !grouped && this.onItemChange({ + originalEvent: event, + processedItem + }); + } + this.popup && this.hide(event, true); + event.preventDefault(); + } else { + var itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex(); + this.changeFocusedItemIndex(event, itemIndex); + event.preventDefault(); + } + }, "onArrowUpKey"), + onArrowLeftKey: /* @__PURE__ */ __name(function onArrowLeftKey4(event) { + var _this = this; + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var parentItem = this.activeItemPath.find(function(p) { + return p.key === processedItem.parentKey; + }); + var root12 = isEmpty(processedItem.parent); + if (!root12) { + this.focusedItemInfo = { + index: -1, + parentKey: parentItem ? parentItem.parentKey : "" + }; + this.searchValue = ""; + this.onArrowDownKey(event); + } + this.activeItemPath = this.activeItemPath.filter(function(p) { + return p.parentKey !== _this.focusedItemInfo.parentKey; + }); + event.preventDefault(); + }, "onArrowLeftKey"), + onArrowRightKey: /* @__PURE__ */ __name(function onArrowRightKey4(event) { + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var grouped = this.isProccessedItemGroup(processedItem); + if (grouped) { + this.onItemChange({ + originalEvent: event, + processedItem + }); + this.focusedItemInfo = { + index: -1, + parentKey: processedItem.key + }; + this.searchValue = ""; + this.onArrowDownKey(event); + } + event.preventDefault(); + }, "onArrowRightKey"), + onHomeKey: /* @__PURE__ */ __name(function onHomeKey4(event) { + this.changeFocusedItemIndex(event, this.findFirstItemIndex()); + event.preventDefault(); + }, "onHomeKey"), + onEndKey: /* @__PURE__ */ __name(function onEndKey4(event) { + this.changeFocusedItemIndex(event, this.findLastItemIndex()); + event.preventDefault(); + }, "onEndKey"), + onEnterKey: /* @__PURE__ */ __name(function onEnterKey4(event) { + if (this.focusedItemInfo.index !== -1) { + var element = findSingle(this.menubar, 'li[id="'.concat("".concat(this.focusedItemId), '"]')); + var anchorElement = element && findSingle(element, '[data-pc-section="itemlink"]'); + anchorElement ? anchorElement.click() : element && element.click(); + if (!this.popup) { + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var grouped = this.isProccessedItemGroup(processedItem); + !grouped && (this.focusedItemInfo.index = this.findFirstFocusedItemIndex()); + } + } + event.preventDefault(); + }, "onEnterKey"), + onSpaceKey: /* @__PURE__ */ __name(function onSpaceKey2(event) { + this.onEnterKey(event); + }, "onSpaceKey"), + onEscapeKey: /* @__PURE__ */ __name(function onEscapeKey3(event) { + if (this.popup || this.focusedItemInfo.level !== 0) { + var _focusedItemInfo = this.focusedItemInfo; + this.hide(event, false); + this.focusedItemInfo = { + index: Number(_focusedItemInfo.parentKey.split("_")[0]), + level: 0, + parentKey: "" + }; + this.popup && focus(this.target); + } + event.preventDefault(); + }, "onEscapeKey"), + onTabKey: /* @__PURE__ */ __name(function onTabKey3(event) { + if (this.focusedItemInfo.index !== -1) { + var processedItem = this.visibleItems[this.focusedItemInfo.index]; + var grouped = this.isProccessedItemGroup(processedItem); + !grouped && this.onItemChange({ + originalEvent: event, + processedItem + }); + } + this.hide(); + }, "onTabKey"), + onEnter: /* @__PURE__ */ __name(function onEnter3(el) { + if (this.autoZIndex) { + ZIndex.set("menu", el, this.baseZIndex + this.$primevue.config.zIndex.menu); + } + addStyle(el, { + position: "absolute", + top: "0", + left: "0" + }); + this.alignOverlay(); + focus(this.menubar); + this.scrollInView(); + }, "onEnter"), + onAfterEnter: /* @__PURE__ */ __name(function onAfterEnter() { + this.bindOutsideClickListener(); + this.bindScrollListener(); + this.bindResizeListener(); + this.$emit("show"); + }, "onAfterEnter"), + onLeave: /* @__PURE__ */ __name(function onLeave2() { + this.unbindOutsideClickListener(); + this.unbindScrollListener(); + this.unbindResizeListener(); + this.$emit("hide"); + this.container = null; + this.dirty = false; + }, "onLeave"), + onAfterLeave: /* @__PURE__ */ __name(function onAfterLeave(el) { + if (this.autoZIndex) { + ZIndex.clear(el); + } + }, "onAfterLeave"), + alignOverlay: /* @__PURE__ */ __name(function alignOverlay2() { + absolutePosition(this.container, this.target); + var targetWidth = getOuterWidth(this.target); + if (targetWidth > getOuterWidth(this.container)) { + this.container.style.minWidth = getOuterWidth(this.target) + "px"; + } + }, "alignOverlay"), + bindOutsideClickListener: /* @__PURE__ */ __name(function bindOutsideClickListener3() { + var _this2 = this; + if (!this.outsideClickListener) { + this.outsideClickListener = function(event) { + var isOutsideContainer = _this2.container && !_this2.container.contains(event.target); + var isOutsideTarget = _this2.popup ? !(_this2.target && (_this2.target === event.target || _this2.target.contains(event.target))) : true; + if (isOutsideContainer && isOutsideTarget) { + _this2.hide(); + } + }; + document.addEventListener("click", this.outsideClickListener); + } + }, "bindOutsideClickListener"), + unbindOutsideClickListener: /* @__PURE__ */ __name(function unbindOutsideClickListener3() { + if (this.outsideClickListener) { + document.removeEventListener("click", this.outsideClickListener); + this.outsideClickListener = null; + } + }, "unbindOutsideClickListener"), + bindScrollListener: /* @__PURE__ */ __name(function bindScrollListener2() { + var _this3 = this; + if (!this.scrollHandler) { + this.scrollHandler = new ConnectedOverlayScrollHandler(this.target, function(event) { + _this3.hide(event, true); + }); + } + this.scrollHandler.bindScrollListener(); + }, "bindScrollListener"), + unbindScrollListener: /* @__PURE__ */ __name(function unbindScrollListener2() { + if (this.scrollHandler) { + this.scrollHandler.unbindScrollListener(); + } + }, "unbindScrollListener"), + bindResizeListener: /* @__PURE__ */ __name(function bindResizeListener3() { + var _this4 = this; + if (!this.resizeListener) { + this.resizeListener = function(event) { + if (!isTouchDevice()) { + _this4.hide(event, true); + } + }; + window.addEventListener("resize", this.resizeListener); + } + }, "bindResizeListener"), + unbindResizeListener: /* @__PURE__ */ __name(function unbindResizeListener3() { + if (this.resizeListener) { + window.removeEventListener("resize", this.resizeListener); + this.resizeListener = null; + } + }, "unbindResizeListener"), + isItemMatched: /* @__PURE__ */ __name(function isItemMatched2(processedItem) { + var _this$getProccessedIt; + return this.isValidItem(processedItem) && ((_this$getProccessedIt = this.getProccessedItemLabel(processedItem)) === null || _this$getProccessedIt === void 0 ? void 0 : _this$getProccessedIt.toLocaleLowerCase().startsWith(this.searchValue.toLocaleLowerCase())); + }, "isItemMatched"), + isValidItem: /* @__PURE__ */ __name(function isValidItem2(processedItem) { + return !!processedItem && !this.isItemDisabled(processedItem.item) && !this.isItemSeparator(processedItem.item) && this.isItemVisible(processedItem.item); + }, "isValidItem"), + isValidSelectedItem: /* @__PURE__ */ __name(function isValidSelectedItem2(processedItem) { + return this.isValidItem(processedItem) && this.isSelected(processedItem); + }, "isValidSelectedItem"), + isSelected: /* @__PURE__ */ __name(function isSelected3(processedItem) { + return this.activeItemPath.some(function(p) { + return p.key === processedItem.key; + }); + }, "isSelected"), + findFirstItemIndex: /* @__PURE__ */ __name(function findFirstItemIndex2() { + var _this5 = this; + return this.visibleItems.findIndex(function(processedItem) { + return _this5.isValidItem(processedItem); + }); + }, "findFirstItemIndex"), + findLastItemIndex: /* @__PURE__ */ __name(function findLastItemIndex2() { + var _this6 = this; + return findLastIndex(this.visibleItems, function(processedItem) { + return _this6.isValidItem(processedItem); + }); + }, "findLastItemIndex"), + findNextItemIndex: /* @__PURE__ */ __name(function findNextItemIndex2(index) { + var _this7 = this; + var matchedItemIndex = index < this.visibleItems.length - 1 ? this.visibleItems.slice(index + 1).findIndex(function(processedItem) { + return _this7.isValidItem(processedItem); + }) : -1; + return matchedItemIndex > -1 ? matchedItemIndex + index + 1 : index; + }, "findNextItemIndex"), + findPrevItemIndex: /* @__PURE__ */ __name(function findPrevItemIndex2(index) { + var _this8 = this; + var matchedItemIndex = index > 0 ? findLastIndex(this.visibleItems.slice(0, index), function(processedItem) { + return _this8.isValidItem(processedItem); + }) : -1; + return matchedItemIndex > -1 ? matchedItemIndex : index; + }, "findPrevItemIndex"), + findSelectedItemIndex: /* @__PURE__ */ __name(function findSelectedItemIndex2() { + var _this9 = this; + return this.visibleItems.findIndex(function(processedItem) { + return _this9.isValidSelectedItem(processedItem); + }); + }, "findSelectedItemIndex"), + findFirstFocusedItemIndex: /* @__PURE__ */ __name(function findFirstFocusedItemIndex2() { + var selectedIndex = this.findSelectedItemIndex(); + return selectedIndex < 0 ? this.findFirstItemIndex() : selectedIndex; + }, "findFirstFocusedItemIndex"), + findLastFocusedItemIndex: /* @__PURE__ */ __name(function findLastFocusedItemIndex2() { + var selectedIndex = this.findSelectedItemIndex(); + return selectedIndex < 0 ? this.findLastItemIndex() : selectedIndex; + }, "findLastFocusedItemIndex"), + searchItems: /* @__PURE__ */ __name(function searchItems2(event, _char) { + var _this10 = this; + this.searchValue = (this.searchValue || "") + _char; + var itemIndex = -1; + var matched = false; + if (this.focusedItemInfo.index !== -1) { + itemIndex = this.visibleItems.slice(this.focusedItemInfo.index).findIndex(function(processedItem) { + return _this10.isItemMatched(processedItem); + }); + itemIndex = itemIndex === -1 ? this.visibleItems.slice(0, this.focusedItemInfo.index).findIndex(function(processedItem) { + return _this10.isItemMatched(processedItem); + }) : itemIndex + this.focusedItemInfo.index; + } else { + itemIndex = this.visibleItems.findIndex(function(processedItem) { + return _this10.isItemMatched(processedItem); + }); + } + if (itemIndex !== -1) { + matched = true; + } + if (itemIndex === -1 && this.focusedItemInfo.index === -1) { + itemIndex = this.findFirstFocusedItemIndex(); + } + if (itemIndex !== -1) { + this.changeFocusedItemIndex(event, itemIndex); + } + if (this.searchTimeout) { + clearTimeout(this.searchTimeout); + } + this.searchTimeout = setTimeout(function() { + _this10.searchValue = ""; + _this10.searchTimeout = null; + }, 500); + return matched; + }, "searchItems"), + changeFocusedItemIndex: /* @__PURE__ */ __name(function changeFocusedItemIndex2(event, index) { + if (this.focusedItemInfo.index !== index) { + this.focusedItemInfo.index = index; + this.scrollInView(); + } + }, "changeFocusedItemIndex"), + scrollInView: /* @__PURE__ */ __name(function scrollInView4() { + var index = arguments.length > 0 && arguments[0] !== void 0 ? arguments[0] : -1; + var id2 = index !== -1 ? "".concat(this.id, "_").concat(index) : this.focusedItemId; + var element = findSingle(this.menubar, 'li[id="'.concat(id2, '"]')); + if (element) { + element.scrollIntoView && element.scrollIntoView({ + block: "nearest", + inline: "start" + }); + } + }, "scrollInView"), + createProcessedItems: /* @__PURE__ */ __name(function createProcessedItems2(items) { + var _this11 = this; + var level = arguments.length > 1 && arguments[1] !== void 0 ? arguments[1] : 0; + var parent = arguments.length > 2 && arguments[2] !== void 0 ? arguments[2] : {}; + var parentKey = arguments.length > 3 && arguments[3] !== void 0 ? arguments[3] : ""; + var processedItems3 = []; + items && items.forEach(function(item3, index) { + var key = (parentKey !== "" ? parentKey + "_" : "") + index; + var newItem = { + item: item3, + index, + level, + key, + parent, + parentKey + }; + newItem["items"] = _this11.createProcessedItems(item3.items, level + 1, newItem, key); + processedItems3.push(newItem); + }); + return processedItems3; + }, "createProcessedItems"), + containerRef: /* @__PURE__ */ __name(function containerRef3(el) { + this.container = el; + }, "containerRef"), + menubarRef: /* @__PURE__ */ __name(function menubarRef2(el) { + this.menubar = el ? el.$el : void 0; + }, "menubarRef") + }, + computed: { + processedItems: /* @__PURE__ */ __name(function processedItems2() { + return this.createProcessedItems(this.model || []); + }, "processedItems"), + visibleItems: /* @__PURE__ */ __name(function visibleItems2() { + var _this12 = this; + var processedItem = this.activeItemPath.find(function(p) { + return p.key === _this12.focusedItemInfo.parentKey; + }); + return processedItem ? processedItem.items : this.processedItems; + }, "visibleItems"), + focusedItemId: /* @__PURE__ */ __name(function focusedItemId2() { + return this.focusedItemInfo.index !== -1 ? "".concat(this.id).concat(isNotEmpty(this.focusedItemInfo.parentKey) ? "_" + this.focusedItemInfo.parentKey : "", "_").concat(this.focusedItemInfo.index) : null; + }, "focusedItemId") + }, + components: { + TieredMenuSub: script$1$1, + Portal: script$m + } +}; +var _hoisted_1$5 = ["id"]; +function render$3(_ctx, _cache, $props, $setup, $data, $options) { + var _component_TieredMenuSub = resolveComponent("TieredMenuSub"); + var _component_Portal = resolveComponent("Portal"); + return openBlock(), createBlock(_component_Portal, { + appendTo: _ctx.appendTo, + disabled: !_ctx.popup + }, { + "default": withCtx(function() { + return [createVNode(Transition, mergeProps({ + name: "p-connected-overlay", + onEnter: $options.onEnter, + onAfterEnter: $options.onAfterEnter, + onLeave: $options.onLeave, + onAfterLeave: $options.onAfterLeave + }, _ctx.ptm("transition")), { + "default": withCtx(function() { + return [$data.visible ? (openBlock(), createElementBlock("div", mergeProps({ + key: 0, + ref: $options.containerRef, + id: $data.id, + "class": _ctx.cx("root"), + onClick: _cache[0] || (_cache[0] = function() { + return $options.onOverlayClick && $options.onOverlayClick.apply($options, arguments); + }) + }, _ctx.ptmi("root")), [_ctx.$slots.start ? (openBlock(), createElementBlock("div", mergeProps({ + key: 0, + "class": _ctx.cx("start") + }, _ctx.ptm("start")), [renderSlot(_ctx.$slots, "start")], 16)) : createCommentVNode("", true), createVNode(_component_TieredMenuSub, { + ref: $options.menubarRef, + id: $data.id + "_list", + tabindex: !_ctx.disabled ? _ctx.tabindex : -1, + role: "menubar", + "aria-label": _ctx.ariaLabel, + "aria-labelledby": _ctx.ariaLabelledby, + "aria-disabled": _ctx.disabled || void 0, + "aria-orientation": "vertical", + "aria-activedescendant": $data.focused ? $options.focusedItemId : void 0, + menuId: $data.id, + focusedItemId: $data.focused ? $options.focusedItemId : void 0, + items: $options.processedItems, + templates: _ctx.$slots, + activeItemPath: $data.activeItemPath, + level: 0, + visible: $data.submenuVisible, + pt: _ctx.pt, + unstyled: _ctx.unstyled, + onFocus: $options.onFocus, + onBlur: $options.onBlur, + onKeydown: $options.onKeyDown, + onItemClick: $options.onItemClick, + onItemMouseenter: $options.onItemMouseEnter, + onItemMousemove: $options.onItemMouseMove + }, null, 8, ["id", "tabindex", "aria-label", "aria-labelledby", "aria-disabled", "aria-activedescendant", "menuId", "focusedItemId", "items", "templates", "activeItemPath", "visible", "pt", "unstyled", "onFocus", "onBlur", "onKeydown", "onItemClick", "onItemMouseenter", "onItemMousemove"]), _ctx.$slots.end ? (openBlock(), createElementBlock("div", mergeProps({ + key: 1, + "class": _ctx.cx("end") + }, _ctx.ptm("end")), [renderSlot(_ctx.$slots, "end")], 16)) : createCommentVNode("", true)], 16, _hoisted_1$5)) : createCommentVNode("", true)]; + }), + _: 3 + }, 16, ["onEnter", "onAfterEnter", "onLeave", "onAfterLeave"])]; + }), + _: 3 + }, 8, ["appendTo", "disabled"]); +} +__name(render$3, "render$3"); +script$3.render = render$3; +var theme9 = /* @__PURE__ */ __name(function theme10(_ref) { + var dt = _ref.dt; + return "\n.p-splitbutton {\n display: inline-flex;\n position: relative;\n border-radius: ".concat(dt("splitbutton.border.radius"), ";\n}\n\n.p-splitbutton-button {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n border-right: 0 none;\n}\n\n.p-splitbutton-button:focus-visible,\n.p-splitbutton-dropdown:focus-visible {\n z-index: 1;\n}\n\n.p-splitbutton-button:not(:disabled):hover,\n.p-splitbutton-button:not(:disabled):active {\n border-right: 0 none;\n}\n\n.p-splitbutton-dropdown {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n\n.p-splitbutton .p-menu {\n min-width: 100%;\n}\n\n.p-splitbutton-fluid {\n display: flex;\n}\n\n.p-splitbutton-rounded .p-splitbutton-dropdown {\n border-top-right-radius: ").concat(dt("splitbutton.rounded.border.radius"), ";\n border-bottom-right-radius: ").concat(dt("splitbutton.rounded.border.radius"), ";\n}\n\n.p-splitbutton-rounded .p-splitbutton-button {\n border-top-left-radius: ").concat(dt("splitbutton.rounded.border.radius"), ";\n border-bottom-left-radius: ").concat(dt("splitbutton.rounded.border.radius"), ";\n}\n\n.p-splitbutton-raised {\n box-shadow: ").concat(dt("splitbutton.raised.shadow"), ";\n}\n"); +}, "theme"); +var classes = { + root: /* @__PURE__ */ __name(function root11(_ref2) { + var instance = _ref2.instance, props = _ref2.props; + return ["p-splitbutton p-component", { + "p-splitbutton-raised": props.raised, + "p-splitbutton-rounded": props.rounded, + "p-splitbutton-fluid": instance.hasFluid + }]; + }, "root"), + pcButton: "p-splitbutton-button", + pcDropdown: "p-splitbutton-dropdown" +}; +var SplitButtonStyle = BaseStyle.extend({ + name: "splitbutton", + theme: theme9, + classes +}); +var script$1 = { + name: "BaseSplitButton", + "extends": script$g, + props: { + label: { + type: String, + "default": null + }, + icon: { + type: String, + "default": null + }, + model: { + type: Array, + "default": null + }, + autoZIndex: { + type: Boolean, + "default": true + }, + baseZIndex: { + type: Number, + "default": 0 + }, + appendTo: { + type: [String, Object], + "default": "body" + }, + disabled: { + type: Boolean, + "default": false + }, + fluid: { + type: Boolean, + "default": null + }, + "class": { + type: null, + "default": null + }, + style: { + type: null, + "default": null + }, + buttonProps: { + type: null, + "default": null + }, + menuButtonProps: { + type: null, + "default": null + }, + menuButtonIcon: { + type: String, + "default": void 0 + }, + dropdownIcon: { + type: String, + "default": void 0 + }, + severity: { + type: String, + "default": null + }, + raised: { + type: Boolean, + "default": false + }, + rounded: { + type: Boolean, + "default": false + }, + text: { + type: Boolean, + "default": false + }, + outlined: { + type: Boolean, + "default": false + }, + size: { + type: String, + "default": null + }, + plain: { + type: Boolean, + "default": false + } + }, + style: SplitButtonStyle, + provide: /* @__PURE__ */ __name(function provide13() { + return { + $pcSplitButton: this, + $parentInstance: this + }; + }, "provide") +}; +var script = { + name: "SplitButton", + "extends": script$1, + inheritAttrs: false, + emits: ["click"], + inject: { + $pcFluid: { + "default": null + } + }, + data: /* @__PURE__ */ __name(function data9() { + return { + id: this.$attrs.id, + isExpanded: false + }; + }, "data"), + watch: { + "$attrs.id": /* @__PURE__ */ __name(function $attrsId5(newValue) { + this.id = newValue || UniqueComponentId(); + }, "$attrsId") + }, + mounted: /* @__PURE__ */ __name(function mounted9() { + var _this = this; + this.id = this.id || UniqueComponentId(); + this.$watch("$refs.menu.visible", function(newValue) { + _this.isExpanded = newValue; + }); + }, "mounted"), + methods: { + onDropdownButtonClick: /* @__PURE__ */ __name(function onDropdownButtonClick(event) { + if (event) { + event.preventDefault(); + } + this.$refs.menu.toggle({ + currentTarget: this.$el, + relatedTarget: this.$refs.button.$el + }); + this.isExpanded = this.$refs.menu.visible; + }, "onDropdownButtonClick"), + onDropdownKeydown: /* @__PURE__ */ __name(function onDropdownKeydown(event) { + if (event.code === "ArrowDown" || event.code === "ArrowUp") { + this.onDropdownButtonClick(); + event.preventDefault(); + } + }, "onDropdownKeydown"), + onDefaultButtonClick: /* @__PURE__ */ __name(function onDefaultButtonClick(event) { + if (this.isExpanded) { + this.$refs.menu.hide(event); + } + this.$emit("click", event); + }, "onDefaultButtonClick") + }, + computed: { + containerClass: /* @__PURE__ */ __name(function containerClass() { + return [this.cx("root"), this["class"]]; + }, "containerClass"), + hasFluid: /* @__PURE__ */ __name(function hasFluid2() { + return isEmpty(this.fluid) ? !!this.$pcFluid : this.fluid; + }, "hasFluid") + }, + components: { + PVSButton: script$f, + PVSMenu: script$3, + ChevronDownIcon: script$n + } +}; +var _hoisted_1$4 = ["data-p-severity"]; +function render$2(_ctx, _cache, $props, $setup, $data, $options) { + var _component_PVSButton = resolveComponent("PVSButton"); + var _component_PVSMenu = resolveComponent("PVSMenu"); + return openBlock(), createElementBlock("div", mergeProps({ + "class": $options.containerClass, + style: _ctx.style + }, _ctx.ptmi("root"), { + "data-p-severity": _ctx.severity + }), [createVNode(_component_PVSButton, mergeProps({ + type: "button", + "class": _ctx.cx("pcButton"), + label: _ctx.label, + disabled: _ctx.disabled, + severity: _ctx.severity, + text: _ctx.text, + icon: _ctx.icon, + outlined: _ctx.outlined, + size: _ctx.size, + fluid: _ctx.fluid, + "aria-label": _ctx.label, + onClick: $options.onDefaultButtonClick + }, _ctx.buttonProps, { + pt: _ctx.ptm("pcButton"), + unstyled: _ctx.unstyled + }), createSlots({ + "default": withCtx(function() { + return [renderSlot(_ctx.$slots, "default")]; + }), + _: 2 + }, [_ctx.$slots.icon ? { + name: "icon", + fn: withCtx(function(slotProps) { + return [renderSlot(_ctx.$slots, "icon", { + "class": normalizeClass(slotProps["class"]) + }, function() { + return [createBaseVNode("span", mergeProps({ + "class": [_ctx.icon, slotProps["class"]] + }, _ctx.ptm("pcButton")["icon"], { + "data-pc-section": "buttonicon" + }), null, 16)]; + })]; + }), + key: "0" + } : void 0]), 1040, ["class", "label", "disabled", "severity", "text", "icon", "outlined", "size", "fluid", "aria-label", "onClick", "pt", "unstyled"]), createVNode(_component_PVSButton, mergeProps({ + ref: "button", + type: "button", + "class": _ctx.cx("pcDropdown"), + disabled: _ctx.disabled, + "aria-haspopup": "true", + "aria-expanded": $data.isExpanded, + "aria-controls": $data.id + "_overlay", + onClick: $options.onDropdownButtonClick, + onKeydown: $options.onDropdownKeydown, + severity: _ctx.severity, + text: _ctx.text, + outlined: _ctx.outlined, + size: _ctx.size, + unstyled: _ctx.unstyled + }, _ctx.menuButtonProps, { + pt: _ctx.ptm("pcDropdown") + }), { + icon: withCtx(function(slotProps) { + return [renderSlot(_ctx.$slots, _ctx.$slots.dropdownicon ? "dropdownicon" : "menubuttonicon", { + "class": normalizeClass(slotProps["class"]) + }, function() { + return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.menuButtonIcon || _ctx.dropdownIcon ? "span" : "ChevronDownIcon"), mergeProps({ + "class": [_ctx.dropdownIcon || _ctx.menuButtonIcon, slotProps["class"]] + }, _ctx.ptm("pcDropdown")["icon"], { + "data-pc-section": "menubuttonicon" + }), null, 16, ["class"]))]; + })]; + }), + _: 3 + }, 16, ["class", "disabled", "aria-expanded", "aria-controls", "onClick", "onKeydown", "severity", "text", "outlined", "size", "unstyled", "pt"]), createVNode(_component_PVSMenu, { + ref: "menu", + id: $data.id + "_overlay", + model: _ctx.model, + popup: true, + autoZIndex: _ctx.autoZIndex, + baseZIndex: _ctx.baseZIndex, + appendTo: _ctx.appendTo, + unstyled: _ctx.unstyled, + pt: _ctx.ptm("pcMenu") + }, createSlots({ + _: 2 + }, [_ctx.$slots.menuitemicon ? { + name: "itemicon", + fn: withCtx(function(slotProps) { + return [renderSlot(_ctx.$slots, "menuitemicon", { + item: slotProps.item, + "class": normalizeClass(slotProps["class"]) + })]; + }), + key: "0" + } : void 0, _ctx.$slots.item ? { + name: "item", + fn: withCtx(function(slotProps) { + return [renderSlot(_ctx.$slots, "item", { + item: slotProps.item, + hasSubmenu: slotProps.hasSubmenu, + label: slotProps.label, + props: slotProps.props + })]; + }), + key: "1" + } : void 0]), 1032, ["id", "model", "autoZIndex", "baseZIndex", "appendTo", "unstyled", "pt"])], 16, _hoisted_1$4); +} +__name(render$2, "render$2"); +script.render = render$2; +const minQueueCount = 1; +const _sfc_main$5 = /* @__PURE__ */ defineComponent({ + __name: "BatchCountEdit", + props: { + class: { default: "" } + }, + setup(__props) { + const props = __props; + const queueSettingsStore = useQueueSettingsStore(); + const { batchCount } = storeToRefs(queueSettingsStore); + const settingStore = useSettingStore(); + const maxQueueCount = computed( + () => settingStore.get("Comfy.QueueButton.BatchCountLimit") + ); + const handleClick = /* @__PURE__ */ __name((increment) => { + let newCount; + if (increment) { + const originalCount = batchCount.value - 1; + newCount = originalCount * 2; + } else { + const originalCount = batchCount.value + 1; + newCount = Math.floor(originalCount / 2); + } + batchCount.value = newCount; + }, "handleClick"); + return (_ctx, _cache) => { + const _directive_tooltip = resolveDirective("tooltip"); + return withDirectives((openBlock(), createElementBlock("div", { + class: normalizeClass(["batch-count", props.class]) + }, [ + createVNode(unref(script$D), { + class: "w-14", + modelValue: unref(batchCount), + "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => isRef(batchCount) ? batchCount.value = $event : null), + min: minQueueCount, + max: maxQueueCount.value, + fluid: "", + showButtons: "", + pt: { + incrementButton: { + class: "w-6", + onmousedown: /* @__PURE__ */ __name(() => { + handleClick(true); + }, "onmousedown") + }, + decrementButton: { + class: "w-6", + onmousedown: /* @__PURE__ */ __name(() => { + handleClick(false); + }, "onmousedown") + } + } + }, null, 8, ["modelValue", "max", "pt"]) + ], 2)), [ + [ + _directive_tooltip, + _ctx.$t("menu.batchCount"), + void 0, + { bottom: true } + ] + ]); + }; + } +}); +const BatchCountEdit = /* @__PURE__ */ _export_sfc(_sfc_main$5, [["__scopeId", "data-v-713442be"]]); +const _withScopeId$1 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2b80bf74"), n = n(), popScopeId(), n), "_withScopeId$1"); +const _hoisted_1$3 = { class: "queue-button-group flex" }; +const _sfc_main$4 = /* @__PURE__ */ defineComponent({ + __name: "ComfyQueueButton", + setup(__props) { + const workspaceStore = useWorkspaceStore(); + const queueCountStore = storeToRefs(useQueuePendingTaskCountStore()); + const { mode: queueMode } = storeToRefs(useQueueSettingsStore()); + const { t } = useI18n(); + const queueModeMenuItemLookup = { + disabled: { + key: "disabled", + label: "Queue", + icon: "pi pi-play", + tooltip: t("menu.disabledTooltip"), + command: /* @__PURE__ */ __name(() => { + queueMode.value = "disabled"; + }, "command") + }, + instant: { + key: "instant", + label: "Queue (Instant)", + icon: "pi pi-forward", + tooltip: t("menu.instantTooltip"), + command: /* @__PURE__ */ __name(() => { + queueMode.value = "instant"; + }, "command") + }, + change: { + key: "change", + label: "Queue (Change)", + icon: "pi pi-step-forward-alt", + tooltip: t("menu.changeTooltip"), + command: /* @__PURE__ */ __name(() => { + queueMode.value = "change"; + }, "command") + } + }; + const activeQueueModeMenuItem = computed( + () => queueModeMenuItemLookup[queueMode.value] + ); + const queueModeMenuItems = computed( + () => Object.values(queueModeMenuItemLookup) + ); + const executingPrompt = computed(() => !!queueCountStore.count.value); + const hasPendingTasks = computed(() => queueCountStore.count.value > 1); + const commandStore = useCommandStore(); + const queuePrompt = /* @__PURE__ */ __name((e) => { + const commandId = e.shiftKey ? "Comfy.QueuePromptFront" : "Comfy.QueuePrompt"; + commandStore.execute(commandId); + }, "queuePrompt"); + return (_ctx, _cache) => { + const _component_i_lucide58list_start = __unplugin_components_0$1; + const _directive_tooltip = resolveDirective("tooltip"); + return openBlock(), createElementBlock("div", _hoisted_1$3, [ + withDirectives((openBlock(), createBlock(unref(script), { + class: "comfyui-queue-button", + label: activeQueueModeMenuItem.value.label, + severity: "primary", + onClick: queuePrompt, + model: queueModeMenuItems.value, + "data-testid": "queue-button" + }, { + icon: withCtx(() => [ + unref(workspaceStore).shiftDown ? (openBlock(), createBlock(_component_i_lucide58list_start, { key: 0 })) : (openBlock(), createElementBlock("i", { + key: 1, + class: normalizeClass(activeQueueModeMenuItem.value.icon) + }, null, 2)) + ]), + item: withCtx(({ item: item3 }) => [ + withDirectives(createVNode(unref(script$f), { + label: item3.label, + icon: item3.icon, + severity: item3.key === unref(queueMode) ? "primary" : "secondary", + text: "" + }, null, 8, ["label", "icon", "severity"]), [ + [_directive_tooltip, item3.tooltip] + ]) + ]), + _: 1 + }, 8, ["label", "model"])), [ + [ + _directive_tooltip, + unref(workspaceStore).shiftDown ? _ctx.$t("menu.queueWorkflowFront") : _ctx.$t("menu.queueWorkflow"), + void 0, + { bottom: true } + ] + ]), + createVNode(BatchCountEdit), + createVNode(unref(script$7), { class: "execution-actions flex flex-nowrap" }, { + default: withCtx(() => [ + withDirectives(createVNode(unref(script$f), { + icon: "pi pi-times", + severity: executingPrompt.value ? "danger" : "secondary", + disabled: !executingPrompt.value, + text: "", + onClick: _cache[0] || (_cache[0] = () => unref(commandStore).execute("Comfy.Interrupt")) + }, null, 8, ["severity", "disabled"]), [ + [ + _directive_tooltip, + _ctx.$t("menu.interrupt"), + void 0, + { bottom: true } + ] + ]), + withDirectives(createVNode(unref(script$f), { + icon: "pi pi-stop", + severity: hasPendingTasks.value ? "danger" : "secondary", + disabled: !hasPendingTasks.value, + text: "", + onClick: _cache[1] || (_cache[1] = () => unref(commandStore).execute("Comfy.ClearPendingTasks")) + }, null, 8, ["severity", "disabled"]), [ + [ + _directive_tooltip, + _ctx.$t("sideToolbar.queueTab.clearPendingTasks"), + void 0, + { bottom: true } + ] + ]) + ]), + _: 1 + }) + ]); + }; + } +}); +const ComfyQueueButton = /* @__PURE__ */ _export_sfc(_sfc_main$4, [["__scopeId", "data-v-2b80bf74"]]); +const overlapThreshold = 20; +const _sfc_main$3 = /* @__PURE__ */ defineComponent({ + __name: "ComfyActionbar", + setup(__props) { + const settingsStore = useSettingStore(); + const commandStore = useCommandStore(); + const visible = computed( + () => settingsStore.get("Comfy.UseNewMenu") !== "Disabled" + ); + const panelRef = ref(null); + const dragHandleRef = ref(null); + const isDocked = useLocalStorage("Comfy.MenuPosition.Docked", false); + const storedPosition = useLocalStorage("Comfy.MenuPosition.Floating", { + x: 0, + y: 0 + }); + const { + x, + y, + style, + isDragging + } = useDraggable(panelRef, { + initialValue: { x: 0, y: 0 }, + handle: dragHandleRef, + containerElement: document.body + }); + watchDebounced( + [x, y], + ([newX, newY]) => { + storedPosition.value = { x: newX, y: newY }; + }, + { debounce: 300 } + ); + const setInitialPosition = /* @__PURE__ */ __name(() => { + if (x.value !== 0 || y.value !== 0) { + return; + } + if (storedPosition.value.x !== 0 || storedPosition.value.y !== 0) { + x.value = storedPosition.value.x; + y.value = storedPosition.value.y; + captureLastDragState(); + return; + } + if (panelRef.value) { + const screenWidth = window.innerWidth; + const screenHeight = window.innerHeight; + const menuWidth = panelRef.value.offsetWidth; + const menuHeight = panelRef.value.offsetHeight; + if (menuWidth === 0 || menuHeight === 0) { + return; + } + x.value = (screenWidth - menuWidth) / 2; + y.value = screenHeight - menuHeight - 10; + captureLastDragState(); + } + }, "setInitialPosition"); + onMounted(setInitialPosition); + watch(visible, (newVisible) => { + if (newVisible) { + nextTick(setInitialPosition); + } + }); + const lastDragState = ref({ + x: x.value, + y: y.value, + windowWidth: window.innerWidth, + windowHeight: window.innerHeight + }); + const captureLastDragState = /* @__PURE__ */ __name(() => { + lastDragState.value = { + x: x.value, + y: y.value, + windowWidth: window.innerWidth, + windowHeight: window.innerHeight + }; + }, "captureLastDragState"); + watch( + isDragging, + (newIsDragging) => { + if (!newIsDragging) { + captureLastDragState(); + } + }, + { immediate: true } + ); + const adjustMenuPosition = /* @__PURE__ */ __name(() => { + if (panelRef.value) { + const screenWidth = window.innerWidth; + const screenHeight = window.innerHeight; + const menuWidth = panelRef.value.offsetWidth; + const menuHeight = panelRef.value.offsetHeight; + const distanceRight = lastDragState.value.windowWidth - (lastDragState.value.x + menuWidth); + const distanceBottom = lastDragState.value.windowHeight - (lastDragState.value.y + menuHeight); + const anchorRight = distanceRight < lastDragState.value.x; + const anchorBottom = distanceBottom < lastDragState.value.y; + if (anchorRight) { + x.value = screenWidth - (lastDragState.value.windowWidth - lastDragState.value.x); + } else { + x.value = lastDragState.value.x; + } + if (anchorBottom) { + y.value = screenHeight - (lastDragState.value.windowHeight - lastDragState.value.y); + } else { + y.value = lastDragState.value.y; + } + x.value = lodashExports.clamp(x.value, 0, screenWidth - menuWidth); + y.value = lodashExports.clamp(y.value, 0, screenHeight - menuHeight); + } + }, "adjustMenuPosition"); + useEventListener(window, "resize", adjustMenuPosition); + const topMenuRef = inject("topMenuRef"); + const topMenuBounds = useElementBounding(topMenuRef); + const isOverlappingWithTopMenu = computed(() => { + if (!panelRef.value) { + return false; + } + const { height } = panelRef.value.getBoundingClientRect(); + const actionbarBottom = y.value + height; + const topMenuBottom = topMenuBounds.bottom.value; + const overlapPixels = Math.min(actionbarBottom, topMenuBottom) - Math.max(y.value, topMenuBounds.top.value); + return overlapPixels > overlapThreshold; + }); + watch(isDragging, (newIsDragging) => { + if (!newIsDragging) { + isDocked.value = isOverlappingWithTopMenu.value; + } else { + isDocked.value = false; + } + }); + const eventBus = useEventBus("topMenu"); + watch([isDragging, isOverlappingWithTopMenu], ([dragging, overlapping]) => { + eventBus.emit("updateHighlight", { + isDragging: dragging, + isOverlapping: overlapping + }); + }); + return (_ctx, _cache) => { + const _directive_tooltip = resolveDirective("tooltip"); + return openBlock(), createBlock(unref(script$4), { + class: normalizeClass(["actionbar w-fit", { "is-dragging": unref(isDragging), "is-docked": unref(isDocked) }]), + style: normalizeStyle(unref(style)) + }, { + default: withCtx(() => [ + createBaseVNode("div", { + class: "actionbar-content flex items-center", + ref_key: "panelRef", + ref: panelRef + }, [ + createBaseVNode("span", { + class: "drag-handle cursor-move mr-2 p-0!", + ref_key: "dragHandleRef", + ref: dragHandleRef + }, null, 512), + createVNode(ComfyQueueButton), + createVNode(unref(script$E), { + layout: "vertical", + class: "mx-1" + }), + createVNode(unref(script$7), { class: "flex flex-nowrap" }, { + default: withCtx(() => [ + withDirectives(createVNode(unref(script$f), { + icon: "pi pi-refresh", + severity: "secondary", + text: "", + onClick: _cache[0] || (_cache[0] = () => unref(commandStore).execute("Comfy.RefreshNodeDefinitions")) + }, null, 512), [ + [ + _directive_tooltip, + _ctx.$t("menu.refresh"), + void 0, + { bottom: true } + ] + ]) + ]), + _: 1 + }) + ], 512) + ]), + _: 1 + }, 8, ["style", "class"]); + }; + } +}); +const Actionbar = /* @__PURE__ */ _export_sfc(_sfc_main$3, [["__scopeId", "data-v-2e54db00"]]); +const _hoisted_1$2 = { + viewBox: "0 0 24 24", + width: "1.2em", + height: "1.2em" +}; +const _hoisted_2$2 = /* @__PURE__ */ createBaseVNode("path", { + fill: "currentColor", + d: "M5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21zm0-5v3h14v-3zm0-2h14V5H5zm0 2v3z" +}, null, -1); +const _hoisted_3$1 = [ + _hoisted_2$2 +]; +function render$1(_ctx, _cache) { + return openBlock(), createElementBlock("svg", _hoisted_1$2, [..._hoisted_3$1]); +} +__name(render$1, "render$1"); +const __unplugin_components_1 = markRaw({ name: "material-symbols-dock-to-bottom-outline", render: render$1 }); +const _hoisted_1$1 = { + viewBox: "0 0 24 24", + width: "1.2em", + height: "1.2em" +}; +const _hoisted_2$1 = /* @__PURE__ */ createBaseVNode("path", { + fill: "currentColor", + d: "M5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21zm0-7h14V5H5z" +}, null, -1); +const _hoisted_3 = [ + _hoisted_2$1 +]; +function render(_ctx, _cache) { + return openBlock(), createElementBlock("svg", _hoisted_1$1, [..._hoisted_3]); +} +__name(render, "render"); +const __unplugin_components_0 = markRaw({ name: "material-symbols-dock-to-bottom", render }); +const _sfc_main$2 = /* @__PURE__ */ defineComponent({ + __name: "BottomPanelToggleButton", + setup(__props) { + const bottomPanelStore = useBottomPanelStore(); + return (_ctx, _cache) => { + const _component_i_material_symbols58dock_to_bottom = __unplugin_components_0; + const _component_i_material_symbols58dock_to_bottom_outline = __unplugin_components_1; + const _directive_tooltip = resolveDirective("tooltip"); + return withDirectives((openBlock(), createBlock(unref(script$f), { + severity: "secondary", + text: "", + onClick: unref(bottomPanelStore).toggleBottomPanel + }, { + icon: withCtx(() => [ + unref(bottomPanelStore).bottomPanelVisible ? (openBlock(), createBlock(_component_i_material_symbols58dock_to_bottom, { key: 0 })) : (openBlock(), createBlock(_component_i_material_symbols58dock_to_bottom_outline, { key: 1 })) + ]), + _: 1 + }, 8, ["onClick"])), [ + [vShow, unref(bottomPanelStore).bottomPanelTabs.length > 0], + [_directive_tooltip, { value: _ctx.$t("menu.toggleBottomPanel"), showDelay: 300 }] + ]); + }; + } +}); +const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-ad2c662b"), n = n(), popScopeId(), n), "_withScopeId"); +const _hoisted_1 = /* @__PURE__ */ _withScopeId(() => /* @__PURE__ */ createBaseVNode("h1", { class: "comfyui-logo mx-2" }, "ComfyUI", -1)); +const _hoisted_2 = { class: "flex-grow" }; +const _sfc_main$1 = /* @__PURE__ */ defineComponent({ + __name: "TopMenubar", + setup(__props) { + const settingStore = useSettingStore(); + const workflowTabsPosition = computed( + () => settingStore.get("Comfy.Workflow.WorkflowTabsPosition") + ); + const betaMenuEnabled = computed( + () => settingStore.get("Comfy.UseNewMenu") !== "Disabled" + ); + const teleportTarget = computed( + () => settingStore.get("Comfy.UseNewMenu") === "Top" ? ".comfyui-body-top" : ".comfyui-body-bottom" + ); + const menuRight = ref(null); + onMounted(() => { + if (menuRight.value) { + menuRight.value.appendChild(app.menu.element); + } + }); + const topMenuRef = ref(null); + provide("topMenuRef", topMenuRef); + const eventBus = useEventBus("topMenu"); + const isDropZone = ref(false); + const isDroppable = ref(false); + eventBus.on((event, payload) => { + if (event === "updateHighlight") { + isDropZone.value = payload.isDragging; + isDroppable.value = payload.isOverlapping && payload.isDragging; + } + }); + return (_ctx, _cache) => { + return openBlock(), createBlock(Teleport, { to: teleportTarget.value }, [ + withDirectives(createBaseVNode("div", { + ref_key: "topMenuRef", + ref: topMenuRef, + class: normalizeClass(["comfyui-menu flex items-center", { dropzone: isDropZone.value, "dropzone-active": isDroppable.value }]) + }, [ + _hoisted_1, + createVNode(CommandMenubar), + createVNode(unref(script$E), { + layout: "vertical", + class: "mx-2" + }), + createBaseVNode("div", _hoisted_2, [ + workflowTabsPosition.value === "Topbar" ? (openBlock(), createBlock(WorkflowTabs, { key: 0 })) : createCommentVNode("", true) + ]), + createBaseVNode("div", { + class: "comfyui-menu-right", + ref_key: "menuRight", + ref: menuRight + }, null, 512), + createVNode(Actionbar), + createVNode(_sfc_main$2) + ], 2), [ + [vShow, betaMenuEnabled.value] + ]) + ], 8, ["to"]); + }; + } +}); +const TopMenubar = /* @__PURE__ */ _export_sfc(_sfc_main$1, [["__scopeId", "data-v-ad2c662b"]]); +function setupAutoQueueHandler() { + const queueCountStore = useQueuePendingTaskCountStore(); + const queueSettingsStore = useQueueSettingsStore(); + let graphHasChanged = false; + let internalCount = 0; + api.addEventListener("graphChanged", () => { + if (queueSettingsStore.mode === "change") { + if (internalCount) { + graphHasChanged = true; + } else { + graphHasChanged = false; + app.queuePrompt(0, queueSettingsStore.batchCount); + internalCount++; + } + } + }); + queueCountStore.$subscribe( + () => { + internalCount = queueCountStore.count; + if (!internalCount && !app.lastExecutionError) { + if (queueSettingsStore.mode === "instant" || queueSettingsStore.mode === "change" && graphHasChanged) { + graphHasChanged = false; + app.queuePrompt(0, queueSettingsStore.batchCount); + } + } + }, + { detached: true } + ); +} +__name(setupAutoQueueHandler, "setupAutoQueueHandler"); +const _sfc_main = /* @__PURE__ */ defineComponent({ + __name: "GraphView", + setup(__props) { + setupAutoQueueHandler(); + const { t } = useI18n(); + const toast = useToast(); + const settingStore = useSettingStore(); + const executionStore = useExecutionStore(); + const theme11 = computed(() => settingStore.get("Comfy.ColorPalette")); + watch( + theme11, + (newTheme) => { + const DARK_THEME_CLASS = "dark-theme"; + const isDarkTheme = newTheme !== "light"; + if (isDarkTheme) { + document.body.classList.add(DARK_THEME_CLASS); + } else { + document.body.classList.remove(DARK_THEME_CLASS); + } + }, + { immediate: true } + ); + watchEffect(() => { + const fontSize = settingStore.get("Comfy.TextareaWidget.FontSize"); + document.documentElement.style.setProperty( + "--comfy-textarea-font-size", + `${fontSize}px` + ); + }); + watchEffect(() => { + const padding = settingStore.get("Comfy.TreeExplorer.ItemPadding"); + document.documentElement.style.setProperty( + "--comfy-tree-explorer-item-padding", + `${padding}px` + ); + }); + watchEffect(() => { + const locale = settingStore.get("Comfy.Locale"); + if (locale) { + i18n.global.locale.value = locale; + } + }); + watchEffect(() => { + const useNewMenu = settingStore.get("Comfy.UseNewMenu"); + if (useNewMenu === "Disabled") { + app.ui.menuContainer.style.removeProperty("display"); + app.ui.restoreMenuPosition(); + } else { + app.ui.menuContainer.style.setProperty("display", "none"); + } + }); + const init = /* @__PURE__ */ __name(() => { + settingStore.addSettings(app.ui.settings); + useKeybindingStore().loadCoreKeybindings(); + useSidebarTabStore().registerCoreSidebarTabs(); + useBottomPanelStore().registerCoreBottomPanelTabs(); + app.extensionManager = useWorkspaceStore(); + }, "init"); + const queuePendingTaskCountStore = useQueuePendingTaskCountStore(); + const onStatus = /* @__PURE__ */ __name((e) => { + queuePendingTaskCountStore.update(e); + }, "onStatus"); + const reconnectingMessage = { + severity: "error", + summary: t("reconnecting") + }; + const onReconnecting = /* @__PURE__ */ __name(() => { + toast.remove(reconnectingMessage); + toast.add(reconnectingMessage); + }, "onReconnecting"); + const onReconnected = /* @__PURE__ */ __name(() => { + toast.remove(reconnectingMessage); + toast.add({ + severity: "success", + summary: t("reconnected"), + life: 2e3 + }); + }, "onReconnected"); + const workflowStore = useWorkflowStore(); + const workflowBookmarkStore = useWorkflowBookmarkStore(); + app.workflowManager.executionStore = executionStore; + app.workflowManager.workflowStore = workflowStore; + app.workflowManager.workflowBookmarkStore = workflowBookmarkStore; + onMounted(() => { + api.addEventListener("status", onStatus); + api.addEventListener("reconnecting", onReconnecting); + api.addEventListener("reconnected", onReconnected); + executionStore.bindExecutionEvents(); + try { + init(); + } catch (e) { + console.error("Failed to init ComfyUI frontend", e); + } + }); + onBeforeUnmount(() => { + api.removeEventListener("status", onStatus); + api.removeEventListener("reconnecting", onReconnecting); + api.removeEventListener("reconnected", onReconnected); + executionStore.unbindExecutionEvents(); + }); + const onGraphReady = /* @__PURE__ */ __name(() => { + requestIdleCallback( + () => { + useKeybindingStore().loadUserKeybindings(); + useNodeBookmarkStore().migrateLegacyBookmarks(); + useNodeDefStore().nodeSearchService.endsWithFilterStartSequence(""); + useNodeFrequencyStore().loadNodeFrequencies(); + }, + { timeout: 1e3 } + ); + }, "onGraphReady"); + return (_ctx, _cache) => { + return openBlock(), createElementBlock(Fragment, null, [ + createVNode(TopMenubar), + createVNode(_sfc_main$b, { onReady: onGraphReady }), + createVNode(_sfc_main$a), + createVNode(_sfc_main$9), + createVNode(_sfc_main$8) + ], 64); + }; + } +}); +export { + _sfc_main as default +}; +//# sourceMappingURL=GraphView-C4blCugc.js.map diff --git a/web/assets/GraphView-C4blCugc.js.map b/web/assets/GraphView-C4blCugc.js.map new file mode 100644 index 0000000..1971ac5 --- /dev/null +++ b/web/assets/GraphView-C4blCugc.js.map @@ -0,0 +1 @@ +{"version":3,"file":"GraphView-C4blCugc.js","sources":["../../src/components/graph/TitleEditor.vue","../../node_modules/primevue/overlaybadge/style/index.mjs","../../node_modules/primevue/overlaybadge/index.mjs","../../src/components/sidebar/SidebarIcon.vue","../../src/components/sidebar/SidebarThemeToggleIcon.vue","../../src/components/sidebar/SidebarSettingsToggleIcon.vue","../../src/components/common/ExtensionSlot.vue","../../src/components/sidebar/SideToolbar.vue","../../node_modules/primevue/tablist/style/index.mjs","../../node_modules/primevue/tablist/index.mjs","../../node_modules/primevue/tab/style/index.mjs","../../node_modules/primevue/tab/index.mjs","../../src/components/bottomPanel/BottomPanel.vue","../../node_modules/primevue/splitter/style/index.mjs","../../node_modules/primevue/splitter/index.mjs","../../node_modules/primevue/splitterpanel/style/index.mjs","../../node_modules/primevue/splitterpanel/index.mjs","../../src/components/LiteGraphCanvasSplitterOverlay.vue","../../node_modules/primevue/autocomplete/style/index.mjs","../../node_modules/primevue/autocomplete/index.mjs","../../src/components/primevueOverride/AutoCompletePlus.vue","../../src/components/searchbox/NodeSearchItem.vue","../../src/components/searchbox/NodeSearchBox.vue","../../src/types/litegraphTypes.ts","../../src/components/searchbox/NodeSearchBoxPopover.vue","../../src/components/graph/NodeTooltip.vue","../../src/components/graph/NodeBadge.vue","../../node_modules/primevue/buttongroup/style/index.mjs","../../node_modules/primevue/buttongroup/index.mjs","../../src/components/graph/GraphCanvasMenu.vue","../../src/components/graph/GraphCanvas.vue","../../node_modules/primevue/toast/style/index.mjs","../../node_modules/primevue/toast/index.mjs","../../src/components/toast/GlobalToast.vue","../../src/components/dialog/UnloadWindowConfirmDialog.vue","../../src/components/BrowserTabTitle.vue","../../src/components/topbar/WorkflowTabs.vue","../../node_modules/primevue/menubar/style/index.mjs","../../node_modules/primevue/menubar/index.mjs","../../src/components/topbar/CommandMenubar.vue","../../node_modules/primevue/panel/style/index.mjs","../../node_modules/primevue/panel/index.mjs","../../node_modules/primevue/tieredmenu/style/index.mjs","../../node_modules/primevue/tieredmenu/index.mjs","../../node_modules/primevue/splitbutton/style/index.mjs","../../node_modules/primevue/splitbutton/index.mjs","../../src/components/actionbar/BatchCountEdit.vue","../../src/components/actionbar/ComfyQueueButton.vue","../../src/components/actionbar/ComfyActionbar.vue","../../src/components/topbar/BottomPanelToggleButton.vue","../../src/components/topbar/TopMenubar.vue","../../src/services/autoQueueService.ts","../../src/views/GraphView.vue"],"sourcesContent":["\n\n\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-overlaybadge {\\n position: relative;\\n}\\n\\n.p-overlaybadge .p-badge {\\n position: absolute;\\n top: 0;\\n right: 0;\\n transform: translate(50%, -50%);\\n transform-origin: 100% 0;\\n margin: 0;\\n outline-width: \".concat(dt('overlaybadge.outline.width'), \";\\n outline-style: solid;\\n outline-color: \").concat(dt('overlaybadge.outline.color'), \";\\n}\\n\");\n};\nvar classes = {\n root: 'p-overlaybadge'\n};\nvar OverlayBadgeStyle = BaseStyle.extend({\n name: 'overlaybadge',\n theme: theme,\n classes: classes\n});\n\nexport { OverlayBadgeStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import Badge from 'primevue/badge';\nimport OverlayBadgeStyle from 'primevue/overlaybadge/style';\nimport { resolveComponent, openBlock, createElementBlock, mergeProps, renderSlot, createVNode } from 'vue';\n\nvar script$1 = {\n name: 'OverlayBadge',\n \"extends\": Badge,\n style: OverlayBadgeStyle,\n provide: function provide() {\n return {\n $pcOverlayBadge: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'OverlayBadge',\n \"extends\": script$1,\n inheritAttrs: false,\n components: {\n Badge: Badge\n }\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_Badge = resolveComponent(\"Badge\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [renderSlot(_ctx.$slots, \"default\"), createVNode(_component_Badge, mergeProps(_ctx.$props, {\n pt: _ctx.ptm('pcBadge')\n }), null, 16, [\"pt\"])], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n\n\n\n\n","\n\n\n","\n\n\n","\n\n\n","\n\n\n\n\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar classes = {\n root: 'p-tablist',\n content: function content(_ref) {\n var instance = _ref.instance;\n return ['p-tablist-content', {\n 'p-tablist-viewport': instance.$pcTabs.scrollable\n }];\n },\n tabList: 'p-tablist-tab-list',\n activeBar: 'p-tablist-active-bar',\n prevButton: 'p-tablist-prev-button p-tablist-nav-button',\n nextButton: 'p-tablist-next-button p-tablist-nav-button'\n};\nvar TabListStyle = BaseStyle.extend({\n name: 'tablist',\n classes: classes\n});\n\nexport { TabListStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { getWidth, findSingle, getOuterHeight, getOffset, getOuterWidth, getHeight } from '@primeuix/utils/dom';\nimport ChevronLeftIcon from '@primevue/icons/chevronleft';\nimport ChevronRightIcon from '@primevue/icons/chevronright';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport TabListStyle from 'primevue/tablist/style';\nimport Ripple from 'primevue/ripple';\nimport { resolveDirective, openBlock, createElementBlock, mergeProps, withDirectives, createBlock, resolveDynamicComponent, createCommentVNode, createElementVNode, renderSlot } from 'vue';\n\nvar script$1 = {\n name: 'BaseTabList',\n \"extends\": BaseComponent,\n props: {},\n style: TabListStyle,\n provide: function provide() {\n return {\n $pcTabList: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'TabList',\n \"extends\": script$1,\n inheritAttrs: false,\n inject: ['$pcTabs'],\n data: function data() {\n return {\n isPrevButtonEnabled: false,\n isNextButtonEnabled: true\n };\n },\n resizeObserver: undefined,\n watch: {\n showNavigators: function showNavigators(newValue) {\n newValue ? this.bindResizeObserver() : this.unbindResizeObserver();\n },\n activeValue: {\n flush: 'post',\n handler: function handler() {\n this.updateInkBar();\n }\n }\n },\n mounted: function mounted() {\n var _this = this;\n this.$nextTick(function () {\n _this.updateInkBar();\n });\n if (this.showNavigators) {\n this.updateButtonState();\n this.bindResizeObserver();\n }\n },\n updated: function updated() {\n this.showNavigators && this.updateButtonState();\n },\n beforeUnmount: function beforeUnmount() {\n this.unbindResizeObserver();\n },\n methods: {\n onScroll: function onScroll(event) {\n this.showNavigators && this.updateButtonState();\n event.preventDefault();\n },\n onPrevButtonClick: function onPrevButtonClick() {\n var content = this.$refs.content;\n var width = getWidth(content);\n var pos = content.scrollLeft - width;\n content.scrollLeft = pos <= 0 ? 0 : pos;\n },\n onNextButtonClick: function onNextButtonClick() {\n var content = this.$refs.content;\n var width = getWidth(content) - this.getVisibleButtonWidths();\n var pos = content.scrollLeft + width;\n var lastPos = content.scrollWidth - width;\n content.scrollLeft = pos >= lastPos ? lastPos : pos;\n },\n bindResizeObserver: function bindResizeObserver() {\n var _this2 = this;\n this.resizeObserver = new ResizeObserver(function () {\n return _this2.updateButtonState();\n });\n this.resizeObserver.observe(this.$refs.list);\n },\n unbindResizeObserver: function unbindResizeObserver() {\n var _this$resizeObserver;\n (_this$resizeObserver = this.resizeObserver) === null || _this$resizeObserver === void 0 || _this$resizeObserver.unobserve(this.$refs.list);\n this.resizeObserver = undefined;\n },\n updateInkBar: function updateInkBar() {\n var _this$$refs = this.$refs,\n content = _this$$refs.content,\n inkbar = _this$$refs.inkbar,\n tabs = _this$$refs.tabs;\n var activeTab = findSingle(content, '[data-pc-name=\"tab\"][data-p-active=\"true\"]');\n if (this.$pcTabs.isVertical()) {\n inkbar.style.height = getOuterHeight(activeTab) + 'px';\n inkbar.style.top = getOffset(activeTab).top - getOffset(tabs).top + 'px';\n } else {\n inkbar.style.width = getOuterWidth(activeTab) + 'px';\n inkbar.style.left = getOffset(activeTab).left - getOffset(tabs).left + 'px';\n }\n },\n updateButtonState: function updateButtonState() {\n var _this$$refs2 = this.$refs,\n list = _this$$refs2.list,\n content = _this$$refs2.content;\n var scrollLeft = content.scrollLeft,\n scrollTop = content.scrollTop,\n scrollWidth = content.scrollWidth,\n scrollHeight = content.scrollHeight,\n offsetWidth = content.offsetWidth,\n offsetHeight = content.offsetHeight;\n var _ref = [getWidth(content), getHeight(content)],\n width = _ref[0],\n height = _ref[1];\n if (this.$pcTabs.isVertical()) {\n this.isPrevButtonEnabled = scrollTop !== 0;\n this.isNextButtonEnabled = list.offsetHeight >= offsetHeight && parseInt(scrollTop) !== scrollHeight - height;\n } else {\n this.isPrevButtonEnabled = scrollLeft !== 0;\n this.isNextButtonEnabled = list.offsetWidth >= offsetWidth && parseInt(scrollLeft) !== scrollWidth - width;\n }\n },\n getVisibleButtonWidths: function getVisibleButtonWidths() {\n var _this$$refs3 = this.$refs,\n prevBtn = _this$$refs3.prevBtn,\n nextBtn = _this$$refs3.nextBtn;\n return [prevBtn, nextBtn].reduce(function (acc, el) {\n return el ? acc + getWidth(el) : acc;\n }, 0);\n }\n },\n computed: {\n templates: function templates() {\n return this.$pcTabs.$slots;\n },\n activeValue: function activeValue() {\n return this.$pcTabs.d_value;\n },\n showNavigators: function showNavigators() {\n return this.$pcTabs.scrollable && this.$pcTabs.showNavigators;\n },\n prevButtonAriaLabel: function prevButtonAriaLabel() {\n return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.previous : undefined;\n },\n nextButtonAriaLabel: function nextButtonAriaLabel() {\n return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.next : undefined;\n }\n },\n components: {\n ChevronLeftIcon: ChevronLeftIcon,\n ChevronRightIcon: ChevronRightIcon\n },\n directives: {\n ripple: Ripple\n }\n};\n\nvar _hoisted_1 = [\"aria-label\", \"tabindex\"];\nvar _hoisted_2 = [\"aria-orientation\"];\nvar _hoisted_3 = [\"aria-label\", \"tabindex\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n ref: \"list\",\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [$options.showNavigators && $data.isPrevButtonEnabled ? withDirectives((openBlock(), createElementBlock(\"button\", mergeProps({\n key: 0,\n ref: \"prevButton\",\n \"class\": _ctx.cx('prevButton'),\n \"aria-label\": $options.prevButtonAriaLabel,\n tabindex: $options.$pcTabs.tabindex,\n onClick: _cache[0] || (_cache[0] = function () {\n return $options.onPrevButtonClick && $options.onPrevButtonClick.apply($options, arguments);\n })\n }, _ctx.ptm('prevButton'), {\n \"data-pc-group-section\": \"navigator\"\n }), [(openBlock(), createBlock(resolveDynamicComponent($options.templates.previcon || 'ChevronLeftIcon'), mergeProps({\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('prevIcon')), null, 16))], 16, _hoisted_1)), [[_directive_ripple]]) : createCommentVNode(\"\", true), createElementVNode(\"div\", mergeProps({\n ref: \"content\",\n \"class\": _ctx.cx('content'),\n onScroll: _cache[1] || (_cache[1] = function () {\n return $options.onScroll && $options.onScroll.apply($options, arguments);\n })\n }, _ctx.ptm('content')), [createElementVNode(\"div\", mergeProps({\n ref: \"tabs\",\n \"class\": _ctx.cx('tabList'),\n role: \"tablist\",\n \"aria-orientation\": $options.$pcTabs.orientation || 'horizontal'\n }, _ctx.ptm('tabList')), [renderSlot(_ctx.$slots, \"default\"), createElementVNode(\"span\", mergeProps({\n ref: \"inkbar\",\n \"class\": _ctx.cx('activeBar'),\n role: \"presentation\",\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('activeBar')), null, 16)], 16, _hoisted_2)], 16), $options.showNavigators && $data.isNextButtonEnabled ? withDirectives((openBlock(), createElementBlock(\"button\", mergeProps({\n key: 1,\n ref: \"nextButton\",\n \"class\": _ctx.cx('nextButton'),\n \"aria-label\": $options.nextButtonAriaLabel,\n tabindex: $options.$pcTabs.tabindex,\n onClick: _cache[2] || (_cache[2] = function () {\n return $options.onNextButtonClick && $options.onNextButtonClick.apply($options, arguments);\n })\n }, _ctx.ptm('nextButton'), {\n \"data-pc-group-section\": \"navigator\"\n }), [(openBlock(), createBlock(resolveDynamicComponent($options.templates.nexticon || 'ChevronRightIcon'), mergeProps({\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('nextIcon')), null, 16))], 16, _hoisted_3)), [[_directive_ripple]]) : createCommentVNode(\"\", true)], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar classes = {\n root: function root(_ref) {\n var instance = _ref.instance,\n props = _ref.props;\n return ['p-tab', {\n 'p-tab-active': instance.active,\n 'p-disabled': props.disabled\n }];\n }\n};\nvar TabStyle = BaseStyle.extend({\n name: 'tab',\n classes: classes\n});\n\nexport { TabStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { getAttribute, findSingle, focus } from '@primeuix/utils/dom';\nimport { equals } from '@primeuix/utils/object';\nimport Ripple from 'primevue/ripple';\nimport { mergeProps, resolveDirective, withDirectives, openBlock, createBlock, resolveDynamicComponent, withCtx, renderSlot, normalizeClass } from 'vue';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport TabStyle from 'primevue/tab/style';\n\nvar script$1 = {\n name: 'BaseTab',\n \"extends\": BaseComponent,\n props: {\n value: {\n type: [String, Number],\n \"default\": undefined\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n as: {\n type: [String, Object],\n \"default\": 'BUTTON'\n },\n asChild: {\n type: Boolean,\n \"default\": false\n }\n },\n style: TabStyle,\n provide: function provide() {\n return {\n $pcTab: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'Tab',\n \"extends\": script$1,\n inheritAttrs: false,\n inject: ['$pcTabs', '$pcTabList'],\n methods: {\n onFocus: function onFocus() {\n this.$pcTabs.selectOnFocus && this.changeActiveValue();\n },\n onClick: function onClick() {\n this.changeActiveValue();\n },\n onKeydown: function onKeydown(event) {\n switch (event.code) {\n case 'ArrowRight':\n this.onArrowRightKey(event);\n break;\n case 'ArrowLeft':\n this.onArrowLeftKey(event);\n break;\n case 'Home':\n this.onHomeKey(event);\n break;\n case 'End':\n this.onEndKey(event);\n break;\n case 'PageDown':\n this.onPageDownKey(event);\n break;\n case 'PageUp':\n this.onPageUpKey(event);\n break;\n case 'Enter':\n case 'NumpadEnter':\n case 'Space':\n this.onEnterKey(event);\n break;\n }\n },\n onArrowRightKey: function onArrowRightKey(event) {\n var nextTab = this.findNextTab(event.currentTarget);\n nextTab ? this.changeFocusedTab(event, nextTab) : this.onHomeKey(event);\n event.preventDefault();\n },\n onArrowLeftKey: function onArrowLeftKey(event) {\n var prevTab = this.findPrevTab(event.currentTarget);\n prevTab ? this.changeFocusedTab(event, prevTab) : this.onEndKey(event);\n event.preventDefault();\n },\n onHomeKey: function onHomeKey(event) {\n var firstTab = this.findFirstTab();\n this.changeFocusedTab(event, firstTab);\n event.preventDefault();\n },\n onEndKey: function onEndKey(event) {\n var lastTab = this.findLastTab();\n this.changeFocusedTab(event, lastTab);\n event.preventDefault();\n },\n onPageDownKey: function onPageDownKey(event) {\n this.scrollInView(this.findLastTab());\n event.preventDefault();\n },\n onPageUpKey: function onPageUpKey(event) {\n this.scrollInView(this.findFirstTab());\n event.preventDefault();\n },\n onEnterKey: function onEnterKey(event) {\n this.changeActiveValue();\n event.preventDefault();\n },\n findNextTab: function findNextTab(tabElement) {\n var selfCheck = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n var element = selfCheck ? tabElement : tabElement.nextElementSibling;\n return element ? getAttribute(element, 'data-p-disabled') || getAttribute(element, 'data-pc-section') === 'inkbar' ? this.findNextTab(element) : findSingle(element, '[data-pc-name=\"tab\"]') : null;\n },\n findPrevTab: function findPrevTab(tabElement) {\n var selfCheck = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n var element = selfCheck ? tabElement : tabElement.previousElementSibling;\n return element ? getAttribute(element, 'data-p-disabled') || getAttribute(element, 'data-pc-section') === 'inkbar' ? this.findPrevTab(element) : findSingle(element, '[data-pc-name=\"tab\"]') : null;\n },\n findFirstTab: function findFirstTab() {\n return this.findNextTab(this.$pcTabList.$refs.content.firstElementChild, true);\n },\n findLastTab: function findLastTab() {\n return this.findPrevTab(this.$pcTabList.$refs.content.lastElementChild, true);\n },\n changeActiveValue: function changeActiveValue() {\n this.$pcTabs.updateValue(this.value);\n },\n changeFocusedTab: function changeFocusedTab(event, element) {\n focus(element);\n this.scrollInView(element);\n },\n scrollInView: function scrollInView(element) {\n var _element$scrollIntoVi;\n element === null || element === void 0 || (_element$scrollIntoVi = element.scrollIntoView) === null || _element$scrollIntoVi === void 0 || _element$scrollIntoVi.call(element, {\n block: 'nearest'\n });\n }\n },\n computed: {\n active: function active() {\n var _this$$pcTabs;\n return equals((_this$$pcTabs = this.$pcTabs) === null || _this$$pcTabs === void 0 ? void 0 : _this$$pcTabs.d_value, this.value);\n },\n id: function id() {\n var _this$$pcTabs2;\n return \"\".concat((_this$$pcTabs2 = this.$pcTabs) === null || _this$$pcTabs2 === void 0 ? void 0 : _this$$pcTabs2.id, \"_tab_\").concat(this.value);\n },\n ariaControls: function ariaControls() {\n var _this$$pcTabs3;\n return \"\".concat((_this$$pcTabs3 = this.$pcTabs) === null || _this$$pcTabs3 === void 0 ? void 0 : _this$$pcTabs3.id, \"_tabpanel_\").concat(this.value);\n },\n attrs: function attrs() {\n return mergeProps(this.asAttrs, this.a11yAttrs, this.ptmi('root', this.ptParams));\n },\n asAttrs: function asAttrs() {\n return this.as === 'BUTTON' ? {\n type: 'button',\n disabled: this.disabled\n } : undefined;\n },\n a11yAttrs: function a11yAttrs() {\n return {\n id: this.id,\n tabindex: this.active ? this.$pcTabs.tabindex : -1,\n role: 'tab',\n 'aria-selected': this.active,\n 'aria-controls': this.ariaControls,\n 'data-pc-name': 'tab',\n 'data-p-disabled': this.disabled,\n 'data-p-active': this.active,\n onFocus: this.onFocus,\n onKeydown: this.onKeydown\n };\n },\n ptParams: function ptParams() {\n return {\n context: {\n active: this.active\n }\n };\n }\n },\n directives: {\n ripple: Ripple\n }\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _directive_ripple = resolveDirective(\"ripple\");\n return !_ctx.asChild ? withDirectives((openBlock(), createBlock(resolveDynamicComponent(_ctx.as), mergeProps({\n key: 0,\n \"class\": _ctx.cx('root'),\n onClick: $options.onClick\n }, $options.attrs), {\n \"default\": withCtx(function () {\n return [renderSlot(_ctx.$slots, \"default\")];\n }),\n _: 3\n }, 16, [\"class\", \"onClick\"])), [[_directive_ripple]]) : renderSlot(_ctx.$slots, \"default\", {\n key: 1,\n \"class\": normalizeClass(_ctx.cx('root')),\n active: $options.active,\n a11yAttrs: $options.a11yAttrs,\n onClick: $options.onClick\n });\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-splitter {\\n display: flex;\\n flex-wrap: nowrap;\\n border: 1px solid \".concat(dt('splitter.border.color'), \";\\n background: \").concat(dt('splitter.background'), \";\\n border-radius: \").concat(dt('border.radius.md'), \";\\n color: \").concat(dt('splitter.color'), \";\\n}\\n\\n.p-splitter-vertical {\\n flex-direction: column;\\n}\\n\\n.p-splitter-gutter {\\n flex-grow: 0;\\n flex-shrink: 0;\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n z-index: 1;\\n background: \").concat(dt('splitter.gutter.background'), \";\\n}\\n\\n.p-splitter-gutter-handle {\\n border-radius: \").concat(dt('splitter.handle.border.radius'), \";\\n background: \").concat(dt('splitter.handle.background'), \";\\n transition: outline-color \").concat(dt('splitter.transition.duration'), \", box-shadow \").concat(dt('splitter.transition.duration'), \";\\n outline-color: transparent;\\n}\\n\\n.p-splitter-gutter-handle:focus-visible {\\n box-shadow: \").concat(dt('splitter.handle.focus.ring.shadow'), \";\\n outline: \").concat(dt('splitter.handle.focus.ring.width'), \" \").concat(dt('splitter.handle.focus.ring.style'), \" \").concat(dt('splitter.handle.focus.ring.color'), \";\\n outline-offset: \").concat(dt('splitter.handle.focus.ring.offset'), \";\\n}\\n\\n.p-splitter-horizontal.p-splitter-resizing {\\n cursor: col-resize;\\n user-select: none;\\n}\\n\\n.p-splitter-vertical.p-splitter-resizing {\\n cursor: row-resize;\\n user-select: none;\\n}\\n\\n.p-splitter-horizontal > .p-splitter-gutter > .p-splitter-gutter-handle {\\n height: \").concat(dt('splitter.handle.size'), \";\\n width: 100%;\\n}\\n\\n.p-splitter-vertical > .p-splitter-gutter > .p-splitter-gutter-handle {\\n width: \").concat(dt('splitter.handle.size'), \";\\n height: 100%;\\n}\\n\\n.p-splitter-horizontal > .p-splitter-gutter {\\n cursor: col-resize;\\n}\\n\\n.p-splitter-vertical > .p-splitter-gutter {\\n cursor: row-resize;\\n}\\n\\n.p-splitterpanel {\\n flex-grow: 1;\\n overflow: hidden;\\n}\\n\\n.p-splitterpanel-nested {\\n display: flex;\\n}\\n\\n.p-splitterpanel .p-splitter {\\n flex-grow: 1;\\n border: 0 none;\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var props = _ref2.props;\n return ['p-splitter p-component', 'p-splitter-' + props.layout];\n },\n gutter: 'p-splitter-gutter',\n gutterHandle: 'p-splitter-gutter-handle'\n};\nvar inlineStyles = {\n root: function root(_ref3) {\n var props = _ref3.props;\n return [{\n display: 'flex',\n 'flex-wrap': 'nowrap'\n }, props.layout === 'vertical' ? {\n 'flex-direction': 'column'\n } : ''];\n }\n};\nvar SplitterStyle = BaseStyle.extend({\n name: 'splitter',\n theme: theme,\n classes: classes,\n inlineStyles: inlineStyles\n});\n\nexport { SplitterStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { getVNodeProp } from '@primevue/core/utils';\nimport { getWidth, getHeight, getOuterWidth, getOuterHeight } from '@primeuix/utils/dom';\nimport { isArray } from '@primeuix/utils/object';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport SplitterStyle from 'primevue/splitter/style';\nimport { openBlock, createElementBlock, mergeProps, Fragment, renderList, createBlock, resolveDynamicComponent, createElementVNode, createCommentVNode } from 'vue';\n\nvar script$1 = {\n name: 'BaseSplitter',\n \"extends\": BaseComponent,\n props: {\n layout: {\n type: String,\n \"default\": 'horizontal'\n },\n gutterSize: {\n type: Number,\n \"default\": 4\n },\n stateKey: {\n type: String,\n \"default\": null\n },\n stateStorage: {\n type: String,\n \"default\": 'session'\n },\n step: {\n type: Number,\n \"default\": 5\n }\n },\n style: SplitterStyle,\n provide: function provide() {\n return {\n $pcSplitter: this,\n $parentInstance: this\n };\n }\n};\n\nfunction _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }\nfunction _nonIterableSpread() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _iterableToArray(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nvar script = {\n name: 'Splitter',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['resizestart', 'resizeend', 'resize'],\n dragging: false,\n mouseMoveListener: null,\n mouseUpListener: null,\n touchMoveListener: null,\n touchEndListener: null,\n size: null,\n gutterElement: null,\n startPos: null,\n prevPanelElement: null,\n nextPanelElement: null,\n nextPanelSize: null,\n prevPanelSize: null,\n panelSizes: null,\n prevPanelIndex: null,\n timer: null,\n data: function data() {\n return {\n prevSize: null\n };\n },\n mounted: function mounted() {\n var _this = this;\n if (this.panels && this.panels.length) {\n var initialized = false;\n if (this.isStateful()) {\n initialized = this.restoreState();\n }\n if (!initialized) {\n var children = _toConsumableArray(this.$el.children).filter(function (child) {\n return child.getAttribute('data-pc-name') === 'splitterpanel';\n });\n var _panelSizes = [];\n this.panels.map(function (panel, i) {\n var panelInitialSize = panel.props && panel.props.size ? panel.props.size : null;\n var panelSize = panelInitialSize || 100 / _this.panels.length;\n _panelSizes[i] = panelSize;\n children[i].style.flexBasis = 'calc(' + panelSize + '% - ' + (_this.panels.length - 1) * _this.gutterSize + 'px)';\n });\n this.panelSizes = _panelSizes;\n this.prevSize = parseFloat(_panelSizes[0]).toFixed(4);\n }\n }\n },\n beforeUnmount: function beforeUnmount() {\n this.clear();\n this.unbindMouseListeners();\n },\n methods: {\n isSplitterPanel: function isSplitterPanel(child) {\n return child.type.name === 'SplitterPanel';\n },\n onResizeStart: function onResizeStart(event, index, isKeyDown) {\n this.gutterElement = event.currentTarget || event.target.parentElement;\n this.size = this.horizontal ? getWidth(this.$el) : getHeight(this.$el);\n if (!isKeyDown) {\n this.dragging = true;\n this.startPos = this.layout === 'horizontal' ? event.pageX || event.changedTouches[0].pageX : event.pageY || event.changedTouches[0].pageY;\n }\n this.prevPanelElement = this.gutterElement.previousElementSibling;\n this.nextPanelElement = this.gutterElement.nextElementSibling;\n if (isKeyDown) {\n this.prevPanelSize = this.horizontal ? getOuterWidth(this.prevPanelElement, true) : getOuterHeight(this.prevPanelElement, true);\n this.nextPanelSize = this.horizontal ? getOuterWidth(this.nextPanelElement, true) : getOuterHeight(this.nextPanelElement, true);\n } else {\n this.prevPanelSize = 100 * (this.horizontal ? getOuterWidth(this.prevPanelElement, true) : getOuterHeight(this.prevPanelElement, true)) / this.size;\n this.nextPanelSize = 100 * (this.horizontal ? getOuterWidth(this.nextPanelElement, true) : getOuterHeight(this.nextPanelElement, true)) / this.size;\n }\n this.prevPanelIndex = index;\n this.$emit('resizestart', {\n originalEvent: event,\n sizes: this.panelSizes\n });\n this.$refs.gutter[index].setAttribute('data-p-gutter-resizing', true);\n this.$el.setAttribute('data-p-resizing', true);\n },\n onResize: function onResize(event, step, isKeyDown) {\n var newPos, newPrevPanelSize, newNextPanelSize;\n if (isKeyDown) {\n if (this.horizontal) {\n newPrevPanelSize = 100 * (this.prevPanelSize + step) / this.size;\n newNextPanelSize = 100 * (this.nextPanelSize - step) / this.size;\n } else {\n newPrevPanelSize = 100 * (this.prevPanelSize - step) / this.size;\n newNextPanelSize = 100 * (this.nextPanelSize + step) / this.size;\n }\n } else {\n if (this.horizontal) newPos = event.pageX * 100 / this.size - this.startPos * 100 / this.size;else newPos = event.pageY * 100 / this.size - this.startPos * 100 / this.size;\n newPrevPanelSize = this.prevPanelSize + newPos;\n newNextPanelSize = this.nextPanelSize - newPos;\n }\n if (this.validateResize(newPrevPanelSize, newNextPanelSize)) {\n this.prevPanelElement.style.flexBasis = 'calc(' + newPrevPanelSize + '% - ' + (this.panels.length - 1) * this.gutterSize + 'px)';\n this.nextPanelElement.style.flexBasis = 'calc(' + newNextPanelSize + '% - ' + (this.panels.length - 1) * this.gutterSize + 'px)';\n this.panelSizes[this.prevPanelIndex] = newPrevPanelSize;\n this.panelSizes[this.prevPanelIndex + 1] = newNextPanelSize;\n this.prevSize = parseFloat(newPrevPanelSize).toFixed(4);\n }\n this.$emit('resize', {\n originalEvent: event,\n sizes: this.panelSizes\n });\n },\n onResizeEnd: function onResizeEnd(event) {\n if (this.isStateful()) {\n this.saveState();\n }\n this.$emit('resizeend', {\n originalEvent: event,\n sizes: this.panelSizes\n });\n this.$refs.gutter.forEach(function (gutter) {\n return gutter.setAttribute('data-p-gutter-resizing', false);\n });\n this.$el.setAttribute('data-p-resizing', false);\n this.clear();\n },\n repeat: function repeat(event, index, step) {\n this.onResizeStart(event, index, true);\n this.onResize(event, step, true);\n },\n setTimer: function setTimer(event, index, step) {\n var _this2 = this;\n if (!this.timer) {\n this.timer = setInterval(function () {\n _this2.repeat(event, index, step);\n }, 40);\n }\n },\n clearTimer: function clearTimer() {\n if (this.timer) {\n clearInterval(this.timer);\n this.timer = null;\n }\n },\n onGutterKeyUp: function onGutterKeyUp() {\n this.clearTimer();\n this.onResizeEnd();\n },\n onGutterKeyDown: function onGutterKeyDown(event, index) {\n switch (event.code) {\n case 'ArrowLeft':\n {\n if (this.layout === 'horizontal') {\n this.setTimer(event, index, this.step * -1);\n }\n event.preventDefault();\n break;\n }\n case 'ArrowRight':\n {\n if (this.layout === 'horizontal') {\n this.setTimer(event, index, this.step);\n }\n event.preventDefault();\n break;\n }\n case 'ArrowDown':\n {\n if (this.layout === 'vertical') {\n this.setTimer(event, index, this.step * -1);\n }\n event.preventDefault();\n break;\n }\n case 'ArrowUp':\n {\n if (this.layout === 'vertical') {\n this.setTimer(event, index, this.step);\n }\n event.preventDefault();\n break;\n }\n }\n },\n onGutterMouseDown: function onGutterMouseDown(event, index) {\n this.onResizeStart(event, index);\n this.bindMouseListeners();\n },\n onGutterTouchStart: function onGutterTouchStart(event, index) {\n this.onResizeStart(event, index);\n this.bindTouchListeners();\n event.preventDefault();\n },\n onGutterTouchMove: function onGutterTouchMove(event) {\n this.onResize(event);\n event.preventDefault();\n },\n onGutterTouchEnd: function onGutterTouchEnd(event) {\n this.onResizeEnd(event);\n this.unbindTouchListeners();\n event.preventDefault();\n },\n bindMouseListeners: function bindMouseListeners() {\n var _this3 = this;\n if (!this.mouseMoveListener) {\n this.mouseMoveListener = function (event) {\n return _this3.onResize(event);\n };\n document.addEventListener('mousemove', this.mouseMoveListener);\n }\n if (!this.mouseUpListener) {\n this.mouseUpListener = function (event) {\n _this3.onResizeEnd(event);\n _this3.unbindMouseListeners();\n };\n document.addEventListener('mouseup', this.mouseUpListener);\n }\n },\n bindTouchListeners: function bindTouchListeners() {\n var _this4 = this;\n if (!this.touchMoveListener) {\n this.touchMoveListener = function (event) {\n return _this4.onResize(event.changedTouches[0]);\n };\n document.addEventListener('touchmove', this.touchMoveListener);\n }\n if (!this.touchEndListener) {\n this.touchEndListener = function (event) {\n _this4.resizeEnd(event);\n _this4.unbindTouchListeners();\n };\n document.addEventListener('touchend', this.touchEndListener);\n }\n },\n validateResize: function validateResize(newPrevPanelSize, newNextPanelSize) {\n if (newPrevPanelSize > 100 || newPrevPanelSize < 0) return false;\n if (newNextPanelSize > 100 || newNextPanelSize < 0) return false;\n var prevPanelMinSize = getVNodeProp(this.panels[this.prevPanelIndex], 'minSize');\n if (this.panels[this.prevPanelIndex].props && prevPanelMinSize && prevPanelMinSize > newPrevPanelSize) {\n return false;\n }\n var newPanelMinSize = getVNodeProp(this.panels[this.prevPanelIndex + 1], 'minSize');\n if (this.panels[this.prevPanelIndex + 1].props && newPanelMinSize && newPanelMinSize > newNextPanelSize) {\n return false;\n }\n return true;\n },\n unbindMouseListeners: function unbindMouseListeners() {\n if (this.mouseMoveListener) {\n document.removeEventListener('mousemove', this.mouseMoveListener);\n this.mouseMoveListener = null;\n }\n if (this.mouseUpListener) {\n document.removeEventListener('mouseup', this.mouseUpListener);\n this.mouseUpListener = null;\n }\n },\n unbindTouchListeners: function unbindTouchListeners() {\n if (this.touchMoveListener) {\n document.removeEventListener('touchmove', this.touchMoveListener);\n this.touchMoveListener = null;\n }\n if (this.touchEndListener) {\n document.removeEventListener('touchend', this.touchEndListener);\n this.touchEndListener = null;\n }\n },\n clear: function clear() {\n this.dragging = false;\n this.size = null;\n this.startPos = null;\n this.prevPanelElement = null;\n this.nextPanelElement = null;\n this.prevPanelSize = null;\n this.nextPanelSize = null;\n this.gutterElement = null;\n this.prevPanelIndex = null;\n },\n isStateful: function isStateful() {\n return this.stateKey != null;\n },\n getStorage: function getStorage() {\n switch (this.stateStorage) {\n case 'local':\n return window.localStorage;\n case 'session':\n return window.sessionStorage;\n default:\n throw new Error(this.stateStorage + ' is not a valid value for the state storage, supported values are \"local\" and \"session\".');\n }\n },\n saveState: function saveState() {\n if (isArray(this.panelSizes)) {\n this.getStorage().setItem(this.stateKey, JSON.stringify(this.panelSizes));\n }\n },\n restoreState: function restoreState() {\n var _this5 = this;\n var storage = this.getStorage();\n var stateString = storage.getItem(this.stateKey);\n if (stateString) {\n this.panelSizes = JSON.parse(stateString);\n var children = _toConsumableArray(this.$el.children).filter(function (child) {\n return child.getAttribute('data-pc-name') === 'splitterpanel';\n });\n children.forEach(function (child, i) {\n child.style.flexBasis = 'calc(' + _this5.panelSizes[i] + '% - ' + (_this5.panels.length - 1) * _this5.gutterSize + 'px)';\n });\n return true;\n }\n return false;\n }\n },\n computed: {\n panels: function panels() {\n var _this6 = this;\n var panels = [];\n this.$slots[\"default\"]().forEach(function (child) {\n if (_this6.isSplitterPanel(child)) {\n panels.push(child);\n } else if (child.children instanceof Array) {\n child.children.forEach(function (nestedChild) {\n if (_this6.isSplitterPanel(nestedChild)) {\n panels.push(nestedChild);\n }\n });\n }\n });\n return panels;\n },\n gutterStyle: function gutterStyle() {\n if (this.horizontal) return {\n width: this.gutterSize + 'px'\n };else return {\n height: this.gutterSize + 'px'\n };\n },\n horizontal: function horizontal() {\n return this.layout === 'horizontal';\n },\n getPTOptions: function getPTOptions() {\n var _this$$parentInstance;\n return {\n context: {\n nested: (_this$$parentInstance = this.$parentInstance) === null || _this$$parentInstance === void 0 ? void 0 : _this$$parentInstance.nestedState\n }\n };\n }\n }\n};\n\nvar _hoisted_1 = [\"onMousedown\", \"onTouchstart\", \"onTouchmove\", \"onTouchend\"];\nvar _hoisted_2 = [\"aria-orientation\", \"aria-valuenow\", \"onKeydown\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root'),\n style: _ctx.sx('root'),\n \"data-p-resizing\": false\n }, _ctx.ptmi('root', $options.getPTOptions)), [(openBlock(true), createElementBlock(Fragment, null, renderList($options.panels, function (panel, i) {\n return openBlock(), createElementBlock(Fragment, {\n key: i\n }, [(openBlock(), createBlock(resolveDynamicComponent(panel), {\n tabindex: \"-1\"\n })), i !== $options.panels.length - 1 ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n ref_for: true,\n ref: \"gutter\",\n \"class\": _ctx.cx('gutter'),\n role: \"separator\",\n tabindex: \"-1\",\n onMousedown: function onMousedown($event) {\n return $options.onGutterMouseDown($event, i);\n },\n onTouchstart: function onTouchstart($event) {\n return $options.onGutterTouchStart($event, i);\n },\n onTouchmove: function onTouchmove($event) {\n return $options.onGutterTouchMove($event, i);\n },\n onTouchend: function onTouchend($event) {\n return $options.onGutterTouchEnd($event, i);\n },\n \"data-p-gutter-resizing\": false\n }, _ctx.ptm('gutter')), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('gutterHandle'),\n tabindex: \"0\",\n style: [$options.gutterStyle],\n \"aria-orientation\": _ctx.layout,\n \"aria-valuenow\": $data.prevSize,\n onKeyup: _cache[0] || (_cache[0] = function () {\n return $options.onGutterKeyUp && $options.onGutterKeyUp.apply($options, arguments);\n }),\n onKeydown: function onKeydown($event) {\n return $options.onGutterKeyDown($event, i);\n },\n ref_for: true\n }, _ctx.ptm('gutterHandle')), null, 16, _hoisted_2)], 16, _hoisted_1)) : createCommentVNode(\"\", true)], 64);\n }), 128))], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar classes = {\n root: function root(_ref) {\n var instance = _ref.instance;\n return ['p-splitterpanel', {\n 'p-splitterpanel-nested': instance.isNested\n }];\n }\n};\nvar SplitterPanelStyle = BaseStyle.extend({\n name: 'splitterpanel',\n classes: classes\n});\n\nexport { SplitterPanelStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseComponent from '@primevue/core/basecomponent';\nimport SplitterPanelStyle from 'primevue/splitterpanel/style';\nimport { openBlock, createElementBlock, mergeProps, renderSlot } from 'vue';\n\nvar script$1 = {\n name: 'BaseSplitterPanel',\n \"extends\": BaseComponent,\n props: {\n size: {\n type: Number,\n \"default\": null\n },\n minSize: {\n type: Number,\n \"default\": null\n }\n },\n style: SplitterPanelStyle,\n provide: function provide() {\n return {\n $pcSplitterPanel: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'SplitterPanel',\n \"extends\": script$1,\n inheritAttrs: false,\n data: function data() {\n return {\n nestedState: null\n };\n },\n computed: {\n isNested: function isNested() {\n var _this = this;\n return this.$slots[\"default\"]().some(function (child) {\n _this.nestedState = child.type.name === 'Splitter' ? true : null;\n return _this.nestedState;\n });\n },\n getPTOptions: function getPTOptions() {\n return {\n context: {\n nested: this.isNested\n }\n };\n }\n }\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n ref: \"container\",\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root', $options.getPTOptions)), [renderSlot(_ctx.$slots, \"default\")], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n\n\n","import { isNotEmpty } from '@primeuix/utils/object';\nimport BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-autocomplete {\\n display: inline-flex;\\n}\\n\\n.p-autocomplete-loader {\\n position: absolute;\\n top: 50%;\\n margin-top: -0.5rem;\\n right: \".concat(dt('autocomplete.padding.x'), \";\\n}\\n\\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-loader {\\n right: calc(\").concat(dt('autocomplete.dropdown.width'), \" + \").concat(dt('autocomplete.padding.x'), \");\\n}\\n\\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input {\\n flex: 1 1 auto;\\n width: 1%;\\n}\\n\\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input,\\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input-multiple {\\n border-top-right-radius: 0;\\n border-bottom-right-radius: 0;\\n}\\n\\n.p-autocomplete-dropdown {\\n cursor: pointer;\\n display: inline-flex;\\n cursor: pointer;\\n user-select: none;\\n align-items: center;\\n justify-content: center;\\n overflow: hidden;\\n position: relative;\\n width: \").concat(dt('autocomplete.dropdown.width'), \";\\n border-top-right-radius: \").concat(dt('autocomplete.dropdown.border.radius'), \";\\n border-bottom-right-radius: \").concat(dt('autocomplete.dropdown.border.radius'), \";\\n background: \").concat(dt('autocomplete.dropdown.background'), \";\\n border: 1px solid \").concat(dt('autocomplete.dropdown.border.color'), \";\\n border-left: 0 none;\\n color: \").concat(dt('autocomplete.dropdown.color'), \";\\n transition: background \").concat(dt('autocomplete.transition.duration'), \", color \").concat(dt('autocomplete.transition.duration'), \", border-color \").concat(dt('autocomplete.transition.duration'), \", outline-color \").concat(dt('autocomplete.transition.duration'), \", box-shadow \").concat(dt('autocomplete.transition.duration'), \";\\n outline-color: transparent;\\n}\\n\\n.p-autocomplete-dropdown:not(:disabled):hover {\\n background: \").concat(dt('autocomplete.dropdown.hover.background'), \";\\n border-color: \").concat(dt('autocomplete.dropdown.hover.border.color'), \";\\n color: \").concat(dt('autocomplete.dropdown.hover.color'), \";\\n}\\n\\n.p-autocomplete-dropdown:not(:disabled):active {\\n background: \").concat(dt('autocomplete.dropdown.active.background'), \";\\n border-color: \").concat(dt('autocomplete.dropdown.active.border.color'), \";\\n color: \").concat(dt('autocomplete.dropdown.active.color'), \";\\n}\\n\\n.p-autocomplete-dropdown:focus-visible {\\n box-shadow: \").concat(dt('autocomplete.dropdown.focus.ring.shadow'), \";\\n outline: \").concat(dt('autocomplete.dropdown.focus.ring.width'), \" \").concat(dt('autocomplete.dropdown.focus.ring.style'), \" \").concat(dt('autocomplete.dropdown.focus.ring.color'), \";\\n outline-offset: \").concat(dt('autocomplete.dropdown.focus.ring.offset'), \";\\n}\\n\\n.p-autocomplete .p-autocomplete-overlay {\\n min-width: 100%;\\n}\\n\\n.p-autocomplete-overlay {\\n position: absolute;\\n overflow: auto;\\n top: 0;\\n left: 0;\\n background: \").concat(dt('autocomplete.overlay.background'), \";\\n color: \").concat(dt('autocomplete.overlay.color'), \";\\n border: 1px solid \").concat(dt('autocomplete.overlay.border.color'), \";\\n border-radius: \").concat(dt('autocomplete.overlay.border.radius'), \";\\n box-shadow: \").concat(dt('autocomplete.overlay.shadow'), \";\\n}\\n\\n.p-autocomplete-list {\\n margin: 0;\\n padding: 0;\\n list-style-type: none;\\n display: flex;\\n flex-direction: column;\\n gap: \").concat(dt('autocomplete.list.gap'), \";\\n padding: \").concat(dt('autocomplete.list.padding'), \";\\n}\\n\\n.p-autocomplete-option {\\n cursor: pointer;\\n white-space: nowrap;\\n position: relative;\\n overflow: hidden;\\n display: flex;\\n align-items: center;\\n padding: \").concat(dt('autocomplete.option.padding'), \";\\n border: 0 none;\\n color: \").concat(dt('autocomplete.option.color'), \";\\n background: transparent;\\n transition: background \").concat(dt('autocomplete.transition.duration'), \", color \").concat(dt('autocomplete.transition.duration'), \", border-color \").concat(dt('autocomplete.transition.duration'), \";\\n border-radius: \").concat(dt('autocomplete.option.border.radius'), \";\\n}\\n\\n.p-autocomplete-option:not(.p-autocomplete-option-selected):not(.p-disabled).p-focus {\\n background: \").concat(dt('autocomplete.option.focus.background'), \";\\n color: \").concat(dt('autocomplete.option.focus.color'), \";\\n}\\n\\n.p-autocomplete-option-selected {\\n background: \").concat(dt('autocomplete.option.selected.background'), \";\\n color: \").concat(dt('autocomplete.option.selected.color'), \";\\n}\\n\\n.p-autocomplete-option-selected.p-focus {\\n background: \").concat(dt('autocomplete.option.selected.focus.background'), \";\\n color: \").concat(dt('autocomplete.option.selected.focus.color'), \";\\n}\\n\\n.p-autocomplete-option-group {\\n margin: 0;\\n padding: \").concat(dt('autocomplete.option.group.padding'), \";\\n color: \").concat(dt('autocomplete.option.group.color'), \";\\n background: \").concat(dt('autocomplete.option.group.background'), \";\\n font-weight: \").concat(dt('autocomplete.option.group.font.weight'), \";\\n}\\n\\n.p-autocomplete-input-multiple {\\n margin: 0;\\n list-style-type: none;\\n cursor: text;\\n overflow: hidden;\\n display: flex;\\n align-items: center;\\n flex-wrap: wrap;\\n padding: calc(\").concat(dt('autocomplete.padding.y'), \" / 2) \").concat(dt('autocomplete.padding.x'), \";\\n gap: calc(\").concat(dt('autocomplete.padding.y'), \" / 2);\\n color: \").concat(dt('autocomplete.color'), \";\\n background: \").concat(dt('autocomplete.background'), \";\\n border: 1px solid \").concat(dt('autocomplete.border.color'), \";\\n border-radius: \").concat(dt('autocomplete.border.radius'), \";\\n width: 100%;\\n transition: background \").concat(dt('autocomplete.transition.duration'), \", color \").concat(dt('autocomplete.transition.duration'), \", border-color \").concat(dt('autocomplete.transition.duration'), \", outline-color \").concat(dt('autocomplete.transition.duration'), \", box-shadow \").concat(dt('autocomplete.transition.duration'), \";\\n outline-color: transparent;\\n box-shadow: \").concat(dt('autocomplete.shadow'), \";\\n}\\n\\n.p-autocomplete:not(.p-disabled):hover .p-autocomplete-input-multiple {\\n border-color: \").concat(dt('autocomplete.hover.border.color'), \";\\n}\\n\\n.p-autocomplete:not(.p-disabled).p-focus .p-autocomplete-input-multiple {\\n border-color: \").concat(dt('autocomplete.focus.border.color'), \";\\n box-shadow: \").concat(dt('autocomplete.focus.ring.shadow'), \";\\n outline: \").concat(dt('autocomplete.focus.ring.width'), \" \").concat(dt('autocomplete.focus.ring.style'), \" \").concat(dt('autocomplete.focus.ring.color'), \";\\n outline-offset: \").concat(dt('autocomplete.focus.ring.offset'), \";\\n}\\n\\n.p-autocomplete.p-invalid .p-autocomplete-input-multiple {\\n border-color: \").concat(dt('autocomplete.invalid.border.color'), \";\\n}\\n\\n.p-variant-filled.p-autocomplete-input-multiple {\\n background: \").concat(dt('autocomplete.filled.background'), \";\\n}\\n\\n.p-autocomplete:not(.p-disabled).p-focus .p-variant-filled.p-autocomplete-input-multiple {\\n background: \").concat(dt('autocomplete.filled.focus.background'), \";\\n}\\n\\n.p-autocomplete.p-disabled .p-autocomplete-input-multiple {\\n opacity: 1;\\n background: \").concat(dt('autocomplete.disabled.background'), \";\\n color: \").concat(dt('autocomplete.disabled.color'), \";\\n}\\n\\n.p-autocomplete-chip.p-chip {\\n padding-top: calc(\").concat(dt('autocomplete.padding.y'), \" / 2);\\n padding-bottom: calc(\").concat(dt('autocomplete.padding.y'), \" / 2);\\n border-radius: \").concat(dt('autocomplete.chip.border.radius'), \";\\n}\\n\\n.p-autocomplete-input-multiple:has(.p-autocomplete-chip) {\\n padding-left: calc(\").concat(dt('autocomplete.padding.y'), \" / 2);\\n padding-right: calc(\").concat(dt('autocomplete.padding.y'), \" / 2);\\n}\\n\\n.p-autocomplete-chip-item.p-focus .p-autocomplete-chip {\\n background: \").concat(dt('inputchips.chip.focus.background'), \";\\n color: \").concat(dt('inputchips.chip.focus.color'), \";\\n}\\n\\n.p-autocomplete-input-chip {\\n flex: 1 1 auto;\\n display: inline-flex;\\n padding-top: calc(\").concat(dt('autocomplete.padding.y'), \" / 2);\\n padding-bottom: calc(\").concat(dt('autocomplete.padding.y'), \" / 2);\\n}\\n\\n.p-autocomplete-input-chip input {\\n border: 0 none;\\n outline: 0 none;\\n background: transparent;\\n margin: 0;\\n padding: 0;\\n box-shadow: none;\\n border-radius: 0;\\n width: 100%;\\n font-family: inherit;\\n font-feature-settings: inherit;\\n font-size: 1rem;\\n color: inherit;\\n}\\n\\n.p-autocomplete-input-chip input::placeholder {\\n color: \").concat(dt('autocomplete.placeholder.color'), \";\\n}\\n\\n.p-autocomplete-empty-message {\\n padding: \").concat(dt('autocomplete.empty.message.padding'), \";\\n}\\n\\n.p-autocomplete-fluid {\\n display: flex;\\n}\\n\\n.p-autocomplete-fluid:has(.p-autocomplete-dropdown) .p-autocomplete-input {\\n width: 1%;\\n}\\n\");\n};\nvar inlineStyles = {\n root: {\n position: 'relative'\n }\n};\nvar classes = {\n root: function root(_ref2) {\n var instance = _ref2.instance,\n props = _ref2.props;\n return ['p-autocomplete p-component p-inputwrapper', {\n 'p-disabled': props.disabled,\n 'p-invalid': props.invalid,\n 'p-focus': instance.focused,\n 'p-inputwrapper-filled': props.modelValue || isNotEmpty(instance.inputValue),\n 'p-inputwrapper-focus': instance.focused,\n 'p-autocomplete-open': instance.overlayVisible,\n 'p-autocomplete-fluid': instance.hasFluid\n }];\n },\n pcInput: 'p-autocomplete-input',\n inputMultiple: function inputMultiple(_ref3) {\n var props = _ref3.props,\n instance = _ref3.instance;\n return ['p-autocomplete-input-multiple', {\n 'p-variant-filled': props.variant ? props.variant === 'filled' : instance.$primevue.config.inputStyle === 'filled' || instance.$primevue.config.inputVariant === 'filled'\n }];\n },\n chipItem: function chipItem(_ref4) {\n var instance = _ref4.instance,\n i = _ref4.i;\n return ['p-autocomplete-chip-item', {\n 'p-focus': instance.focusedMultipleOptionIndex === i\n }];\n },\n pcChip: 'p-autocomplete-chip',\n chipIcon: 'p-autocomplete-chip-icon',\n inputChip: 'p-autocomplete-input-chip',\n loader: 'p-autocomplete-loader',\n dropdown: 'p-autocomplete-dropdown',\n overlay: 'p-autocomplete-overlay p-component',\n list: 'p-autocomplete-list',\n optionGroup: 'p-autocomplete-option-group',\n option: function option(_ref5) {\n var instance = _ref5.instance,\n _option = _ref5.option,\n i = _ref5.i,\n getItemOptions = _ref5.getItemOptions;\n return ['p-autocomplete-option', {\n 'p-autocomplete-option-selected': instance.isSelected(_option),\n 'p-focus': instance.focusedOptionIndex === instance.getOptionIndex(i, getItemOptions),\n 'p-disabled': instance.isOptionDisabled(_option)\n }];\n },\n emptyMessage: 'p-autocomplete-empty-message'\n};\nvar AutoCompleteStyle = BaseStyle.extend({\n name: 'autocomplete',\n theme: theme,\n classes: classes,\n inlineStyles: inlineStyles\n});\n\nexport { AutoCompleteStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { focus, addStyle, relativePosition, getOuterWidth, absolutePosition, isTouchDevice, findSingle } from '@primeuix/utils/dom';\nimport { resolveFieldData, isEmpty, isNotEmpty, equals, findLastIndex } from '@primeuix/utils/object';\nimport { ZIndex } from '@primeuix/utils/zindex';\nimport { UniqueComponentId, ConnectedOverlayScrollHandler } from '@primevue/core/utils';\nimport ChevronDownIcon from '@primevue/icons/chevrondown';\nimport SpinnerIcon from '@primevue/icons/spinner';\nimport Chip from 'primevue/chip';\nimport InputText from 'primevue/inputtext';\nimport OverlayEventBus from 'primevue/overlayeventbus';\nimport Portal from 'primevue/portal';\nimport Ripple from 'primevue/ripple';\nimport VirtualScroller from 'primevue/virtualscroller';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport AutoCompleteStyle from 'primevue/autocomplete/style';\nimport { resolveComponent, resolveDirective, openBlock, createElementBlock, mergeProps, createBlock, normalizeClass, normalizeStyle, createCommentVNode, Fragment, renderList, renderSlot, createVNode, withCtx, createElementVNode, resolveDynamicComponent, toDisplayString, Transition, createSlots, createTextVNode, withDirectives } from 'vue';\n\nvar script$1 = {\n name: 'BaseAutoComplete',\n \"extends\": BaseComponent,\n props: {\n modelValue: null,\n suggestions: {\n type: Array,\n \"default\": null\n },\n optionLabel: null,\n optionDisabled: null,\n optionGroupLabel: null,\n optionGroupChildren: null,\n scrollHeight: {\n type: String,\n \"default\": '14rem'\n },\n dropdown: {\n type: Boolean,\n \"default\": false\n },\n dropdownMode: {\n type: String,\n \"default\": 'blank'\n },\n multiple: {\n type: Boolean,\n \"default\": false\n },\n loading: {\n type: Boolean,\n \"default\": false\n },\n variant: {\n type: String,\n \"default\": null\n },\n invalid: {\n type: Boolean,\n \"default\": false\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n placeholder: {\n type: String,\n \"default\": null\n },\n dataKey: {\n type: String,\n \"default\": null\n },\n minLength: {\n type: Number,\n \"default\": 1\n },\n delay: {\n type: Number,\n \"default\": 300\n },\n appendTo: {\n type: [String, Object],\n \"default\": 'body'\n },\n forceSelection: {\n type: Boolean,\n \"default\": false\n },\n completeOnFocus: {\n type: Boolean,\n \"default\": false\n },\n inputId: {\n type: String,\n \"default\": null\n },\n inputStyle: {\n type: Object,\n \"default\": null\n },\n inputClass: {\n type: [String, Object],\n \"default\": null\n },\n panelStyle: {\n type: Object,\n \"default\": null\n },\n panelClass: {\n type: [String, Object],\n \"default\": null\n },\n overlayStyle: {\n type: Object,\n \"default\": null\n },\n overlayClass: {\n type: [String, Object],\n \"default\": null\n },\n dropdownIcon: {\n type: String,\n \"default\": null\n },\n dropdownClass: {\n type: [String, Object],\n \"default\": null\n },\n loader: {\n type: String,\n \"default\": null\n },\n loadingIcon: {\n type: String,\n \"default\": null\n },\n removeTokenIcon: {\n type: String,\n \"default\": null\n },\n chipIcon: {\n type: String,\n \"default\": null\n },\n virtualScrollerOptions: {\n type: Object,\n \"default\": null\n },\n autoOptionFocus: {\n type: Boolean,\n \"default\": false\n },\n selectOnFocus: {\n type: Boolean,\n \"default\": false\n },\n focusOnHover: {\n type: Boolean,\n \"default\": true\n },\n searchLocale: {\n type: String,\n \"default\": undefined\n },\n searchMessage: {\n type: String,\n \"default\": null\n },\n selectionMessage: {\n type: String,\n \"default\": null\n },\n emptySelectionMessage: {\n type: String,\n \"default\": null\n },\n emptySearchMessage: {\n type: String,\n \"default\": null\n },\n tabindex: {\n type: Number,\n \"default\": 0\n },\n typeahead: {\n type: Boolean,\n \"default\": true\n },\n ariaLabel: {\n type: String,\n \"default\": null\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n fluid: {\n type: Boolean,\n \"default\": null\n }\n },\n style: AutoCompleteStyle,\n provide: function provide() {\n return {\n $pcAutoComplete: this,\n $parentInstance: this\n };\n }\n};\n\nfunction _typeof$1(o) { \"@babel/helpers - typeof\"; return _typeof$1 = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof$1(o); }\nfunction _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }\nfunction _nonIterableSpread() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _iterableToArray(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nvar script = {\n name: 'AutoComplete',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:modelValue', 'change', 'focus', 'blur', 'item-select', 'item-unselect', 'option-select', 'option-unselect', 'dropdown-click', 'clear', 'complete', 'before-show', 'before-hide', 'show', 'hide'],\n inject: {\n $pcFluid: {\n \"default\": null\n }\n },\n outsideClickListener: null,\n resizeListener: null,\n scrollHandler: null,\n overlay: null,\n virtualScroller: null,\n searchTimeout: null,\n dirty: false,\n data: function data() {\n return {\n id: this.$attrs.id,\n clicked: false,\n focused: false,\n focusedOptionIndex: -1,\n focusedMultipleOptionIndex: -1,\n overlayVisible: false,\n searching: false\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n },\n suggestions: function suggestions() {\n if (this.searching) {\n this.show();\n this.focusedOptionIndex = this.overlayVisible && this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1;\n this.searching = false;\n }\n this.autoUpdateModel();\n }\n },\n mounted: function mounted() {\n this.id = this.id || UniqueComponentId();\n this.autoUpdateModel();\n },\n updated: function updated() {\n if (this.overlayVisible) {\n this.alignOverlay();\n }\n },\n beforeUnmount: function beforeUnmount() {\n this.unbindOutsideClickListener();\n this.unbindResizeListener();\n if (this.scrollHandler) {\n this.scrollHandler.destroy();\n this.scrollHandler = null;\n }\n if (this.overlay) {\n ZIndex.clear(this.overlay);\n this.overlay = null;\n }\n },\n methods: {\n getOptionIndex: function getOptionIndex(index, fn) {\n return this.virtualScrollerDisabled ? index : fn && fn(index)['index'];\n },\n getOptionLabel: function getOptionLabel(option) {\n return this.optionLabel ? resolveFieldData(option, this.optionLabel) : option;\n },\n getOptionValue: function getOptionValue(option) {\n return option; // TODO: The 'optionValue' properties can be added.\n },\n getOptionRenderKey: function getOptionRenderKey(option, index) {\n return (this.dataKey ? resolveFieldData(option, this.dataKey) : this.getOptionLabel(option)) + '_' + index;\n },\n getPTOptions: function getPTOptions(option, itemOptions, index, key) {\n return this.ptm(key, {\n context: {\n selected: this.isSelected(option),\n focused: this.focusedOptionIndex === this.getOptionIndex(index, itemOptions),\n disabled: this.isOptionDisabled(option)\n }\n });\n },\n isOptionDisabled: function isOptionDisabled(option) {\n return this.optionDisabled ? resolveFieldData(option, this.optionDisabled) : false;\n },\n isOptionGroup: function isOptionGroup(option) {\n return this.optionGroupLabel && option.optionGroup && option.group;\n },\n getOptionGroupLabel: function getOptionGroupLabel(optionGroup) {\n return resolveFieldData(optionGroup, this.optionGroupLabel);\n },\n getOptionGroupChildren: function getOptionGroupChildren(optionGroup) {\n return resolveFieldData(optionGroup, this.optionGroupChildren);\n },\n getAriaPosInset: function getAriaPosInset(index) {\n var _this = this;\n return (this.optionGroupLabel ? index - this.visibleOptions.slice(0, index).filter(function (option) {\n return _this.isOptionGroup(option);\n }).length : index) + 1;\n },\n show: function show(isFocus) {\n this.$emit('before-show');\n this.dirty = true;\n this.overlayVisible = true;\n this.focusedOptionIndex = this.focusedOptionIndex !== -1 ? this.focusedOptionIndex : this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1;\n isFocus && focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el);\n },\n hide: function hide(isFocus) {\n var _this2 = this;\n var _hide = function _hide() {\n _this2.$emit('before-hide');\n _this2.dirty = isFocus;\n _this2.overlayVisible = false;\n _this2.clicked = false;\n _this2.focusedOptionIndex = -1;\n isFocus && focus(_this2.multiple ? _this2.$refs.focusInput : _this2.$refs.focusInput.$el);\n };\n setTimeout(function () {\n _hide();\n }, 0); // For ScreenReaders\n },\n onFocus: function onFocus(event) {\n if (this.disabled) {\n // For ScreenReaders\n return;\n }\n if (!this.dirty && this.completeOnFocus) {\n this.search(event, event.target.value, 'focus');\n }\n this.dirty = true;\n this.focused = true;\n if (this.overlayVisible) {\n this.focusedOptionIndex = this.focusedOptionIndex !== -1 ? this.focusedOptionIndex : this.overlayVisible && this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1;\n this.scrollInView(this.focusedOptionIndex);\n }\n this.$emit('focus', event);\n },\n onBlur: function onBlur(event) {\n this.dirty = false;\n this.focused = false;\n this.focusedOptionIndex = -1;\n this.$emit('blur', event);\n },\n onKeyDown: function onKeyDown(event) {\n if (this.disabled) {\n event.preventDefault();\n return;\n }\n switch (event.code) {\n case 'ArrowDown':\n this.onArrowDownKey(event);\n break;\n case 'ArrowUp':\n this.onArrowUpKey(event);\n break;\n case 'ArrowLeft':\n this.onArrowLeftKey(event);\n break;\n case 'ArrowRight':\n this.onArrowRightKey(event);\n break;\n case 'Home':\n this.onHomeKey(event);\n break;\n case 'End':\n this.onEndKey(event);\n break;\n case 'PageDown':\n this.onPageDownKey(event);\n break;\n case 'PageUp':\n this.onPageUpKey(event);\n break;\n case 'Enter':\n case 'NumpadEnter':\n this.onEnterKey(event);\n break;\n case 'Escape':\n this.onEscapeKey(event);\n break;\n case 'Tab':\n this.onTabKey(event);\n break;\n case 'Backspace':\n this.onBackspaceKey(event);\n break;\n }\n this.clicked = false;\n },\n onInput: function onInput(event) {\n var _this3 = this;\n if (this.typeahead) {\n if (this.searchTimeout) {\n clearTimeout(this.searchTimeout);\n }\n var query = event.target.value;\n if (!this.multiple) {\n this.updateModel(event, query);\n }\n if (query.length === 0) {\n this.hide();\n this.$emit('clear');\n } else {\n if (query.length >= this.minLength) {\n this.focusedOptionIndex = -1;\n this.searchTimeout = setTimeout(function () {\n _this3.search(event, query, 'input');\n }, this.delay);\n } else {\n this.hide();\n }\n }\n }\n },\n onChange: function onChange(event) {\n var _this4 = this;\n if (this.forceSelection) {\n var valid = false;\n\n // when forceSelection is on, prevent called twice onOptionSelect()\n if (this.visibleOptions && !this.multiple) {\n var value = this.multiple ? this.$refs.focusInput.value : this.$refs.focusInput.$el.value;\n var matchedValue = this.visibleOptions.find(function (option) {\n return _this4.isOptionMatched(option, value || '');\n });\n if (matchedValue !== undefined) {\n valid = true;\n !this.isSelected(matchedValue) && this.onOptionSelect(event, matchedValue);\n }\n }\n if (!valid) {\n if (this.multiple) this.$refs.focusInput.value = '';else this.$refs.focusInput.$el.value = '';\n this.$emit('clear');\n !this.multiple && this.updateModel(event, null);\n }\n }\n },\n onMultipleContainerFocus: function onMultipleContainerFocus() {\n if (this.disabled) {\n // For ScreenReaders\n return;\n }\n this.focused = true;\n },\n onMultipleContainerBlur: function onMultipleContainerBlur() {\n this.focusedMultipleOptionIndex = -1;\n this.focused = false;\n },\n onMultipleContainerKeyDown: function onMultipleContainerKeyDown(event) {\n if (this.disabled) {\n event.preventDefault();\n return;\n }\n switch (event.code) {\n case 'ArrowLeft':\n this.onArrowLeftKeyOnMultiple(event);\n break;\n case 'ArrowRight':\n this.onArrowRightKeyOnMultiple(event);\n break;\n case 'Backspace':\n this.onBackspaceKeyOnMultiple(event);\n break;\n }\n },\n onContainerClick: function onContainerClick(event) {\n this.clicked = true;\n if (this.disabled || this.searching || this.loading || this.isInputClicked(event) || this.isDropdownClicked(event)) {\n return;\n }\n if (!this.overlay || !this.overlay.contains(event.target)) {\n focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el);\n }\n },\n onDropdownClick: function onDropdownClick(event) {\n var query = undefined;\n if (this.overlayVisible) {\n this.hide(true);\n } else {\n var target = this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el;\n focus(target);\n query = target.value;\n if (this.dropdownMode === 'blank') this.search(event, '', 'dropdown');else if (this.dropdownMode === 'current') this.search(event, query, 'dropdown');\n }\n this.$emit('dropdown-click', {\n originalEvent: event,\n query: query\n });\n },\n onOptionSelect: function onOptionSelect(event, option) {\n var isHide = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : true;\n var value = this.getOptionValue(option);\n if (this.multiple) {\n this.$refs.focusInput.value = '';\n if (!this.isSelected(option)) {\n this.updateModel(event, [].concat(_toConsumableArray(this.modelValue || []), [value]));\n }\n } else {\n this.updateModel(event, value);\n }\n this.$emit('item-select', {\n originalEvent: event,\n value: option\n });\n this.$emit('option-select', {\n originalEvent: event,\n value: option\n });\n isHide && this.hide(true);\n },\n onOptionMouseMove: function onOptionMouseMove(event, index) {\n if (this.focusOnHover) {\n this.changeFocusedOptionIndex(event, index);\n }\n },\n onOverlayClick: function onOverlayClick(event) {\n OverlayEventBus.emit('overlay-click', {\n originalEvent: event,\n target: this.$el\n });\n },\n onOverlayKeyDown: function onOverlayKeyDown(event) {\n switch (event.code) {\n case 'Escape':\n this.onEscapeKey(event);\n break;\n }\n },\n onArrowDownKey: function onArrowDownKey(event) {\n if (!this.overlayVisible) {\n return;\n }\n var optionIndex = this.focusedOptionIndex !== -1 ? this.findNextOptionIndex(this.focusedOptionIndex) : this.clicked ? this.findFirstOptionIndex() : this.findFirstFocusedOptionIndex();\n this.changeFocusedOptionIndex(event, optionIndex);\n event.preventDefault();\n },\n onArrowUpKey: function onArrowUpKey(event) {\n if (!this.overlayVisible) {\n return;\n }\n if (event.altKey) {\n if (this.focusedOptionIndex !== -1) {\n this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]);\n }\n this.overlayVisible && this.hide();\n event.preventDefault();\n } else {\n var optionIndex = this.focusedOptionIndex !== -1 ? this.findPrevOptionIndex(this.focusedOptionIndex) : this.clicked ? this.findLastOptionIndex() : this.findLastFocusedOptionIndex();\n this.changeFocusedOptionIndex(event, optionIndex);\n event.preventDefault();\n }\n },\n onArrowLeftKey: function onArrowLeftKey(event) {\n var target = event.currentTarget;\n this.focusedOptionIndex = -1;\n if (this.multiple) {\n if (isEmpty(target.value) && this.hasSelectedOption) {\n focus(this.$refs.multiContainer);\n this.focusedMultipleOptionIndex = this.modelValue.length;\n } else {\n event.stopPropagation(); // To prevent onArrowLeftKeyOnMultiple method\n }\n }\n },\n onArrowRightKey: function onArrowRightKey(event) {\n this.focusedOptionIndex = -1;\n this.multiple && event.stopPropagation(); // To prevent onArrowRightKeyOnMultiple method\n },\n onHomeKey: function onHomeKey(event) {\n var currentTarget = event.currentTarget;\n var len = currentTarget.value.length;\n currentTarget.setSelectionRange(0, event.shiftKey ? len : 0);\n this.focusedOptionIndex = -1;\n event.preventDefault();\n },\n onEndKey: function onEndKey(event) {\n var currentTarget = event.currentTarget;\n var len = currentTarget.value.length;\n currentTarget.setSelectionRange(event.shiftKey ? 0 : len, len);\n this.focusedOptionIndex = -1;\n event.preventDefault();\n },\n onPageUpKey: function onPageUpKey(event) {\n this.scrollInView(0);\n event.preventDefault();\n },\n onPageDownKey: function onPageDownKey(event) {\n this.scrollInView(this.visibleOptions.length - 1);\n event.preventDefault();\n },\n onEnterKey: function onEnterKey(event) {\n if (!this.typeahead) {\n if (this.multiple) {\n this.updateModel(event, [].concat(_toConsumableArray(this.modelValue || []), [event.target.value]));\n this.$refs.focusInput.value = '';\n }\n } else {\n if (!this.overlayVisible) {\n this.focusedOptionIndex = -1; // reset\n this.onArrowDownKey(event);\n } else {\n if (this.focusedOptionIndex !== -1) {\n this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]);\n }\n this.hide();\n }\n }\n },\n onEscapeKey: function onEscapeKey(event) {\n this.overlayVisible && this.hide(true);\n event.preventDefault();\n },\n onTabKey: function onTabKey(event) {\n if (this.focusedOptionIndex !== -1) {\n this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]);\n }\n this.overlayVisible && this.hide();\n },\n onBackspaceKey: function onBackspaceKey(event) {\n if (this.multiple) {\n if (isNotEmpty(this.modelValue) && !this.$refs.focusInput.value) {\n var removedValue = this.modelValue[this.modelValue.length - 1];\n var newValue = this.modelValue.slice(0, -1);\n this.$emit('update:modelValue', newValue);\n this.$emit('item-unselect', {\n originalEvent: event,\n value: removedValue\n });\n this.$emit('option-unselect', {\n originalEvent: event,\n value: removedValue\n });\n }\n event.stopPropagation(); // To prevent onBackspaceKeyOnMultiple method\n }\n },\n onArrowLeftKeyOnMultiple: function onArrowLeftKeyOnMultiple() {\n this.focusedMultipleOptionIndex = this.focusedMultipleOptionIndex < 1 ? 0 : this.focusedMultipleOptionIndex - 1;\n },\n onArrowRightKeyOnMultiple: function onArrowRightKeyOnMultiple() {\n this.focusedMultipleOptionIndex++;\n if (this.focusedMultipleOptionIndex > this.modelValue.length - 1) {\n this.focusedMultipleOptionIndex = -1;\n focus(this.$refs.focusInput);\n }\n },\n onBackspaceKeyOnMultiple: function onBackspaceKeyOnMultiple(event) {\n if (this.focusedMultipleOptionIndex !== -1) {\n this.removeOption(event, this.focusedMultipleOptionIndex);\n }\n },\n onOverlayEnter: function onOverlayEnter(el) {\n ZIndex.set('overlay', el, this.$primevue.config.zIndex.overlay);\n addStyle(el, {\n position: 'absolute',\n top: '0',\n left: '0'\n });\n this.alignOverlay();\n },\n onOverlayAfterEnter: function onOverlayAfterEnter() {\n this.bindOutsideClickListener();\n this.bindScrollListener();\n this.bindResizeListener();\n this.$emit('show');\n },\n onOverlayLeave: function onOverlayLeave() {\n this.unbindOutsideClickListener();\n this.unbindScrollListener();\n this.unbindResizeListener();\n this.$emit('hide');\n this.overlay = null;\n },\n onOverlayAfterLeave: function onOverlayAfterLeave(el) {\n ZIndex.clear(el);\n },\n alignOverlay: function alignOverlay() {\n var target = this.multiple ? this.$refs.multiContainer : this.$refs.focusInput.$el;\n if (this.appendTo === 'self') {\n relativePosition(this.overlay, target);\n } else {\n this.overlay.style.minWidth = getOuterWidth(target) + 'px';\n absolutePosition(this.overlay, target);\n }\n },\n bindOutsideClickListener: function bindOutsideClickListener() {\n var _this5 = this;\n if (!this.outsideClickListener) {\n this.outsideClickListener = function (event) {\n if (_this5.overlayVisible && _this5.overlay && _this5.isOutsideClicked(event)) {\n _this5.hide();\n }\n };\n document.addEventListener('click', this.outsideClickListener);\n }\n },\n unbindOutsideClickListener: function unbindOutsideClickListener() {\n if (this.outsideClickListener) {\n document.removeEventListener('click', this.outsideClickListener);\n this.outsideClickListener = null;\n }\n },\n bindScrollListener: function bindScrollListener() {\n var _this6 = this;\n if (!this.scrollHandler) {\n this.scrollHandler = new ConnectedOverlayScrollHandler(this.$refs.container, function () {\n if (_this6.overlayVisible) {\n _this6.hide();\n }\n });\n }\n this.scrollHandler.bindScrollListener();\n },\n unbindScrollListener: function unbindScrollListener() {\n if (this.scrollHandler) {\n this.scrollHandler.unbindScrollListener();\n }\n },\n bindResizeListener: function bindResizeListener() {\n var _this7 = this;\n if (!this.resizeListener) {\n this.resizeListener = function () {\n if (_this7.overlayVisible && !isTouchDevice()) {\n _this7.hide();\n }\n };\n window.addEventListener('resize', this.resizeListener);\n }\n },\n unbindResizeListener: function unbindResizeListener() {\n if (this.resizeListener) {\n window.removeEventListener('resize', this.resizeListener);\n this.resizeListener = null;\n }\n },\n isOutsideClicked: function isOutsideClicked(event) {\n return !this.overlay.contains(event.target) && !this.isInputClicked(event) && !this.isDropdownClicked(event);\n },\n isInputClicked: function isInputClicked(event) {\n if (this.multiple) return event.target === this.$refs.multiContainer || this.$refs.multiContainer.contains(event.target);else return event.target === this.$refs.focusInput.$el;\n },\n isDropdownClicked: function isDropdownClicked(event) {\n return this.$refs.dropdownButton ? event.target === this.$refs.dropdownButton || this.$refs.dropdownButton.contains(event.target) : false;\n },\n isOptionMatched: function isOptionMatched(option, value) {\n var _this$getOptionLabel;\n return this.isValidOption(option) && ((_this$getOptionLabel = this.getOptionLabel(option)) === null || _this$getOptionLabel === void 0 ? void 0 : _this$getOptionLabel.toLocaleLowerCase(this.searchLocale)) === value.toLocaleLowerCase(this.searchLocale);\n },\n isValidOption: function isValidOption(option) {\n return isNotEmpty(option) && !(this.isOptionDisabled(option) || this.isOptionGroup(option));\n },\n isValidSelectedOption: function isValidSelectedOption(option) {\n return this.isValidOption(option) && this.isSelected(option);\n },\n isEquals: function isEquals(value1, value2) {\n return equals(value1, value2, this.equalityKey);\n },\n isSelected: function isSelected(option) {\n var _this8 = this;\n var optionValue = this.getOptionValue(option);\n return this.multiple ? (this.modelValue || []).some(function (value) {\n return _this8.isEquals(value, optionValue);\n }) : this.isEquals(this.modelValue, this.getOptionValue(option));\n },\n findFirstOptionIndex: function findFirstOptionIndex() {\n var _this9 = this;\n return this.visibleOptions.findIndex(function (option) {\n return _this9.isValidOption(option);\n });\n },\n findLastOptionIndex: function findLastOptionIndex() {\n var _this10 = this;\n return findLastIndex(this.visibleOptions, function (option) {\n return _this10.isValidOption(option);\n });\n },\n findNextOptionIndex: function findNextOptionIndex(index) {\n var _this11 = this;\n var matchedOptionIndex = index < this.visibleOptions.length - 1 ? this.visibleOptions.slice(index + 1).findIndex(function (option) {\n return _this11.isValidOption(option);\n }) : -1;\n return matchedOptionIndex > -1 ? matchedOptionIndex + index + 1 : index;\n },\n findPrevOptionIndex: function findPrevOptionIndex(index) {\n var _this12 = this;\n var matchedOptionIndex = index > 0 ? findLastIndex(this.visibleOptions.slice(0, index), function (option) {\n return _this12.isValidOption(option);\n }) : -1;\n return matchedOptionIndex > -1 ? matchedOptionIndex : index;\n },\n findSelectedOptionIndex: function findSelectedOptionIndex() {\n var _this13 = this;\n return this.hasSelectedOption ? this.visibleOptions.findIndex(function (option) {\n return _this13.isValidSelectedOption(option);\n }) : -1;\n },\n findFirstFocusedOptionIndex: function findFirstFocusedOptionIndex() {\n var selectedIndex = this.findSelectedOptionIndex();\n return selectedIndex < 0 ? this.findFirstOptionIndex() : selectedIndex;\n },\n findLastFocusedOptionIndex: function findLastFocusedOptionIndex() {\n var selectedIndex = this.findSelectedOptionIndex();\n return selectedIndex < 0 ? this.findLastOptionIndex() : selectedIndex;\n },\n search: function search(event, query, source) {\n //allow empty string but not undefined or null\n if (query === undefined || query === null) {\n return;\n }\n\n //do not search blank values on input change\n if (source === 'input' && query.trim().length === 0) {\n return;\n }\n this.searching = true;\n this.$emit('complete', {\n originalEvent: event,\n query: query\n });\n },\n removeOption: function removeOption(event, index) {\n var _this14 = this;\n var removedOption = this.modelValue[index];\n var value = this.modelValue.filter(function (_, i) {\n return i !== index;\n }).map(function (option) {\n return _this14.getOptionValue(option);\n });\n this.updateModel(event, value);\n this.$emit('item-unselect', {\n originalEvent: event,\n value: removedOption\n });\n this.$emit('option-unselect', {\n originalEvent: event,\n value: removedOption\n });\n this.dirty = true;\n focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el);\n },\n changeFocusedOptionIndex: function changeFocusedOptionIndex(event, index) {\n if (this.focusedOptionIndex !== index) {\n this.focusedOptionIndex = index;\n this.scrollInView();\n if (this.selectOnFocus) {\n this.onOptionSelect(event, this.visibleOptions[index], false);\n }\n }\n },\n scrollInView: function scrollInView() {\n var _this15 = this;\n var index = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : -1;\n this.$nextTick(function () {\n var id = index !== -1 ? \"\".concat(_this15.id, \"_\").concat(index) : _this15.focusedOptionId;\n var element = findSingle(_this15.list, \"li[id=\\\"\".concat(id, \"\\\"]\"));\n if (element) {\n element.scrollIntoView && element.scrollIntoView({\n block: 'nearest',\n inline: 'start'\n });\n } else if (!_this15.virtualScrollerDisabled) {\n _this15.virtualScroller && _this15.virtualScroller.scrollToIndex(index !== -1 ? index : _this15.focusedOptionIndex);\n }\n });\n },\n autoUpdateModel: function autoUpdateModel() {\n if (this.selectOnFocus && this.autoOptionFocus && !this.hasSelectedOption) {\n this.focusedOptionIndex = this.findFirstFocusedOptionIndex();\n this.onOptionSelect(null, this.visibleOptions[this.focusedOptionIndex], false);\n }\n },\n updateModel: function updateModel(event, value) {\n this.$emit('update:modelValue', value);\n this.$emit('change', {\n originalEvent: event,\n value: value\n });\n },\n flatOptions: function flatOptions(options) {\n var _this16 = this;\n return (options || []).reduce(function (result, option, index) {\n result.push({\n optionGroup: option,\n group: true,\n index: index\n });\n var optionGroupChildren = _this16.getOptionGroupChildren(option);\n optionGroupChildren && optionGroupChildren.forEach(function (o) {\n return result.push(o);\n });\n return result;\n }, []);\n },\n overlayRef: function overlayRef(el) {\n this.overlay = el;\n },\n listRef: function listRef(el, contentRef) {\n this.list = el;\n contentRef && contentRef(el); // For VirtualScroller\n },\n virtualScrollerRef: function virtualScrollerRef(el) {\n this.virtualScroller = el;\n }\n },\n computed: {\n visibleOptions: function visibleOptions() {\n return this.optionGroupLabel ? this.flatOptions(this.suggestions) : this.suggestions || [];\n },\n inputValue: function inputValue() {\n if (isNotEmpty(this.modelValue)) {\n if (_typeof$1(this.modelValue) === 'object') {\n var label = this.getOptionLabel(this.modelValue);\n return label != null ? label : this.modelValue;\n } else {\n return this.modelValue;\n }\n } else {\n return '';\n }\n },\n hasSelectedOption: function hasSelectedOption() {\n return isNotEmpty(this.modelValue);\n },\n equalityKey: function equalityKey() {\n return this.dataKey; // TODO: The 'optionValue' properties can be added.\n },\n searchResultMessageText: function searchResultMessageText() {\n return isNotEmpty(this.visibleOptions) && this.overlayVisible ? this.searchMessageText.replaceAll('{0}', this.visibleOptions.length) : this.emptySearchMessageText;\n },\n searchMessageText: function searchMessageText() {\n return this.searchMessage || this.$primevue.config.locale.searchMessage || '';\n },\n emptySearchMessageText: function emptySearchMessageText() {\n return this.emptySearchMessage || this.$primevue.config.locale.emptySearchMessage || '';\n },\n selectionMessageText: function selectionMessageText() {\n return this.selectionMessage || this.$primevue.config.locale.selectionMessage || '';\n },\n emptySelectionMessageText: function emptySelectionMessageText() {\n return this.emptySelectionMessage || this.$primevue.config.locale.emptySelectionMessage || '';\n },\n selectedMessageText: function selectedMessageText() {\n return this.hasSelectedOption ? this.selectionMessageText.replaceAll('{0}', this.multiple ? this.modelValue.length : '1') : this.emptySelectionMessageText;\n },\n listAriaLabel: function listAriaLabel() {\n return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.listLabel : undefined;\n },\n focusedOptionId: function focusedOptionId() {\n return this.focusedOptionIndex !== -1 ? \"\".concat(this.id, \"_\").concat(this.focusedOptionIndex) : null;\n },\n focusedMultipleOptionId: function focusedMultipleOptionId() {\n return this.focusedMultipleOptionIndex !== -1 ? \"\".concat(this.id, \"_multiple_option_\").concat(this.focusedMultipleOptionIndex) : null;\n },\n ariaSetSize: function ariaSetSize() {\n var _this17 = this;\n return this.visibleOptions.filter(function (option) {\n return !_this17.isOptionGroup(option);\n }).length;\n },\n virtualScrollerDisabled: function virtualScrollerDisabled() {\n return !this.virtualScrollerOptions;\n },\n panelId: function panelId() {\n return this.id + '_panel';\n },\n hasFluid: function hasFluid() {\n return isEmpty(this.fluid) ? !!this.$pcFluid : this.fluid;\n }\n },\n components: {\n InputText: InputText,\n VirtualScroller: VirtualScroller,\n Portal: Portal,\n ChevronDownIcon: ChevronDownIcon,\n SpinnerIcon: SpinnerIcon,\n Chip: Chip\n },\n directives: {\n ripple: Ripple\n }\n};\n\nfunction _typeof(o) { \"@babel/helpers - typeof\"; return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof(o); }\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty(e, r, t) { return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey(t) { var i = _toPrimitive(t, \"string\"); return \"symbol\" == _typeof(i) ? i : i + \"\"; }\nfunction _toPrimitive(t, r) { if (\"object\" != _typeof(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nvar _hoisted_1 = [\"aria-activedescendant\"];\nvar _hoisted_2 = [\"id\", \"aria-label\", \"aria-setsize\", \"aria-posinset\"];\nvar _hoisted_3 = [\"id\", \"placeholder\", \"tabindex\", \"disabled\", \"aria-label\", \"aria-labelledby\", \"aria-expanded\", \"aria-controls\", \"aria-activedescendant\", \"aria-invalid\"];\nvar _hoisted_4 = [\"disabled\", \"aria-expanded\", \"aria-controls\"];\nvar _hoisted_5 = [\"id\"];\nvar _hoisted_6 = [\"id\", \"aria-label\"];\nvar _hoisted_7 = [\"id\"];\nvar _hoisted_8 = [\"id\", \"aria-label\", \"aria-selected\", \"aria-disabled\", \"aria-setsize\", \"aria-posinset\", \"onClick\", \"onMousemove\", \"data-p-selected\", \"data-p-focus\", \"data-p-disabled\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_InputText = resolveComponent(\"InputText\");\n var _component_Chip = resolveComponent(\"Chip\");\n var _component_SpinnerIcon = resolveComponent(\"SpinnerIcon\");\n var _component_VirtualScroller = resolveComponent(\"VirtualScroller\");\n var _component_Portal = resolveComponent(\"Portal\");\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n ref: \"container\",\n \"class\": _ctx.cx('root'),\n style: _ctx.sx('root'),\n onClick: _cache[11] || (_cache[11] = function () {\n return $options.onContainerClick && $options.onContainerClick.apply($options, arguments);\n })\n }, _ctx.ptmi('root')), [!_ctx.multiple ? (openBlock(), createBlock(_component_InputText, {\n key: 0,\n ref: \"focusInput\",\n id: _ctx.inputId,\n type: \"text\",\n \"class\": normalizeClass([_ctx.cx('pcInput'), _ctx.inputClass]),\n style: normalizeStyle(_ctx.inputStyle),\n value: $options.inputValue,\n placeholder: _ctx.placeholder,\n tabindex: !_ctx.disabled ? _ctx.tabindex : -1,\n fluid: $options.hasFluid,\n disabled: _ctx.disabled,\n invalid: _ctx.invalid,\n variant: _ctx.variant,\n autocomplete: \"off\",\n role: \"combobox\",\n \"aria-label\": _ctx.ariaLabel,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-haspopup\": \"listbox\",\n \"aria-autocomplete\": \"list\",\n \"aria-expanded\": $data.overlayVisible,\n \"aria-controls\": $options.panelId,\n \"aria-activedescendant\": $data.focused ? $options.focusedOptionId : undefined,\n onFocus: $options.onFocus,\n onBlur: $options.onBlur,\n onKeydown: $options.onKeyDown,\n onInput: $options.onInput,\n onChange: $options.onChange,\n unstyled: _ctx.unstyled,\n pt: _ctx.ptm('pcInput')\n }, null, 8, [\"id\", \"class\", \"style\", \"value\", \"placeholder\", \"tabindex\", \"fluid\", \"disabled\", \"invalid\", \"variant\", \"aria-label\", \"aria-labelledby\", \"aria-expanded\", \"aria-controls\", \"aria-activedescendant\", \"onFocus\", \"onBlur\", \"onKeydown\", \"onInput\", \"onChange\", \"unstyled\", \"pt\"])) : createCommentVNode(\"\", true), _ctx.multiple ? (openBlock(), createElementBlock(\"ul\", mergeProps({\n key: 1,\n ref: \"multiContainer\",\n \"class\": _ctx.cx('inputMultiple'),\n tabindex: \"-1\",\n role: \"listbox\",\n \"aria-orientation\": \"horizontal\",\n \"aria-activedescendant\": $data.focused ? $options.focusedMultipleOptionId : undefined,\n onFocus: _cache[5] || (_cache[5] = function () {\n return $options.onMultipleContainerFocus && $options.onMultipleContainerFocus.apply($options, arguments);\n }),\n onBlur: _cache[6] || (_cache[6] = function () {\n return $options.onMultipleContainerBlur && $options.onMultipleContainerBlur.apply($options, arguments);\n }),\n onKeydown: _cache[7] || (_cache[7] = function () {\n return $options.onMultipleContainerKeyDown && $options.onMultipleContainerKeyDown.apply($options, arguments);\n })\n }, _ctx.ptm('inputMultiple')), [(openBlock(true), createElementBlock(Fragment, null, renderList(_ctx.modelValue, function (option, i) {\n return openBlock(), createElementBlock(\"li\", mergeProps({\n key: \"\".concat(i, \"_\").concat($options.getOptionLabel(option)),\n id: $data.id + '_multiple_option_' + i,\n \"class\": _ctx.cx('chipItem', {\n i: i\n }),\n role: \"option\",\n \"aria-label\": $options.getOptionLabel(option),\n \"aria-selected\": true,\n \"aria-setsize\": _ctx.modelValue.length,\n \"aria-posinset\": i + 1,\n ref_for: true\n }, _ctx.ptm('chipItem')), [renderSlot(_ctx.$slots, \"chip\", mergeProps({\n \"class\": _ctx.cx('pcChip'),\n value: option,\n index: i,\n removeCallback: function removeCallback(event) {\n return $options.removeOption(event, i);\n },\n ref_for: true\n }, _ctx.ptm('pcChip')), function () {\n return [createVNode(_component_Chip, {\n \"class\": normalizeClass(_ctx.cx('pcChip')),\n label: $options.getOptionLabel(option),\n removeIcon: _ctx.chipIcon || _ctx.removeTokenIcon,\n removable: \"\",\n unstyled: _ctx.unstyled,\n onRemove: function onRemove($event) {\n return $options.removeOption($event, i);\n },\n pt: _ctx.ptm('pcChip')\n }, {\n removeicon: withCtx(function () {\n return [renderSlot(_ctx.$slots, _ctx.$slots.chipicon ? 'chipicon' : 'removetokenicon', {\n \"class\": normalizeClass(_ctx.cx('chipIcon')),\n index: i,\n removeCallback: function removeCallback(event) {\n return $options.removeOption(event, i);\n }\n })];\n }),\n _: 2\n }, 1032, [\"class\", \"label\", \"removeIcon\", \"unstyled\", \"onRemove\", \"pt\"])];\n })], 16, _hoisted_2);\n }), 128)), createElementVNode(\"li\", mergeProps({\n \"class\": _ctx.cx('inputChip'),\n role: \"option\"\n }, _ctx.ptm('inputChip')), [createElementVNode(\"input\", mergeProps({\n ref: \"focusInput\",\n id: _ctx.inputId,\n type: \"text\",\n style: _ctx.inputStyle,\n \"class\": _ctx.inputClass,\n placeholder: _ctx.placeholder,\n tabindex: !_ctx.disabled ? _ctx.tabindex : -1,\n disabled: _ctx.disabled,\n autocomplete: \"off\",\n role: \"combobox\",\n \"aria-label\": _ctx.ariaLabel,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-haspopup\": \"listbox\",\n \"aria-autocomplete\": \"list\",\n \"aria-expanded\": $data.overlayVisible,\n \"aria-controls\": $data.id + '_list',\n \"aria-activedescendant\": $data.focused ? $options.focusedOptionId : undefined,\n \"aria-invalid\": _ctx.invalid || undefined,\n onFocus: _cache[0] || (_cache[0] = function () {\n return $options.onFocus && $options.onFocus.apply($options, arguments);\n }),\n onBlur: _cache[1] || (_cache[1] = function () {\n return $options.onBlur && $options.onBlur.apply($options, arguments);\n }),\n onKeydown: _cache[2] || (_cache[2] = function () {\n return $options.onKeyDown && $options.onKeyDown.apply($options, arguments);\n }),\n onInput: _cache[3] || (_cache[3] = function () {\n return $options.onInput && $options.onInput.apply($options, arguments);\n }),\n onChange: _cache[4] || (_cache[4] = function () {\n return $options.onChange && $options.onChange.apply($options, arguments);\n })\n }, _ctx.ptm('input')), null, 16, _hoisted_3)], 16)], 16, _hoisted_1)) : createCommentVNode(\"\", true), $data.searching || _ctx.loading ? renderSlot(_ctx.$slots, _ctx.$slots.loader ? 'loader' : 'loadingicon', {\n key: 2,\n \"class\": normalizeClass(_ctx.cx('loader'))\n }, function () {\n return [_ctx.loader || _ctx.loadingIcon ? (openBlock(), createElementBlock(\"i\", mergeProps({\n key: 0,\n \"class\": ['pi-spin', _ctx.cx('loader'), _ctx.loader, _ctx.loadingIcon],\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('loader')), null, 16)) : (openBlock(), createBlock(_component_SpinnerIcon, mergeProps({\n key: 1,\n \"class\": _ctx.cx('loader'),\n spin: \"\",\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('loader')), null, 16, [\"class\"]))];\n }) : createCommentVNode(\"\", true), renderSlot(_ctx.$slots, _ctx.$slots.dropdown ? 'dropdown' : 'dropdownbutton', {\n toggleCallback: function toggleCallback(event) {\n return $options.onDropdownClick(event);\n }\n }, function () {\n return [_ctx.dropdown ? (openBlock(), createElementBlock(\"button\", mergeProps({\n key: 0,\n ref: \"dropdownButton\",\n type: \"button\",\n \"class\": [_ctx.cx('dropdown'), _ctx.dropdownClass],\n disabled: _ctx.disabled,\n \"aria-haspopup\": \"listbox\",\n \"aria-expanded\": $data.overlayVisible,\n \"aria-controls\": $options.panelId,\n onClick: _cache[8] || (_cache[8] = function () {\n return $options.onDropdownClick && $options.onDropdownClick.apply($options, arguments);\n })\n }, _ctx.ptm('dropdown')), [renderSlot(_ctx.$slots, \"dropdownicon\", {\n \"class\": normalizeClass(_ctx.dropdownIcon)\n }, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.dropdownIcon ? 'span' : 'ChevronDownIcon'), mergeProps({\n \"class\": _ctx.dropdownIcon\n }, _ctx.ptm('dropdownIcon')), null, 16, [\"class\"]))];\n })], 16, _hoisted_4)) : createCommentVNode(\"\", true)];\n }), createElementVNode(\"span\", mergeProps({\n role: \"status\",\n \"aria-live\": \"polite\",\n \"class\": \"p-hidden-accessible\"\n }, _ctx.ptm('hiddenSearchResult'), {\n \"data-p-hidden-accessible\": true\n }), toDisplayString($options.searchResultMessageText), 17), createVNode(_component_Portal, {\n appendTo: _ctx.appendTo\n }, {\n \"default\": withCtx(function () {\n return [createVNode(Transition, mergeProps({\n name: \"p-connected-overlay\",\n onEnter: $options.onOverlayEnter,\n onAfterEnter: $options.onOverlayAfterEnter,\n onLeave: $options.onOverlayLeave,\n onAfterLeave: $options.onOverlayAfterLeave\n }, _ctx.ptm('transition')), {\n \"default\": withCtx(function () {\n return [$data.overlayVisible ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n ref: $options.overlayRef,\n id: $options.panelId,\n \"class\": [_ctx.cx('overlay'), _ctx.panelClass, _ctx.overlayClass],\n style: _objectSpread(_objectSpread(_objectSpread({}, _ctx.panelStyle), _ctx.overlayStyle), {}, {\n 'max-height': $options.virtualScrollerDisabled ? _ctx.scrollHeight : ''\n }),\n onClick: _cache[9] || (_cache[9] = function () {\n return $options.onOverlayClick && $options.onOverlayClick.apply($options, arguments);\n }),\n onKeydown: _cache[10] || (_cache[10] = function () {\n return $options.onOverlayKeyDown && $options.onOverlayKeyDown.apply($options, arguments);\n })\n }, _ctx.ptm('overlay')), [renderSlot(_ctx.$slots, \"header\", {\n value: _ctx.modelValue,\n suggestions: $options.visibleOptions\n }), createVNode(_component_VirtualScroller, mergeProps({\n ref: $options.virtualScrollerRef\n }, _ctx.virtualScrollerOptions, {\n style: {\n height: _ctx.scrollHeight\n },\n items: $options.visibleOptions,\n tabindex: -1,\n disabled: $options.virtualScrollerDisabled,\n pt: _ctx.ptm('virtualScroller')\n }), createSlots({\n content: withCtx(function (_ref) {\n var styleClass = _ref.styleClass,\n contentRef = _ref.contentRef,\n items = _ref.items,\n getItemOptions = _ref.getItemOptions,\n contentStyle = _ref.contentStyle,\n itemSize = _ref.itemSize;\n return [createElementVNode(\"ul\", mergeProps({\n ref: function ref(el) {\n return $options.listRef(el, contentRef);\n },\n id: $data.id + '_list',\n \"class\": [_ctx.cx('list'), styleClass],\n style: contentStyle,\n role: \"listbox\",\n \"aria-label\": $options.listAriaLabel\n }, _ctx.ptm('list')), [(openBlock(true), createElementBlock(Fragment, null, renderList(items, function (option, i) {\n return openBlock(), createElementBlock(Fragment, {\n key: $options.getOptionRenderKey(option, $options.getOptionIndex(i, getItemOptions))\n }, [$options.isOptionGroup(option) ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 0,\n id: $data.id + '_' + $options.getOptionIndex(i, getItemOptions),\n style: {\n height: itemSize ? itemSize + 'px' : undefined\n },\n \"class\": _ctx.cx('optionGroup'),\n role: \"option\",\n ref_for: true\n }, _ctx.ptm('optionGroup')), [renderSlot(_ctx.$slots, \"optiongroup\", {\n option: option.optionGroup,\n index: $options.getOptionIndex(i, getItemOptions)\n }, function () {\n return [createTextVNode(toDisplayString($options.getOptionGroupLabel(option.optionGroup)), 1)];\n })], 16, _hoisted_7)) : withDirectives((openBlock(), createElementBlock(\"li\", mergeProps({\n key: 1,\n id: $data.id + '_' + $options.getOptionIndex(i, getItemOptions),\n style: {\n height: itemSize ? itemSize + 'px' : undefined\n },\n \"class\": _ctx.cx('option', {\n option: option,\n i: i,\n getItemOptions: getItemOptions\n }),\n role: \"option\",\n \"aria-label\": $options.getOptionLabel(option),\n \"aria-selected\": $options.isSelected(option),\n \"aria-disabled\": $options.isOptionDisabled(option),\n \"aria-setsize\": $options.ariaSetSize,\n \"aria-posinset\": $options.getAriaPosInset($options.getOptionIndex(i, getItemOptions)),\n onClick: function onClick($event) {\n return $options.onOptionSelect($event, option);\n },\n onMousemove: function onMousemove($event) {\n return $options.onOptionMouseMove($event, $options.getOptionIndex(i, getItemOptions));\n },\n \"data-p-selected\": $options.isSelected(option),\n \"data-p-focus\": $data.focusedOptionIndex === $options.getOptionIndex(i, getItemOptions),\n \"data-p-disabled\": $options.isOptionDisabled(option),\n ref_for: true\n }, $options.getPTOptions(option, getItemOptions, i, 'option')), [renderSlot(_ctx.$slots, \"option\", {\n option: option,\n index: $options.getOptionIndex(i, getItemOptions)\n }, function () {\n return [createTextVNode(toDisplayString($options.getOptionLabel(option)), 1)];\n })], 16, _hoisted_8)), [[_directive_ripple]])], 64);\n }), 128)), !items || items && items.length === 0 ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('emptyMessage'),\n role: \"option\"\n }, _ctx.ptm('emptyMessage')), [renderSlot(_ctx.$slots, \"empty\", {}, function () {\n return [createTextVNode(toDisplayString($options.searchResultMessageText), 1)];\n })], 16)) : createCommentVNode(\"\", true)], 16, _hoisted_6)];\n }),\n _: 2\n }, [_ctx.$slots.loader ? {\n name: \"loader\",\n fn: withCtx(function (_ref2) {\n var options = _ref2.options;\n return [renderSlot(_ctx.$slots, \"loader\", {\n options: options\n })];\n }),\n key: \"0\"\n } : undefined]), 1040, [\"style\", \"items\", \"disabled\", \"pt\"]), renderSlot(_ctx.$slots, \"footer\", {\n value: _ctx.modelValue,\n suggestions: $options.visibleOptions\n }), createElementVNode(\"span\", mergeProps({\n role: \"status\",\n \"aria-live\": \"polite\",\n \"class\": \"p-hidden-accessible\"\n }, _ctx.ptm('hiddenSelectedMessage'), {\n \"data-p-hidden-accessible\": true\n }), toDisplayString($options.selectedMessageText), 17)], 16, _hoisted_5)) : createCommentVNode(\"\", true)];\n }),\n _: 3\n }, 16, [\"onEnter\", \"onAfterEnter\", \"onLeave\", \"onAfterLeave\"])];\n }),\n _: 3\n }, 8, [\"appendTo\"])], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n","\n\n\n\n\n","\n\n\n\n\n","// @ts-strict-ignore\nimport type {\n ConnectingLink,\n LGraphNode,\n Vector2,\n INodeInputSlot,\n INodeOutputSlot,\n INodeSlot\n} from '@comfyorg/litegraph'\nimport type { ISlotType } from '@comfyorg/litegraph'\nimport { LiteGraph } from '@comfyorg/litegraph'\n\nexport class ConnectingLinkImpl implements ConnectingLink {\n node: LGraphNode\n slot: number\n input: INodeInputSlot | null\n output: INodeOutputSlot | null\n pos: Vector2\n\n constructor(\n node: LGraphNode,\n slot: number,\n input: INodeInputSlot | null,\n output: INodeOutputSlot | null,\n pos: Vector2\n ) {\n this.node = node\n this.slot = slot\n this.input = input\n this.output = output\n this.pos = pos\n }\n\n static createFromPlainObject(obj: ConnectingLink) {\n return new ConnectingLinkImpl(\n obj.node,\n obj.slot,\n obj.input,\n obj.output,\n obj.pos\n )\n }\n\n get type(): ISlotType | null {\n const result = this.input ? this.input.type : this.output.type\n return result === -1 ? null : result\n }\n\n /**\n * Which slot type is release and need to be reconnected.\n * - 'output' means we need a new node's outputs slot to connect with this link\n */\n get releaseSlotType(): 'input' | 'output' {\n return this.output ? 'input' : 'output'\n }\n\n connectTo(newNode: LGraphNode) {\n const newNodeSlots =\n this.releaseSlotType === 'output' ? newNode.outputs : newNode.inputs\n if (!newNodeSlots) return\n\n const newNodeSlot = newNodeSlots.findIndex((slot: INodeSlot) =>\n LiteGraph.isValidConnection(slot.type, this.type)\n )\n\n if (newNodeSlot === -1) {\n console.warn(\n `Could not find slot with type ${this.type} on node ${newNode.title}. This should never happen`\n )\n return\n }\n\n if (this.releaseSlotType === 'input') {\n this.node.connect(this.slot, newNode, newNodeSlot)\n } else {\n newNode.connect(newNodeSlot, this.node, this.slot)\n }\n }\n}\n\nexport type CanvasDragAndDropData = {\n type: 'add-node'\n data: T\n}\n","\n\n\n\n\n","\n\n\n\n\n","\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n _ref.dt;\n return \"\\n.p-buttongroup .p-button {\\n margin: 0;\\n}\\n\\n.p-buttongroup .p-button:not(:last-child),\\n.p-buttongroup .p-button:not(:last-child):hover {\\n border-right: 0 none;\\n}\\n\\n.p-buttongroup .p-button:not(:first-of-type):not(:last-of-type) {\\n border-radius: 0;\\n}\\n\\n.p-buttongroup .p-button:first-of-type:not(:only-of-type) {\\n border-top-right-radius: 0;\\n border-bottom-right-radius: 0;\\n}\\n\\n.p-buttongroup .p-button:last-of-type:not(:only-of-type) {\\n border-top-left-radius: 0;\\n border-bottom-left-radius: 0;\\n}\\n\\n.p-buttongroup .p-button:focus {\\n position: relative;\\n z-index: 1;\\n}\\n\";\n};\nvar classes = {\n root: 'p-buttongroup p-component'\n};\nvar ButtonGroupStyle = BaseStyle.extend({\n name: 'buttongroup',\n theme: theme,\n classes: classes\n});\n\nexport { ButtonGroupStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseComponent from '@primevue/core/basecomponent';\nimport ButtonGroupStyle from 'primevue/buttongroup/style';\nimport { openBlock, createElementBlock, mergeProps, renderSlot } from 'vue';\n\nvar script$1 = {\n name: 'BaseButtonGroup',\n \"extends\": BaseComponent,\n style: ButtonGroupStyle,\n provide: function provide() {\n return {\n $pcButtonGroup: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'ButtonGroup',\n \"extends\": script$1,\n inheritAttrs: false\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"span\", mergeProps({\n \"class\": _ctx.cx('root'),\n role: \"group\"\n }, _ctx.ptmi('root')), [renderSlot(_ctx.$slots, \"default\")], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n\n\n","\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nfunction _typeof(o) { \"@babel/helpers - typeof\"; return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof(o); }\nfunction _defineProperty(e, r, t) { return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey(t) { var i = _toPrimitive(t, \"string\"); return \"symbol\" == _typeof(i) ? i : i + \"\"; }\nfunction _toPrimitive(t, r) { if (\"object\" != _typeof(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-toast {\\n width: \".concat(dt('toast.width'), \";\\n white-space: pre-line;\\n word-break: break-word;\\n}\\n\\n.p-toast-message {\\n margin: 0 0 1rem 0;\\n}\\n\\n.p-toast-message-icon {\\n flex-shrink: 0;\\n font-size: \").concat(dt('toast.icon.size'), \";\\n width: \").concat(dt('toast.icon.size'), \";\\n height: \").concat(dt('toast.icon.size'), \";\\n}\\n\\n.p-toast-message-content {\\n display: flex;\\n align-items: flex-start;\\n padding: \").concat(dt('toast.content.padding'), \";\\n gap: \").concat(dt('toast.content.gap'), \";\\n}\\n\\n.p-toast-message-text {\\n flex: 1 1 auto;\\n display: flex;\\n flex-direction: column;\\n gap: \").concat(dt('toast.text.gap'), \";\\n}\\n\\n.p-toast-summary {\\n font-weight: \").concat(dt('toast.summary.font.weight'), \";\\n font-size: \").concat(dt('toast.summary.font.size'), \";\\n}\\n\\n.p-toast-detail {\\n font-weight: \").concat(dt('toast.detail.font.weight'), \";\\n font-size: \").concat(dt('toast.detail.font.size'), \";\\n}\\n\\n.p-toast-close-button {\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n overflow: hidden;\\n position: relative;\\n cursor: pointer;\\n background: transparent;\\n transition: background \").concat(dt('toast.transition.duration'), \", color \").concat(dt('toast.transition.duration'), \", outline-color \").concat(dt('toast.transition.duration'), \", box-shadow \").concat(dt('toast.transition.duration'), \";\\n outline-color: transparent;\\n color: inherit;\\n width: \").concat(dt('toast.close.button.width'), \";\\n height: \").concat(dt('toast.close.button.height'), \";\\n border-radius: \").concat(dt('toast.close.button.border.radius'), \";\\n margin: -25% 0 0 0;\\n right: -25%;\\n padding: 0;\\n border: none;\\n user-select: none;\\n}\\n\\n.p-toast-message-info,\\n.p-toast-message-success,\\n.p-toast-message-warn,\\n.p-toast-message-error,\\n.p-toast-message-secondary,\\n.p-toast-message-contrast {\\n border-width: \").concat(dt('toast.border.width'), \";\\n border-style: solid;\\n backdrop-filter: blur(\").concat(dt('toast.blur'), \");\\n border-radius: \").concat(dt('toast.border.radius'), \";\\n}\\n\\n.p-toast-close-icon {\\n font-size: \").concat(dt('toast.close.icon.size'), \";\\n width: \").concat(dt('toast.close.icon.size'), \";\\n height: \").concat(dt('toast.close.icon.size'), \";\\n}\\n\\n.p-toast-close-button:focus-visible {\\n outline-width: \").concat(dt('focus.ring.width'), \";\\n outline-style: \").concat(dt('focus.ring.style'), \";\\n outline-offset: \").concat(dt('focus.ring.offset'), \";\\n}\\n\\n.p-toast-message-info {\\n background: \").concat(dt('toast.info.background'), \";\\n border-color: \").concat(dt('toast.info.border.color'), \";\\n color: \").concat(dt('toast.info.color'), \";\\n box-shadow: \").concat(dt('toast.info.shadow'), \";\\n}\\n\\n.p-toast-message-info .p-toast-detail {\\n color: \").concat(dt('toast.info.detail.color'), \";\\n}\\n\\n.p-toast-message-info .p-toast-close-button:focus-visible {\\n outline-color: \").concat(dt('toast.info.close.button.focus.ring.color'), \";\\n box-shadow: \").concat(dt('toast.info.close.button.focus.ring.shadow'), \";\\n}\\n\\n.p-toast-message-info .p-toast-close-button:hover {\\n background: \").concat(dt('toast.info.close.button.hover.background'), \";\\n}\\n\\n.p-toast-message-success {\\n background: \").concat(dt('toast.success.background'), \";\\n border-color: \").concat(dt('toast.success.border.color'), \";\\n color: \").concat(dt('toast.success.color'), \";\\n box-shadow: \").concat(dt('toast.success.shadow'), \";\\n}\\n\\n.p-toast-message-success .p-toast-detail {\\n color: \").concat(dt('toast.success.detail.color'), \";\\n}\\n\\n.p-toast-message-success .p-toast-close-button:focus-visible {\\n outline-color: \").concat(dt('toast.success.close.button.focus.ring.color'), \";\\n box-shadow: \").concat(dt('toast.success.close.button.focus.ring.shadow'), \";\\n}\\n\\n.p-toast-message-success .p-toast-close-button:hover {\\n background: \").concat(dt('toast.success.close.button.hover.background'), \";\\n}\\n\\n.p-toast-message-warn {\\n background: \").concat(dt('toast.warn.background'), \";\\n border-color: \").concat(dt('toast.warn.border.color'), \";\\n color: \").concat(dt('toast.warn.color'), \";\\n box-shadow: \").concat(dt('toast.warn.shadow'), \";\\n}\\n\\n.p-toast-message-warn .p-toast-detail {\\n color: \").concat(dt('toast.warn.detail.color'), \";\\n}\\n\\n.p-toast-message-warn .p-toast-close-button:focus-visible {\\n outline-color: \").concat(dt('toast.warn.close.button.focus.ring.color'), \";\\n box-shadow: \").concat(dt('toast.warn.close.button.focus.ring.shadow'), \";\\n}\\n\\n.p-toast-message-warn .p-toast-close-button:hover {\\n background: \").concat(dt('toast.warn.close.button.hover.background'), \";\\n}\\n\\n.p-toast-message-error {\\n background: \").concat(dt('toast.error.background'), \";\\n border-color: \").concat(dt('toast.error.border.color'), \";\\n color: \").concat(dt('toast.error.color'), \";\\n box-shadow: \").concat(dt('toast.error.shadow'), \";\\n}\\n\\n.p-toast-message-error .p-toast-detail {\\n color: \").concat(dt('toast.error.detail.color'), \";\\n}\\n\\n.p-toast-message-error .p-toast-close-button:focus-visible {\\n outline-color: \").concat(dt('toast.error.close.button.focus.ring.color'), \";\\n box-shadow: \").concat(dt('toast.error.close.button.focus.ring.shadow'), \";\\n}\\n\\n.p-toast-message-error .p-toast-close-button:hover {\\n background: \").concat(dt('toast.error.close.button.hover.background'), \";\\n}\\n\\n.p-toast-message-secondary {\\n background: \").concat(dt('toast.secondary.background'), \";\\n border-color: \").concat(dt('toast.secondary.border.color'), \";\\n color: \").concat(dt('toast.secondary.color'), \";\\n box-shadow: \").concat(dt('toast.secondary.shadow'), \";\\n}\\n\\n.p-toast-message-secondary .p-toast-detail {\\n color: \").concat(dt('toast.secondary.detail.color'), \";\\n}\\n\\n.p-toast-message-secondary .p-toast-close-button:focus-visible {\\n outline-color: \").concat(dt('toast.secondary.close.button.focus.ring.color'), \";\\n box-shadow: \").concat(dt('toast.secondary.close.button.focus.ring.shadow'), \";\\n}\\n\\n.p-toast-message-secondary .p-toast-close-button:hover {\\n background: \").concat(dt('toast.secondary.close.button.hover.background'), \";\\n}\\n\\n.p-toast-message-contrast {\\n background: \").concat(dt('toast.contrast.background'), \";\\n border-color: \").concat(dt('toast.contrast.border.color'), \";\\n color: \").concat(dt('toast.contrast.color'), \";\\n box-shadow: \").concat(dt('toast.contrast.shadow'), \";\\n}\\n\\n.p-toast-message-contrast .p-toast-detail {\\n color: \").concat(dt('toast.contrast.detail.color'), \";\\n}\\n\\n.p-toast-message-contrast .p-toast-close-button:focus-visible {\\n outline-color: \").concat(dt('toast.contrast.close.button.focus.ring.color'), \";\\n box-shadow: \").concat(dt('toast.contrast.close.button.focus.ring.shadow'), \";\\n}\\n\\n.p-toast-message-contrast .p-toast-close-button:hover {\\n background: \").concat(dt('toast.contrast.close.button.hover.background'), \";\\n}\\n\\n.p-toast-top-center {\\n transform: translateX(-50%);\\n}\\n\\n.p-toast-bottom-center {\\n transform: translateX(-50%);\\n}\\n\\n.p-toast-center {\\n min-width: 20vw;\\n transform: translate(-50%, -50%);\\n}\\n\\n.p-toast-message-enter-from {\\n opacity: 0;\\n transform: translateY(50%);\\n}\\n\\n.p-toast-message-leave-from {\\n max-height: 1000px;\\n}\\n\\n.p-toast .p-toast-message.p-toast-message-leave-to {\\n max-height: 0;\\n opacity: 0;\\n margin-bottom: 0;\\n overflow: hidden;\\n}\\n\\n.p-toast-message-enter-active {\\n transition: transform 0.3s, opacity 0.3s;\\n}\\n\\n.p-toast-message-leave-active {\\n transition: max-height 0.45s cubic-bezier(0, 1, 0, 1), opacity 0.3s, margin-bottom 0.3s;\\n}\\n\");\n};\n\n// Position\nvar inlineStyles = {\n root: function root(_ref2) {\n var position = _ref2.position;\n return {\n position: 'fixed',\n top: position === 'top-right' || position === 'top-left' || position === 'top-center' ? '20px' : position === 'center' ? '50%' : null,\n right: (position === 'top-right' || position === 'bottom-right') && '20px',\n bottom: (position === 'bottom-left' || position === 'bottom-right' || position === 'bottom-center') && '20px',\n left: position === 'top-left' || position === 'bottom-left' ? '20px' : position === 'center' || position === 'top-center' || position === 'bottom-center' ? '50%' : null\n };\n }\n};\nvar classes = {\n root: function root(_ref3) {\n var props = _ref3.props;\n return ['p-toast p-component p-toast-' + props.position];\n },\n message: function message(_ref4) {\n var props = _ref4.props;\n return ['p-toast-message', {\n 'p-toast-message-info': props.message.severity === 'info' || props.message.severity === undefined,\n 'p-toast-message-warn': props.message.severity === 'warn',\n 'p-toast-message-error': props.message.severity === 'error',\n 'p-toast-message-success': props.message.severity === 'success',\n 'p-toast-message-secondary': props.message.severity === 'secondary',\n 'p-toast-message-contrast': props.message.severity === 'contrast'\n }];\n },\n messageContent: 'p-toast-message-content',\n messageIcon: function messageIcon(_ref5) {\n var props = _ref5.props;\n return ['p-toast-message-icon', _defineProperty(_defineProperty(_defineProperty(_defineProperty({}, props.infoIcon, props.message.severity === 'info'), props.warnIcon, props.message.severity === 'warn'), props.errorIcon, props.message.severity === 'error'), props.successIcon, props.message.severity === 'success')];\n },\n messageText: 'p-toast-message-text',\n summary: 'p-toast-summary',\n detail: 'p-toast-detail',\n closeButton: 'p-toast-close-button',\n closeIcon: 'p-toast-close-icon'\n};\nvar ToastStyle = BaseStyle.extend({\n name: 'toast',\n theme: theme,\n classes: classes,\n inlineStyles: inlineStyles\n});\n\nexport { ToastStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { setAttribute } from '@primeuix/utils/dom';\nimport { isEmpty } from '@primeuix/utils/object';\nimport { ZIndex } from '@primeuix/utils/zindex';\nimport { UniqueComponentId } from '@primevue/core/utils';\nimport Portal from 'primevue/portal';\nimport ToastEventBus from 'primevue/toasteventbus';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport ToastStyle from 'primevue/toast/style';\nimport CheckIcon from '@primevue/icons/check';\nimport ExclamationTriangleIcon from '@primevue/icons/exclamationtriangle';\nimport InfoCircleIcon from '@primevue/icons/infocircle';\nimport TimesIcon from '@primevue/icons/times';\nimport TimesCircleIcon from '@primevue/icons/timescircle';\nimport Ripple from 'primevue/ripple';\nimport { resolveDirective, openBlock, createElementBlock, mergeProps, createBlock, resolveDynamicComponent, Fragment, createElementVNode, toDisplayString, normalizeProps, withDirectives, createCommentVNode, resolveComponent, withCtx, createVNode, TransitionGroup, renderList } from 'vue';\n\nvar script$2 = {\n name: 'BaseToast',\n \"extends\": BaseComponent,\n props: {\n group: {\n type: String,\n \"default\": null\n },\n position: {\n type: String,\n \"default\": 'top-right'\n },\n autoZIndex: {\n type: Boolean,\n \"default\": true\n },\n baseZIndex: {\n type: Number,\n \"default\": 0\n },\n breakpoints: {\n type: Object,\n \"default\": null\n },\n closeIcon: {\n type: String,\n \"default\": undefined\n },\n infoIcon: {\n type: String,\n \"default\": undefined\n },\n warnIcon: {\n type: String,\n \"default\": undefined\n },\n errorIcon: {\n type: String,\n \"default\": undefined\n },\n successIcon: {\n type: String,\n \"default\": undefined\n },\n closeButtonProps: {\n type: null,\n \"default\": null\n }\n },\n style: ToastStyle,\n provide: function provide() {\n return {\n $pcToast: this,\n $parentInstance: this\n };\n }\n};\n\nvar script$1 = {\n name: 'ToastMessage',\n hostName: 'Toast',\n \"extends\": BaseComponent,\n emits: ['close'],\n closeTimeout: null,\n props: {\n message: {\n type: null,\n \"default\": null\n },\n templates: {\n type: Object,\n \"default\": null\n },\n closeIcon: {\n type: String,\n \"default\": null\n },\n infoIcon: {\n type: String,\n \"default\": null\n },\n warnIcon: {\n type: String,\n \"default\": null\n },\n errorIcon: {\n type: String,\n \"default\": null\n },\n successIcon: {\n type: String,\n \"default\": null\n },\n closeButtonProps: {\n type: null,\n \"default\": null\n }\n },\n mounted: function mounted() {\n var _this = this;\n if (this.message.life) {\n this.closeTimeout = setTimeout(function () {\n _this.close({\n message: _this.message,\n type: 'life-end'\n });\n }, this.message.life);\n }\n },\n beforeUnmount: function beforeUnmount() {\n this.clearCloseTimeout();\n },\n methods: {\n close: function close(params) {\n this.$emit('close', params);\n },\n onCloseClick: function onCloseClick() {\n this.clearCloseTimeout();\n this.close({\n message: this.message,\n type: 'close'\n });\n },\n clearCloseTimeout: function clearCloseTimeout() {\n if (this.closeTimeout) {\n clearTimeout(this.closeTimeout);\n this.closeTimeout = null;\n }\n }\n },\n computed: {\n iconComponent: function iconComponent() {\n return {\n info: !this.infoIcon && InfoCircleIcon,\n success: !this.successIcon && CheckIcon,\n warn: !this.warnIcon && ExclamationTriangleIcon,\n error: !this.errorIcon && TimesCircleIcon\n }[this.message.severity];\n },\n closeAriaLabel: function closeAriaLabel() {\n return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.close : undefined;\n }\n },\n components: {\n TimesIcon: TimesIcon,\n InfoCircleIcon: InfoCircleIcon,\n CheckIcon: CheckIcon,\n ExclamationTriangleIcon: ExclamationTriangleIcon,\n TimesCircleIcon: TimesCircleIcon\n },\n directives: {\n ripple: Ripple\n }\n};\n\nfunction _typeof$1(o) { \"@babel/helpers - typeof\"; return _typeof$1 = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof$1(o); }\nfunction ownKeys$1(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread$1(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys$1(Object(t), !0).forEach(function (r) { _defineProperty$1(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys$1(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty$1(e, r, t) { return (r = _toPropertyKey$1(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey$1(t) { var i = _toPrimitive$1(t, \"string\"); return \"symbol\" == _typeof$1(i) ? i : i + \"\"; }\nfunction _toPrimitive$1(t, r) { if (\"object\" != _typeof$1(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof$1(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nvar _hoisted_1 = [\"aria-label\"];\nfunction render$1(_ctx, _cache, $props, $setup, $data, $options) {\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": [_ctx.cx('message'), $props.message.styleClass],\n role: \"alert\",\n \"aria-live\": \"assertive\",\n \"aria-atomic\": \"true\"\n }, _ctx.ptm('message')), [$props.templates.container ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.container), {\n key: 0,\n message: $props.message,\n closeCallback: $options.onCloseClick\n }, null, 8, [\"message\", \"closeCallback\"])) : (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 1,\n \"class\": [_ctx.cx('messageContent'), $props.message.contentStyleClass]\n }, _ctx.ptm('messageContent')), [!$props.templates.message ? (openBlock(), createElementBlock(Fragment, {\n key: 0\n }, [(openBlock(), createBlock(resolveDynamicComponent($props.templates.messageicon ? $props.templates.messageicon : $props.templates.icon ? $props.templates.icon : $options.iconComponent && $options.iconComponent.name ? $options.iconComponent : 'span'), mergeProps({\n \"class\": _ctx.cx('messageIcon')\n }, _ctx.ptm('messageIcon')), null, 16, [\"class\"])), createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('messageText')\n }, _ctx.ptm('messageText')), [createElementVNode(\"span\", mergeProps({\n \"class\": _ctx.cx('summary')\n }, _ctx.ptm('summary')), toDisplayString($props.message.summary), 17), createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('detail')\n }, _ctx.ptm('detail')), toDisplayString($props.message.detail), 17)], 16)], 64)) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.message), {\n key: 1,\n message: $props.message\n }, null, 8, [\"message\"])), $props.message.closable !== false ? (openBlock(), createElementBlock(\"div\", normalizeProps(mergeProps({\n key: 2\n }, _ctx.ptm('buttonContainer'))), [withDirectives((openBlock(), createElementBlock(\"button\", mergeProps({\n \"class\": _ctx.cx('closeButton'),\n type: \"button\",\n \"aria-label\": $options.closeAriaLabel,\n onClick: _cache[0] || (_cache[0] = function () {\n return $options.onCloseClick && $options.onCloseClick.apply($options, arguments);\n }),\n autofocus: \"\"\n }, _objectSpread$1(_objectSpread$1({}, $props.closeButtonProps), _ctx.ptm('closeButton'))), [(openBlock(), createBlock(resolveDynamicComponent($props.templates.closeicon || 'TimesIcon'), mergeProps({\n \"class\": [_ctx.cx('closeIcon'), $props.closeIcon]\n }, _ctx.ptm('closeIcon')), null, 16, [\"class\"]))], 16, _hoisted_1)), [[_directive_ripple]])], 16)) : createCommentVNode(\"\", true)], 16))], 16);\n}\n\nscript$1.render = render$1;\n\nfunction _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }\nfunction _nonIterableSpread() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _iterableToArray(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nvar messageIdx = 0;\nvar script = {\n name: 'Toast',\n \"extends\": script$2,\n inheritAttrs: false,\n emits: ['close', 'life-end'],\n data: function data() {\n return {\n messages: []\n };\n },\n styleElement: null,\n mounted: function mounted() {\n ToastEventBus.on('add', this.onAdd);\n ToastEventBus.on('remove', this.onRemove);\n ToastEventBus.on('remove-group', this.onRemoveGroup);\n ToastEventBus.on('remove-all-groups', this.onRemoveAllGroups);\n if (this.breakpoints) {\n this.createStyle();\n }\n },\n beforeUnmount: function beforeUnmount() {\n this.destroyStyle();\n if (this.$refs.container && this.autoZIndex) {\n ZIndex.clear(this.$refs.container);\n }\n ToastEventBus.off('add', this.onAdd);\n ToastEventBus.off('remove', this.onRemove);\n ToastEventBus.off('remove-group', this.onRemoveGroup);\n ToastEventBus.off('remove-all-groups', this.onRemoveAllGroups);\n },\n methods: {\n add: function add(message) {\n if (message.id == null) {\n message.id = messageIdx++;\n }\n this.messages = [].concat(_toConsumableArray(this.messages), [message]);\n },\n remove: function remove(params) {\n var index = this.messages.findIndex(function (m) {\n return m.id === params.message.id;\n });\n if (index !== -1) {\n this.messages.splice(index, 1);\n this.$emit(params.type, {\n message: params.message\n });\n }\n },\n onAdd: function onAdd(message) {\n if (this.group == message.group) {\n this.add(message);\n }\n },\n onRemove: function onRemove(message) {\n this.remove({\n message: message,\n type: 'close'\n });\n },\n onRemoveGroup: function onRemoveGroup(group) {\n if (this.group === group) {\n this.messages = [];\n }\n },\n onRemoveAllGroups: function onRemoveAllGroups() {\n this.messages = [];\n },\n onEnter: function onEnter() {\n this.$refs.container.setAttribute(this.attributeSelector, '');\n if (this.autoZIndex) {\n ZIndex.set('modal', this.$refs.container, this.baseZIndex || this.$primevue.config.zIndex.modal);\n }\n },\n onLeave: function onLeave() {\n var _this = this;\n if (this.$refs.container && this.autoZIndex && isEmpty(this.messages)) {\n setTimeout(function () {\n ZIndex.clear(_this.$refs.container);\n }, 200);\n }\n },\n createStyle: function createStyle() {\n if (!this.styleElement && !this.isUnstyled) {\n var _this$$primevue;\n this.styleElement = document.createElement('style');\n this.styleElement.type = 'text/css';\n setAttribute(this.styleElement, 'nonce', (_this$$primevue = this.$primevue) === null || _this$$primevue === void 0 || (_this$$primevue = _this$$primevue.config) === null || _this$$primevue === void 0 || (_this$$primevue = _this$$primevue.csp) === null || _this$$primevue === void 0 ? void 0 : _this$$primevue.nonce);\n document.head.appendChild(this.styleElement);\n var innerHTML = '';\n for (var breakpoint in this.breakpoints) {\n var breakpointStyle = '';\n for (var styleProp in this.breakpoints[breakpoint]) {\n breakpointStyle += styleProp + ':' + this.breakpoints[breakpoint][styleProp] + '!important;';\n }\n innerHTML += \"\\n @media screen and (max-width: \".concat(breakpoint, \") {\\n .p-toast[\").concat(this.attributeSelector, \"] {\\n \").concat(breakpointStyle, \"\\n }\\n }\\n \");\n }\n this.styleElement.innerHTML = innerHTML;\n }\n },\n destroyStyle: function destroyStyle() {\n if (this.styleElement) {\n document.head.removeChild(this.styleElement);\n this.styleElement = null;\n }\n }\n },\n computed: {\n attributeSelector: function attributeSelector() {\n return UniqueComponentId();\n }\n },\n components: {\n ToastMessage: script$1,\n Portal: Portal\n }\n};\n\nfunction _typeof(o) { \"@babel/helpers - typeof\"; return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof(o); }\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty(e, r, t) { return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey(t) { var i = _toPrimitive(t, \"string\"); return \"symbol\" == _typeof(i) ? i : i + \"\"; }\nfunction _toPrimitive(t, r) { if (\"object\" != _typeof(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_ToastMessage = resolveComponent(\"ToastMessage\");\n var _component_Portal = resolveComponent(\"Portal\");\n return openBlock(), createBlock(_component_Portal, null, {\n \"default\": withCtx(function () {\n return [createElementVNode(\"div\", mergeProps({\n ref: \"container\",\n \"class\": _ctx.cx('root'),\n style: _ctx.sx('root', true, {\n position: _ctx.position\n })\n }, _ctx.ptmi('root')), [createVNode(TransitionGroup, mergeProps({\n name: \"p-toast-message\",\n tag: \"div\",\n onEnter: $options.onEnter,\n onLeave: $options.onLeave\n }, _objectSpread({}, _ctx.ptm('transition'))), {\n \"default\": withCtx(function () {\n return [(openBlock(true), createElementBlock(Fragment, null, renderList($data.messages, function (msg) {\n return openBlock(), createBlock(_component_ToastMessage, {\n key: msg.id,\n message: msg,\n templates: _ctx.$slots,\n closeIcon: _ctx.closeIcon,\n infoIcon: _ctx.infoIcon,\n warnIcon: _ctx.warnIcon,\n errorIcon: _ctx.errorIcon,\n successIcon: _ctx.successIcon,\n closeButtonProps: _ctx.closeButtonProps,\n unstyled: _ctx.unstyled,\n onClose: _cache[0] || (_cache[0] = function ($event) {\n return $options.remove($event);\n }),\n pt: _ctx.pt\n }, null, 8, [\"message\", \"templates\", \"closeIcon\", \"infoIcon\", \"warnIcon\", \"errorIcon\", \"successIcon\", \"closeButtonProps\", \"unstyled\", \"pt\"]);\n }), 128))];\n }),\n _: 1\n }, 16, [\"onEnter\", \"onLeave\"])], 16)];\n }),\n _: 1\n });\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n","\n\n\n","\n\n\n","\n\n\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-menubar {\\n display: flex;\\n align-items: center;\\n background: \".concat(dt('menubar.background'), \";\\n border: 1px solid \").concat(dt('menubar.border.color'), \";\\n border-radius: \").concat(dt('menubar.border.radius'), \";\\n color: \").concat(dt('menubar.color'), \";\\n padding: \").concat(dt('menubar.padding'), \";\\n gap: \").concat(dt('menubar.gap'), \";\\n}\\n\\n.p-menubar-start,\\n.p-megamenu-end {\\n display: flex;\\n align-items: center;\\n}\\n\\n.p-menubar-root-list,\\n.p-menubar-submenu {\\n display: flex;\\n margin: 0;\\n padding: 0;\\n list-style: none;\\n outline: 0 none;\\n}\\n\\n.p-menubar-root-list {\\n align-items: center;\\n flex-wrap: wrap;\\n gap: \").concat(dt('menubar.gap'), \";\\n}\\n\\n.p-menubar-root-list > .p-menubar-item > .p-menubar-item-content {\\n border-radius: \").concat(dt('menubar.base.item.border.radius'), \";\\n}\\n\\n.p-menubar-root-list > .p-menubar-item > .p-menubar-item-content > .p-menubar-item-link {\\n padding: \").concat(dt('menubar.base.item.padding'), \";\\n}\\n\\n.p-menubar-item-content {\\n transition: background \").concat(dt('menubar.transition.duration'), \", color \").concat(dt('menubar.transition.duration'), \";\\n border-radius: \").concat(dt('menubar.item.border.radius'), \";\\n color: \").concat(dt('menubar.item.color'), \";\\n}\\n\\n.p-menubar-item-link {\\n cursor: pointer;\\n display: flex;\\n align-items: center;\\n text-decoration: none;\\n overflow: hidden;\\n position: relative;\\n color: inherit;\\n padding: \").concat(dt('menubar.item.padding'), \";\\n gap: \").concat(dt('menubar.item.gap'), \";\\n user-select: none;\\n outline: 0 none;\\n}\\n\\n.p-menubar-item-label {\\n line-height: 1;\\n}\\n\\n.p-menubar-item-icon {\\n color: \").concat(dt('menubar.item.icon.color'), \";\\n}\\n\\n.p-menubar-submenu-icon {\\n color: \").concat(dt('menubar.submenu.icon.color'), \";\\n margin-left: auto;\\n font-size: \").concat(dt('menubar.submenu.icon.size'), \";\\n width: \").concat(dt('menubar.submenu.icon.size'), \";\\n height: \").concat(dt('menubar.submenu.icon.size'), \";\\n}\\n\\n.p-menubar-item.p-focus > .p-menubar-item-content {\\n color: \").concat(dt('menubar.item.focus.color'), \";\\n background: \").concat(dt('menubar.item.focus.background'), \";\\n}\\n\\n.p-menubar-item.p-focus > .p-menubar-item-content .p-menubar-item-icon {\\n color: \").concat(dt('menubar.item.icon.focus.color'), \";\\n}\\n\\n.p-menubar-item.p-focus > .p-menubar-item-content .p-menubar-submenu-icon {\\n color: \").concat(dt('menubar.submenu.icon.focus.color'), \";\\n}\\n\\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover {\\n color: \").concat(dt('menubar.item.focus.color'), \";\\n background: \").concat(dt('menubar.item.focus.background'), \";\\n}\\n\\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover .p-menubar-item-icon {\\n color: \").concat(dt('menubar.item.icon.focus.color'), \";\\n}\\n\\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover .p-menubar-submenu-icon {\\n color: \").concat(dt('menubar.submenu.icon.focus.color'), \";\\n}\\n\\n.p-menubar-item-active > .p-menubar-item-content {\\n color: \").concat(dt('menubar.item.active.color'), \";\\n background: \").concat(dt('menubar.item.active.background'), \";\\n}\\n\\n.p-menubar-item-active > .p-menubar-item-content .p-menubar-item-icon {\\n color: \").concat(dt('menubar.item.icon.active.color'), \";\\n}\\n\\n.p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\\n color: \").concat(dt('menubar.submenu.icon.active.color'), \";\\n}\\n\\n.p-menubar-submenu {\\n display: none;\\n position: absolute;\\n min-width: 12.5rem;\\n z-index: 1;\\n background: \").concat(dt('menubar.submenu.background'), \";\\n border: 1px solid \").concat(dt('menubar.submenu.border.color'), \";\\n border-radius: \").concat(dt('menubar.border.radius'), \";\\n box-shadow: \").concat(dt('menubar.submenu.shadow'), \";\\n color: \").concat(dt('menubar.submenu.color'), \";\\n flex-direction: column;\\n padding: \").concat(dt('menubar.submenu.padding'), \";\\n gap: \").concat(dt('menubar.submenu.gap'), \";\\n}\\n\\n.p-menubar-submenu .p-menubar-separator {\\n border-top: 1px solid \").concat(dt('menubar.separator.border.color'), \";\\n}\\n\\n.p-menubar-submenu .p-menubar-item {\\n position: relative;\\n}\\n\\n .p-menubar-submenu > .p-menubar-item-active > .p-menubar-submenu {\\n display: block;\\n left: 100%;\\n top: 0;\\n}\\n\\n.p-menubar-end {\\n margin-left: auto;\\n align-self: center;\\n}\\n\\n.p-menubar-button {\\n display: none;\\n justify-content: center;\\n align-items: center;\\n cursor: pointer;\\n width: \").concat(dt('menubar.mobile.button.size'), \";\\n height: \").concat(dt('menubar.mobile.button.size'), \";\\n position: relative;\\n color: \").concat(dt('menubar.mobile.button.color'), \";\\n border: 0 none;\\n background: transparent;\\n border-radius: \").concat(dt('menubar.mobile.button.border.radius'), \";\\n transition: background \").concat(dt('menubar.transition.duration'), \", color \").concat(dt('menubar.transition.duration'), \", outline-color \").concat(dt('menubar.transition.duration'), \";\\n outline-color: transparent;\\n}\\n\\n.p-menubar-button:hover {\\n color: \").concat(dt('menubar.mobile.button.hover.color'), \";\\n background: \").concat(dt('menubar.mobile.button.hover.background'), \";\\n}\\n\\n.p-menubar-button:focus-visible {\\n box-shadow: \").concat(dt('menubar.mobile.button.focus.ring.shadow'), \";\\n outline: \").concat(dt('menubar.mobile.button.focus.ring.width'), \" \").concat(dt('menubar.mobile.button.focus.ring.style'), \" \").concat(dt('menubar.mobile.button.focus.ring.color'), \";\\n outline-offset: \").concat(dt('menubar.mobile.button.focus.ring.offset'), \";\\n}\\n\\n.p-menubar-mobile {\\n position: relative;\\n}\\n\\n.p-menubar-mobile .p-menubar-button {\\n display: flex;\\n}\\n\\n.p-menubar-mobile .p-menubar-root-list {\\n position: absolute;\\n display: none;\\n width: 100%;\\n padding: \").concat(dt('menubar.submenu.padding'), \";\\n background: \").concat(dt('menubar.submenu.background'), \";\\n border: 1px solid \").concat(dt('menubar.submenu.border.color'), \";\\n box-shadow: \").concat(dt('menubar.submenu.shadow'), \";\\n}\\n\\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content {\\n border-radius: \").concat(dt('menubar.item.border.radius'), \";\\n}\\n\\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content > .p-menubar-item-link {\\n padding: \").concat(dt('menubar.item.padding'), \";\\n}\\n\\n.p-menubar-mobile-active .p-menubar-root-list {\\n display: flex;\\n flex-direction: column;\\n top: 100%;\\n left: 0;\\n z-index: 1;\\n}\\n\\n.p-menubar-mobile .p-menubar-root-list .p-menubar-item {\\n width: 100%;\\n position: static;\\n}\\n\\n.p-menubar-mobile .p-menubar-root-list .p-menubar-separator {\\n border-top: 1px solid \").concat(dt('menubar.separator.border.color'), \";\\n}\\n\\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content .p-menubar-submenu-icon {\\n margin-left: auto;\\n transition: transform 0.2s;\\n}\\n\\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\\n transform: rotate(-180deg);\\n}\\n\\n.p-menubar-mobile .p-menubar-submenu .p-menubar-submenu-icon {\\n transition: transform 0.2s;\\n transform: rotate(90deg);\\n}\\n\\n.p-menubar-mobile .p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\\n transform: rotate(-90deg);\\n}\\n\\n.p-menubar-mobile .p-menubar-submenu {\\n width: 100%;\\n position: static;\\n box-shadow: none;\\n border: 0 none;\\n padding-left: \").concat(dt('menubar.submenu.mobile.indent'), \";\\n}\\n\");\n};\nvar inlineStyles = {\n submenu: function submenu(_ref2) {\n var instance = _ref2.instance,\n processedItem = _ref2.processedItem;\n return {\n display: instance.isItemActive(processedItem) ? 'flex' : 'none'\n };\n }\n};\nvar classes = {\n root: function root(_ref3) {\n var instance = _ref3.instance;\n return ['p-menubar p-component', {\n 'p-menubar-mobile': instance.queryMatches,\n 'p-menubar-mobile-active': instance.mobileActive\n }];\n },\n start: 'p-menubar-start',\n button: 'p-menubar-button',\n rootList: 'p-menubar-root-list',\n item: function item(_ref4) {\n var instance = _ref4.instance,\n processedItem = _ref4.processedItem;\n return ['p-menubar-item', {\n 'p-menubar-item-active': instance.isItemActive(processedItem),\n 'p-focus': instance.isItemFocused(processedItem),\n 'p-disabled': instance.isItemDisabled(processedItem)\n }];\n },\n itemContent: 'p-menubar-item-content',\n itemLink: 'p-menubar-item-link',\n itemIcon: 'p-menubar-item-icon',\n itemLabel: 'p-menubar-item-label',\n submenuIcon: 'p-menubar-submenu-icon',\n submenu: 'p-menubar-submenu',\n separator: 'p-menubar-separator',\n end: 'p-menubar-end'\n};\nvar MenubarStyle = BaseStyle.extend({\n name: 'menubar',\n theme: theme,\n classes: classes,\n inlineStyles: inlineStyles\n});\n\nexport { MenubarStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { UniqueComponentId } from '@primevue/core/utils';\nimport { focus, findSingle, isTouchDevice } from '@primeuix/utils/dom';\nimport { resolve, isNotEmpty, isPrintableCharacter, isEmpty, findLastIndex } from '@primeuix/utils/object';\nimport { ZIndex } from '@primeuix/utils/zindex';\nimport BarsIcon from '@primevue/icons/bars';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport MenubarStyle from 'primevue/menubar/style';\nimport AngleDownIcon from '@primevue/icons/angledown';\nimport AngleRightIcon from '@primevue/icons/angleright';\nimport Ripple from 'primevue/ripple';\nimport { mergeProps, resolveComponent, resolveDirective, openBlock, createElementBlock, Fragment, renderList, createElementVNode, withDirectives, createBlock, resolveDynamicComponent, normalizeClass, createCommentVNode, toDisplayString, normalizeStyle, renderSlot, createVNode, normalizeProps, guardReactiveProps } from 'vue';\n\nvar script$2 = {\n name: 'BaseMenubar',\n \"extends\": BaseComponent,\n props: {\n model: {\n type: Array,\n \"default\": null\n },\n buttonProps: {\n type: null,\n \"default\": null\n },\n breakpoint: {\n type: String,\n \"default\": '960px'\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n ariaLabel: {\n type: String,\n \"default\": null\n }\n },\n style: MenubarStyle,\n provide: function provide() {\n return {\n $pcMenubar: this,\n $parentInstance: this\n };\n }\n};\n\nvar script$1 = {\n name: 'MenubarSub',\n hostName: 'Menubar',\n \"extends\": BaseComponent,\n emits: ['item-mouseenter', 'item-click', 'item-mousemove'],\n props: {\n items: {\n type: Array,\n \"default\": null\n },\n root: {\n type: Boolean,\n \"default\": false\n },\n popup: {\n type: Boolean,\n \"default\": false\n },\n mobileActive: {\n type: Boolean,\n \"default\": false\n },\n templates: {\n type: Object,\n \"default\": null\n },\n level: {\n type: Number,\n \"default\": 0\n },\n menuId: {\n type: String,\n \"default\": null\n },\n focusedItemId: {\n type: String,\n \"default\": null\n },\n activeItemPath: {\n type: Object,\n \"default\": null\n }\n },\n list: null,\n methods: {\n getItemId: function getItemId(processedItem) {\n return \"\".concat(this.menuId, \"_\").concat(processedItem.key);\n },\n getItemKey: function getItemKey(processedItem) {\n return this.getItemId(processedItem);\n },\n getItemProp: function getItemProp(processedItem, name, params) {\n return processedItem && processedItem.item ? resolve(processedItem.item[name], params) : undefined;\n },\n getItemLabel: function getItemLabel(processedItem) {\n return this.getItemProp(processedItem, 'label');\n },\n getItemLabelId: function getItemLabelId(processedItem) {\n return \"\".concat(this.menuId, \"_\").concat(processedItem.key, \"_label\");\n },\n getPTOptions: function getPTOptions(processedItem, index, key) {\n return this.ptm(key, {\n context: {\n item: processedItem.item,\n index: index,\n active: this.isItemActive(processedItem),\n focused: this.isItemFocused(processedItem),\n disabled: this.isItemDisabled(processedItem),\n level: this.level\n }\n });\n },\n isItemActive: function isItemActive(processedItem) {\n return this.activeItemPath.some(function (path) {\n return path.key === processedItem.key;\n });\n },\n isItemVisible: function isItemVisible(processedItem) {\n return this.getItemProp(processedItem, 'visible') !== false;\n },\n isItemDisabled: function isItemDisabled(processedItem) {\n return this.getItemProp(processedItem, 'disabled');\n },\n isItemFocused: function isItemFocused(processedItem) {\n return this.focusedItemId === this.getItemId(processedItem);\n },\n isItemGroup: function isItemGroup(processedItem) {\n return isNotEmpty(processedItem.items);\n },\n onItemClick: function onItemClick(event, processedItem) {\n this.getItemProp(processedItem, 'command', {\n originalEvent: event,\n item: processedItem.item\n });\n this.$emit('item-click', {\n originalEvent: event,\n processedItem: processedItem,\n isFocus: true\n });\n },\n onItemMouseEnter: function onItemMouseEnter(event, processedItem) {\n this.$emit('item-mouseenter', {\n originalEvent: event,\n processedItem: processedItem\n });\n },\n onItemMouseMove: function onItemMouseMove(event, processedItem) {\n this.$emit('item-mousemove', {\n originalEvent: event,\n processedItem: processedItem\n });\n },\n getAriaPosInset: function getAriaPosInset(index) {\n return index - this.calculateAriaSetSize.slice(0, index).length + 1;\n },\n getMenuItemProps: function getMenuItemProps(processedItem, index) {\n return {\n action: mergeProps({\n \"class\": this.cx('itemLink'),\n tabindex: -1,\n 'aria-hidden': true\n }, this.getPTOptions(processedItem, index, 'itemLink')),\n icon: mergeProps({\n \"class\": [this.cx('itemIcon'), this.getItemProp(processedItem, 'icon')]\n }, this.getPTOptions(processedItem, index, 'itemIcon')),\n label: mergeProps({\n \"class\": this.cx('itemLabel')\n }, this.getPTOptions(processedItem, index, 'itemLabel')),\n submenuicon: mergeProps({\n \"class\": this.cx('submenuIcon')\n }, this.getPTOptions(processedItem, index, 'submenuIcon'))\n };\n }\n },\n computed: {\n calculateAriaSetSize: function calculateAriaSetSize() {\n var _this = this;\n return this.items.filter(function (processedItem) {\n return _this.isItemVisible(processedItem) && _this.getItemProp(processedItem, 'separator');\n });\n },\n getAriaSetSize: function getAriaSetSize() {\n var _this2 = this;\n return this.items.filter(function (processedItem) {\n return _this2.isItemVisible(processedItem) && !_this2.getItemProp(processedItem, 'separator');\n }).length;\n }\n },\n components: {\n AngleRightIcon: AngleRightIcon,\n AngleDownIcon: AngleDownIcon\n },\n directives: {\n ripple: Ripple\n }\n};\n\nvar _hoisted_1$1 = [\"id\", \"aria-label\", \"aria-disabled\", \"aria-expanded\", \"aria-haspopup\", \"aria-level\", \"aria-setsize\", \"aria-posinset\", \"data-p-active\", \"data-p-focused\", \"data-p-disabled\"];\nvar _hoisted_2 = [\"onClick\", \"onMouseenter\", \"onMousemove\"];\nvar _hoisted_3 = [\"href\", \"target\"];\nvar _hoisted_4 = [\"id\"];\nvar _hoisted_5 = [\"id\"];\nfunction render$1(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_MenubarSub = resolveComponent(\"MenubarSub\", true);\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createElementBlock(\"ul\", mergeProps({\n \"class\": $props.level === 0 ? _ctx.cx('rootList') : _ctx.cx('submenu')\n }, $props.level === 0 ? _ctx.ptm('rootList') : _ctx.ptm('submenu')), [(openBlock(true), createElementBlock(Fragment, null, renderList($props.items, function (processedItem, index) {\n return openBlock(), createElementBlock(Fragment, {\n key: $options.getItemKey(processedItem)\n }, [$options.isItemVisible(processedItem) && !$options.getItemProp(processedItem, 'separator') ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 0,\n id: $options.getItemId(processedItem),\n style: $options.getItemProp(processedItem, 'style'),\n \"class\": [_ctx.cx('item', {\n processedItem: processedItem\n }), $options.getItemProp(processedItem, 'class')],\n role: \"menuitem\",\n \"aria-label\": $options.getItemLabel(processedItem),\n \"aria-disabled\": $options.isItemDisabled(processedItem) || undefined,\n \"aria-expanded\": $options.isItemGroup(processedItem) ? $options.isItemActive(processedItem) : undefined,\n \"aria-haspopup\": $options.isItemGroup(processedItem) && !$options.getItemProp(processedItem, 'to') ? 'menu' : undefined,\n \"aria-level\": $props.level + 1,\n \"aria-setsize\": $options.getAriaSetSize,\n \"aria-posinset\": $options.getAriaPosInset(index),\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'item'), {\n \"data-p-active\": $options.isItemActive(processedItem),\n \"data-p-focused\": $options.isItemFocused(processedItem),\n \"data-p-disabled\": $options.isItemDisabled(processedItem)\n }), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('itemContent'),\n onClick: function onClick($event) {\n return $options.onItemClick($event, processedItem);\n },\n onMouseenter: function onMouseenter($event) {\n return $options.onItemMouseEnter($event, processedItem);\n },\n onMousemove: function onMousemove($event) {\n return $options.onItemMouseMove($event, processedItem);\n },\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'itemContent')), [!$props.templates.item ? withDirectives((openBlock(), createElementBlock(\"a\", mergeProps({\n key: 0,\n href: $options.getItemProp(processedItem, 'url'),\n \"class\": _ctx.cx('itemLink'),\n target: $options.getItemProp(processedItem, 'target'),\n tabindex: \"-1\",\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'itemLink')), [$props.templates.itemicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.itemicon), {\n key: 0,\n item: processedItem.item,\n \"class\": normalizeClass(_ctx.cx('itemIcon'))\n }, null, 8, [\"item\", \"class\"])) : $options.getItemProp(processedItem, 'icon') ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 1,\n \"class\": [_ctx.cx('itemIcon'), $options.getItemProp(processedItem, 'icon')],\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'itemIcon')), null, 16)) : createCommentVNode(\"\", true), createElementVNode(\"span\", mergeProps({\n id: $options.getItemLabelId(processedItem),\n \"class\": _ctx.cx('itemLabel'),\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'itemLabel')), toDisplayString($options.getItemLabel(processedItem)), 17, _hoisted_4), $options.getItemProp(processedItem, 'items') ? (openBlock(), createElementBlock(Fragment, {\n key: 2\n }, [$props.templates.submenuicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.submenuicon), {\n key: 0,\n root: $props.root,\n active: $options.isItemActive(processedItem),\n \"class\": normalizeClass(_ctx.cx('submenuIcon'))\n }, null, 8, [\"root\", \"active\", \"class\"])) : (openBlock(), createBlock(resolveDynamicComponent($props.root ? 'AngleDownIcon' : 'AngleRightIcon'), mergeProps({\n key: 1,\n \"class\": _ctx.cx('submenuIcon'),\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'submenuIcon')), null, 16, [\"class\"]))], 64)) : createCommentVNode(\"\", true)], 16, _hoisted_3)), [[_directive_ripple]]) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.item), {\n key: 1,\n item: processedItem.item,\n root: $props.root,\n hasSubmenu: $options.getItemProp(processedItem, 'items'),\n label: $options.getItemLabel(processedItem),\n props: $options.getMenuItemProps(processedItem, index)\n }, null, 8, [\"item\", \"root\", \"hasSubmenu\", \"label\", \"props\"]))], 16, _hoisted_2), $options.isItemVisible(processedItem) && $options.isItemGroup(processedItem) ? (openBlock(), createBlock(_component_MenubarSub, {\n key: 0,\n id: $options.getItemId(processedItem) + '_list',\n menuId: $props.menuId,\n role: \"menu\",\n style: normalizeStyle(_ctx.sx('submenu', true, {\n processedItem: processedItem\n })),\n focusedItemId: $props.focusedItemId,\n items: processedItem.items,\n mobileActive: $props.mobileActive,\n activeItemPath: $props.activeItemPath,\n templates: $props.templates,\n level: $props.level + 1,\n \"aria-labelledby\": $options.getItemLabelId(processedItem),\n pt: _ctx.pt,\n unstyled: _ctx.unstyled,\n onItemClick: _cache[0] || (_cache[0] = function ($event) {\n return _ctx.$emit('item-click', $event);\n }),\n onItemMouseenter: _cache[1] || (_cache[1] = function ($event) {\n return _ctx.$emit('item-mouseenter', $event);\n }),\n onItemMousemove: _cache[2] || (_cache[2] = function ($event) {\n return _ctx.$emit('item-mousemove', $event);\n })\n }, null, 8, [\"id\", \"menuId\", \"style\", \"focusedItemId\", \"items\", \"mobileActive\", \"activeItemPath\", \"templates\", \"level\", \"aria-labelledby\", \"pt\", \"unstyled\"])) : createCommentVNode(\"\", true)], 16, _hoisted_1$1)) : createCommentVNode(\"\", true), $options.isItemVisible(processedItem) && $options.getItemProp(processedItem, 'separator') ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 1,\n id: $options.getItemId(processedItem),\n \"class\": [_ctx.cx('separator'), $options.getItemProp(processedItem, 'class')],\n style: $options.getItemProp(processedItem, 'style'),\n role: \"separator\",\n ref_for: true\n }, _ctx.ptm('separator')), null, 16, _hoisted_5)) : createCommentVNode(\"\", true)], 64);\n }), 128))], 16);\n}\n\nscript$1.render = render$1;\n\nvar script = {\n name: 'Menubar',\n \"extends\": script$2,\n inheritAttrs: false,\n emits: ['focus', 'blur'],\n matchMediaListener: null,\n data: function data() {\n return {\n id: this.$attrs.id,\n mobileActive: false,\n focused: false,\n focusedItemInfo: {\n index: -1,\n level: 0,\n parentKey: ''\n },\n activeItemPath: [],\n dirty: false,\n query: null,\n queryMatches: false\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n },\n activeItemPath: function activeItemPath(newPath) {\n if (isNotEmpty(newPath)) {\n this.bindOutsideClickListener();\n this.bindResizeListener();\n } else {\n this.unbindOutsideClickListener();\n this.unbindResizeListener();\n }\n }\n },\n outsideClickListener: null,\n container: null,\n menubar: null,\n mounted: function mounted() {\n this.id = this.id || UniqueComponentId();\n this.bindMatchMediaListener();\n },\n beforeUnmount: function beforeUnmount() {\n this.mobileActive = false;\n this.unbindOutsideClickListener();\n this.unbindResizeListener();\n this.unbindMatchMediaListener();\n if (this.container) {\n ZIndex.clear(this.container);\n }\n this.container = null;\n },\n methods: {\n getItemProp: function getItemProp(item, name) {\n return item ? resolve(item[name]) : undefined;\n },\n getItemLabel: function getItemLabel(item) {\n return this.getItemProp(item, 'label');\n },\n isItemDisabled: function isItemDisabled(item) {\n return this.getItemProp(item, 'disabled');\n },\n isItemVisible: function isItemVisible(item) {\n return this.getItemProp(item, 'visible') !== false;\n },\n isItemGroup: function isItemGroup(item) {\n return isNotEmpty(this.getItemProp(item, 'items'));\n },\n isItemSeparator: function isItemSeparator(item) {\n return this.getItemProp(item, 'separator');\n },\n getProccessedItemLabel: function getProccessedItemLabel(processedItem) {\n return processedItem ? this.getItemLabel(processedItem.item) : undefined;\n },\n isProccessedItemGroup: function isProccessedItemGroup(processedItem) {\n return processedItem && isNotEmpty(processedItem.items);\n },\n toggle: function toggle(event) {\n var _this = this;\n if (this.mobileActive) {\n this.mobileActive = false;\n ZIndex.clear(this.menubar);\n this.hide();\n } else {\n this.mobileActive = true;\n ZIndex.set('menu', this.menubar, this.$primevue.config.zIndex.menu);\n setTimeout(function () {\n _this.show();\n }, 1);\n }\n this.bindOutsideClickListener();\n event.preventDefault();\n },\n show: function show() {\n focus(this.menubar);\n },\n hide: function hide(event, isFocus) {\n var _this2 = this;\n if (this.mobileActive) {\n this.mobileActive = false;\n setTimeout(function () {\n focus(_this2.$refs.menubutton);\n }, 0);\n }\n this.activeItemPath = [];\n this.focusedItemInfo = {\n index: -1,\n level: 0,\n parentKey: ''\n };\n isFocus && focus(this.menubar);\n this.dirty = false;\n },\n onFocus: function onFocus(event) {\n this.focused = true;\n this.focusedItemInfo = this.focusedItemInfo.index !== -1 ? this.focusedItemInfo : {\n index: this.findFirstFocusedItemIndex(),\n level: 0,\n parentKey: ''\n };\n this.$emit('focus', event);\n },\n onBlur: function onBlur(event) {\n this.focused = false;\n this.focusedItemInfo = {\n index: -1,\n level: 0,\n parentKey: ''\n };\n this.searchValue = '';\n this.dirty = false;\n this.$emit('blur', event);\n },\n onKeyDown: function onKeyDown(event) {\n var metaKey = event.metaKey || event.ctrlKey;\n switch (event.code) {\n case 'ArrowDown':\n this.onArrowDownKey(event);\n break;\n case 'ArrowUp':\n this.onArrowUpKey(event);\n break;\n case 'ArrowLeft':\n this.onArrowLeftKey(event);\n break;\n case 'ArrowRight':\n this.onArrowRightKey(event);\n break;\n case 'Home':\n this.onHomeKey(event);\n break;\n case 'End':\n this.onEndKey(event);\n break;\n case 'Space':\n this.onSpaceKey(event);\n break;\n case 'Enter':\n case 'NumpadEnter':\n this.onEnterKey(event);\n break;\n case 'Escape':\n this.onEscapeKey(event);\n break;\n case 'Tab':\n this.onTabKey(event);\n break;\n case 'PageDown':\n case 'PageUp':\n case 'Backspace':\n case 'ShiftLeft':\n case 'ShiftRight':\n //NOOP\n break;\n default:\n if (!metaKey && isPrintableCharacter(event.key)) {\n this.searchItems(event, event.key);\n }\n break;\n }\n },\n onItemChange: function onItemChange(event) {\n var processedItem = event.processedItem,\n isFocus = event.isFocus;\n if (isEmpty(processedItem)) return;\n var index = processedItem.index,\n key = processedItem.key,\n level = processedItem.level,\n parentKey = processedItem.parentKey,\n items = processedItem.items;\n var grouped = isNotEmpty(items);\n var activeItemPath = this.activeItemPath.filter(function (p) {\n return p.parentKey !== parentKey && p.parentKey !== key;\n });\n grouped && activeItemPath.push(processedItem);\n this.focusedItemInfo = {\n index: index,\n level: level,\n parentKey: parentKey\n };\n this.activeItemPath = activeItemPath;\n grouped && (this.dirty = true);\n isFocus && focus(this.menubar);\n },\n onItemClick: function onItemClick(event) {\n var originalEvent = event.originalEvent,\n processedItem = event.processedItem;\n var grouped = this.isProccessedItemGroup(processedItem);\n var root = isEmpty(processedItem.parent);\n var selected = this.isSelected(processedItem);\n if (selected) {\n var index = processedItem.index,\n key = processedItem.key,\n level = processedItem.level,\n parentKey = processedItem.parentKey;\n this.activeItemPath = this.activeItemPath.filter(function (p) {\n return key !== p.key && key.startsWith(p.key);\n });\n this.focusedItemInfo = {\n index: index,\n level: level,\n parentKey: parentKey\n };\n this.dirty = !root;\n focus(this.menubar);\n } else {\n if (grouped) {\n this.onItemChange(event);\n } else {\n var rootProcessedItem = root ? processedItem : this.activeItemPath.find(function (p) {\n return p.parentKey === '';\n });\n this.hide(originalEvent);\n this.changeFocusedItemIndex(originalEvent, rootProcessedItem ? rootProcessedItem.index : -1);\n this.mobileActive = false;\n focus(this.menubar);\n }\n }\n },\n onItemMouseEnter: function onItemMouseEnter(event) {\n if (this.dirty) {\n this.onItemChange(event);\n }\n },\n onItemMouseMove: function onItemMouseMove(event) {\n if (this.focused) {\n this.changeFocusedItemIndex(event, event.processedItem.index);\n }\n },\n menuButtonClick: function menuButtonClick(event) {\n this.toggle(event);\n },\n menuButtonKeydown: function menuButtonKeydown(event) {\n (event.code === 'Enter' || event.code === 'NumpadEnter' || event.code === 'Space') && this.menuButtonClick(event);\n },\n onArrowDownKey: function onArrowDownKey(event) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var root = processedItem ? isEmpty(processedItem.parent) : null;\n if (root) {\n var grouped = this.isProccessedItemGroup(processedItem);\n if (grouped) {\n this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n this.focusedItemInfo = {\n index: -1,\n parentKey: processedItem.key\n };\n this.onArrowRightKey(event);\n }\n } else {\n var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex();\n this.changeFocusedItemIndex(event, itemIndex);\n }\n event.preventDefault();\n },\n onArrowUpKey: function onArrowUpKey(event) {\n var _this3 = this;\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var root = isEmpty(processedItem.parent);\n if (root) {\n var grouped = this.isProccessedItemGroup(processedItem);\n if (grouped) {\n this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n this.focusedItemInfo = {\n index: -1,\n parentKey: processedItem.key\n };\n var itemIndex = this.findLastItemIndex();\n this.changeFocusedItemIndex(event, itemIndex);\n }\n } else {\n var parentItem = this.activeItemPath.find(function (p) {\n return p.key === processedItem.parentKey;\n });\n if (this.focusedItemInfo.index === 0) {\n this.focusedItemInfo = {\n index: -1,\n parentKey: parentItem ? parentItem.parentKey : ''\n };\n this.searchValue = '';\n this.onArrowLeftKey(event);\n this.activeItemPath = this.activeItemPath.filter(function (p) {\n return p.parentKey !== _this3.focusedItemInfo.parentKey;\n });\n } else {\n var _itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex();\n this.changeFocusedItemIndex(event, _itemIndex);\n }\n }\n event.preventDefault();\n },\n onArrowLeftKey: function onArrowLeftKey(event) {\n var _this4 = this;\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var parentItem = processedItem ? this.activeItemPath.find(function (p) {\n return p.key === processedItem.parentKey;\n }) : null;\n if (parentItem) {\n this.onItemChange({\n originalEvent: event,\n processedItem: parentItem\n });\n this.activeItemPath = this.activeItemPath.filter(function (p) {\n return p.parentKey !== _this4.focusedItemInfo.parentKey;\n });\n event.preventDefault();\n } else {\n var itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex();\n this.changeFocusedItemIndex(event, itemIndex);\n event.preventDefault();\n }\n },\n onArrowRightKey: function onArrowRightKey(event) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var parentItem = processedItem ? this.activeItemPath.find(function (p) {\n return p.key === processedItem.parentKey;\n }) : null;\n if (parentItem) {\n var grouped = this.isProccessedItemGroup(processedItem);\n if (grouped) {\n this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n this.focusedItemInfo = {\n index: -1,\n parentKey: processedItem.key\n };\n this.onArrowDownKey(event);\n }\n } else {\n var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex();\n this.changeFocusedItemIndex(event, itemIndex);\n event.preventDefault();\n }\n },\n onHomeKey: function onHomeKey(event) {\n this.changeFocusedItemIndex(event, this.findFirstItemIndex());\n event.preventDefault();\n },\n onEndKey: function onEndKey(event) {\n this.changeFocusedItemIndex(event, this.findLastItemIndex());\n event.preventDefault();\n },\n onEnterKey: function onEnterKey(event) {\n if (this.focusedItemInfo.index !== -1) {\n var element = findSingle(this.menubar, \"li[id=\\\"\".concat(\"\".concat(this.focusedItemId), \"\\\"]\"));\n var anchorElement = element && findSingle(element, 'a[data-pc-section=\"itemlink\"]');\n anchorElement ? anchorElement.click() : element && element.click();\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n !grouped && (this.focusedItemInfo.index = this.findFirstFocusedItemIndex());\n }\n event.preventDefault();\n },\n onSpaceKey: function onSpaceKey(event) {\n this.onEnterKey(event);\n },\n onEscapeKey: function onEscapeKey(event) {\n if (this.focusedItemInfo.level !== 0) {\n var _focusedItemInfo = this.focusedItemInfo;\n this.hide(event, false);\n this.focusedItemInfo = {\n index: Number(_focusedItemInfo.parentKey.split('_')[0]),\n level: 0,\n parentKey: ''\n };\n }\n event.preventDefault();\n },\n onTabKey: function onTabKey(event) {\n if (this.focusedItemInfo.index !== -1) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n !grouped && this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n }\n this.hide();\n },\n bindOutsideClickListener: function bindOutsideClickListener() {\n var _this5 = this;\n if (!this.outsideClickListener) {\n this.outsideClickListener = function (event) {\n var isOutsideContainer = _this5.container && !_this5.container.contains(event.target);\n var isOutsideTarget = !(_this5.target && (_this5.target === event.target || _this5.target.contains(event.target)));\n if (isOutsideContainer && isOutsideTarget) {\n _this5.hide();\n }\n };\n document.addEventListener('click', this.outsideClickListener);\n }\n },\n unbindOutsideClickListener: function unbindOutsideClickListener() {\n if (this.outsideClickListener) {\n document.removeEventListener('click', this.outsideClickListener);\n this.outsideClickListener = null;\n }\n },\n bindResizeListener: function bindResizeListener() {\n var _this6 = this;\n if (!this.resizeListener) {\n this.resizeListener = function (event) {\n if (!isTouchDevice()) {\n _this6.hide(event, true);\n }\n _this6.mobileActive = false;\n };\n window.addEventListener('resize', this.resizeListener);\n }\n },\n unbindResizeListener: function unbindResizeListener() {\n if (this.resizeListener) {\n window.removeEventListener('resize', this.resizeListener);\n this.resizeListener = null;\n }\n },\n bindMatchMediaListener: function bindMatchMediaListener() {\n var _this7 = this;\n if (!this.matchMediaListener) {\n var query = matchMedia(\"(max-width: \".concat(this.breakpoint, \")\"));\n this.query = query;\n this.queryMatches = query.matches;\n this.matchMediaListener = function () {\n _this7.queryMatches = query.matches;\n _this7.mobileActive = false;\n };\n this.query.addEventListener('change', this.matchMediaListener);\n }\n },\n unbindMatchMediaListener: function unbindMatchMediaListener() {\n if (this.matchMediaListener) {\n this.query.removeEventListener('change', this.matchMediaListener);\n this.matchMediaListener = null;\n }\n },\n isItemMatched: function isItemMatched(processedItem) {\n var _this$getProccessedIt;\n return this.isValidItem(processedItem) && ((_this$getProccessedIt = this.getProccessedItemLabel(processedItem)) === null || _this$getProccessedIt === void 0 ? void 0 : _this$getProccessedIt.toLocaleLowerCase().startsWith(this.searchValue.toLocaleLowerCase()));\n },\n isValidItem: function isValidItem(processedItem) {\n return !!processedItem && !this.isItemDisabled(processedItem.item) && !this.isItemSeparator(processedItem.item) && this.isItemVisible(processedItem.item);\n },\n isValidSelectedItem: function isValidSelectedItem(processedItem) {\n return this.isValidItem(processedItem) && this.isSelected(processedItem);\n },\n isSelected: function isSelected(processedItem) {\n return this.activeItemPath.some(function (p) {\n return p.key === processedItem.key;\n });\n },\n findFirstItemIndex: function findFirstItemIndex() {\n var _this8 = this;\n return this.visibleItems.findIndex(function (processedItem) {\n return _this8.isValidItem(processedItem);\n });\n },\n findLastItemIndex: function findLastItemIndex() {\n var _this9 = this;\n return findLastIndex(this.visibleItems, function (processedItem) {\n return _this9.isValidItem(processedItem);\n });\n },\n findNextItemIndex: function findNextItemIndex(index) {\n var _this10 = this;\n var matchedItemIndex = index < this.visibleItems.length - 1 ? this.visibleItems.slice(index + 1).findIndex(function (processedItem) {\n return _this10.isValidItem(processedItem);\n }) : -1;\n return matchedItemIndex > -1 ? matchedItemIndex + index + 1 : index;\n },\n findPrevItemIndex: function findPrevItemIndex(index) {\n var _this11 = this;\n var matchedItemIndex = index > 0 ? findLastIndex(this.visibleItems.slice(0, index), function (processedItem) {\n return _this11.isValidItem(processedItem);\n }) : -1;\n return matchedItemIndex > -1 ? matchedItemIndex : index;\n },\n findSelectedItemIndex: function findSelectedItemIndex() {\n var _this12 = this;\n return this.visibleItems.findIndex(function (processedItem) {\n return _this12.isValidSelectedItem(processedItem);\n });\n },\n findFirstFocusedItemIndex: function findFirstFocusedItemIndex() {\n var selectedIndex = this.findSelectedItemIndex();\n return selectedIndex < 0 ? this.findFirstItemIndex() : selectedIndex;\n },\n findLastFocusedItemIndex: function findLastFocusedItemIndex() {\n var selectedIndex = this.findSelectedItemIndex();\n return selectedIndex < 0 ? this.findLastItemIndex() : selectedIndex;\n },\n searchItems: function searchItems(event, _char) {\n var _this13 = this;\n this.searchValue = (this.searchValue || '') + _char;\n var itemIndex = -1;\n var matched = false;\n if (this.focusedItemInfo.index !== -1) {\n itemIndex = this.visibleItems.slice(this.focusedItemInfo.index).findIndex(function (processedItem) {\n return _this13.isItemMatched(processedItem);\n });\n itemIndex = itemIndex === -1 ? this.visibleItems.slice(0, this.focusedItemInfo.index).findIndex(function (processedItem) {\n return _this13.isItemMatched(processedItem);\n }) : itemIndex + this.focusedItemInfo.index;\n } else {\n itemIndex = this.visibleItems.findIndex(function (processedItem) {\n return _this13.isItemMatched(processedItem);\n });\n }\n if (itemIndex !== -1) {\n matched = true;\n }\n if (itemIndex === -1 && this.focusedItemInfo.index === -1) {\n itemIndex = this.findFirstFocusedItemIndex();\n }\n if (itemIndex !== -1) {\n this.changeFocusedItemIndex(event, itemIndex);\n }\n if (this.searchTimeout) {\n clearTimeout(this.searchTimeout);\n }\n this.searchTimeout = setTimeout(function () {\n _this13.searchValue = '';\n _this13.searchTimeout = null;\n }, 500);\n return matched;\n },\n changeFocusedItemIndex: function changeFocusedItemIndex(event, index) {\n if (this.focusedItemInfo.index !== index) {\n this.focusedItemInfo.index = index;\n this.scrollInView();\n }\n },\n scrollInView: function scrollInView() {\n var index = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : -1;\n var id = index !== -1 ? \"\".concat(this.id, \"_\").concat(index) : this.focusedItemId;\n var element = findSingle(this.menubar, \"li[id=\\\"\".concat(id, \"\\\"]\"));\n if (element) {\n element.scrollIntoView && element.scrollIntoView({\n block: 'nearest',\n inline: 'start'\n });\n }\n },\n createProcessedItems: function createProcessedItems(items) {\n var _this14 = this;\n var level = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0;\n var parent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};\n var parentKey = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : '';\n var processedItems = [];\n items && items.forEach(function (item, index) {\n var key = (parentKey !== '' ? parentKey + '_' : '') + index;\n var newItem = {\n item: item,\n index: index,\n level: level,\n key: key,\n parent: parent,\n parentKey: parentKey\n };\n newItem['items'] = _this14.createProcessedItems(item.items, level + 1, newItem, key);\n processedItems.push(newItem);\n });\n return processedItems;\n },\n containerRef: function containerRef(el) {\n this.container = el;\n },\n menubarRef: function menubarRef(el) {\n this.menubar = el ? el.$el : undefined;\n }\n },\n computed: {\n processedItems: function processedItems() {\n return this.createProcessedItems(this.model || []);\n },\n visibleItems: function visibleItems() {\n var _this15 = this;\n var processedItem = this.activeItemPath.find(function (p) {\n return p.key === _this15.focusedItemInfo.parentKey;\n });\n return processedItem ? processedItem.items : this.processedItems;\n },\n focusedItemId: function focusedItemId() {\n return this.focusedItemInfo.index !== -1 ? \"\".concat(this.id).concat(isNotEmpty(this.focusedItemInfo.parentKey) ? '_' + this.focusedItemInfo.parentKey : '', \"_\").concat(this.focusedItemInfo.index) : null;\n }\n },\n components: {\n MenubarSub: script$1,\n BarsIcon: BarsIcon\n }\n};\n\nfunction _typeof(o) { \"@babel/helpers - typeof\"; return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof(o); }\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty(e, r, t) { return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey(t) { var i = _toPrimitive(t, \"string\"); return \"symbol\" == _typeof(i) ? i : i + \"\"; }\nfunction _toPrimitive(t, r) { if (\"object\" != _typeof(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nvar _hoisted_1 = [\"aria-haspopup\", \"aria-expanded\", \"aria-controls\", \"aria-label\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_BarsIcon = resolveComponent(\"BarsIcon\");\n var _component_MenubarSub = resolveComponent(\"MenubarSub\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n ref: $options.containerRef,\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [_ctx.$slots.start ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('start')\n }, _ctx.ptm('start')), [renderSlot(_ctx.$slots, \"start\")], 16)) : createCommentVNode(\"\", true), renderSlot(_ctx.$slots, _ctx.$slots.button ? 'button' : 'menubutton', {\n id: $data.id,\n \"class\": normalizeClass(_ctx.cx('button')),\n toggleCallback: function toggleCallback(event) {\n return $options.menuButtonClick(event);\n }\n }, function () {\n var _ctx$$primevue$config;\n return [_ctx.model && _ctx.model.length > 0 ? (openBlock(), createElementBlock(\"a\", mergeProps({\n key: 0,\n ref: \"menubutton\",\n role: \"button\",\n tabindex: \"0\",\n \"class\": _ctx.cx('button'),\n \"aria-haspopup\": _ctx.model.length && _ctx.model.length > 0 ? true : false,\n \"aria-expanded\": $data.mobileActive,\n \"aria-controls\": $data.id,\n \"aria-label\": (_ctx$$primevue$config = _ctx.$primevue.config.locale.aria) === null || _ctx$$primevue$config === void 0 ? void 0 : _ctx$$primevue$config.navigation,\n onClick: _cache[0] || (_cache[0] = function ($event) {\n return $options.menuButtonClick($event);\n }),\n onKeydown: _cache[1] || (_cache[1] = function ($event) {\n return $options.menuButtonKeydown($event);\n })\n }, _objectSpread(_objectSpread({}, _ctx.buttonProps), _ctx.ptm('button'))), [renderSlot(_ctx.$slots, _ctx.$slots.buttonicon ? 'buttonicon' : 'menubuttonicon', {}, function () {\n return [createVNode(_component_BarsIcon, normalizeProps(guardReactiveProps(_ctx.ptm('buttonicon'))), null, 16)];\n })], 16, _hoisted_1)) : createCommentVNode(\"\", true)];\n }), createVNode(_component_MenubarSub, {\n ref: $options.menubarRef,\n id: $data.id + '_list',\n role: \"menubar\",\n items: $options.processedItems,\n templates: _ctx.$slots,\n root: true,\n mobileActive: $data.mobileActive,\n tabindex: \"0\",\n \"aria-activedescendant\": $data.focused ? $options.focusedItemId : undefined,\n menuId: $data.id,\n focusedItemId: $data.focused ? $options.focusedItemId : undefined,\n activeItemPath: $data.activeItemPath,\n level: 0,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel,\n pt: _ctx.pt,\n unstyled: _ctx.unstyled,\n onFocus: $options.onFocus,\n onBlur: $options.onBlur,\n onKeydown: $options.onKeyDown,\n onItemClick: $options.onItemClick,\n onItemMouseenter: $options.onItemMouseEnter,\n onItemMousemove: $options.onItemMouseMove\n }, null, 8, [\"id\", \"items\", \"templates\", \"mobileActive\", \"aria-activedescendant\", \"menuId\", \"focusedItemId\", \"activeItemPath\", \"aria-labelledby\", \"aria-label\", \"pt\", \"unstyled\", \"onFocus\", \"onBlur\", \"onKeydown\", \"onItemClick\", \"onItemMouseenter\", \"onItemMousemove\"]), _ctx.$slots.end ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 1,\n \"class\": _ctx.cx('end')\n }, _ctx.ptm('end')), [renderSlot(_ctx.$slots, \"end\")], 16)) : createCommentVNode(\"\", true)], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-panel {\\n border: 1px solid \".concat(dt('panel.border.color'), \";\\n border-radius: \").concat(dt('panel.border.radius'), \";\\n background: \").concat(dt('panel.background'), \";\\n color: \").concat(dt('panel.color'), \";\\n}\\n\\n.p-panel-header {\\n display: flex;\\n justify-content: space-between;\\n align-items: center;\\n padding: \").concat(dt('panel.header.padding'), \";\\n background: \").concat(dt('panel.header.background'), \";\\n color: \").concat(dt('panel.header.color'), \";\\n border-style: solid;\\n border-width: \").concat(dt('panel.header.border.width'), \";\\n border-color: \").concat(dt('panel.header.border.color'), \";\\n border-radius: \").concat(dt('panel.header.border.radius'), \";\\n}\\n\\n.p-panel-toggleable .p-panel-header {\\n padding: \").concat(dt('panel.toggleable.header.padding'), \";\\n}\\n\\n.p-panel-title {\\n line-height: 1;\\n font-weight: \").concat(dt('panel.title.font.weight'), \";\\n}\\n\\n.p-panel-content {\\n padding: \").concat(dt('panel.content.padding'), \";\\n}\\n\\n.p-panel-footer {\\n padding: \").concat(dt('panel.footer.padding'), \";\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var props = _ref2.props;\n return ['p-panel p-component', {\n 'p-panel-toggleable': props.toggleable\n }];\n },\n header: 'p-panel-header',\n title: 'p-panel-title',\n headerActions: 'p-panel-header-actions',\n pcToggleButton: 'p-panel-toggle-button',\n contentContainer: 'p-panel-content-container',\n content: 'p-panel-content',\n footer: 'p-panel-footer'\n};\nvar PanelStyle = BaseStyle.extend({\n name: 'panel',\n theme: theme,\n classes: classes\n});\n\nexport { PanelStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { UniqueComponentId } from '@primevue/core/utils';\nimport MinusIcon from '@primevue/icons/minus';\nimport PlusIcon from '@primevue/icons/plus';\nimport Button from 'primevue/button';\nimport Ripple from 'primevue/ripple';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport PanelStyle from 'primevue/panel/style';\nimport { resolveComponent, openBlock, createElementBlock, mergeProps, createElementVNode, renderSlot, normalizeClass, toDisplayString, createCommentVNode, createBlock, withCtx, resolveDynamicComponent, createVNode, Transition, withDirectives, vShow } from 'vue';\n\nvar script$1 = {\n name: 'BasePanel',\n \"extends\": BaseComponent,\n props: {\n header: String,\n toggleable: Boolean,\n collapsed: Boolean,\n toggleButtonProps: {\n type: Object,\n \"default\": function _default() {\n return {\n severity: 'secondary',\n text: true,\n rounded: true\n };\n }\n }\n },\n style: PanelStyle,\n provide: function provide() {\n return {\n $pcPanel: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'Panel',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:collapsed', 'toggle'],\n data: function data() {\n return {\n id: this.$attrs.id,\n d_collapsed: this.collapsed\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n },\n collapsed: function collapsed(newValue) {\n this.d_collapsed = newValue;\n }\n },\n mounted: function mounted() {\n this.id = this.id || UniqueComponentId();\n },\n methods: {\n toggle: function toggle(event) {\n this.d_collapsed = !this.d_collapsed;\n this.$emit('update:collapsed', this.d_collapsed);\n this.$emit('toggle', {\n originalEvent: event,\n value: this.d_collapsed\n });\n },\n onKeyDown: function onKeyDown(event) {\n if (event.code === 'Enter' || event.code === 'NumpadEnter' || event.code === 'Space') {\n this.toggle(event);\n event.preventDefault();\n }\n }\n },\n computed: {\n buttonAriaLabel: function buttonAriaLabel() {\n return this.toggleButtonProps && this.toggleButtonProps.ariaLabel ? this.toggleButtonProps.ariaLabel : this.header;\n }\n },\n components: {\n PlusIcon: PlusIcon,\n MinusIcon: MinusIcon,\n Button: Button\n },\n directives: {\n ripple: Ripple\n }\n};\n\nvar _hoisted_1 = [\"id\"];\nvar _hoisted_2 = [\"id\", \"aria-labelledby\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_Button = resolveComponent(\"Button\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('header')\n }, _ctx.ptm('header')), [renderSlot(_ctx.$slots, \"header\", {\n id: $data.id + '_header',\n \"class\": normalizeClass(_ctx.cx('title'))\n }, function () {\n return [_ctx.header ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 0,\n id: $data.id + '_header',\n \"class\": _ctx.cx('title')\n }, _ctx.ptm('title')), toDisplayString(_ctx.header), 17, _hoisted_1)) : createCommentVNode(\"\", true)];\n }), createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('headerActions')\n }, _ctx.ptm('headerActions')), [renderSlot(_ctx.$slots, \"icons\"), _ctx.toggleable ? (openBlock(), createBlock(_component_Button, mergeProps({\n key: 0,\n id: $data.id + '_header',\n \"class\": _ctx.cx('pcToggleButton'),\n \"aria-label\": $options.buttonAriaLabel,\n \"aria-controls\": $data.id + '_content',\n \"aria-expanded\": !$data.d_collapsed,\n unstyled: _ctx.unstyled,\n onClick: $options.toggle,\n onKeydown: $options.onKeyDown\n }, _ctx.toggleButtonProps, {\n pt: _ctx.ptm('pcToggleButton')\n }), {\n icon: withCtx(function (slotProps) {\n return [renderSlot(_ctx.$slots, _ctx.$slots.toggleicon ? 'toggleicon' : 'togglericon', {\n collapsed: $data.d_collapsed\n }, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent($data.d_collapsed ? 'PlusIcon' : 'MinusIcon'), mergeProps({\n \"class\": slotProps[\"class\"]\n }, _ctx.ptm('pcToggleButton')['icon']), null, 16, [\"class\"]))];\n })];\n }),\n _: 3\n }, 16, [\"id\", \"class\", \"aria-label\", \"aria-controls\", \"aria-expanded\", \"unstyled\", \"onClick\", \"onKeydown\", \"pt\"])) : createCommentVNode(\"\", true)], 16)], 16), createVNode(Transition, mergeProps({\n name: \"p-toggleable-content\"\n }, _ctx.ptm('transition')), {\n \"default\": withCtx(function () {\n return [withDirectives(createElementVNode(\"div\", mergeProps({\n id: $data.id + '_content',\n \"class\": _ctx.cx('contentContainer'),\n role: \"region\",\n \"aria-labelledby\": $data.id + '_header'\n }, _ctx.ptm('contentContainer')), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('content')\n }, _ctx.ptm('content')), [renderSlot(_ctx.$slots, \"default\")], 16), _ctx.$slots.footer ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('footer')\n }, _ctx.ptm('footer')), [renderSlot(_ctx.$slots, \"footer\")], 16)) : createCommentVNode(\"\", true)], 16, _hoisted_2), [[vShow, !$data.d_collapsed]])];\n }),\n _: 3\n }, 16)], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-tieredmenu {\\n background: \".concat(dt('tieredmenu.background'), \";\\n color: \").concat(dt('tieredmenu.color'), \";\\n border: 1px solid \").concat(dt('tieredmenu.border.color'), \";\\n border-radius: \").concat(dt('tieredmenu.border.radius'), \";\\n min-width: 12.5rem;\\n}\\n\\n.p-tieredmenu-root-list,\\n.p-tieredmenu-submenu {\\n margin: 0;\\n padding: \").concat(dt('tieredmenu.list.padding'), \";\\n list-style: none;\\n outline: 0 none;\\n display: flex;\\n flex-direction: column;\\n gap: \").concat(dt('tieredmenu.list.gap'), \";\\n}\\n\\n.p-tieredmenu-submenu {\\n position: absolute;\\n min-width: 100%;\\n z-index: 1;\\n background: \").concat(dt('tieredmenu.background'), \";\\n color: \").concat(dt('tieredmenu.color'), \";\\n border: 1px solid \").concat(dt('tieredmenu.border.color'), \";\\n border-radius: \").concat(dt('tieredmenu.border.radius'), \";\\n box-shadow: \").concat(dt('tieredmenu.shadow'), \";\\n}\\n\\n.p-tieredmenu-item {\\n position: relative;\\n}\\n\\n.p-tieredmenu-item-content {\\n transition: background \").concat(dt('tieredmenu.transition.duration'), \", color \").concat(dt('tieredmenu.transition.duration'), \";\\n border-radius: \").concat(dt('tieredmenu.item.border.radius'), \";\\n color: \").concat(dt('tieredmenu.item.color'), \";\\n}\\n\\n.p-tieredmenu-item-link {\\n cursor: pointer;\\n display: flex;\\n align-items: center;\\n text-decoration: none;\\n overflow: hidden;\\n position: relative;\\n color: inherit;\\n padding: \").concat(dt('tieredmenu.item.padding'), \";\\n gap: \").concat(dt('tieredmenu.item.gap'), \";\\n user-select: none;\\n outline: 0 none;\\n}\\n\\n.p-tieredmenu-item-label {\\n line-height: 1;\\n}\\n\\n.p-tieredmenu-item-icon {\\n color: \").concat(dt('tieredmenu.item.icon.color'), \";\\n}\\n\\n.p-tieredmenu-submenu-icon {\\n color: \").concat(dt('tieredmenu.submenu.icon.color'), \";\\n margin-left: auto;\\n font-size: \").concat(dt('tieredmenu.submenu.icon.size'), \";\\n width: \").concat(dt('tieredmenu.submenu.icon.size'), \";\\n height: \").concat(dt('tieredmenu.submenu.icon.size'), \";\\n}\\n\\n.p-tieredmenu-item.p-focus > .p-tieredmenu-item-content {\\n color: \").concat(dt('tieredmenu.item.focus.color'), \";\\n background: \").concat(dt('tieredmenu.item.focus.background'), \";\\n}\\n\\n.p-tieredmenu-item.p-focus > .p-tieredmenu-item-content .p-tieredmenu-item-icon {\\n color: \").concat(dt('tieredmenu.item.icon.focus.color'), \";\\n}\\n\\n.p-tieredmenu-item.p-focus > .p-tieredmenu-item-content .p-tieredmenu-submenu-icon {\\n color: \").concat(dt('tieredmenu.submenu.icon.focus.color'), \";\\n}\\n\\n.p-tieredmenu-item:not(.p-disabled) > .p-tieredmenu-item-content:hover {\\n color: \").concat(dt('tieredmenu.item.focus.color'), \";\\n background: \").concat(dt('tieredmenu.item.focus.background'), \";\\n}\\n\\n.p-tieredmenu-item:not(.p-disabled) > .p-tieredmenu-item-content:hover .p-tieredmenu-item-icon {\\n color: \").concat(dt('tieredmenu.item.icon.focus.color'), \";\\n}\\n\\n.p-tieredmenu-item:not(.p-disabled) > .p-tieredmenu-item-content:hover .p-tieredmenu-submenu-icon {\\n color: \").concat(dt('tieredmenu.submenu.icon.focus.color'), \";\\n}\\n\\n.p-tieredmenu-item-active > .p-tieredmenu-item-content {\\n color: \").concat(dt('tieredmenu.item.active.color'), \";\\n background: \").concat(dt('tieredmenu.item.active.background'), \";\\n}\\n\\n.p-tieredmenu-item-active > .p-tieredmenu-item-content .p-tieredmenu-item-icon {\\n color: \").concat(dt('tieredmenu.item.icon.active.color'), \";\\n}\\n\\n.p-tieredmenu-item-active > .p-tieredmenu-item-content .p-tieredmenu-submenu-icon {\\n color: \").concat(dt('tieredmenu.submenu.icon.active.color'), \";\\n}\\n\\n.p-tieredmenu-separator {\\n border-top: 1px solid \").concat(dt('tieredmenu.separator.border.color'), \";\\n}\\n\\n.p-tieredmenu-overlay {\\n box-shadow: \").concat(dt('tieredmenu.shadow'), \";\\n}\\n\\n.p-tieredmenu-enter-from,\\n.p-tieredmenu-leave-active {\\n opacity: 0;\\n}\\n\\n.p-tieredmenu-enter-active {\\n transition: opacity 250ms;\\n}\\n\");\n};\nvar inlineStyles = {\n submenu: function submenu(_ref2) {\n var instance = _ref2.instance,\n processedItem = _ref2.processedItem;\n return {\n display: instance.isItemActive(processedItem) ? 'flex' : 'none'\n };\n }\n};\nvar classes = {\n root: function root(_ref3) {\n _ref3.instance;\n var props = _ref3.props;\n return ['p-tieredmenu p-component', {\n 'p-tieredmenu-overlay': props.popup\n }];\n },\n start: 'p-tieredmenu-start',\n rootList: 'p-tieredmenu-root-list',\n item: function item(_ref4) {\n var instance = _ref4.instance,\n processedItem = _ref4.processedItem;\n return ['p-tieredmenu-item', {\n 'p-tieredmenu-item-active': instance.isItemActive(processedItem),\n 'p-focus': instance.isItemFocused(processedItem),\n 'p-disabled': instance.isItemDisabled(processedItem)\n }];\n },\n itemContent: 'p-tieredmenu-item-content',\n itemLink: 'p-tieredmenu-item-link',\n itemIcon: 'p-tieredmenu-item-icon',\n itemLabel: 'p-tieredmenu-item-label',\n submenuIcon: 'p-tieredmenu-submenu-icon',\n submenu: 'p-tieredmenu-submenu',\n separator: 'p-tieredmenu-separator',\n end: 'p-tieredmenu-end'\n};\nvar TieredMenuStyle = BaseStyle.extend({\n name: 'tieredmenu',\n theme: theme,\n classes: classes,\n inlineStyles: inlineStyles\n});\n\nexport { TieredMenuStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { UniqueComponentId, ConnectedOverlayScrollHandler } from '@primevue/core/utils';\nimport { nestedPosition, focus, findSingle, addStyle, absolutePosition, getOuterWidth, isTouchDevice } from '@primeuix/utils/dom';\nimport { resolve, isNotEmpty, isPrintableCharacter, isEmpty, findLastIndex } from '@primeuix/utils/object';\nimport { ZIndex } from '@primeuix/utils/zindex';\nimport OverlayEventBus from 'primevue/overlayeventbus';\nimport Portal from 'primevue/portal';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport TieredMenuStyle from 'primevue/tieredmenu/style';\nimport AngleRightIcon from '@primevue/icons/angleright';\nimport Ripple from 'primevue/ripple';\nimport { mergeProps, resolveComponent, resolveDirective, openBlock, createBlock, Transition, withCtx, createElementBlock, Fragment, renderList, createElementVNode, withDirectives, resolveDynamicComponent, normalizeClass, createCommentVNode, toDisplayString, normalizeStyle, createVNode, renderSlot } from 'vue';\n\nvar script$2 = {\n name: 'BaseTieredMenu',\n \"extends\": BaseComponent,\n props: {\n popup: {\n type: Boolean,\n \"default\": false\n },\n model: {\n type: Array,\n \"default\": null\n },\n appendTo: {\n type: [String, Object],\n \"default\": 'body'\n },\n autoZIndex: {\n type: Boolean,\n \"default\": true\n },\n baseZIndex: {\n type: Number,\n \"default\": 0\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n tabindex: {\n type: Number,\n \"default\": 0\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n ariaLabel: {\n type: String,\n \"default\": null\n }\n },\n style: TieredMenuStyle,\n provide: function provide() {\n return {\n $pcTieredMenu: this,\n $parentInstance: this\n };\n }\n};\n\nvar script$1 = {\n name: 'TieredMenuSub',\n hostName: 'TieredMenu',\n \"extends\": BaseComponent,\n emits: ['item-click', 'item-mouseenter', 'item-mousemove'],\n container: null,\n props: {\n menuId: {\n type: String,\n \"default\": null\n },\n focusedItemId: {\n type: String,\n \"default\": null\n },\n items: {\n type: Array,\n \"default\": null\n },\n visible: {\n type: Boolean,\n \"default\": false\n },\n level: {\n type: Number,\n \"default\": 0\n },\n templates: {\n type: Object,\n \"default\": null\n },\n activeItemPath: {\n type: Object,\n \"default\": null\n },\n tabindex: {\n type: Number,\n \"default\": 0\n }\n },\n methods: {\n getItemId: function getItemId(processedItem) {\n return \"\".concat(this.menuId, \"_\").concat(processedItem.key);\n },\n getItemKey: function getItemKey(processedItem) {\n return this.getItemId(processedItem);\n },\n getItemProp: function getItemProp(processedItem, name, params) {\n return processedItem && processedItem.item ? resolve(processedItem.item[name], params) : undefined;\n },\n getItemLabel: function getItemLabel(processedItem) {\n return this.getItemProp(processedItem, 'label');\n },\n getItemLabelId: function getItemLabelId(processedItem) {\n return \"\".concat(this.menuId, \"_\").concat(processedItem.key, \"_label\");\n },\n getPTOptions: function getPTOptions(processedItem, index, key) {\n return this.ptm(key, {\n context: {\n item: processedItem.item,\n index: index,\n active: this.isItemActive(processedItem),\n focused: this.isItemFocused(processedItem),\n disabled: this.isItemDisabled(processedItem)\n }\n });\n },\n isItemActive: function isItemActive(processedItem) {\n return this.activeItemPath.some(function (path) {\n return path.key === processedItem.key;\n });\n },\n isItemVisible: function isItemVisible(processedItem) {\n return this.getItemProp(processedItem, 'visible') !== false;\n },\n isItemDisabled: function isItemDisabled(processedItem) {\n return this.getItemProp(processedItem, 'disabled');\n },\n isItemFocused: function isItemFocused(processedItem) {\n return this.focusedItemId === this.getItemId(processedItem);\n },\n isItemGroup: function isItemGroup(processedItem) {\n return isNotEmpty(processedItem.items);\n },\n onEnter: function onEnter() {\n nestedPosition(this.container, this.level);\n },\n onItemClick: function onItemClick(event, processedItem) {\n this.getItemProp(processedItem, 'command', {\n originalEvent: event,\n item: processedItem.item\n });\n this.$emit('item-click', {\n originalEvent: event,\n processedItem: processedItem,\n isFocus: true\n });\n },\n onItemMouseEnter: function onItemMouseEnter(event, processedItem) {\n this.$emit('item-mouseenter', {\n originalEvent: event,\n processedItem: processedItem\n });\n },\n onItemMouseMove: function onItemMouseMove(event, processedItem) {\n this.$emit('item-mousemove', {\n originalEvent: event,\n processedItem: processedItem\n });\n },\n getAriaSetSize: function getAriaSetSize() {\n var _this = this;\n return this.items.filter(function (processedItem) {\n return _this.isItemVisible(processedItem) && !_this.getItemProp(processedItem, 'separator');\n }).length;\n },\n getAriaPosInset: function getAriaPosInset(index) {\n var _this2 = this;\n return index - this.items.slice(0, index).filter(function (processedItem) {\n return _this2.isItemVisible(processedItem) && _this2.getItemProp(processedItem, 'separator');\n }).length + 1;\n },\n getMenuItemProps: function getMenuItemProps(processedItem, index) {\n return {\n action: mergeProps({\n \"class\": this.cx('itemLink'),\n tabindex: -1,\n 'aria-hidden': true\n }, this.getPTOptions(processedItem, index, 'itemLink')),\n icon: mergeProps({\n \"class\": [this.cx('itemIcon'), this.getItemProp(processedItem, 'icon')]\n }, this.getPTOptions(processedItem, index, 'itemIcon')),\n label: mergeProps({\n \"class\": this.cx('itemLabel')\n }, this.getPTOptions(processedItem, index, 'itemLabel')),\n submenuicon: mergeProps({\n \"class\": this.cx('submenuIcon')\n }, this.getPTOptions(processedItem, index, 'submenuIcon'))\n };\n },\n containerRef: function containerRef(el) {\n this.container = el;\n }\n },\n components: {\n AngleRightIcon: AngleRightIcon\n },\n directives: {\n ripple: Ripple\n }\n};\n\nvar _hoisted_1$1 = [\"tabindex\"];\nvar _hoisted_2 = [\"id\", \"aria-label\", \"aria-disabled\", \"aria-expanded\", \"aria-haspopup\", \"aria-level\", \"aria-setsize\", \"aria-posinset\", \"data-p-active\", \"data-p-focused\", \"data-p-disabled\"];\nvar _hoisted_3 = [\"onClick\", \"onMouseenter\", \"onMousemove\"];\nvar _hoisted_4 = [\"href\", \"target\"];\nvar _hoisted_5 = [\"id\"];\nvar _hoisted_6 = [\"id\"];\nfunction render$1(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_AngleRightIcon = resolveComponent(\"AngleRightIcon\");\n var _component_TieredMenuSub = resolveComponent(\"TieredMenuSub\", true);\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createBlock(Transition, mergeProps({\n name: \"p-tieredmenu\",\n onEnter: $options.onEnter\n }, _ctx.ptm('menu.transition')), {\n \"default\": withCtx(function () {\n return [($props.level === 0 ? true : $props.visible) ? (openBlock(), createElementBlock(\"ul\", mergeProps({\n key: 0,\n ref: $options.containerRef,\n \"class\": $props.level === 0 ? _ctx.cx('rootList') : _ctx.cx('submenu'),\n tabindex: $props.tabindex\n }, $props.level === 0 ? _ctx.ptm('rootList') : _ctx.ptm('submenu')), [(openBlock(true), createElementBlock(Fragment, null, renderList($props.items, function (processedItem, index) {\n return openBlock(), createElementBlock(Fragment, {\n key: $options.getItemKey(processedItem)\n }, [$options.isItemVisible(processedItem) && !$options.getItemProp(processedItem, 'separator') ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 0,\n id: $options.getItemId(processedItem),\n style: $options.getItemProp(processedItem, 'style'),\n \"class\": [_ctx.cx('item', {\n processedItem: processedItem\n }), $options.getItemProp(processedItem, 'class')],\n role: \"menuitem\",\n \"aria-label\": $options.getItemLabel(processedItem),\n \"aria-disabled\": $options.isItemDisabled(processedItem) || undefined,\n \"aria-expanded\": $options.isItemGroup(processedItem) ? $options.isItemActive(processedItem) : undefined,\n \"aria-haspopup\": $options.isItemGroup(processedItem) && !$options.getItemProp(processedItem, 'to') ? 'menu' : undefined,\n \"aria-level\": $props.level + 1,\n \"aria-setsize\": $options.getAriaSetSize(),\n \"aria-posinset\": $options.getAriaPosInset(index),\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'item'), {\n \"data-p-active\": $options.isItemActive(processedItem),\n \"data-p-focused\": $options.isItemFocused(processedItem),\n \"data-p-disabled\": $options.isItemDisabled(processedItem)\n }), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('itemContent'),\n onClick: function onClick($event) {\n return $options.onItemClick($event, processedItem);\n },\n onMouseenter: function onMouseenter($event) {\n return $options.onItemMouseEnter($event, processedItem);\n },\n onMousemove: function onMousemove($event) {\n return $options.onItemMouseMove($event, processedItem);\n },\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'itemContent')), [!$props.templates.item ? withDirectives((openBlock(), createElementBlock(\"a\", mergeProps({\n key: 0,\n href: $options.getItemProp(processedItem, 'url'),\n \"class\": _ctx.cx('itemLink'),\n target: $options.getItemProp(processedItem, 'target'),\n tabindex: \"-1\",\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'itemLink')), [$props.templates.itemicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.itemicon), {\n key: 0,\n item: processedItem.item,\n \"class\": normalizeClass(_ctx.cx('itemIcon'))\n }, null, 8, [\"item\", \"class\"])) : $options.getItemProp(processedItem, 'icon') ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 1,\n \"class\": [_ctx.cx('itemIcon'), $options.getItemProp(processedItem, 'icon')],\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'itemIcon')), null, 16)) : createCommentVNode(\"\", true), createElementVNode(\"span\", mergeProps({\n id: $options.getItemLabelId(processedItem),\n \"class\": _ctx.cx('itemLabel'),\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'itemLabel')), toDisplayString($options.getItemLabel(processedItem)), 17, _hoisted_5), $options.getItemProp(processedItem, 'items') ? (openBlock(), createElementBlock(Fragment, {\n key: 2\n }, [$props.templates.submenuicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.submenuicon), mergeProps({\n key: 0,\n \"class\": _ctx.cx('submenuIcon'),\n active: $options.isItemActive(processedItem),\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'submenuIcon')), null, 16, [\"class\", \"active\"])) : (openBlock(), createBlock(_component_AngleRightIcon, mergeProps({\n key: 1,\n \"class\": _ctx.cx('submenuIcon'),\n ref_for: true\n }, $options.getPTOptions(processedItem, index, 'submenuIcon')), null, 16, [\"class\"]))], 64)) : createCommentVNode(\"\", true)], 16, _hoisted_4)), [[_directive_ripple]]) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.item), {\n key: 1,\n item: processedItem.item,\n hasSubmenu: $options.getItemProp(processedItem, 'items'),\n label: $options.getItemLabel(processedItem),\n props: $options.getMenuItemProps(processedItem, index)\n }, null, 8, [\"item\", \"hasSubmenu\", \"label\", \"props\"]))], 16, _hoisted_3), $options.isItemVisible(processedItem) && $options.isItemGroup(processedItem) ? (openBlock(), createBlock(_component_TieredMenuSub, {\n key: 0,\n id: $options.getItemId(processedItem) + '_list',\n style: normalizeStyle(_ctx.sx('submenu', true, {\n processedItem: processedItem\n })),\n \"aria-labelledby\": $options.getItemLabelId(processedItem),\n role: \"menu\",\n menuId: $props.menuId,\n focusedItemId: $props.focusedItemId,\n items: processedItem.items,\n templates: $props.templates,\n activeItemPath: $props.activeItemPath,\n level: $props.level + 1,\n visible: $options.isItemActive(processedItem) && $options.isItemGroup(processedItem),\n pt: _ctx.pt,\n unstyled: _ctx.unstyled,\n onItemClick: _cache[0] || (_cache[0] = function ($event) {\n return _ctx.$emit('item-click', $event);\n }),\n onItemMouseenter: _cache[1] || (_cache[1] = function ($event) {\n return _ctx.$emit('item-mouseenter', $event);\n }),\n onItemMousemove: _cache[2] || (_cache[2] = function ($event) {\n return _ctx.$emit('item-mousemove', $event);\n })\n }, null, 8, [\"id\", \"style\", \"aria-labelledby\", \"menuId\", \"focusedItemId\", \"items\", \"templates\", \"activeItemPath\", \"level\", \"visible\", \"pt\", \"unstyled\"])) : createCommentVNode(\"\", true)], 16, _hoisted_2)) : createCommentVNode(\"\", true), $options.isItemVisible(processedItem) && $options.getItemProp(processedItem, 'separator') ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 1,\n id: $options.getItemId(processedItem),\n style: $options.getItemProp(processedItem, 'style'),\n \"class\": [_ctx.cx('separator'), $options.getItemProp(processedItem, 'class')],\n role: \"separator\",\n ref_for: true\n }, _ctx.ptm('separator')), null, 16, _hoisted_6)) : createCommentVNode(\"\", true)], 64);\n }), 128))], 16, _hoisted_1$1)) : createCommentVNode(\"\", true)];\n }),\n _: 1\n }, 16, [\"onEnter\"]);\n}\n\nscript$1.render = render$1;\n\nvar script = {\n name: 'TieredMenu',\n \"extends\": script$2,\n inheritAttrs: false,\n emits: ['focus', 'blur', 'before-show', 'before-hide', 'hide', 'show'],\n outsideClickListener: null,\n scrollHandler: null,\n resizeListener: null,\n target: null,\n container: null,\n menubar: null,\n searchTimeout: null,\n searchValue: null,\n data: function data() {\n return {\n id: this.$attrs.id,\n focused: false,\n focusedItemInfo: {\n index: -1,\n level: 0,\n parentKey: ''\n },\n activeItemPath: [],\n visible: !this.popup,\n submenuVisible: false,\n dirty: false\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n },\n activeItemPath: function activeItemPath(newPath) {\n if (!this.popup) {\n if (isNotEmpty(newPath)) {\n this.bindOutsideClickListener();\n this.bindResizeListener();\n } else {\n this.unbindOutsideClickListener();\n this.unbindResizeListener();\n }\n }\n }\n },\n mounted: function mounted() {\n this.id = this.id || UniqueComponentId();\n },\n beforeUnmount: function beforeUnmount() {\n this.unbindOutsideClickListener();\n this.unbindResizeListener();\n if (this.scrollHandler) {\n this.scrollHandler.destroy();\n this.scrollHandler = null;\n }\n if (this.container && this.autoZIndex) {\n ZIndex.clear(this.container);\n }\n this.target = null;\n this.container = null;\n },\n methods: {\n getItemProp: function getItemProp(item, name) {\n return item ? resolve(item[name]) : undefined;\n },\n getItemLabel: function getItemLabel(item) {\n return this.getItemProp(item, 'label');\n },\n isItemDisabled: function isItemDisabled(item) {\n return this.getItemProp(item, 'disabled');\n },\n isItemVisible: function isItemVisible(item) {\n return this.getItemProp(item, 'visible') !== false;\n },\n isItemGroup: function isItemGroup(item) {\n return isNotEmpty(this.getItemProp(item, 'items'));\n },\n isItemSeparator: function isItemSeparator(item) {\n return this.getItemProp(item, 'separator');\n },\n getProccessedItemLabel: function getProccessedItemLabel(processedItem) {\n return processedItem ? this.getItemLabel(processedItem.item) : undefined;\n },\n isProccessedItemGroup: function isProccessedItemGroup(processedItem) {\n return processedItem && isNotEmpty(processedItem.items);\n },\n toggle: function toggle(event) {\n this.visible ? this.hide(event, true) : this.show(event);\n },\n show: function show(event, isFocus) {\n if (this.popup) {\n this.$emit('before-show');\n this.visible = true;\n this.target = this.target || event.currentTarget;\n this.relatedTarget = event.relatedTarget || null;\n }\n isFocus && focus(this.menubar);\n },\n hide: function hide(event, isFocus) {\n if (this.popup) {\n this.$emit('before-hide');\n this.visible = false;\n }\n this.activeItemPath = [];\n this.focusedItemInfo = {\n index: -1,\n level: 0,\n parentKey: ''\n };\n isFocus && focus(this.relatedTarget || this.target || this.menubar);\n this.dirty = false;\n },\n onFocus: function onFocus(event) {\n this.focused = true;\n if (!this.popup) {\n this.focusedItemInfo = this.focusedItemInfo.index !== -1 ? this.focusedItemInfo : {\n index: this.findFirstFocusedItemIndex(),\n level: 0,\n parentKey: ''\n };\n }\n this.$emit('focus', event);\n },\n onBlur: function onBlur(event) {\n this.focused = false;\n this.focusedItemInfo = {\n index: -1,\n level: 0,\n parentKey: ''\n };\n this.searchValue = '';\n this.dirty = false;\n this.$emit('blur', event);\n },\n onKeyDown: function onKeyDown(event) {\n if (this.disabled) {\n event.preventDefault();\n return;\n }\n var metaKey = event.metaKey || event.ctrlKey;\n switch (event.code) {\n case 'ArrowDown':\n this.onArrowDownKey(event);\n break;\n case 'ArrowUp':\n this.onArrowUpKey(event);\n break;\n case 'ArrowLeft':\n this.onArrowLeftKey(event);\n break;\n case 'ArrowRight':\n this.onArrowRightKey(event);\n break;\n case 'Home':\n this.onHomeKey(event);\n break;\n case 'End':\n this.onEndKey(event);\n break;\n case 'Space':\n this.onSpaceKey(event);\n break;\n case 'Enter':\n case 'NumpadEnter':\n this.onEnterKey(event);\n break;\n case 'Escape':\n this.onEscapeKey(event);\n break;\n case 'Tab':\n this.onTabKey(event);\n break;\n case 'PageDown':\n case 'PageUp':\n case 'Backspace':\n case 'ShiftLeft':\n case 'ShiftRight':\n //NOOP\n break;\n default:\n if (!metaKey && isPrintableCharacter(event.key)) {\n this.searchItems(event, event.key);\n }\n break;\n }\n },\n onItemChange: function onItemChange(event) {\n var processedItem = event.processedItem,\n isFocus = event.isFocus;\n if (isEmpty(processedItem)) return;\n var index = processedItem.index,\n key = processedItem.key,\n level = processedItem.level,\n parentKey = processedItem.parentKey,\n items = processedItem.items;\n var grouped = isNotEmpty(items);\n var activeItemPath = this.activeItemPath.filter(function (p) {\n return p.parentKey !== parentKey && p.parentKey !== key;\n });\n if (grouped) {\n activeItemPath.push(processedItem);\n this.submenuVisible = true;\n }\n this.focusedItemInfo = {\n index: index,\n level: level,\n parentKey: parentKey\n };\n this.activeItemPath = activeItemPath;\n grouped && (this.dirty = true);\n isFocus && focus(this.menubar);\n },\n onOverlayClick: function onOverlayClick(event) {\n OverlayEventBus.emit('overlay-click', {\n originalEvent: event,\n target: this.target\n });\n },\n onItemClick: function onItemClick(event) {\n var originalEvent = event.originalEvent,\n processedItem = event.processedItem;\n var grouped = this.isProccessedItemGroup(processedItem);\n var root = isEmpty(processedItem.parent);\n var selected = this.isSelected(processedItem);\n if (selected) {\n var index = processedItem.index,\n key = processedItem.key,\n level = processedItem.level,\n parentKey = processedItem.parentKey;\n this.activeItemPath = this.activeItemPath.filter(function (p) {\n return key !== p.key && key.startsWith(p.key);\n });\n this.focusedItemInfo = {\n index: index,\n level: level,\n parentKey: parentKey\n };\n this.dirty = !root;\n focus(this.menubar);\n } else {\n if (grouped) {\n this.onItemChange(event);\n } else {\n var rootProcessedItem = root ? processedItem : this.activeItemPath.find(function (p) {\n return p.parentKey === '';\n });\n this.hide(originalEvent);\n this.changeFocusedItemIndex(originalEvent, rootProcessedItem ? rootProcessedItem.index : -1);\n focus(this.menubar);\n }\n }\n },\n onItemMouseEnter: function onItemMouseEnter(event) {\n if (this.dirty) {\n this.onItemChange(event);\n }\n },\n onItemMouseMove: function onItemMouseMove(event) {\n if (this.focused) {\n this.changeFocusedItemIndex(event, event.processedItem.index);\n }\n },\n onArrowDownKey: function onArrowDownKey(event) {\n var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex();\n this.changeFocusedItemIndex(event, itemIndex);\n event.preventDefault();\n },\n onArrowUpKey: function onArrowUpKey(event) {\n if (event.altKey) {\n if (this.focusedItemInfo.index !== -1) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n !grouped && this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n }\n this.popup && this.hide(event, true);\n event.preventDefault();\n } else {\n var itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex();\n this.changeFocusedItemIndex(event, itemIndex);\n event.preventDefault();\n }\n },\n onArrowLeftKey: function onArrowLeftKey(event) {\n var _this = this;\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var parentItem = this.activeItemPath.find(function (p) {\n return p.key === processedItem.parentKey;\n });\n var root = isEmpty(processedItem.parent);\n if (!root) {\n this.focusedItemInfo = {\n index: -1,\n parentKey: parentItem ? parentItem.parentKey : ''\n };\n this.searchValue = '';\n this.onArrowDownKey(event);\n }\n this.activeItemPath = this.activeItemPath.filter(function (p) {\n return p.parentKey !== _this.focusedItemInfo.parentKey;\n });\n event.preventDefault();\n },\n onArrowRightKey: function onArrowRightKey(event) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n if (grouped) {\n this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n this.focusedItemInfo = {\n index: -1,\n parentKey: processedItem.key\n };\n this.searchValue = '';\n this.onArrowDownKey(event);\n }\n event.preventDefault();\n },\n onHomeKey: function onHomeKey(event) {\n this.changeFocusedItemIndex(event, this.findFirstItemIndex());\n event.preventDefault();\n },\n onEndKey: function onEndKey(event) {\n this.changeFocusedItemIndex(event, this.findLastItemIndex());\n event.preventDefault();\n },\n onEnterKey: function onEnterKey(event) {\n if (this.focusedItemInfo.index !== -1) {\n var element = findSingle(this.menubar, \"li[id=\\\"\".concat(\"\".concat(this.focusedItemId), \"\\\"]\"));\n var anchorElement = element && findSingle(element, '[data-pc-section=\"itemlink\"]');\n anchorElement ? anchorElement.click() : element && element.click();\n if (!this.popup) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n !grouped && (this.focusedItemInfo.index = this.findFirstFocusedItemIndex());\n }\n }\n event.preventDefault();\n },\n onSpaceKey: function onSpaceKey(event) {\n this.onEnterKey(event);\n },\n onEscapeKey: function onEscapeKey(event) {\n if (this.popup || this.focusedItemInfo.level !== 0) {\n var _focusedItemInfo = this.focusedItemInfo;\n this.hide(event, false);\n this.focusedItemInfo = {\n index: Number(_focusedItemInfo.parentKey.split('_')[0]),\n level: 0,\n parentKey: ''\n };\n this.popup && focus(this.target);\n }\n event.preventDefault();\n },\n onTabKey: function onTabKey(event) {\n if (this.focusedItemInfo.index !== -1) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n !grouped && this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n }\n this.hide();\n },\n onEnter: function onEnter(el) {\n if (this.autoZIndex) {\n ZIndex.set('menu', el, this.baseZIndex + this.$primevue.config.zIndex.menu);\n }\n addStyle(el, {\n position: 'absolute',\n top: '0',\n left: '0'\n });\n this.alignOverlay();\n focus(this.menubar);\n this.scrollInView();\n },\n onAfterEnter: function onAfterEnter() {\n this.bindOutsideClickListener();\n this.bindScrollListener();\n this.bindResizeListener();\n this.$emit('show');\n },\n onLeave: function onLeave() {\n this.unbindOutsideClickListener();\n this.unbindScrollListener();\n this.unbindResizeListener();\n this.$emit('hide');\n this.container = null;\n this.dirty = false;\n },\n onAfterLeave: function onAfterLeave(el) {\n if (this.autoZIndex) {\n ZIndex.clear(el);\n }\n },\n alignOverlay: function alignOverlay() {\n absolutePosition(this.container, this.target);\n var targetWidth = getOuterWidth(this.target);\n if (targetWidth > getOuterWidth(this.container)) {\n this.container.style.minWidth = getOuterWidth(this.target) + 'px';\n }\n },\n bindOutsideClickListener: function bindOutsideClickListener() {\n var _this2 = this;\n if (!this.outsideClickListener) {\n this.outsideClickListener = function (event) {\n var isOutsideContainer = _this2.container && !_this2.container.contains(event.target);\n var isOutsideTarget = _this2.popup ? !(_this2.target && (_this2.target === event.target || _this2.target.contains(event.target))) : true;\n if (isOutsideContainer && isOutsideTarget) {\n _this2.hide();\n }\n };\n document.addEventListener('click', this.outsideClickListener);\n }\n },\n unbindOutsideClickListener: function unbindOutsideClickListener() {\n if (this.outsideClickListener) {\n document.removeEventListener('click', this.outsideClickListener);\n this.outsideClickListener = null;\n }\n },\n bindScrollListener: function bindScrollListener() {\n var _this3 = this;\n if (!this.scrollHandler) {\n this.scrollHandler = new ConnectedOverlayScrollHandler(this.target, function (event) {\n _this3.hide(event, true);\n });\n }\n this.scrollHandler.bindScrollListener();\n },\n unbindScrollListener: function unbindScrollListener() {\n if (this.scrollHandler) {\n this.scrollHandler.unbindScrollListener();\n }\n },\n bindResizeListener: function bindResizeListener() {\n var _this4 = this;\n if (!this.resizeListener) {\n this.resizeListener = function (event) {\n if (!isTouchDevice()) {\n _this4.hide(event, true);\n }\n };\n window.addEventListener('resize', this.resizeListener);\n }\n },\n unbindResizeListener: function unbindResizeListener() {\n if (this.resizeListener) {\n window.removeEventListener('resize', this.resizeListener);\n this.resizeListener = null;\n }\n },\n isItemMatched: function isItemMatched(processedItem) {\n var _this$getProccessedIt;\n return this.isValidItem(processedItem) && ((_this$getProccessedIt = this.getProccessedItemLabel(processedItem)) === null || _this$getProccessedIt === void 0 ? void 0 : _this$getProccessedIt.toLocaleLowerCase().startsWith(this.searchValue.toLocaleLowerCase()));\n },\n isValidItem: function isValidItem(processedItem) {\n return !!processedItem && !this.isItemDisabled(processedItem.item) && !this.isItemSeparator(processedItem.item) && this.isItemVisible(processedItem.item);\n },\n isValidSelectedItem: function isValidSelectedItem(processedItem) {\n return this.isValidItem(processedItem) && this.isSelected(processedItem);\n },\n isSelected: function isSelected(processedItem) {\n return this.activeItemPath.some(function (p) {\n return p.key === processedItem.key;\n });\n },\n findFirstItemIndex: function findFirstItemIndex() {\n var _this5 = this;\n return this.visibleItems.findIndex(function (processedItem) {\n return _this5.isValidItem(processedItem);\n });\n },\n findLastItemIndex: function findLastItemIndex() {\n var _this6 = this;\n return findLastIndex(this.visibleItems, function (processedItem) {\n return _this6.isValidItem(processedItem);\n });\n },\n findNextItemIndex: function findNextItemIndex(index) {\n var _this7 = this;\n var matchedItemIndex = index < this.visibleItems.length - 1 ? this.visibleItems.slice(index + 1).findIndex(function (processedItem) {\n return _this7.isValidItem(processedItem);\n }) : -1;\n return matchedItemIndex > -1 ? matchedItemIndex + index + 1 : index;\n },\n findPrevItemIndex: function findPrevItemIndex(index) {\n var _this8 = this;\n var matchedItemIndex = index > 0 ? findLastIndex(this.visibleItems.slice(0, index), function (processedItem) {\n return _this8.isValidItem(processedItem);\n }) : -1;\n return matchedItemIndex > -1 ? matchedItemIndex : index;\n },\n findSelectedItemIndex: function findSelectedItemIndex() {\n var _this9 = this;\n return this.visibleItems.findIndex(function (processedItem) {\n return _this9.isValidSelectedItem(processedItem);\n });\n },\n findFirstFocusedItemIndex: function findFirstFocusedItemIndex() {\n var selectedIndex = this.findSelectedItemIndex();\n return selectedIndex < 0 ? this.findFirstItemIndex() : selectedIndex;\n },\n findLastFocusedItemIndex: function findLastFocusedItemIndex() {\n var selectedIndex = this.findSelectedItemIndex();\n return selectedIndex < 0 ? this.findLastItemIndex() : selectedIndex;\n },\n searchItems: function searchItems(event, _char) {\n var _this10 = this;\n this.searchValue = (this.searchValue || '') + _char;\n var itemIndex = -1;\n var matched = false;\n if (this.focusedItemInfo.index !== -1) {\n itemIndex = this.visibleItems.slice(this.focusedItemInfo.index).findIndex(function (processedItem) {\n return _this10.isItemMatched(processedItem);\n });\n itemIndex = itemIndex === -1 ? this.visibleItems.slice(0, this.focusedItemInfo.index).findIndex(function (processedItem) {\n return _this10.isItemMatched(processedItem);\n }) : itemIndex + this.focusedItemInfo.index;\n } else {\n itemIndex = this.visibleItems.findIndex(function (processedItem) {\n return _this10.isItemMatched(processedItem);\n });\n }\n if (itemIndex !== -1) {\n matched = true;\n }\n if (itemIndex === -1 && this.focusedItemInfo.index === -1) {\n itemIndex = this.findFirstFocusedItemIndex();\n }\n if (itemIndex !== -1) {\n this.changeFocusedItemIndex(event, itemIndex);\n }\n if (this.searchTimeout) {\n clearTimeout(this.searchTimeout);\n }\n this.searchTimeout = setTimeout(function () {\n _this10.searchValue = '';\n _this10.searchTimeout = null;\n }, 500);\n return matched;\n },\n changeFocusedItemIndex: function changeFocusedItemIndex(event, index) {\n if (this.focusedItemInfo.index !== index) {\n this.focusedItemInfo.index = index;\n this.scrollInView();\n }\n },\n scrollInView: function scrollInView() {\n var index = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : -1;\n var id = index !== -1 ? \"\".concat(this.id, \"_\").concat(index) : this.focusedItemId;\n var element = findSingle(this.menubar, \"li[id=\\\"\".concat(id, \"\\\"]\"));\n if (element) {\n element.scrollIntoView && element.scrollIntoView({\n block: 'nearest',\n inline: 'start'\n });\n }\n },\n createProcessedItems: function createProcessedItems(items) {\n var _this11 = this;\n var level = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0;\n var parent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};\n var parentKey = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : '';\n var processedItems = [];\n items && items.forEach(function (item, index) {\n var key = (parentKey !== '' ? parentKey + '_' : '') + index;\n var newItem = {\n item: item,\n index: index,\n level: level,\n key: key,\n parent: parent,\n parentKey: parentKey\n };\n newItem['items'] = _this11.createProcessedItems(item.items, level + 1, newItem, key);\n processedItems.push(newItem);\n });\n return processedItems;\n },\n containerRef: function containerRef(el) {\n this.container = el;\n },\n menubarRef: function menubarRef(el) {\n this.menubar = el ? el.$el : undefined;\n }\n },\n computed: {\n processedItems: function processedItems() {\n return this.createProcessedItems(this.model || []);\n },\n visibleItems: function visibleItems() {\n var _this12 = this;\n var processedItem = this.activeItemPath.find(function (p) {\n return p.key === _this12.focusedItemInfo.parentKey;\n });\n return processedItem ? processedItem.items : this.processedItems;\n },\n focusedItemId: function focusedItemId() {\n return this.focusedItemInfo.index !== -1 ? \"\".concat(this.id).concat(isNotEmpty(this.focusedItemInfo.parentKey) ? '_' + this.focusedItemInfo.parentKey : '', \"_\").concat(this.focusedItemInfo.index) : null;\n }\n },\n components: {\n TieredMenuSub: script$1,\n Portal: Portal\n }\n};\n\nvar _hoisted_1 = [\"id\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_TieredMenuSub = resolveComponent(\"TieredMenuSub\");\n var _component_Portal = resolveComponent(\"Portal\");\n return openBlock(), createBlock(_component_Portal, {\n appendTo: _ctx.appendTo,\n disabled: !_ctx.popup\n }, {\n \"default\": withCtx(function () {\n return [createVNode(Transition, mergeProps({\n name: \"p-connected-overlay\",\n onEnter: $options.onEnter,\n onAfterEnter: $options.onAfterEnter,\n onLeave: $options.onLeave,\n onAfterLeave: $options.onAfterLeave\n }, _ctx.ptm('transition')), {\n \"default\": withCtx(function () {\n return [$data.visible ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n ref: $options.containerRef,\n id: $data.id,\n \"class\": _ctx.cx('root'),\n onClick: _cache[0] || (_cache[0] = function () {\n return $options.onOverlayClick && $options.onOverlayClick.apply($options, arguments);\n })\n }, _ctx.ptmi('root')), [_ctx.$slots.start ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('start')\n }, _ctx.ptm('start')), [renderSlot(_ctx.$slots, \"start\")], 16)) : createCommentVNode(\"\", true), createVNode(_component_TieredMenuSub, {\n ref: $options.menubarRef,\n id: $data.id + '_list',\n tabindex: !_ctx.disabled ? _ctx.tabindex : -1,\n role: \"menubar\",\n \"aria-label\": _ctx.ariaLabel,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-disabled\": _ctx.disabled || undefined,\n \"aria-orientation\": \"vertical\",\n \"aria-activedescendant\": $data.focused ? $options.focusedItemId : undefined,\n menuId: $data.id,\n focusedItemId: $data.focused ? $options.focusedItemId : undefined,\n items: $options.processedItems,\n templates: _ctx.$slots,\n activeItemPath: $data.activeItemPath,\n level: 0,\n visible: $data.submenuVisible,\n pt: _ctx.pt,\n unstyled: _ctx.unstyled,\n onFocus: $options.onFocus,\n onBlur: $options.onBlur,\n onKeydown: $options.onKeyDown,\n onItemClick: $options.onItemClick,\n onItemMouseenter: $options.onItemMouseEnter,\n onItemMousemove: $options.onItemMouseMove\n }, null, 8, [\"id\", \"tabindex\", \"aria-label\", \"aria-labelledby\", \"aria-disabled\", \"aria-activedescendant\", \"menuId\", \"focusedItemId\", \"items\", \"templates\", \"activeItemPath\", \"visible\", \"pt\", \"unstyled\", \"onFocus\", \"onBlur\", \"onKeydown\", \"onItemClick\", \"onItemMouseenter\", \"onItemMousemove\"]), _ctx.$slots.end ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 1,\n \"class\": _ctx.cx('end')\n }, _ctx.ptm('end')), [renderSlot(_ctx.$slots, \"end\")], 16)) : createCommentVNode(\"\", true)], 16, _hoisted_1)) : createCommentVNode(\"\", true)];\n }),\n _: 3\n }, 16, [\"onEnter\", \"onAfterEnter\", \"onLeave\", \"onAfterLeave\"])];\n }),\n _: 3\n }, 8, [\"appendTo\", \"disabled\"]);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-splitbutton {\\n display: inline-flex;\\n position: relative;\\n border-radius: \".concat(dt('splitbutton.border.radius'), \";\\n}\\n\\n.p-splitbutton-button {\\n border-top-right-radius: 0;\\n border-bottom-right-radius: 0;\\n border-right: 0 none;\\n}\\n\\n.p-splitbutton-button:focus-visible,\\n.p-splitbutton-dropdown:focus-visible {\\n z-index: 1;\\n}\\n\\n.p-splitbutton-button:not(:disabled):hover,\\n.p-splitbutton-button:not(:disabled):active {\\n border-right: 0 none;\\n}\\n\\n.p-splitbutton-dropdown {\\n border-top-left-radius: 0;\\n border-bottom-left-radius: 0;\\n}\\n\\n.p-splitbutton .p-menu {\\n min-width: 100%;\\n}\\n\\n.p-splitbutton-fluid {\\n display: flex;\\n}\\n\\n.p-splitbutton-rounded .p-splitbutton-dropdown {\\n border-top-right-radius: \").concat(dt('splitbutton.rounded.border.radius'), \";\\n border-bottom-right-radius: \").concat(dt('splitbutton.rounded.border.radius'), \";\\n}\\n\\n.p-splitbutton-rounded .p-splitbutton-button {\\n border-top-left-radius: \").concat(dt('splitbutton.rounded.border.radius'), \";\\n border-bottom-left-radius: \").concat(dt('splitbutton.rounded.border.radius'), \";\\n}\\n\\n.p-splitbutton-raised {\\n box-shadow: \").concat(dt('splitbutton.raised.shadow'), \";\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var instance = _ref2.instance,\n props = _ref2.props;\n return ['p-splitbutton p-component', {\n 'p-splitbutton-raised': props.raised,\n 'p-splitbutton-rounded': props.rounded,\n 'p-splitbutton-fluid': instance.hasFluid\n }];\n },\n pcButton: 'p-splitbutton-button',\n pcDropdown: 'p-splitbutton-dropdown'\n};\nvar SplitButtonStyle = BaseStyle.extend({\n name: 'splitbutton',\n theme: theme,\n classes: classes\n});\n\nexport { SplitButtonStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { isEmpty } from '@primeuix/utils/object';\nimport { UniqueComponentId } from '@primevue/core/utils';\nimport ChevronDownIcon from '@primevue/icons/chevrondown';\nimport Button from 'primevue/button';\nimport TieredMenu from 'primevue/tieredmenu';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport SplitButtonStyle from 'primevue/splitbutton/style';\nimport { resolveComponent, openBlock, createElementBlock, mergeProps, createVNode, createSlots, withCtx, renderSlot, normalizeClass, createElementVNode, createBlock, resolveDynamicComponent } from 'vue';\n\nvar script$1 = {\n name: 'BaseSplitButton',\n \"extends\": BaseComponent,\n props: {\n label: {\n type: String,\n \"default\": null\n },\n icon: {\n type: String,\n \"default\": null\n },\n model: {\n type: Array,\n \"default\": null\n },\n autoZIndex: {\n type: Boolean,\n \"default\": true\n },\n baseZIndex: {\n type: Number,\n \"default\": 0\n },\n appendTo: {\n type: [String, Object],\n \"default\": 'body'\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n fluid: {\n type: Boolean,\n \"default\": null\n },\n \"class\": {\n type: null,\n \"default\": null\n },\n style: {\n type: null,\n \"default\": null\n },\n buttonProps: {\n type: null,\n \"default\": null\n },\n menuButtonProps: {\n type: null,\n \"default\": null\n },\n menuButtonIcon: {\n type: String,\n \"default\": undefined\n },\n dropdownIcon: {\n type: String,\n \"default\": undefined\n },\n severity: {\n type: String,\n \"default\": null\n },\n raised: {\n type: Boolean,\n \"default\": false\n },\n rounded: {\n type: Boolean,\n \"default\": false\n },\n text: {\n type: Boolean,\n \"default\": false\n },\n outlined: {\n type: Boolean,\n \"default\": false\n },\n size: {\n type: String,\n \"default\": null\n },\n plain: {\n type: Boolean,\n \"default\": false\n }\n },\n style: SplitButtonStyle,\n provide: function provide() {\n return {\n $pcSplitButton: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'SplitButton',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['click'],\n inject: {\n $pcFluid: {\n \"default\": null\n }\n },\n data: function data() {\n return {\n id: this.$attrs.id,\n isExpanded: false\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n }\n },\n mounted: function mounted() {\n var _this = this;\n this.id = this.id || UniqueComponentId();\n this.$watch('$refs.menu.visible', function (newValue) {\n _this.isExpanded = newValue;\n });\n },\n methods: {\n onDropdownButtonClick: function onDropdownButtonClick(event) {\n if (event) {\n event.preventDefault();\n }\n this.$refs.menu.toggle({\n currentTarget: this.$el,\n relatedTarget: this.$refs.button.$el\n });\n this.isExpanded = this.$refs.menu.visible;\n },\n onDropdownKeydown: function onDropdownKeydown(event) {\n if (event.code === 'ArrowDown' || event.code === 'ArrowUp') {\n this.onDropdownButtonClick();\n event.preventDefault();\n }\n },\n onDefaultButtonClick: function onDefaultButtonClick(event) {\n if (this.isExpanded) {\n this.$refs.menu.hide(event);\n }\n this.$emit('click', event);\n }\n },\n computed: {\n containerClass: function containerClass() {\n return [this.cx('root'), this[\"class\"]];\n },\n hasFluid: function hasFluid() {\n return isEmpty(this.fluid) ? !!this.$pcFluid : this.fluid;\n }\n },\n components: {\n PVSButton: Button,\n PVSMenu: TieredMenu,\n ChevronDownIcon: ChevronDownIcon\n }\n};\n\nvar _hoisted_1 = [\"data-p-severity\"];\nfunction 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return [renderSlot(_ctx.$slots, \"icon\", {\n \"class\": normalizeClass(slotProps[\"class\"])\n }, function () {\n return [createElementVNode(\"span\", mergeProps({\n \"class\": [_ctx.icon, slotProps[\"class\"]]\n }, _ctx.ptm('pcButton')['icon'], {\n \"data-pc-section\": \"buttonicon\"\n }), null, 16)];\n })];\n }),\n key: \"0\"\n } : undefined]), 1040, [\"class\", \"label\", \"disabled\", \"severity\", \"text\", \"icon\", \"outlined\", \"size\", \"fluid\", \"aria-label\", \"onClick\", \"pt\", \"unstyled\"]), createVNode(_component_PVSButton, mergeProps({\n ref: \"button\",\n type: \"button\",\n \"class\": _ctx.cx('pcDropdown'),\n disabled: _ctx.disabled,\n \"aria-haspopup\": \"true\",\n \"aria-expanded\": $data.isExpanded,\n \"aria-controls\": $data.id + '_overlay',\n onClick: $options.onDropdownButtonClick,\n onKeydown: $options.onDropdownKeydown,\n severity: _ctx.severity,\n text: _ctx.text,\n outlined: _ctx.outlined,\n size: _ctx.size,\n unstyled: _ctx.unstyled\n }, 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\ No newline at end of file diff --git a/web/assets/GraphView-Cf7ubG48.css b/web/assets/GraphView-Cf7ubG48.css new file mode 100644 index 0000000..959bfde --- /dev/null +++ b/web/assets/GraphView-Cf7ubG48.css @@ -0,0 +1,281 @@ + +.group-title-editor.node-title-editor[data-v-8a100d5a] { + z-index: 9999; + padding: 0.25rem; +} +[data-v-8a100d5a] .editable-text { + width: 100%; + height: 100%; +} +[data-v-8a100d5a] .editable-text input { + width: 100%; + height: 100%; + /* Override the default font size */ + font-size: inherit; +} + +.side-bar-button-icon { + font-size: var(--sidebar-icon-size) !important; +} +.side-bar-button-selected .side-bar-button-icon { + font-size: var(--sidebar-icon-size) !important; + font-weight: bold; +} + +.side-bar-button[data-v-caa3ee9c] { + width: var(--sidebar-width); + height: var(--sidebar-width); + border-radius: 0; +} +.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-caa3ee9c], +.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-caa3ee9c]:hover { + border-left: 4px solid var(--p-button-text-primary-color); +} +.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-caa3ee9c], +.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-caa3ee9c]:hover { + border-right: 4px solid var(--p-button-text-primary-color); +} + +:root { + --sidebar-width: 64px; + --sidebar-icon-size: 1.5rem; +} +:root .small-sidebar { + --sidebar-width: 40px; + --sidebar-icon-size: 1rem; +} + +.side-tool-bar-container[data-v-37fd2fa4] { + display: flex; + flex-direction: column; + align-items: center; + + pointer-events: auto; + + width: var(--sidebar-width); + height: 100%; + + background-color: var(--comfy-menu-bg); + color: var(--fg-color); +} +.side-tool-bar-end[data-v-37fd2fa4] { + align-self: flex-end; + margin-top: auto; +} + +[data-v-b49f20b1] .p-splitter-gutter { + pointer-events: auto; +} +.side-bar-panel[data-v-b49f20b1] { + background-color: var(--bg-color); + pointer-events: auto; +} +.bottom-panel[data-v-b49f20b1] { + background-color: var(--bg-color); + pointer-events: auto; +} +.splitter-overlay[data-v-b49f20b1] { + pointer-events: none; + border-style: none; + background-color: transparent; +} +.splitter-overlay-root[data-v-b49f20b1] { + position: absolute; + top: 0px; + left: 0px; + height: 100%; + width: 100%; + + /* Set it the same as the ComfyUI menu */ + /* Note: Lite-graph DOM widgets have the same z-index as the node id, so + 999 should be sufficient to make sure splitter overlays on node's DOM + widgets */ + z-index: 999; +} + +[data-v-37f672ab] .highlight { + background-color: var(--p-primary-color); + color: var(--p-primary-contrast-color); + font-weight: bold; + border-radius: 0.25rem; + padding: 0rem 0.125rem; + margin: -0.125rem 0.125rem; +} + +.comfy-vue-node-search-container[data-v-2d409367] { + display: flex; + width: 100%; + min-width: 26rem; + align-items: center; + justify-content: center; +} +.comfy-vue-node-search-container[data-v-2d409367] * { + pointer-events: auto; +} +.comfy-vue-node-preview-container[data-v-2d409367] { + position: absolute; + left: -350px; + top: 50px; +} +.comfy-vue-node-search-box[data-v-2d409367] { + z-index: 10; + flex-grow: 1; +} +._filter-button[data-v-2d409367] { + z-index: 10; +} +._dialog[data-v-2d409367] { + min-width: 26rem; +} + +.invisible-dialog-root { + width: 60%; + min-width: 24rem; + max-width: 48rem; + border: 0 !important; + background-color: transparent !important; + margin-top: 25vh; + margin-left: 400px; +} +@media all and (max-width: 768px) { +.invisible-dialog-root { + margin-left: 0px; +} +} +.node-search-box-dialog-mask { + align-items: flex-start !important; +} + +.node-tooltip[data-v-79ec8c53] { + background: var(--comfy-input-bg); + border-radius: 5px; + box-shadow: 0 0 5px rgba(0, 0, 0, 0.4); + color: var(--input-text); + font-family: sans-serif; + left: 0; + max-width: 30vw; + padding: 4px 8px; + position: absolute; + top: 0; + transform: translate(5px, calc(-100% - 5px)); + white-space: pre-wrap; + z-index: 99999; +} + +.p-buttongroup-vertical[data-v-444d3768] { + display: flex; + flex-direction: column; + border-radius: var(--p-button-border-radius); + overflow: hidden; + border: 1px solid var(--p-panel-border-color); +} +.p-buttongroup-vertical .p-button[data-v-444d3768] { + margin: 0; + border-radius: 0; +} + +[data-v-84e785b8] .p-togglebutton::before { + display: none +} +[data-v-84e785b8] .p-togglebutton { + position: relative; + flex-shrink: 0; + border-radius: 0px; + background-color: transparent; + padding-left: 0.5rem; + padding-right: 0.5rem +} +[data-v-84e785b8] .p-togglebutton.p-togglebutton-checked { + border-bottom-width: 2px; + border-bottom-color: var(--p-button-text-primary-color) +} +[data-v-84e785b8] .p-togglebutton-checked .close-button,[data-v-84e785b8] .p-togglebutton:hover .close-button { + visibility: visible +} +.status-indicator[data-v-84e785b8] { + position: absolute; + font-weight: 700; + font-size: 1.5rem; + top: 50%; + left: 50%; + transform: translate(-50%, -50%) +} +[data-v-84e785b8] .p-togglebutton:hover .status-indicator { + display: none +} +[data-v-84e785b8] .p-togglebutton .close-button { + visibility: hidden +} + +.top-menubar[data-v-9646ca0a] .p-menubar-item-link svg { + display: none; +} +[data-v-9646ca0a] .p-menubar-submenu.dropdown-direction-up { + top: auto; + bottom: 100%; + flex-direction: column-reverse; +} +.keybinding-tag[data-v-9646ca0a] { + background: var(--p-content-hover-background); + border-color: var(--p-content-border-color); + border-style: solid; +} + +[data-v-713442be] .p-inputtext { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} + +.comfyui-queue-button[data-v-2b80bf74] .p-splitbutton-dropdown { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} + +.actionbar[data-v-2e54db00] { + pointer-events: all; + position: fixed; + z-index: 1000; +} +.actionbar.is-docked[data-v-2e54db00] { + position: static; + border-style: none; + background-color: transparent; + padding: 0px; +} +.actionbar.is-dragging[data-v-2e54db00] { + -webkit-user-select: none; + -moz-user-select: none; + user-select: none; +} +[data-v-2e54db00] .p-panel-content { + padding: 0.25rem; +} +[data-v-2e54db00] .p-panel-header { + display: none; +} + +.comfyui-menu[data-v-ad2c662b] { + width: 100vw; + background: var(--comfy-menu-bg); + color: var(--fg-color); + font-family: Arial, Helvetica, sans-serif; + font-size: 0.8em; + box-sizing: border-box; + z-index: 1000; + order: 0; + grid-column: 1/-1; + max-height: 90vh; +} +.comfyui-menu.dropzone[data-v-ad2c662b] { + background: var(--p-highlight-background); +} +.comfyui-menu.dropzone-active[data-v-ad2c662b] { + background: var(--p-highlight-background-focus); +} +.comfyui-logo[data-v-ad2c662b] { + font-size: 1.2em; + -webkit-user-select: none; + -moz-user-select: none; + user-select: none; + cursor: default; +} diff --git a/web/assets/KeybindingPanel-BNYKhW1k.css b/web/assets/KeybindingPanel-BNYKhW1k.css new file mode 100644 index 0000000..c14704b --- /dev/null +++ b/web/assets/KeybindingPanel-BNYKhW1k.css @@ -0,0 +1,8 @@ + +[data-v-e5724e4d] .p-datatable-tbody > tr > td { + padding: 1px; + min-height: 2rem; +} +[data-v-e5724e4d] .p-datatable-row-selected .actions,[data-v-e5724e4d] .p-datatable-selectable-row:hover .actions { + visibility: visible; +} diff --git a/web/assets/KeybindingPanel-Dm_3sBT5.js b/web/assets/KeybindingPanel-Dm_3sBT5.js new file mode 100644 index 0000000..74dcd28 --- /dev/null +++ b/web/assets/KeybindingPanel-Dm_3sBT5.js @@ -0,0 +1,264 @@ +var __defProp = Object.defineProperty; +var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); +import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, N as Fragment, O as renderList, i as createVNode, y as withCtx, aw as createTextVNode, a6 as toDisplayString, z as unref, aA as script, j as createCommentVNode, r as ref, bN as FilterMatchMode, M as useKeybindingStore, F as useCommandStore, aJ as watchEffect, b9 as useToast, t as resolveDirective, bO as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, ao as script$4, be as withModifiers, aH as script$6, v as withDirectives, P as pushScopeId, Q as popScopeId, bJ as KeyComboImpl, bP as KeybindingImpl, _ as _export_sfc } from "./index-BHayQCxv.js"; +import { s as script$1, a as script$3, b as script$5 } from "./index-CwRXxFdA.js"; +import "./index-C_wOqB0f.js"; +const _hoisted_1$1 = { + key: 0, + class: "px-2" +}; +const _sfc_main$1 = /* @__PURE__ */ defineComponent({ + __name: "KeyComboDisplay", + props: { + keyCombo: {}, + isModified: { type: Boolean, default: false } + }, + setup(__props) { + const props = __props; + const keySequences = computed(() => props.keyCombo.getKeySequences()); + return (_ctx, _cache) => { + return openBlock(), createElementBlock("span", null, [ + (openBlock(true), createElementBlock(Fragment, null, renderList(keySequences.value, (sequence, index) => { + return openBlock(), createElementBlock(Fragment, { key: index }, [ + createVNode(unref(script), { + severity: _ctx.isModified ? "info" : "secondary" + }, { + default: withCtx(() => [ + createTextVNode(toDisplayString(sequence), 1) + ]), + _: 2 + }, 1032, ["severity"]), + index < keySequences.value.length - 1 ? (openBlock(), createElementBlock("span", _hoisted_1$1, "+")) : createCommentVNode("", true) + ], 64); + }), 128)) + ]); + }; + } +}); +const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-e5724e4d"), n = n(), popScopeId(), n), "_withScopeId"); +const _hoisted_1 = { class: "keybinding-panel" }; +const _hoisted_2 = { class: "actions invisible" }; +const _hoisted_3 = { key: 1 }; +const _sfc_main = /* @__PURE__ */ defineComponent({ + __name: "KeybindingPanel", + setup(__props) { + const filters = ref({ + global: { value: "", matchMode: FilterMatchMode.CONTAINS } + }); + const keybindingStore = useKeybindingStore(); + const commandStore = useCommandStore(); + const commandsData = computed(() => { + return Object.values(commandStore.commands).map((command) => ({ + id: command.id, + keybinding: keybindingStore.getKeybindingByCommandId(command.id) + })); + }); + const selectedCommandData = ref(null); + const editDialogVisible = ref(false); + const newBindingKeyCombo = ref(null); + const currentEditingCommand = ref(null); + const keybindingInput = ref(null); + const existingKeybindingOnCombo = computed(() => { + if (!currentEditingCommand.value) { + return null; + } + if (currentEditingCommand.value.keybinding?.combo?.equals( + newBindingKeyCombo.value + )) { + return null; + } + if (!newBindingKeyCombo.value) { + return null; + } + return keybindingStore.getKeybinding(newBindingKeyCombo.value); + }); + function editKeybinding(commandData) { + currentEditingCommand.value = commandData; + newBindingKeyCombo.value = commandData.keybinding ? commandData.keybinding.combo : null; + editDialogVisible.value = true; + } + __name(editKeybinding, "editKeybinding"); + watchEffect(() => { + if (editDialogVisible.value) { + setTimeout(() => { + keybindingInput.value?.$el?.focus(); + }, 300); + } + }); + function removeKeybinding(commandData) { + if (commandData.keybinding) { + keybindingStore.unsetKeybinding(commandData.keybinding); + keybindingStore.persistUserKeybindings(); + } + } + __name(removeKeybinding, "removeKeybinding"); + function captureKeybinding(event) { + const keyCombo = KeyComboImpl.fromEvent(event); + newBindingKeyCombo.value = keyCombo; + } + __name(captureKeybinding, "captureKeybinding"); + function cancelEdit() { + editDialogVisible.value = false; + currentEditingCommand.value = null; + newBindingKeyCombo.value = null; + } + __name(cancelEdit, "cancelEdit"); + function saveKeybinding() { + if (currentEditingCommand.value && newBindingKeyCombo.value) { + const updated = keybindingStore.updateKeybindingOnCommand( + new KeybindingImpl({ + commandId: currentEditingCommand.value.id, + combo: newBindingKeyCombo.value + }) + ); + if (updated) { + keybindingStore.persistUserKeybindings(); + } + } + cancelEdit(); + } + __name(saveKeybinding, "saveKeybinding"); + const toast = useToast(); + async function resetKeybindings() { + keybindingStore.resetKeybindings(); + await keybindingStore.persistUserKeybindings(); + toast.add({ + severity: "info", + summary: "Info", + detail: "Keybindings reset", + life: 3e3 + }); + } + __name(resetKeybindings, "resetKeybindings"); + return (_ctx, _cache) => { + const _directive_tooltip = resolveDirective("tooltip"); + return openBlock(), createElementBlock("div", _hoisted_1, [ + createVNode(unref(script$3), { + value: commandsData.value, + selection: selectedCommandData.value, + "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event), + "global-filter-fields": ["id"], + filters: filters.value, + selectionMode: "single", + stripedRows: "", + pt: { + header: "px-0" + } + }, { + header: withCtx(() => [ + createVNode(SearchBox, { + modelValue: filters.value["global"].value, + "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event), + placeholder: _ctx.$t("searchKeybindings") + "..." + }, null, 8, ["modelValue", "placeholder"]) + ]), + default: withCtx(() => [ + createVNode(unref(script$1), { + field: "actions", + header: "" + }, { + body: withCtx((slotProps) => [ + createBaseVNode("div", _hoisted_2, [ + createVNode(unref(script$2), { + icon: "pi pi-pencil", + class: "p-button-text", + onClick: /* @__PURE__ */ __name(($event) => editKeybinding(slotProps.data), "onClick") + }, null, 8, ["onClick"]), + createVNode(unref(script$2), { + icon: "pi pi-trash", + class: "p-button-text p-button-danger", + onClick: /* @__PURE__ */ __name(($event) => removeKeybinding(slotProps.data), "onClick"), + disabled: !slotProps.data.keybinding + }, null, 8, ["onClick", "disabled"]) + ]) + ]), + _: 1 + }), + createVNode(unref(script$1), { + field: "id", + header: "Command ID", + sortable: "" + }), + createVNode(unref(script$1), { + field: "keybinding", + header: "Keybinding" + }, { + body: withCtx((slotProps) => [ + slotProps.data.keybinding ? (openBlock(), createBlock(_sfc_main$1, { + key: 0, + keyCombo: slotProps.data.keybinding.combo, + isModified: unref(keybindingStore).isCommandKeybindingModified(slotProps.data.id) + }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_3, "-")) + ]), + _: 1 + }) + ]), + _: 1 + }, 8, ["value", "selection", "filters"]), + createVNode(unref(script$6), { + class: "min-w-96", + visible: editDialogVisible.value, + "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event), + modal: "", + header: currentEditingCommand.value?.id, + onHide: cancelEdit + }, { + footer: withCtx(() => [ + createVNode(unref(script$2), { + label: "Save", + icon: "pi pi-check", + onClick: saveKeybinding, + disabled: !!existingKeybindingOnCombo.value, + autofocus: "" + }, null, 8, ["disabled"]) + ]), + default: withCtx(() => [ + createBaseVNode("div", null, [ + createVNode(unref(script$4), { + class: "mb-2 text-center", + ref_key: "keybindingInput", + ref: keybindingInput, + modelValue: newBindingKeyCombo.value?.toString() ?? "", + placeholder: "Press keys for new binding", + onKeydown: withModifiers(captureKeybinding, ["stop", "prevent"]), + autocomplete: "off", + fluid: "", + invalid: !!existingKeybindingOnCombo.value + }, null, 8, ["modelValue", "invalid"]), + existingKeybindingOnCombo.value ? 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+const VOID_TAGS = "area,base,br,col,embed,hr,img,input,link,meta,param,source,track,wbr"; +const isHTMLTag = /* @__PURE__ */ makeMap(HTML_TAGS); +const isSVGTag = /* @__PURE__ */ makeMap(SVG_TAGS); +const isMathMLTag = /* @__PURE__ */ makeMap(MATH_TAGS); +const isVoidTag = /* @__PURE__ */ makeMap(VOID_TAGS); +const specialBooleanAttrs = `itemscope,allowfullscreen,formnovalidate,ismap,nomodule,novalidate,readonly`; +const isSpecialBooleanAttr = /* @__PURE__ */ makeMap(specialBooleanAttrs); +const isBooleanAttr = /* @__PURE__ */ makeMap( + specialBooleanAttrs + `,async,autofocus,autoplay,controls,default,defer,disabled,hidden,inert,loop,open,required,reversed,scoped,seamless,checked,muted,multiple,selected` +); +function includeBooleanAttr(value3) { + return !!value3 || value3 === ""; +} +__name(includeBooleanAttr, "includeBooleanAttr"); +const unsafeAttrCharRE = /[>/="'\u0009\u000a\u000c\u0020]/; +const attrValidationCache = {}; +function isSSRSafeAttrName(name) { + if (attrValidationCache.hasOwnProperty(name)) { + return attrValidationCache[name]; + } + const isUnsafe = unsafeAttrCharRE.test(name); + if (isUnsafe) { + console.error(`unsafe attribute name: ${name}`); + } + return attrValidationCache[name] = !isUnsafe; +} +__name(isSSRSafeAttrName, "isSSRSafeAttrName"); +const propsToAttrMap = { + acceptCharset: "accept-charset", + className: "class", + htmlFor: "for", + httpEquiv: "http-equiv" +}; +const isKnownHtmlAttr = /* @__PURE__ */ makeMap( + `accept,accept-charset,accesskey,action,align,allow,alt,async,autocapitalize,autocomplete,autofocus,autoplay,background,bgcolor,border,buffered,capture,challenge,charset,checked,cite,class,code,codebase,color,cols,colspan,content,contenteditable,contextmenu,controls,coords,crossorigin,csp,data,datetime,decoding,default,defer,dir,dirname,disabled,download,draggable,dropzone,enctype,enterkeyhint,for,form,formaction,formenctype,formmethod,formnovalidate,formtarget,headers,height,hidden,high,href,hreflang,http-equiv,icon,id,importance,inert,integrity,ismap,itemprop,keytype,kind,label,lang,language,loading,list,loop,low,manifest,max,maxlength,minlength,media,min,multiple,muted,name,novalidate,open,optimum,pattern,ping,placeholder,poster,preload,radiogroup,readonly,referrerpolicy,rel,required,reversed,rows,rowspan,sandbox,scope,scoped,selected,shape,size,sizes,slot,span,spellcheck,src,srcdoc,srclang,srcset,start,step,style,summary,tabindex,target,title,translate,type,usemap,value,width,wrap` +); +const isKnownSvgAttr = /* @__PURE__ */ makeMap( + `xmlns,accent-height,accumulate,additive,alignment-baseline,alphabetic,amplitude,arabic-form,ascent,attributeName,attributeType,azimuth,baseFrequency,baseline-shift,baseProfile,bbox,begin,bias,by,calcMode,cap-height,class,clip,clipPathUnits,clip-path,clip-rule,color,color-interpolation,color-interpolation-filters,color-profile,color-rendering,contentScriptType,contentStyleType,crossorigin,cursor,cx,cy,d,decelerate,descent,diffuseConstant,direction,display,divisor,dominant-baseline,dur,dx,dy,edgeMode,elevation,enable-background,end,exponent,fill,fill-opacity,fill-rule,filter,filterRes,filterUnits,flood-color,flood-opacity,font-family,font-size,font-size-adjust,font-stretch,font-style,font-variant,font-weight,format,from,fr,fx,fy,g1,g2,glyph-name,glyph-orientation-horizontal,glyph-orientation-vertical,glyphRef,gradientTransform,gradientUnits,hanging,height,href,hreflang,horiz-adv-x,horiz-origin-x,id,ideographic,image-rendering,in,in2,intercept,k,k1,k2,k3,k4,kernelMatrix,kernelUnitLength,kerning,keyPoints,keySplines,keyTimes,lang,lengthAdjust,letter-spacing,lighting-color,limitingConeAngle,local,marker-end,marker-mid,marker-start,markerHeight,markerUnits,markerWidth,mask,maskContentUnits,maskUnits,mathematical,max,media,method,min,mode,name,numOctaves,offset,opacity,operator,order,orient,orientation,origin,overflow,overline-position,overline-thickness,panose-1,paint-order,path,pathLength,patternContentUnits,patternTransform,patternUnits,ping,pointer-events,points,pointsAtX,pointsAtY,pointsAtZ,preserveAlpha,preserveAspectRatio,primitiveUnits,r,radius,referrerPolicy,refX,refY,rel,rendering-intent,repeatCount,repeatDur,requiredExtensions,requiredFeatures,restart,result,rotate,rx,ry,scale,seed,shape-rendering,slope,spacing,specularConstant,specularExponent,speed,spreadMethod,startOffset,stdDeviation,stemh,stemv,stitchTiles,stop-color,stop-opacity,strikethrough-position,strikethrough-thickness,string,stroke,stroke-dasharray,stroke-dashoffset,stroke-linecap,stroke-linejoin,stroke-miterlimit,stroke-opacity,stroke-width,style,surfaceScale,systemLanguage,tabindex,tableValues,target,targetX,targetY,text-anchor,text-decoration,text-rendering,textLength,to,transform,transform-origin,type,u1,u2,underline-position,underline-thickness,unicode,unicode-bidi,unicode-range,units-per-em,v-alphabetic,v-hanging,v-ideographic,v-mathematical,values,vector-effect,version,vert-adv-y,vert-origin-x,vert-origin-y,viewBox,viewTarget,visibility,width,widths,word-spacing,writing-mode,x,x-height,x1,x2,xChannelSelector,xlink:actuate,xlink:arcrole,xlink:href,xlink:role,xlink:show,xlink:title,xlink:type,xmlns:xlink,xml:base,xml:lang,xml:space,y,y1,y2,yChannelSelector,z,zoomAndPan` +); +function isRenderableAttrValue(value3) { + if (value3 == null) { + return false; + } + const type = typeof value3; + return type === "string" || type === "number" || type === "boolean"; +} +__name(isRenderableAttrValue, "isRenderableAttrValue"); +const escapeRE = /["'&<>]/; +function escapeHtml$1(string) { + const str = "" + string; + const match = escapeRE.exec(str); + if (!match) { + return str; + } + let html = ""; + let escaped; + let index2; + let lastIndex = 0; + for (index2 = match.index; index2 < str.length; index2++) { + switch (str.charCodeAt(index2)) { + case 34: + escaped = """; + break; + case 38: + escaped = "&"; + break; + case 39: + escaped = "'"; + break; + case 60: + escaped = "<"; + break; + case 62: + escaped = ">"; + break; + default: + continue; + } + if (lastIndex !== index2) { + html += str.slice(lastIndex, index2); + } + lastIndex = index2 + 1; + html += escaped; + } + return lastIndex !== index2 ? html + str.slice(lastIndex, index2) : html; +} +__name(escapeHtml$1, "escapeHtml$1"); +const commentStripRE = /^-?>||--!>| looseEqual(item2, val)); +} +__name(looseIndexOf, "looseIndexOf"); +const isRef$1 = /* @__PURE__ */ __name((val) => { + return !!(val && val.__v_isRef === true); +}, "isRef$1"); +const toDisplayString$1 = /* @__PURE__ */ __name((val) => { + return isString$5(val) ? val : val == null ? "" : isArray$5(val) || isObject$6(val) && (val.toString === objectToString$1 || !isFunction$3(val.toString)) ? isRef$1(val) ? toDisplayString$1(val.value) : JSON.stringify(val, replacer, 2) : String(val); +}, "toDisplayString$1"); +const replacer = /* @__PURE__ */ __name((_key, val) => { + if (isRef$1(val)) { + return replacer(_key, val.value); + } else if (isMap(val)) { + return { + [`Map(${val.size})`]: [...val.entries()].reduce( + (entries, [key, val2], i2) => { + entries[stringifySymbol(key, i2) + " =>"] = val2; + return entries; + }, + {} + ) + }; + } else if (isSet(val)) { + return { + [`Set(${val.size})`]: [...val.values()].map((v2) => stringifySymbol(v2)) + }; + } else if (isSymbol$1(val)) { + return stringifySymbol(val); + } else if (isObject$6(val) && !isArray$5(val) && !isPlainObject$3(val)) { + return String(val); + } + return val; +}, "replacer"); +const stringifySymbol = /* @__PURE__ */ __name((v2, i2 = "") => { + var _a; + return ( + // Symbol.description in es2019+ so we need to cast here to pass + // the lib: es2016 check + isSymbol$1(v2) ? `Symbol(${(_a = v2.description) != null ? _a : i2})` : v2 + ); +}, "stringifySymbol"); +/** +* @vue/reactivity v3.4.31 +* (c) 2018-present Yuxi (Evan) You and Vue contributors +* @license MIT +**/ +function warn$3(msg, ...args) { + console.warn(`[Vue warn] ${msg}`, ...args); +} +__name(warn$3, "warn$3"); +let activeEffectScope; +class EffectScope { + static { + __name(this, "EffectScope"); + } + constructor(detached = false) { + this.detached = detached; + this._active = true; + this.effects = []; + this.cleanups = []; + this.parent = activeEffectScope; + if (!detached && activeEffectScope) { + this.index = (activeEffectScope.scopes || (activeEffectScope.scopes = [])).push( + this + ) - 1; + } + } + get active() { + return this._active; + } + run(fn) { + if (this._active) { + const currentEffectScope = activeEffectScope; + try { + activeEffectScope = this; + return fn(); + } finally { + activeEffectScope = currentEffectScope; + } + } else if (false) { + warn$3(`cannot run an inactive effect scope.`); + } + } + /** + * This should only be called on non-detached scopes + * @internal + */ + on() { + activeEffectScope = this; + } + /** + * This should only be called on non-detached scopes + * @internal + */ + off() { + activeEffectScope = this.parent; + } + stop(fromParent) { + if (this._active) { + let i2, l; + for (i2 = 0, l = this.effects.length; i2 < l; i2++) { + this.effects[i2].stop(); + } + for (i2 = 0, l = this.cleanups.length; i2 < l; i2++) { + this.cleanups[i2](); + } + if (this.scopes) { + for (i2 = 0, l = this.scopes.length; i2 < l; i2++) { + this.scopes[i2].stop(true); + } + } + if (!this.detached && this.parent && !fromParent) { + const last = this.parent.scopes.pop(); + if (last && last !== this) { + this.parent.scopes[this.index] = last; + last.index = this.index; + } + } + this.parent = void 0; + this._active = false; + } + } +} +function effectScope(detached) { + return new EffectScope(detached); +} +__name(effectScope, "effectScope"); +function recordEffectScope(effect2, scope = activeEffectScope) { + if (scope && scope.active) { + scope.effects.push(effect2); + } +} +__name(recordEffectScope, "recordEffectScope"); +function getCurrentScope() { + return activeEffectScope; +} +__name(getCurrentScope, "getCurrentScope"); +function onScopeDispose(fn) { + if (activeEffectScope) { + activeEffectScope.cleanups.push(fn); + } else if (false) { + warn$3( + `onScopeDispose() is called when there is no active effect scope to be associated with.` + ); + } +} +__name(onScopeDispose, "onScopeDispose"); +let activeEffect; +class ReactiveEffect { + static { + __name(this, "ReactiveEffect"); + } + constructor(fn, trigger2, scheduler, scope) { + this.fn = fn; + this.trigger = trigger2; + this.scheduler = scheduler; + this.active = true; + this.deps = []; + this._dirtyLevel = 4; + this._trackId = 0; + this._runnings = 0; + this._shouldSchedule = false; + this._depsLength = 0; + recordEffectScope(this, scope); + } + get dirty() { + if (this._dirtyLevel === 2 || this._dirtyLevel === 3) { + this._dirtyLevel = 1; + pauseTracking(); + for (let i2 = 0; i2 < this._depsLength; i2++) { + const dep = this.deps[i2]; + if (dep.computed) { + triggerComputed(dep.computed); + if (this._dirtyLevel >= 4) { + break; + } + } + } + if (this._dirtyLevel === 1) { + this._dirtyLevel = 0; + } + resetTracking(); + } + return this._dirtyLevel >= 4; + } + set dirty(v2) { + this._dirtyLevel = v2 ? 4 : 0; + } + run() { + this._dirtyLevel = 0; + if (!this.active) { + return this.fn(); + } + let lastShouldTrack = shouldTrack; + let lastEffect = activeEffect; + try { + shouldTrack = true; + activeEffect = this; + this._runnings++; + preCleanupEffect(this); + return this.fn(); + } finally { + postCleanupEffect(this); + this._runnings--; + activeEffect = lastEffect; + shouldTrack = lastShouldTrack; + } + } + stop() { + if (this.active) { + preCleanupEffect(this); + postCleanupEffect(this); + this.onStop && this.onStop(); + this.active = false; + } + } +} +function triggerComputed(computed2) { + return computed2.value; +} +__name(triggerComputed, "triggerComputed"); +function preCleanupEffect(effect2) { + effect2._trackId++; + effect2._depsLength = 0; +} +__name(preCleanupEffect, "preCleanupEffect"); +function postCleanupEffect(effect2) { + if (effect2.deps.length > effect2._depsLength) { + for (let i2 = effect2._depsLength; i2 < effect2.deps.length; i2++) { + cleanupDepEffect(effect2.deps[i2], effect2); + } + effect2.deps.length = effect2._depsLength; + } +} +__name(postCleanupEffect, "postCleanupEffect"); +function cleanupDepEffect(dep, effect2) { + const trackId = dep.get(effect2); + if (trackId !== void 0 && effect2._trackId !== trackId) { + dep.delete(effect2); + if (dep.size === 0) { + dep.cleanup(); + } + } +} +__name(cleanupDepEffect, "cleanupDepEffect"); +function effect(fn, options3) { + if (fn.effect instanceof ReactiveEffect) { + fn = fn.effect.fn; + } + const _effect = new ReactiveEffect(fn, NOOP, () => { + if (_effect.dirty) { + _effect.run(); + } + }); + if (options3) { + extend$1(_effect, options3); + if (options3.scope) recordEffectScope(_effect, options3.scope); + } + if (!options3 || !options3.lazy) { + _effect.run(); + } + const runner = _effect.run.bind(_effect); + runner.effect = _effect; + return runner; +} +__name(effect, "effect"); +function stop(runner) { + runner.effect.stop(); +} +__name(stop, "stop"); +let shouldTrack = true; +let pauseScheduleStack = 0; +const trackStack = []; +function pauseTracking() { + trackStack.push(shouldTrack); + shouldTrack = false; +} +__name(pauseTracking, "pauseTracking"); +function enableTracking() { + trackStack.push(shouldTrack); + shouldTrack = true; +} +__name(enableTracking, "enableTracking"); +function resetTracking() { + const last = trackStack.pop(); + shouldTrack = last === void 0 ? true : last; +} +__name(resetTracking, "resetTracking"); +function pauseScheduling() { + pauseScheduleStack++; +} +__name(pauseScheduling, "pauseScheduling"); +function resetScheduling() { + pauseScheduleStack--; + while (!pauseScheduleStack && queueEffectSchedulers.length) { + queueEffectSchedulers.shift()(); + } +} +__name(resetScheduling, "resetScheduling"); +function trackEffect(effect2, dep, debuggerEventExtraInfo) { + var _a; + if (dep.get(effect2) !== effect2._trackId) { + dep.set(effect2, effect2._trackId); + const oldDep = effect2.deps[effect2._depsLength]; + if (oldDep !== dep) { + if (oldDep) { + cleanupDepEffect(oldDep, effect2); + } + effect2.deps[effect2._depsLength++] = dep; + } else { + effect2._depsLength++; + } + if (false) { + (_a = effect2.onTrack) == null ? void 0 : _a.call(effect2, extend$1({ effect: effect2 }, debuggerEventExtraInfo)); + } + } +} +__name(trackEffect, "trackEffect"); +const queueEffectSchedulers = []; +function triggerEffects(dep, dirtyLevel, debuggerEventExtraInfo) { + var _a; + pauseScheduling(); + for (const effect2 of dep.keys()) { + let tracking; + if (effect2._dirtyLevel < dirtyLevel && (tracking != null ? tracking : tracking = dep.get(effect2) === effect2._trackId)) { + effect2._shouldSchedule || (effect2._shouldSchedule = effect2._dirtyLevel === 0); + effect2._dirtyLevel = dirtyLevel; + } + if (effect2._shouldSchedule && (tracking != null ? tracking : tracking = dep.get(effect2) === effect2._trackId)) { + if (false) { + (_a = effect2.onTrigger) == null ? void 0 : _a.call(effect2, extend$1({ effect: effect2 }, debuggerEventExtraInfo)); + } + effect2.trigger(); + if ((!effect2._runnings || effect2.allowRecurse) && effect2._dirtyLevel !== 2) { + effect2._shouldSchedule = false; + if (effect2.scheduler) { + queueEffectSchedulers.push(effect2.scheduler); + } + } + } + } + resetScheduling(); +} +__name(triggerEffects, "triggerEffects"); +const createDep = /* @__PURE__ */ __name((cleanup, computed2) => { + const dep = /* @__PURE__ */ new Map(); + dep.cleanup = cleanup; + dep.computed = computed2; + return dep; +}, "createDep"); +const targetMap = /* @__PURE__ */ new WeakMap(); +const ITERATE_KEY = Symbol(false ? "iterate" : ""); +const MAP_KEY_ITERATE_KEY = Symbol(false ? "Map key iterate" : ""); +function track(target, type, key) { + if (shouldTrack && activeEffect) { + let depsMap = targetMap.get(target); + if (!depsMap) { + targetMap.set(target, depsMap = /* @__PURE__ */ new Map()); + } + let dep = depsMap.get(key); + if (!dep) { + depsMap.set(key, dep = createDep(() => depsMap.delete(key))); + } + trackEffect( + activeEffect, + dep, + false ? { + target, + type, + key + } : void 0 + ); + } +} +__name(track, "track"); +function trigger(target, type, key, newValue, oldValue, oldTarget) { + const depsMap = targetMap.get(target); + if (!depsMap) { + return; + } + let deps = []; + if (type === "clear") { + deps = [...depsMap.values()]; + } else if (key === "length" && isArray$5(target)) { + const newLength = Number(newValue); + depsMap.forEach((dep, key2) => { + if (key2 === "length" || !isSymbol$1(key2) && key2 >= newLength) { + deps.push(dep); + } + }); + } else { + if (key !== void 0) { + deps.push(depsMap.get(key)); + } + switch (type) { + case "add": + if (!isArray$5(target)) { + deps.push(depsMap.get(ITERATE_KEY)); + if (isMap(target)) { + deps.push(depsMap.get(MAP_KEY_ITERATE_KEY)); + } + } else if (isIntegerKey(key)) { + deps.push(depsMap.get("length")); + } + break; + case "delete": + if (!isArray$5(target)) { + deps.push(depsMap.get(ITERATE_KEY)); + if (isMap(target)) { + deps.push(depsMap.get(MAP_KEY_ITERATE_KEY)); + } + } + break; + case "set": + if (isMap(target)) { + deps.push(depsMap.get(ITERATE_KEY)); + } + break; + } + } + pauseScheduling(); + for (const dep of deps) { + if (dep) { + triggerEffects( + dep, + 4, + false ? { + target, + type, + key, + newValue, + oldValue, + oldTarget + } : void 0 + ); + } + } + resetScheduling(); +} +__name(trigger, "trigger"); +function getDepFromReactive(object, key) { + const depsMap = targetMap.get(object); + return depsMap && depsMap.get(key); +} +__name(getDepFromReactive, "getDepFromReactive"); +const isNonTrackableKeys = /* @__PURE__ */ makeMap(`__proto__,__v_isRef,__isVue`); +const builtInSymbols = new Set( + /* @__PURE__ */ Object.getOwnPropertyNames(Symbol).filter((key) => key !== "arguments" && key !== "caller").map((key) => Symbol[key]).filter(isSymbol$1) +); +const arrayInstrumentations = /* @__PURE__ */ createArrayInstrumentations(); +function createArrayInstrumentations() { + const instrumentations = {}; + ["includes", "indexOf", "lastIndexOf"].forEach((key) => { + instrumentations[key] = function(...args) { + const arr = toRaw(this); + for (let i2 = 0, l = this.length; i2 < l; i2++) { + track(arr, "get", i2 + ""); + } + const res = arr[key](...args); + if (res === -1 || res === false) { + return arr[key](...args.map(toRaw)); + } else { + return res; + } + }; + }); + ["push", "pop", "shift", "unshift", "splice"].forEach((key) => { + instrumentations[key] = function(...args) { + pauseTracking(); + pauseScheduling(); + const res = toRaw(this)[key].apply(this, args); + resetScheduling(); + resetTracking(); + return res; + }; + }); + return instrumentations; +} +__name(createArrayInstrumentations, "createArrayInstrumentations"); +function hasOwnProperty$2(key) { + if (!isSymbol$1(key)) key = String(key); + const obj = toRaw(this); + track(obj, "has", key); + return obj.hasOwnProperty(key); +} +__name(hasOwnProperty$2, "hasOwnProperty$2"); +class BaseReactiveHandler { + static { + __name(this, "BaseReactiveHandler"); + } + constructor(_isReadonly = false, _isShallow = false) { + this._isReadonly = _isReadonly; + this._isShallow = _isShallow; + } + get(target, key, receiver) { + const isReadonly2 = this._isReadonly, isShallow2 = this._isShallow; + if (key === "__v_isReactive") { + return !isReadonly2; + } else if (key === "__v_isReadonly") { + return isReadonly2; + } else if (key === "__v_isShallow") { + return isShallow2; + } else if (key === "__v_raw") { + if (receiver === (isReadonly2 ? isShallow2 ? shallowReadonlyMap : readonlyMap : isShallow2 ? shallowReactiveMap : reactiveMap).get(target) || // receiver is not the reactive proxy, but has the same prototype + // this means the reciever is a user proxy of the reactive proxy + Object.getPrototypeOf(target) === Object.getPrototypeOf(receiver)) { + return target; + } + return; + } + const targetIsArray = isArray$5(target); + if (!isReadonly2) { + if (targetIsArray && hasOwn$3(arrayInstrumentations, key)) { + return Reflect.get(arrayInstrumentations, key, receiver); + } + if (key === "hasOwnProperty") { + return hasOwnProperty$2; + } + } + const res = Reflect.get(target, key, receiver); + if (isSymbol$1(key) ? builtInSymbols.has(key) : isNonTrackableKeys(key)) { + return res; + } + if (!isReadonly2) { + track(target, "get", key); + } + if (isShallow2) { + return res; + } + if (isRef(res)) { + return targetIsArray && isIntegerKey(key) ? res : res.value; + } + if (isObject$6(res)) { + return isReadonly2 ? readonly(res) : reactive(res); + } + return res; + } +} +class MutableReactiveHandler extends BaseReactiveHandler { + static { + __name(this, "MutableReactiveHandler"); + } + constructor(isShallow2 = false) { + super(false, isShallow2); + } + set(target, key, value3, receiver) { + let oldValue = target[key]; + if (!this._isShallow) { + const isOldValueReadonly = isReadonly(oldValue); + if (!isShallow(value3) && !isReadonly(value3)) { + oldValue = toRaw(oldValue); + value3 = toRaw(value3); + } + if (!isArray$5(target) && isRef(oldValue) && !isRef(value3)) { + if (isOldValueReadonly) { + return false; + } else { + oldValue.value = value3; + return true; + } + } + } + const hadKey = isArray$5(target) && isIntegerKey(key) ? Number(key) < target.length : hasOwn$3(target, key); + const result = Reflect.set(target, key, value3, receiver); + if (target === toRaw(receiver)) { + if (!hadKey) { + trigger(target, "add", key, value3); + } else if (hasChanged(value3, oldValue)) { + trigger(target, "set", key, value3, oldValue); + } + } + return result; + } + deleteProperty(target, key) { + const hadKey = hasOwn$3(target, key); + const oldValue = target[key]; + const result = Reflect.deleteProperty(target, key); + if (result && hadKey) { + trigger(target, "delete", key, void 0, oldValue); + } + return result; + } + has(target, key) { + const result = Reflect.has(target, key); + if (!isSymbol$1(key) || !builtInSymbols.has(key)) { + track(target, "has", key); + } + return result; + } + ownKeys(target) { + track( + target, + "iterate", + isArray$5(target) ? "length" : ITERATE_KEY + ); + return Reflect.ownKeys(target); + } +} +class ReadonlyReactiveHandler extends BaseReactiveHandler { + static { + __name(this, "ReadonlyReactiveHandler"); + } + constructor(isShallow2 = false) { + super(true, isShallow2); + } + set(target, key) { + if (false) { + warn$3( + `Set operation on key "${String(key)}" failed: target is readonly.`, + target + ); + } + return true; + } + deleteProperty(target, key) { + if (false) { + warn$3( + `Delete operation on key "${String(key)}" failed: target is readonly.`, + target + ); + } + return true; + } +} +const mutableHandlers = /* @__PURE__ */ new MutableReactiveHandler(); +const readonlyHandlers = /* @__PURE__ */ new ReadonlyReactiveHandler(); +const shallowReactiveHandlers = /* @__PURE__ */ new MutableReactiveHandler( + true +); +const shallowReadonlyHandlers = /* @__PURE__ */ new ReadonlyReactiveHandler(true); +const toShallow = /* @__PURE__ */ __name((value3) => value3, "toShallow"); +const getProto = /* @__PURE__ */ __name((v2) => Reflect.getPrototypeOf(v2), "getProto"); +function get$2(target, key, isReadonly2 = false, isShallow2 = false) { + target = target["__v_raw"]; + const rawTarget = toRaw(target); + const rawKey = toRaw(key); + if (!isReadonly2) { + if (hasChanged(key, rawKey)) { + track(rawTarget, "get", key); + } + track(rawTarget, "get", rawKey); + } + const { has: has2 } = getProto(rawTarget); + const wrap = isShallow2 ? toShallow : isReadonly2 ? toReadonly : toReactive$1; + if (has2.call(rawTarget, key)) { + return wrap(target.get(key)); + } else if (has2.call(rawTarget, rawKey)) { + return wrap(target.get(rawKey)); + } else if (target !== rawTarget) { + target.get(key); + } +} +__name(get$2, "get$2"); +function has(key, isReadonly2 = false) { + const target = this["__v_raw"]; + const rawTarget = toRaw(target); + const rawKey = toRaw(key); + if (!isReadonly2) { + if (hasChanged(key, rawKey)) { + track(rawTarget, "has", key); + } + track(rawTarget, "has", rawKey); + } + return key === rawKey ? target.has(key) : target.has(key) || target.has(rawKey); +} +__name(has, "has"); +function size(target, isReadonly2 = false) { + target = target["__v_raw"]; + !isReadonly2 && track(toRaw(target), "iterate", ITERATE_KEY); + return Reflect.get(target, "size", target); +} +__name(size, "size"); +function add(value3) { + value3 = toRaw(value3); + const target = toRaw(this); + const proto = getProto(target); + const hadKey = proto.has.call(target, value3); + if (!hadKey) { + target.add(value3); + trigger(target, "add", value3, value3); + } + return this; +} +__name(add, "add"); +function set$4(key, value3) { + value3 = toRaw(value3); + const target = toRaw(this); + const { has: has2, get: get2 } = getProto(target); + let hadKey = has2.call(target, key); + if (!hadKey) { + key = toRaw(key); + hadKey = has2.call(target, key); + } else if (false) { + checkIdentityKeys(target, has2, key); + } + const oldValue = get2.call(target, key); + target.set(key, value3); + if (!hadKey) { + trigger(target, "add", key, value3); + } else if (hasChanged(value3, oldValue)) { + trigger(target, "set", key, value3, oldValue); + } + return this; +} +__name(set$4, "set$4"); +function deleteEntry(key) { + const target = toRaw(this); + const { has: has2, get: get2 } = getProto(target); + let hadKey = has2.call(target, key); + if (!hadKey) { + key = toRaw(key); + hadKey = has2.call(target, key); + } else if (false) { + checkIdentityKeys(target, has2, key); + } + const oldValue = get2 ? get2.call(target, key) : void 0; + const result = target.delete(key); + if (hadKey) { + trigger(target, "delete", key, void 0, oldValue); + } + return result; +} +__name(deleteEntry, "deleteEntry"); +function clear() { + const target = toRaw(this); + const hadItems = target.size !== 0; + const oldTarget = false ? isMap(target) ? new Map(target) : new Set(target) : void 0; + const result = target.clear(); + if (hadItems) { + trigger(target, "clear", void 0, void 0, oldTarget); + } + return result; +} +__name(clear, "clear"); +function createForEach(isReadonly2, isShallow2) { + return /* @__PURE__ */ __name(function forEach2(callback, thisArg) { + const observed = this; + const target = observed["__v_raw"]; + const rawTarget = toRaw(target); + const wrap = isShallow2 ? toShallow : isReadonly2 ? toReadonly : toReactive$1; + !isReadonly2 && track(rawTarget, "iterate", ITERATE_KEY); + return target.forEach((value3, key) => { + return callback.call(thisArg, wrap(value3), wrap(key), observed); + }); + }, "forEach"); +} +__name(createForEach, "createForEach"); +function createIterableMethod(method, isReadonly2, isShallow2) { + return function(...args) { + const target = this["__v_raw"]; + const rawTarget = toRaw(target); + const targetIsMap = isMap(rawTarget); + const isPair = method === "entries" || method === Symbol.iterator && targetIsMap; + const isKeyOnly = method === "keys" && targetIsMap; + const innerIterator = target[method](...args); + const wrap = isShallow2 ? toShallow : isReadonly2 ? toReadonly : toReactive$1; + !isReadonly2 && track( + rawTarget, + "iterate", + isKeyOnly ? MAP_KEY_ITERATE_KEY : ITERATE_KEY + ); + return { + // iterator protocol + next() { + const { value: value3, done } = innerIterator.next(); + return done ? { value: value3, done } : { + value: isPair ? [wrap(value3[0]), wrap(value3[1])] : wrap(value3), + done + }; + }, + // iterable protocol + [Symbol.iterator]() { + return this; + } + }; + }; +} +__name(createIterableMethod, "createIterableMethod"); +function createReadonlyMethod(type) { + return function(...args) { + if (false) { + const key = args[0] ? `on key "${args[0]}" ` : ``; + warn$3( + `${capitalize$1(type)} operation ${key}failed: target is readonly.`, + toRaw(this) + ); + } + return type === "delete" ? false : type === "clear" ? void 0 : this; + }; +} +__name(createReadonlyMethod, "createReadonlyMethod"); +function createInstrumentations() { + const mutableInstrumentations2 = { + get(key) { + return get$2(this, key); + }, + get size() { + return size(this); + }, + has, + add, + set: set$4, + delete: deleteEntry, + clear, + forEach: createForEach(false, false) + }; + const shallowInstrumentations2 = { + get(key) { + return get$2(this, key, false, true); + }, + get size() { + return size(this); + }, + has, + add, + set: set$4, + delete: deleteEntry, + clear, + forEach: createForEach(false, true) + }; + const readonlyInstrumentations2 = { + get(key) { + return get$2(this, key, true); + }, + get size() { + return size(this, true); + }, + has(key) { + return has.call(this, key, true); + }, + add: createReadonlyMethod("add"), + set: createReadonlyMethod("set"), + delete: createReadonlyMethod("delete"), + clear: createReadonlyMethod("clear"), + forEach: createForEach(true, false) + }; + const shallowReadonlyInstrumentations2 = { + get(key) { + return get$2(this, key, true, true); + }, + get size() { + return size(this, true); + }, + has(key) { + return has.call(this, key, true); + }, + add: createReadonlyMethod("add"), + set: createReadonlyMethod("set"), + delete: createReadonlyMethod("delete"), + clear: createReadonlyMethod("clear"), + forEach: createForEach(true, true) + }; + const iteratorMethods = [ + "keys", + "values", + "entries", + Symbol.iterator + ]; + iteratorMethods.forEach((method) => { + mutableInstrumentations2[method] = createIterableMethod(method, false, false); + readonlyInstrumentations2[method] = createIterableMethod(method, true, false); + shallowInstrumentations2[method] = createIterableMethod(method, false, true); + shallowReadonlyInstrumentations2[method] = createIterableMethod( + method, + true, + true + ); + }); + return [ + mutableInstrumentations2, + readonlyInstrumentations2, + shallowInstrumentations2, + shallowReadonlyInstrumentations2 + ]; +} +__name(createInstrumentations, "createInstrumentations"); +const [ + mutableInstrumentations, + readonlyInstrumentations, + shallowInstrumentations, + shallowReadonlyInstrumentations +] = /* @__PURE__ */ createInstrumentations(); +function createInstrumentationGetter(isReadonly2, shallow) { + const instrumentations = shallow ? isReadonly2 ? shallowReadonlyInstrumentations : shallowInstrumentations : isReadonly2 ? readonlyInstrumentations : mutableInstrumentations; + return (target, key, receiver) => { + if (key === "__v_isReactive") { + return !isReadonly2; + } else if (key === "__v_isReadonly") { + return isReadonly2; + } else if (key === "__v_raw") { + return target; + } + return Reflect.get( + hasOwn$3(instrumentations, key) && key in target ? instrumentations : target, + key, + receiver + ); + }; +} +__name(createInstrumentationGetter, "createInstrumentationGetter"); +const mutableCollectionHandlers = { + get: /* @__PURE__ */ createInstrumentationGetter(false, false) +}; +const shallowCollectionHandlers = { + get: /* @__PURE__ */ createInstrumentationGetter(false, true) +}; +const readonlyCollectionHandlers = { + get: /* @__PURE__ */ createInstrumentationGetter(true, false) +}; +const shallowReadonlyCollectionHandlers = { + get: /* @__PURE__ */ createInstrumentationGetter(true, true) +}; +function checkIdentityKeys(target, has2, key) { + const rawKey = toRaw(key); + if (rawKey !== key && has2.call(target, rawKey)) { + const type = toRawType(target); + warn$3( + `Reactive ${type} contains both the raw and reactive versions of the same object${type === `Map` ? ` as keys` : ``}, which can lead to inconsistencies. Avoid differentiating between the raw and reactive versions of an object and only use the reactive version if possible.` + ); + } +} +__name(checkIdentityKeys, "checkIdentityKeys"); +const reactiveMap = /* @__PURE__ */ new WeakMap(); +const shallowReactiveMap = /* @__PURE__ */ new WeakMap(); +const readonlyMap = /* @__PURE__ */ new WeakMap(); +const shallowReadonlyMap = /* @__PURE__ */ new WeakMap(); +function targetTypeMap(rawType) { + switch (rawType) { + case "Object": + case "Array": + return 1; + case "Map": + case "Set": + case "WeakMap": + case "WeakSet": + return 2; + default: + return 0; + } +} +__name(targetTypeMap, "targetTypeMap"); +function getTargetType(value3) { + return value3["__v_skip"] || !Object.isExtensible(value3) ? 0 : targetTypeMap(toRawType(value3)); +} +__name(getTargetType, "getTargetType"); +function reactive(target) { + if (isReadonly(target)) { + return target; + } + return createReactiveObject( + target, + false, + mutableHandlers, + mutableCollectionHandlers, + reactiveMap + ); +} +__name(reactive, "reactive"); +function shallowReactive(target) { + return createReactiveObject( + target, + false, + shallowReactiveHandlers, + shallowCollectionHandlers, + shallowReactiveMap + ); +} +__name(shallowReactive, "shallowReactive"); +function readonly(target) { + return createReactiveObject( + target, + true, + readonlyHandlers, + readonlyCollectionHandlers, + readonlyMap + ); +} +__name(readonly, "readonly"); +function shallowReadonly(target) { + return createReactiveObject( + target, + true, + shallowReadonlyHandlers, + shallowReadonlyCollectionHandlers, + shallowReadonlyMap + ); +} +__name(shallowReadonly, "shallowReadonly"); +function createReactiveObject(target, isReadonly2, baseHandlers, collectionHandlers, proxyMap) { + if (!isObject$6(target)) { + if (false) { + warn$3( + `value cannot be made ${isReadonly2 ? "readonly" : "reactive"}: ${String( + target + )}` + ); + } + return target; + } + if (target["__v_raw"] && !(isReadonly2 && target["__v_isReactive"])) { + return target; + } + const existingProxy = proxyMap.get(target); + if (existingProxy) { + return existingProxy; + } + const targetType = getTargetType(target); + if (targetType === 0) { + return target; + } + const proxy = new Proxy( + target, + targetType === 2 ? collectionHandlers : baseHandlers + ); + proxyMap.set(target, proxy); + return proxy; +} +__name(createReactiveObject, "createReactiveObject"); +function isReactive(value3) { + if (isReadonly(value3)) { + return isReactive(value3["__v_raw"]); + } + return !!(value3 && value3["__v_isReactive"]); +} +__name(isReactive, "isReactive"); +function isReadonly(value3) { + return !!(value3 && value3["__v_isReadonly"]); +} +__name(isReadonly, "isReadonly"); +function isShallow(value3) { + return !!(value3 && value3["__v_isShallow"]); +} +__name(isShallow, "isShallow"); +function isProxy(value3) { + return value3 ? !!value3["__v_raw"] : false; +} +__name(isProxy, "isProxy"); +function toRaw(observed) { + const raw = observed && observed["__v_raw"]; + return raw ? toRaw(raw) : observed; +} +__name(toRaw, "toRaw"); +function markRaw(value3) { + if (Object.isExtensible(value3)) { + def(value3, "__v_skip", true); + } + return value3; +} +__name(markRaw, "markRaw"); +const toReactive$1 = /* @__PURE__ */ __name((value3) => isObject$6(value3) ? reactive(value3) : value3, "toReactive$1"); +const toReadonly = /* @__PURE__ */ __name((value3) => isObject$6(value3) ? readonly(value3) : value3, "toReadonly"); +const COMPUTED_SIDE_EFFECT_WARN = `Computed is still dirty after getter evaluation, likely because a computed is mutating its own dependency in its getter. State mutations in computed getters should be avoided. Check the docs for more details: https://vuejs.org/guide/essentials/computed.html#getters-should-be-side-effect-free`; +class ComputedRefImpl { + static { + __name(this, "ComputedRefImpl"); + } + constructor(getter, _setter, isReadonly2, isSSR) { + this.getter = getter; + this._setter = _setter; + this.dep = void 0; + this.__v_isRef = true; + this["__v_isReadonly"] = false; + this.effect = new ReactiveEffect( + () => getter(this._value), + () => triggerRefValue( + this, + this.effect._dirtyLevel === 2 ? 2 : 3 + ) + ); + this.effect.computed = this; + this.effect.active = this._cacheable = !isSSR; + this["__v_isReadonly"] = isReadonly2; + } + get value() { + const self2 = toRaw(this); + if ((!self2._cacheable || self2.effect.dirty) && hasChanged(self2._value, self2._value = self2.effect.run())) { + triggerRefValue(self2, 4); + } + trackRefValue(self2); + if (self2.effect._dirtyLevel >= 2) { + if (false) { + warn$3(COMPUTED_SIDE_EFFECT_WARN, ` + +getter: `, this.getter); + } + triggerRefValue(self2, 2); + } + return self2._value; + } + set value(newValue) { + this._setter(newValue); + } + // #region polyfill _dirty for backward compatibility third party code for Vue <= 3.3.x + get _dirty() { + return this.effect.dirty; + } + set _dirty(v2) { + this.effect.dirty = v2; + } + // #endregion +} +function computed$1(getterOrOptions, debugOptions, isSSR = false) { + let getter; + let setter; + const onlyGetter = isFunction$3(getterOrOptions); + if (onlyGetter) { + getter = getterOrOptions; + setter = false ? () => { + warn$3("Write operation failed: computed value is readonly"); + } : NOOP; + } else { + getter = getterOrOptions.get; + setter = getterOrOptions.set; + } + const cRef = new ComputedRefImpl(getter, setter, onlyGetter || !setter, isSSR); + if (false) { + cRef.effect.onTrack = debugOptions.onTrack; + cRef.effect.onTrigger = debugOptions.onTrigger; + } + return cRef; +} +__name(computed$1, "computed$1"); +function trackRefValue(ref2) { + var _a; + if (shouldTrack && activeEffect) { + ref2 = toRaw(ref2); + trackEffect( + activeEffect, + (_a = ref2.dep) != null ? _a : ref2.dep = createDep( + () => ref2.dep = void 0, + ref2 instanceof ComputedRefImpl ? ref2 : void 0 + ), + false ? { + target: ref2, + type: "get", + key: "value" + } : void 0 + ); + } +} +__name(trackRefValue, "trackRefValue"); +function triggerRefValue(ref2, dirtyLevel = 4, newVal, oldVal) { + ref2 = toRaw(ref2); + const dep = ref2.dep; + if (dep) { + triggerEffects( + dep, + dirtyLevel, + false ? { + target: ref2, + type: "set", + key: "value", + newValue: newVal, + oldValue: oldVal + } : void 0 + ); + } +} +__name(triggerRefValue, "triggerRefValue"); +function isRef(r) { + return !!(r && r.__v_isRef === true); +} +__name(isRef, "isRef"); +function ref(value3) { + return createRef(value3, false); +} +__name(ref, "ref"); +function shallowRef(value3) { + return createRef(value3, true); +} +__name(shallowRef, "shallowRef"); +function createRef(rawValue, shallow) { + if (isRef(rawValue)) { + return rawValue; + } + return new RefImpl(rawValue, shallow); +} +__name(createRef, "createRef"); +class RefImpl { + static { + __name(this, "RefImpl"); + } + constructor(value3, __v_isShallow) { + this.__v_isShallow = __v_isShallow; + this.dep = void 0; + this.__v_isRef = true; + this._rawValue = __v_isShallow ? value3 : toRaw(value3); + this._value = __v_isShallow ? value3 : toReactive$1(value3); + } + get value() { + trackRefValue(this); + return this._value; + } + set value(newVal) { + const useDirectValue = this.__v_isShallow || isShallow(newVal) || isReadonly(newVal); + newVal = useDirectValue ? newVal : toRaw(newVal); + if (hasChanged(newVal, this._rawValue)) { + const oldVal = this._rawValue; + this._rawValue = newVal; + this._value = useDirectValue ? newVal : toReactive$1(newVal); + triggerRefValue(this, 4, newVal, oldVal); + } + } +} +function triggerRef(ref2) { + triggerRefValue(ref2, 4, false ? ref2.value : void 0); +} +__name(triggerRef, "triggerRef"); +function unref(ref2) { + return isRef(ref2) ? ref2.value : ref2; +} +__name(unref, "unref"); +function toValue$2(source) { + return isFunction$3(source) ? source() : unref(source); +} +__name(toValue$2, "toValue$2"); +const shallowUnwrapHandlers = { + get: /* @__PURE__ */ __name((target, key, receiver) => unref(Reflect.get(target, key, receiver)), "get"), + set: /* @__PURE__ */ __name((target, key, value3, receiver) => { + const oldValue = target[key]; + if (isRef(oldValue) && !isRef(value3)) { + oldValue.value = value3; + return true; + } else { + return Reflect.set(target, key, value3, receiver); + } + }, "set") +}; +function proxyRefs(objectWithRefs) { + return isReactive(objectWithRefs) ? objectWithRefs : new Proxy(objectWithRefs, shallowUnwrapHandlers); +} +__name(proxyRefs, "proxyRefs"); +class CustomRefImpl { + static { + __name(this, "CustomRefImpl"); + } + constructor(factory) { + this.dep = void 0; + this.__v_isRef = true; + const { get: get2, set: set2 } = factory( + () => trackRefValue(this), + () => triggerRefValue(this) + ); + this._get = get2; + this._set = set2; + } + get value() { + return this._get(); + } + set value(newVal) { + this._set(newVal); + } +} +function customRef(factory) { + return new CustomRefImpl(factory); +} +__name(customRef, "customRef"); +function toRefs$1(object) { + if (false) { + warn$3(`toRefs() expects a reactive object but received a plain one.`); + } + const ret = isArray$5(object) ? new Array(object.length) : {}; + for (const key in object) { + ret[key] = propertyToRef(object, key); + } + return ret; +} +__name(toRefs$1, "toRefs$1"); +class ObjectRefImpl { + static { + __name(this, "ObjectRefImpl"); + } + constructor(_object, _key, _defaultValue) { + this._object = _object; + this._key = _key; + this._defaultValue = _defaultValue; + this.__v_isRef = true; + } + get value() { + const val = this._object[this._key]; + return val === void 0 ? this._defaultValue : val; + } + set value(newVal) { + this._object[this._key] = newVal; + } + get dep() { + return getDepFromReactive(toRaw(this._object), this._key); + } +} +class GetterRefImpl { + static { + __name(this, "GetterRefImpl"); + } + constructor(_getter) { + this._getter = _getter; + this.__v_isRef = true; + this.__v_isReadonly = true; + } + get value() { + return this._getter(); + } +} +function toRef$1(source, key, defaultValue) { + if (isRef(source)) { + return source; + } else if (isFunction$3(source)) { + return new GetterRefImpl(source); + } else if (isObject$6(source) && arguments.length > 1) { + return propertyToRef(source, key, defaultValue); + } else { + return ref(source); + } +} +__name(toRef$1, "toRef$1"); +function propertyToRef(source, key, defaultValue) { + const val = source[key]; + return isRef(val) ? val : new ObjectRefImpl(source, key, defaultValue); +} +__name(propertyToRef, "propertyToRef"); +const deferredComputed = computed$1; +const TrackOpTypes = { + "GET": "get", + "HAS": "has", + "ITERATE": "iterate" +}; +const TriggerOpTypes = { + "SET": "set", + "ADD": "add", + "DELETE": "delete", + "CLEAR": "clear" +}; +const ReactiveFlags = { + "SKIP": "__v_skip", + "IS_REACTIVE": "__v_isReactive", + "IS_READONLY": "__v_isReadonly", + "IS_SHALLOW": "__v_isShallow", + "RAW": "__v_raw" +}; +/** +* @vue/runtime-core v3.4.31 +* (c) 2018-present Yuxi (Evan) You and Vue contributors +* @license MIT +**/ +const stack = []; +function pushWarningContext(vnode) { + stack.push(vnode); +} +__name(pushWarningContext, "pushWarningContext"); +function popWarningContext() { + stack.pop(); +} +__name(popWarningContext, "popWarningContext"); +function warn$1$1(msg, ...args) { + pauseTracking(); + const instance = stack.length ? stack[stack.length - 1].component : null; + const appWarnHandler = instance && instance.appContext.config.warnHandler; + const trace = getComponentTrace(); + if (appWarnHandler) { + callWithErrorHandling( + appWarnHandler, + instance, + 11, + [ + // eslint-disable-next-line no-restricted-syntax + msg + args.map((a) => { + var _a, _b; + return (_b = (_a = a.toString) == null ? void 0 : _a.call(a)) != null ? _b : JSON.stringify(a); + }).join(""), + instance && instance.proxy, + trace.map( + ({ vnode }) => `at <${formatComponentName(instance, vnode.type)}>` + ).join("\n"), + trace + ] + ); + } else { + const warnArgs = [`[Vue warn]: ${msg}`, ...args]; + if (trace.length && // avoid spamming console during tests + true) { + warnArgs.push(` +`, ...formatTrace(trace)); + } + console.warn(...warnArgs); + } + resetTracking(); +} +__name(warn$1$1, "warn$1$1"); +function getComponentTrace() { + let currentVNode = stack[stack.length - 1]; + if (!currentVNode) { + return []; + } + const normalizedStack = []; + while (currentVNode) { + const last = normalizedStack[0]; + if (last && last.vnode === currentVNode) { + last.recurseCount++; + } else { + normalizedStack.push({ + vnode: currentVNode, + recurseCount: 0 + }); + } + const parentInstance = currentVNode.component && currentVNode.component.parent; + currentVNode = parentInstance && parentInstance.vnode; + } + return normalizedStack; +} +__name(getComponentTrace, "getComponentTrace"); +function formatTrace(trace) { + const logs = []; + trace.forEach((entry, i2) => { + logs.push(...i2 === 0 ? [] : [` +`], ...formatTraceEntry(entry)); + }); + return logs; +} +__name(formatTrace, "formatTrace"); +function formatTraceEntry({ vnode, recurseCount }) { + const postfix = recurseCount > 0 ? `... (${recurseCount} recursive calls)` : ``; + const isRoot = vnode.component ? vnode.component.parent == null : false; + const open2 = ` at <${formatComponentName( + vnode.component, + vnode.type, + isRoot + )}`; + const close3 = `>` + postfix; + return vnode.props ? [open2, ...formatProps(vnode.props), close3] : [open2 + close3]; +} +__name(formatTraceEntry, "formatTraceEntry"); +function formatProps(props) { + const res = []; + const keys2 = Object.keys(props); + keys2.slice(0, 3).forEach((key) => { + res.push(...formatProp(key, props[key])); + }); + if (keys2.length > 3) { + res.push(` ...`); + } + return res; +} +__name(formatProps, "formatProps"); +function formatProp(key, value3, raw) { + if (isString$5(value3)) { + value3 = JSON.stringify(value3); + return raw ? value3 : [`${key}=${value3}`]; + } else if (typeof value3 === "number" || typeof value3 === "boolean" || value3 == null) { + return raw ? value3 : [`${key}=${value3}`]; + } else if (isRef(value3)) { + value3 = formatProp(key, toRaw(value3.value), true); + return raw ? value3 : [`${key}=Ref<`, value3, `>`]; + } else if (isFunction$3(value3)) { + return [`${key}=fn${value3.name ? `<${value3.name}>` : ``}`]; + } else { + value3 = toRaw(value3); + return raw ? value3 : [`${key}=`, value3]; + } +} +__name(formatProp, "formatProp"); +function assertNumber(val, type) { + if (true) return; + if (val === void 0) { + return; + } else if (typeof val !== "number") { + warn$1$1(`${type} is not a valid number - got ${JSON.stringify(val)}.`); + } else if (isNaN(val)) { + warn$1$1(`${type} is NaN - the duration expression might be incorrect.`); + } +} +__name(assertNumber, "assertNumber"); +const ErrorCodes = { + "SETUP_FUNCTION": 0, + "0": "SETUP_FUNCTION", + "RENDER_FUNCTION": 1, + "1": "RENDER_FUNCTION", + "WATCH_GETTER": 2, + "2": "WATCH_GETTER", + "WATCH_CALLBACK": 3, + "3": "WATCH_CALLBACK", + "WATCH_CLEANUP": 4, + "4": "WATCH_CLEANUP", + "NATIVE_EVENT_HANDLER": 5, + "5": "NATIVE_EVENT_HANDLER", + "COMPONENT_EVENT_HANDLER": 6, + "6": "COMPONENT_EVENT_HANDLER", + "VNODE_HOOK": 7, + "7": "VNODE_HOOK", + "DIRECTIVE_HOOK": 8, + "8": "DIRECTIVE_HOOK", + "TRANSITION_HOOK": 9, + "9": "TRANSITION_HOOK", + "APP_ERROR_HANDLER": 10, + "10": "APP_ERROR_HANDLER", + "APP_WARN_HANDLER": 11, + "11": "APP_WARN_HANDLER", + "FUNCTION_REF": 12, + "12": "FUNCTION_REF", + "ASYNC_COMPONENT_LOADER": 13, + "13": "ASYNC_COMPONENT_LOADER", + "SCHEDULER": 14, + "14": "SCHEDULER" +}; +const ErrorTypeStrings$1 = { + ["sp"]: "serverPrefetch hook", + ["bc"]: "beforeCreate hook", + ["c"]: "created hook", + ["bm"]: "beforeMount hook", + ["m"]: "mounted hook", + ["bu"]: "beforeUpdate hook", + ["u"]: "updated", + ["bum"]: "beforeUnmount hook", + ["um"]: "unmounted hook", + ["a"]: "activated hook", + ["da"]: "deactivated hook", + ["ec"]: "errorCaptured hook", + ["rtc"]: "renderTracked hook", + ["rtg"]: "renderTriggered hook", + [0]: "setup function", + [1]: "render function", + [2]: "watcher getter", + [3]: "watcher callback", + [4]: "watcher cleanup function", + [5]: "native event handler", + [6]: "component event handler", + [7]: "vnode hook", + [8]: "directive hook", + [9]: "transition hook", + [10]: "app errorHandler", + [11]: "app warnHandler", + [12]: "ref function", + [13]: "async component loader", + [14]: "scheduler flush. This is likely a Vue internals bug. Please open an issue at https://github.com/vuejs/core ." +}; +function callWithErrorHandling(fn, instance, type, args) { + try { + return args ? fn(...args) : fn(); + } catch (err) { + handleError(err, instance, type); + } +} +__name(callWithErrorHandling, "callWithErrorHandling"); +function callWithAsyncErrorHandling(fn, instance, type, args) { + if (isFunction$3(fn)) { + const res = callWithErrorHandling(fn, instance, type, args); + if (res && isPromise$1(res)) { + res.catch((err) => { + handleError(err, instance, type); + }); + } + return res; + } + if (isArray$5(fn)) { + const values2 = []; + for (let i2 = 0; i2 < fn.length; i2++) { + values2.push(callWithAsyncErrorHandling(fn[i2], instance, type, args)); + } + return values2; + } else if (false) { + warn$1$1( + `Invalid value type passed to callWithAsyncErrorHandling(): ${typeof fn}` + ); + } +} +__name(callWithAsyncErrorHandling, "callWithAsyncErrorHandling"); +function handleError(err, instance, type, throwInDev = true) { + const contextVNode = instance ? instance.vnode : null; + if (instance) { + let cur = instance.parent; + const exposedInstance = instance.proxy; + const errorInfo = false ? ErrorTypeStrings$1[type] : `https://vuejs.org/error-reference/#runtime-${type}`; + while (cur) { + const errorCapturedHooks = cur.ec; + if (errorCapturedHooks) { + for (let i2 = 0; i2 < errorCapturedHooks.length; i2++) { + if (errorCapturedHooks[i2](err, exposedInstance, errorInfo) === false) { + return; + } + } + } + cur = cur.parent; + } + const appErrorHandler = instance.appContext.config.errorHandler; + if (appErrorHandler) { + pauseTracking(); + callWithErrorHandling( + appErrorHandler, + null, + 10, + [err, exposedInstance, errorInfo] + ); + resetTracking(); + return; + } + } + logError(err, type, contextVNode, throwInDev); +} +__name(handleError, "handleError"); +function logError(err, type, contextVNode, throwInDev = true) { + if (false) { + const info = ErrorTypeStrings$1[type]; + if (contextVNode) { + pushWarningContext(contextVNode); + } + warn$1$1(`Unhandled error${info ? ` during execution of ${info}` : ``}`); + if (contextVNode) { + popWarningContext(); + } + if (throwInDev) { + throw err; + } else { + console.error(err); + } + } else { + console.error(err); + } +} +__name(logError, "logError"); +let isFlushing = false; +let isFlushPending = false; +const queue = []; +let flushIndex = 0; +const pendingPostFlushCbs = []; +let activePostFlushCbs = null; +let postFlushIndex = 0; +const resolvedPromise = /* @__PURE__ */ Promise.resolve(); +let currentFlushPromise = null; +const RECURSION_LIMIT = 100; +function nextTick(fn) { + const p2 = currentFlushPromise || resolvedPromise; + return fn ? p2.then(this ? fn.bind(this) : fn) : p2; +} +__name(nextTick, "nextTick"); +function findInsertionIndex$1(id3) { + let start2 = flushIndex + 1; + let end = queue.length; + while (start2 < end) { + const middle = start2 + end >>> 1; + const middleJob = queue[middle]; + const middleJobId = getId(middleJob); + if (middleJobId < id3 || middleJobId === id3 && middleJob.pre) { + start2 = middle + 1; + } else { + end = middle; + } + } + return start2; +} +__name(findInsertionIndex$1, "findInsertionIndex$1"); +function queueJob(job) { + if (!queue.length || !queue.includes( + job, + isFlushing && job.allowRecurse ? flushIndex + 1 : flushIndex + )) { + if (job.id == null) { + queue.push(job); + } else { + queue.splice(findInsertionIndex$1(job.id), 0, job); + } + queueFlush(); + } +} +__name(queueJob, "queueJob"); +function queueFlush() { + if (!isFlushing && !isFlushPending) { + isFlushPending = true; + currentFlushPromise = resolvedPromise.then(flushJobs); + } +} +__name(queueFlush, "queueFlush"); +function invalidateJob(job) { + const i2 = queue.indexOf(job); + if (i2 > flushIndex) { + queue.splice(i2, 1); + } +} +__name(invalidateJob, "invalidateJob"); +function queuePostFlushCb(cb) { + if (!isArray$5(cb)) { + if (!activePostFlushCbs || !activePostFlushCbs.includes( + cb, + cb.allowRecurse ? postFlushIndex + 1 : postFlushIndex + )) { + pendingPostFlushCbs.push(cb); + } + } else { + pendingPostFlushCbs.push(...cb); + } + queueFlush(); +} +__name(queuePostFlushCb, "queuePostFlushCb"); +function flushPreFlushCbs(instance, seen2, i2 = isFlushing ? flushIndex + 1 : 0) { + if (false) { + seen2 = seen2 || /* @__PURE__ */ new Map(); + } + for (; i2 < queue.length; i2++) { + const cb = queue[i2]; + if (cb && cb.pre) { + if (instance && cb.id !== instance.uid) { + continue; + } + if (false) { + continue; + } + queue.splice(i2, 1); + i2--; + cb(); + } + } +} +__name(flushPreFlushCbs, "flushPreFlushCbs"); +function flushPostFlushCbs(seen2) { + if (pendingPostFlushCbs.length) { + const deduped = [...new Set(pendingPostFlushCbs)].sort( + (a, b) => getId(a) - getId(b) + ); + pendingPostFlushCbs.length = 0; + if (activePostFlushCbs) { + activePostFlushCbs.push(...deduped); + return; + } + activePostFlushCbs = deduped; + if (false) { + seen2 = seen2 || /* @__PURE__ */ new Map(); + } + for (postFlushIndex = 0; postFlushIndex < activePostFlushCbs.length; postFlushIndex++) { + const cb = activePostFlushCbs[postFlushIndex]; + if (false) { + continue; + } + if (cb.active !== false) cb(); + } + activePostFlushCbs = null; + postFlushIndex = 0; + } +} +__name(flushPostFlushCbs, "flushPostFlushCbs"); +const getId = /* @__PURE__ */ __name((job) => job.id == null ? Infinity : job.id, "getId"); +const comparator = /* @__PURE__ */ __name((a, b) => { + const diff = getId(a) - getId(b); + if (diff === 0) { + if (a.pre && !b.pre) return -1; + if (b.pre && !a.pre) return 1; + } + return diff; +}, "comparator"); +function flushJobs(seen2) { + isFlushPending = false; + isFlushing = true; + if (false) { + seen2 = seen2 || /* @__PURE__ */ new Map(); + } + queue.sort(comparator); + const check = false ? (job) => checkRecursiveUpdates(seen2, job) : NOOP; + try { + for (flushIndex = 0; flushIndex < queue.length; flushIndex++) { + const job = queue[flushIndex]; + if (job && job.active !== false) { + if (false) { + continue; + } + callWithErrorHandling(job, null, 14); + } + } + } finally { + flushIndex = 0; + queue.length = 0; + flushPostFlushCbs(seen2); + isFlushing = false; + currentFlushPromise = null; + if (queue.length || pendingPostFlushCbs.length) { + flushJobs(seen2); + } + } +} +__name(flushJobs, "flushJobs"); +function checkRecursiveUpdates(seen2, fn) { + if (!seen2.has(fn)) { + seen2.set(fn, 1); + } else { + const count = seen2.get(fn); + if (count > RECURSION_LIMIT) { + const instance = fn.ownerInstance; + const componentName = instance && getComponentName(instance.type); + handleError( + `Maximum recursive updates exceeded${componentName ? ` in component <${componentName}>` : ``}. This means you have a reactive effect that is mutating its own dependencies and thus recursively triggering itself. Possible sources include component template, render function, updated hook or watcher source function.`, + null, + 10 + ); + return true; + } else { + seen2.set(fn, count + 1); + } + } +} +__name(checkRecursiveUpdates, "checkRecursiveUpdates"); +let isHmrUpdating = false; +const hmrDirtyComponents = /* @__PURE__ */ new Set(); +if (false) { + getGlobalThis$1().__VUE_HMR_RUNTIME__ = { + createRecord: tryWrap(createRecord), + rerender: tryWrap(rerender), + reload: tryWrap(reload) + }; +} +const map = /* @__PURE__ */ new Map(); +function registerHMR(instance) { + const id3 = instance.type.__hmrId; + let record = map.get(id3); + if (!record) { + createRecord(id3, instance.type); + record = map.get(id3); + } + record.instances.add(instance); +} +__name(registerHMR, "registerHMR"); +function unregisterHMR(instance) { + map.get(instance.type.__hmrId).instances.delete(instance); +} +__name(unregisterHMR, "unregisterHMR"); +function createRecord(id3, initialDef) { + if (map.has(id3)) { + return false; + } + map.set(id3, { + initialDef: normalizeClassComponent(initialDef), + instances: /* @__PURE__ */ new Set() + }); + return true; +} +__name(createRecord, "createRecord"); +function normalizeClassComponent(component) { + return isClassComponent(component) ? component.__vccOpts : component; +} +__name(normalizeClassComponent, "normalizeClassComponent"); +function rerender(id3, newRender) { + const record = map.get(id3); + if (!record) { + return; + } + record.initialDef.render = newRender; + [...record.instances].forEach((instance) => { + if (newRender) { + instance.render = newRender; + normalizeClassComponent(instance.type).render = newRender; + } + instance.renderCache = []; + isHmrUpdating = true; + instance.effect.dirty = true; + instance.update(); + isHmrUpdating = false; + }); +} +__name(rerender, "rerender"); +function reload(id3, newComp) { + const record = map.get(id3); + if (!record) return; + newComp = normalizeClassComponent(newComp); + updateComponentDef(record.initialDef, newComp); + const instances = [...record.instances]; + for (const instance of instances) { + const oldComp = normalizeClassComponent(instance.type); + if (!hmrDirtyComponents.has(oldComp)) { + if (oldComp !== record.initialDef) { + updateComponentDef(oldComp, newComp); + } + hmrDirtyComponents.add(oldComp); + } + instance.appContext.propsCache.delete(instance.type); + instance.appContext.emitsCache.delete(instance.type); + instance.appContext.optionsCache.delete(instance.type); + if (instance.ceReload) { + hmrDirtyComponents.add(oldComp); + instance.ceReload(newComp.styles); + hmrDirtyComponents.delete(oldComp); + } else if (instance.parent) { + instance.parent.effect.dirty = true; + queueJob(() => { + instance.parent.update(); + hmrDirtyComponents.delete(oldComp); + }); + } else if (instance.appContext.reload) { + instance.appContext.reload(); + } else if (typeof window !== "undefined") { + window.location.reload(); + } else { + console.warn( + "[HMR] Root or manually mounted instance modified. Full reload required." + ); + } + } + queuePostFlushCb(() => { + for (const instance of instances) { + hmrDirtyComponents.delete( + normalizeClassComponent(instance.type) + ); + } + }); +} +__name(reload, "reload"); +function updateComponentDef(oldComp, newComp) { + extend$1(oldComp, newComp); + for (const key in oldComp) { + if (key !== "__file" && !(key in newComp)) { + delete oldComp[key]; + } + } +} +__name(updateComponentDef, "updateComponentDef"); +function tryWrap(fn) { + return (id3, arg) => { + try { + return fn(id3, arg); + } catch (e) { + console.error(e); + console.warn( + `[HMR] Something went wrong during Vue component hot-reload. Full reload required.` + ); + } + }; +} +__name(tryWrap, "tryWrap"); +let devtools$1; +let buffer = []; +let devtoolsNotInstalled = false; +function emit$1(event2, ...args) { + if (devtools$1) { + devtools$1.emit(event2, ...args); + } else if (!devtoolsNotInstalled) { + buffer.push({ event: event2, args }); + } +} +__name(emit$1, "emit$1"); +function setDevtoolsHook$1(hook, target) { + var _a, _b; + devtools$1 = hook; + if (devtools$1) { + devtools$1.enabled = true; + buffer.forEach(({ event: event2, args }) => devtools$1.emit(event2, ...args)); + buffer = []; + } else if ( + // handle late devtools injection - only do this if we are in an actual + // browser environment to avoid the timer handle stalling test runner exit + // (#4815) + typeof window !== "undefined" && // some envs mock window but not fully + window.HTMLElement && // also exclude jsdom + // eslint-disable-next-line no-restricted-syntax + !((_b = (_a = window.navigator) == null ? void 0 : _a.userAgent) == null ? void 0 : _b.includes("jsdom")) + ) { + const replay = target.__VUE_DEVTOOLS_HOOK_REPLAY__ = target.__VUE_DEVTOOLS_HOOK_REPLAY__ || []; + replay.push((newHook) => { + setDevtoolsHook$1(newHook, target); + }); + setTimeout(() => { + if (!devtools$1) { + target.__VUE_DEVTOOLS_HOOK_REPLAY__ = null; + devtoolsNotInstalled = true; + buffer = []; + } + }, 3e3); + } else { + devtoolsNotInstalled = true; + buffer = []; + } +} +__name(setDevtoolsHook$1, "setDevtoolsHook$1"); +function devtoolsInitApp(app2, version2) { + emit$1("app:init", app2, version2, { + Fragment, + Text, + Comment, + Static + }); +} +__name(devtoolsInitApp, "devtoolsInitApp"); +function devtoolsUnmountApp(app2) { + emit$1("app:unmount", app2); +} +__name(devtoolsUnmountApp, "devtoolsUnmountApp"); +const devtoolsComponentAdded = /* @__PURE__ */ createDevtoolsComponentHook( + "component:added" + /* COMPONENT_ADDED */ +); +const devtoolsComponentUpdated = /* @__PURE__ */ createDevtoolsComponentHook( + "component:updated" + /* COMPONENT_UPDATED */ +); +const _devtoolsComponentRemoved = /* @__PURE__ */ createDevtoolsComponentHook( + "component:removed" + /* COMPONENT_REMOVED */ +); +const devtoolsComponentRemoved = /* @__PURE__ */ __name((component) => { + if (devtools$1 && typeof devtools$1.cleanupBuffer === "function" && // remove the component if it wasn't buffered + !devtools$1.cleanupBuffer(component)) { + _devtoolsComponentRemoved(component); + } +}, "devtoolsComponentRemoved"); +/*! #__NO_SIDE_EFFECTS__ */ +// @__NO_SIDE_EFFECTS__ +function createDevtoolsComponentHook(hook) { + return (component) => { + emit$1( + hook, + component.appContext.app, + component.uid, + component.parent ? component.parent.uid : void 0, + component + ); + }; +} +__name(createDevtoolsComponentHook, "createDevtoolsComponentHook"); +const devtoolsPerfStart = /* @__PURE__ */ createDevtoolsPerformanceHook( + "perf:start" + /* PERFORMANCE_START */ +); +const devtoolsPerfEnd = /* @__PURE__ */ createDevtoolsPerformanceHook( + "perf:end" + /* PERFORMANCE_END */ +); +function createDevtoolsPerformanceHook(hook) { + return (component, type, time) => { + emit$1(hook, component.appContext.app, component.uid, component, type, time); + }; +} +__name(createDevtoolsPerformanceHook, "createDevtoolsPerformanceHook"); +function devtoolsComponentEmit(component, event2, params) { + emit$1( + "component:emit", + component.appContext.app, + component, + event2, + params + ); +} +__name(devtoolsComponentEmit, "devtoolsComponentEmit"); +function emit(instance, event2, ...rawArgs) { + if (instance.isUnmounted) return; + const props = instance.vnode.props || EMPTY_OBJ; + if (false) { + const { + emitsOptions, + propsOptions: [propsOptions] + } = instance; + if (emitsOptions) { + if (!(event2 in emitsOptions) && true) { + if (!propsOptions || !(toHandlerKey(event2) in propsOptions)) { + warn$1$1( + `Component emitted event "${event2}" but it is neither declared in the emits option nor as an "${toHandlerKey(event2)}" prop.` + ); + } + } else { + const validator3 = emitsOptions[event2]; + if (isFunction$3(validator3)) { + const isValid2 = validator3(...rawArgs); + if (!isValid2) { + warn$1$1( + `Invalid event arguments: event validation failed for event "${event2}".` + ); + } + } + } + } + } + let args = rawArgs; + const isModelListener2 = event2.startsWith("update:"); + const modelArg = isModelListener2 && event2.slice(7); + if (modelArg && modelArg in props) { + const modifiersKey = `${modelArg === "modelValue" ? "model" : modelArg}Modifiers`; + const { number: number2, trim: trim2 } = props[modifiersKey] || EMPTY_OBJ; + if (trim2) { + args = rawArgs.map((a) => isString$5(a) ? a.trim() : a); + } + if (number2) { + args = rawArgs.map(looseToNumber); + } + } + if (false) { + devtoolsComponentEmit(instance, event2, args); + } + if (false) { + const lowerCaseEvent = event2.toLowerCase(); + if (lowerCaseEvent !== event2 && props[toHandlerKey(lowerCaseEvent)]) { + warn$1$1( + `Event "${lowerCaseEvent}" is emitted in component ${formatComponentName( + instance, + instance.type + )} but the handler is registered for "${event2}". Note that HTML attributes are case-insensitive and you cannot use v-on to listen to camelCase events when using in-DOM templates. You should probably use "${hyphenate$1( + event2 + )}" instead of "${event2}".` + ); + } + } + let handlerName; + let handler6 = props[handlerName = toHandlerKey(event2)] || // also try camelCase event handler (#2249) + props[handlerName = toHandlerKey(camelize$1(event2))]; + if (!handler6 && isModelListener2) { + handler6 = props[handlerName = toHandlerKey(hyphenate$1(event2))]; + } + if (handler6) { + callWithAsyncErrorHandling( + handler6, + instance, + 6, + args + ); + } + const onceHandler = props[handlerName + `Once`]; + if (onceHandler) { + if (!instance.emitted) { + instance.emitted = {}; + } else if (instance.emitted[handlerName]) { + return; + } + instance.emitted[handlerName] = true; + callWithAsyncErrorHandling( + onceHandler, + instance, + 6, + args + ); + } +} +__name(emit, "emit"); +function normalizeEmitsOptions(comp, appContext, asMixin = false) { + const cache2 = appContext.emitsCache; + const cached = cache2.get(comp); + if (cached !== void 0) { + return cached; + } + const raw = comp.emits; + let normalized = {}; + let hasExtends = false; + if (!isFunction$3(comp)) { + const extendEmits = /* @__PURE__ */ __name((raw2) => { + const normalizedFromExtend = normalizeEmitsOptions(raw2, appContext, true); + if (normalizedFromExtend) { + hasExtends = true; + extend$1(normalized, normalizedFromExtend); + } + }, "extendEmits"); + if (!asMixin && appContext.mixins.length) { + appContext.mixins.forEach(extendEmits); + } + if (comp.extends) { + extendEmits(comp.extends); + } + if (comp.mixins) { + comp.mixins.forEach(extendEmits); + } + } + if (!raw && !hasExtends) { + if (isObject$6(comp)) { + cache2.set(comp, null); + } + return null; + } + if (isArray$5(raw)) { + raw.forEach((key) => normalized[key] = null); + } else { + extend$1(normalized, raw); + } + if (isObject$6(comp)) { + cache2.set(comp, normalized); + } + return normalized; +} +__name(normalizeEmitsOptions, "normalizeEmitsOptions"); +function isEmitListener(options3, key) { + if (!options3 || !isOn(key)) { + return false; + } + key = key.slice(2).replace(/Once$/, ""); + return hasOwn$3(options3, key[0].toLowerCase() + key.slice(1)) || hasOwn$3(options3, hyphenate$1(key)) || hasOwn$3(options3, key); +} +__name(isEmitListener, "isEmitListener"); +let currentRenderingInstance = null; +let currentScopeId = null; +function setCurrentRenderingInstance(instance) { + const prev2 = currentRenderingInstance; + currentRenderingInstance = instance; + currentScopeId = instance && instance.type.__scopeId || null; + return prev2; +} +__name(setCurrentRenderingInstance, "setCurrentRenderingInstance"); +function pushScopeId(id3) { + currentScopeId = id3; +} +__name(pushScopeId, "pushScopeId"); +function popScopeId() { + currentScopeId = null; +} +__name(popScopeId, "popScopeId"); +const withScopeId = /* @__PURE__ */ __name((_id2) => withCtx, "withScopeId"); +function withCtx(fn, ctx = currentRenderingInstance, isNonScopedSlot) { + if (!ctx) return fn; + if (fn._n) { + return fn; + } + const renderFnWithContext = /* @__PURE__ */ __name((...args) => { + if (renderFnWithContext._d) { + setBlockTracking(-1); + } + const prevInstance = setCurrentRenderingInstance(ctx); + let res; + try { + res = fn(...args); + } finally { + setCurrentRenderingInstance(prevInstance); + if (renderFnWithContext._d) { + setBlockTracking(1); + } + } + if (false) { + devtoolsComponentUpdated(ctx); + } + return res; + }, "renderFnWithContext"); + renderFnWithContext._n = true; + renderFnWithContext._c = true; + renderFnWithContext._d = true; + return renderFnWithContext; +} +__name(withCtx, "withCtx"); +let accessedAttrs = false; +function markAttrsAccessed() { + accessedAttrs = true; +} +__name(markAttrsAccessed, "markAttrsAccessed"); +function renderComponentRoot(instance) { + const { + type: Component, + vnode, + proxy, + withProxy, + propsOptions: [propsOptions], + slots, + attrs: attrs3, + emit: emit2, + render: render2, + renderCache, + props, + data: data21, + setupState, + ctx, + inheritAttrs + } = instance; + const prev2 = setCurrentRenderingInstance(instance); + let result; + let fallthroughAttrs; + if (false) { + accessedAttrs = false; + } + try { + if (vnode.shapeFlag & 4) { + const proxyToUse = withProxy || proxy; + const thisProxy = false ? new Proxy(proxyToUse, { + get(target, key, receiver) { + warn$1$1( + `Property '${String( + key + )}' was accessed via 'this'. Avoid using 'this' in templates.` + ); + return Reflect.get(target, key, receiver); + } + }) : proxyToUse; + result = normalizeVNode( + render2.call( + thisProxy, + proxyToUse, + renderCache, + false ? shallowReadonly(props) : props, + setupState, + data21, + ctx + ) + ); + fallthroughAttrs = attrs3; + } else { + const render22 = Component; + if (false) { + markAttrsAccessed(); + } + result = normalizeVNode( + render22.length > 1 ? render22( + false ? shallowReadonly(props) : props, + false ? { + get attrs() { + markAttrsAccessed(); + return shallowReadonly(attrs3); + }, + slots, + emit: emit2 + } : { attrs: attrs3, slots, emit: emit2 } + ) : render22( + false ? shallowReadonly(props) : props, + null + ) + ); + fallthroughAttrs = Component.props ? attrs3 : getFunctionalFallthrough(attrs3); + } + } catch (err) { + blockStack.length = 0; + handleError(err, instance, 1); + result = createVNode(Comment); + } + let root21 = result; + let setRoot = void 0; + if (false) { + [root21, setRoot] = getChildRoot(result); + } + if (fallthroughAttrs && inheritAttrs !== false) { + const keys2 = Object.keys(fallthroughAttrs); + const { shapeFlag } = root21; + if (keys2.length) { + if (shapeFlag & (1 | 6)) { + if (propsOptions && keys2.some(isModelListener)) { + fallthroughAttrs = filterModelListeners( + fallthroughAttrs, + propsOptions + ); + } + root21 = cloneVNode(root21, fallthroughAttrs, false, true); + } else if (false) { + const allAttrs = Object.keys(attrs3); + const eventAttrs = []; + const extraAttrs = []; + for (let i2 = 0, l = allAttrs.length; i2 < l; i2++) { + const key = allAttrs[i2]; + if (isOn(key)) { + if (!isModelListener(key)) { + eventAttrs.push(key[2].toLowerCase() + key.slice(3)); + } + } else { + extraAttrs.push(key); + } + } + if (extraAttrs.length) { + warn$1$1( + `Extraneous non-props attributes (${extraAttrs.join(", ")}) were passed to component but could not be automatically inherited because component renders fragment or text root nodes.` + ); + } + if (eventAttrs.length) { + warn$1$1( + `Extraneous non-emits event listeners (${eventAttrs.join(", ")}) were passed to component but could not be automatically inherited because component renders fragment or text root nodes. If the listener is intended to be a component custom event listener only, declare it using the "emits" option.` + ); + } + } + } + } + if (vnode.dirs) { + if (false) { + warn$1$1( + `Runtime directive used on component with non-element root node. The directives will not function as intended.` + ); + } + root21 = cloneVNode(root21, null, false, true); + root21.dirs = root21.dirs ? root21.dirs.concat(vnode.dirs) : vnode.dirs; + } + if (vnode.transition) { + if (false) { + warn$1$1( + `Component inside renders non-element root node that cannot be animated.` + ); + } + root21.transition = vnode.transition; + } + if (false) { + setRoot(root21); + } else { + result = root21; + } + setCurrentRenderingInstance(prev2); + return result; +} +__name(renderComponentRoot, "renderComponentRoot"); +const getChildRoot = /* @__PURE__ */ __name((vnode) => { + const rawChildren = vnode.children; + const dynamicChildren = vnode.dynamicChildren; + const childRoot = filterSingleRoot(rawChildren, false); + if (!childRoot) { + return [vnode, void 0]; + } else if (false) { + return getChildRoot(childRoot); + } + const index2 = rawChildren.indexOf(childRoot); + const dynamicIndex = dynamicChildren ? dynamicChildren.indexOf(childRoot) : -1; + const setRoot = /* @__PURE__ */ __name((updatedRoot) => { + rawChildren[index2] = updatedRoot; + if (dynamicChildren) { + if (dynamicIndex > -1) { + dynamicChildren[dynamicIndex] = updatedRoot; + } else if (updatedRoot.patchFlag > 0) { + vnode.dynamicChildren = [...dynamicChildren, updatedRoot]; + } + } + }, "setRoot"); + return [normalizeVNode(childRoot), setRoot]; +}, "getChildRoot"); +function filterSingleRoot(children, recurse = true) { + let singleRoot; + for (let i2 = 0; i2 < children.length; i2++) { + const child = children[i2]; + if (isVNode$1(child)) { + if (child.type !== Comment || child.children === "v-if") { + if (singleRoot) { + return; + } else { + singleRoot = child; + if (false) { + return filterSingleRoot(singleRoot.children); + } + } + } + } else { + return; + } + } + return singleRoot; +} +__name(filterSingleRoot, "filterSingleRoot"); +const getFunctionalFallthrough = /* @__PURE__ */ __name((attrs3) => { + let res; + for (const key in attrs3) { + if (key === "class" || key === "style" || isOn(key)) { + (res || (res = {}))[key] = attrs3[key]; + } + } + return res; +}, "getFunctionalFallthrough"); +const filterModelListeners = /* @__PURE__ */ __name((attrs3, props) => { + const res = {}; + for (const key in attrs3) { + if (!isModelListener(key) || !(key.slice(9) in props)) { + res[key] = attrs3[key]; + } + } + return res; +}, "filterModelListeners"); +const isElementRoot = /* @__PURE__ */ __name((vnode) => { + return vnode.shapeFlag & (6 | 1) || vnode.type === Comment; +}, "isElementRoot"); +function shouldUpdateComponent(prevVNode, nextVNode, optimized) { + const { props: prevProps, children: prevChildren, component } = prevVNode; + const { props: nextProps, children: nextChildren, patchFlag } = nextVNode; + const emits = component.emitsOptions; + if (false) { + return true; + } + if (nextVNode.dirs || nextVNode.transition) { + return true; + } + if (optimized && patchFlag >= 0) { + if (patchFlag & 1024) { + return true; + } + if (patchFlag & 16) { + if (!prevProps) { + return !!nextProps; + } + return hasPropsChanged(prevProps, nextProps, emits); + } else if (patchFlag & 8) { + const dynamicProps = nextVNode.dynamicProps; + for (let i2 = 0; i2 < dynamicProps.length; i2++) { + const key = dynamicProps[i2]; + if (nextProps[key] !== prevProps[key] && !isEmitListener(emits, key)) { + return true; + } + } + } + } else { + if (prevChildren || nextChildren) { + if (!nextChildren || !nextChildren.$stable) { + return true; + } + } + if (prevProps === nextProps) { + return false; + } + if (!prevProps) { + return !!nextProps; + } + if (!nextProps) { + return true; + } + return hasPropsChanged(prevProps, nextProps, emits); + } + return false; +} +__name(shouldUpdateComponent, "shouldUpdateComponent"); +function hasPropsChanged(prevProps, nextProps, emitsOptions) { + const nextKeys = Object.keys(nextProps); + if (nextKeys.length !== Object.keys(prevProps).length) { + return true; + } + for (let i2 = 0; i2 < nextKeys.length; i2++) { + const key = nextKeys[i2]; + if (nextProps[key] !== prevProps[key] && !isEmitListener(emitsOptions, key)) { + return true; + } + } + return false; +} +__name(hasPropsChanged, "hasPropsChanged"); +function updateHOCHostEl({ vnode, parent }, el) { + while (parent) { + const root21 = parent.subTree; + if (root21.suspense && root21.suspense.activeBranch === vnode) { + root21.el = vnode.el; + } + if (root21 === vnode) { + (vnode = parent.vnode).el = el; + parent = parent.parent; + } else { + break; + } + } +} +__name(updateHOCHostEl, "updateHOCHostEl"); +const COMPONENTS = "components"; +const DIRECTIVES = "directives"; +function resolveComponent(name, maybeSelfReference) { + return resolveAsset(COMPONENTS, name, true, maybeSelfReference) || name; +} +__name(resolveComponent, "resolveComponent"); +const NULL_DYNAMIC_COMPONENT = Symbol.for("v-ndc"); +function resolveDynamicComponent(component) { + if (isString$5(component)) { + return resolveAsset(COMPONENTS, component, false) || component; + } else { + return component || NULL_DYNAMIC_COMPONENT; + } +} +__name(resolveDynamicComponent, "resolveDynamicComponent"); +function resolveDirective(name) { + return resolveAsset(DIRECTIVES, name); +} +__name(resolveDirective, "resolveDirective"); +function resolveAsset(type, name, warnMissing = true, maybeSelfReference = false) { + const instance = currentRenderingInstance || currentInstance; + if (instance) { + const Component = instance.type; + if (type === COMPONENTS) { + const selfName = getComponentName( + Component, + false + ); + if (selfName && (selfName === name || selfName === camelize$1(name) || selfName === capitalize$1(camelize$1(name)))) { + return Component; + } + } + const res = ( + // local registration + // check instance[type] first which is resolved for options API + resolve$1(instance[type] || Component[type], name) || // global registration + resolve$1(instance.appContext[type], name) + ); + if (!res && maybeSelfReference) { + return Component; + } + if (false) { + const extra = type === COMPONENTS ? ` +If this is a native custom element, make sure to exclude it from component resolution via compilerOptions.isCustomElement.` : ``; + warn$1$1(`Failed to resolve ${type.slice(0, -1)}: ${name}${extra}`); + } + return res; + } else if (false) { + warn$1$1( + `resolve${capitalize$1(type.slice(0, -1))} can only be used in render() or setup().` + ); + } +} +__name(resolveAsset, "resolveAsset"); +function resolve$1(registry, name) { + return registry && (registry[name] || registry[camelize$1(name)] || registry[capitalize$1(camelize$1(name))]); +} +__name(resolve$1, "resolve$1"); +const isSuspense = /* @__PURE__ */ __name((type) => type.__isSuspense, "isSuspense"); +let suspenseId = 0; +const SuspenseImpl = { + name: "Suspense", + // In order to make Suspense tree-shakable, we need to avoid importing it + // directly in the renderer. The renderer checks for the __isSuspense flag + // on a vnode's type and calls the `process` method, passing in renderer + // internals. + __isSuspense: true, + process(n1, n2, container, anchor, parentComponent, parentSuspense, namespace, slotScopeIds, optimized, rendererInternals) { + if (n1 == null) { + mountSuspense( + n2, + container, + anchor, + parentComponent, + parentSuspense, + namespace, + slotScopeIds, + optimized, + rendererInternals + ); + } else { + if (parentSuspense && parentSuspense.deps > 0 && !n1.suspense.isInFallback) { + n2.suspense = n1.suspense; + n2.suspense.vnode = n2; + n2.el = n1.el; + return; + } + patchSuspense( + n1, + n2, + container, + anchor, + parentComponent, + namespace, + slotScopeIds, + optimized, + rendererInternals + ); + } + }, + hydrate: hydrateSuspense, + normalize: normalizeSuspenseChildren +}; +const Suspense = SuspenseImpl; +function triggerEvent(vnode, name) { + const eventListener = vnode.props && vnode.props[name]; + if (isFunction$3(eventListener)) { + eventListener(); + } +} +__name(triggerEvent, "triggerEvent"); +function mountSuspense(vnode, container, anchor, parentComponent, parentSuspense, namespace, slotScopeIds, optimized, rendererInternals) { + const { + p: patch, + o: { createElement: createElement2 } + } = rendererInternals; + const hiddenContainer = createElement2("div"); + const suspense = vnode.suspense = createSuspenseBoundary( + vnode, + parentSuspense, + parentComponent, + container, + hiddenContainer, + anchor, + namespace, + slotScopeIds, + optimized, + rendererInternals + ); + patch( + null, + suspense.pendingBranch = vnode.ssContent, + hiddenContainer, + null, + parentComponent, + suspense, + namespace, + slotScopeIds + ); + if (suspense.deps > 0) { + triggerEvent(vnode, "onPending"); + triggerEvent(vnode, "onFallback"); + patch( + null, + vnode.ssFallback, + container, + anchor, + parentComponent, + null, + // fallback tree will not have suspense context + namespace, + slotScopeIds + ); + setActiveBranch(suspense, vnode.ssFallback); + } else { + suspense.resolve(false, true); + } +} +__name(mountSuspense, "mountSuspense"); +function patchSuspense(n1, n2, container, anchor, parentComponent, namespace, slotScopeIds, optimized, { p: patch, um: unmount, o: { createElement: createElement2 } }) { + const suspense = n2.suspense = n1.suspense; + suspense.vnode = n2; + n2.el = n1.el; + const newBranch = n2.ssContent; + const newFallback = n2.ssFallback; + const { activeBranch, pendingBranch, isInFallback, isHydrating } = suspense; + if (pendingBranch) { + suspense.pendingBranch = newBranch; + if (isSameVNodeType(newBranch, pendingBranch)) { + patch( + pendingBranch, + newBranch, + suspense.hiddenContainer, + null, + parentComponent, + suspense, + namespace, + slotScopeIds, + optimized + ); + if (suspense.deps <= 0) { + suspense.resolve(); + } else if (isInFallback) { + if (!isHydrating) { + patch( + activeBranch, + newFallback, + container, + anchor, + parentComponent, + null, + // fallback tree will not have suspense context + namespace, + slotScopeIds, + optimized + ); + setActiveBranch(suspense, newFallback); + } + } + } else { + suspense.pendingId = suspenseId++; + if (isHydrating) { + suspense.isHydrating = false; + suspense.activeBranch = pendingBranch; + } else { + unmount(pendingBranch, parentComponent, suspense); + } + suspense.deps = 0; + suspense.effects.length = 0; + suspense.hiddenContainer = createElement2("div"); + if (isInFallback) { + patch( + null, + newBranch, + suspense.hiddenContainer, + null, + parentComponent, + suspense, + namespace, + slotScopeIds, + optimized + ); + if (suspense.deps <= 0) { + suspense.resolve(); + } else { + patch( + activeBranch, + newFallback, + container, + anchor, + parentComponent, + null, + // fallback tree will not have suspense context + namespace, + slotScopeIds, + optimized + ); + setActiveBranch(suspense, newFallback); + } + } else if (activeBranch && isSameVNodeType(newBranch, activeBranch)) { + patch( + activeBranch, + newBranch, + container, + anchor, + parentComponent, + suspense, + namespace, + slotScopeIds, + optimized + ); + suspense.resolve(true); + } else { + patch( + null, + newBranch, + suspense.hiddenContainer, + null, + parentComponent, + suspense, + namespace, + slotScopeIds, + optimized + ); + if (suspense.deps <= 0) { + suspense.resolve(); + } + } + } + } else { + if (activeBranch && isSameVNodeType(newBranch, activeBranch)) { + patch( + activeBranch, + newBranch, + container, + anchor, + parentComponent, + suspense, + namespace, + slotScopeIds, + optimized + ); + setActiveBranch(suspense, newBranch); + } else { + triggerEvent(n2, "onPending"); + suspense.pendingBranch = newBranch; + if (newBranch.shapeFlag & 512) { + suspense.pendingId = newBranch.component.suspenseId; + } else { + suspense.pendingId = suspenseId++; + } + patch( + null, + newBranch, + suspense.hiddenContainer, + null, + parentComponent, + suspense, + namespace, + slotScopeIds, + optimized + ); + if (suspense.deps <= 0) { + suspense.resolve(); + } else { + const { timeout, pendingId } = suspense; + if (timeout > 0) { + setTimeout(() => { + if (suspense.pendingId === pendingId) { + suspense.fallback(newFallback); + } + }, timeout); + } else if (timeout === 0) { + suspense.fallback(newFallback); + } + } + } + } +} +__name(patchSuspense, "patchSuspense"); +let hasWarned$1 = false; +function createSuspenseBoundary(vnode, parentSuspense, parentComponent, container, hiddenContainer, anchor, namespace, slotScopeIds, optimized, rendererInternals, isHydrating = false) { + if (false) { + hasWarned$1 = true; + console[console.info ? "info" : "log"]( + ` is an experimental feature and its API will likely change.` + ); + } + const { + p: patch, + m: move, + um: unmount, + n: next2, + o: { parentNode, remove: remove22 } + } = rendererInternals; + let parentSuspenseId; + const isSuspensible = isVNodeSuspensible(vnode); + if (isSuspensible) { + if (parentSuspense && parentSuspense.pendingBranch) { + parentSuspenseId = parentSuspense.pendingId; + parentSuspense.deps++; + } + } + const timeout = vnode.props ? toNumber(vnode.props.timeout) : void 0; + if (false) { + assertNumber(timeout, `Suspense timeout`); + } + const initialAnchor = anchor; + const suspense = { + vnode, + parent: parentSuspense, + parentComponent, + namespace, + container, + hiddenContainer, + deps: 0, + pendingId: suspenseId++, + timeout: typeof timeout === "number" ? timeout : -1, + activeBranch: null, + pendingBranch: null, + isInFallback: !isHydrating, + isHydrating, + isUnmounted: false, + effects: [], + resolve(resume = false, sync = false) { + if (false) { + if (!resume && !suspense.pendingBranch) { + throw new Error( + `suspense.resolve() is called without a pending branch.` + ); + } + if (suspense.isUnmounted) { + throw new Error( + `suspense.resolve() is called on an already unmounted suspense boundary.` + ); + } + } + const { + vnode: vnode2, + activeBranch, + pendingBranch, + pendingId, + effects, + parentComponent: parentComponent2, + container: container2 + } = suspense; + let delayEnter = false; + if (suspense.isHydrating) { + suspense.isHydrating = false; + } else if (!resume) { + delayEnter = activeBranch && pendingBranch.transition && pendingBranch.transition.mode === "out-in"; + if (delayEnter) { + activeBranch.transition.afterLeave = () => { + if (pendingId === suspense.pendingId) { + move( + pendingBranch, + container2, + anchor === initialAnchor ? next2(activeBranch) : anchor, + 0 + ); + queuePostFlushCb(effects); + } + }; + } + if (activeBranch) { + if (parentNode(activeBranch.el) !== suspense.hiddenContainer) { + anchor = next2(activeBranch); + } + unmount(activeBranch, parentComponent2, suspense, true); + } + if (!delayEnter) { + move(pendingBranch, container2, anchor, 0); + } + } + setActiveBranch(suspense, pendingBranch); + suspense.pendingBranch = null; + suspense.isInFallback = false; + let parent = suspense.parent; + let hasUnresolvedAncestor = false; + while (parent) { + if (parent.pendingBranch) { + parent.effects.push(...effects); + hasUnresolvedAncestor = true; + break; + } + parent = parent.parent; + } + if (!hasUnresolvedAncestor && !delayEnter) { + queuePostFlushCb(effects); + } + suspense.effects = []; + if (isSuspensible) { + if (parentSuspense && parentSuspense.pendingBranch && parentSuspenseId === parentSuspense.pendingId) { + parentSuspense.deps--; + if (parentSuspense.deps === 0 && !sync) { + parentSuspense.resolve(); + } + } + } + triggerEvent(vnode2, "onResolve"); + }, + fallback(fallbackVNode) { + if (!suspense.pendingBranch) { + return; + } + const { vnode: vnode2, activeBranch, parentComponent: parentComponent2, container: container2, namespace: namespace2 } = suspense; + triggerEvent(vnode2, "onFallback"); + const anchor2 = next2(activeBranch); + const mountFallback = /* @__PURE__ */ __name(() => { + if (!suspense.isInFallback) { + return; + } + patch( + null, + fallbackVNode, + container2, + anchor2, + parentComponent2, + null, + // fallback tree will not have suspense context + namespace2, + slotScopeIds, + optimized + ); + setActiveBranch(suspense, fallbackVNode); + }, "mountFallback"); + const delayEnter = fallbackVNode.transition && fallbackVNode.transition.mode === "out-in"; + if (delayEnter) { + activeBranch.transition.afterLeave = mountFallback; + } + suspense.isInFallback = true; + unmount( + activeBranch, + parentComponent2, + null, + // no suspense so unmount hooks fire now + true + // shouldRemove + ); + if (!delayEnter) { + mountFallback(); + } + }, + move(container2, anchor2, type) { + suspense.activeBranch && move(suspense.activeBranch, container2, anchor2, type); + suspense.container = container2; + }, + next() { + return suspense.activeBranch && next2(suspense.activeBranch); + }, + registerDep(instance, setupRenderEffect, optimized2) { + const isInPendingSuspense = !!suspense.pendingBranch; + if (isInPendingSuspense) { + suspense.deps++; + } + const hydratedEl = instance.vnode.el; + instance.asyncDep.catch((err) => { + handleError(err, instance, 0); + }).then((asyncSetupResult) => { + if (instance.isUnmounted || suspense.isUnmounted || suspense.pendingId !== instance.suspenseId) { + return; + } + instance.asyncResolved = true; + const { vnode: vnode2 } = instance; + if (false) { + pushWarningContext(vnode2); + } + handleSetupResult(instance, asyncSetupResult, false); + if (hydratedEl) { + vnode2.el = hydratedEl; + } + const placeholder = !hydratedEl && instance.subTree.el; + setupRenderEffect( + instance, + vnode2, + // component may have been moved before resolve. + // if this is not a hydration, instance.subTree will be the comment + // placeholder. + parentNode(hydratedEl || instance.subTree.el), + // anchor will not be used if this is hydration, so only need to + // consider the comment placeholder case. + hydratedEl ? null : next2(instance.subTree), + suspense, + namespace, + optimized2 + ); + if (placeholder) { + remove22(placeholder); + } + updateHOCHostEl(instance, vnode2.el); + if (false) { + popWarningContext(); + } + if (isInPendingSuspense && --suspense.deps === 0) { + suspense.resolve(); + } + }); + }, + unmount(parentSuspense2, doRemove) { + suspense.isUnmounted = true; + if (suspense.activeBranch) { + unmount( + suspense.activeBranch, + parentComponent, + parentSuspense2, + doRemove + ); + } + if (suspense.pendingBranch) { + unmount( + suspense.pendingBranch, + parentComponent, + parentSuspense2, + doRemove + ); + } + } + }; + return suspense; +} +__name(createSuspenseBoundary, "createSuspenseBoundary"); +function hydrateSuspense(node3, vnode, parentComponent, parentSuspense, namespace, slotScopeIds, optimized, rendererInternals, hydrateNode) { + const suspense = vnode.suspense = createSuspenseBoundary( + vnode, + parentSuspense, + parentComponent, + node3.parentNode, + // eslint-disable-next-line no-restricted-globals + document.createElement("div"), + null, + namespace, + slotScopeIds, + optimized, + rendererInternals, + true + ); + const result = hydrateNode( + node3, + suspense.pendingBranch = vnode.ssContent, + parentComponent, + suspense, + slotScopeIds, + optimized + ); + if (suspense.deps === 0) { + suspense.resolve(false, true); + } + return result; +} +__name(hydrateSuspense, "hydrateSuspense"); +function normalizeSuspenseChildren(vnode) { + const { shapeFlag, children } = vnode; + const isSlotChildren = shapeFlag & 32; + vnode.ssContent = normalizeSuspenseSlot( + isSlotChildren ? children.default : children + ); + vnode.ssFallback = isSlotChildren ? normalizeSuspenseSlot(children.fallback) : createVNode(Comment); +} +__name(normalizeSuspenseChildren, "normalizeSuspenseChildren"); +function normalizeSuspenseSlot(s) { + let block2; + if (isFunction$3(s)) { + const trackBlock = isBlockTreeEnabled && s._c; + if (trackBlock) { + s._d = false; + openBlock(); + } + s = s(); + if (trackBlock) { + s._d = true; + block2 = currentBlock; + closeBlock(); + } + } + if (isArray$5(s)) { + const singleChild = filterSingleRoot(s); + if (false) { + warn$1$1(` slots expect a single root node.`); + } + s = singleChild; + } + s = normalizeVNode(s); + if (block2 && !s.dynamicChildren) { + s.dynamicChildren = block2.filter((c) => c !== s); + } + return s; +} +__name(normalizeSuspenseSlot, "normalizeSuspenseSlot"); +function queueEffectWithSuspense(fn, suspense) { + if (suspense && suspense.pendingBranch) { + if (isArray$5(fn)) { + suspense.effects.push(...fn); + } else { + suspense.effects.push(fn); + } + } else { + queuePostFlushCb(fn); + } +} +__name(queueEffectWithSuspense, "queueEffectWithSuspense"); +function setActiveBranch(suspense, branch) { + suspense.activeBranch = branch; + const { vnode, parentComponent } = suspense; + let el = branch.el; + while (!el && branch.component) { + branch = branch.component.subTree; + el = branch.el; + } + vnode.el = el; + if (parentComponent && parentComponent.subTree === vnode) { + parentComponent.vnode.el = el; + updateHOCHostEl(parentComponent, el); + } +} +__name(setActiveBranch, "setActiveBranch"); +function isVNodeSuspensible(vnode) { + const suspensible = vnode.props && vnode.props.suspensible; + return suspensible != null && suspensible !== false; +} +__name(isVNodeSuspensible, "isVNodeSuspensible"); +function injectHook(type, hook, target = currentInstance, prepend = false) { + if (target) { + const hooks = target[type] || (target[type] = []); + const wrappedHook = hook.__weh || (hook.__weh = (...args) => { + pauseTracking(); + const reset = setCurrentInstance(target); + const res = callWithAsyncErrorHandling(hook, target, type, args); + reset(); + resetTracking(); + return res; + }); + if (prepend) { + hooks.unshift(wrappedHook); + } else { + hooks.push(wrappedHook); + } + return wrappedHook; + } else if (false) { + const apiName = toHandlerKey(ErrorTypeStrings$1[type].replace(/ hook$/, "")); + warn$1$1( + `${apiName} is called when there is no active component instance to be associated with. Lifecycle injection APIs can only be used during execution of setup(). If you are using async setup(), make sure to register lifecycle hooks before the first await statement.` + ); + } +} +__name(injectHook, "injectHook"); +const createHook = /* @__PURE__ */ __name((lifecycle2) => (hook, target = currentInstance) => { + if (!isInSSRComponentSetup || lifecycle2 === "sp") { + injectHook(lifecycle2, (...args) => hook(...args), target); + } +}, "createHook"); +const onBeforeMount = createHook("bm"); +const onMounted = createHook("m"); +const onBeforeUpdate = createHook("bu"); +const onUpdated = createHook("u"); +const onBeforeUnmount = createHook("bum"); +const onUnmounted = createHook("um"); +const onServerPrefetch = createHook("sp"); +const onRenderTriggered = createHook( + "rtg" +); +const onRenderTracked = createHook( + "rtc" +); +function onErrorCaptured(hook, target = currentInstance) { + injectHook("ec", hook, target); +} +__name(onErrorCaptured, "onErrorCaptured"); +function validateDirectiveName(name) { + if (isBuiltInDirective(name)) { + warn$1$1("Do not use built-in directive ids as custom directive id: " + name); + } +} +__name(validateDirectiveName, "validateDirectiveName"); +function withDirectives(vnode, directives) { + if (currentRenderingInstance === null) { + return vnode; + } + const instance = getComponentPublicInstance(currentRenderingInstance); + const bindings = vnode.dirs || (vnode.dirs = []); + for (let i2 = 0; i2 < directives.length; i2++) { + let [dir, value3, arg, modifiers = EMPTY_OBJ] = directives[i2]; + if (dir) { + if (isFunction$3(dir)) { + dir = { + mounted: dir, + updated: dir + }; + } + if (dir.deep) { + traverse(value3); + } + bindings.push({ + dir, + instance, + value: value3, + oldValue: void 0, + arg, + modifiers + }); + } + } + return vnode; +} +__name(withDirectives, "withDirectives"); +function invokeDirectiveHook(vnode, prevVNode, instance, name) { + const bindings = vnode.dirs; + const oldBindings = prevVNode && prevVNode.dirs; + for (let i2 = 0; i2 < bindings.length; i2++) { + const binding = bindings[i2]; + if (oldBindings) { + binding.oldValue = oldBindings[i2].value; + } + let hook = binding.dir[name]; + if (hook) { + pauseTracking(); + callWithAsyncErrorHandling(hook, instance, 8, [ + vnode.el, + binding, + vnode, + prevVNode + ]); + resetTracking(); + } + } +} +__name(invokeDirectiveHook, "invokeDirectiveHook"); +function renderList(source, renderItem, cache2, index2) { + let ret; + const cached = cache2 && cache2[index2]; + if (isArray$5(source) || isString$5(source)) { + ret = new Array(source.length); + for (let i2 = 0, l = source.length; i2 < l; i2++) { + ret[i2] = renderItem(source[i2], i2, void 0, cached && cached[i2]); + } + } else if (typeof source === "number") { + if (false) { + warn$1$1(`The v-for range expect an integer value but got ${source}.`); + } + ret = new Array(source); + for (let i2 = 0; i2 < source; i2++) { + ret[i2] = renderItem(i2 + 1, i2, void 0, cached && cached[i2]); + } + } else if (isObject$6(source)) { + if (source[Symbol.iterator]) { + ret = Array.from( + source, + (item2, i2) => renderItem(item2, i2, void 0, cached && cached[i2]) + ); + } else { + const keys2 = Object.keys(source); + ret = new Array(keys2.length); + for (let i2 = 0, l = keys2.length; i2 < l; i2++) { + const key = keys2[i2]; + ret[i2] = renderItem(source[key], key, i2, cached && cached[i2]); + } + } + } else { + ret = []; + } + if (cache2) { + cache2[index2] = ret; + } + return ret; +} +__name(renderList, "renderList"); +function createSlots(slots, dynamicSlots) { + for (let i2 = 0; i2 < dynamicSlots.length; i2++) { + const slot = dynamicSlots[i2]; + if (isArray$5(slot)) { + for (let j = 0; j < slot.length; j++) { + slots[slot[j].name] = slot[j].fn; + } + } else if (slot) { + slots[slot.name] = slot.key ? (...args) => { + const res = slot.fn(...args); + if (res) res.key = slot.key; + return res; + } : slot.fn; + } + } + return slots; +} +__name(createSlots, "createSlots"); +/*! #__NO_SIDE_EFFECTS__ */ +// @__NO_SIDE_EFFECTS__ +function defineComponent(options3, extraOptions) { + return isFunction$3(options3) ? ( + // #8326: extend call and options.name access are considered side-effects + // by Rollup, so we have to wrap it in a pure-annotated IIFE. + /* @__PURE__ */ (() => extend$1({ name: options3.name }, extraOptions, { setup: options3 }))() + ) : options3; +} +__name(defineComponent, "defineComponent"); +const isAsyncWrapper = /* @__PURE__ */ __name((i2) => !!i2.type.__asyncLoader, "isAsyncWrapper"); +/*! #__NO_SIDE_EFFECTS__ */ +// @__NO_SIDE_EFFECTS__ +function defineAsyncComponent(source) { + if (isFunction$3(source)) { + source = { loader: source }; + } + const { + loader, + loadingComponent, + errorComponent, + delay = 200, + timeout, + // undefined = never times out + suspensible = true, + onError: userOnError + } = source; + let pendingRequest = null; + let resolvedComp; + let retries = 0; + const retry = /* @__PURE__ */ __name(() => { + retries++; + pendingRequest = null; + return load2(); + }, "retry"); + const load2 = /* @__PURE__ */ __name(() => { + let thisRequest; + return pendingRequest || (thisRequest = pendingRequest = loader().catch((err) => { + err = err instanceof Error ? err : new Error(String(err)); + if (userOnError) { + return new Promise((resolve2, reject2) => { + const userRetry = /* @__PURE__ */ __name(() => resolve2(retry()), "userRetry"); + const userFail = /* @__PURE__ */ __name(() => reject2(err), "userFail"); + userOnError(err, userRetry, userFail, retries + 1); + }); + } else { + throw err; + } + }).then((comp) => { + if (thisRequest !== pendingRequest && pendingRequest) { + return pendingRequest; + } + if (false) { + warn$1$1( + `Async component loader resolved to undefined. If you are using retry(), make sure to return its return value.` + ); + } + if (comp && (comp.__esModule || comp[Symbol.toStringTag] === "Module")) { + comp = comp.default; + } + if (false) { + throw new Error(`Invalid async component load result: ${comp}`); + } + resolvedComp = comp; + return comp; + })); + }, "load"); + return /* @__PURE__ */ defineComponent({ + name: "AsyncComponentWrapper", + __asyncLoader: load2, + get __asyncResolved() { + return resolvedComp; + }, + setup() { + const instance = currentInstance; + if (resolvedComp) { + return () => createInnerComp(resolvedComp, instance); + } + const onError = /* @__PURE__ */ __name((err) => { + pendingRequest = null; + handleError( + err, + instance, + 13, + !errorComponent + ); + }, "onError"); + if (suspensible && instance.suspense || isInSSRComponentSetup) { + return load2().then((comp) => { + return () => createInnerComp(comp, instance); + }).catch((err) => { + onError(err); + return () => errorComponent ? createVNode(errorComponent, { + error: err + }) : null; + }); + } + const loaded = ref(false); + const error = ref(); + const delayed = ref(!!delay); + if (delay) { + setTimeout(() => { + delayed.value = false; + }, delay); + } + if (timeout != null) { + setTimeout(() => { + if (!loaded.value && !error.value) { + const err = new Error( + `Async component timed out after ${timeout}ms.` + ); + onError(err); + error.value = err; + } + }, timeout); + } + load2().then(() => { + loaded.value = true; + if (instance.parent && isKeepAlive(instance.parent.vnode)) { + instance.parent.effect.dirty = true; + queueJob(instance.parent.update); + } + }).catch((err) => { + onError(err); + error.value = err; + }); + return () => { + if (loaded.value && resolvedComp) { + return createInnerComp(resolvedComp, instance); + } else if (error.value && errorComponent) { + return createVNode(errorComponent, { + error: error.value + }); + } else if (loadingComponent && !delayed.value) { + return createVNode(loadingComponent); + } + }; + } + }); +} +__name(defineAsyncComponent, "defineAsyncComponent"); +function createInnerComp(comp, parent) { + const { ref: ref22, props, children, ce } = parent.vnode; + const vnode = createVNode(comp, props, children); + vnode.ref = ref22; + vnode.ce = ce; + delete parent.vnode.ce; + return vnode; +} +__name(createInnerComp, "createInnerComp"); +function renderSlot(slots, name, props = {}, fallback, noSlotted) { + if (currentRenderingInstance.isCE || currentRenderingInstance.parent && isAsyncWrapper(currentRenderingInstance.parent) && currentRenderingInstance.parent.isCE) { + if (name !== "default") props.name = name; + return createVNode("slot", props, fallback && fallback()); + } + let slot = slots[name]; + if (false) { + warn$1$1( + `SSR-optimized slot function detected in a non-SSR-optimized render function. You need to mark this component with $dynamic-slots in the parent template.` + ); + slot = /* @__PURE__ */ __name(() => [], "slot"); + } + if (slot && slot._c) { + slot._d = false; + } + openBlock(); + const validSlotContent = slot && ensureValidVNode(slot(props)); + const rendered = createBlock( + Fragment, + { + key: props.key || // slot content array of a dynamic conditional slot may have a branch + // key attached in the `createSlots` helper, respect that + validSlotContent && validSlotContent.key || `_${name}` + }, + validSlotContent || (fallback ? fallback() : []), + validSlotContent && slots._ === 1 ? 64 : -2 + ); + if (!noSlotted && rendered.scopeId) { + rendered.slotScopeIds = [rendered.scopeId + "-s"]; + } + if (slot && slot._c) { + slot._d = true; + } + return rendered; +} +__name(renderSlot, "renderSlot"); +function ensureValidVNode(vnodes) { + return vnodes.some((child) => { + if (!isVNode$1(child)) return true; + if (child.type === Comment) return false; + if (child.type === Fragment && !ensureValidVNode(child.children)) + return false; + return true; + }) ? vnodes : null; +} +__name(ensureValidVNode, "ensureValidVNode"); +function toHandlers(obj, preserveCaseIfNecessary) { + const ret = {}; + if (false) { + warn$1$1(`v-on with no argument expects an object value.`); + return ret; + } + for (const key in obj) { + ret[preserveCaseIfNecessary && /[A-Z]/.test(key) ? `on:${key}` : toHandlerKey(key)] = obj[key]; + } + return ret; +} +__name(toHandlers, "toHandlers"); +const getPublicInstance = /* @__PURE__ */ __name((i2) => { + if (!i2) return null; + if (isStatefulComponent(i2)) return getComponentPublicInstance(i2); + return getPublicInstance(i2.parent); +}, "getPublicInstance"); +const publicPropertiesMap = ( + // Move PURE marker to new line to workaround compiler discarding it + // due to type annotation + /* @__PURE__ */ extend$1(/* @__PURE__ */ Object.create(null), { + $: /* @__PURE__ */ __name((i2) => i2, "$"), + $el: /* @__PURE__ */ __name((i2) => i2.vnode.el, "$el"), + $data: /* @__PURE__ */ __name((i2) => i2.data, "$data"), + $props: /* @__PURE__ */ __name((i2) => false ? shallowReadonly(i2.props) : i2.props, "$props"), + $attrs: /* @__PURE__ */ __name((i2) => false ? shallowReadonly(i2.attrs) : i2.attrs, "$attrs"), + $slots: /* @__PURE__ */ __name((i2) => false ? shallowReadonly(i2.slots) : i2.slots, "$slots"), + $refs: /* @__PURE__ */ __name((i2) => false ? shallowReadonly(i2.refs) : i2.refs, "$refs"), + $parent: /* @__PURE__ */ __name((i2) => getPublicInstance(i2.parent), "$parent"), + $root: /* @__PURE__ */ __name((i2) => getPublicInstance(i2.root), "$root"), + $emit: /* @__PURE__ */ __name((i2) => i2.emit, "$emit"), + $options: /* @__PURE__ */ __name((i2) => true ? resolveMergedOptions(i2) : i2.type, "$options"), + $forceUpdate: /* @__PURE__ */ __name((i2) => i2.f || (i2.f = () => { + i2.effect.dirty = true; + queueJob(i2.update); + }), "$forceUpdate"), + $nextTick: /* @__PURE__ */ __name((i2) => i2.n || (i2.n = nextTick.bind(i2.proxy)), "$nextTick"), + $watch: /* @__PURE__ */ __name((i2) => true ? instanceWatch.bind(i2) : NOOP, "$watch") + }) +); +const isReservedPrefix = /* @__PURE__ */ __name((key) => key === "_" || key === "$", "isReservedPrefix"); +const hasSetupBinding = /* @__PURE__ */ __name((state, key) => state !== EMPTY_OBJ && !state.__isScriptSetup && hasOwn$3(state, key), "hasSetupBinding"); +const PublicInstanceProxyHandlers = { + get({ _: instance }, key) { + if (key === "__v_skip") { + return true; + } + const { ctx, setupState, data: data21, props, accessCache, type, appContext } = instance; + if (false) { + return true; + } + let normalizedProps; + if (key[0] !== "$") { + const n = accessCache[key]; + if (n !== void 0) { + switch (n) { + case 1: + return setupState[key]; + case 2: + return data21[key]; + case 4: + return ctx[key]; + case 3: + return props[key]; + } + } else if (hasSetupBinding(setupState, key)) { + accessCache[key] = 1; + return setupState[key]; + } else if (data21 !== EMPTY_OBJ && hasOwn$3(data21, key)) { + accessCache[key] = 2; + return data21[key]; + } else if ( + // only cache other properties when instance has declared (thus stable) + // props + (normalizedProps = instance.propsOptions[0]) && hasOwn$3(normalizedProps, key) + ) { + accessCache[key] = 3; + return props[key]; + } else if (ctx !== EMPTY_OBJ && hasOwn$3(ctx, key)) { + accessCache[key] = 4; + return ctx[key]; + } else if (shouldCacheAccess) { + accessCache[key] = 0; + } + } + const publicGetter = publicPropertiesMap[key]; + let cssModule, globalProperties; + if (publicGetter) { + if (key === "$attrs") { + track(instance.attrs, "get", ""); + } else if (false) { + track(instance, "get", key); + } + return publicGetter(instance); + } else if ( + // css module (injected by vue-loader) + (cssModule = type.__cssModules) && (cssModule = cssModule[key]) + ) { + return cssModule; + } else if (ctx !== EMPTY_OBJ && hasOwn$3(ctx, key)) { + accessCache[key] = 4; + return ctx[key]; + } else if ( + // global properties + globalProperties = appContext.config.globalProperties, hasOwn$3(globalProperties, key) + ) { + { + return globalProperties[key]; + } + } else if (false) { + if (data21 !== EMPTY_OBJ && isReservedPrefix(key[0]) && hasOwn$3(data21, key)) { + warn$1$1( + `Property ${JSON.stringify( + key + )} must be accessed via $data because it starts with a reserved character ("$" or "_") and is not proxied on the render context.` + ); + } else if (instance === currentRenderingInstance) { + warn$1$1( + `Property ${JSON.stringify(key)} was accessed during render but is not defined on instance.` + ); + } + } + }, + set({ _: instance }, key, value3) { + const { data: data21, setupState, ctx } = instance; + if (hasSetupBinding(setupState, key)) { + setupState[key] = value3; + return true; + } else if (false) { + warn$1$1(`Cannot mutate \n\n\n","\n\n\n\n\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'MinusIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n d: \"M13.2222 7.77778H0.777778C0.571498 7.77778 0.373667 7.69584 0.227806 7.54998C0.0819442 7.40412 0 7.20629 0 7.00001C0 6.79373 0.0819442 6.5959 0.227806 6.45003C0.373667 6.30417 0.571498 6.22223 0.777778 6.22223H13.2222C13.4285 6.22223 13.6263 6.30417 13.7722 6.45003C13.9181 6.5959 14 6.79373 14 7.00001C14 7.20629 13.9181 7.40412 13.7722 7.54998C13.6263 7.69584 13.4285 7.77778 13.2222 7.77778Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-checkbox {\\n position: relative;\\n display: inline-flex;\\n user-select: none;\\n vertical-align: bottom;\\n width: \".concat(dt('checkbox.width'), \";\\n height: \").concat(dt('checkbox.height'), \";\\n}\\n\\n.p-checkbox-input {\\n cursor: pointer;\\n appearance: none;\\n position: absolute;\\n top: 0;\\n left: 0;\\n width: 100%;\\n height: 100%;\\n padding: 0;\\n margin: 0;\\n opacity: 0;\\n z-index: 1;\\n outline: 0 none;\\n border: 1px solid transparent;\\n border-radius: \").concat(dt('checkbox.border.radius'), \";\\n}\\n\\n.p-checkbox-box {\\n display: flex;\\n justify-content: center;\\n align-items: center;\\n border-radius: \").concat(dt('checkbox.border.radius'), \";\\n border: 1px solid \").concat(dt('checkbox.border.color'), \";\\n background: \").concat(dt('checkbox.background'), \";\\n width: \").concat(dt('checkbox.width'), \";\\n height: \").concat(dt('checkbox.height'), \";\\n transition: background \").concat(dt('checkbox.transition.duration'), \", color \").concat(dt('checkbox.transition.duration'), \", border-color \").concat(dt('checkbox.transition.duration'), \", box-shadow \").concat(dt('checkbox.transition.duration'), \", outline-color \").concat(dt('checkbox.transition.duration'), \";\\n outline-color: transparent;\\n box-shadow: \").concat(dt('checkbox.shadow'), \";\\n}\\n\\n.p-checkbox-icon {\\n transition-duration: \").concat(dt('checkbox.transition.duration'), \";\\n color: \").concat(dt('checkbox.icon.color'), \";\\n font-size: \").concat(dt('checkbox.icon.size'), \";\\n width: \").concat(dt('checkbox.icon.size'), \";\\n height: \").concat(dt('checkbox.icon.size'), \";\\n}\\n\\n.p-checkbox:not(.p-disabled):has(.p-checkbox-input:hover) .p-checkbox-box {\\n border-color: \").concat(dt('checkbox.hover.border.color'), \";\\n}\\n\\n.p-checkbox-checked .p-checkbox-box {\\n border-color: \").concat(dt('checkbox.checked.border.color'), \";\\n background: \").concat(dt('checkbox.checked.background'), \";\\n}\\n\\n.p-checkbox-checked .p-checkbox-icon {\\n color: \").concat(dt('checkbox.icon.checked.color'), \";\\n}\\n\\n.p-checkbox-checked:not(.p-disabled):has(.p-checkbox-input:hover) .p-checkbox-box {\\n background: \").concat(dt('checkbox.checked.hover.background'), \";\\n border-color: \").concat(dt('checkbox.checked.hover.border.color'), \";\\n}\\n\\n.p-checkbox-checked:not(.p-disabled):has(.p-checkbox-input:hover) .p-checkbox-icon {\\n color: \").concat(dt('checkbox.icon.checked.hover.color'), \";\\n}\\n\\n.p-checkbox:not(.p-disabled):has(.p-checkbox-input:focus-visible) .p-checkbox-box {\\n border-color: \").concat(dt('checkbox.focus.border.color'), \";\\n box-shadow: \").concat(dt('checkbox.focus.ring.shadow'), \";\\n outline: \").concat(dt('checkbox.focus.ring.width'), \" \").concat(dt('checkbox.focus.ring.style'), \" \").concat(dt('checkbox.focus.ring.color'), \";\\n outline-offset: \").concat(dt('checkbox.focus.ring.offset'), \";\\n}\\n\\n.p-checkbox-checked:not(.p-disabled):has(.p-checkbox-input:focus-visible) .p-checkbox-box {\\n border-color: \").concat(dt('checkbox.checked.focus.border.color'), \";\\n}\\n\\n.p-checkbox.p-invalid > .p-checkbox-box {\\n border-color: \").concat(dt('checkbox.invalid.border.color'), \";\\n}\\n\\n.p-checkbox.p-variant-filled .p-checkbox-box {\\n background: \").concat(dt('checkbox.filled.background'), \";\\n}\\n\\n.p-checkbox-checked.p-variant-filled .p-checkbox-box {\\n background: \").concat(dt('checkbox.checked.background'), \";\\n}\\n\\n.p-checkbox-checked.p-variant-filled:not(.p-disabled):has(.p-checkbox-input:hover) .p-checkbox-box {\\n background: \").concat(dt('checkbox.checked.hover.background'), \";\\n}\\n\\n.p-checkbox.p-disabled {\\n opacity: 1;\\n}\\n\\n.p-checkbox.p-disabled .p-checkbox-box {\\n background: \").concat(dt('checkbox.disabled.background'), \";\\n border-color: \").concat(dt('checkbox.checked.disabled.border.color'), \";\\n}\\n\\n.p-checkbox.p-disabled .p-checkbox-box .p-checkbox-icon {\\n color: \").concat(dt('checkbox.icon.disabled.color'), \";\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var instance = _ref2.instance,\n props = _ref2.props;\n return ['p-checkbox p-component', {\n 'p-checkbox-checked': instance.checked,\n 'p-disabled': props.disabled,\n 'p-invalid': props.invalid,\n 'p-variant-filled': props.variant ? props.variant === 'filled' : instance.$primevue.config.inputStyle === 'filled' || instance.$primevue.config.inputVariant === 'filled'\n }];\n },\n box: 'p-checkbox-box',\n input: 'p-checkbox-input',\n icon: 'p-checkbox-icon'\n};\nvar CheckboxStyle = BaseStyle.extend({\n name: 'checkbox',\n theme: theme,\n classes: classes\n});\n\nexport { CheckboxStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { equals, contains } from '@primeuix/utils/object';\nimport CheckIcon from '@primevue/icons/check';\nimport MinusIcon from '@primevue/icons/minus';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport CheckboxStyle from 'primevue/checkbox/style';\nimport { resolveComponent, openBlock, createElementBlock, mergeProps, createElementVNode, renderSlot, normalizeClass, createBlock, createCommentVNode } from 'vue';\n\nvar script$1 = {\n name: 'BaseCheckbox',\n \"extends\": BaseComponent,\n props: {\n value: null,\n modelValue: null,\n binary: Boolean,\n name: {\n type: String,\n \"default\": null\n },\n indeterminate: {\n type: Boolean,\n \"default\": false\n },\n trueValue: {\n type: null,\n \"default\": true\n },\n falseValue: {\n type: null,\n \"default\": false\n },\n variant: {\n type: String,\n \"default\": null\n },\n invalid: {\n type: Boolean,\n \"default\": false\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n readonly: {\n type: Boolean,\n \"default\": false\n },\n required: {\n type: Boolean,\n \"default\": false\n },\n tabindex: {\n type: Number,\n \"default\": null\n },\n inputId: {\n type: String,\n \"default\": null\n },\n inputClass: {\n type: [String, Object],\n \"default\": null\n },\n inputStyle: {\n type: Object,\n \"default\": null\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n ariaLabel: {\n type: String,\n \"default\": null\n }\n },\n style: CheckboxStyle,\n provide: function provide() {\n return {\n $pcCheckbox: this,\n $parentInstance: this\n };\n }\n};\n\nfunction _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }\nfunction _nonIterableSpread() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _iterableToArray(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nvar script = {\n name: 'Checkbox',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:modelValue', 'change', 'focus', 'blur', 'update:indeterminate'],\n data: function data() {\n return {\n d_indeterminate: this.indeterminate\n };\n },\n watch: {\n indeterminate: function indeterminate(newValue) {\n this.d_indeterminate = newValue;\n }\n },\n methods: {\n getPTOptions: function getPTOptions(key) {\n var _ptm = key === 'root' ? this.ptmi : this.ptm;\n return _ptm(key, {\n context: {\n checked: this.checked,\n indeterminate: this.d_indeterminate,\n disabled: this.disabled\n }\n });\n },\n onChange: function onChange(event) {\n var _this = this;\n if (!this.disabled && !this.readonly) {\n var newModelValue;\n if (this.binary) {\n newModelValue = this.d_indeterminate ? this.trueValue : this.checked ? this.falseValue : this.trueValue;\n } else {\n if (this.checked || this.d_indeterminate) newModelValue = this.modelValue.filter(function (val) {\n return !equals(val, _this.value);\n });else newModelValue = this.modelValue ? [].concat(_toConsumableArray(this.modelValue), [this.value]) : [this.value];\n }\n if (this.d_indeterminate) {\n this.d_indeterminate = false;\n this.$emit('update:indeterminate', this.d_indeterminate);\n }\n this.$emit('update:modelValue', newModelValue);\n this.$emit('change', event);\n }\n },\n onFocus: function onFocus(event) {\n this.$emit('focus', event);\n },\n onBlur: function onBlur(event) {\n this.$emit('blur', event);\n }\n },\n computed: {\n checked: function checked() {\n return this.d_indeterminate ? false : this.binary ? this.modelValue === this.trueValue : contains(this.value, this.modelValue);\n }\n },\n components: {\n CheckIcon: CheckIcon,\n MinusIcon: MinusIcon\n }\n};\n\nvar _hoisted_1 = [\"data-p-checked\", \"data-p-indeterminate\", \"data-p-disabled\"];\nvar _hoisted_2 = [\"id\", \"value\", \"name\", \"checked\", \"tabindex\", \"disabled\", \"readonly\", \"required\", \"aria-labelledby\", \"aria-label\", \"aria-invalid\", \"aria-checked\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_CheckIcon = resolveComponent(\"CheckIcon\");\n var _component_MinusIcon = resolveComponent(\"MinusIcon\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root')\n }, $options.getPTOptions('root'), {\n \"data-p-checked\": $options.checked,\n \"data-p-indeterminate\": $data.d_indeterminate || undefined,\n \"data-p-disabled\": _ctx.disabled\n }), [createElementVNode(\"input\", mergeProps({\n id: _ctx.inputId,\n type: \"checkbox\",\n \"class\": [_ctx.cx('input'), _ctx.inputClass],\n style: _ctx.inputStyle,\n value: _ctx.value,\n name: _ctx.name,\n checked: $options.checked,\n tabindex: _ctx.tabindex,\n disabled: _ctx.disabled,\n readonly: _ctx.readonly,\n required: _ctx.required,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel,\n \"aria-invalid\": _ctx.invalid || undefined,\n \"aria-checked\": $data.d_indeterminate ? 'mixed' : undefined,\n onFocus: _cache[0] || (_cache[0] = function () {\n return $options.onFocus && $options.onFocus.apply($options, arguments);\n }),\n onBlur: _cache[1] || (_cache[1] = function () {\n return $options.onBlur && $options.onBlur.apply($options, arguments);\n }),\n onChange: _cache[2] || (_cache[2] = function () {\n return $options.onChange && $options.onChange.apply($options, arguments);\n })\n }, $options.getPTOptions('input')), null, 16, _hoisted_2), createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('box')\n }, $options.getPTOptions('box')), [renderSlot(_ctx.$slots, \"icon\", {\n checked: $options.checked,\n indeterminate: $data.d_indeterminate,\n \"class\": normalizeClass(_ctx.cx('icon'))\n }, function () {\n return [$options.checked ? (openBlock(), createBlock(_component_CheckIcon, mergeProps({\n key: 0,\n \"class\": _ctx.cx('icon')\n }, $options.getPTOptions('icon')), null, 16, [\"class\"])) : $data.d_indeterminate ? (openBlock(), createBlock(_component_MinusIcon, mergeProps({\n key: 1,\n \"class\": _ctx.cx('icon')\n }, $options.getPTOptions('icon')), null, 16, [\"class\"])) : createCommentVNode(\"\", true)];\n })], 16)], 16, _hoisted_1);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'ChevronDownIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n d: \"M7.01744 10.398C6.91269 10.3985 6.8089 10.378 6.71215 10.3379C6.61541 10.2977 6.52766 10.2386 6.45405 10.1641L1.13907 4.84913C1.03306 4.69404 0.985221 4.5065 1.00399 4.31958C1.02276 4.13266 1.10693 3.95838 1.24166 3.82747C1.37639 3.69655 1.55301 3.61742 1.74039 3.60402C1.92777 3.59062 2.11386 3.64382 2.26584 3.75424L7.01744 8.47394L11.769 3.75424C11.9189 3.65709 12.097 3.61306 12.2748 3.62921C12.4527 3.64535 12.6199 3.72073 12.7498 3.84328C12.8797 3.96582 12.9647 4.12842 12.9912 4.30502C13.0177 4.48162 12.9841 4.662 12.8958 4.81724L7.58083 10.1322C7.50996 10.2125 7.42344 10.2775 7.32656 10.3232C7.22968 10.3689 7.12449 10.3944 7.01744 10.398Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'TimesIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n d: \"M8.01186 7.00933L12.27 2.75116C12.341 2.68501 12.398 2.60524 12.4375 2.51661C12.4769 2.42798 12.4982 2.3323 12.4999 2.23529C12.5016 2.13827 12.4838 2.0419 12.4474 1.95194C12.4111 1.86197 12.357 1.78024 12.2884 1.71163C12.2198 1.64302 12.138 1.58893 12.0481 1.55259C11.9581 1.51625 11.8617 1.4984 11.7647 1.50011C11.6677 1.50182 11.572 1.52306 11.4834 1.56255C11.3948 1.60204 11.315 1.65898 11.2488 1.72997L6.99067 5.98814L2.7325 1.72997C2.59553 1.60234 2.41437 1.53286 2.22718 1.53616C2.03999 1.53946 1.8614 1.61529 1.72901 1.74767C1.59663 1.88006 1.5208 2.05865 1.5175 2.24584C1.5142 2.43303 1.58368 2.61419 1.71131 2.75116L5.96948 7.00933L1.71131 11.2675C1.576 11.403 1.5 11.5866 1.5 11.7781C1.5 11.9696 1.576 12.1532 1.71131 12.2887C1.84679 12.424 2.03043 12.5 2.2219 12.5C2.41338 12.5 2.59702 12.424 2.7325 12.2887L6.99067 8.03052L11.2488 12.2887C11.3843 12.424 11.568 12.5 11.7594 12.5C11.9509 12.5 12.1346 12.424 12.27 12.2887C12.4053 12.1532 12.4813 11.9696 12.4813 11.7781C12.4813 11.5866 12.4053 11.403 12.27 11.2675L8.01186 7.00933Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import { EventBus } from '@primeuix/utils/eventbus';\n\nvar OverlayEventBus = EventBus();\n\nexport { OverlayEventBus as default };\n//# sourceMappingURL=index.mjs.map\n","import { isClient } from '@primeuix/utils/dom';\nimport { renderSlot, openBlock, createBlock, Teleport, createCommentVNode } from 'vue';\n\nvar script = {\n name: 'Portal',\n props: {\n appendTo: {\n type: [String, Object],\n \"default\": 'body'\n },\n disabled: {\n type: Boolean,\n \"default\": false\n }\n },\n data: function data() {\n return {\n mounted: false\n };\n },\n mounted: function mounted() {\n this.mounted = isClient();\n },\n computed: {\n inline: function inline() {\n return this.disabled || this.appendTo === 'self';\n }\n }\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return $options.inline ? renderSlot(_ctx.$slots, \"default\", {\n key: 0\n }) : $data.mounted ? (openBlock(), createBlock(Teleport, {\n key: 1,\n to: $props.appendTo\n }, [renderSlot(_ctx.$slots, \"default\")], 8, [\"to\"])) : createCommentVNode(\"\", true);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-select {\\n display: inline-flex;\\n cursor: pointer;\\n position: relative;\\n user-select: none;\\n background: \".concat(dt('select.background'), \";\\n border: 1px solid \").concat(dt('select.border.color'), \";\\n transition: background \").concat(dt('select.transition.duration'), \", color \").concat(dt('select.transition.duration'), \", border-color \").concat(dt('select.transition.duration'), \",\\n outline-color \").concat(dt('select.transition.duration'), \", box-shadow \").concat(dt('select.transition.duration'), \";\\n border-radius: \").concat(dt('select.border.radius'), \";\\n outline-color: transparent;\\n box-shadow: \").concat(dt('select.shadow'), \";\\n}\\n\\n.p-select:not(.p-disabled):hover {\\n border-color: \").concat(dt('select.hover.border.color'), \";\\n}\\n\\n.p-select:not(.p-disabled).p-focus {\\n border-color: \").concat(dt('select.focus.border.color'), \";\\n box-shadow: \").concat(dt('select.focus.ring.shadow'), \";\\n outline: \").concat(dt('select.focus.ring.width'), \" \").concat(dt('select.focus.ring.style'), \" \").concat(dt('select.focus.ring.color'), \";\\n outline-offset: \").concat(dt('select.focus.ring.offset'), \";\\n}\\n\\n.p-select.p-variant-filled {\\n background: \").concat(dt('select.filled.background'), \";\\n}\\n\\n.p-select.p-variant-filled.p-focus {\\n background: \").concat(dt('select.filled.focus.background'), \";\\n}\\n\\n.p-select.p-invalid {\\n border-color: \").concat(dt('select.invalid.border.color'), \";\\n}\\n\\n.p-select.p-disabled {\\n opacity: 1;\\n background: \").concat(dt('select.disabled.background'), \";\\n}\\n\\n.p-select-clear-icon {\\n position: absolute;\\n top: 50%;\\n margin-top: -0.5rem;\\n color: \").concat(dt('select.clear.icon.color'), \";\\n right: \").concat(dt('select.dropdown.width'), \";\\n}\\n\\n.p-select-dropdown {\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n flex-shrink: 0;\\n background: transparent;\\n color: \").concat(dt('select.dropdown.color'), \";\\n width: \").concat(dt('select.dropdown.width'), \";\\n border-top-right-radius: \").concat(dt('select.border.radius'), \";\\n border-bottom-right-radius: \").concat(dt('select.border.radius'), \";\\n}\\n\\n.p-select-label {\\n display: block;\\n white-space: nowrap;\\n overflow: hidden;\\n flex: 1 1 auto;\\n width: 1%;\\n padding: \").concat(dt('select.padding.y'), \" \").concat(dt('select.padding.x'), \";\\n text-overflow: ellipsis;\\n cursor: pointer;\\n color: \").concat(dt('select.color'), \";\\n background: transparent;\\n border: 0 none;\\n outline: 0 none;\\n}\\n\\n.p-select-label.p-placeholder {\\n color: \").concat(dt('select.placeholder.color'), \";\\n}\\n\\n.p-select:has(.p-select-clear-icon) .p-select-label {\\n padding-right: calc(1rem + \").concat(dt('select.padding.x'), \");\\n}\\n\\n.p-select.p-disabled .p-select-label {\\n color: \").concat(dt('select.disabled.color'), \";\\n}\\n\\n.p-select-label-empty {\\n overflow: hidden;\\n opacity: 0;\\n}\\n\\ninput.p-select-label {\\n cursor: default;\\n}\\n\\n.p-select .p-select-overlay {\\n min-width: 100%;\\n}\\n\\n.p-select-overlay {\\n position: absolute;\\n top: 0;\\n left: 0;\\n background: \").concat(dt('select.overlay.background'), \";\\n color: \").concat(dt('select.overlay.color'), \";\\n border: 1px solid \").concat(dt('select.overlay.border.color'), \";\\n border-radius: \").concat(dt('select.overlay.border.radius'), \";\\n box-shadow: \").concat(dt('select.overlay.shadow'), \";\\n}\\n\\n.p-select-header {\\n padding: \").concat(dt('select.list.header.padding'), \";\\n}\\n\\n.p-select-filter {\\n width: 100%;\\n}\\n\\n.p-select-list-container {\\n overflow: auto;\\n}\\n\\n.p-select-option-group {\\n cursor: auto;\\n margin: 0;\\n padding: \").concat(dt('select.option.group.padding'), \";\\n background: \").concat(dt('select.option.group.background'), \";\\n color: \").concat(dt('select.option.group.color'), \";\\n font-weight: \").concat(dt('select.option.group.font.weight'), \";\\n}\\n\\n.p-select-list {\\n margin: 0;\\n padding: 0;\\n list-style-type: none;\\n padding: \").concat(dt('select.list.padding'), \";\\n gap: \").concat(dt('select.list.gap'), \";\\n display: flex;\\n flex-direction: column;\\n}\\n\\n.p-select-option {\\n cursor: pointer;\\n font-weight: normal;\\n white-space: nowrap;\\n position: relative;\\n overflow: hidden;\\n display: flex;\\n align-items: center;\\n padding: \").concat(dt('select.option.padding'), \";\\n border: 0 none;\\n color: \").concat(dt('select.option.color'), \";\\n background: transparent;\\n transition: background \").concat(dt('select.transition.duration'), \", color \").concat(dt('select.transition.duration'), \", border-color \").concat(dt('select.transition.duration'), \",\\n box-shadow \").concat(dt('select.transition.duration'), \", outline-color \").concat(dt('select.transition.duration'), \";\\n border-radius: \").concat(dt('select.option.border.radius'), \";\\n}\\n\\n.p-select-option:not(.p-select-option-selected):not(.p-disabled).p-focus {\\n background: \").concat(dt('select.option.focus.background'), \";\\n color: \").concat(dt('select.option.focus.color'), \";\\n}\\n\\n.p-select-option.p-select-option-selected {\\n background: \").concat(dt('select.option.selected.background'), \";\\n color: \").concat(dt('select.option.selected.color'), \";\\n}\\n\\n.p-select-option.p-select-option-selected.p-focus {\\n background: \").concat(dt('select.option.selected.focus.background'), \";\\n color: \").concat(dt('select.option.selected.focus.color'), \";\\n}\\n\\n.p-select-option-check-icon {\\n position: relative;\\n margin-inline-start: \").concat(dt('select.checkmark.gutter.start'), \";\\n margin-inline-end: \").concat(dt('select.checkmark.gutter.end'), \";\\n color: \").concat(dt('select.checkmark.color'), \";\\n}\\n\\n.p-select-empty-message {\\n padding: \").concat(dt('select.empty.message.padding'), \";\\n}\\n\\n.p-select-fluid {\\n display: flex;\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var instance = _ref2.instance,\n props = _ref2.props,\n state = _ref2.state;\n return ['p-select p-component p-inputwrapper', {\n 'p-disabled': props.disabled,\n 'p-invalid': props.invalid,\n 'p-variant-filled': props.variant ? props.variant === 'filled' : instance.$primevue.config.inputStyle === 'filled' || instance.$primevue.config.inputVariant === 'filled',\n 'p-focus': state.focused,\n 'p-inputwrapper-filled': instance.hasSelectedOption,\n 'p-inputwrapper-focus': state.focused || state.overlayVisible,\n 'p-select-open': state.overlayVisible,\n 'p-select-fluid': instance.hasFluid\n }];\n },\n label: function label(_ref3) {\n var instance = _ref3.instance,\n props = _ref3.props;\n return ['p-select-label', {\n 'p-placeholder': !props.editable && instance.label === props.placeholder,\n 'p-select-label-empty': !props.editable && !instance.$slots['value'] && (instance.label === 'p-emptylabel' || instance.label.length === 0)\n }];\n },\n clearIcon: 'p-select-clear-icon',\n dropdown: 'p-select-dropdown',\n loadingicon: 'p-select-loading-icon',\n dropdownIcon: 'p-select-dropdown-icon',\n overlay: 'p-select-overlay p-component',\n header: 'p-select-header',\n pcFilter: 'p-select-filter',\n listContainer: 'p-select-list-container',\n list: 'p-select-list',\n optionGroup: 'p-select-option-group',\n optionGroupLabel: 'p-select-option-group-label',\n option: function option(_ref4) {\n var instance = _ref4.instance,\n props = _ref4.props,\n state = _ref4.state,\n _option = _ref4.option,\n focusedOption = _ref4.focusedOption;\n return ['p-select-option', {\n 'p-select-option-selected': instance.isSelected(_option) && props.highlightOnSelect,\n 'p-focus': state.focusedOptionIndex === focusedOption,\n 'p-disabled': instance.isOptionDisabled(_option)\n }];\n },\n optionLabel: 'p-select-option-label',\n optionCheckIcon: 'p-select-option-check-icon',\n optionBlankIcon: 'p-select-option-blank-icon',\n emptyMessage: 'p-select-empty-message'\n};\nvar SelectStyle = BaseStyle.extend({\n name: 'select',\n theme: theme,\n classes: classes\n});\n\nexport { SelectStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { focus, isAndroid, getFirstFocusableElement, getLastFocusableElement, addStyle, relativePosition, getOuterWidth, absolutePosition, isTouchDevice, isVisible, getFocusableElements, findSingle } from '@primeuix/utils/dom';\nimport { resolveFieldData, isPrintableCharacter, isNotEmpty, equals, findLastIndex, isEmpty } from '@primeuix/utils/object';\nimport { ZIndex } from '@primeuix/utils/zindex';\nimport { FilterService } from '@primevue/core/api';\nimport { UniqueComponentId, ConnectedOverlayScrollHandler } from '@primevue/core/utils';\nimport BlankIcon from '@primevue/icons/blank';\nimport CheckIcon from '@primevue/icons/check';\nimport ChevronDownIcon from '@primevue/icons/chevrondown';\nimport SearchIcon from '@primevue/icons/search';\nimport SpinnerIcon from '@primevue/icons/spinner';\nimport TimesIcon from '@primevue/icons/times';\nimport IconField from 'primevue/iconfield';\nimport InputIcon from 'primevue/inputicon';\nimport InputText from 'primevue/inputtext';\nimport OverlayEventBus from 'primevue/overlayeventbus';\nimport Portal from 'primevue/portal';\nimport Ripple from 'primevue/ripple';\nimport VirtualScroller from 'primevue/virtualscroller';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport SelectStyle from 'primevue/select/style';\nimport { resolveComponent, resolveDirective, openBlock, createElementBlock, mergeProps, renderSlot, createTextVNode, toDisplayString, normalizeClass, createBlock, resolveDynamicComponent, createCommentVNode, createElementVNode, createVNode, withCtx, Transition, normalizeProps, createSlots, Fragment, renderList, withDirectives } from 'vue';\n\nvar script$1 = {\n name: 'BaseSelect',\n \"extends\": BaseComponent,\n props: {\n modelValue: null,\n options: Array,\n optionLabel: [String, Function],\n optionValue: [String, Function],\n optionDisabled: [String, Function],\n optionGroupLabel: [String, Function],\n optionGroupChildren: [String, Function],\n scrollHeight: {\n type: String,\n \"default\": '14rem'\n },\n filter: Boolean,\n filterPlaceholder: String,\n filterLocale: String,\n filterMatchMode: {\n type: String,\n \"default\": 'contains'\n },\n filterFields: {\n type: Array,\n \"default\": null\n },\n editable: Boolean,\n placeholder: {\n type: String,\n \"default\": null\n },\n variant: {\n type: String,\n \"default\": null\n },\n invalid: {\n type: Boolean,\n \"default\": false\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n dataKey: null,\n showClear: {\n type: Boolean,\n \"default\": false\n },\n fluid: {\n type: Boolean,\n \"default\": null\n },\n inputId: {\n type: String,\n \"default\": null\n },\n inputClass: {\n type: [String, Object],\n \"default\": null\n },\n inputStyle: {\n type: Object,\n \"default\": null\n },\n labelId: {\n type: String,\n \"default\": null\n },\n labelClass: {\n type: [String, Object],\n \"default\": null\n },\n labelStyle: {\n type: Object,\n \"default\": null\n },\n panelClass: {\n type: [String, Object],\n \"default\": null\n },\n overlayStyle: {\n type: Object,\n \"default\": null\n },\n overlayClass: {\n type: [String, Object],\n \"default\": null\n },\n panelStyle: {\n type: Object,\n \"default\": null\n },\n appendTo: {\n type: [String, Object],\n \"default\": 'body'\n },\n loading: {\n type: Boolean,\n \"default\": false\n },\n clearIcon: {\n type: String,\n \"default\": undefined\n },\n dropdownIcon: {\n type: String,\n \"default\": undefined\n },\n filterIcon: {\n type: String,\n \"default\": undefined\n },\n loadingIcon: {\n type: String,\n \"default\": undefined\n },\n resetFilterOnHide: {\n type: Boolean,\n \"default\": false\n },\n resetFilterOnClear: {\n type: Boolean,\n \"default\": false\n },\n virtualScrollerOptions: {\n type: Object,\n \"default\": null\n },\n autoOptionFocus: {\n type: Boolean,\n \"default\": false\n },\n autoFilterFocus: {\n type: Boolean,\n \"default\": false\n },\n selectOnFocus: {\n type: Boolean,\n \"default\": false\n },\n focusOnHover: {\n type: Boolean,\n \"default\": true\n },\n highlightOnSelect: {\n type: Boolean,\n \"default\": true\n },\n checkmark: {\n type: Boolean,\n \"default\": false\n },\n filterMessage: {\n type: String,\n \"default\": null\n },\n selectionMessage: {\n type: String,\n \"default\": null\n },\n emptySelectionMessage: {\n type: String,\n \"default\": null\n },\n emptyFilterMessage: {\n type: String,\n \"default\": null\n },\n emptyMessage: {\n type: String,\n \"default\": null\n },\n tabindex: {\n type: Number,\n \"default\": 0\n },\n ariaLabel: {\n type: String,\n \"default\": null\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n }\n },\n style: SelectStyle,\n provide: function provide() {\n return {\n $pcSelect: this,\n $parentInstance: this\n };\n }\n};\n\nfunction _typeof(o) { \"@babel/helpers - typeof\"; return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof(o); }\nfunction _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }\nfunction _nonIterableSpread() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _iterableToArray(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty(e, r, t) { return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey(t) { var i = _toPrimitive(t, \"string\"); return \"symbol\" == _typeof(i) ? i : i + \"\"; }\nfunction _toPrimitive(t, r) { if (\"object\" != _typeof(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nvar script = {\n name: 'Select',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:modelValue', 'change', 'focus', 'blur', 'before-show', 'before-hide', 'show', 'hide', 'filter'],\n inject: {\n $pcFluid: {\n \"default\": null\n }\n },\n outsideClickListener: null,\n scrollHandler: null,\n resizeListener: null,\n labelClickListener: null,\n overlay: null,\n list: null,\n virtualScroller: null,\n searchTimeout: null,\n searchValue: null,\n isModelValueChanged: false,\n data: function data() {\n return {\n id: this.$attrs.id,\n clicked: false,\n focused: false,\n focusedOptionIndex: -1,\n filterValue: null,\n overlayVisible: false\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n },\n modelValue: function modelValue() {\n this.isModelValueChanged = true;\n },\n options: function options() {\n this.autoUpdateModel();\n }\n },\n mounted: function mounted() {\n this.id = this.id || UniqueComponentId();\n this.autoUpdateModel();\n this.bindLabelClickListener();\n },\n updated: function updated() {\n if (this.overlayVisible && this.isModelValueChanged) {\n this.scrollInView(this.findSelectedOptionIndex());\n }\n this.isModelValueChanged = false;\n },\n beforeUnmount: function beforeUnmount() {\n this.unbindOutsideClickListener();\n this.unbindResizeListener();\n this.unbindLabelClickListener();\n if (this.scrollHandler) {\n this.scrollHandler.destroy();\n this.scrollHandler = null;\n }\n if (this.overlay) {\n ZIndex.clear(this.overlay);\n this.overlay = null;\n }\n },\n methods: {\n getOptionIndex: function getOptionIndex(index, fn) {\n return this.virtualScrollerDisabled ? index : fn && fn(index)['index'];\n },\n getOptionLabel: function getOptionLabel(option) {\n return this.optionLabel ? resolveFieldData(option, this.optionLabel) : option;\n },\n getOptionValue: function getOptionValue(option) {\n return this.optionValue ? resolveFieldData(option, this.optionValue) : option;\n },\n getOptionRenderKey: function getOptionRenderKey(option, index) {\n return (this.dataKey ? resolveFieldData(option, this.dataKey) : this.getOptionLabel(option)) + '_' + index;\n },\n getPTItemOptions: function getPTItemOptions(option, itemOptions, index, key) {\n return this.ptm(key, {\n context: {\n option: option,\n index: index,\n selected: this.isSelected(option),\n focused: this.focusedOptionIndex === this.getOptionIndex(index, itemOptions),\n disabled: this.isOptionDisabled(option)\n }\n });\n },\n isOptionDisabled: function isOptionDisabled(option) {\n return this.optionDisabled ? resolveFieldData(option, this.optionDisabled) : false;\n },\n isOptionGroup: function isOptionGroup(option) {\n return this.optionGroupLabel && option.optionGroup && option.group;\n },\n getOptionGroupLabel: function getOptionGroupLabel(optionGroup) {\n return resolveFieldData(optionGroup, this.optionGroupLabel);\n },\n getOptionGroupChildren: function getOptionGroupChildren(optionGroup) {\n return resolveFieldData(optionGroup, this.optionGroupChildren);\n },\n getAriaPosInset: function getAriaPosInset(index) {\n var _this = this;\n return (this.optionGroupLabel ? index - this.visibleOptions.slice(0, index).filter(function (option) {\n return _this.isOptionGroup(option);\n }).length : index) + 1;\n },\n show: function show(isFocus) {\n this.$emit('before-show');\n this.overlayVisible = true;\n this.focusedOptionIndex = this.focusedOptionIndex !== -1 ? this.focusedOptionIndex : this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : this.editable ? -1 : this.findSelectedOptionIndex();\n isFocus && focus(this.$refs.focusInput);\n },\n hide: function hide(isFocus) {\n var _this2 = this;\n var _hide = function _hide() {\n _this2.$emit('before-hide');\n _this2.overlayVisible = false;\n _this2.clicked = false;\n _this2.focusedOptionIndex = -1;\n _this2.searchValue = '';\n _this2.resetFilterOnHide && (_this2.filterValue = null);\n isFocus && focus(_this2.$refs.focusInput);\n };\n setTimeout(function () {\n _hide();\n }, 0); // For ScreenReaders\n },\n onFocus: function onFocus(event) {\n if (this.disabled) {\n // For ScreenReaders\n return;\n }\n this.focused = true;\n if (this.overlayVisible) {\n this.focusedOptionIndex = this.focusedOptionIndex !== -1 ? this.focusedOptionIndex : this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : this.editable ? -1 : this.findSelectedOptionIndex();\n this.scrollInView(this.focusedOptionIndex);\n }\n this.$emit('focus', event);\n },\n onBlur: function onBlur(event) {\n this.focused = false;\n this.focusedOptionIndex = -1;\n this.searchValue = '';\n this.$emit('blur', event);\n },\n onKeyDown: function onKeyDown(event) {\n if (this.disabled || isAndroid()) {\n event.preventDefault();\n return;\n }\n var metaKey = event.metaKey || event.ctrlKey;\n switch (event.code) {\n case 'ArrowDown':\n this.onArrowDownKey(event);\n break;\n case 'ArrowUp':\n this.onArrowUpKey(event, this.editable);\n break;\n case 'ArrowLeft':\n case 'ArrowRight':\n this.onArrowLeftKey(event, this.editable);\n break;\n case 'Home':\n this.onHomeKey(event, this.editable);\n break;\n case 'End':\n this.onEndKey(event, this.editable);\n break;\n case 'PageDown':\n this.onPageDownKey(event);\n break;\n case 'PageUp':\n this.onPageUpKey(event);\n break;\n case 'Space':\n this.onSpaceKey(event, this.editable);\n break;\n case 'Enter':\n case 'NumpadEnter':\n this.onEnterKey(event);\n break;\n case 'Escape':\n this.onEscapeKey(event);\n break;\n case 'Tab':\n this.onTabKey(event);\n break;\n case 'Backspace':\n this.onBackspaceKey(event, this.editable);\n break;\n case 'ShiftLeft':\n case 'ShiftRight':\n //NOOP\n break;\n default:\n if (!metaKey && isPrintableCharacter(event.key)) {\n !this.overlayVisible && this.show();\n !this.editable && this.searchOptions(event, event.key);\n }\n break;\n }\n this.clicked = false;\n },\n onEditableInput: function onEditableInput(event) {\n var value = event.target.value;\n this.searchValue = '';\n var matched = this.searchOptions(event, value);\n !matched && (this.focusedOptionIndex = -1);\n this.updateModel(event, value);\n !this.overlayVisible && isNotEmpty(value) && this.show();\n },\n onContainerClick: function onContainerClick(event) {\n if (this.disabled || this.loading) {\n return;\n }\n if (event.target.tagName === 'INPUT' || event.target.getAttribute('data-pc-section') === 'clearicon' || event.target.closest('[data-pc-section=\"clearicon\"]')) {\n return;\n } else if (!this.overlay || !this.overlay.contains(event.target)) {\n this.overlayVisible ? this.hide(true) : this.show(true);\n }\n this.clicked = true;\n },\n onClearClick: function onClearClick(event) {\n this.updateModel(event, null);\n this.resetFilterOnClear && (this.filterValue = null);\n },\n onFirstHiddenFocus: function onFirstHiddenFocus(event) {\n var focusableEl = event.relatedTarget === this.$refs.focusInput ? getFirstFocusableElement(this.overlay, ':not([data-p-hidden-focusable=\"true\"])') : this.$refs.focusInput;\n focus(focusableEl);\n },\n onLastHiddenFocus: function onLastHiddenFocus(event) {\n var focusableEl = event.relatedTarget === this.$refs.focusInput ? getLastFocusableElement(this.overlay, ':not([data-p-hidden-focusable=\"true\"])') : this.$refs.focusInput;\n focus(focusableEl);\n },\n onOptionSelect: function onOptionSelect(event, option) {\n var isHide = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : true;\n var value = this.getOptionValue(option);\n this.updateModel(event, value);\n isHide && this.hide(true);\n },\n onOptionMouseMove: function onOptionMouseMove(event, index) {\n if (this.focusOnHover) {\n this.changeFocusedOptionIndex(event, index);\n }\n },\n onFilterChange: function onFilterChange(event) {\n var value = event.target.value;\n this.filterValue = value;\n this.focusedOptionIndex = -1;\n this.$emit('filter', {\n originalEvent: event,\n value: value\n });\n !this.virtualScrollerDisabled && this.virtualScroller.scrollToIndex(0);\n },\n onFilterKeyDown: function onFilterKeyDown(event) {\n switch (event.code) {\n case 'ArrowDown':\n this.onArrowDownKey(event);\n break;\n case 'ArrowUp':\n this.onArrowUpKey(event, true);\n break;\n case 'ArrowLeft':\n case 'ArrowRight':\n this.onArrowLeftKey(event, true);\n break;\n case 'Home':\n this.onHomeKey(event, true);\n break;\n case 'End':\n this.onEndKey(event, true);\n break;\n case 'Enter':\n case 'NumpadEnter':\n this.onEnterKey(event);\n break;\n case 'Escape':\n this.onEscapeKey(event);\n break;\n case 'Tab':\n this.onTabKey(event, true);\n break;\n }\n },\n onFilterBlur: function onFilterBlur() {\n this.focusedOptionIndex = -1;\n },\n onFilterUpdated: function onFilterUpdated() {\n if (this.overlayVisible) {\n this.alignOverlay();\n }\n },\n onOverlayClick: function onOverlayClick(event) {\n OverlayEventBus.emit('overlay-click', {\n originalEvent: event,\n target: this.$el\n });\n },\n onOverlayKeyDown: function onOverlayKeyDown(event) {\n switch (event.code) {\n case 'Escape':\n this.onEscapeKey(event);\n break;\n }\n },\n onArrowDownKey: function onArrowDownKey(event) {\n if (!this.overlayVisible) {\n this.show();\n this.editable && this.changeFocusedOptionIndex(event, this.findSelectedOptionIndex());\n } else {\n var optionIndex = this.focusedOptionIndex !== -1 ? this.findNextOptionIndex(this.focusedOptionIndex) : this.clicked ? this.findFirstOptionIndex() : this.findFirstFocusedOptionIndex();\n this.changeFocusedOptionIndex(event, optionIndex);\n }\n event.preventDefault();\n },\n onArrowUpKey: function onArrowUpKey(event) {\n var pressedInInputText = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n if (event.altKey && !pressedInInputText) {\n if (this.focusedOptionIndex !== -1) {\n this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]);\n }\n this.overlayVisible && this.hide();\n event.preventDefault();\n } else {\n var optionIndex = this.focusedOptionIndex !== -1 ? this.findPrevOptionIndex(this.focusedOptionIndex) : this.clicked ? this.findLastOptionIndex() : this.findLastFocusedOptionIndex();\n this.changeFocusedOptionIndex(event, optionIndex);\n !this.overlayVisible && this.show();\n event.preventDefault();\n }\n },\n onArrowLeftKey: function onArrowLeftKey(event) {\n var pressedInInputText = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n pressedInInputText && (this.focusedOptionIndex = -1);\n },\n onHomeKey: function onHomeKey(event) {\n var pressedInInputText = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n if (pressedInInputText) {\n var target = event.currentTarget;\n if (event.shiftKey) {\n target.setSelectionRange(0, event.target.selectionStart);\n } else {\n target.setSelectionRange(0, 0);\n this.focusedOptionIndex = -1;\n }\n } else {\n this.changeFocusedOptionIndex(event, this.findFirstOptionIndex());\n !this.overlayVisible && this.show();\n }\n event.preventDefault();\n },\n onEndKey: function onEndKey(event) {\n var pressedInInputText = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n if (pressedInInputText) {\n var target = event.currentTarget;\n if (event.shiftKey) {\n target.setSelectionRange(event.target.selectionStart, target.value.length);\n } else {\n var len = target.value.length;\n target.setSelectionRange(len, len);\n this.focusedOptionIndex = -1;\n }\n } else {\n this.changeFocusedOptionIndex(event, this.findLastOptionIndex());\n !this.overlayVisible && this.show();\n }\n event.preventDefault();\n },\n onPageUpKey: function onPageUpKey(event) {\n this.scrollInView(0);\n event.preventDefault();\n },\n onPageDownKey: function onPageDownKey(event) {\n this.scrollInView(this.visibleOptions.length - 1);\n event.preventDefault();\n },\n onEnterKey: function onEnterKey(event) {\n if (!this.overlayVisible) {\n this.focusedOptionIndex = -1; // reset\n this.onArrowDownKey(event);\n } else {\n if (this.focusedOptionIndex !== -1) {\n this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]);\n }\n this.hide();\n }\n event.preventDefault();\n },\n onSpaceKey: function onSpaceKey(event) {\n var pressedInInputText = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n !pressedInInputText && this.onEnterKey(event);\n },\n onEscapeKey: function onEscapeKey(event) {\n this.overlayVisible && this.hide(true);\n event.preventDefault();\n event.stopPropagation(); //@todo will be changed next versionss\n },\n onTabKey: function onTabKey(event) {\n var pressedInInputText = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n if (!pressedInInputText) {\n if (this.overlayVisible && this.hasFocusableElements()) {\n focus(this.$refs.firstHiddenFocusableElementOnOverlay);\n event.preventDefault();\n } else {\n if (this.focusedOptionIndex !== -1) {\n this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]);\n }\n this.overlayVisible && this.hide(this.filter);\n }\n }\n },\n onBackspaceKey: function onBackspaceKey(event) {\n var pressedInInputText = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n if (pressedInInputText) {\n !this.overlayVisible && this.show();\n }\n },\n onOverlayEnter: function onOverlayEnter(el) {\n ZIndex.set('overlay', el, this.$primevue.config.zIndex.overlay);\n addStyle(el, {\n position: 'absolute',\n top: '0',\n left: '0'\n });\n this.alignOverlay();\n this.scrollInView();\n this.autoFilterFocus && focus(this.$refs.filterInput.$el);\n },\n onOverlayAfterEnter: function onOverlayAfterEnter() {\n this.bindOutsideClickListener();\n this.bindScrollListener();\n this.bindResizeListener();\n this.$emit('show');\n },\n onOverlayLeave: function onOverlayLeave() {\n this.unbindOutsideClickListener();\n this.unbindScrollListener();\n this.unbindResizeListener();\n this.autoFilterFocus && focus(this.$refs.focusInput);\n this.$emit('hide');\n this.overlay = null;\n },\n onOverlayAfterLeave: function onOverlayAfterLeave(el) {\n ZIndex.clear(el);\n },\n alignOverlay: function alignOverlay() {\n if (this.appendTo === 'self') {\n relativePosition(this.overlay, this.$el);\n } else {\n this.overlay.style.minWidth = getOuterWidth(this.$el) + 'px';\n absolutePosition(this.overlay, this.$el);\n }\n },\n bindOutsideClickListener: function bindOutsideClickListener() {\n var _this3 = this;\n if (!this.outsideClickListener) {\n this.outsideClickListener = function (event) {\n if (_this3.overlayVisible && _this3.overlay && !_this3.$el.contains(event.target) && !_this3.overlay.contains(event.target)) {\n _this3.hide();\n }\n };\n document.addEventListener('click', this.outsideClickListener);\n }\n },\n unbindOutsideClickListener: function unbindOutsideClickListener() {\n if (this.outsideClickListener) {\n document.removeEventListener('click', this.outsideClickListener);\n this.outsideClickListener = null;\n }\n },\n bindScrollListener: function bindScrollListener() {\n var _this4 = this;\n if (!this.scrollHandler) {\n this.scrollHandler = new ConnectedOverlayScrollHandler(this.$refs.container, function () {\n if (_this4.overlayVisible) {\n _this4.hide();\n }\n });\n }\n this.scrollHandler.bindScrollListener();\n },\n unbindScrollListener: function unbindScrollListener() {\n if (this.scrollHandler) {\n this.scrollHandler.unbindScrollListener();\n }\n },\n bindResizeListener: function bindResizeListener() {\n var _this5 = this;\n if (!this.resizeListener) {\n this.resizeListener = function () {\n if (_this5.overlayVisible && !isTouchDevice()) {\n _this5.hide();\n }\n };\n window.addEventListener('resize', this.resizeListener);\n }\n },\n unbindResizeListener: function unbindResizeListener() {\n if (this.resizeListener) {\n window.removeEventListener('resize', this.resizeListener);\n this.resizeListener = null;\n }\n },\n bindLabelClickListener: function bindLabelClickListener() {\n var _this6 = this;\n if (!this.editable && !this.labelClickListener) {\n var label = document.querySelector(\"label[for=\\\"\".concat(this.inputId, \"\\\"]\"));\n if (label && isVisible(label)) {\n this.labelClickListener = function () {\n focus(_this6.$refs.focusInput);\n };\n label.addEventListener('click', this.labelClickListener);\n }\n }\n },\n unbindLabelClickListener: function unbindLabelClickListener() {\n if (this.labelClickListener) {\n var label = document.querySelector(\"label[for=\\\"\".concat(this.inputId, \"\\\"]\"));\n if (label && isVisible(label)) {\n label.removeEventListener('click', this.labelClickListener);\n }\n }\n },\n hasFocusableElements: function hasFocusableElements() {\n return getFocusableElements(this.overlay, ':not([data-p-hidden-focusable=\"true\"])').length > 0;\n },\n isOptionMatched: function isOptionMatched(option) {\n var _this$getOptionLabel;\n return this.isValidOption(option) && typeof this.getOptionLabel(option) === 'string' && ((_this$getOptionLabel = this.getOptionLabel(option)) === null || _this$getOptionLabel === void 0 ? void 0 : _this$getOptionLabel.toLocaleLowerCase(this.filterLocale).startsWith(this.searchValue.toLocaleLowerCase(this.filterLocale)));\n },\n isValidOption: function isValidOption(option) {\n return isNotEmpty(option) && !(this.isOptionDisabled(option) || this.isOptionGroup(option));\n },\n isValidSelectedOption: function isValidSelectedOption(option) {\n return this.isValidOption(option) && this.isSelected(option);\n },\n isSelected: function isSelected(option) {\n return this.isValidOption(option) && equals(this.modelValue, this.getOptionValue(option), this.equalityKey);\n },\n findFirstOptionIndex: function findFirstOptionIndex() {\n var _this7 = this;\n return this.visibleOptions.findIndex(function (option) {\n return _this7.isValidOption(option);\n });\n },\n findLastOptionIndex: function findLastOptionIndex() {\n var _this8 = this;\n return findLastIndex(this.visibleOptions, function (option) {\n return _this8.isValidOption(option);\n });\n },\n findNextOptionIndex: function findNextOptionIndex(index) {\n var _this9 = this;\n var matchedOptionIndex = index < this.visibleOptions.length - 1 ? this.visibleOptions.slice(index + 1).findIndex(function (option) {\n return _this9.isValidOption(option);\n }) : -1;\n return matchedOptionIndex > -1 ? matchedOptionIndex + index + 1 : index;\n },\n findPrevOptionIndex: function findPrevOptionIndex(index) {\n var _this10 = this;\n var matchedOptionIndex = index > 0 ? findLastIndex(this.visibleOptions.slice(0, index), function (option) {\n return _this10.isValidOption(option);\n }) : -1;\n return matchedOptionIndex > -1 ? matchedOptionIndex : index;\n },\n findSelectedOptionIndex: function findSelectedOptionIndex() {\n var _this11 = this;\n return this.hasSelectedOption ? this.visibleOptions.findIndex(function (option) {\n return _this11.isValidSelectedOption(option);\n }) : -1;\n },\n findFirstFocusedOptionIndex: function findFirstFocusedOptionIndex() {\n var selectedIndex = this.findSelectedOptionIndex();\n return selectedIndex < 0 ? this.findFirstOptionIndex() : selectedIndex;\n },\n findLastFocusedOptionIndex: function findLastFocusedOptionIndex() {\n var selectedIndex = this.findSelectedOptionIndex();\n return selectedIndex < 0 ? this.findLastOptionIndex() : selectedIndex;\n },\n searchOptions: function searchOptions(event, _char) {\n var _this12 = this;\n this.searchValue = (this.searchValue || '') + _char;\n var optionIndex = -1;\n var matched = false;\n if (isNotEmpty(this.searchValue)) {\n if (this.focusedOptionIndex !== -1) {\n optionIndex = this.visibleOptions.slice(this.focusedOptionIndex).findIndex(function (option) {\n return _this12.isOptionMatched(option);\n });\n optionIndex = optionIndex === -1 ? this.visibleOptions.slice(0, this.focusedOptionIndex).findIndex(function (option) {\n return _this12.isOptionMatched(option);\n }) : optionIndex + this.focusedOptionIndex;\n } else {\n optionIndex = this.visibleOptions.findIndex(function (option) {\n return _this12.isOptionMatched(option);\n });\n }\n if (optionIndex !== -1) {\n matched = true;\n }\n if (optionIndex === -1 && this.focusedOptionIndex === -1) {\n optionIndex = this.findFirstFocusedOptionIndex();\n }\n if (optionIndex !== -1) {\n this.changeFocusedOptionIndex(event, optionIndex);\n }\n }\n if (this.searchTimeout) {\n clearTimeout(this.searchTimeout);\n }\n this.searchTimeout = setTimeout(function () {\n _this12.searchValue = '';\n _this12.searchTimeout = null;\n }, 500);\n return matched;\n },\n changeFocusedOptionIndex: function changeFocusedOptionIndex(event, index) {\n if (this.focusedOptionIndex !== index) {\n this.focusedOptionIndex = index;\n this.scrollInView();\n if (this.selectOnFocus) {\n this.onOptionSelect(event, this.visibleOptions[index], false);\n }\n }\n },\n scrollInView: function scrollInView() {\n var _this13 = this;\n var index = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : -1;\n this.$nextTick(function () {\n var id = index !== -1 ? \"\".concat(_this13.id, \"_\").concat(index) : _this13.focusedOptionId;\n var element = findSingle(_this13.list, \"li[id=\\\"\".concat(id, \"\\\"]\"));\n if (element) {\n element.scrollIntoView && element.scrollIntoView({\n block: 'nearest',\n inline: 'start'\n });\n } else if (!_this13.virtualScrollerDisabled) {\n _this13.virtualScroller && _this13.virtualScroller.scrollToIndex(index !== -1 ? index : _this13.focusedOptionIndex);\n }\n });\n },\n autoUpdateModel: function autoUpdateModel() {\n if (this.selectOnFocus && this.autoOptionFocus && !this.hasSelectedOption) {\n this.focusedOptionIndex = this.findFirstFocusedOptionIndex();\n this.onOptionSelect(null, this.visibleOptions[this.focusedOptionIndex], false);\n }\n },\n updateModel: function updateModel(event, value) {\n this.$emit('update:modelValue', value);\n this.$emit('change', {\n originalEvent: event,\n value: value\n });\n },\n flatOptions: function flatOptions(options) {\n var _this14 = this;\n return (options || []).reduce(function (result, option, index) {\n result.push({\n optionGroup: option,\n group: true,\n index: index\n });\n var optionGroupChildren = _this14.getOptionGroupChildren(option);\n optionGroupChildren && optionGroupChildren.forEach(function (o) {\n return result.push(o);\n });\n return result;\n }, []);\n },\n overlayRef: function overlayRef(el) {\n this.overlay = el;\n },\n listRef: function listRef(el, contentRef) {\n this.list = el;\n contentRef && contentRef(el); // For VirtualScroller\n },\n virtualScrollerRef: function virtualScrollerRef(el) {\n this.virtualScroller = el;\n }\n },\n computed: {\n visibleOptions: function visibleOptions() {\n var _this15 = this;\n var options = this.optionGroupLabel ? this.flatOptions(this.options) : this.options || [];\n if (this.filterValue) {\n var filteredOptions = FilterService.filter(options, this.searchFields, this.filterValue, this.filterMatchMode, this.filterLocale);\n if (this.optionGroupLabel) {\n var optionGroups = this.options || [];\n var filtered = [];\n optionGroups.forEach(function (group) {\n var groupChildren = _this15.getOptionGroupChildren(group);\n var filteredItems = groupChildren.filter(function (item) {\n return filteredOptions.includes(item);\n });\n if (filteredItems.length > 0) filtered.push(_objectSpread(_objectSpread({}, group), {}, _defineProperty({}, typeof _this15.optionGroupChildren === 'string' ? _this15.optionGroupChildren : 'items', _toConsumableArray(filteredItems))));\n });\n return this.flatOptions(filtered);\n }\n return filteredOptions;\n }\n return options;\n },\n hasSelectedOption: function hasSelectedOption() {\n return isNotEmpty(this.modelValue);\n },\n label: function label() {\n var selectedOptionIndex = this.findSelectedOptionIndex();\n return selectedOptionIndex !== -1 ? this.getOptionLabel(this.visibleOptions[selectedOptionIndex]) : this.placeholder || 'p-emptylabel';\n },\n editableInputValue: function editableInputValue() {\n var selectedOptionIndex = this.findSelectedOptionIndex();\n return selectedOptionIndex !== -1 ? this.getOptionLabel(this.visibleOptions[selectedOptionIndex]) : this.modelValue || '';\n },\n equalityKey: function equalityKey() {\n return this.optionValue ? null : this.dataKey;\n },\n searchFields: function searchFields() {\n return this.filterFields || [this.optionLabel];\n },\n filterResultMessageText: function filterResultMessageText() {\n return isNotEmpty(this.visibleOptions) ? this.filterMessageText.replaceAll('{0}', this.visibleOptions.length) : this.emptyFilterMessageText;\n },\n filterMessageText: function filterMessageText() {\n return this.filterMessage || this.$primevue.config.locale.searchMessage || '';\n },\n emptyFilterMessageText: function emptyFilterMessageText() {\n return this.emptyFilterMessage || this.$primevue.config.locale.emptySearchMessage || this.$primevue.config.locale.emptyFilterMessage || '';\n },\n emptyMessageText: function emptyMessageText() {\n return this.emptyMessage || this.$primevue.config.locale.emptyMessage || '';\n },\n selectionMessageText: function selectionMessageText() {\n return this.selectionMessage || this.$primevue.config.locale.selectionMessage || '';\n },\n emptySelectionMessageText: function emptySelectionMessageText() {\n return this.emptySelectionMessage || this.$primevue.config.locale.emptySelectionMessage || '';\n },\n selectedMessageText: function selectedMessageText() {\n return this.hasSelectedOption ? this.selectionMessageText.replaceAll('{0}', '1') : this.emptySelectionMessageText;\n },\n focusedOptionId: function focusedOptionId() {\n return this.focusedOptionIndex !== -1 ? \"\".concat(this.id, \"_\").concat(this.focusedOptionIndex) : null;\n },\n ariaSetSize: function ariaSetSize() {\n var _this16 = this;\n return this.visibleOptions.filter(function (option) {\n return !_this16.isOptionGroup(option);\n }).length;\n },\n isClearIconVisible: function isClearIconVisible() {\n return this.showClear && this.modelValue != null && isNotEmpty(this.options);\n },\n virtualScrollerDisabled: function virtualScrollerDisabled() {\n return !this.virtualScrollerOptions;\n },\n hasFluid: function hasFluid() {\n return isEmpty(this.fluid) ? !!this.$pcFluid : this.fluid;\n }\n },\n directives: {\n ripple: Ripple\n },\n components: {\n InputText: InputText,\n VirtualScroller: VirtualScroller,\n Portal: Portal,\n InputIcon: InputIcon,\n IconField: IconField,\n TimesIcon: TimesIcon,\n ChevronDownIcon: ChevronDownIcon,\n SpinnerIcon: SpinnerIcon,\n SearchIcon: SearchIcon,\n CheckIcon: CheckIcon,\n BlankIcon: BlankIcon\n }\n};\n\nvar _hoisted_1 = [\"id\"];\nvar _hoisted_2 = [\"id\", \"value\", \"placeholder\", \"tabindex\", \"disabled\", \"aria-label\", \"aria-labelledby\", \"aria-expanded\", \"aria-controls\", \"aria-activedescendant\", \"aria-invalid\"];\nvar _hoisted_3 = [\"id\", \"tabindex\", \"aria-label\", \"aria-labelledby\", \"aria-expanded\", \"aria-controls\", \"aria-activedescendant\", \"aria-disabled\"];\nvar _hoisted_4 = [\"id\"];\nvar _hoisted_5 = [\"id\"];\nvar _hoisted_6 = [\"id\", \"aria-label\", \"aria-selected\", \"aria-disabled\", \"aria-setsize\", \"aria-posinset\", \"onClick\", \"onMousemove\", \"data-p-selected\", \"data-p-focused\", \"data-p-disabled\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_SpinnerIcon = resolveComponent(\"SpinnerIcon\");\n var _component_InputText = resolveComponent(\"InputText\");\n var _component_SearchIcon = resolveComponent(\"SearchIcon\");\n var _component_InputIcon = resolveComponent(\"InputIcon\");\n var _component_IconField = resolveComponent(\"IconField\");\n var _component_CheckIcon = resolveComponent(\"CheckIcon\");\n var _component_BlankIcon = resolveComponent(\"BlankIcon\");\n var _component_VirtualScroller = resolveComponent(\"VirtualScroller\");\n var _component_Portal = resolveComponent(\"Portal\");\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n ref: \"container\",\n id: $data.id,\n \"class\": _ctx.cx('root'),\n onClick: _cache[11] || (_cache[11] = function () {\n return $options.onContainerClick && $options.onContainerClick.apply($options, arguments);\n })\n }, _ctx.ptmi('root')), [_ctx.editable ? (openBlock(), createElementBlock(\"input\", mergeProps({\n key: 0,\n ref: \"focusInput\",\n id: _ctx.labelId || _ctx.inputId,\n type: \"text\",\n \"class\": [_ctx.cx('label'), _ctx.inputClass, _ctx.labelClass],\n style: [_ctx.inputStyle, _ctx.labelStyle],\n value: $options.editableInputValue,\n placeholder: _ctx.placeholder,\n tabindex: !_ctx.disabled ? _ctx.tabindex : -1,\n disabled: _ctx.disabled,\n autocomplete: \"off\",\n role: \"combobox\",\n \"aria-label\": _ctx.ariaLabel,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-haspopup\": \"listbox\",\n \"aria-expanded\": $data.overlayVisible,\n \"aria-controls\": $data.id + '_list',\n \"aria-activedescendant\": $data.focused ? $options.focusedOptionId : undefined,\n \"aria-invalid\": _ctx.invalid || undefined,\n onFocus: _cache[0] || (_cache[0] = function () {\n return $options.onFocus && $options.onFocus.apply($options, arguments);\n }),\n onBlur: _cache[1] || (_cache[1] = function () {\n return $options.onBlur && $options.onBlur.apply($options, arguments);\n }),\n onKeydown: _cache[2] || (_cache[2] = function () {\n return $options.onKeyDown && $options.onKeyDown.apply($options, arguments);\n }),\n onInput: _cache[3] || (_cache[3] = function () {\n return $options.onEditableInput && $options.onEditableInput.apply($options, arguments);\n })\n }, _ctx.ptm('label')), null, 16, _hoisted_2)) : (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 1,\n ref: \"focusInput\",\n id: _ctx.labelId || _ctx.inputId,\n \"class\": [_ctx.cx('label'), _ctx.inputClass, _ctx.labelClass],\n style: [_ctx.inputStyle, _ctx.labelStyle],\n tabindex: !_ctx.disabled ? _ctx.tabindex : -1,\n role: \"combobox\",\n \"aria-label\": _ctx.ariaLabel || ($options.label === 'p-emptylabel' ? undefined : $options.label),\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-haspopup\": \"listbox\",\n \"aria-expanded\": $data.overlayVisible,\n \"aria-controls\": $data.id + '_list',\n \"aria-activedescendant\": $data.focused ? $options.focusedOptionId : undefined,\n \"aria-disabled\": _ctx.disabled,\n onFocus: _cache[4] || (_cache[4] = function () {\n return $options.onFocus && $options.onFocus.apply($options, arguments);\n }),\n onBlur: _cache[5] || (_cache[5] = function () {\n return $options.onBlur && $options.onBlur.apply($options, arguments);\n }),\n onKeydown: _cache[6] || (_cache[6] = function () {\n return $options.onKeyDown && $options.onKeyDown.apply($options, arguments);\n })\n }, _ctx.ptm('label')), [renderSlot(_ctx.$slots, \"value\", {\n value: _ctx.modelValue,\n placeholder: _ctx.placeholder\n }, function () {\n return [createTextVNode(toDisplayString($options.label === 'p-emptylabel' ? ' ' : $options.label || 'empty'), 1)];\n })], 16, _hoisted_3)), $options.isClearIconVisible ? renderSlot(_ctx.$slots, \"clearicon\", {\n key: 2,\n \"class\": normalizeClass(_ctx.cx('clearIcon')),\n clearCallback: $options.onClearClick\n }, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.clearIcon ? 'i' : 'TimesIcon'), mergeProps({\n ref: \"clearIcon\",\n \"class\": [_ctx.cx('clearIcon'), _ctx.clearIcon],\n onClick: $options.onClearClick\n }, _ctx.ptm('clearIcon'), {\n \"data-pc-section\": \"clearicon\"\n }), null, 16, [\"class\", \"onClick\"]))];\n }) : createCommentVNode(\"\", true), createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('dropdown')\n }, _ctx.ptm('dropdown')), [_ctx.loading ? renderSlot(_ctx.$slots, \"loadingicon\", {\n key: 0,\n \"class\": normalizeClass(_ctx.cx('loadingIcon'))\n }, function () {\n return [_ctx.loadingIcon ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 0,\n \"class\": [_ctx.cx('loadingIcon'), 'pi-spin', _ctx.loadingIcon],\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('loadingIcon')), null, 16)) : (openBlock(), createBlock(_component_SpinnerIcon, mergeProps({\n key: 1,\n \"class\": _ctx.cx('loadingIcon'),\n spin: \"\",\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('loadingIcon')), null, 16, [\"class\"]))];\n }) : renderSlot(_ctx.$slots, \"dropdownicon\", {\n key: 1,\n \"class\": normalizeClass(_ctx.cx('dropdownIcon'))\n }, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.dropdownIcon ? 'span' : 'ChevronDownIcon'), mergeProps({\n \"class\": [_ctx.cx('dropdownIcon'), _ctx.dropdownIcon],\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('dropdownIcon')), null, 16, [\"class\"]))];\n })], 16), createVNode(_component_Portal, {\n appendTo: _ctx.appendTo\n }, {\n \"default\": withCtx(function () {\n return [createVNode(Transition, mergeProps({\n name: \"p-connected-overlay\",\n onEnter: $options.onOverlayEnter,\n onAfterEnter: $options.onOverlayAfterEnter,\n onLeave: $options.onOverlayLeave,\n onAfterLeave: $options.onOverlayAfterLeave\n }, _ctx.ptm('transition')), {\n \"default\": withCtx(function () {\n return [$data.overlayVisible ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n ref: $options.overlayRef,\n \"class\": [_ctx.cx('overlay'), _ctx.panelClass, _ctx.overlayClass],\n style: [_ctx.panelStyle, _ctx.overlayStyle],\n onClick: _cache[9] || (_cache[9] = function () {\n return $options.onOverlayClick && $options.onOverlayClick.apply($options, arguments);\n }),\n onKeydown: _cache[10] || (_cache[10] = function () {\n return $options.onOverlayKeyDown && $options.onOverlayKeyDown.apply($options, arguments);\n })\n }, _ctx.ptm('overlay')), [createElementVNode(\"span\", mergeProps({\n ref: \"firstHiddenFocusableElementOnOverlay\",\n role: \"presentation\",\n \"aria-hidden\": \"true\",\n \"class\": \"p-hidden-accessible p-hidden-focusable\",\n tabindex: 0,\n onFocus: _cache[7] || (_cache[7] = function () {\n return $options.onFirstHiddenFocus && $options.onFirstHiddenFocus.apply($options, arguments);\n })\n }, _ctx.ptm('hiddenFirstFocusableEl'), {\n \"data-p-hidden-accessible\": true,\n \"data-p-hidden-focusable\": true\n }), null, 16), renderSlot(_ctx.$slots, \"header\", {\n value: _ctx.modelValue,\n options: $options.visibleOptions\n }), _ctx.filter ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('header')\n }, _ctx.ptm('header')), [createVNode(_component_IconField, {\n unstyled: _ctx.unstyled,\n pt: _ctx.ptm('pcFilterContainer')\n }, {\n \"default\": withCtx(function () {\n return [createVNode(_component_InputText, {\n ref: \"filterInput\",\n type: \"text\",\n value: $data.filterValue,\n onVnodeMounted: $options.onFilterUpdated,\n onVnodeUpdated: $options.onFilterUpdated,\n \"class\": normalizeClass(_ctx.cx('pcFilter')),\n placeholder: _ctx.filterPlaceholder,\n variant: _ctx.variant,\n unstyled: _ctx.unstyled,\n role: \"searchbox\",\n autocomplete: \"off\",\n \"aria-owns\": $data.id + '_list',\n \"aria-activedescendant\": $options.focusedOptionId,\n onKeydown: $options.onFilterKeyDown,\n onBlur: $options.onFilterBlur,\n onInput: $options.onFilterChange,\n pt: _ctx.ptm('pcFilter')\n }, null, 8, [\"value\", \"onVnodeMounted\", \"onVnodeUpdated\", \"class\", \"placeholder\", \"variant\", \"unstyled\", \"aria-owns\", \"aria-activedescendant\", \"onKeydown\", \"onBlur\", \"onInput\", \"pt\"]), createVNode(_component_InputIcon, mergeProps({\n unstyled: _ctx.unstyled\n }, _ctx.ptm('pcFilterIconContainer')), {\n \"default\": withCtx(function () {\n return [renderSlot(_ctx.$slots, \"filtericon\", {}, function () {\n return [_ctx.filterIcon ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 0,\n \"class\": _ctx.filterIcon\n }, _ctx.ptm('filterIcon')), null, 16)) : (openBlock(), createBlock(_component_SearchIcon, normalizeProps(mergeProps({\n key: 1\n }, _ctx.ptm('filterIcon'))), null, 16))];\n })];\n }),\n _: 3\n }, 16, [\"unstyled\"])];\n }),\n _: 3\n }, 8, [\"unstyled\", \"pt\"]), createElementVNode(\"span\", mergeProps({\n role: \"status\",\n \"aria-live\": \"polite\",\n \"class\": \"p-hidden-accessible\"\n }, _ctx.ptm('hiddenFilterResult'), {\n \"data-p-hidden-accessible\": true\n }), toDisplayString($options.filterResultMessageText), 17)], 16)) : createCommentVNode(\"\", true), createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('listContainer'),\n style: {\n 'max-height': $options.virtualScrollerDisabled ? _ctx.scrollHeight : ''\n }\n }, _ctx.ptm('listContainer')), [createVNode(_component_VirtualScroller, mergeProps({\n ref: $options.virtualScrollerRef\n }, _ctx.virtualScrollerOptions, {\n items: $options.visibleOptions,\n style: {\n height: _ctx.scrollHeight\n },\n tabindex: -1,\n disabled: $options.virtualScrollerDisabled,\n pt: _ctx.ptm('virtualScroller')\n }), createSlots({\n content: withCtx(function (_ref) {\n var styleClass = _ref.styleClass,\n contentRef = _ref.contentRef,\n items = _ref.items,\n getItemOptions = _ref.getItemOptions,\n contentStyle = _ref.contentStyle,\n itemSize = _ref.itemSize;\n return [createElementVNode(\"ul\", mergeProps({\n ref: function ref(el) {\n return $options.listRef(el, contentRef);\n },\n id: $data.id + '_list',\n \"class\": [_ctx.cx('list'), styleClass],\n style: contentStyle,\n role: \"listbox\"\n }, _ctx.ptm('list')), [(openBlock(true), createElementBlock(Fragment, null, renderList(items, function (option, i) {\n return openBlock(), createElementBlock(Fragment, {\n key: $options.getOptionRenderKey(option, $options.getOptionIndex(i, getItemOptions))\n }, [$options.isOptionGroup(option) ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 0,\n id: $data.id + '_' + $options.getOptionIndex(i, getItemOptions),\n style: {\n height: itemSize ? itemSize + 'px' : undefined\n },\n \"class\": _ctx.cx('optionGroup'),\n role: \"option\",\n ref_for: true\n }, _ctx.ptm('optionGroup')), [renderSlot(_ctx.$slots, \"optiongroup\", {\n option: option.optionGroup,\n index: $options.getOptionIndex(i, getItemOptions)\n }, function () {\n return [createElementVNode(\"span\", mergeProps({\n \"class\": _ctx.cx('optionGroupLabel'),\n ref_for: true\n }, _ctx.ptm('optionGroupLabel')), toDisplayString($options.getOptionGroupLabel(option.optionGroup)), 17)];\n })], 16, _hoisted_5)) : withDirectives((openBlock(), createElementBlock(\"li\", mergeProps({\n key: 1,\n id: $data.id + '_' + $options.getOptionIndex(i, getItemOptions),\n \"class\": _ctx.cx('option', {\n option: option,\n focusedOption: $options.getOptionIndex(i, getItemOptions)\n }),\n style: {\n height: itemSize ? itemSize + 'px' : undefined\n },\n role: \"option\",\n \"aria-label\": $options.getOptionLabel(option),\n \"aria-selected\": $options.isSelected(option),\n \"aria-disabled\": $options.isOptionDisabled(option),\n \"aria-setsize\": $options.ariaSetSize,\n \"aria-posinset\": $options.getAriaPosInset($options.getOptionIndex(i, getItemOptions)),\n onClick: function onClick($event) {\n return $options.onOptionSelect($event, option);\n },\n onMousemove: function onMousemove($event) {\n return $options.onOptionMouseMove($event, $options.getOptionIndex(i, getItemOptions));\n },\n \"data-p-selected\": $options.isSelected(option),\n \"data-p-focused\": $data.focusedOptionIndex === $options.getOptionIndex(i, getItemOptions),\n \"data-p-disabled\": $options.isOptionDisabled(option),\n ref_for: true\n }, $options.getPTItemOptions(option, getItemOptions, i, 'option')), [_ctx.checkmark ? (openBlock(), createElementBlock(Fragment, {\n key: 0\n }, [$options.isSelected(option) ? (openBlock(), createBlock(_component_CheckIcon, mergeProps({\n key: 0,\n \"class\": _ctx.cx('optionCheckIcon'),\n ref_for: true\n }, _ctx.ptm('optionCheckIcon')), null, 16, [\"class\"])) : (openBlock(), createBlock(_component_BlankIcon, mergeProps({\n key: 1,\n \"class\": _ctx.cx('optionBlankIcon'),\n ref_for: true\n }, _ctx.ptm('optionBlankIcon')), null, 16, [\"class\"]))], 64)) : createCommentVNode(\"\", true), renderSlot(_ctx.$slots, \"option\", {\n option: option,\n selected: $options.isSelected(option),\n index: $options.getOptionIndex(i, getItemOptions)\n }, function () {\n return [createElementVNode(\"span\", mergeProps({\n \"class\": _ctx.cx('optionLabel'),\n ref_for: true\n }, _ctx.ptm('optionLabel')), toDisplayString($options.getOptionLabel(option)), 17)];\n })], 16, _hoisted_6)), [[_directive_ripple]])], 64);\n }), 128)), $data.filterValue && (!items || items && items.length === 0) ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('emptyMessage'),\n role: \"option\"\n }, _ctx.ptm('emptyMessage'), {\n \"data-p-hidden-accessible\": true\n }), [renderSlot(_ctx.$slots, \"emptyfilter\", {}, function () {\n return [createTextVNode(toDisplayString($options.emptyFilterMessageText), 1)];\n })], 16)) : !_ctx.options || _ctx.options && _ctx.options.length === 0 ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 1,\n \"class\": _ctx.cx('emptyMessage'),\n role: \"option\"\n }, _ctx.ptm('emptyMessage'), {\n \"data-p-hidden-accessible\": true\n }), [renderSlot(_ctx.$slots, \"empty\", {}, function () {\n return [createTextVNode(toDisplayString($options.emptyMessageText), 1)];\n })], 16)) : createCommentVNode(\"\", true)], 16, _hoisted_4)];\n }),\n _: 2\n }, [_ctx.$slots.loader ? {\n name: \"loader\",\n fn: withCtx(function (_ref2) {\n var options = _ref2.options;\n return [renderSlot(_ctx.$slots, \"loader\", {\n options: options\n })];\n }),\n key: \"0\"\n } : undefined]), 1040, [\"items\", \"style\", \"disabled\", \"pt\"])], 16), renderSlot(_ctx.$slots, \"footer\", {\n value: _ctx.modelValue,\n options: $options.visibleOptions\n }), !_ctx.options || _ctx.options && _ctx.options.length === 0 ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 1,\n role: \"status\",\n \"aria-live\": \"polite\",\n \"class\": \"p-hidden-accessible\"\n }, _ctx.ptm('hiddenEmptyMessage'), {\n \"data-p-hidden-accessible\": true\n }), toDisplayString($options.emptyMessageText), 17)) : createCommentVNode(\"\", true), createElementVNode(\"span\", mergeProps({\n role: \"status\",\n \"aria-live\": \"polite\",\n \"class\": \"p-hidden-accessible\"\n }, _ctx.ptm('hiddenSelectedMessage'), {\n \"data-p-hidden-accessible\": true\n }), toDisplayString($options.selectedMessageText), 17), createElementVNode(\"span\", mergeProps({\n ref: \"lastHiddenFocusableElementOnOverlay\",\n role: \"presentation\",\n \"aria-hidden\": \"true\",\n \"class\": \"p-hidden-accessible p-hidden-focusable\",\n tabindex: 0,\n onFocus: _cache[8] || (_cache[8] = function () {\n return $options.onLastHiddenFocus && $options.onLastHiddenFocus.apply($options, arguments);\n })\n }, _ctx.ptm('hiddenLastFocusableEl'), {\n \"data-p-hidden-accessible\": true,\n \"data-p-hidden-focusable\": true\n }), null, 16)], 16)) : createCommentVNode(\"\", true)];\n }),\n _: 3\n }, 16, [\"onEnter\", \"onAfterEnter\", \"onLeave\", \"onAfterLeave\"])];\n }),\n _: 3\n }, 8, [\"appendTo\"])], 16, _hoisted_1);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n\n\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-tabs {\\n display: flex;\\n flex-direction: column;\\n}\\n\\n.p-tablist {\\n display: flex;\\n position: relative;\\n}\\n\\n.p-tabs-scrollable > .p-tablist {\\n overflow: hidden;\\n}\\n\\n.p-tablist-viewport {\\n overflow-x: auto;\\n overflow-y: hidden;\\n scroll-behavior: smooth;\\n scrollbar-width: none;\\n overscroll-behavior: contain auto;\\n}\\n\\n.p-tablist-viewport::-webkit-scrollbar {\\n display: none;\\n}\\n\\n.p-tablist-tab-list {\\n position: relative;\\n display: flex;\\n background: \".concat(dt('tabs.tablist.background'), \";\\n border-style: solid;\\n border-color: \").concat(dt('tabs.tablist.border.color'), \";\\n border-width: \").concat(dt('tabs.tablist.border.width'), \";\\n}\\n\\n.p-tablist-content {\\n flex-grow: 1;\\n}\\n\\n.p-tablist-nav-button {\\n all: unset;\\n position: absolute !important;\\n flex-shrink: 0;\\n top: 0;\\n z-index: 2;\\n height: 100%;\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n background: \").concat(dt('tabs.nav.button.background'), \";\\n color: \").concat(dt('tabs.nav.button.color'), \";\\n width: \").concat(dt('tabs.nav.button.width'), \";\\n transition: color \").concat(dt('tabs.transition.duration'), \", outline-color \").concat(dt('tabs.transition.duration'), \", box-shadow \").concat(dt('tabs.transition.duration'), \";\\n box-shadow: \").concat(dt('tabs.nav.button.shadow'), \";\\n outline-color: transparent;\\n cursor: pointer;\\n}\\n\\n.p-tablist-nav-button:focus-visible {\\n z-index: 1;\\n box-shadow: \").concat(dt('tabs.nav.button.focus.ring.shadow'), \";\\n outline: \").concat(dt('tabs.nav.button.focus.ring.width'), \" \").concat(dt('tabs.nav.button.focus.ring.style'), \" \").concat(dt('tabs.nav.button.focus.ring.color'), \";\\n outline-offset: \").concat(dt('tabs.nav.button.focus.ring.offset'), \";\\n}\\n\\n.p-tablist-nav-button:hover {\\n color: \").concat(dt('tabs.nav.button.hover.color'), \";\\n}\\n\\n.p-tablist-prev-button {\\n left: 0;\\n}\\n\\n.p-tablist-next-button {\\n right: 0;\\n}\\n\\n.p-tab {\\n flex-shrink: 0;\\n cursor: pointer;\\n user-select: none;\\n position: relative;\\n border-style: solid;\\n white-space: nowrap;\\n background: \").concat(dt('tabs.tab.background'), \";\\n border-width: \").concat(dt('tabs.tab.border.width'), \";\\n border-color: \").concat(dt('tabs.tab.border.color'), \";\\n color: \").concat(dt('tabs.tab.color'), \";\\n padding: \").concat(dt('tabs.tab.padding'), \";\\n font-weight: \").concat(dt('tabs.tab.font.weight'), \";\\n transition: background \").concat(dt('tabs.transition.duration'), \", border-color \").concat(dt('tabs.transition.duration'), \", color \").concat(dt('tabs.transition.duration'), \", outline-color \").concat(dt('tabs.transition.duration'), \", box-shadow \").concat(dt('tabs.transition.duration'), \";\\n margin: \").concat(dt('tabs.tab.margin'), \";\\n outline-color: transparent;\\n}\\n\\n.p-tab:not(.p-disabled):focus-visible {\\n z-index: 1;\\n box-shadow: \").concat(dt('tabs.tab.focus.ring.shadow'), \";\\n outline: \").concat(dt('tabs.tab.focus.ring.width'), \" \").concat(dt('tabs.tab.focus.ring.style'), \" \").concat(dt('tabs.tab.focus.ring.color'), \";\\n outline-offset: \").concat(dt('tabs.tab.focus.ring.offset'), \";\\n}\\n\\n.p-tab:not(.p-tab-active):not(.p-disabled):hover {\\n background: \").concat(dt('tabs.tab.hover.background'), \";\\n border-color: \").concat(dt('tabs.tab.hover.border.color'), \";\\n color: \").concat(dt('tabs.tab.hover.color'), \";\\n}\\n\\n.p-tab-active {\\n background: \").concat(dt('tabs.tab.active.background'), \";\\n border-color: \").concat(dt('tabs.tab.active.border.color'), \";\\n color: \").concat(dt('tabs.tab.active.color'), \";\\n}\\n\\n.p-tabpanels {\\n background: \").concat(dt('tabs.tabpanel.background'), \";\\n color: \").concat(dt('tabs.tabpanel.color'), \";\\n padding: \").concat(dt('tabs.tabpanel.padding'), \";\\n outline: 0 none;\\n}\\n\\n.p-tabpanel:focus-visible {\\n box-shadow: \").concat(dt('tabs.tabpanel.focus.ring.shadow'), \";\\n outline: \").concat(dt('tabs.tabpanel.focus.ring.width'), \" \").concat(dt('tabs.tabpanel.focus.ring.style'), \" \").concat(dt('tabs.tabpanel.focus.ring.color'), \";\\n outline-offset: \").concat(dt('tabs.tabpanel.focus.ring.offset'), \";\\n}\\n\\n.p-tablist-active-bar {\\n z-index: 1;\\n display: block;\\n position: absolute;\\n bottom: \").concat(dt('tabs.active.bar.bottom'), \";\\n height: \").concat(dt('tabs.active.bar.height'), \";\\n background: \").concat(dt('tabs.active.bar.background'), \";\\n transition: 250ms cubic-bezier(0.35, 0, 0.25, 1);\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var props = _ref2.props;\n return ['p-tabs p-component', {\n 'p-tabs-scrollable': props.scrollable\n }];\n }\n};\nvar TabsStyle = BaseStyle.extend({\n name: 'tabs',\n theme: theme,\n classes: classes\n});\n\nexport { TabsStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { UniqueComponentId } from '@primevue/core/utils';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport TabsStyle from 'primevue/tabs/style';\nimport { openBlock, createElementBlock, mergeProps, renderSlot } from 'vue';\n\nvar script$1 = {\n name: 'BaseTabs',\n \"extends\": BaseComponent,\n props: {\n value: {\n type: [String, Number],\n \"default\": undefined\n },\n lazy: {\n type: Boolean,\n \"default\": false\n },\n scrollable: {\n type: Boolean,\n \"default\": false\n },\n showNavigators: {\n type: Boolean,\n \"default\": true\n },\n tabindex: {\n type: Number,\n \"default\": 0\n },\n selectOnFocus: {\n type: Boolean,\n \"default\": false\n }\n },\n style: TabsStyle,\n provide: function provide() {\n return {\n $pcTabs: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'Tabs',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:value'],\n data: function data() {\n return {\n id: this.$attrs.id,\n d_value: this.value\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n },\n value: function value(newValue) {\n this.d_value = newValue;\n }\n },\n mounted: function mounted() {\n this.id = this.id || UniqueComponentId();\n },\n methods: {\n updateValue: function updateValue(newValue) {\n if (this.d_value !== newValue) {\n this.d_value = newValue;\n this.$emit('update:value', newValue);\n }\n },\n isVertical: function isVertical() {\n return this.orientation === 'vertical';\n }\n }\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [renderSlot(_ctx.$slots, \"default\")], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar classes = {\n root: 'p-tabpanels'\n};\nvar TabPanelsStyle = BaseStyle.extend({\n name: 'tabpanels',\n classes: classes\n});\n\nexport { TabPanelsStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseComponent from '@primevue/core/basecomponent';\nimport TabPanelsStyle from 'primevue/tabpanels/style';\nimport { openBlock, createElementBlock, mergeProps, renderSlot } from 'vue';\n\nvar script$1 = {\n name: 'BaseTabPanels',\n \"extends\": BaseComponent,\n props: {},\n style: TabPanelsStyle,\n provide: function provide() {\n return {\n $pcTabPanels: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'TabPanels',\n \"extends\": script$1,\n inheritAttrs: false\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root'),\n role: \"presentation\"\n }, _ctx.ptmi('root')), [renderSlot(_ctx.$slots, \"default\")], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar classes = {\n root: function root(_ref) {\n var instance = _ref.instance;\n return ['p-tabpanel', {\n 'p-tabpanel-active': instance.active\n }];\n }\n};\nvar TabPanelStyle = BaseStyle.extend({\n name: 'tabpanel',\n classes: classes\n});\n\nexport { TabPanelStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { equals } from '@primeuix/utils/object';\nimport { mergeProps, renderSlot, openBlock, createElementBlock, Fragment, withDirectives, createBlock, resolveDynamicComponent, withCtx, vShow, createCommentVNode, normalizeClass } from 'vue';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport TabPanelStyle from 'primevue/tabpanel/style';\n\nvar script$1 = {\n name: 'BaseTabPanel',\n \"extends\": BaseComponent,\n props: {\n // in Tabs\n value: {\n type: [String, Number],\n \"default\": undefined\n },\n as: {\n type: [String, Object],\n \"default\": 'DIV'\n },\n asChild: {\n type: Boolean,\n \"default\": false\n },\n // in TabView\n header: null,\n headerStyle: null,\n headerClass: null,\n headerProps: null,\n headerActionProps: null,\n contentStyle: null,\n contentClass: null,\n contentProps: null,\n disabled: Boolean\n },\n style: TabPanelStyle,\n provide: function provide() {\n return {\n $pcTabPanel: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'TabPanel',\n \"extends\": script$1,\n inheritAttrs: false,\n inject: ['$pcTabs'],\n computed: {\n active: function active() {\n var _this$$pcTabs;\n return equals((_this$$pcTabs = this.$pcTabs) === null || _this$$pcTabs === void 0 ? void 0 : _this$$pcTabs.d_value, this.value);\n },\n id: function id() {\n var _this$$pcTabs2;\n return \"\".concat((_this$$pcTabs2 = this.$pcTabs) === null || _this$$pcTabs2 === void 0 ? void 0 : _this$$pcTabs2.id, \"_tabpanel_\").concat(this.value);\n },\n ariaLabelledby: function ariaLabelledby() {\n var _this$$pcTabs3;\n return \"\".concat((_this$$pcTabs3 = this.$pcTabs) === null || _this$$pcTabs3 === void 0 ? void 0 : _this$$pcTabs3.id, \"_tab_\").concat(this.value);\n },\n attrs: function attrs() {\n return mergeProps(this.a11yAttrs, this.ptmi('root', this.ptParams));\n },\n a11yAttrs: function a11yAttrs() {\n var _this$$pcTabs4;\n return {\n id: this.id,\n tabindex: (_this$$pcTabs4 = this.$pcTabs) === null || _this$$pcTabs4 === void 0 ? void 0 : _this$$pcTabs4.tabindex,\n role: 'tabpanel',\n 'aria-labelledby': this.ariaLabelledby,\n 'data-pc-name': 'tabpanel',\n 'data-p-active': this.active\n };\n },\n ptParams: function ptParams() {\n return {\n context: {\n active: this.active\n }\n };\n }\n }\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _$options$$pcTabs, _$options$$pcTabs2;\n return !$options.$pcTabs ? renderSlot(_ctx.$slots, \"default\", {\n key: 0\n }) : (openBlock(), createElementBlock(Fragment, {\n key: 1\n }, [!_ctx.asChild ? (openBlock(), createElementBlock(Fragment, {\n key: 0\n }, [((_$options$$pcTabs = $options.$pcTabs) !== null && _$options$$pcTabs !== void 0 && _$options$$pcTabs.lazy ? $options.active : true) ? withDirectives((openBlock(), createBlock(resolveDynamicComponent(_ctx.as), mergeProps({\n key: 0,\n \"class\": _ctx.cx('root')\n }, $options.attrs), {\n \"default\": withCtx(function () {\n return [renderSlot(_ctx.$slots, \"default\")];\n }),\n _: 3\n }, 16, [\"class\"])), [[vShow, (_$options$$pcTabs2 = $options.$pcTabs) !== null && _$options$$pcTabs2 !== void 0 && _$options$$pcTabs2.lazy ? true : $options.active]]) : createCommentVNode(\"\", true)], 64)) : renderSlot(_ctx.$slots, \"default\", {\n key: 1,\n \"class\": normalizeClass(_ctx.cx('root')),\n active: $options.active,\n a11yAttrs: $options.a11yAttrs\n })], 64));\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-divider-horizontal {\\n display: flex;\\n width: 100%;\\n position: relative;\\n align-items: center;\\n margin: \".concat(dt('divider.horizontal.margin'), \";\\n padding: \").concat(dt('divider.horizontal.padding'), \";\\n}\\n\\n.p-divider-horizontal:before {\\n position: absolute;\\n display: block;\\n top: 50%;\\n left: 0;\\n width: 100%;\\n content: \\\"\\\";\\n border-top: 1px solid \").concat(dt('divider.border.color'), \";\\n}\\n\\n.p-divider-horizontal .p-divider-content {\\n padding: \").concat(dt('divider.horizontal.content.padding'), \";\\n}\\n\\n.p-divider-vertical {\\n min-height: 100%;\\n margin: 0 1rem;\\n display: flex;\\n position: relative;\\n justify-content: center;\\n margin: \").concat(dt('divider.vertical.margin'), \";\\n padding: \").concat(dt('divider.vertical.padding'), \";\\n}\\n\\n.p-divider-vertical:before {\\n position: absolute;\\n display: block;\\n top: 0;\\n left: 50%;\\n height: 100%;\\n content: \\\"\\\";\\n border-left: 1px solid \").concat(dt('divider.border.color'), \";\\n}\\n\\n.p-divider.p-divider-vertical .p-divider-content {\\n padding: \").concat(dt('divider.vertical.content.padding'), \";\\n}\\n\\n.p-divider-content {\\n z-index: 1;\\n background: \").concat(dt('divider.content.background'), \";\\n color: \").concat(dt('divider.content.color'), \";\\n}\\n\\n.p-divider-solid.p-divider-horizontal:before {\\n border-top-style: solid;\\n}\\n\\n.p-divider-solid.p-divider-vertical:before {\\n border-left-style: solid;\\n}\\n\\n.p-divider-dashed.p-divider-horizontal:before {\\n border-top-style: dashed;\\n}\\n\\n.p-divider-dashed.p-divider-vertical:before {\\n border-left-style: dashed;\\n}\\n\\n.p-divider-dotted.p-divider-horizontal:before {\\n border-top-style: dotted;\\n}\\n\\n.p-divider-dotted.p-divider-vertical:before {\\n border-left-style: dotted;\\n}\\n\");\n};\n\n/* Position */\nvar inlineStyles = {\n root: function root(_ref2) {\n var props = _ref2.props;\n return {\n justifyContent: props.layout === 'horizontal' ? props.align === 'center' || props.align === null ? 'center' : props.align === 'left' ? 'flex-start' : props.align === 'right' ? 'flex-end' : null : null,\n alignItems: props.layout === 'vertical' ? props.align === 'center' || props.align === null ? 'center' : props.align === 'top' ? 'flex-start' : props.align === 'bottom' ? 'flex-end' : null : null\n };\n }\n};\nvar classes = {\n root: function root(_ref3) {\n var props = _ref3.props;\n return ['p-divider p-component', 'p-divider-' + props.layout, 'p-divider-' + props.type, {\n 'p-divider-left': props.layout === 'horizontal' && (!props.align || props.align === 'left')\n }, {\n 'p-divider-center': props.layout === 'horizontal' && props.align === 'center'\n }, {\n 'p-divider-right': props.layout === 'horizontal' && props.align === 'right'\n }, {\n 'p-divider-top': props.layout === 'vertical' && props.align === 'top'\n }, {\n 'p-divider-center': props.layout === 'vertical' && (!props.align || props.align === 'center')\n }, {\n 'p-divider-bottom': props.layout === 'vertical' && props.align === 'bottom'\n }];\n },\n content: 'p-divider-content'\n};\nvar DividerStyle = BaseStyle.extend({\n name: 'divider',\n theme: theme,\n classes: classes,\n inlineStyles: inlineStyles\n});\n\nexport { DividerStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseComponent from '@primevue/core/basecomponent';\nimport DividerStyle from 'primevue/divider/style';\nimport { openBlock, createElementBlock, mergeProps, renderSlot, createCommentVNode } from 'vue';\n\nvar script$1 = {\n name: 'BaseDivider',\n \"extends\": BaseComponent,\n props: {\n align: {\n type: String,\n \"default\": null\n },\n layout: {\n type: String,\n \"default\": 'horizontal'\n },\n type: {\n type: String,\n \"default\": 'solid'\n }\n },\n style: DividerStyle,\n provide: function provide() {\n return {\n $pcDivider: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'Divider',\n \"extends\": script$1,\n inheritAttrs: false\n};\n\nvar _hoisted_1 = [\"aria-orientation\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root'),\n style: _ctx.sx('root'),\n role: \"separator\",\n \"aria-orientation\": _ctx.layout\n }, _ctx.ptmi('root')), [_ctx.$slots[\"default\"] ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('content')\n }, _ctx.ptm('content')), [renderSlot(_ctx.$slots, \"default\")], 16)) : createCommentVNode(\"\", true)], 16, _hoisted_1);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-scrollpanel-content-container {\\n overflow: hidden;\\n width: 100%;\\n height: 100%;\\n position: relative;\\n z-index: 1;\\n float: left;\\n}\\n\\n.p-scrollpanel-content {\\n height: calc(100% + calc(2 * \".concat(dt('scrollpanel.bar.size'), \"));\\n width: calc(100% + calc(2 * \").concat(dt('scrollpanel.bar.size'), \"));\\n padding: 0 calc(2 * \").concat(dt('scrollpanel.bar.size'), \") calc(2 * \").concat(dt('scrollpanel.bar.size'), \") 0;\\n position: relative;\\n overflow: auto;\\n box-sizing: border-box;\\n scrollbar-width: none;\\n}\\n\\n.p-scrollpanel-content::-webkit-scrollbar {\\n display: none;\\n}\\n\\n.p-scrollpanel-bar {\\n position: relative;\\n border-radius: \").concat(dt('scrollpanel.bar.border.radius'), \";\\n z-index: 2;\\n cursor: pointer;\\n opacity: 0;\\n outline-color: transparent;\\n transition: outline-color \").concat(dt('scrollpanel.transition.duration'), \";\\n background: \").concat(dt('scrollpanel.bar.background'), \";\\n border: 0 none;\\n transition: outline-color \").concat(dt('scrollpanel.transition.duration'), \", opacity \").concat(dt('scrollpanel.transition.duration'), \";\\n}\\n\\n.p-scrollpanel-bar:focus-visible {\\n box-shadow: \").concat(dt('scrollpanel.bar.focus.ring.shadow'), \";\\n outline: \").concat(dt('scrollpanel.barfocus.ring.width'), \" \").concat(dt('scrollpanel.bar.focus.ring.style'), \" \").concat(dt('scrollpanel.bar.focus.ring.color'), \";\\n outline-offset: \").concat(dt('scrollpanel.barfocus.ring.offset'), \";\\n}\\n\\n.p-scrollpanel-bar-y {\\n width: \").concat(dt('scrollpanel.bar.size'), \";\\n top: 0;\\n}\\n\\n.p-scrollpanel-bar-x {\\n height: \").concat(dt('scrollpanel.bar.size'), \";\\n bottom: 0;\\n}\\n\\n.p-scrollpanel-hidden {\\n visibility: hidden;\\n}\\n\\n.p-scrollpanel:hover .p-scrollpanel-bar,\\n.p-scrollpanel:active .p-scrollpanel-bar {\\n opacity: 1;\\n}\\n\\n.p-scrollpanel-grabbed {\\n user-select: none;\\n}\\n\");\n};\nvar classes = {\n root: 'p-scrollpanel p-component',\n contentContainer: 'p-scrollpanel-content-container',\n content: 'p-scrollpanel-content',\n barX: 'p-scrollpanel-bar p-scrollpanel-bar-x',\n barY: 'p-scrollpanel-bar p-scrollpanel-bar-y'\n};\nvar ScrollPanelStyle = BaseStyle.extend({\n name: 'scrollpanel',\n theme: theme,\n classes: classes\n});\n\nexport { ScrollPanelStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { UniqueComponentId } from '@primevue/core/utils';\nimport { getHeight, addClass, removeClass } from '@primeuix/utils/dom';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport ScrollPanelStyle from 'primevue/scrollpanel/style';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode, renderSlot } from 'vue';\n\nvar script$1 = {\n name: 'BaseScrollPanel',\n \"extends\": BaseComponent,\n props: {\n step: {\n type: Number,\n \"default\": 5\n }\n },\n style: ScrollPanelStyle,\n provide: function provide() {\n return {\n $pcScrollPanel: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'ScrollPanel',\n \"extends\": script$1,\n inheritAttrs: false,\n initialized: false,\n documentResizeListener: null,\n documentMouseMoveListener: null,\n documentMouseUpListener: null,\n frame: null,\n scrollXRatio: null,\n scrollYRatio: null,\n isXBarClicked: false,\n isYBarClicked: false,\n lastPageX: null,\n lastPageY: null,\n timer: null,\n outsideClickListener: null,\n data: function data() {\n return {\n id: this.$attrs.id,\n orientation: 'vertical',\n lastScrollTop: 0,\n lastScrollLeft: 0\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n }\n },\n mounted: function mounted() {\n this.id = this.id || UniqueComponentId();\n if (this.$el.offsetParent) {\n this.initialize();\n }\n },\n updated: function updated() {\n if (!this.initialized && this.$el.offsetParent) {\n this.initialize();\n }\n },\n beforeUnmount: function beforeUnmount() {\n this.unbindDocumentResizeListener();\n if (this.frame) {\n window.cancelAnimationFrame(this.frame);\n }\n },\n methods: {\n initialize: function initialize() {\n this.moveBar();\n this.bindDocumentResizeListener();\n this.calculateContainerHeight();\n },\n calculateContainerHeight: function calculateContainerHeight() {\n var containerStyles = getComputedStyle(this.$el),\n xBarStyles = getComputedStyle(this.$refs.xBar),\n pureContainerHeight = getHeight(this.$el) - parseInt(xBarStyles['height'], 10);\n if (containerStyles['max-height'] !== 'none' && pureContainerHeight === 0) {\n if (this.$refs.content.offsetHeight + parseInt(xBarStyles['height'], 10) > parseInt(containerStyles['max-height'], 10)) {\n this.$el.style.height = containerStyles['max-height'];\n } else {\n this.$el.style.height = this.$refs.content.offsetHeight + parseFloat(containerStyles.paddingTop) + parseFloat(containerStyles.paddingBottom) + parseFloat(containerStyles.borderTopWidth) + parseFloat(containerStyles.borderBottomWidth) + 'px';\n }\n }\n },\n moveBar: function moveBar() {\n var _this = this;\n if (this.$refs.content) {\n /* horizontal scroll */\n var totalWidth = this.$refs.content.scrollWidth;\n var ownWidth = this.$refs.content.clientWidth;\n var bottom = (this.$el.clientHeight - this.$refs.xBar.clientHeight) * -1;\n this.scrollXRatio = ownWidth / totalWidth;\n\n /* vertical scroll */\n var totalHeight = this.$refs.content.scrollHeight;\n var ownHeight = this.$refs.content.clientHeight;\n var right = (this.$el.clientWidth - this.$refs.yBar.clientWidth) * -1;\n this.scrollYRatio = ownHeight / totalHeight;\n this.frame = this.requestAnimationFrame(function () {\n if (_this.$refs.xBar) {\n if (_this.scrollXRatio >= 1) {\n _this.$refs.xBar.setAttribute('data-p-scrollpanel-hidden', 'true');\n !_this.isUnstyled && addClass(_this.$refs.xBar, 'p-scrollpanel-hidden');\n } else {\n _this.$refs.xBar.setAttribute('data-p-scrollpanel-hidden', 'false');\n !_this.isUnstyled && removeClass(_this.$refs.xBar, 'p-scrollpanel-hidden');\n _this.$refs.xBar.style.cssText = 'width:' + Math.max(_this.scrollXRatio * 100, 10) + '%; left:' + _this.$refs.content.scrollLeft / totalWidth * 100 + '%;bottom:' + bottom + 'px;';\n }\n }\n if (_this.$refs.yBar) {\n if (_this.scrollYRatio >= 1) {\n _this.$refs.yBar.setAttribute('data-p-scrollpanel-hidden', 'true');\n !_this.isUnstyled && addClass(_this.$refs.yBar, 'p-scrollpanel-hidden');\n } else {\n _this.$refs.yBar.setAttribute('data-p-scrollpanel-hidden', 'false');\n !_this.isUnstyled && removeClass(_this.$refs.yBar, 'p-scrollpanel-hidden');\n _this.$refs.yBar.style.cssText = 'height:' + Math.max(_this.scrollYRatio * 100, 10) + '%; top: calc(' + _this.$refs.content.scrollTop / totalHeight * 100 + '% - ' + _this.$refs.xBar.clientHeight + 'px);right:' + right + 'px;';\n }\n }\n });\n }\n },\n onYBarMouseDown: function onYBarMouseDown(e) {\n this.isYBarClicked = true;\n this.$refs.yBar.focus();\n this.lastPageY = e.pageY;\n this.$refs.yBar.setAttribute('data-p-scrollpanel-grabbed', 'true');\n !this.isUnstyled && addClass(this.$refs.yBar, 'p-scrollpanel-grabbed');\n document.body.setAttribute('data-p-scrollpanel-grabbed', 'true');\n !this.isUnstyled && addClass(document.body, 'p-scrollpanel-grabbed');\n this.bindDocumentMouseListeners();\n e.preventDefault();\n },\n onXBarMouseDown: function onXBarMouseDown(e) {\n this.isXBarClicked = true;\n this.$refs.xBar.focus();\n this.lastPageX = e.pageX;\n this.$refs.yBar.setAttribute('data-p-scrollpanel-grabbed', 'false');\n !this.isUnstyled && addClass(this.$refs.xBar, 'p-scrollpanel-grabbed');\n document.body.setAttribute('data-p-scrollpanel-grabbed', 'false');\n !this.isUnstyled && addClass(document.body, 'p-scrollpanel-grabbed');\n this.bindDocumentMouseListeners();\n e.preventDefault();\n },\n onScroll: function onScroll(event) {\n if (this.lastScrollLeft !== event.target.scrollLeft) {\n this.lastScrollLeft = event.target.scrollLeft;\n this.orientation = 'horizontal';\n } else if (this.lastScrollTop !== event.target.scrollTop) {\n this.lastScrollTop = event.target.scrollTop;\n this.orientation = 'vertical';\n }\n this.moveBar();\n },\n onKeyDown: function onKeyDown(event) {\n if (this.orientation === 'vertical') {\n switch (event.code) {\n case 'ArrowDown':\n {\n this.setTimer('scrollTop', this.step);\n event.preventDefault();\n break;\n }\n case 'ArrowUp':\n {\n this.setTimer('scrollTop', this.step * -1);\n event.preventDefault();\n break;\n }\n case 'ArrowLeft':\n case 'ArrowRight':\n {\n event.preventDefault();\n break;\n }\n }\n } else if (this.orientation === 'horizontal') {\n switch (event.code) {\n case 'ArrowRight':\n {\n this.setTimer('scrollLeft', this.step);\n event.preventDefault();\n break;\n }\n case 'ArrowLeft':\n {\n this.setTimer('scrollLeft', this.step * -1);\n event.preventDefault();\n break;\n }\n case 'ArrowDown':\n case 'ArrowUp':\n {\n event.preventDefault();\n break;\n }\n }\n }\n },\n onKeyUp: function onKeyUp() {\n this.clearTimer();\n },\n repeat: function repeat(bar, step) {\n this.$refs.content[bar] += step;\n this.moveBar();\n },\n setTimer: function setTimer(bar, step) {\n var _this2 = this;\n this.clearTimer();\n this.timer = setTimeout(function () {\n _this2.repeat(bar, step);\n }, 40);\n },\n clearTimer: function clearTimer() {\n if (this.timer) {\n clearTimeout(this.timer);\n }\n },\n onDocumentMouseMove: function onDocumentMouseMove(e) {\n if (this.isXBarClicked) {\n this.onMouseMoveForXBar(e);\n } else if (this.isYBarClicked) {\n this.onMouseMoveForYBar(e);\n } else {\n this.onMouseMoveForXBar(e);\n this.onMouseMoveForYBar(e);\n }\n },\n onMouseMoveForXBar: function onMouseMoveForXBar(e) {\n var _this3 = this;\n var deltaX = e.pageX - this.lastPageX;\n this.lastPageX = e.pageX;\n this.frame = this.requestAnimationFrame(function () {\n _this3.$refs.content.scrollLeft += deltaX / _this3.scrollXRatio;\n });\n },\n onMouseMoveForYBar: function onMouseMoveForYBar(e) {\n var _this4 = this;\n var deltaY = e.pageY - this.lastPageY;\n this.lastPageY = e.pageY;\n this.frame = this.requestAnimationFrame(function () {\n _this4.$refs.content.scrollTop += deltaY / _this4.scrollYRatio;\n });\n },\n onFocus: function onFocus(event) {\n if (this.$refs.xBar.isSameNode(event.target)) {\n this.orientation = 'horizontal';\n } else if (this.$refs.yBar.isSameNode(event.target)) {\n this.orientation = 'vertical';\n }\n },\n onBlur: function onBlur() {\n if (this.orientation === 'horizontal') {\n this.orientation = 'vertical';\n }\n },\n onDocumentMouseUp: function onDocumentMouseUp() {\n this.$refs.yBar.setAttribute('data-p-scrollpanel-grabbed', 'false');\n !this.isUnstyled && removeClass(this.$refs.yBar, 'p-scrollpanel-grabbed');\n this.$refs.xBar.setAttribute('data-p-scrollpanel-grabbed', 'false');\n !this.isUnstyled && removeClass(this.$refs.xBar, 'p-scrollpanel-grabbed');\n document.body.setAttribute('data-p-scrollpanel-grabbed', 'false');\n !this.isUnstyled && removeClass(document.body, 'p-scrollpanel-grabbed');\n this.unbindDocumentMouseListeners();\n this.isXBarClicked = false;\n this.isYBarClicked = false;\n },\n requestAnimationFrame: function requestAnimationFrame(f) {\n var frame = window.requestAnimationFrame || this.timeoutFrame;\n return frame(f);\n },\n refresh: function refresh() {\n this.moveBar();\n },\n scrollTop: function scrollTop(_scrollTop) {\n var scrollableHeight = this.$refs.content.scrollHeight - this.$refs.content.clientHeight;\n _scrollTop = _scrollTop > scrollableHeight ? scrollableHeight : _scrollTop > 0 ? _scrollTop : 0;\n this.$refs.content.scrollTop = _scrollTop;\n },\n timeoutFrame: function timeoutFrame(fn) {\n setTimeout(fn, 0);\n },\n bindDocumentMouseListeners: function bindDocumentMouseListeners() {\n var _this5 = this;\n if (!this.documentMouseMoveListener) {\n this.documentMouseMoveListener = function (e) {\n _this5.onDocumentMouseMove(e);\n };\n document.addEventListener('mousemove', this.documentMouseMoveListener);\n }\n if (!this.documentMouseUpListener) {\n this.documentMouseUpListener = function (e) {\n _this5.onDocumentMouseUp(e);\n };\n document.addEventListener('mouseup', this.documentMouseUpListener);\n }\n },\n unbindDocumentMouseListeners: function unbindDocumentMouseListeners() {\n if (this.documentMouseMoveListener) {\n document.removeEventListener('mousemove', this.documentMouseMoveListener);\n this.documentMouseMoveListener = null;\n }\n if (this.documentMouseUpListener) {\n document.removeEventListener('mouseup', this.documentMouseUpListener);\n this.documentMouseUpListener = null;\n }\n },\n bindDocumentResizeListener: function bindDocumentResizeListener() {\n var _this6 = this;\n if (!this.documentResizeListener) {\n this.documentResizeListener = function () {\n _this6.moveBar();\n };\n window.addEventListener('resize', this.documentResizeListener);\n }\n },\n unbindDocumentResizeListener: function unbindDocumentResizeListener() {\n if (this.documentResizeListener) {\n window.removeEventListener('resize', this.documentResizeListener);\n this.documentResizeListener = null;\n }\n }\n },\n computed: {\n contentId: function contentId() {\n return this.id + '_content';\n }\n }\n};\n\nvar _hoisted_1 = [\"id\"];\nvar _hoisted_2 = [\"aria-controls\", \"aria-valuenow\"];\nvar _hoisted_3 = [\"aria-controls\", \"aria-valuenow\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('contentContainer')\n }, _ctx.ptm('contentContainer')), [createElementVNode(\"div\", mergeProps({\n ref: \"content\",\n id: $options.contentId,\n \"class\": _ctx.cx('content'),\n onScroll: _cache[0] || (_cache[0] = function () {\n return $options.onScroll && $options.onScroll.apply($options, arguments);\n }),\n onMouseenter: _cache[1] || (_cache[1] = function () {\n return $options.moveBar && $options.moveBar.apply($options, arguments);\n })\n }, _ctx.ptm('content')), [renderSlot(_ctx.$slots, \"default\")], 16, _hoisted_1)], 16), createElementVNode(\"div\", mergeProps({\n ref: \"xBar\",\n \"class\": _ctx.cx('barx'),\n tabindex: \"0\",\n role: \"scrollbar\",\n \"aria-orientation\": \"horizontal\",\n \"aria-controls\": $options.contentId,\n \"aria-valuenow\": $data.lastScrollLeft,\n onMousedown: _cache[2] || (_cache[2] = function () {\n return $options.onXBarMouseDown && $options.onXBarMouseDown.apply($options, arguments);\n }),\n onKeydown: _cache[3] || (_cache[3] = function ($event) {\n return $options.onKeyDown($event);\n }),\n onKeyup: _cache[4] || (_cache[4] = function () {\n return $options.onKeyUp && $options.onKeyUp.apply($options, arguments);\n }),\n onFocus: _cache[5] || (_cache[5] = function () {\n return $options.onFocus && $options.onFocus.apply($options, arguments);\n }),\n onBlur: _cache[6] || (_cache[6] = function () {\n return $options.onBlur && $options.onBlur.apply($options, arguments);\n })\n }, _ctx.ptm('barx'), {\n \"data-pc-group-section\": \"bar\"\n }), null, 16, _hoisted_2), createElementVNode(\"div\", mergeProps({\n ref: \"yBar\",\n \"class\": _ctx.cx('bary'),\n tabindex: \"0\",\n role: \"scrollbar\",\n \"aria-orientation\": \"vertical\",\n \"aria-controls\": $options.contentId,\n \"aria-valuenow\": $data.lastScrollTop,\n onMousedown: _cache[7] || (_cache[7] = function () {\n return $options.onYBarMouseDown && $options.onYBarMouseDown.apply($options, arguments);\n }),\n onKeydown: _cache[8] || (_cache[8] = function ($event) {\n return $options.onKeyDown($event);\n }),\n onKeyup: _cache[9] || (_cache[9] = function () {\n return $options.onKeyUp && $options.onKeyUp.apply($options, arguments);\n }),\n onFocus: _cache[10] || (_cache[10] = function () {\n return $options.onFocus && $options.onFocus.apply($options, arguments);\n })\n }, _ctx.ptm('bary'), {\n \"data-pc-group-section\": \"bar\"\n }), null, 16, _hoisted_3)], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'AngleDownIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n d: \"M3.58659 4.5007C3.68513 4.50023 3.78277 4.51945 3.87379 4.55723C3.9648 4.59501 4.04735 4.65058 4.11659 4.7207L7.11659 7.7207L10.1166 4.7207C10.2619 4.65055 10.4259 4.62911 10.5843 4.65956C10.7427 4.69002 10.8871 4.77074 10.996 4.88976C11.1049 5.00877 11.1726 5.15973 11.1889 5.32022C11.2052 5.48072 11.1693 5.6422 11.0866 5.7807L7.58659 9.2807C7.44597 9.42115 7.25534 9.50004 7.05659 9.50004C6.85784 9.50004 6.66722 9.42115 6.52659 9.2807L3.02659 5.7807C2.88614 5.64007 2.80725 5.44945 2.80725 5.2507C2.80725 5.05195 2.88614 4.86132 3.02659 4.7207C3.09932 4.64685 3.18675 4.58911 3.28322 4.55121C3.37969 4.51331 3.48305 4.4961 3.58659 4.5007Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'AngleUpIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n d: \"M10.4134 9.49931C10.3148 9.49977 10.2172 9.48055 10.1262 9.44278C10.0352 9.405 9.95263 9.34942 9.88338 9.27931L6.88338 6.27931L3.88338 9.27931C3.73811 9.34946 3.57409 9.3709 3.41567 9.34044C3.25724 9.30999 3.11286 9.22926 3.00395 9.11025C2.89504 8.99124 2.82741 8.84028 2.8111 8.67978C2.79478 8.51928 2.83065 8.35781 2.91338 8.21931L6.41338 4.71931C6.55401 4.57886 6.74463 4.49997 6.94338 4.49997C7.14213 4.49997 7.33276 4.57886 7.47338 4.71931L10.9734 8.21931C11.1138 8.35994 11.1927 8.55056 11.1927 8.74931C11.1927 8.94806 11.1138 9.13868 10.9734 9.27931C10.9007 9.35315 10.8132 9.41089 10.7168 9.44879C10.6203 9.48669 10.5169 9.5039 10.4134 9.49931Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-inputnumber {\\n display: inline-flex;\\n position: relative;\\n}\\n\\n.p-inputnumber-button {\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n flex: 0 0 auto;\\n cursor: pointer;\\n background: \".concat(dt('inputnumber.button.background'), \";\\n color: \").concat(dt('inputnumber.button.color'), \";\\n width: \").concat(dt('inputnumber.button.width'), \";\\n transition: background \").concat(dt('inputnumber.transition.duration'), \", color \").concat(dt('inputnumber.transition.duration'), \", border-color \").concat(dt('inputnumber.transition.duration'), \", outline-color \").concat(dt('inputnumber.transition.duration'), \";\\n}\\n\\n.p-inputnumber-button:hover {\\n background: \").concat(dt('inputnumber.button.hover.background'), \";\\n color: \").concat(dt('inputnumber.button.hover.color'), \";\\n}\\n\\n.p-inputnumber-button:active {\\n background: \").concat(dt('inputnumber.button.active.background'), \";\\n color: \").concat(dt('inputnumber.button.active.color'), \";\\n}\\n\\n.p-inputnumber-stacked .p-inputnumber-button {\\n position: relative;\\n border: 0 none;\\n}\\n\\n.p-inputnumber-stacked .p-inputnumber-button-group {\\n display: flex;\\n flex-direction: column;\\n position: absolute;\\n top: 1px;\\n right: 1px;\\n height: calc(100% - 2px);\\n z-index: 1;\\n}\\n\\n.p-inputnumber-stacked .p-inputnumber-increment-button {\\n padding: 0;\\n border-top-right-radius: calc(\").concat(dt('inputnumber.button.border.radius'), \" - 1px);\\n}\\n\\n.p-inputnumber-stacked .p-inputnumber-decrement-button {\\n padding: 0;\\n border-bottom-right-radius: calc(\").concat(dt('inputnumber.button.border.radius'), \" - 1px);\\n}\\n\\n.p-inputnumber-stacked .p-inputnumber-button {\\n flex: 1 1 auto;\\n border: 0 none;\\n}\\n\\n.p-inputnumber-horizontal .p-inputnumber-button {\\n border: 1px solid \").concat(dt('inputnumber.button.border.color'), \";\\n}\\n\\n.p-inputnumber-horizontal .p-inputnumber-button:hover {\\n border-color: \").concat(dt('inputnumber.button.hover.border.color'), \";\\n}\\n\\n.p-inputnumber-horizontal .p-inputnumber-button:active {\\n border-color: \").concat(dt('inputnumber.button.active.border.color'), \";\\n}\\n\\n.p-inputnumber-horizontal .p-inputnumber-increment-button {\\n order: 3;\\n border-top-right-radius: \").concat(dt('inputnumber.button.border.radius'), \";\\n border-bottom-right-radius: \").concat(dt('inputnumber.button.border.radius'), \";\\n border-left: 0 none;\\n}\\n\\n.p-inputnumber-horizontal .p-inputnumber-input {\\n order: 2;\\n border-radius: 0;\\n}\\n\\n.p-inputnumber-horizontal .p-inputnumber-decrement-button {\\n order: 1;\\n border-top-left-radius: \").concat(dt('inputnumber.button.border.radius'), \";\\n border-bottom-left-radius: \").concat(dt('inputnumber.button.border.radius'), \";\\n border-right: 0 none;\\n}\\n\\n.p-inputnumber-vertical {\\n flex-direction: column;\\n}\\n\\n.p-inputnumber-vertical .p-inputnumber-button {\\n border: 1px solid \").concat(dt('inputnumber.button.border.color'), \";\\n padding: \").concat(dt('inputnumber.button.vertical.padding'), \"; 0;\\n}\\n\\n.p-inputnumber-vertical .p-inputnumber-button:hover {\\n border-color: \").concat(dt('inputnumber.button.hover.border.color'), \";\\n}\\n\\n.p-inputnumber-vertical .p-inputnumber-button:active {\\n border-color: \").concat(dt('inputnumber.button.active.border.color'), \";\\n}\\n\\n.p-inputnumber-vertical .p-inputnumber-increment-button {\\n order: 1;\\n border-top-left-radius: \").concat(dt('inputnumber.button.border.radius'), \";\\n border-top-right-radius: \").concat(dt('inputnumber.button.border.radius'), \";\\n width: 100%;\\n border-bottom: 0 none;\\n}\\n\\n.p-inputnumber-vertical .p-inputnumber-input {\\n order: 2;\\n border-radius: 0;\\n text-align: center;\\n}\\n\\n.p-inputnumber-vertical .p-inputnumber-decrement-button {\\n order: 3;\\n border-bottom-left-radius: \").concat(dt('inputnumber.button.border.radius'), \";\\n border-bottom-right-radius: \").concat(dt('inputnumber.button.border.radius'), \";\\n width: 100%;\\n border-top: 0 none;\\n}\\n\\n.p-inputnumber-input {\\n flex: 1 1 auto;\\n}\\n\\n.p-inputnumber-fluid {\\n width: 100%;\\n}\\n\\n.p-inputnumber-fluid .p-inputnumber-input {\\n width: 1%;\\n}\\n\\n.p-inputnumber-fluid.p-inputnumber-vertical .p-inputnumber-input {\\n width: 100%;\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var instance = _ref2.instance,\n props = _ref2.props;\n return ['p-inputnumber p-component p-inputwrapper', {\n 'p-inputwrapper-filled': instance.filled || props.allowEmpty === false,\n 'p-inputwrapper-focus': instance.focused,\n 'p-inputnumber-stacked': props.showButtons && props.buttonLayout === 'stacked',\n 'p-inputnumber-horizontal': props.showButtons && props.buttonLayout === 'horizontal',\n 'p-inputnumber-vertical': props.showButtons && props.buttonLayout === 'vertical',\n 'p-inputnumber-fluid': instance.hasFluid\n }];\n },\n pcInput: 'p-inputnumber-input',\n buttonGroup: 'p-inputnumber-button-group',\n incrementButton: function incrementButton(_ref3) {\n var instance = _ref3.instance,\n props = _ref3.props;\n return ['p-inputnumber-button p-inputnumber-increment-button', {\n 'p-disabled': props.showButtons && props.max !== null && instance.maxBoundry()\n }];\n },\n decrementButton: function decrementButton(_ref4) {\n var instance = _ref4.instance,\n props = _ref4.props;\n return ['p-inputnumber-button p-inputnumber-decrement-button', {\n 'p-disabled': props.showButtons && props.min !== null && instance.minBoundry()\n }];\n }\n};\nvar InputNumberStyle = BaseStyle.extend({\n name: 'inputnumber',\n theme: theme,\n classes: classes\n});\n\nexport { InputNumberStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { getSelection, clearSelection } from '@primeuix/utils/dom';\nimport { isNotEmpty, isEmpty } from '@primeuix/utils/object';\nimport AngleDownIcon from '@primevue/icons/angledown';\nimport AngleUpIcon from '@primevue/icons/angleup';\nimport InputText from 'primevue/inputtext';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport InputNumberStyle from 'primevue/inputnumber/style';\nimport { resolveComponent, openBlock, createElementBlock, mergeProps, createVNode, normalizeClass, normalizeStyle, renderSlot, createElementVNode, toHandlers, createBlock, resolveDynamicComponent, createCommentVNode } from 'vue';\n\nvar script$1 = {\n name: 'BaseInputNumber',\n \"extends\": BaseComponent,\n props: {\n modelValue: {\n type: Number,\n \"default\": null\n },\n format: {\n type: Boolean,\n \"default\": true\n },\n showButtons: {\n type: Boolean,\n \"default\": false\n },\n buttonLayout: {\n type: String,\n \"default\": 'stacked'\n },\n incrementButtonClass: {\n type: String,\n \"default\": null\n },\n decrementButtonClass: {\n type: String,\n \"default\": null\n },\n incrementButtonIcon: {\n type: String,\n \"default\": undefined\n },\n incrementIcon: {\n type: String,\n \"default\": undefined\n },\n decrementButtonIcon: {\n type: String,\n \"default\": undefined\n },\n decrementIcon: {\n type: String,\n \"default\": undefined\n },\n locale: {\n type: String,\n \"default\": undefined\n },\n localeMatcher: {\n type: String,\n \"default\": undefined\n },\n mode: {\n type: String,\n \"default\": 'decimal'\n },\n prefix: {\n type: String,\n \"default\": null\n },\n suffix: {\n type: String,\n \"default\": null\n },\n currency: {\n type: String,\n \"default\": undefined\n },\n currencyDisplay: {\n type: String,\n \"default\": undefined\n },\n useGrouping: {\n type: Boolean,\n \"default\": true\n },\n minFractionDigits: {\n type: Number,\n \"default\": undefined\n },\n maxFractionDigits: {\n type: Number,\n \"default\": undefined\n },\n roundingMode: {\n type: String,\n \"default\": 'halfExpand',\n validator: function validator(value) {\n return ['ceil', 'floor', 'expand', 'trunc', 'halfCeil', 'halfFloor', 'halfExpand', 'halfTrunc', 'halfEven'].includes(value);\n }\n },\n min: {\n type: Number,\n \"default\": null\n },\n max: {\n type: Number,\n \"default\": null\n },\n step: {\n type: Number,\n \"default\": 1\n },\n allowEmpty: {\n type: Boolean,\n \"default\": true\n },\n highlightOnFocus: {\n type: Boolean,\n \"default\": false\n },\n readonly: {\n type: Boolean,\n \"default\": false\n },\n variant: {\n type: String,\n \"default\": null\n },\n invalid: {\n type: Boolean,\n \"default\": false\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n placeholder: {\n type: String,\n \"default\": null\n },\n fluid: {\n type: Boolean,\n \"default\": null\n },\n inputId: {\n type: String,\n \"default\": null\n },\n inputClass: {\n type: [String, Object],\n \"default\": null\n },\n inputStyle: {\n type: Object,\n \"default\": null\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n ariaLabel: {\n type: String,\n \"default\": null\n }\n },\n style: InputNumberStyle,\n provide: function provide() {\n return {\n $pcInputNumber: this,\n $parentInstance: this\n };\n }\n};\n\nfunction _typeof(o) { \"@babel/helpers - typeof\"; return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof(o); }\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty(e, r, t) { return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey(t) { var i = _toPrimitive(t, \"string\"); return \"symbol\" == _typeof(i) ? i : i + \"\"; }\nfunction _toPrimitive(t, r) { if (\"object\" != _typeof(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nfunction _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }\nfunction _nonIterableSpread() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _iterableToArray(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nvar script = {\n name: 'InputNumber',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:modelValue', 'input', 'focus', 'blur'],\n inject: {\n $pcFluid: {\n \"default\": null\n }\n },\n numberFormat: null,\n _numeral: null,\n _decimal: null,\n _group: null,\n _minusSign: null,\n _currency: null,\n _suffix: null,\n _prefix: null,\n _index: null,\n groupChar: '',\n isSpecialChar: null,\n prefixChar: null,\n suffixChar: null,\n timer: null,\n data: function data() {\n return {\n d_modelValue: this.modelValue,\n focused: false\n };\n },\n watch: {\n modelValue: function modelValue(newValue) {\n this.d_modelValue = newValue;\n },\n locale: function locale(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n localeMatcher: function localeMatcher(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n mode: function mode(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n currency: function currency(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n currencyDisplay: function currencyDisplay(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n useGrouping: function useGrouping(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n minFractionDigits: function minFractionDigits(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n maxFractionDigits: function maxFractionDigits(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n suffix: function suffix(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n },\n prefix: function prefix(newValue, oldValue) {\n this.updateConstructParser(newValue, oldValue);\n }\n },\n created: function created() {\n this.constructParser();\n },\n methods: {\n getOptions: function getOptions() {\n return {\n localeMatcher: this.localeMatcher,\n style: this.mode,\n currency: this.currency,\n currencyDisplay: this.currencyDisplay,\n useGrouping: this.useGrouping,\n minimumFractionDigits: this.minFractionDigits,\n maximumFractionDigits: this.maxFractionDigits,\n roundingMode: this.roundingMode\n };\n },\n constructParser: function constructParser() {\n this.numberFormat = new Intl.NumberFormat(this.locale, this.getOptions());\n var numerals = _toConsumableArray(new Intl.NumberFormat(this.locale, {\n useGrouping: false\n }).format(9876543210)).reverse();\n var index = new Map(numerals.map(function (d, i) {\n return [d, i];\n }));\n this._numeral = new RegExp(\"[\".concat(numerals.join(''), \"]\"), 'g');\n this._group = this.getGroupingExpression();\n this._minusSign = this.getMinusSignExpression();\n this._currency = this.getCurrencyExpression();\n this._decimal = this.getDecimalExpression();\n this._suffix = this.getSuffixExpression();\n this._prefix = this.getPrefixExpression();\n this._index = function (d) {\n return index.get(d);\n };\n },\n updateConstructParser: function updateConstructParser(newValue, oldValue) {\n if (newValue !== oldValue) {\n this.constructParser();\n }\n },\n escapeRegExp: function escapeRegExp(text) {\n return text.replace(/[-[\\]{}()*+?.,\\\\^$|#\\s]/g, '\\\\$&');\n },\n getDecimalExpression: function getDecimalExpression() {\n var formatter = new Intl.NumberFormat(this.locale, _objectSpread(_objectSpread({}, this.getOptions()), {}, {\n useGrouping: false\n }));\n return new RegExp(\"[\".concat(formatter.format(1.1).replace(this._currency, '').trim().replace(this._numeral, ''), \"]\"), 'g');\n },\n getGroupingExpression: function getGroupingExpression() {\n var formatter = new Intl.NumberFormat(this.locale, {\n useGrouping: true\n });\n this.groupChar = formatter.format(1000000).trim().replace(this._numeral, '').charAt(0);\n return new RegExp(\"[\".concat(this.groupChar, \"]\"), 'g');\n },\n getMinusSignExpression: function getMinusSignExpression() {\n var formatter = new Intl.NumberFormat(this.locale, {\n useGrouping: false\n });\n return new RegExp(\"[\".concat(formatter.format(-1).trim().replace(this._numeral, ''), \"]\"), 'g');\n },\n getCurrencyExpression: function getCurrencyExpression() {\n if (this.currency) {\n var formatter = new Intl.NumberFormat(this.locale, {\n style: 'currency',\n currency: this.currency,\n currencyDisplay: this.currencyDisplay,\n minimumFractionDigits: 0,\n maximumFractionDigits: 0,\n roundingMode: this.roundingMode\n });\n return new RegExp(\"[\".concat(formatter.format(1).replace(/\\s/g, '').replace(this._numeral, '').replace(this._group, ''), \"]\"), 'g');\n }\n return new RegExp(\"[]\", 'g');\n },\n getPrefixExpression: function getPrefixExpression() {\n if (this.prefix) {\n this.prefixChar = this.prefix;\n } else {\n var formatter = new Intl.NumberFormat(this.locale, {\n style: this.mode,\n currency: this.currency,\n currencyDisplay: this.currencyDisplay\n });\n this.prefixChar = formatter.format(1).split('1')[0];\n }\n return new RegExp(\"\".concat(this.escapeRegExp(this.prefixChar || '')), 'g');\n },\n getSuffixExpression: function getSuffixExpression() {\n if (this.suffix) {\n this.suffixChar = this.suffix;\n } else {\n var formatter = new Intl.NumberFormat(this.locale, {\n style: this.mode,\n currency: this.currency,\n currencyDisplay: this.currencyDisplay,\n minimumFractionDigits: 0,\n maximumFractionDigits: 0,\n roundingMode: this.roundingMode\n });\n this.suffixChar = formatter.format(1).split('1')[1];\n }\n return new RegExp(\"\".concat(this.escapeRegExp(this.suffixChar || '')), 'g');\n },\n formatValue: function formatValue(value) {\n if (value != null) {\n if (value === '-') {\n // Minus sign\n return value;\n }\n if (this.format) {\n var formatter = new Intl.NumberFormat(this.locale, this.getOptions());\n var formattedValue = formatter.format(value);\n if (this.prefix) {\n formattedValue = this.prefix + formattedValue;\n }\n if (this.suffix) {\n formattedValue = formattedValue + this.suffix;\n }\n return formattedValue;\n }\n return value.toString();\n }\n return '';\n },\n parseValue: function parseValue(text) {\n var filteredText = text.replace(this._suffix, '').replace(this._prefix, '').trim().replace(/\\s/g, '').replace(this._currency, '').replace(this._group, '').replace(this._minusSign, '-').replace(this._decimal, '.').replace(this._numeral, this._index);\n if (filteredText) {\n if (filteredText === '-')\n // Minus sign\n return filteredText;\n var parsedValue = +filteredText;\n return isNaN(parsedValue) ? null : parsedValue;\n }\n return null;\n },\n repeat: function repeat(event, interval, dir) {\n var _this = this;\n if (this.readonly) {\n return;\n }\n var i = interval || 500;\n this.clearTimer();\n this.timer = setTimeout(function () {\n _this.repeat(event, 40, dir);\n }, i);\n this.spin(event, dir);\n },\n spin: function spin(event, dir) {\n if (this.$refs.input) {\n var step = this.step * dir;\n var currentValue = this.parseValue(this.$refs.input.$el.value) || 0;\n var newValue = this.validateValue(currentValue + step);\n this.updateInput(newValue, null, 'spin');\n this.updateModel(event, newValue);\n this.handleOnInput(event, currentValue, newValue);\n }\n },\n onUpButtonMouseDown: function onUpButtonMouseDown(event) {\n if (!this.disabled) {\n this.$refs.input.$el.focus();\n this.repeat(event, null, 1);\n event.preventDefault();\n }\n },\n onUpButtonMouseUp: function onUpButtonMouseUp() {\n if (!this.disabled) {\n this.clearTimer();\n }\n },\n onUpButtonMouseLeave: function onUpButtonMouseLeave() {\n if (!this.disabled) {\n this.clearTimer();\n }\n },\n onUpButtonKeyUp: function onUpButtonKeyUp() {\n if (!this.disabled) {\n this.clearTimer();\n }\n },\n onUpButtonKeyDown: function onUpButtonKeyDown(event) {\n if (event.code === 'Space' || event.code === 'Enter' || event.code === 'NumpadEnter') {\n this.repeat(event, null, 1);\n }\n },\n onDownButtonMouseDown: function onDownButtonMouseDown(event) {\n if (!this.disabled) {\n this.$refs.input.$el.focus();\n this.repeat(event, null, -1);\n event.preventDefault();\n }\n },\n onDownButtonMouseUp: function onDownButtonMouseUp() {\n if (!this.disabled) {\n this.clearTimer();\n }\n },\n onDownButtonMouseLeave: function onDownButtonMouseLeave() {\n if (!this.disabled) {\n this.clearTimer();\n }\n },\n onDownButtonKeyUp: function onDownButtonKeyUp() {\n if (!this.disabled) {\n this.clearTimer();\n }\n },\n onDownButtonKeyDown: function onDownButtonKeyDown(event) {\n if (event.code === 'Space' || event.code === 'Enter' || event.code === 'NumpadEnter') {\n this.repeat(event, null, -1);\n }\n },\n onUserInput: function onUserInput() {\n if (this.isSpecialChar) {\n this.$refs.input.$el.value = this.lastValue;\n }\n this.isSpecialChar = false;\n },\n onInputKeyDown: function onInputKeyDown(event) {\n if (this.readonly) {\n return;\n }\n if (event.altKey || event.ctrlKey || event.metaKey) {\n this.isSpecialChar = true;\n this.lastValue = this.$refs.input.$el.value;\n return;\n }\n this.lastValue = event.target.value;\n var selectionStart = event.target.selectionStart;\n var selectionEnd = event.target.selectionEnd;\n var inputValue = event.target.value;\n var newValueStr = null;\n switch (event.code) {\n case 'ArrowUp':\n this.spin(event, 1);\n event.preventDefault();\n break;\n case 'ArrowDown':\n this.spin(event, -1);\n event.preventDefault();\n break;\n case 'ArrowLeft':\n if (!this.isNumeralChar(inputValue.charAt(selectionStart - 1))) {\n event.preventDefault();\n }\n break;\n case 'ArrowRight':\n if (!this.isNumeralChar(inputValue.charAt(selectionStart))) {\n event.preventDefault();\n }\n break;\n case 'Tab':\n case 'Enter':\n case 'NumpadEnter':\n newValueStr = this.validateValue(this.parseValue(inputValue));\n this.$refs.input.$el.value = this.formatValue(newValueStr);\n this.$refs.input.$el.setAttribute('aria-valuenow', newValueStr);\n this.updateModel(event, newValueStr);\n break;\n case 'Backspace':\n {\n event.preventDefault();\n if (selectionStart === selectionEnd) {\n var deleteChar = inputValue.charAt(selectionStart - 1);\n var _this$getDecimalCharI = this.getDecimalCharIndexes(inputValue),\n decimalCharIndex = _this$getDecimalCharI.decimalCharIndex,\n decimalCharIndexWithoutPrefix = _this$getDecimalCharI.decimalCharIndexWithoutPrefix;\n if (this.isNumeralChar(deleteChar)) {\n var decimalLength = this.getDecimalLength(inputValue);\n if (this._group.test(deleteChar)) {\n this._group.lastIndex = 0;\n newValueStr = inputValue.slice(0, selectionStart - 2) + inputValue.slice(selectionStart - 1);\n } else if (this._decimal.test(deleteChar)) {\n this._decimal.lastIndex = 0;\n if (decimalLength) {\n this.$refs.input.$el.setSelectionRange(selectionStart - 1, selectionStart - 1);\n } else {\n newValueStr = inputValue.slice(0, selectionStart - 1) + inputValue.slice(selectionStart);\n }\n } else if (decimalCharIndex > 0 && selectionStart > decimalCharIndex) {\n var insertedText = this.isDecimalMode() && (this.minFractionDigits || 0) < decimalLength ? '' : '0';\n newValueStr = inputValue.slice(0, selectionStart - 1) + insertedText + inputValue.slice(selectionStart);\n } else if (decimalCharIndexWithoutPrefix === 1) {\n newValueStr = inputValue.slice(0, selectionStart - 1) + '0' + inputValue.slice(selectionStart);\n newValueStr = this.parseValue(newValueStr) > 0 ? newValueStr : '';\n } else {\n newValueStr = inputValue.slice(0, selectionStart - 1) + inputValue.slice(selectionStart);\n }\n }\n this.updateValue(event, newValueStr, null, 'delete-single');\n } else {\n newValueStr = this.deleteRange(inputValue, selectionStart, selectionEnd);\n this.updateValue(event, newValueStr, null, 'delete-range');\n }\n break;\n }\n case 'Delete':\n event.preventDefault();\n if (selectionStart === selectionEnd) {\n var _deleteChar = inputValue.charAt(selectionStart);\n var _this$getDecimalCharI2 = this.getDecimalCharIndexes(inputValue),\n _decimalCharIndex = _this$getDecimalCharI2.decimalCharIndex,\n _decimalCharIndexWithoutPrefix = _this$getDecimalCharI2.decimalCharIndexWithoutPrefix;\n if (this.isNumeralChar(_deleteChar)) {\n var _decimalLength = this.getDecimalLength(inputValue);\n if (this._group.test(_deleteChar)) {\n this._group.lastIndex = 0;\n newValueStr = inputValue.slice(0, selectionStart) + inputValue.slice(selectionStart + 2);\n } else if (this._decimal.test(_deleteChar)) {\n this._decimal.lastIndex = 0;\n if (_decimalLength) {\n this.$refs.input.$el.setSelectionRange(selectionStart + 1, selectionStart + 1);\n } else {\n newValueStr = inputValue.slice(0, selectionStart) + inputValue.slice(selectionStart + 1);\n }\n } else if (_decimalCharIndex > 0 && selectionStart > _decimalCharIndex) {\n var _insertedText = this.isDecimalMode() && (this.minFractionDigits || 0) < _decimalLength ? '' : '0';\n newValueStr = inputValue.slice(0, selectionStart) + _insertedText + inputValue.slice(selectionStart + 1);\n } else if (_decimalCharIndexWithoutPrefix === 1) {\n newValueStr = inputValue.slice(0, selectionStart) + '0' + inputValue.slice(selectionStart + 1);\n newValueStr = this.parseValue(newValueStr) > 0 ? newValueStr : '';\n } else {\n newValueStr = inputValue.slice(0, selectionStart) + inputValue.slice(selectionStart + 1);\n }\n }\n this.updateValue(event, newValueStr, null, 'delete-back-single');\n } else {\n newValueStr = this.deleteRange(inputValue, selectionStart, selectionEnd);\n this.updateValue(event, newValueStr, null, 'delete-range');\n }\n break;\n case 'Home':\n event.preventDefault();\n if (isNotEmpty(this.min)) {\n this.updateModel(event, this.min);\n }\n break;\n case 'End':\n event.preventDefault();\n if (isNotEmpty(this.max)) {\n this.updateModel(event, this.max);\n }\n break;\n }\n },\n onInputKeyPress: function onInputKeyPress(event) {\n if (this.readonly) {\n return;\n }\n var _char = event.key;\n var isDecimalSign = this.isDecimalSign(_char);\n var isMinusSign = this.isMinusSign(_char);\n if (event.code !== 'Enter') {\n event.preventDefault();\n }\n if (Number(_char) >= 0 && Number(_char) <= 9 || isMinusSign || isDecimalSign) {\n this.insert(event, _char, {\n isDecimalSign: isDecimalSign,\n isMinusSign: isMinusSign\n });\n }\n },\n onPaste: function onPaste(event) {\n event.preventDefault();\n var data = (event.clipboardData || window['clipboardData']).getData('Text');\n if (data) {\n var filteredData = this.parseValue(data);\n if (filteredData != null) {\n this.insert(event, filteredData.toString());\n }\n }\n },\n allowMinusSign: function allowMinusSign() {\n return this.min === null || this.min < 0;\n },\n isMinusSign: function isMinusSign(_char2) {\n if (this._minusSign.test(_char2) || _char2 === '-') {\n this._minusSign.lastIndex = 0;\n return true;\n }\n return false;\n },\n isDecimalSign: function isDecimalSign(_char3) {\n if (this._decimal.test(_char3)) {\n this._decimal.lastIndex = 0;\n return true;\n }\n return false;\n },\n isDecimalMode: function isDecimalMode() {\n return this.mode === 'decimal';\n },\n getDecimalCharIndexes: function getDecimalCharIndexes(val) {\n var decimalCharIndex = val.search(this._decimal);\n this._decimal.lastIndex = 0;\n var filteredVal = val.replace(this._prefix, '').trim().replace(/\\s/g, '').replace(this._currency, '');\n var decimalCharIndexWithoutPrefix = filteredVal.search(this._decimal);\n this._decimal.lastIndex = 0;\n return {\n decimalCharIndex: decimalCharIndex,\n decimalCharIndexWithoutPrefix: decimalCharIndexWithoutPrefix\n };\n },\n getCharIndexes: function getCharIndexes(val) {\n var decimalCharIndex = val.search(this._decimal);\n this._decimal.lastIndex = 0;\n var minusCharIndex = val.search(this._minusSign);\n this._minusSign.lastIndex = 0;\n var suffixCharIndex = val.search(this._suffix);\n this._suffix.lastIndex = 0;\n var currencyCharIndex = val.search(this._currency);\n this._currency.lastIndex = 0;\n return {\n decimalCharIndex: decimalCharIndex,\n minusCharIndex: minusCharIndex,\n suffixCharIndex: suffixCharIndex,\n currencyCharIndex: currencyCharIndex\n };\n },\n insert: function insert(event, text) {\n var sign = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {\n isDecimalSign: false,\n isMinusSign: false\n };\n var minusCharIndexOnText = text.search(this._minusSign);\n this._minusSign.lastIndex = 0;\n if (!this.allowMinusSign() && minusCharIndexOnText !== -1) {\n return;\n }\n var selectionStart = this.$refs.input.$el.selectionStart;\n var selectionEnd = this.$refs.input.$el.selectionEnd;\n var inputValue = this.$refs.input.$el.value.trim();\n var _this$getCharIndexes = this.getCharIndexes(inputValue),\n decimalCharIndex = _this$getCharIndexes.decimalCharIndex,\n minusCharIndex = _this$getCharIndexes.minusCharIndex,\n suffixCharIndex = _this$getCharIndexes.suffixCharIndex,\n currencyCharIndex = _this$getCharIndexes.currencyCharIndex;\n var newValueStr;\n if (sign.isMinusSign) {\n if (selectionStart === 0) {\n newValueStr = inputValue;\n if (minusCharIndex === -1 || selectionEnd !== 0) {\n newValueStr = this.insertText(inputValue, text, 0, selectionEnd);\n }\n this.updateValue(event, newValueStr, text, 'insert');\n }\n } else if (sign.isDecimalSign) {\n if (decimalCharIndex > 0 && selectionStart === decimalCharIndex) {\n this.updateValue(event, inputValue, text, 'insert');\n } else if (decimalCharIndex > selectionStart && decimalCharIndex < selectionEnd) {\n newValueStr = this.insertText(inputValue, text, selectionStart, selectionEnd);\n this.updateValue(event, newValueStr, text, 'insert');\n } else if (decimalCharIndex === -1 && this.maxFractionDigits) {\n newValueStr = this.insertText(inputValue, text, selectionStart, selectionEnd);\n this.updateValue(event, newValueStr, text, 'insert');\n }\n } else {\n var maxFractionDigits = this.numberFormat.resolvedOptions().maximumFractionDigits;\n var operation = selectionStart !== selectionEnd ? 'range-insert' : 'insert';\n if (decimalCharIndex > 0 && selectionStart > decimalCharIndex) {\n if (selectionStart + text.length - (decimalCharIndex + 1) <= maxFractionDigits) {\n var charIndex = currencyCharIndex >= selectionStart ? currencyCharIndex - 1 : suffixCharIndex >= selectionStart ? suffixCharIndex : inputValue.length;\n newValueStr = inputValue.slice(0, selectionStart) + text + inputValue.slice(selectionStart + text.length, charIndex) + inputValue.slice(charIndex);\n this.updateValue(event, newValueStr, text, operation);\n }\n } else {\n newValueStr = this.insertText(inputValue, text, selectionStart, selectionEnd);\n this.updateValue(event, newValueStr, text, operation);\n }\n }\n },\n insertText: function insertText(value, text, start, end) {\n var textSplit = text === '.' ? text : text.split('.');\n if (textSplit.length === 2) {\n var decimalCharIndex = value.slice(start, end).search(this._decimal);\n this._decimal.lastIndex = 0;\n return decimalCharIndex > 0 ? value.slice(0, start) + this.formatValue(text) + value.slice(end) : this.formatValue(text) || value;\n } else if (end - start === value.length) {\n return this.formatValue(text);\n } else if (start === 0) {\n return text + value.slice(end);\n } else if (end === value.length) {\n return value.slice(0, start) + text;\n } else {\n return value.slice(0, start) + text + value.slice(end);\n }\n },\n deleteRange: function deleteRange(value, start, end) {\n var newValueStr;\n if (end - start === value.length) newValueStr = '';else if (start === 0) newValueStr = value.slice(end);else if (end === value.length) newValueStr = value.slice(0, start);else newValueStr = value.slice(0, start) + value.slice(end);\n return newValueStr;\n },\n initCursor: function initCursor() {\n var selectionStart = this.$refs.input.$el.selectionStart;\n var inputValue = this.$refs.input.$el.value;\n var valueLength = inputValue.length;\n var index = null;\n\n // remove prefix\n var prefixLength = (this.prefixChar || '').length;\n inputValue = inputValue.replace(this._prefix, '');\n selectionStart = selectionStart - prefixLength;\n var _char4 = inputValue.charAt(selectionStart);\n if (this.isNumeralChar(_char4)) {\n return selectionStart + prefixLength;\n }\n\n //left\n var i = selectionStart - 1;\n while (i >= 0) {\n _char4 = inputValue.charAt(i);\n if (this.isNumeralChar(_char4)) {\n index = i + prefixLength;\n break;\n } else {\n i--;\n }\n }\n if (index !== null) {\n this.$refs.input.$el.setSelectionRange(index + 1, index + 1);\n } else {\n i = selectionStart;\n while (i < valueLength) {\n _char4 = inputValue.charAt(i);\n if (this.isNumeralChar(_char4)) {\n index = i + prefixLength;\n break;\n } else {\n i++;\n }\n }\n if (index !== null) {\n this.$refs.input.$el.setSelectionRange(index, index);\n }\n }\n return index || 0;\n },\n onInputClick: function onInputClick() {\n var currentValue = this.$refs.input.$el.value;\n if (!this.readonly && currentValue !== getSelection()) {\n this.initCursor();\n }\n },\n isNumeralChar: function isNumeralChar(_char5) {\n if (_char5.length === 1 && (this._numeral.test(_char5) || this._decimal.test(_char5) || this._group.test(_char5) || this._minusSign.test(_char5))) {\n this.resetRegex();\n return true;\n }\n return false;\n },\n resetRegex: function resetRegex() {\n this._numeral.lastIndex = 0;\n this._decimal.lastIndex = 0;\n this._group.lastIndex = 0;\n this._minusSign.lastIndex = 0;\n },\n updateValue: function updateValue(event, valueStr, insertedValueStr, operation) {\n var currentValue = this.$refs.input.$el.value;\n var newValue = null;\n if (valueStr != null) {\n newValue = this.parseValue(valueStr);\n newValue = !newValue && !this.allowEmpty ? 0 : newValue;\n this.updateInput(newValue, insertedValueStr, operation, valueStr);\n this.handleOnInput(event, currentValue, newValue);\n }\n },\n handleOnInput: function handleOnInput(event, currentValue, newValue) {\n if (this.isValueChanged(currentValue, newValue)) {\n this.$emit('input', {\n originalEvent: event,\n value: newValue,\n formattedValue: currentValue\n });\n }\n },\n isValueChanged: function isValueChanged(currentValue, newValue) {\n if (newValue === null && currentValue !== null) {\n return true;\n }\n if (newValue != null) {\n var parsedCurrentValue = typeof currentValue === 'string' ? this.parseValue(currentValue) : currentValue;\n return newValue !== parsedCurrentValue;\n }\n return false;\n },\n validateValue: function validateValue(value) {\n if (value === '-' || value == null) {\n return null;\n }\n if (this.min != null && value < this.min) {\n return this.min;\n }\n if (this.max != null && value > this.max) {\n return this.max;\n }\n return value;\n },\n updateInput: function updateInput(value, insertedValueStr, operation, valueStr) {\n insertedValueStr = insertedValueStr || '';\n var inputValue = this.$refs.input.$el.value;\n var newValue = this.formatValue(value);\n var currentLength = inputValue.length;\n if (newValue !== valueStr) {\n newValue = this.concatValues(newValue, valueStr);\n }\n if (currentLength === 0) {\n this.$refs.input.$el.value = newValue;\n this.$refs.input.$el.setSelectionRange(0, 0);\n var index = this.initCursor();\n var selectionEnd = index + insertedValueStr.length;\n this.$refs.input.$el.setSelectionRange(selectionEnd, selectionEnd);\n } else {\n var selectionStart = this.$refs.input.$el.selectionStart;\n var _selectionEnd = this.$refs.input.$el.selectionEnd;\n this.$refs.input.$el.value = newValue;\n var newLength = newValue.length;\n if (operation === 'range-insert') {\n var startValue = this.parseValue((inputValue || '').slice(0, selectionStart));\n var startValueStr = startValue !== null ? startValue.toString() : '';\n var startExpr = startValueStr.split('').join(\"(\".concat(this.groupChar, \")?\"));\n var sRegex = new RegExp(startExpr, 'g');\n sRegex.test(newValue);\n var tExpr = insertedValueStr.split('').join(\"(\".concat(this.groupChar, \")?\"));\n var tRegex = new RegExp(tExpr, 'g');\n tRegex.test(newValue.slice(sRegex.lastIndex));\n _selectionEnd = sRegex.lastIndex + tRegex.lastIndex;\n this.$refs.input.$el.setSelectionRange(_selectionEnd, _selectionEnd);\n } else if (newLength === currentLength) {\n if (operation === 'insert' || operation === 'delete-back-single') {\n this.$refs.input.$el.setSelectionRange(_selectionEnd + 1, _selectionEnd + 1);\n } else if (operation === 'delete-single') {\n this.$refs.input.$el.setSelectionRange(_selectionEnd - 1, _selectionEnd - 1);\n } else if (operation === 'delete-range' || operation === 'spin') {\n this.$refs.input.$el.setSelectionRange(_selectionEnd, _selectionEnd);\n }\n } else if (operation === 'delete-back-single') {\n var prevChar = inputValue.charAt(_selectionEnd - 1);\n var nextChar = inputValue.charAt(_selectionEnd);\n var diff = currentLength - newLength;\n var isGroupChar = this._group.test(nextChar);\n if (isGroupChar && diff === 1) {\n _selectionEnd += 1;\n } else if (!isGroupChar && this.isNumeralChar(prevChar)) {\n _selectionEnd += -1 * diff + 1;\n }\n this._group.lastIndex = 0;\n this.$refs.input.$el.setSelectionRange(_selectionEnd, _selectionEnd);\n } else if (inputValue === '-' && operation === 'insert') {\n this.$refs.input.$el.setSelectionRange(0, 0);\n var _index = this.initCursor();\n var _selectionEnd2 = _index + insertedValueStr.length + 1;\n this.$refs.input.$el.setSelectionRange(_selectionEnd2, _selectionEnd2);\n } else {\n _selectionEnd = _selectionEnd + (newLength - currentLength);\n this.$refs.input.$el.setSelectionRange(_selectionEnd, _selectionEnd);\n }\n }\n this.$refs.input.$el.setAttribute('aria-valuenow', value);\n },\n concatValues: function concatValues(val1, val2) {\n if (val1 && val2) {\n var decimalCharIndex = val2.search(this._decimal);\n this._decimal.lastIndex = 0;\n if (this.suffixChar) {\n return decimalCharIndex !== -1 ? val1.replace(this.suffixChar, '').split(this._decimal)[0] + val2.replace(this.suffixChar, '').slice(decimalCharIndex) + this.suffixChar : val1;\n } else {\n return decimalCharIndex !== -1 ? val1.split(this._decimal)[0] + val2.slice(decimalCharIndex) : val1;\n }\n }\n return val1;\n },\n getDecimalLength: function getDecimalLength(value) {\n if (value) {\n var valueSplit = value.split(this._decimal);\n if (valueSplit.length === 2) {\n return valueSplit[1].replace(this._suffix, '').trim().replace(/\\s/g, '').replace(this._currency, '').length;\n }\n }\n return 0;\n },\n updateModel: function updateModel(event, value) {\n this.d_modelValue = value;\n this.$emit('update:modelValue', value);\n },\n onInputFocus: function onInputFocus(event) {\n this.focused = true;\n if (!this.disabled && !this.readonly && this.$refs.input.$el.value !== getSelection() && this.highlightOnFocus) {\n event.target.select();\n }\n this.$emit('focus', event);\n },\n onInputBlur: function onInputBlur(event) {\n this.focused = false;\n var input = event.target;\n var newValue = this.validateValue(this.parseValue(input.value));\n this.$emit('blur', {\n originalEvent: event,\n value: input.value\n });\n input.value = this.formatValue(newValue);\n input.setAttribute('aria-valuenow', newValue);\n this.updateModel(event, newValue);\n if (!this.disabled && !this.readonly && this.highlightOnFocus) {\n clearSelection();\n }\n },\n clearTimer: function clearTimer() {\n if (this.timer) {\n clearInterval(this.timer);\n }\n },\n maxBoundry: function maxBoundry() {\n return this.d_modelValue >= this.max;\n },\n minBoundry: function minBoundry() {\n return this.d_modelValue <= this.min;\n }\n },\n computed: {\n filled: function filled() {\n return this.modelValue != null && this.modelValue.toString().length > 0;\n },\n upButtonListeners: function upButtonListeners() {\n var _this2 = this;\n return {\n mousedown: function mousedown(event) {\n return _this2.onUpButtonMouseDown(event);\n },\n mouseup: function mouseup(event) {\n return _this2.onUpButtonMouseUp(event);\n },\n mouseleave: function mouseleave(event) {\n return _this2.onUpButtonMouseLeave(event);\n },\n keydown: function keydown(event) {\n return _this2.onUpButtonKeyDown(event);\n },\n keyup: function keyup(event) {\n return _this2.onUpButtonKeyUp(event);\n }\n };\n },\n downButtonListeners: function downButtonListeners() {\n var _this3 = this;\n return {\n mousedown: function mousedown(event) {\n return _this3.onDownButtonMouseDown(event);\n },\n mouseup: function mouseup(event) {\n return _this3.onDownButtonMouseUp(event);\n },\n mouseleave: function mouseleave(event) {\n return _this3.onDownButtonMouseLeave(event);\n },\n keydown: function keydown(event) {\n return _this3.onDownButtonKeyDown(event);\n },\n keyup: function keyup(event) {\n return _this3.onDownButtonKeyUp(event);\n }\n };\n },\n formattedValue: function formattedValue() {\n var val = !this.modelValue && !this.allowEmpty ? 0 : this.modelValue;\n return this.formatValue(val);\n },\n getFormatter: function getFormatter() {\n return this.numberFormat;\n },\n hasFluid: function hasFluid() {\n return isEmpty(this.fluid) ? !!this.$pcFluid : this.fluid;\n }\n },\n components: {\n InputText: InputText,\n AngleUpIcon: AngleUpIcon,\n AngleDownIcon: AngleDownIcon\n }\n};\n\nvar _hoisted_1 = [\"disabled\"];\nvar _hoisted_2 = [\"disabled\"];\nvar _hoisted_3 = [\"disabled\"];\nvar _hoisted_4 = [\"disabled\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_InputText = resolveComponent(\"InputText\");\n return openBlock(), createElementBlock(\"span\", mergeProps({\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [createVNode(_component_InputText, {\n ref: \"input\",\n id: _ctx.inputId,\n role: \"spinbutton\",\n \"class\": normalizeClass([_ctx.cx('pcInput'), _ctx.inputClass]),\n style: normalizeStyle(_ctx.inputStyle),\n value: $options.formattedValue,\n \"aria-valuemin\": _ctx.min,\n \"aria-valuemax\": _ctx.max,\n \"aria-valuenow\": _ctx.modelValue,\n inputmode: _ctx.mode === 'decimal' && !_ctx.minFractionDigits ? 'numeric' : 'decimal',\n disabled: _ctx.disabled,\n readonly: _ctx.readonly,\n placeholder: _ctx.placeholder,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel,\n invalid: _ctx.invalid,\n variant: _ctx.variant,\n onInput: $options.onUserInput,\n onKeydown: $options.onInputKeyDown,\n onKeypress: $options.onInputKeyPress,\n onPaste: $options.onPaste,\n onClick: $options.onInputClick,\n onFocus: $options.onInputFocus,\n onBlur: $options.onInputBlur,\n pt: _ctx.ptm('pcInput'),\n unstyled: _ctx.unstyled\n }, null, 8, [\"id\", \"class\", \"style\", \"value\", \"aria-valuemin\", \"aria-valuemax\", \"aria-valuenow\", \"inputmode\", \"disabled\", \"readonly\", \"placeholder\", \"aria-labelledby\", \"aria-label\", \"invalid\", \"variant\", \"onInput\", \"onKeydown\", \"onKeypress\", \"onPaste\", \"onClick\", \"onFocus\", \"onBlur\", \"pt\", \"unstyled\"]), _ctx.showButtons && _ctx.buttonLayout === 'stacked' ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('buttonGroup')\n }, _ctx.ptm('buttonGroup')), [renderSlot(_ctx.$slots, \"incrementbutton\", {\n listeners: $options.upButtonListeners\n }, function () {\n return [createElementVNode(\"button\", mergeProps({\n \"class\": [_ctx.cx('incrementButton'), _ctx.incrementButtonClass]\n }, toHandlers($options.upButtonListeners, true), {\n disabled: _ctx.disabled,\n tabindex: -1,\n \"aria-hidden\": \"true\",\n type: \"button\"\n }, _ctx.ptm('incrementButton')), [renderSlot(_ctx.$slots, _ctx.$slots.incrementicon ? 'incrementicon' : 'incrementbuttonicon', {}, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.incrementIcon || _ctx.incrementButtonIcon ? 'span' : 'AngleUpIcon'), mergeProps({\n \"class\": [_ctx.incrementIcon, _ctx.incrementButtonIcon]\n }, _ctx.ptm('incrementIcon'), {\n \"data-pc-section\": \"incrementicon\"\n }), null, 16, [\"class\"]))];\n })], 16, _hoisted_1)];\n }), renderSlot(_ctx.$slots, \"decrementbutton\", {\n listeners: $options.downButtonListeners\n }, function () {\n return [createElementVNode(\"button\", mergeProps({\n \"class\": [_ctx.cx('decrementButton'), _ctx.decrementButtonClass]\n }, toHandlers($options.downButtonListeners, true), {\n disabled: _ctx.disabled,\n tabindex: -1,\n \"aria-hidden\": \"true\",\n type: \"button\"\n }, _ctx.ptm('decrementButton')), [renderSlot(_ctx.$slots, _ctx.$slots.decrementicon ? 'decrementicon' : 'decrementbuttonicon', {}, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.decrementIcon || _ctx.decrementButtonIcon ? 'span' : 'AngleDownIcon'), mergeProps({\n \"class\": [_ctx.decrementIcon, _ctx.decrementButtonIcon]\n }, _ctx.ptm('decrementIcon'), {\n \"data-pc-section\": \"decrementicon\"\n }), null, 16, [\"class\"]))];\n })], 16, _hoisted_2)];\n })], 16)) : createCommentVNode(\"\", true), renderSlot(_ctx.$slots, \"incrementbutton\", {\n listeners: $options.upButtonListeners\n }, function () {\n return [_ctx.showButtons && _ctx.buttonLayout !== 'stacked' ? (openBlock(), createElementBlock(\"button\", mergeProps({\n key: 0,\n \"class\": [_ctx.cx('incrementButton'), _ctx.incrementButtonClass]\n }, toHandlers($options.upButtonListeners, true), {\n disabled: _ctx.disabled,\n tabindex: -1,\n \"aria-hidden\": \"true\",\n type: \"button\"\n }, _ctx.ptm('incrementButton')), [renderSlot(_ctx.$slots, _ctx.$slots.incrementicon ? 'incrementicon' : 'incrementbuttonicon', {}, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.incrementIcon || _ctx.incrementButtonIcon ? 'span' : 'AngleUpIcon'), mergeProps({\n \"class\": [_ctx.incrementIcon, _ctx.incrementButtonIcon]\n }, _ctx.ptm('incrementIcon'), {\n \"data-pc-section\": \"incrementicon\"\n }), null, 16, [\"class\"]))];\n })], 16, _hoisted_3)) : createCommentVNode(\"\", true)];\n }), renderSlot(_ctx.$slots, \"decrementbutton\", {\n listeners: $options.downButtonListeners\n }, function () {\n return [_ctx.showButtons && _ctx.buttonLayout !== 'stacked' ? (openBlock(), createElementBlock(\"button\", mergeProps({\n key: 0,\n \"class\": [_ctx.cx('decrementButton'), _ctx.decrementButtonClass]\n }, toHandlers($options.downButtonListeners, true), {\n disabled: _ctx.disabled,\n tabindex: -1,\n \"aria-hidden\": \"true\",\n type: \"button\"\n }, _ctx.ptm('decrementButton')), [renderSlot(_ctx.$slots, _ctx.$slots.decrementicon ? 'decrementicon' : 'decrementbuttonicon', {}, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.decrementIcon || _ctx.decrementButtonIcon ? 'span' : 'AngleDownIcon'), mergeProps({\n \"class\": [_ctx.decrementIcon, _ctx.decrementButtonIcon]\n }, _ctx.ptm('decrementIcon'), {\n \"data-pc-section\": \"decrementicon\"\n }), null, 16, [\"class\"]))];\n })], 16, _hoisted_4)) : createCommentVNode(\"\", true)];\n })], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-toggleswitch {\\n display: inline-block;\\n width: \".concat(dt('toggleswitch.width'), \";\\n height: \").concat(dt('toggleswitch.height'), \";\\n}\\n\\n.p-toggleswitch-input {\\n cursor: pointer;\\n appearance: none;\\n position: absolute;\\n top: 0;\\n left: 0;\\n width: 100%;\\n height: 100%;\\n padding: 0;\\n margin: 0;\\n opacity: 0;\\n z-index: 1;\\n outline: 0 none;\\n border-radius: \").concat(dt('toggleswitch.border.radius'), \";\\n}\\n\\n.p-toggleswitch-slider {\\n display: inline-block;\\n cursor: pointer;\\n width: 100%;\\n height: 100%;\\n border-width: \").concat(dt('toggleswitch.border.width'), \";\\n border-style: solid;\\n border-color: \").concat(dt('toggleswitch.border.color'), \";\\n background: \").concat(dt('toggleswitch.background'), \";\\n transition: background \").concat(dt('toggleswitch.transition.duration'), \", color \").concat(dt('toggleswitch.transition.duration'), \", border-color \").concat(dt('toggleswitch.transition.duration'), \", outline-color \").concat(dt('toggleswitch.transition.duration'), \", box-shadow \").concat(dt('toggleswitch.transition.duration'), \";\\n border-radius: \").concat(dt('toggleswitch.border.radius'), \";\\n outline-color: transparent;\\n box-shadow: \").concat(dt('toggleswitch.shadow'), \";\\n}\\n\\n.p-toggleswitch-slider:before {\\n position: absolute;\\n content: \\\"\\\";\\n top: 50%;\\n background: \").concat(dt('toggleswitch.handle.background'), \";\\n width: \").concat(dt('toggleswitch.handle.size'), \";\\n height: \").concat(dt('toggleswitch.handle.size'), \";\\n left: \").concat(dt('toggleswitch.gap'), \";\\n margin-top: calc(-1 * calc(\").concat(dt('toggleswitch.handle.size'), \" / 2));\\n border-radius: \").concat(dt('toggleswitch.handle.border.radius'), \";\\n transition: background \").concat(dt('toggleswitch.transition.duration'), \", left \").concat(dt('toggleswitch.slide.duration'), \";\\n}\\n\\n.p-toggleswitch.p-toggleswitch-checked .p-toggleswitch-slider {\\n background: \").concat(dt('toggleswitch.checked.background'), \";\\n border-color: \").concat(dt('toggleswitch.checked.border.color'), \";\\n}\\n\\n.p-toggleswitch.p-toggleswitch-checked .p-toggleswitch-slider:before {\\n background: \").concat(dt('toggleswitch.handle.checked.background'), \";\\n left: calc(\").concat(dt('toggleswitch.width'), \" - calc(\").concat(dt('toggleswitch.handle.size'), \" + \").concat(dt('toggleswitch.gap'), \"));\\n}\\n\\n.p-toggleswitch:not(.p-disabled):has(.p-toggleswitch-input:hover) .p-toggleswitch-slider {\\n background: \").concat(dt('toggleswitch.hover.background'), \";\\n border-color: \").concat(dt('toggleswitch.hover.border.color'), \";\\n}\\n\\n.p-toggleswitch:not(.p-disabled):has(.p-toggleswitch-input:hover) .p-toggleswitch-slider:before {\\n background: \").concat(dt('toggleswitch.handle.hover.background'), \";\\n}\\n\\n.p-toggleswitch:not(.p-disabled):has(.p-toggleswitch-input:hover).p-toggleswitch-checked .p-toggleswitch-slider {\\n background: \").concat(dt('toggleswitch.checked.hover.background'), \";\\n border-color: \").concat(dt('toggleswitch.checked.hover.border.color'), \";\\n}\\n\\n.p-toggleswitch:not(.p-disabled):has(.p-toggleswitch-input:hover).p-toggleswitch-checked .p-toggleswitch-slider:before {\\n background: \").concat(dt('toggleswitch.handle.checked.hover.background'), \";\\n}\\n\\n.p-toggleswitch:not(.p-disabled):has(.p-toggleswitch-input:focus-visible) .p-toggleswitch-slider {\\n box-shadow: \").concat(dt('toggleswitch.focus.ring.shadow'), \";\\n outline: \").concat(dt('toggleswitch.focus.ring.width'), \" \").concat(dt('toggleswitch.focus.ring.style'), \" \").concat(dt('toggleswitch.focus.ring.color'), \";\\n outline-offset: \").concat(dt('toggleswitch.focus.ring.offset'), \";\\n}\\n\\n.p-toggleswitch.p-invalid > .p-toggleswitch-slider {\\n border-color: \").concat(dt('toggleswitch.invalid.border.color'), \";\\n}\\n\\n.p-toggleswitch.p-disabled {\\n opacity: 1;\\n}\\n\\n.p-toggleswitch.p-disabled .p-toggleswitch-slider {\\n background: \").concat(dt('toggleswitch.disabled.background'), \";\\n}\\n\\n.p-toggleswitch.p-disabled .p-toggleswitch-slider:before {\\n background: \").concat(dt('toggleswitch.handle.disabled.background'), \";\\n}\\n\");\n};\nvar inlineStyles = {\n root: {\n position: 'relative'\n }\n};\nvar classes = {\n root: function root(_ref2) {\n var instance = _ref2.instance,\n props = _ref2.props;\n return ['p-toggleswitch p-component', {\n 'p-toggleswitch-checked': instance.checked,\n 'p-disabled': props.disabled,\n 'p-invalid': props.invalid\n }];\n },\n input: 'p-toggleswitch-input',\n slider: 'p-toggleswitch-slider'\n};\nvar ToggleSwitchStyle = BaseStyle.extend({\n name: 'toggleswitch',\n theme: theme,\n classes: classes,\n inlineStyles: inlineStyles\n});\n\nexport { ToggleSwitchStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseComponent from '@primevue/core/basecomponent';\nimport ToggleSwitchStyle from 'primevue/toggleswitch/style';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script$1 = {\n name: 'BaseToggleSwitch',\n \"extends\": BaseComponent,\n props: {\n modelValue: {\n type: null,\n \"default\": false\n },\n trueValue: {\n type: null,\n \"default\": true\n },\n falseValue: {\n type: null,\n \"default\": false\n },\n invalid: {\n type: Boolean,\n \"default\": false\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n readonly: {\n type: Boolean,\n \"default\": false\n },\n tabindex: {\n type: Number,\n \"default\": null\n },\n inputId: {\n type: String,\n \"default\": null\n },\n inputClass: {\n type: [String, Object],\n \"default\": null\n },\n inputStyle: {\n type: Object,\n \"default\": null\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n ariaLabel: {\n type: String,\n \"default\": null\n }\n },\n style: ToggleSwitchStyle,\n provide: function provide() {\n return {\n $pcToggleSwitch: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'ToggleSwitch',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:modelValue', 'change', 'focus', 'blur'],\n methods: {\n getPTOptions: function getPTOptions(key) {\n var _ptm = key === 'root' ? this.ptmi : this.ptm;\n return _ptm(key, {\n context: {\n checked: this.checked,\n disabled: this.disabled\n }\n });\n },\n onChange: function onChange(event) {\n if (!this.disabled && !this.readonly) {\n var newValue = this.checked ? this.falseValue : this.trueValue;\n this.$emit('update:modelValue', newValue);\n this.$emit('change', event);\n }\n },\n onFocus: function onFocus(event) {\n this.$emit('focus', event);\n },\n onBlur: function onBlur(event) {\n this.$emit('blur', event);\n }\n },\n computed: {\n checked: function checked() {\n return this.modelValue === this.trueValue;\n }\n }\n};\n\nvar _hoisted_1 = [\"data-p-checked\", \"data-p-disabled\"];\nvar _hoisted_2 = [\"id\", \"checked\", \"tabindex\", \"disabled\", \"readonly\", \"aria-checked\", \"aria-labelledby\", \"aria-label\", \"aria-invalid\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root'),\n style: _ctx.sx('root')\n }, $options.getPTOptions('root'), {\n \"data-p-checked\": $options.checked,\n \"data-p-disabled\": _ctx.disabled\n }), [createElementVNode(\"input\", mergeProps({\n id: _ctx.inputId,\n type: \"checkbox\",\n role: \"switch\",\n \"class\": [_ctx.cx('input'), _ctx.inputClass],\n style: _ctx.inputStyle,\n checked: $options.checked,\n tabindex: _ctx.tabindex,\n disabled: _ctx.disabled,\n readonly: _ctx.readonly,\n \"aria-checked\": $options.checked,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel,\n \"aria-invalid\": _ctx.invalid || undefined,\n onFocus: _cache[0] || (_cache[0] = function () {\n return $options.onFocus && $options.onFocus.apply($options, arguments);\n }),\n onBlur: _cache[1] || (_cache[1] = function () {\n return $options.onBlur && $options.onBlur.apply($options, arguments);\n }),\n onChange: _cache[2] || (_cache[2] = function () {\n return $options.onChange && $options.onChange.apply($options, arguments);\n })\n }, $options.getPTOptions('input')), null, 16, _hoisted_2), createElementVNode(\"span\", mergeProps({\n \"class\": _ctx.cx('slider')\n }, $options.getPTOptions('slider')), null, 16)], 16, _hoisted_1);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-tag {\\n display: inline-flex;\\n align-items: center;\\n justify-content: center;\\n background: \".concat(dt('tag.primary.background'), \";\\n color: \").concat(dt('tag.primary.color'), \";\\n font-size: \").concat(dt('tag.font.size'), \";\\n font-weight: \").concat(dt('tag.font.weight'), \";\\n padding: \").concat(dt('tag.padding'), \";\\n border-radius: \").concat(dt('tag.border.radius'), \";\\n gap: \").concat(dt('tag.gap'), \";\\n}\\n\\n.p-tag-icon {\\n font-size: \").concat(dt('tag.icon.size'), \";\\n width: \").concat(dt('tag.icon.size'), \";\\n height:\").concat(dt('tag.icon.size'), \";\\n}\\n\\n.p-tag-rounded {\\n border-radius: \").concat(dt('tag.rounded.border.radius'), \";\\n}\\n\\n.p-tag-success {\\n background: \").concat(dt('tag.success.background'), \";\\n color: \").concat(dt('tag.success.color'), \";\\n}\\n\\n.p-tag-info {\\n background: \").concat(dt('tag.info.background'), \";\\n color: \").concat(dt('tag.info.color'), \";\\n}\\n\\n.p-tag-warn {\\n background: \").concat(dt('tag.warn.background'), \";\\n color: \").concat(dt('tag.warn.color'), \";\\n}\\n\\n.p-tag-danger {\\n background: \").concat(dt('tag.danger.background'), \";\\n color: \").concat(dt('tag.danger.color'), \";\\n}\\n\\n.p-tag-secondary {\\n background: \").concat(dt('tag.secondary.background'), \";\\n color: \").concat(dt('tag.secondary.color'), \";\\n}\\n\\n.p-tag-contrast {\\n background: \").concat(dt('tag.contrast.background'), \";\\n color: \").concat(dt('tag.contrast.color'), \";\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var props = _ref2.props;\n return ['p-tag p-component', {\n 'p-tag-info': props.severity === 'info',\n 'p-tag-success': props.severity === 'success',\n 'p-tag-warn': props.severity === 'warn',\n 'p-tag-danger': props.severity === 'danger',\n 'p-tag-secondary': props.severity === 'secondary',\n 'p-tag-contrast': props.severity === 'contrast',\n 'p-tag-rounded': props.rounded\n }];\n },\n icon: 'p-tag-icon',\n label: 'p-tag-label'\n};\nvar TagStyle = BaseStyle.extend({\n name: 'tag',\n theme: theme,\n classes: classes\n});\n\nexport { TagStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseComponent from '@primevue/core/basecomponent';\nimport TagStyle from 'primevue/tag/style';\nimport { openBlock, createElementBlock, mergeProps, createBlock, resolveDynamicComponent, createCommentVNode, renderSlot, createElementVNode, toDisplayString } from 'vue';\n\nvar script$1 = {\n name: 'BaseTag',\n \"extends\": BaseComponent,\n props: {\n value: null,\n severity: null,\n rounded: Boolean,\n icon: String\n },\n style: TagStyle,\n provide: function provide() {\n return {\n $pcTag: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'Tag',\n \"extends\": script$1,\n inheritAttrs: false\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"span\", mergeProps({\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [_ctx.$slots.icon ? (openBlock(), createBlock(resolveDynamicComponent(_ctx.$slots.icon), mergeProps({\n key: 0,\n \"class\": _ctx.cx('icon')\n }, _ctx.ptm('icon')), null, 16, [\"class\"])) : _ctx.icon ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 1,\n \"class\": [_ctx.cx('icon'), _ctx.icon]\n }, _ctx.ptm('icon')), null, 16)) : createCommentVNode(\"\", true), _ctx.value != null || _ctx.$slots[\"default\"] ? renderSlot(_ctx.$slots, \"default\", {\n key: 2\n }, function () {\n return [createElementVNode(\"span\", mergeProps({\n \"class\": _ctx.cx('label')\n }, _ctx.ptm('label')), toDisplayString(_ctx.value), 17)];\n }) : createCommentVNode(\"\", true)], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-slider {\\n position: relative;\\n background: \".concat(dt('slider.track.background'), \";\\n border-radius: \").concat(dt('slider.border.radius'), \";\\n}\\n\\n.p-slider-handle {\\n cursor: grab;\\n touch-action: none;\\n display: flex;\\n justify-content: center;\\n align-items: center;\\n height: \").concat(dt('slider.handle.height'), \";\\n width: \").concat(dt('slider.handle.width'), \";\\n background: \").concat(dt('slider.handle.background'), \";\\n border-radius: \").concat(dt('slider.handle.border.radius'), \";\\n transition: background \").concat(dt('slider.transition.duration'), \", color \").concat(dt('slider.transition.duration'), \", border-color \").concat(dt('slider.transition.duration'), \", box-shadow \").concat(dt('slider.transition.duration'), \", outline-color \").concat(dt('slider.transition.duration'), \";\\n outline-color: transparent;\\n}\\n\\n.p-slider-handle::before {\\n content: \\\"\\\";\\n width: \").concat(dt('slider.handle.content.width'), \";\\n height: \").concat(dt('slider.handle.content.height'), \";\\n display: block;\\n background: \").concat(dt('slider.handle.content.background'), \";\\n border-radius: \").concat(dt('slider.handle.content.border.radius'), \";\\n box-shadow: \").concat(dt('slider.handle.content.shadow'), \";\\n transition: background \").concat(dt('slider.transition.duration'), \";\\n}\\n\\n.p-slider:not(.p-disabled) .p-slider-handle:hover {\\n background: \").concat(dt('slider.handle.hover.background'), \";\\n}\\n\\n.p-slider:not(.p-disabled) .p-slider-handle:hover::before {\\n background: \").concat(dt('slider.handle.content.hover.background'), \";\\n}\\n\\n.p-slider-handle:focus-visible {\\n border-color: \").concat(dt('slider.handle.focus.border.color'), \";\\n box-shadow: \").concat(dt('slider.handle.focus.ring.shadow'), \";\\n outline: \").concat(dt('slider.handle.focus.ring.width'), \" \").concat(dt('slider.handle.focus.ring.style'), \" \").concat(dt('slider.handle.focus.ring.color'), \";\\n outline-offset: \").concat(dt('slider.handle.focus.ring.offset'), \";\\n}\\n\\n.p-slider-range {\\n display: block;\\n background: \").concat(dt('slider.range.background'), \";\\n border-radius: \").concat(dt('slider.border.radius'), \";\\n}\\n\\n.p-slider.p-slider-horizontal {\\n height: \").concat(dt('slider.track.size'), \";\\n}\\n\\n.p-slider-horizontal .p-slider-range {\\n top: 0;\\n left: 0;\\n height: 100%;\\n}\\n\\n.p-slider-horizontal .p-slider-handle {\\n top: 50%;\\n margin-top: calc(-1 * calc(\").concat(dt('slider.handle.height'), \" / 2));\\n margin-left: calc(-1 * calc(\").concat(dt('slider.handle.width'), \" / 2));\\n}\\n\\n.p-slider-vertical {\\n min-height: 100px;\\n width: \").concat(dt('slider.track.size'), \";\\n}\\n\\n.p-slider-vertical .p-slider-handle {\\n left: 50%;\\n margin-left: calc(-1 * calc(\").concat(dt('slider.handle.width'), \" / 2));\\n margin-bottom: calc(-1 * calc(\").concat(dt('slider.handle.height'), \" / 2));\\n}\\n\\n.p-slider-vertical .p-slider-range {\\n bottom: 0;\\n left: 0;\\n width: 100%;\\n}\\n\");\n};\nvar inlineStyles = {\n handle: {\n position: 'absolute'\n },\n range: {\n position: 'absolute'\n }\n};\nvar classes = {\n root: function root(_ref2) {\n var props = _ref2.props;\n return ['p-slider p-component', {\n 'p-disabled': props.disabled,\n 'p-slider-horizontal': props.orientation === 'horizontal',\n 'p-slider-vertical': props.orientation === 'vertical'\n }];\n },\n range: 'p-slider-range',\n handle: 'p-slider-handle'\n};\nvar SliderStyle = BaseStyle.extend({\n name: 'slider',\n theme: theme,\n classes: classes,\n inlineStyles: inlineStyles\n});\n\nexport { SliderStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { getWindowScrollLeft, getWindowScrollTop, getAttribute } from '@primeuix/utils/dom';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport SliderStyle from 'primevue/slider/style';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode, createCommentVNode } from 'vue';\n\nvar script$1 = {\n name: 'BaseSlider',\n \"extends\": BaseComponent,\n props: {\n modelValue: [Number, Array],\n min: {\n type: Number,\n \"default\": 0\n },\n max: {\n type: Number,\n \"default\": 100\n },\n orientation: {\n type: String,\n \"default\": 'horizontal'\n },\n step: {\n type: Number,\n \"default\": null\n },\n range: {\n type: Boolean,\n \"default\": false\n },\n disabled: {\n type: Boolean,\n \"default\": false\n },\n tabindex: {\n type: Number,\n \"default\": 0\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n ariaLabel: {\n type: String,\n \"default\": null\n }\n },\n style: SliderStyle,\n provide: function provide() {\n return {\n $pcSlider: this,\n $parentInstance: this\n };\n }\n};\n\nfunction _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }\nfunction _nonIterableSpread() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _iterableToArray(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nvar script = {\n name: 'Slider',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:modelValue', 'change', 'slideend'],\n dragging: false,\n handleIndex: null,\n initX: null,\n initY: null,\n barWidth: null,\n barHeight: null,\n dragListener: null,\n dragEndListener: null,\n beforeUnmount: function beforeUnmount() {\n this.unbindDragListeners();\n },\n methods: {\n updateDomData: function updateDomData() {\n var rect = this.$el.getBoundingClientRect();\n this.initX = rect.left + getWindowScrollLeft();\n this.initY = rect.top + getWindowScrollTop();\n this.barWidth = this.$el.offsetWidth;\n this.barHeight = this.$el.offsetHeight;\n },\n setValue: function setValue(event) {\n var handleValue;\n var pageX = event.touches ? event.touches[0].pageX : event.pageX;\n var pageY = event.touches ? event.touches[0].pageY : event.pageY;\n if (this.orientation === 'horizontal') handleValue = (pageX - this.initX) * 100 / this.barWidth;else handleValue = (this.initY + this.barHeight - pageY) * 100 / this.barHeight;\n var newValue = (this.max - this.min) * (handleValue / 100) + this.min;\n if (this.step) {\n var oldValue = this.range ? this.value[this.handleIndex] : this.value;\n var diff = newValue - oldValue;\n if (diff < 0) newValue = oldValue + Math.ceil(newValue / this.step - oldValue / this.step) * this.step;else if (diff > 0) newValue = oldValue + Math.floor(newValue / this.step - oldValue / this.step) * this.step;\n } else {\n newValue = Math.floor(newValue);\n }\n this.updateModel(event, newValue);\n },\n updateModel: function updateModel(event, value) {\n var newValue = parseFloat(value.toFixed(10));\n var modelValue;\n if (this.range) {\n modelValue = this.value ? _toConsumableArray(this.value) : [];\n if (this.handleIndex == 0) {\n if (newValue < this.min) newValue = this.min;else if (newValue >= this.max) newValue = this.max;\n modelValue[0] = newValue;\n } else {\n if (newValue > this.max) newValue = this.max;else if (newValue <= this.min) newValue = this.min;\n modelValue[1] = newValue;\n }\n } else {\n if (newValue < this.min) newValue = this.min;else if (newValue > this.max) newValue = this.max;\n modelValue = newValue;\n }\n this.$emit('update:modelValue', modelValue);\n this.$emit('change', modelValue);\n },\n onDragStart: function onDragStart(event, index) {\n if (this.disabled) {\n return;\n }\n this.$el.setAttribute('data-p-sliding', true);\n this.dragging = true;\n this.updateDomData();\n if (this.range && this.value[0] === this.max) {\n this.handleIndex = 0;\n } else {\n this.handleIndex = index;\n }\n event.currentTarget.focus();\n event.preventDefault();\n },\n onDrag: function onDrag(event) {\n if (this.dragging) {\n this.setValue(event);\n event.preventDefault();\n }\n },\n onDragEnd: function onDragEnd(event) {\n if (this.dragging) {\n this.dragging = false;\n this.$el.setAttribute('data-p-sliding', false);\n this.$emit('slideend', {\n originalEvent: event,\n value: this.value\n });\n }\n },\n onBarClick: function onBarClick(event) {\n if (this.disabled) {\n return;\n }\n if (getAttribute(event.target, 'data-pc-section') !== 'handle') {\n this.updateDomData();\n this.setValue(event);\n }\n },\n onMouseDown: function onMouseDown(event, index) {\n this.bindDragListeners();\n this.onDragStart(event, index);\n },\n onKeyDown: function onKeyDown(event, index) {\n this.handleIndex = index;\n switch (event.code) {\n case 'ArrowDown':\n case 'ArrowLeft':\n this.decrementValue(event, index);\n event.preventDefault();\n break;\n case 'ArrowUp':\n case 'ArrowRight':\n this.incrementValue(event, index);\n event.preventDefault();\n break;\n case 'PageDown':\n this.decrementValue(event, index, true);\n event.preventDefault();\n break;\n case 'PageUp':\n this.incrementValue(event, index, true);\n event.preventDefault();\n break;\n case 'Home':\n this.updateModel(event, this.min);\n event.preventDefault();\n break;\n case 'End':\n this.updateModel(event, this.max);\n event.preventDefault();\n break;\n }\n },\n decrementValue: function decrementValue(event, index) {\n var pageKey = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;\n var newValue;\n if (this.range) {\n if (this.step) newValue = this.value[index] - this.step;else newValue = this.value[index] - 1;\n } else {\n if (this.step) newValue = this.value - this.step;else if (!this.step && pageKey) newValue = this.value - 10;else newValue = this.value - 1;\n }\n this.updateModel(event, newValue);\n event.preventDefault();\n },\n incrementValue: function incrementValue(event, index) {\n var pageKey = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;\n var newValue;\n if (this.range) {\n if (this.step) newValue = this.value[index] + this.step;else newValue = this.value[index] + 1;\n } else {\n if (this.step) newValue = this.value + this.step;else if (!this.step && pageKey) newValue = this.value + 10;else newValue = this.value + 1;\n }\n this.updateModel(event, newValue);\n event.preventDefault();\n },\n bindDragListeners: function bindDragListeners() {\n if (!this.dragListener) {\n this.dragListener = this.onDrag.bind(this);\n document.addEventListener('mousemove', this.dragListener);\n }\n if (!this.dragEndListener) {\n this.dragEndListener = this.onDragEnd.bind(this);\n document.addEventListener('mouseup', this.dragEndListener);\n }\n },\n unbindDragListeners: function unbindDragListeners() {\n if (this.dragListener) {\n document.removeEventListener('mousemove', this.dragListener);\n this.dragListener = null;\n }\n if (this.dragEndListener) {\n document.removeEventListener('mouseup', this.dragEndListener);\n this.dragEndListener = null;\n }\n }\n },\n computed: {\n value: function value() {\n var _this$modelValue3;\n if (this.range) {\n var _this$modelValue$, _this$modelValue, _this$modelValue$2, _this$modelValue2;\n return [(_this$modelValue$ = (_this$modelValue = this.modelValue) === null || _this$modelValue === void 0 ? void 0 : _this$modelValue[0]) !== null && _this$modelValue$ !== void 0 ? _this$modelValue$ : this.min, (_this$modelValue$2 = (_this$modelValue2 = this.modelValue) === null || _this$modelValue2 === void 0 ? void 0 : _this$modelValue2[1]) !== null && _this$modelValue$2 !== void 0 ? _this$modelValue$2 : this.max];\n }\n return (_this$modelValue3 = this.modelValue) !== null && _this$modelValue3 !== void 0 ? _this$modelValue3 : this.min;\n },\n horizontal: function horizontal() {\n return this.orientation === 'horizontal';\n },\n vertical: function vertical() {\n return this.orientation === 'vertical';\n },\n rangeStyle: function rangeStyle() {\n if (this.range) {\n var rangeSliderWidth = this.rangeEndPosition > this.rangeStartPosition ? this.rangeEndPosition - this.rangeStartPosition : this.rangeStartPosition - this.rangeEndPosition;\n var rangeSliderPosition = this.rangeEndPosition > this.rangeStartPosition ? this.rangeStartPosition : this.rangeEndPosition;\n if (this.horizontal) return {\n left: rangeSliderPosition + '%',\n width: rangeSliderWidth + '%'\n };else return {\n bottom: rangeSliderPosition + '%',\n height: rangeSliderWidth + '%'\n };\n } else {\n if (this.horizontal) return {\n width: this.handlePosition + '%'\n };else return {\n height: this.handlePosition + '%'\n };\n }\n },\n handleStyle: function handleStyle() {\n if (this.horizontal) return {\n left: this.handlePosition + '%'\n };else return {\n bottom: this.handlePosition + '%'\n };\n },\n handlePosition: function handlePosition() {\n if (this.value < this.min) return 0;else if (this.value > this.max) return 100;else return (this.value - this.min) * 100 / (this.max - this.min);\n },\n rangeStartPosition: function rangeStartPosition() {\n if (this.value && this.value[0]) return (this.value[0] < this.min ? 0 : this.value[0] - this.min) * 100 / (this.max - this.min);else return 0;\n },\n rangeEndPosition: function rangeEndPosition() {\n if (this.value && this.value.length === 2) return (this.value[1] > this.max ? 100 : this.value[1] - this.min) * 100 / (this.max - this.min);else return 100;\n },\n rangeStartHandleStyle: function rangeStartHandleStyle() {\n if (this.horizontal) return {\n left: this.rangeStartPosition + '%'\n };else return {\n bottom: this.rangeStartPosition + '%'\n };\n },\n rangeEndHandleStyle: function rangeEndHandleStyle() {\n if (this.horizontal) return {\n left: this.rangeEndPosition + '%'\n };else return {\n bottom: this.rangeEndPosition + '%'\n };\n }\n }\n};\n\nvar _hoisted_1 = [\"tabindex\", \"aria-valuemin\", \"aria-valuenow\", \"aria-valuemax\", \"aria-labelledby\", \"aria-label\", \"aria-orientation\"];\nvar _hoisted_2 = [\"tabindex\", \"aria-valuemin\", \"aria-valuenow\", \"aria-valuemax\", \"aria-labelledby\", \"aria-label\", \"aria-orientation\"];\nvar _hoisted_3 = [\"tabindex\", \"aria-valuemin\", \"aria-valuenow\", \"aria-valuemax\", \"aria-labelledby\", \"aria-label\", \"aria-orientation\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root'),\n onClick: _cache[15] || (_cache[15] = function () {\n return $options.onBarClick && $options.onBarClick.apply($options, arguments);\n })\n }, _ctx.ptmi('root'), {\n \"data-p-sliding\": false\n }), [createElementVNode(\"span\", mergeProps({\n \"class\": _ctx.cx('range'),\n style: [_ctx.sx('range'), $options.rangeStyle]\n }, _ctx.ptm('range')), null, 16), !_ctx.range ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('handle'),\n style: [_ctx.sx('handle'), $options.handleStyle],\n onTouchstartPassive: _cache[0] || (_cache[0] = function ($event) {\n return $options.onDragStart($event);\n }),\n onTouchmovePassive: _cache[1] || (_cache[1] = function ($event) {\n return $options.onDrag($event);\n }),\n onTouchend: _cache[2] || (_cache[2] = function ($event) {\n return $options.onDragEnd($event);\n }),\n onMousedown: _cache[3] || (_cache[3] = function ($event) {\n return $options.onMouseDown($event);\n }),\n onKeydown: _cache[4] || (_cache[4] = function ($event) {\n return $options.onKeyDown($event);\n }),\n tabindex: _ctx.tabindex,\n role: \"slider\",\n \"aria-valuemin\": _ctx.min,\n \"aria-valuenow\": _ctx.modelValue,\n \"aria-valuemax\": _ctx.max,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel,\n \"aria-orientation\": _ctx.orientation\n }, _ctx.ptm('handle')), null, 16, _hoisted_1)) : createCommentVNode(\"\", true), _ctx.range ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 1,\n \"class\": _ctx.cx('handle'),\n style: [_ctx.sx('handle'), $options.rangeStartHandleStyle],\n onTouchstartPassive: _cache[5] || (_cache[5] = function ($event) {\n return $options.onDragStart($event, 0);\n }),\n onTouchmovePassive: _cache[6] || (_cache[6] = function ($event) {\n return $options.onDrag($event);\n }),\n onTouchend: _cache[7] || (_cache[7] = function ($event) {\n return $options.onDragEnd($event);\n }),\n onMousedown: _cache[8] || (_cache[8] = function ($event) {\n return $options.onMouseDown($event, 0);\n }),\n onKeydown: _cache[9] || (_cache[9] = function ($event) {\n return $options.onKeyDown($event, 0);\n }),\n tabindex: _ctx.tabindex,\n role: \"slider\",\n \"aria-valuemin\": _ctx.min,\n \"aria-valuenow\": _ctx.modelValue ? _ctx.modelValue[0] : null,\n \"aria-valuemax\": _ctx.max,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel,\n \"aria-orientation\": _ctx.orientation\n }, _ctx.ptm('startHandler')), null, 16, _hoisted_2)) : createCommentVNode(\"\", true), _ctx.range ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 2,\n \"class\": _ctx.cx('handle'),\n style: [_ctx.sx('handle'), $options.rangeEndHandleStyle],\n onTouchstartPassive: _cache[10] || (_cache[10] = function ($event) {\n return $options.onDragStart($event, 1);\n }),\n onTouchmovePassive: _cache[11] || (_cache[11] = function ($event) {\n return $options.onDrag($event);\n }),\n onTouchend: _cache[12] || (_cache[12] = function ($event) {\n return $options.onDragEnd($event);\n }),\n onMousedown: _cache[13] || (_cache[13] = function ($event) {\n return $options.onMouseDown($event, 1);\n }),\n onKeydown: _cache[14] || (_cache[14] = function ($event) {\n return $options.onKeyDown($event, 1);\n }),\n tabindex: _ctx.tabindex,\n role: \"slider\",\n \"aria-valuemin\": _ctx.min,\n \"aria-valuenow\": _ctx.modelValue ? _ctx.modelValue[1] : null,\n \"aria-valuemax\": _ctx.max,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel,\n \"aria-orientation\": _ctx.orientation\n }, _ctx.ptm('endHandler')), null, 16, _hoisted_3)) : createCommentVNode(\"\", true)], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n","export function formatCamelCase(str: string): string {\n // Check if the string is camel case\n const isCamelCase = /^([A-Z][a-z]*)+$/.test(str)\n\n if (!isCamelCase) {\n return str // Return original string if not camel case\n }\n\n // Split the string into words, keeping acronyms together\n const words = str.split(/(?=[A-Z][a-z])|\\d+/)\n\n // Process each word\n const processedWords = words.map((word) => {\n // If the word is all uppercase and longer than one character, it's likely an acronym\n if (word.length > 1 && word === word.toUpperCase()) {\n return word // Keep acronyms as is\n }\n // For other words, ensure the first letter is capitalized\n return word.charAt(0).toUpperCase() + word.slice(1)\n })\n\n // Join the words with spaces\n return processedWords.join(' ')\n}\n\nexport function appendJsonExt(path: string) {\n if (!path.toLowerCase().endsWith('.json')) {\n path += '.json'\n }\n return path\n}\n\nexport function trimJsonExt(path?: string) {\n return path?.replace(/\\.json$/, '')\n}\n\nexport function highlightQuery(text: string, query: string) {\n if (!query) return text\n const regex = new RegExp(`(${query})`, 'gi')\n return text.replace(regex, '$1')\n}\n\nexport function formatNumberWithSuffix(\n num: number,\n {\n precision = 1,\n roundToInt = false\n }: { precision?: number; roundToInt?: boolean } = {}\n): string {\n const suffixes = ['', 'k', 'm', 'b', 't']\n const absNum = Math.abs(num)\n\n if (absNum < 1000) {\n return roundToInt ? Math.round(num).toString() : num.toFixed(precision)\n }\n\n const exp = Math.min(Math.floor(Math.log10(absNum) / 3), suffixes.length - 1)\n const formattedNum = (num / Math.pow(1000, exp)).toFixed(precision)\n\n return `${formattedNum}${suffixes[exp]}`\n}\n\nexport function formatMemory(value?: number) {\n if (value === null || value === undefined) {\n return '-'\n }\n\n const mb = Math.round(value / (1024 * 1024))\n if (mb >= 1024) {\n return `${(mb / 1024).toFixed(2)} GB`\n }\n return `${mb} MB`\n}\n","\n\n\n\n\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'TimesCircleIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n \"fill-rule\": \"evenodd\",\n \"clip-rule\": \"evenodd\",\n d: \"M7 14C5.61553 14 4.26215 13.5895 3.11101 12.8203C1.95987 12.0511 1.06266 10.9579 0.532846 9.67879C0.00303296 8.3997 -0.13559 6.99224 0.134506 5.63437C0.404603 4.2765 1.07129 3.02922 2.05026 2.05026C3.02922 1.07129 4.2765 0.404603 5.63437 0.134506C6.99224 -0.13559 8.3997 0.00303296 9.67879 0.532846C10.9579 1.06266 12.0511 1.95987 12.8203 3.11101C13.5895 4.26215 14 5.61553 14 7C14 8.85652 13.2625 10.637 11.9497 11.9497C10.637 13.2625 8.85652 14 7 14ZM7 1.16667C5.84628 1.16667 4.71846 1.50879 3.75918 2.14976C2.79989 2.79074 2.05222 3.70178 1.61071 4.76768C1.16919 5.83358 1.05367 7.00647 1.27876 8.13803C1.50384 9.26958 2.05941 10.309 2.87521 11.1248C3.69102 11.9406 4.73042 12.4962 5.86198 12.7212C6.99353 12.9463 8.16642 12.8308 9.23232 12.3893C10.2982 11.9478 11.2093 11.2001 11.8502 10.2408C12.4912 9.28154 12.8333 8.15373 12.8333 7C12.8333 5.45291 12.2188 3.96918 11.1248 2.87521C10.0308 1.78125 8.5471 1.16667 7 1.16667ZM4.66662 9.91668C4.58998 9.91704 4.51404 9.90209 4.44325 9.87271C4.37246 9.84333 4.30826 9.8001 4.2544 9.74557C4.14516 9.6362 4.0838 9.48793 4.0838 9.33335C4.0838 9.17876 4.14516 9.0305 4.2544 8.92113L6.17553 7L4.25443 5.07891C4.15139 4.96832 4.09529 4.82207 4.09796 4.67094C4.10063 4.51982 4.16185 4.37563 4.26872 4.26876C4.3756 4.16188 4.51979 4.10066 4.67091 4.09799C4.82204 4.09532 4.96829 4.15142 5.07887 4.25446L6.99997 6.17556L8.92106 4.25446C9.03164 4.15142 9.1779 4.09532 9.32903 4.09799C9.48015 4.10066 9.62434 4.16188 9.73121 4.26876C9.83809 4.37563 9.89931 4.51982 9.90198 4.67094C9.90464 4.82207 9.84855 4.96832 9.74551 5.07891L7.82441 7L9.74554 8.92113C9.85478 9.0305 9.91614 9.17876 9.91614 9.33335C9.91614 9.48793 9.85478 9.6362 9.74554 9.74557C9.69168 9.8001 9.62748 9.84333 9.55669 9.87271C9.4859 9.90209 9.40996 9.91704 9.33332 9.91668C9.25668 9.91704 9.18073 9.90209 9.10995 9.87271C9.03916 9.84333 8.97495 9.8001 8.9211 9.74557L6.99997 7.82444L5.07884 9.74557C5.02499 9.8001 4.96078 9.84333 4.88999 9.87271C4.81921 9.90209 4.74326 9.91704 4.66662 9.91668Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-chip {\\n display: inline-flex;\\n align-items: center;\\n background: \".concat(dt('chip.background'), \";\\n color: \").concat(dt('chip.color'), \";\\n border-radius: \").concat(dt('chip.border.radius'), \";\\n padding: \").concat(dt('chip.padding.y'), \" \").concat(dt('chip.padding.x'), \";\\n gap: \").concat(dt('chip.gap'), \";\\n}\\n\\n.p-chip-icon {\\n color: \").concat(dt('chip.icon.color'), \";\\n font-size: \").concat(dt('chip.icon.font.size'), \";\\n width: \").concat(dt('chip.icon.size'), \";\\n height: \").concat(dt('chip.icon.size'), \";\\n}\\n\\n.p-chip-image {\\n border-radius: 50%;\\n width: \").concat(dt('chip.image.width'), \";\\n height: \").concat(dt('chip.image.height'), \";\\n margin-left: calc(-1 * \").concat(dt('chip.padding.y'), \");\\n}\\n\\n.p-chip:has(.p-chip-remove-icon) {\\n padding-right: \").concat(dt('chip.padding.y'), \";\\n}\\n\\n.p-chip:has(.p-chip-image) {\\n padding-top: calc(\").concat(dt('chip.padding.y'), \" / 2);\\n padding-bottom: calc(\").concat(dt('chip.padding.y'), \" / 2);\\n}\\n\\n.p-chip-remove-icon {\\n cursor: pointer;\\n font-size: \").concat(dt('chip.remove.icon.size'), \";\\n width: \").concat(dt('chip.remove.icon.size'), \";\\n height: \").concat(dt('chip.remove.icon.size'), \";\\n color: \").concat(dt('chip.remove.icon.color'), \";\\n border-radius: 50%;\\n transition: outline-color \").concat(dt('chip.transition.duration'), \", box-shadow \").concat(dt('chip.transition.duration'), \";\\n outline-color: transparent;\\n}\\n\\n.p-chip-remove-icon:focus-visible {\\n box-shadow: \").concat(dt('chip.remove.icon.focus.ring.shadow'), \";\\n outline: \").concat(dt('chip.remove.icon.focus.ring.width'), \" \").concat(dt('chip.remove.icon.focus.ring.style'), \" \").concat(dt('chip.remove.icon.focus.ring.color'), \";\\n outline-offset: \").concat(dt('chip.remove.icon.focus.ring.offset'), \";\\n}\\n\");\n};\nvar classes = {\n root: 'p-chip p-component',\n image: 'p-chip-image',\n icon: 'p-chip-icon',\n label: 'p-chip-label',\n removeIcon: 'p-chip-remove-icon'\n};\nvar ChipStyle = BaseStyle.extend({\n name: 'chip',\n theme: theme,\n classes: classes\n});\n\nexport { ChipStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import TimesCircleIcon from '@primevue/icons/timescircle';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport ChipStyle from 'primevue/chip/style';\nimport { openBlock, createElementBlock, mergeProps, renderSlot, createBlock, resolveDynamicComponent, createCommentVNode, toDisplayString } from 'vue';\n\nvar script$1 = {\n name: 'BaseChip',\n \"extends\": BaseComponent,\n props: {\n label: {\n type: String,\n \"default\": null\n },\n icon: {\n type: String,\n \"default\": null\n },\n image: {\n type: String,\n \"default\": null\n },\n removable: {\n type: Boolean,\n \"default\": false\n },\n removeIcon: {\n type: String,\n \"default\": undefined\n }\n },\n style: ChipStyle,\n provide: function provide() {\n return {\n $pcChip: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'Chip',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['remove'],\n data: function data() {\n return {\n visible: true\n };\n },\n methods: {\n onKeydown: function onKeydown(event) {\n if (event.key === 'Enter' || event.key === 'Backspace') {\n this.close(event);\n }\n },\n close: function close(event) {\n this.visible = false;\n this.$emit('remove', event);\n }\n },\n components: {\n TimesCircleIcon: TimesCircleIcon\n }\n};\n\nvar _hoisted_1 = [\"aria-label\"];\nvar _hoisted_2 = [\"src\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return $data.visible ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('root'),\n \"aria-label\": _ctx.label\n }, _ctx.ptmi('root')), [renderSlot(_ctx.$slots, \"default\", {}, function () {\n return [_ctx.image ? (openBlock(), createElementBlock(\"img\", mergeProps({\n key: 0,\n src: _ctx.image\n }, _ctx.ptm('image'), {\n \"class\": _ctx.cx('image')\n }), null, 16, _hoisted_2)) : _ctx.$slots.icon ? (openBlock(), createBlock(resolveDynamicComponent(_ctx.$slots.icon), mergeProps({\n key: 1,\n \"class\": _ctx.cx('icon')\n }, _ctx.ptm('icon')), null, 16, [\"class\"])) : _ctx.icon ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 2,\n \"class\": [_ctx.cx('icon'), _ctx.icon]\n }, _ctx.ptm('icon')), null, 16)) : createCommentVNode(\"\", true), _ctx.label ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 3,\n \"class\": _ctx.cx('label')\n }, _ctx.ptm('label')), toDisplayString(_ctx.label), 17)) : createCommentVNode(\"\", true)];\n }), _ctx.removable ? renderSlot(_ctx.$slots, \"removeicon\", {\n key: 0,\n removeCallback: $options.close,\n keydownCallback: $options.onKeydown\n }, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.removeIcon ? 'span' : 'TimesCircleIcon'), mergeProps({\n tabindex: \"0\",\n \"class\": [_ctx.cx('removeIcon'), _ctx.removeIcon],\n onClick: $options.close,\n onKeydown: $options.onKeydown\n }, _ctx.ptm('removeIcon')), null, 16, [\"class\", \"onClick\", \"onKeydown\"]))];\n }) : createCommentVNode(\"\", true)], 16, _hoisted_1)) : createCommentVNode(\"\", true);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n\n\n","import { defineStore } from 'pinia'\nimport { ref } from 'vue'\nimport { api } from '@/scripts/api'\nimport type { SystemStats } from '@/types/apiTypes'\n\nexport const useSystemStatsStore = defineStore('systemStats', () => {\n const systemStats = ref(null)\n const isLoading = ref(false)\n const error = ref(null)\n\n async function fetchSystemStats() {\n isLoading.value = true\n error.value = null\n\n try {\n systemStats.value = await api.getSystemStats()\n } catch (err) {\n error.value =\n err instanceof Error\n ? err.message\n : 'An error occurred while fetching system stats'\n console.error('Error fetching system stats:', err)\n } finally {\n isLoading.value = false\n }\n }\n\n return {\n systemStats,\n isLoading,\n error,\n fetchSystemStats\n }\n})\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'ChevronLeftIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n d: \"M9.61296 13C9.50997 13.0005 9.40792 12.9804 9.3128 12.9409C9.21767 12.9014 9.13139 12.8433 9.05902 12.7701L3.83313 7.54416C3.68634 7.39718 3.60388 7.19795 3.60388 6.99022C3.60388 6.78249 3.68634 6.58325 3.83313 6.43628L9.05902 1.21039C9.20762 1.07192 9.40416 0.996539 9.60724 1.00012C9.81032 1.00371 10.0041 1.08597 10.1477 1.22959C10.2913 1.37322 10.3736 1.56698 10.3772 1.77005C10.3808 1.97313 10.3054 2.16968 10.1669 2.31827L5.49496 6.99022L10.1669 11.6622C10.3137 11.8091 10.3962 12.0084 10.3962 12.2161C10.3962 12.4238 10.3137 12.6231 10.1669 12.7701C10.0945 12.8433 10.0083 12.9014 9.91313 12.9409C9.81801 12.9804 9.71596 13.0005 9.61296 13Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'ChevronRightIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n d: \"M4.38708 13C4.28408 13.0005 4.18203 12.9804 4.08691 12.9409C3.99178 12.9014 3.9055 12.8433 3.83313 12.7701C3.68634 12.6231 3.60388 12.4238 3.60388 12.2161C3.60388 12.0084 3.68634 11.8091 3.83313 11.6622L8.50507 6.99022L3.83313 2.31827C3.69467 2.16968 3.61928 1.97313 3.62287 1.77005C3.62645 1.56698 3.70872 1.37322 3.85234 1.22959C3.99596 1.08597 4.18972 1.00371 4.3928 1.00012C4.59588 0.996539 4.79242 1.07192 4.94102 1.21039L10.1669 6.43628C10.3137 6.58325 10.3962 6.78249 10.3962 6.99022C10.3962 7.19795 10.3137 7.39718 10.1669 7.54416L4.94102 12.7701C4.86865 12.8433 4.78237 12.9014 4.68724 12.9409C4.59212 12.9804 4.49007 13.0005 4.38708 13Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-tabview-tablist-container {\\n position: relative;\\n}\\n\\n.p-tabview-scrollable > .p-tabview-tablist-container {\\n overflow: hidden;\\n}\\n\\n.p-tabview-tablist-scroll-container {\\n overflow-x: auto;\\n overflow-y: hidden;\\n scroll-behavior: smooth;\\n scrollbar-width: none;\\n overscroll-behavior: contain auto;\\n}\\n\\n.p-tabview-tablist-scroll-container::-webkit-scrollbar {\\n display: none;\\n}\\n\\n.p-tabview-tablist {\\n display: flex;\\n margin: 0;\\n padding: 0;\\n list-style-type: none;\\n flex: 1 1 auto;\\n background: \".concat(dt('tabview.tab.list.background'), \";\\n border: 1px solid \").concat(dt('tabview.tab.list.border.color'), \";\\n border-width: 0 0 1px 0;\\n position: relative;\\n}\\n\\n.p-tabview-tab-header {\\n cursor: pointer;\\n user-select: none;\\n display: flex;\\n align-items: center;\\n text-decoration: none;\\n position: relative;\\n overflow: hidden;\\n border-style: solid;\\n border-width: 0 0 1px 0;\\n border-color: transparent transparent \").concat(dt('tabview.tab.border.color'), \" transparent;\\n color: \").concat(dt('tabview.tab.color'), \";\\n padding: 1rem 1.125rem;\\n font-weight: 600;\\n border-top-right-radius: \").concat(dt('border.radius.md'), \";\\n border-top-left-radius: \").concat(dt('border.radius.md'), \";\\n transition: color \").concat(dt('tabview.transition.duration'), \", outline-color \").concat(dt('tabview.transition.duration'), \";\\n margin: 0 0 -1px 0;\\n outline-color: transparent;\\n}\\n\\n.p-tabview-tablist-item:not(.p-disabled) .p-tabview-tab-header:focus-visible {\\n outline: \").concat(dt('focus.ring.width'), \" \").concat(dt('focus.ring.style'), \" \").concat(dt('focus.ring.color'), \";\\n outline-offset: -1px;\\n}\\n\\n.p-tabview-tablist-item:not(.p-highlight):not(.p-disabled):hover > .p-tabview-tab-header {\\n color: \").concat(dt('tabview.tab.hover.color'), \";\\n}\\n\\n.p-tabview-tablist-item.p-highlight > .p-tabview-tab-header {\\n color: \").concat(dt('tabview.tab.active.color'), \";\\n}\\n\\n.p-tabview-tab-title {\\n line-height: 1;\\n white-space: nowrap;\\n}\\n\\n.p-tabview-next-button,\\n.p-tabview-prev-button {\\n position: absolute;\\n top: 0;\\n margin: 0;\\n padding: 0;\\n z-index: 2;\\n height: 100%;\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n background: \").concat(dt('tabview.nav.button.background'), \";\\n color: \").concat(dt('tabview.nav.button.color'), \";\\n width: 2.5rem;\\n border-radius: 0;\\n outline-color: transparent;\\n transition: color \").concat(dt('tabview.transition.duration'), \", outline-color \").concat(dt('tabview.transition.duration'), \";\\n box-shadow: \").concat(dt('tabview.nav.button.shadow'), \";\\n border: none;\\n cursor: pointer;\\n user-select: none;\\n}\\n\\n.p-tabview-next-button:focus-visible,\\n.p-tabview-prev-button:focus-visible {\\n outline: \").concat(dt('focus.ring.width'), \" \").concat(dt('focus.ring.style'), \" \").concat(dt('focus.ring.color'), \";\\n outline-offset: \").concat(dt('focus.ring.offset'), \";\\n}\\n\\n.p-tabview-next-button:hover,\\n.p-tabview-prev-button:hover {\\n color: \").concat(dt('tabview.nav.button.hover.color'), \";\\n}\\n\\n.p-tabview-prev-button {\\n left: 0;\\n}\\n\\n.p-tabview-next-button {\\n right: 0;\\n}\\n\\n.p-tabview-panels {\\n background: \").concat(dt('tabview.tab.panel.background'), \";\\n color: \").concat(dt('tabview.tab.panel.color'), \";\\n padding: 0.875rem 1.125rem 1.125rem 1.125rem;\\n}\\n\\n.p-tabview-ink-bar {\\n z-index: 1;\\n display: block;\\n position: absolute;\\n bottom: -1px;\\n height: 1px;\\n background: \").concat(dt('tabview.tab.active.border.color'), \";\\n transition: 250ms cubic-bezier(0.35, 0, 0.25, 1);\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var props = _ref2.props;\n return ['p-tabview p-component', {\n 'p-tabview-scrollable': props.scrollable\n }];\n },\n navContainer: 'p-tabview-tablist-container',\n prevButton: 'p-tabview-prev-button',\n navContent: 'p-tabview-tablist-scroll-container',\n nav: 'p-tabview-tablist',\n tab: {\n header: function header(_ref3) {\n var instance = _ref3.instance,\n tab = _ref3.tab,\n index = _ref3.index;\n return ['p-tabview-tablist-item', instance.getTabProp(tab, 'headerClass'), {\n 'p-tabview-tablist-item-active': instance.d_activeIndex === index,\n 'p-disabled': instance.getTabProp(tab, 'disabled')\n }];\n },\n headerAction: 'p-tabview-tab-header',\n headerTitle: 'p-tabview-tab-title',\n content: function content(_ref4) {\n var instance = _ref4.instance,\n tab = _ref4.tab;\n return ['p-tabview-panel', instance.getTabProp(tab, 'contentClass')];\n }\n },\n inkbar: 'p-tabview-ink-bar',\n nextButton: 'p-tabview-next-button',\n panelContainer: 'p-tabview-panels'\n};\nvar TabViewStyle = BaseStyle.extend({\n name: 'tabview',\n theme: theme,\n classes: classes\n});\n\nexport { TabViewStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { UniqueComponentId } from '@primevue/core/utils';\nimport { getWidth, getAttribute, findSingle, focus, getOffset } from '@primeuix/utils/dom';\nimport ChevronLeftIcon from '@primevue/icons/chevronleft';\nimport ChevronRightIcon from '@primevue/icons/chevronright';\nimport Ripple from 'primevue/ripple';\nimport { mergeProps, resolveDirective, openBlock, createElementBlock, createElementVNode, withDirectives, renderSlot, createBlock, resolveDynamicComponent, createCommentVNode, Fragment, renderList, toDisplayString, vShow } from 'vue';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport TabViewStyle from 'primevue/tabview/style';\n\nvar script$1 = {\n name: 'BaseTabView',\n \"extends\": BaseComponent,\n props: {\n activeIndex: {\n type: Number,\n \"default\": 0\n },\n lazy: {\n type: Boolean,\n \"default\": false\n },\n scrollable: {\n type: Boolean,\n \"default\": false\n },\n tabindex: {\n type: Number,\n \"default\": 0\n },\n selectOnFocus: {\n type: Boolean,\n \"default\": false\n },\n prevButtonProps: {\n type: null,\n \"default\": null\n },\n nextButtonProps: {\n type: null,\n \"default\": null\n },\n prevIcon: {\n type: String,\n \"default\": undefined\n },\n nextIcon: {\n type: String,\n \"default\": undefined\n }\n },\n style: TabViewStyle,\n provide: function provide() {\n return {\n $pcTabs: undefined,\n // Backwards compatible to prevent component from breaking\n $pcTabView: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'TabView',\n \"extends\": script$1,\n inheritAttrs: false,\n emits: ['update:activeIndex', 'tab-change', 'tab-click'],\n data: function data() {\n return {\n id: this.$attrs.id,\n d_activeIndex: this.activeIndex,\n isPrevButtonDisabled: true,\n isNextButtonDisabled: false\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n },\n activeIndex: function activeIndex(newValue) {\n this.d_activeIndex = newValue;\n this.scrollInView({\n index: newValue\n });\n }\n },\n mounted: function mounted() {\n console.warn('Deprecated since v4. Use Tabs component instead.');\n this.id = this.id || UniqueComponentId();\n this.updateInkBar();\n this.scrollable && this.updateButtonState();\n },\n updated: function updated() {\n this.updateInkBar();\n this.scrollable && this.updateButtonState();\n },\n methods: {\n isTabPanel: function isTabPanel(child) {\n return child.type.name === 'TabPanel';\n },\n isTabActive: function isTabActive(index) {\n return this.d_activeIndex === index;\n },\n getTabProp: function getTabProp(tab, name) {\n return tab.props ? tab.props[name] : undefined;\n },\n getKey: function getKey(tab, index) {\n return this.getTabProp(tab, 'header') || index;\n },\n getTabHeaderActionId: function getTabHeaderActionId(index) {\n return \"\".concat(this.id, \"_\").concat(index, \"_header_action\");\n },\n getTabContentId: function getTabContentId(index) {\n return \"\".concat(this.id, \"_\").concat(index, \"_content\");\n },\n getTabPT: function getTabPT(tab, key, index) {\n var count = this.tabs.length;\n var tabMetaData = {\n props: tab.props,\n parent: {\n instance: this,\n props: this.$props,\n state: this.$data\n },\n context: {\n index: index,\n count: count,\n first: index === 0,\n last: index === count - 1,\n active: this.isTabActive(index)\n }\n };\n return mergeProps(this.ptm(\"tabpanel.\".concat(key), {\n tabpanel: tabMetaData\n }), this.ptm(\"tabpanel.\".concat(key), tabMetaData), this.ptmo(this.getTabProp(tab, 'pt'), key, tabMetaData));\n },\n onScroll: function onScroll(event) {\n this.scrollable && this.updateButtonState();\n event.preventDefault();\n },\n onPrevButtonClick: function onPrevButtonClick() {\n var content = this.$refs.content;\n var width = getWidth(content);\n var pos = content.scrollLeft - width;\n content.scrollLeft = pos <= 0 ? 0 : pos;\n },\n onNextButtonClick: function onNextButtonClick() {\n var content = this.$refs.content;\n var width = getWidth(content) - this.getVisibleButtonWidths();\n var pos = content.scrollLeft + width;\n var lastPos = content.scrollWidth - width;\n content.scrollLeft = pos >= lastPos ? lastPos : pos;\n },\n onTabClick: function onTabClick(event, tab, index) {\n this.changeActiveIndex(event, tab, index);\n this.$emit('tab-click', {\n originalEvent: event,\n index: index\n });\n },\n onTabKeyDown: function onTabKeyDown(event, tab, index) {\n switch (event.code) {\n case 'ArrowLeft':\n this.onTabArrowLeftKey(event);\n break;\n case 'ArrowRight':\n this.onTabArrowRightKey(event);\n break;\n case 'Home':\n this.onTabHomeKey(event);\n break;\n case 'End':\n this.onTabEndKey(event);\n break;\n case 'PageDown':\n this.onPageDownKey(event);\n break;\n case 'PageUp':\n this.onPageUpKey(event);\n break;\n case 'Enter':\n case 'NumpadEnter':\n case 'Space':\n this.onTabEnterKey(event, tab, index);\n break;\n }\n },\n onTabArrowRightKey: function onTabArrowRightKey(event) {\n var nextHeaderAction = this.findNextHeaderAction(event.target.parentElement);\n nextHeaderAction ? this.changeFocusedTab(event, nextHeaderAction) : this.onTabHomeKey(event);\n event.preventDefault();\n },\n onTabArrowLeftKey: function onTabArrowLeftKey(event) {\n var prevHeaderAction = this.findPrevHeaderAction(event.target.parentElement);\n prevHeaderAction ? this.changeFocusedTab(event, prevHeaderAction) : this.onTabEndKey(event);\n event.preventDefault();\n },\n onTabHomeKey: function onTabHomeKey(event) {\n var firstHeaderAction = this.findFirstHeaderAction();\n this.changeFocusedTab(event, firstHeaderAction);\n event.preventDefault();\n },\n onTabEndKey: function onTabEndKey(event) {\n var lastHeaderAction = this.findLastHeaderAction();\n this.changeFocusedTab(event, lastHeaderAction);\n event.preventDefault();\n },\n onPageDownKey: function onPageDownKey(event) {\n this.scrollInView({\n index: this.$refs.nav.children.length - 2\n });\n event.preventDefault();\n },\n onPageUpKey: function onPageUpKey(event) {\n this.scrollInView({\n index: 0\n });\n event.preventDefault();\n },\n onTabEnterKey: function onTabEnterKey(event, tab, index) {\n this.changeActiveIndex(event, tab, index);\n event.preventDefault();\n },\n findNextHeaderAction: function findNextHeaderAction(tabElement) {\n var selfCheck = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n var headerElement = selfCheck ? tabElement : tabElement.nextElementSibling;\n return headerElement ? getAttribute(headerElement, 'data-p-disabled') || getAttribute(headerElement, 'data-pc-section') === 'inkbar' ? this.findNextHeaderAction(headerElement) : findSingle(headerElement, '[data-pc-section=\"headeraction\"]') : null;\n },\n findPrevHeaderAction: function findPrevHeaderAction(tabElement) {\n var selfCheck = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;\n var headerElement = selfCheck ? tabElement : tabElement.previousElementSibling;\n return headerElement ? getAttribute(headerElement, 'data-p-disabled') || getAttribute(headerElement, 'data-pc-section') === 'inkbar' ? this.findPrevHeaderAction(headerElement) : findSingle(headerElement, '[data-pc-section=\"headeraction\"]') : null;\n },\n findFirstHeaderAction: function findFirstHeaderAction() {\n return this.findNextHeaderAction(this.$refs.nav.firstElementChild, true);\n },\n findLastHeaderAction: function findLastHeaderAction() {\n return this.findPrevHeaderAction(this.$refs.nav.lastElementChild, true);\n },\n changeActiveIndex: function changeActiveIndex(event, tab, index) {\n if (!this.getTabProp(tab, 'disabled') && this.d_activeIndex !== index) {\n this.d_activeIndex = index;\n this.$emit('update:activeIndex', index);\n this.$emit('tab-change', {\n originalEvent: event,\n index: index\n });\n this.scrollInView({\n index: index\n });\n }\n },\n changeFocusedTab: function changeFocusedTab(event, element) {\n if (element) {\n focus(element);\n this.scrollInView({\n element: element\n });\n if (this.selectOnFocus) {\n var index = parseInt(element.parentElement.dataset.pcIndex, 10);\n var tab = this.tabs[index];\n this.changeActiveIndex(event, tab, index);\n }\n }\n },\n scrollInView: function scrollInView(_ref) {\n var element = _ref.element,\n _ref$index = _ref.index,\n index = _ref$index === void 0 ? -1 : _ref$index;\n var currentElement = element || this.$refs.nav.children[index];\n if (currentElement) {\n currentElement.scrollIntoView && currentElement.scrollIntoView({\n block: 'nearest'\n });\n }\n },\n updateInkBar: function updateInkBar() {\n var tabHeader = this.$refs.nav.children[this.d_activeIndex];\n this.$refs.inkbar.style.width = getWidth(tabHeader) + 'px';\n this.$refs.inkbar.style.left = getOffset(tabHeader).left - getOffset(this.$refs.nav).left + 'px';\n },\n updateButtonState: function updateButtonState() {\n var content = this.$refs.content;\n var scrollLeft = content.scrollLeft,\n scrollWidth = content.scrollWidth;\n var width = getWidth(content);\n this.isPrevButtonDisabled = scrollLeft === 0;\n this.isNextButtonDisabled = parseInt(scrollLeft) === scrollWidth - width;\n },\n getVisibleButtonWidths: function getVisibleButtonWidths() {\n var _this$$refs = this.$refs,\n prevBtn = _this$$refs.prevBtn,\n nextBtn = _this$$refs.nextBtn;\n return [prevBtn, nextBtn].reduce(function (acc, el) {\n return el ? acc + getWidth(el) : acc;\n }, 0);\n }\n },\n computed: {\n tabs: function tabs() {\n var _this = this;\n return this.$slots[\"default\"]().reduce(function (tabs, child) {\n if (_this.isTabPanel(child)) {\n tabs.push(child);\n } else if (child.children && child.children instanceof Array) {\n child.children.forEach(function (nestedChild) {\n if (_this.isTabPanel(nestedChild)) {\n tabs.push(nestedChild);\n }\n });\n }\n return tabs;\n }, []);\n },\n prevButtonAriaLabel: function prevButtonAriaLabel() {\n return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.previous : undefined;\n },\n nextButtonAriaLabel: function nextButtonAriaLabel() {\n return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.next : undefined;\n }\n },\n directives: {\n ripple: Ripple\n },\n components: {\n ChevronLeftIcon: ChevronLeftIcon,\n ChevronRightIcon: ChevronRightIcon\n }\n};\n\nfunction _typeof(o) { \"@babel/helpers - typeof\"; return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof(o); }\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty(e, r, t) { return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey(t) { var i = _toPrimitive(t, \"string\"); return \"symbol\" == _typeof(i) ? i : i + \"\"; }\nfunction _toPrimitive(t, r) { if (\"object\" != _typeof(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nvar _hoisted_1 = [\"tabindex\", \"aria-label\"];\nvar _hoisted_2 = [\"data-p-active\", \"data-p-disabled\", \"data-pc-index\"];\nvar _hoisted_3 = [\"id\", \"tabindex\", \"aria-disabled\", \"aria-selected\", \"aria-controls\", \"onClick\", \"onKeydown\"];\nvar _hoisted_4 = [\"tabindex\", \"aria-label\"];\nvar _hoisted_5 = [\"id\", \"aria-labelledby\", \"data-pc-index\", \"data-p-active\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root'),\n role: \"tablist\"\n }, _ctx.ptmi('root')), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('navContainer')\n }, _ctx.ptm('navContainer')), [_ctx.scrollable && !$data.isPrevButtonDisabled ? withDirectives((openBlock(), createElementBlock(\"button\", mergeProps({\n key: 0,\n ref: \"prevBtn\",\n type: \"button\",\n \"class\": _ctx.cx('prevButton'),\n tabindex: _ctx.tabindex,\n \"aria-label\": $options.prevButtonAriaLabel,\n onClick: _cache[0] || (_cache[0] = function () {\n return $options.onPrevButtonClick && $options.onPrevButtonClick.apply($options, arguments);\n })\n }, _objectSpread(_objectSpread({}, _ctx.prevButtonProps), _ctx.ptm('prevButton')), {\n \"data-pc-group-section\": \"navbutton\"\n }), [renderSlot(_ctx.$slots, \"previcon\", {}, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.prevIcon ? 'span' : 'ChevronLeftIcon'), mergeProps({\n \"aria-hidden\": \"true\",\n \"class\": _ctx.prevIcon\n }, _ctx.ptm('prevIcon')), null, 16, [\"class\"]))];\n })], 16, _hoisted_1)), [[_directive_ripple]]) : createCommentVNode(\"\", true), createElementVNode(\"div\", mergeProps({\n ref: \"content\",\n \"class\": _ctx.cx('navContent'),\n onScroll: _cache[1] || (_cache[1] = function () {\n return $options.onScroll && $options.onScroll.apply($options, arguments);\n })\n }, _ctx.ptm('navContent')), [createElementVNode(\"ul\", mergeProps({\n ref: \"nav\",\n \"class\": _ctx.cx('nav')\n }, _ctx.ptm('nav')), [(openBlock(true), createElementBlock(Fragment, null, renderList($options.tabs, function (tab, index) {\n return openBlock(), createElementBlock(\"li\", mergeProps({\n key: $options.getKey(tab, index),\n style: $options.getTabProp(tab, 'headerStyle'),\n \"class\": _ctx.cx('tab.header', {\n tab: tab,\n index: index\n }),\n role: \"presentation\",\n ref_for: true\n }, _objectSpread(_objectSpread(_objectSpread({}, $options.getTabProp(tab, 'headerProps')), $options.getTabPT(tab, 'root', index)), $options.getTabPT(tab, 'header', index)), {\n \"data-pc-name\": \"tabpanel\",\n \"data-p-active\": $data.d_activeIndex === index,\n \"data-p-disabled\": $options.getTabProp(tab, 'disabled'),\n \"data-pc-index\": index\n }), [withDirectives((openBlock(), createElementBlock(\"a\", mergeProps({\n id: $options.getTabHeaderActionId(index),\n \"class\": _ctx.cx('tab.headerAction'),\n tabindex: $options.getTabProp(tab, 'disabled') || !$options.isTabActive(index) ? -1 : _ctx.tabindex,\n role: \"tab\",\n \"aria-disabled\": $options.getTabProp(tab, 'disabled'),\n \"aria-selected\": $options.isTabActive(index),\n \"aria-controls\": $options.getTabContentId(index),\n onClick: function onClick($event) {\n return $options.onTabClick($event, tab, index);\n },\n onKeydown: function onKeydown($event) {\n return $options.onTabKeyDown($event, tab, index);\n },\n ref_for: true\n }, _objectSpread(_objectSpread({}, $options.getTabProp(tab, 'headerActionProps')), $options.getTabPT(tab, 'headerAction', index))), [tab.props && tab.props.header ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('tab.headerTitle'),\n ref_for: true\n }, $options.getTabPT(tab, 'headerTitle', index)), toDisplayString(tab.props.header), 17)) : createCommentVNode(\"\", true), tab.children && tab.children.header ? (openBlock(), createBlock(resolveDynamicComponent(tab.children.header), {\n key: 1\n })) : createCommentVNode(\"\", true)], 16, _hoisted_3)), [[_directive_ripple]])], 16, _hoisted_2);\n }), 128)), createElementVNode(\"li\", mergeProps({\n ref: \"inkbar\",\n \"class\": _ctx.cx('inkbar'),\n role: \"presentation\",\n \"aria-hidden\": \"true\"\n }, _ctx.ptm('inkbar')), null, 16)], 16)], 16), _ctx.scrollable && !$data.isNextButtonDisabled ? withDirectives((openBlock(), createElementBlock(\"button\", mergeProps({\n key: 1,\n ref: \"nextBtn\",\n type: \"button\",\n \"class\": _ctx.cx('nextButton'),\n tabindex: _ctx.tabindex,\n \"aria-label\": $options.nextButtonAriaLabel,\n onClick: _cache[2] || (_cache[2] = function () {\n return $options.onNextButtonClick && $options.onNextButtonClick.apply($options, arguments);\n })\n }, _objectSpread(_objectSpread({}, _ctx.nextButtonProps), _ctx.ptm('nextButton')), {\n \"data-pc-group-section\": \"navbutton\"\n }), [renderSlot(_ctx.$slots, \"nexticon\", {}, function () {\n return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.nextIcon ? 'span' : 'ChevronRightIcon'), mergeProps({\n \"aria-hidden\": \"true\",\n \"class\": _ctx.nextIcon\n }, _ctx.ptm('nextIcon')), null, 16, [\"class\"]))];\n })], 16, _hoisted_4)), [[_directive_ripple]]) : createCommentVNode(\"\", true)], 16), createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('panelContainer')\n }, _ctx.ptm('panelContainer')), [(openBlock(true), createElementBlock(Fragment, null, renderList($options.tabs, function (tab, index) {\n return openBlock(), createElementBlock(Fragment, {\n key: $options.getKey(tab, index)\n }, [(_ctx.lazy ? $options.isTabActive(index) : true) ? withDirectives((openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n id: $options.getTabContentId(index),\n style: $options.getTabProp(tab, 'contentStyle'),\n \"class\": _ctx.cx('tab.content', {\n tab: tab\n }),\n role: \"tabpanel\",\n \"aria-labelledby\": $options.getTabHeaderActionId(index),\n ref_for: true\n }, _objectSpread(_objectSpread(_objectSpread({}, $options.getTabProp(tab, 'contentProps')), $options.getTabPT(tab, 'root', index)), $options.getTabPT(tab, 'content', index)), {\n \"data-pc-name\": \"tabpanel\",\n \"data-pc-index\": index,\n \"data-p-active\": $data.d_activeIndex === index\n }), [(openBlock(), createBlock(resolveDynamicComponent(tab)))], 16, _hoisted_5)), [[vShow, _ctx.lazy ? true : $options.isTabActive(index)]]) : createCommentVNode(\"\", true)], 64);\n }), 128))], 16)], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n","\n\n\n","\n\n\n","\n\n\n\n\n\n\n","\n\n\n","import * as Vue from 'vue'\n\nvar isVue2 = false\nvar isVue3 = true\nvar Vue2 = undefined\n\nfunction install() {}\n\nexport function set(target, key, val) {\n if (Array.isArray(target)) {\n target.length = Math.max(target.length, key)\n target.splice(key, 1, val)\n return val\n }\n target[key] = val\n return val\n}\n\nexport function del(target, key) {\n if (Array.isArray(target)) {\n target.splice(key, 1)\n return\n }\n delete target[key]\n}\n\nexport * from 'vue'\nexport {\n Vue,\n Vue2,\n isVue2,\n isVue3,\n install,\n}\n","import { shallowRef, watchEffect, readonly, ref, watch, customRef, getCurrentScope, onScopeDispose, effectScope, getCurrentInstance, provide, inject, isVue3, version, isRef, unref, computed, reactive, toRefs as toRefs$1, toRef as toRef$1, isVue2, set as set$1, onBeforeMount, nextTick, onBeforeUnmount, onMounted, onUnmounted, isReactive } from 'vue-demi';\n\nfunction computedEager(fn, options) {\n var _a;\n const result = shallowRef();\n watchEffect(() => {\n result.value = fn();\n }, {\n ...options,\n flush: (_a = options == null ? void 0 : options.flush) != null ? _a : \"sync\"\n });\n return readonly(result);\n}\n\nfunction computedWithControl(source, fn) {\n let v = void 0;\n let track;\n let trigger;\n const dirty = ref(true);\n const update = () => {\n dirty.value = true;\n trigger();\n };\n watch(source, update, { flush: \"sync\" });\n const get = typeof fn === \"function\" ? fn : fn.get;\n const set = typeof fn === \"function\" ? void 0 : fn.set;\n const result = customRef((_track, _trigger) => {\n track = _track;\n trigger = _trigger;\n return {\n get() {\n if (dirty.value) {\n v = get(v);\n dirty.value = false;\n }\n track();\n return v;\n },\n set(v2) {\n set == null ? void 0 : set(v2);\n }\n };\n });\n if (Object.isExtensible(result))\n result.trigger = update;\n return result;\n}\n\nfunction tryOnScopeDispose(fn) {\n if (getCurrentScope()) {\n onScopeDispose(fn);\n return true;\n }\n return false;\n}\n\nfunction createEventHook() {\n const fns = /* @__PURE__ */ new Set();\n const off = (fn) => {\n fns.delete(fn);\n };\n const on = (fn) => {\n fns.add(fn);\n const offFn = () => off(fn);\n tryOnScopeDispose(offFn);\n return {\n off: offFn\n };\n };\n const trigger = (...args) => {\n return Promise.all(Array.from(fns).map((fn) => fn(...args)));\n };\n return {\n on,\n off,\n trigger\n };\n}\n\nfunction createGlobalState(stateFactory) {\n let initialized = false;\n let state;\n const scope = effectScope(true);\n return (...args) => {\n if (!initialized) {\n state = scope.run(() => stateFactory(...args));\n initialized = true;\n }\n return state;\n };\n}\n\nconst localProvidedStateMap = /* @__PURE__ */ new WeakMap();\n\nconst provideLocal = (key, value) => {\n var _a;\n const instance = (_a = getCurrentInstance()) == null ? void 0 : _a.proxy;\n if (instance == null)\n throw new Error(\"provideLocal must be called in setup\");\n if (!localProvidedStateMap.has(instance))\n localProvidedStateMap.set(instance, /* @__PURE__ */ Object.create(null));\n const localProvidedState = localProvidedStateMap.get(instance);\n localProvidedState[key] = value;\n provide(key, value);\n};\n\nconst injectLocal = (...args) => {\n var _a;\n const key = args[0];\n const instance = (_a = getCurrentInstance()) == null ? void 0 : _a.proxy;\n if (instance == null)\n throw new Error(\"injectLocal must be called in setup\");\n if (localProvidedStateMap.has(instance) && key in localProvidedStateMap.get(instance))\n return localProvidedStateMap.get(instance)[key];\n return inject(...args);\n};\n\nfunction createInjectionState(composable, options) {\n const key = (options == null ? void 0 : options.injectionKey) || Symbol(composable.name || \"InjectionState\");\n const defaultValue = options == null ? void 0 : options.defaultValue;\n const useProvidingState = (...args) => {\n const state = composable(...args);\n provideLocal(key, state);\n return state;\n };\n const useInjectedState = () => injectLocal(key, defaultValue);\n return [useProvidingState, useInjectedState];\n}\n\nfunction createSharedComposable(composable) {\n let subscribers = 0;\n let state;\n let scope;\n const dispose = () => {\n subscribers -= 1;\n if (scope && subscribers <= 0) {\n scope.stop();\n state = void 0;\n scope = void 0;\n }\n };\n return (...args) => {\n subscribers += 1;\n if (!state) {\n scope = effectScope(true);\n state = scope.run(() => composable(...args));\n }\n tryOnScopeDispose(dispose);\n return state;\n };\n}\n\nfunction extendRef(ref, extend, { enumerable = false, unwrap = true } = {}) {\n if (!isVue3 && !version.startsWith(\"2.7.\")) {\n if (process.env.NODE_ENV !== \"production\")\n throw new Error(\"[VueUse] extendRef only works in Vue 2.7 or above.\");\n return;\n }\n for (const [key, value] of Object.entries(extend)) {\n if (key === \"value\")\n continue;\n if (isRef(value) && unwrap) {\n Object.defineProperty(ref, key, {\n get() {\n return value.value;\n },\n set(v) {\n value.value = v;\n },\n enumerable\n });\n } else {\n Object.defineProperty(ref, key, { value, enumerable });\n }\n }\n return ref;\n}\n\nfunction get(obj, key) {\n if (key == null)\n return unref(obj);\n return unref(obj)[key];\n}\n\nfunction isDefined(v) {\n return unref(v) != null;\n}\n\nfunction makeDestructurable(obj, arr) {\n if (typeof Symbol !== \"undefined\") {\n const clone = { ...obj };\n Object.defineProperty(clone, Symbol.iterator, {\n enumerable: false,\n value() {\n let index = 0;\n return {\n next: () => ({\n value: arr[index++],\n done: index > arr.length\n })\n };\n }\n });\n return clone;\n } else {\n return Object.assign([...arr], obj);\n }\n}\n\nfunction toValue(r) {\n return typeof r === \"function\" ? r() : unref(r);\n}\nconst resolveUnref = toValue;\n\nfunction reactify(fn, options) {\n const unrefFn = (options == null ? void 0 : options.computedGetter) === false ? unref : toValue;\n return function(...args) {\n return computed(() => fn.apply(this, args.map((i) => unrefFn(i))));\n };\n}\n\nfunction reactifyObject(obj, optionsOrKeys = {}) {\n let keys = [];\n let options;\n if (Array.isArray(optionsOrKeys)) {\n keys = optionsOrKeys;\n } else {\n options = optionsOrKeys;\n const { includeOwnProperties = true } = optionsOrKeys;\n keys.push(...Object.keys(obj));\n if (includeOwnProperties)\n keys.push(...Object.getOwnPropertyNames(obj));\n }\n return Object.fromEntries(\n keys.map((key) => {\n const value = obj[key];\n return [\n key,\n typeof value === \"function\" ? reactify(value.bind(obj), options) : value\n ];\n })\n );\n}\n\nfunction toReactive(objectRef) {\n if (!isRef(objectRef))\n return reactive(objectRef);\n const proxy = new Proxy({}, {\n get(_, p, receiver) {\n return unref(Reflect.get(objectRef.value, p, receiver));\n },\n set(_, p, value) {\n if (isRef(objectRef.value[p]) && !isRef(value))\n objectRef.value[p].value = value;\n else\n objectRef.value[p] = value;\n return true;\n },\n deleteProperty(_, p) {\n return Reflect.deleteProperty(objectRef.value, p);\n },\n has(_, p) {\n return Reflect.has(objectRef.value, p);\n },\n ownKeys() {\n return Object.keys(objectRef.value);\n },\n getOwnPropertyDescriptor() {\n return {\n enumerable: true,\n configurable: true\n };\n }\n });\n return reactive(proxy);\n}\n\nfunction reactiveComputed(fn) {\n return toReactive(computed(fn));\n}\n\nfunction reactiveOmit(obj, ...keys) {\n const flatKeys = keys.flat();\n const predicate = flatKeys[0];\n return reactiveComputed(() => typeof predicate === \"function\" ? Object.fromEntries(Object.entries(toRefs$1(obj)).filter(([k, v]) => !predicate(toValue(v), k))) : Object.fromEntries(Object.entries(toRefs$1(obj)).filter((e) => !flatKeys.includes(e[0]))));\n}\n\nconst isClient = typeof window !== \"undefined\" && typeof document !== \"undefined\";\nconst isWorker = typeof WorkerGlobalScope !== \"undefined\" && globalThis instanceof WorkerGlobalScope;\nconst isDef = (val) => typeof val !== \"undefined\";\nconst notNullish = (val) => val != null;\nconst assert = (condition, ...infos) => {\n if (!condition)\n console.warn(...infos);\n};\nconst toString = Object.prototype.toString;\nconst isObject = (val) => toString.call(val) === \"[object Object]\";\nconst now = () => Date.now();\nconst timestamp = () => +Date.now();\nconst clamp = (n, min, max) => Math.min(max, Math.max(min, n));\nconst noop = () => {\n};\nconst rand = (min, max) => {\n min = Math.ceil(min);\n max = Math.floor(max);\n return Math.floor(Math.random() * (max - min + 1)) + min;\n};\nconst hasOwn = (val, key) => Object.prototype.hasOwnProperty.call(val, key);\nconst isIOS = /* @__PURE__ */ getIsIOS();\nfunction getIsIOS() {\n var _a, _b;\n return isClient && ((_a = window == null ? void 0 : window.navigator) == null ? void 0 : _a.userAgent) && (/iP(?:ad|hone|od)/.test(window.navigator.userAgent) || ((_b = window == null ? void 0 : window.navigator) == null ? void 0 : _b.maxTouchPoints) > 2 && /iPad|Macintosh/.test(window == null ? void 0 : window.navigator.userAgent));\n}\n\nfunction createFilterWrapper(filter, fn) {\n function wrapper(...args) {\n return new Promise((resolve, reject) => {\n Promise.resolve(filter(() => fn.apply(this, args), { fn, thisArg: this, args })).then(resolve).catch(reject);\n });\n }\n return wrapper;\n}\nconst bypassFilter = (invoke) => {\n return invoke();\n};\nfunction debounceFilter(ms, options = {}) {\n let timer;\n let maxTimer;\n let lastRejector = noop;\n const _clearTimeout = (timer2) => {\n clearTimeout(timer2);\n lastRejector();\n lastRejector = noop;\n };\n const filter = (invoke) => {\n const duration = toValue(ms);\n const maxDuration = toValue(options.maxWait);\n if (timer)\n _clearTimeout(timer);\n if (duration <= 0 || maxDuration !== void 0 && maxDuration <= 0) {\n if (maxTimer) {\n _clearTimeout(maxTimer);\n maxTimer = null;\n }\n return Promise.resolve(invoke());\n }\n return new Promise((resolve, reject) => {\n lastRejector = options.rejectOnCancel ? reject : resolve;\n if (maxDuration && !maxTimer) {\n maxTimer = setTimeout(() => {\n if (timer)\n _clearTimeout(timer);\n maxTimer = null;\n resolve(invoke());\n }, maxDuration);\n }\n timer = setTimeout(() => {\n if (maxTimer)\n _clearTimeout(maxTimer);\n maxTimer = null;\n resolve(invoke());\n }, duration);\n });\n };\n return filter;\n}\nfunction throttleFilter(...args) {\n let lastExec = 0;\n let timer;\n let isLeading = true;\n let lastRejector = noop;\n let lastValue;\n let ms;\n let trailing;\n let leading;\n let rejectOnCancel;\n if (!isRef(args[0]) && typeof args[0] === \"object\")\n ({ delay: ms, trailing = true, leading = true, rejectOnCancel = false } = args[0]);\n else\n [ms, trailing = true, leading = true, rejectOnCancel = false] = args;\n const clear = () => {\n if (timer) {\n clearTimeout(timer);\n timer = void 0;\n lastRejector();\n lastRejector = noop;\n }\n };\n const filter = (_invoke) => {\n const duration = toValue(ms);\n const elapsed = Date.now() - lastExec;\n const invoke = () => {\n return lastValue = _invoke();\n };\n clear();\n if (duration <= 0) {\n lastExec = Date.now();\n return invoke();\n }\n if (elapsed > duration && (leading || !isLeading)) {\n lastExec = Date.now();\n invoke();\n } else if (trailing) {\n lastValue = new Promise((resolve, reject) => {\n lastRejector = rejectOnCancel ? reject : resolve;\n timer = setTimeout(() => {\n lastExec = Date.now();\n isLeading = true;\n resolve(invoke());\n clear();\n }, Math.max(0, duration - elapsed));\n });\n }\n if (!leading && !timer)\n timer = setTimeout(() => isLeading = true, duration);\n isLeading = false;\n return lastValue;\n };\n return filter;\n}\nfunction pausableFilter(extendFilter = bypassFilter) {\n const isActive = ref(true);\n function pause() {\n isActive.value = false;\n }\n function resume() {\n isActive.value = true;\n }\n const eventFilter = (...args) => {\n if (isActive.value)\n extendFilter(...args);\n };\n return { isActive: readonly(isActive), pause, resume, eventFilter };\n}\n\nconst directiveHooks = {\n mounted: isVue3 ? \"mounted\" : \"inserted\",\n updated: isVue3 ? \"updated\" : \"componentUpdated\",\n unmounted: isVue3 ? \"unmounted\" : \"unbind\"\n};\n\nfunction cacheStringFunction(fn) {\n const cache = /* @__PURE__ */ Object.create(null);\n return (str) => {\n const hit = cache[str];\n return hit || (cache[str] = fn(str));\n };\n}\nconst hyphenateRE = /\\B([A-Z])/g;\nconst hyphenate = cacheStringFunction((str) => str.replace(hyphenateRE, \"-$1\").toLowerCase());\nconst camelizeRE = /-(\\w)/g;\nconst camelize = cacheStringFunction((str) => {\n return str.replace(camelizeRE, (_, c) => c ? c.toUpperCase() : \"\");\n});\n\nfunction promiseTimeout(ms, throwOnTimeout = false, reason = \"Timeout\") {\n return new Promise((resolve, reject) => {\n if (throwOnTimeout)\n setTimeout(() => reject(reason), ms);\n else\n setTimeout(resolve, ms);\n });\n}\nfunction identity(arg) {\n return arg;\n}\nfunction createSingletonPromise(fn) {\n let _promise;\n function wrapper() {\n if (!_promise)\n _promise = fn();\n return _promise;\n }\n wrapper.reset = async () => {\n const _prev = _promise;\n _promise = void 0;\n if (_prev)\n await _prev;\n };\n return wrapper;\n}\nfunction invoke(fn) {\n return fn();\n}\nfunction containsProp(obj, ...props) {\n return props.some((k) => k in obj);\n}\nfunction increaseWithUnit(target, delta) {\n var _a;\n if (typeof target === \"number\")\n return target + delta;\n const value = ((_a = target.match(/^-?\\d+\\.?\\d*/)) == null ? void 0 : _a[0]) || \"\";\n const unit = target.slice(value.length);\n const result = Number.parseFloat(value) + delta;\n if (Number.isNaN(result))\n return target;\n return result + unit;\n}\nfunction objectPick(obj, keys, omitUndefined = false) {\n return keys.reduce((n, k) => {\n if (k in obj) {\n if (!omitUndefined || obj[k] !== void 0)\n n[k] = obj[k];\n }\n return n;\n }, {});\n}\nfunction objectOmit(obj, keys, omitUndefined = false) {\n return Object.fromEntries(Object.entries(obj).filter(([key, value]) => {\n return (!omitUndefined || value !== void 0) && !keys.includes(key);\n }));\n}\nfunction objectEntries(obj) {\n return Object.entries(obj);\n}\nfunction getLifeCycleTarget(target) {\n return target || getCurrentInstance();\n}\n\nfunction toRef(...args) {\n if (args.length !== 1)\n return toRef$1(...args);\n const r = args[0];\n return typeof r === \"function\" ? readonly(customRef(() => ({ get: r, set: noop }))) : ref(r);\n}\nconst resolveRef = toRef;\n\nfunction reactivePick(obj, ...keys) {\n const flatKeys = keys.flat();\n const predicate = flatKeys[0];\n return reactiveComputed(() => typeof predicate === \"function\" ? Object.fromEntries(Object.entries(toRefs$1(obj)).filter(([k, v]) => predicate(toValue(v), k))) : Object.fromEntries(flatKeys.map((k) => [k, toRef(obj, k)])));\n}\n\nfunction refAutoReset(defaultValue, afterMs = 1e4) {\n return customRef((track, trigger) => {\n let value = toValue(defaultValue);\n let timer;\n const resetAfter = () => setTimeout(() => {\n value = toValue(defaultValue);\n trigger();\n }, toValue(afterMs));\n tryOnScopeDispose(() => {\n clearTimeout(timer);\n });\n return {\n get() {\n track();\n return value;\n },\n set(newValue) {\n value = newValue;\n trigger();\n clearTimeout(timer);\n timer = resetAfter();\n }\n };\n });\n}\n\nfunction useDebounceFn(fn, ms = 200, options = {}) {\n return createFilterWrapper(\n debounceFilter(ms, options),\n fn\n );\n}\n\nfunction refDebounced(value, ms = 200, options = {}) {\n const debounced = ref(value.value);\n const updater = useDebounceFn(() => {\n debounced.value = value.value;\n }, ms, options);\n watch(value, () => updater());\n return debounced;\n}\n\nfunction refDefault(source, defaultValue) {\n return computed({\n get() {\n var _a;\n return (_a = source.value) != null ? _a : defaultValue;\n },\n set(value) {\n source.value = value;\n }\n });\n}\n\nfunction useThrottleFn(fn, ms = 200, trailing = false, leading = true, rejectOnCancel = false) {\n return createFilterWrapper(\n throttleFilter(ms, trailing, leading, rejectOnCancel),\n fn\n );\n}\n\nfunction refThrottled(value, delay = 200, trailing = true, leading = true) {\n if (delay <= 0)\n return value;\n const throttled = ref(value.value);\n const updater = useThrottleFn(() => {\n throttled.value = value.value;\n }, delay, trailing, leading);\n watch(value, () => updater());\n return throttled;\n}\n\nfunction refWithControl(initial, options = {}) {\n let source = initial;\n let track;\n let trigger;\n const ref = customRef((_track, _trigger) => {\n track = _track;\n trigger = _trigger;\n return {\n get() {\n return get();\n },\n set(v) {\n set(v);\n }\n };\n });\n function get(tracking = true) {\n if (tracking)\n track();\n return source;\n }\n function set(value, triggering = true) {\n var _a, _b;\n if (value === source)\n return;\n const old = source;\n if (((_a = options.onBeforeChange) == null ? void 0 : _a.call(options, value, old)) === false)\n return;\n source = value;\n (_b = options.onChanged) == null ? void 0 : _b.call(options, value, old);\n if (triggering)\n trigger();\n }\n const untrackedGet = () => get(false);\n const silentSet = (v) => set(v, false);\n const peek = () => get(false);\n const lay = (v) => set(v, false);\n return extendRef(\n ref,\n {\n get,\n set,\n untrackedGet,\n silentSet,\n peek,\n lay\n },\n { enumerable: true }\n );\n}\nconst controlledRef = refWithControl;\n\nfunction set(...args) {\n if (args.length === 2) {\n const [ref, value] = args;\n ref.value = value;\n }\n if (args.length === 3) {\n if (isVue2) {\n set$1(...args);\n } else {\n const [target, key, value] = args;\n target[key] = value;\n }\n }\n}\n\nfunction watchWithFilter(source, cb, options = {}) {\n const {\n eventFilter = bypassFilter,\n ...watchOptions\n } = options;\n return watch(\n source,\n createFilterWrapper(\n eventFilter,\n cb\n ),\n watchOptions\n );\n}\n\nfunction watchPausable(source, cb, options = {}) {\n const {\n eventFilter: filter,\n ...watchOptions\n } = options;\n const { eventFilter, pause, resume, isActive } = pausableFilter(filter);\n const stop = watchWithFilter(\n source,\n cb,\n {\n ...watchOptions,\n eventFilter\n }\n );\n return { stop, pause, resume, isActive };\n}\n\nfunction syncRef(left, right, ...[options]) {\n const {\n flush = \"sync\",\n deep = false,\n immediate = true,\n direction = \"both\",\n transform = {}\n } = options || {};\n const watchers = [];\n const transformLTR = \"ltr\" in transform && transform.ltr || ((v) => v);\n const transformRTL = \"rtl\" in transform && transform.rtl || ((v) => v);\n if (direction === \"both\" || direction === \"ltr\") {\n watchers.push(watchPausable(\n left,\n (newValue) => {\n watchers.forEach((w) => w.pause());\n right.value = transformLTR(newValue);\n watchers.forEach((w) => w.resume());\n },\n { flush, deep, immediate }\n ));\n }\n if (direction === \"both\" || direction === \"rtl\") {\n watchers.push(watchPausable(\n right,\n (newValue) => {\n watchers.forEach((w) => w.pause());\n left.value = transformRTL(newValue);\n watchers.forEach((w) => w.resume());\n },\n { flush, deep, immediate }\n ));\n }\n const stop = () => {\n watchers.forEach((w) => w.stop());\n };\n return stop;\n}\n\nfunction syncRefs(source, targets, options = {}) {\n const {\n flush = \"sync\",\n deep = false,\n immediate = true\n } = options;\n if (!Array.isArray(targets))\n targets = [targets];\n return watch(\n source,\n (newValue) => targets.forEach((target) => target.value = newValue),\n { flush, deep, immediate }\n );\n}\n\nfunction toRefs(objectRef, options = {}) {\n if (!isRef(objectRef))\n return toRefs$1(objectRef);\n const result = Array.isArray(objectRef.value) ? Array.from({ length: objectRef.value.length }) : {};\n for (const key in objectRef.value) {\n result[key] = customRef(() => ({\n get() {\n return objectRef.value[key];\n },\n set(v) {\n var _a;\n const replaceRef = (_a = toValue(options.replaceRef)) != null ? _a : true;\n if (replaceRef) {\n if (Array.isArray(objectRef.value)) {\n const copy = [...objectRef.value];\n copy[key] = v;\n objectRef.value = copy;\n } else {\n const newObject = { ...objectRef.value, [key]: v };\n Object.setPrototypeOf(newObject, Object.getPrototypeOf(objectRef.value));\n objectRef.value = newObject;\n }\n } else {\n objectRef.value[key] = v;\n }\n }\n }));\n }\n return result;\n}\n\nfunction tryOnBeforeMount(fn, sync = true, target) {\n const instance = getLifeCycleTarget(target);\n if (instance)\n onBeforeMount(fn, target);\n else if (sync)\n fn();\n else\n nextTick(fn);\n}\n\nfunction tryOnBeforeUnmount(fn, target) {\n const instance = getLifeCycleTarget(target);\n if (instance)\n onBeforeUnmount(fn, target);\n}\n\nfunction tryOnMounted(fn, sync = true, target) {\n const instance = getLifeCycleTarget();\n if (instance)\n onMounted(fn, target);\n else if (sync)\n fn();\n else\n nextTick(fn);\n}\n\nfunction tryOnUnmounted(fn, target) {\n const instance = getLifeCycleTarget(target);\n if (instance)\n onUnmounted(fn, target);\n}\n\nfunction createUntil(r, isNot = false) {\n function toMatch(condition, { flush = \"sync\", deep = false, timeout, throwOnTimeout } = {}) {\n let stop = null;\n const watcher = new Promise((resolve) => {\n stop = watch(\n r,\n (v) => {\n if (condition(v) !== isNot) {\n if (stop)\n stop();\n else\n nextTick(() => stop == null ? void 0 : stop());\n resolve(v);\n }\n },\n {\n flush,\n deep,\n immediate: true\n }\n );\n });\n const promises = [watcher];\n if (timeout != null) {\n promises.push(\n promiseTimeout(timeout, throwOnTimeout).then(() => toValue(r)).finally(() => stop == null ? void 0 : stop())\n );\n }\n return Promise.race(promises);\n }\n function toBe(value, options) {\n if (!isRef(value))\n return toMatch((v) => v === value, options);\n const { flush = \"sync\", deep = false, timeout, throwOnTimeout } = options != null ? options : {};\n let stop = null;\n const watcher = new Promise((resolve) => {\n stop = watch(\n [r, value],\n ([v1, v2]) => {\n if (isNot !== (v1 === v2)) {\n if (stop)\n stop();\n else\n nextTick(() => stop == null ? void 0 : stop());\n resolve(v1);\n }\n },\n {\n flush,\n deep,\n immediate: true\n }\n );\n });\n const promises = [watcher];\n if (timeout != null) {\n promises.push(\n promiseTimeout(timeout, throwOnTimeout).then(() => toValue(r)).finally(() => {\n stop == null ? void 0 : stop();\n return toValue(r);\n })\n );\n }\n return Promise.race(promises);\n }\n function toBeTruthy(options) {\n return toMatch((v) => Boolean(v), options);\n }\n function toBeNull(options) {\n return toBe(null, options);\n }\n function toBeUndefined(options) {\n return toBe(void 0, options);\n }\n function toBeNaN(options) {\n return toMatch(Number.isNaN, options);\n }\n function toContains(value, options) {\n return toMatch((v) => {\n const array = Array.from(v);\n return array.includes(value) || array.includes(toValue(value));\n }, options);\n }\n function changed(options) {\n return changedTimes(1, options);\n }\n function changedTimes(n = 1, options) {\n let count = -1;\n return toMatch(() => {\n count += 1;\n return count >= n;\n }, options);\n }\n if (Array.isArray(toValue(r))) {\n const instance = {\n toMatch,\n toContains,\n changed,\n changedTimes,\n get not() {\n return createUntil(r, !isNot);\n }\n };\n return instance;\n } else {\n const instance = {\n toMatch,\n toBe,\n toBeTruthy,\n toBeNull,\n toBeNaN,\n toBeUndefined,\n changed,\n changedTimes,\n get not() {\n return createUntil(r, !isNot);\n }\n };\n return instance;\n }\n}\nfunction until(r) {\n return createUntil(r);\n}\n\nfunction defaultComparator(value, othVal) {\n return value === othVal;\n}\nfunction useArrayDifference(...args) {\n var _a;\n const list = args[0];\n const values = args[1];\n let compareFn = (_a = args[2]) != null ? _a : defaultComparator;\n if (typeof compareFn === \"string\") {\n const key = compareFn;\n compareFn = (value, othVal) => value[key] === othVal[key];\n }\n return computed(() => toValue(list).filter((x) => toValue(values).findIndex((y) => compareFn(x, y)) === -1));\n}\n\nfunction useArrayEvery(list, fn) {\n return computed(() => toValue(list).every((element, index, array) => fn(toValue(element), index, array)));\n}\n\nfunction useArrayFilter(list, fn) {\n return computed(() => toValue(list).map((i) => toValue(i)).filter(fn));\n}\n\nfunction useArrayFind(list, fn) {\n return computed(() => toValue(\n toValue(list).find((element, index, array) => fn(toValue(element), index, array))\n ));\n}\n\nfunction useArrayFindIndex(list, fn) {\n return computed(() => toValue(list).findIndex((element, index, array) => fn(toValue(element), index, array)));\n}\n\nfunction findLast(arr, cb) {\n let index = arr.length;\n while (index-- > 0) {\n if (cb(arr[index], index, arr))\n return arr[index];\n }\n return void 0;\n}\nfunction useArrayFindLast(list, fn) {\n return computed(() => toValue(\n !Array.prototype.findLast ? findLast(toValue(list), (element, index, array) => fn(toValue(element), index, array)) : toValue(list).findLast((element, index, array) => fn(toValue(element), index, array))\n ));\n}\n\nfunction isArrayIncludesOptions(obj) {\n return isObject(obj) && containsProp(obj, \"formIndex\", \"comparator\");\n}\nfunction useArrayIncludes(...args) {\n var _a;\n const list = args[0];\n const value = args[1];\n let comparator = args[2];\n let formIndex = 0;\n if (isArrayIncludesOptions(comparator)) {\n formIndex = (_a = comparator.fromIndex) != null ? _a : 0;\n comparator = comparator.comparator;\n }\n if (typeof comparator === \"string\") {\n const key = comparator;\n comparator = (element, value2) => element[key] === toValue(value2);\n }\n comparator = comparator != null ? comparator : (element, value2) => element === toValue(value2);\n return computed(() => toValue(list).slice(formIndex).some((element, index, array) => comparator(\n toValue(element),\n toValue(value),\n index,\n toValue(array)\n )));\n}\n\nfunction useArrayJoin(list, separator) {\n return computed(() => toValue(list).map((i) => toValue(i)).join(toValue(separator)));\n}\n\nfunction useArrayMap(list, fn) {\n return computed(() => toValue(list).map((i) => toValue(i)).map(fn));\n}\n\nfunction useArrayReduce(list, reducer, ...args) {\n const reduceCallback = (sum, value, index) => reducer(toValue(sum), toValue(value), index);\n return computed(() => {\n const resolved = toValue(list);\n return args.length ? resolved.reduce(reduceCallback, toValue(args[0])) : resolved.reduce(reduceCallback);\n });\n}\n\nfunction useArraySome(list, fn) {\n return computed(() => toValue(list).some((element, index, array) => fn(toValue(element), index, array)));\n}\n\nfunction uniq(array) {\n return Array.from(new Set(array));\n}\nfunction uniqueElementsBy(array, fn) {\n return array.reduce((acc, v) => {\n if (!acc.some((x) => fn(v, x, array)))\n acc.push(v);\n return acc;\n }, []);\n}\nfunction useArrayUnique(list, compareFn) {\n return computed(() => {\n const resolvedList = toValue(list).map((element) => toValue(element));\n return compareFn ? uniqueElementsBy(resolvedList, compareFn) : uniq(resolvedList);\n });\n}\n\nfunction useCounter(initialValue = 0, options = {}) {\n let _initialValue = unref(initialValue);\n const count = ref(initialValue);\n const {\n max = Number.POSITIVE_INFINITY,\n min = Number.NEGATIVE_INFINITY\n } = options;\n const inc = (delta = 1) => count.value = Math.max(Math.min(max, count.value + delta), min);\n const dec = (delta = 1) => count.value = Math.min(Math.max(min, count.value - delta), max);\n const get = () => count.value;\n const set = (val) => count.value = Math.max(min, Math.min(max, val));\n const reset = (val = _initialValue) => {\n _initialValue = val;\n return set(val);\n };\n return { count, inc, dec, get, set, reset };\n}\n\nconst REGEX_PARSE = /^(\\d{4})[-/]?(\\d{1,2})?[-/]?(\\d{0,2})[T\\s]*(\\d{1,2})?:?(\\d{1,2})?:?(\\d{1,2})?[.:]?(\\d+)?$/i;\nconst REGEX_FORMAT = /[YMDHhms]o|\\[([^\\]]+)\\]|Y{1,4}|M{1,4}|D{1,2}|d{1,4}|H{1,2}|h{1,2}|a{1,2}|A{1,2}|m{1,2}|s{1,2}|Z{1,2}|SSS/g;\nfunction defaultMeridiem(hours, minutes, isLowercase, hasPeriod) {\n let m = hours < 12 ? \"AM\" : \"PM\";\n if (hasPeriod)\n m = m.split(\"\").reduce((acc, curr) => acc += `${curr}.`, \"\");\n return isLowercase ? m.toLowerCase() : m;\n}\nfunction formatOrdinal(num) {\n const suffixes = [\"th\", \"st\", \"nd\", \"rd\"];\n const v = num % 100;\n return num + (suffixes[(v - 20) % 10] || suffixes[v] || suffixes[0]);\n}\nfunction formatDate(date, formatStr, options = {}) {\n var _a;\n const years = date.getFullYear();\n const month = date.getMonth();\n const days = date.getDate();\n const hours = date.getHours();\n const minutes = date.getMinutes();\n const seconds = date.getSeconds();\n const milliseconds = date.getMilliseconds();\n const day = date.getDay();\n const meridiem = (_a = options.customMeridiem) != null ? _a : defaultMeridiem;\n const matches = {\n Yo: () => formatOrdinal(years),\n YY: () => String(years).slice(-2),\n YYYY: () => years,\n M: () => month + 1,\n Mo: () => formatOrdinal(month + 1),\n MM: () => `${month + 1}`.padStart(2, \"0\"),\n MMM: () => date.toLocaleDateString(toValue(options.locales), { month: \"short\" }),\n MMMM: () => date.toLocaleDateString(toValue(options.locales), { month: \"long\" }),\n D: () => String(days),\n Do: () => formatOrdinal(days),\n DD: () => `${days}`.padStart(2, \"0\"),\n H: () => String(hours),\n Ho: () => formatOrdinal(hours),\n HH: () => `${hours}`.padStart(2, \"0\"),\n h: () => `${hours % 12 || 12}`.padStart(1, \"0\"),\n ho: () => formatOrdinal(hours % 12 || 12),\n hh: () => `${hours % 12 || 12}`.padStart(2, \"0\"),\n m: () => String(minutes),\n mo: () => formatOrdinal(minutes),\n mm: () => `${minutes}`.padStart(2, \"0\"),\n s: () => String(seconds),\n so: () => formatOrdinal(seconds),\n ss: () => `${seconds}`.padStart(2, \"0\"),\n SSS: () => `${milliseconds}`.padStart(3, \"0\"),\n d: () => day,\n dd: () => date.toLocaleDateString(toValue(options.locales), { weekday: \"narrow\" }),\n ddd: () => date.toLocaleDateString(toValue(options.locales), { weekday: \"short\" }),\n dddd: () => date.toLocaleDateString(toValue(options.locales), { weekday: \"long\" }),\n A: () => meridiem(hours, minutes),\n AA: () => meridiem(hours, minutes, false, true),\n a: () => meridiem(hours, minutes, true),\n aa: () => meridiem(hours, minutes, true, true)\n };\n return formatStr.replace(REGEX_FORMAT, (match, $1) => {\n var _a2, _b;\n return (_b = $1 != null ? $1 : (_a2 = matches[match]) == null ? void 0 : _a2.call(matches)) != null ? _b : match;\n });\n}\nfunction normalizeDate(date) {\n if (date === null)\n return new Date(Number.NaN);\n if (date === void 0)\n return /* @__PURE__ */ new Date();\n if (date instanceof Date)\n return new Date(date);\n if (typeof date === \"string\" && !/Z$/i.test(date)) {\n const d = date.match(REGEX_PARSE);\n if (d) {\n const m = d[2] - 1 || 0;\n const ms = (d[7] || \"0\").substring(0, 3);\n return new Date(d[1], m, d[3] || 1, d[4] || 0, d[5] || 0, d[6] || 0, ms);\n }\n }\n return new Date(date);\n}\nfunction useDateFormat(date, formatStr = \"HH:mm:ss\", options = {}) {\n return computed(() => formatDate(normalizeDate(toValue(date)), toValue(formatStr), options));\n}\n\nfunction useIntervalFn(cb, interval = 1e3, options = {}) {\n const {\n immediate = true,\n immediateCallback = false\n } = options;\n let timer = null;\n const isActive = ref(false);\n function clean() {\n if (timer) {\n clearInterval(timer);\n timer = null;\n }\n }\n function pause() {\n isActive.value = false;\n clean();\n }\n function resume() {\n const intervalValue = toValue(interval);\n if (intervalValue <= 0)\n return;\n isActive.value = true;\n if (immediateCallback)\n cb();\n clean();\n timer = setInterval(cb, intervalValue);\n }\n if (immediate && isClient)\n resume();\n if (isRef(interval) || typeof interval === \"function\") {\n const stopWatch = watch(interval, () => {\n if (isActive.value && isClient)\n resume();\n });\n tryOnScopeDispose(stopWatch);\n }\n tryOnScopeDispose(pause);\n return {\n isActive,\n pause,\n resume\n };\n}\n\nfunction useInterval(interval = 1e3, options = {}) {\n const {\n controls: exposeControls = false,\n immediate = true,\n callback\n } = options;\n const counter = ref(0);\n const update = () => counter.value += 1;\n const reset = () => {\n counter.value = 0;\n };\n const controls = useIntervalFn(\n callback ? () => {\n update();\n callback(counter.value);\n } : update,\n interval,\n { immediate }\n );\n if (exposeControls) {\n return {\n counter,\n reset,\n ...controls\n };\n } else {\n return counter;\n }\n}\n\nfunction useLastChanged(source, options = {}) {\n var _a;\n const ms = ref((_a = options.initialValue) != null ? _a : null);\n watch(\n source,\n () => ms.value = timestamp(),\n options\n );\n return ms;\n}\n\nfunction useTimeoutFn(cb, interval, options = {}) {\n const {\n immediate = true\n } = options;\n const isPending = ref(false);\n let timer = null;\n function clear() {\n if (timer) {\n clearTimeout(timer);\n timer = null;\n }\n }\n function stop() {\n isPending.value = false;\n clear();\n }\n function start(...args) {\n clear();\n isPending.value = true;\n timer = setTimeout(() => {\n isPending.value = false;\n timer = null;\n cb(...args);\n }, toValue(interval));\n }\n if (immediate) {\n isPending.value = true;\n if (isClient)\n start();\n }\n tryOnScopeDispose(stop);\n return {\n isPending: readonly(isPending),\n start,\n stop\n };\n}\n\nfunction useTimeout(interval = 1e3, options = {}) {\n const {\n controls: exposeControls = false,\n callback\n } = options;\n const controls = useTimeoutFn(\n callback != null ? callback : noop,\n interval,\n options\n );\n const ready = computed(() => !controls.isPending.value);\n if (exposeControls) {\n return {\n ready,\n ...controls\n };\n } else {\n return ready;\n }\n}\n\nfunction useToNumber(value, options = {}) {\n const {\n method = \"parseFloat\",\n radix,\n nanToZero\n } = options;\n return computed(() => {\n let resolved = toValue(value);\n if (typeof resolved === \"string\")\n resolved = Number[method](resolved, radix);\n if (nanToZero && Number.isNaN(resolved))\n resolved = 0;\n return resolved;\n });\n}\n\nfunction useToString(value) {\n return computed(() => `${toValue(value)}`);\n}\n\nfunction useToggle(initialValue = false, options = {}) {\n const {\n truthyValue = true,\n falsyValue = false\n } = options;\n const valueIsRef = isRef(initialValue);\n const _value = ref(initialValue);\n function toggle(value) {\n if (arguments.length) {\n _value.value = value;\n return _value.value;\n } else {\n const truthy = toValue(truthyValue);\n _value.value = _value.value === truthy ? toValue(falsyValue) : truthy;\n return _value.value;\n }\n }\n if (valueIsRef)\n return toggle;\n else\n return [_value, toggle];\n}\n\nfunction watchArray(source, cb, options) {\n let oldList = (options == null ? void 0 : options.immediate) ? [] : [...source instanceof Function ? source() : Array.isArray(source) ? source : toValue(source)];\n return watch(source, (newList, _, onCleanup) => {\n const oldListRemains = Array.from({ length: oldList.length });\n const added = [];\n for (const obj of newList) {\n let found = false;\n for (let i = 0; i < oldList.length; i++) {\n if (!oldListRemains[i] && obj === oldList[i]) {\n oldListRemains[i] = true;\n found = true;\n break;\n }\n }\n if (!found)\n added.push(obj);\n }\n const removed = oldList.filter((_2, i) => !oldListRemains[i]);\n cb(newList, oldList, added, removed, onCleanup);\n oldList = [...newList];\n }, options);\n}\n\nfunction watchAtMost(source, cb, options) {\n const {\n count,\n ...watchOptions\n } = options;\n const current = ref(0);\n const stop = watchWithFilter(\n source,\n (...args) => {\n current.value += 1;\n if (current.value >= toValue(count))\n nextTick(() => stop());\n cb(...args);\n },\n watchOptions\n );\n return { count: current, stop };\n}\n\nfunction watchDebounced(source, cb, options = {}) {\n const {\n debounce = 0,\n maxWait = void 0,\n ...watchOptions\n } = options;\n return watchWithFilter(\n source,\n cb,\n {\n ...watchOptions,\n eventFilter: debounceFilter(debounce, { maxWait })\n }\n );\n}\n\nfunction watchDeep(source, cb, options) {\n return watch(\n source,\n cb,\n {\n ...options,\n deep: true\n }\n );\n}\n\nfunction watchIgnorable(source, cb, options = {}) {\n const {\n eventFilter = bypassFilter,\n ...watchOptions\n } = options;\n const filteredCb = createFilterWrapper(\n eventFilter,\n cb\n );\n let ignoreUpdates;\n let ignorePrevAsyncUpdates;\n let stop;\n if (watchOptions.flush === \"sync\") {\n const ignore = ref(false);\n ignorePrevAsyncUpdates = () => {\n };\n ignoreUpdates = (updater) => {\n ignore.value = true;\n updater();\n ignore.value = false;\n };\n stop = watch(\n source,\n (...args) => {\n if (!ignore.value)\n filteredCb(...args);\n },\n watchOptions\n );\n } else {\n const disposables = [];\n const ignoreCounter = ref(0);\n const syncCounter = ref(0);\n ignorePrevAsyncUpdates = () => {\n ignoreCounter.value = syncCounter.value;\n };\n disposables.push(\n watch(\n source,\n () => {\n syncCounter.value++;\n },\n { ...watchOptions, flush: \"sync\" }\n )\n );\n ignoreUpdates = (updater) => {\n const syncCounterPrev = syncCounter.value;\n updater();\n ignoreCounter.value += syncCounter.value - syncCounterPrev;\n };\n disposables.push(\n watch(\n source,\n (...args) => {\n const ignore = ignoreCounter.value > 0 && ignoreCounter.value === syncCounter.value;\n ignoreCounter.value = 0;\n syncCounter.value = 0;\n if (ignore)\n return;\n filteredCb(...args);\n },\n watchOptions\n )\n );\n stop = () => {\n disposables.forEach((fn) => fn());\n };\n }\n return { stop, ignoreUpdates, ignorePrevAsyncUpdates };\n}\n\nfunction watchImmediate(source, cb, options) {\n return watch(\n source,\n cb,\n {\n ...options,\n immediate: true\n }\n );\n}\n\nfunction watchOnce(source, cb, options) {\n const stop = watch(source, (...args) => {\n nextTick(() => stop());\n return cb(...args);\n }, options);\n return stop;\n}\n\nfunction watchThrottled(source, cb, options = {}) {\n const {\n throttle = 0,\n trailing = true,\n leading = true,\n ...watchOptions\n } = options;\n return watchWithFilter(\n source,\n cb,\n {\n ...watchOptions,\n eventFilter: throttleFilter(throttle, trailing, leading)\n }\n );\n}\n\nfunction watchTriggerable(source, cb, options = {}) {\n let cleanupFn;\n function onEffect() {\n if (!cleanupFn)\n return;\n const fn = cleanupFn;\n cleanupFn = void 0;\n fn();\n }\n function onCleanup(callback) {\n cleanupFn = callback;\n }\n const _cb = (value, oldValue) => {\n onEffect();\n return cb(value, oldValue, onCleanup);\n };\n const res = watchIgnorable(source, _cb, options);\n const { ignoreUpdates } = res;\n const trigger = () => {\n let res2;\n ignoreUpdates(() => {\n res2 = _cb(getWatchSources(source), getOldValue(source));\n });\n return res2;\n };\n return {\n ...res,\n trigger\n };\n}\nfunction getWatchSources(sources) {\n if (isReactive(sources))\n return sources;\n if (Array.isArray(sources))\n return sources.map((item) => toValue(item));\n return toValue(sources);\n}\nfunction getOldValue(source) {\n return Array.isArray(source) ? source.map(() => void 0) : void 0;\n}\n\nfunction whenever(source, cb, options) {\n const stop = watch(\n source,\n (v, ov, onInvalidate) => {\n if (v) {\n if (options == null ? void 0 : options.once)\n nextTick(() => stop());\n cb(v, ov, onInvalidate);\n }\n },\n {\n ...options,\n once: false\n }\n );\n return stop;\n}\n\nexport { assert, refAutoReset as autoResetRef, bypassFilter, camelize, clamp, computedEager, computedWithControl, containsProp, computedWithControl as controlledComputed, controlledRef, createEventHook, createFilterWrapper, createGlobalState, createInjectionState, reactify as createReactiveFn, createSharedComposable, createSingletonPromise, debounceFilter, refDebounced as debouncedRef, watchDebounced as debouncedWatch, directiveHooks, computedEager as eagerComputed, extendRef, formatDate, get, getLifeCycleTarget, hasOwn, hyphenate, identity, watchIgnorable as ignorableWatch, increaseWithUnit, injectLocal, invoke, isClient, isDef, isDefined, isIOS, isObject, isWorker, makeDestructurable, noop, normalizeDate, notNullish, now, objectEntries, objectOmit, objectPick, pausableFilter, watchPausable as pausableWatch, promiseTimeout, provideLocal, rand, reactify, reactifyObject, reactiveComputed, reactiveOmit, reactivePick, refAutoReset, refDebounced, refDefault, refThrottled, refWithControl, resolveRef, resolveUnref, set, syncRef, syncRefs, throttleFilter, refThrottled as throttledRef, watchThrottled as throttledWatch, timestamp, toReactive, toRef, toRefs, toValue, tryOnBeforeMount, tryOnBeforeUnmount, tryOnMounted, tryOnScopeDispose, tryOnUnmounted, until, useArrayDifference, useArrayEvery, useArrayFilter, useArrayFind, useArrayFindIndex, useArrayFindLast, useArrayIncludes, useArrayJoin, useArrayMap, useArrayReduce, useArraySome, useArrayUnique, useCounter, useDateFormat, refDebounced as useDebounce, useDebounceFn, useInterval, useIntervalFn, useLastChanged, refThrottled as useThrottle, useThrottleFn, useTimeout, useTimeoutFn, useToNumber, useToString, useToggle, watchArray, watchAtMost, watchDebounced, watchDeep, watchIgnorable, watchImmediate, watchOnce, watchPausable, watchThrottled, watchTriggerable, watchWithFilter, whenever };\n","import * as Vue from 'vue'\n\nvar isVue2 = false\nvar isVue3 = true\nvar Vue2 = undefined\n\nfunction install() {}\n\nexport function set(target, key, val) {\n if (Array.isArray(target)) {\n target.length = Math.max(target.length, key)\n target.splice(key, 1, val)\n return val\n }\n target[key] = val\n return val\n}\n\nexport function del(target, key) {\n if (Array.isArray(target)) {\n target.splice(key, 1)\n return\n }\n delete target[key]\n}\n\nexport * from 'vue'\nexport {\n Vue,\n Vue2,\n isVue2,\n isVue3,\n install,\n}\n","import { noop, makeDestructurable, camelize, toValue, isClient, isObject, tryOnScopeDispose, isIOS, tryOnMounted, notNullish, objectOmit, promiseTimeout, until, increaseWithUnit, objectEntries, createSingletonPromise, useTimeoutFn, pausableWatch, toRef, createEventHook, computedWithControl, timestamp, pausableFilter, watchIgnorable, debounceFilter, createFilterWrapper, bypassFilter, toRefs, useIntervalFn, containsProp, hasOwn, throttleFilter, useDebounceFn, useThrottleFn, clamp, syncRef, objectPick, tryOnUnmounted, watchWithFilter, tryOnBeforeUnmount, identity, isDef, whenever, isWorker } from '@vueuse/shared';\nexport * from '@vueuse/shared';\nimport { isRef, ref, shallowRef, watchEffect, computed, inject, isVue3, version, defineComponent, h, TransitionGroup, shallowReactive, Fragment, watch, getCurrentInstance, customRef, onUpdated, onMounted, isVue2, readonly, reactive, nextTick, markRaw, unref, getCurrentScope, set, del, isReadonly, onBeforeUpdate } from 'vue-demi';\n\nfunction computedAsync(evaluationCallback, initialState, optionsOrRef) {\n let options;\n if (isRef(optionsOrRef)) {\n options = {\n evaluating: optionsOrRef\n };\n } else {\n options = optionsOrRef || {};\n }\n const {\n lazy = false,\n evaluating = void 0,\n shallow = true,\n onError = noop\n } = options;\n const started = ref(!lazy);\n const current = shallow ? shallowRef(initialState) : ref(initialState);\n let counter = 0;\n watchEffect(async (onInvalidate) => {\n if (!started.value)\n return;\n counter++;\n const counterAtBeginning = counter;\n let hasFinished = false;\n if (evaluating) {\n Promise.resolve().then(() => {\n evaluating.value = true;\n });\n }\n try {\n const result = await evaluationCallback((cancelCallback) => {\n onInvalidate(() => {\n if (evaluating)\n evaluating.value = false;\n if (!hasFinished)\n cancelCallback();\n });\n });\n if (counterAtBeginning === counter)\n current.value = result;\n } catch (e) {\n onError(e);\n } finally {\n if (evaluating && counterAtBeginning === counter)\n evaluating.value = false;\n hasFinished = true;\n }\n });\n if (lazy) {\n return computed(() => {\n started.value = true;\n return current.value;\n });\n } else {\n return current;\n }\n}\n\nfunction computedInject(key, options, defaultSource, treatDefaultAsFactory) {\n let source = inject(key);\n if (defaultSource)\n source = inject(key, defaultSource);\n if (treatDefaultAsFactory)\n source = inject(key, defaultSource, treatDefaultAsFactory);\n if (typeof options === \"function\") {\n return computed((ctx) => options(source, ctx));\n } else {\n return computed({\n get: (ctx) => options.get(source, ctx),\n set: options.set\n });\n }\n}\n\nfunction createReusableTemplate(options = {}) {\n if (!isVue3 && !version.startsWith(\"2.7.\")) {\n if (process.env.NODE_ENV !== \"production\")\n throw new Error(\"[VueUse] createReusableTemplate only works in Vue 2.7 or above.\");\n return;\n }\n const {\n inheritAttrs = true\n } = options;\n const render = shallowRef();\n const define = /* #__PURE__ */ defineComponent({\n setup(_, { slots }) {\n return () => {\n render.value = slots.default;\n };\n }\n });\n const reuse = /* #__PURE__ */ defineComponent({\n inheritAttrs,\n setup(_, { attrs, slots }) {\n return () => {\n var _a;\n if (!render.value && process.env.NODE_ENV !== \"production\")\n throw new Error(\"[VueUse] Failed to find the definition of reusable template\");\n const vnode = (_a = render.value) == null ? void 0 : _a.call(render, { ...keysToCamelKebabCase(attrs), $slots: slots });\n return inheritAttrs && (vnode == null ? void 0 : vnode.length) === 1 ? vnode[0] : vnode;\n };\n }\n });\n return makeDestructurable(\n { define, reuse },\n [define, reuse]\n );\n}\nfunction keysToCamelKebabCase(obj) {\n const newObj = {};\n for (const key in obj)\n newObj[camelize(key)] = obj[key];\n return newObj;\n}\n\nfunction createTemplatePromise(options = {}) {\n if (!isVue3) {\n if (process.env.NODE_ENV !== \"production\")\n throw new Error(\"[VueUse] createTemplatePromise only works in Vue 3 or above.\");\n return;\n }\n let index = 0;\n const instances = ref([]);\n function create(...args) {\n const props = shallowReactive({\n key: index++,\n args,\n promise: void 0,\n resolve: () => {\n },\n reject: () => {\n },\n isResolving: false,\n options\n });\n instances.value.push(props);\n props.promise = new Promise((_resolve, _reject) => {\n props.resolve = (v) => {\n props.isResolving = true;\n return _resolve(v);\n };\n props.reject = _reject;\n }).finally(() => {\n props.promise = void 0;\n const index2 = instances.value.indexOf(props);\n if (index2 !== -1)\n instances.value.splice(index2, 1);\n });\n return props.promise;\n }\n function start(...args) {\n if (options.singleton && instances.value.length > 0)\n return instances.value[0].promise;\n return create(...args);\n }\n const component = /* #__PURE__ */ defineComponent((_, { slots }) => {\n const renderList = () => instances.value.map((props) => {\n var _a;\n return h(Fragment, { key: props.key }, (_a = slots.default) == null ? void 0 : _a.call(slots, props));\n });\n if (options.transition)\n return () => h(TransitionGroup, options.transition, renderList);\n return renderList;\n });\n component.start = start;\n return component;\n}\n\nfunction createUnrefFn(fn) {\n return function(...args) {\n return fn.apply(this, args.map((i) => toValue(i)));\n };\n}\n\nfunction unrefElement(elRef) {\n var _a;\n const plain = toValue(elRef);\n return (_a = plain == null ? void 0 : plain.$el) != null ? _a : plain;\n}\n\nconst defaultWindow = isClient ? window : void 0;\nconst defaultDocument = isClient ? window.document : void 0;\nconst defaultNavigator = isClient ? window.navigator : void 0;\nconst defaultLocation = isClient ? window.location : void 0;\n\nfunction useEventListener(...args) {\n let target;\n let events;\n let listeners;\n let options;\n if (typeof args[0] === \"string\" || Array.isArray(args[0])) {\n [events, listeners, options] = args;\n target = defaultWindow;\n } else {\n [target, events, listeners, options] = args;\n }\n if (!target)\n return noop;\n if (!Array.isArray(events))\n events = [events];\n if (!Array.isArray(listeners))\n listeners = [listeners];\n const cleanups = [];\n const cleanup = () => {\n cleanups.forEach((fn) => fn());\n cleanups.length = 0;\n };\n const register = (el, event, listener, options2) => {\n el.addEventListener(event, listener, options2);\n return () => el.removeEventListener(event, listener, options2);\n };\n const stopWatch = watch(\n () => [unrefElement(target), toValue(options)],\n ([el, options2]) => {\n cleanup();\n if (!el)\n return;\n const optionsClone = isObject(options2) ? { ...options2 } : options2;\n cleanups.push(\n ...events.flatMap((event) => {\n return listeners.map((listener) => register(el, event, listener, optionsClone));\n })\n );\n },\n { immediate: true, flush: \"post\" }\n );\n const stop = () => {\n stopWatch();\n cleanup();\n };\n tryOnScopeDispose(stop);\n return stop;\n}\n\nlet _iOSWorkaround = false;\nfunction onClickOutside(target, handler, options = {}) {\n const { window = defaultWindow, ignore = [], capture = true, detectIframe = false } = options;\n if (!window)\n return noop;\n if (isIOS && !_iOSWorkaround) {\n _iOSWorkaround = true;\n Array.from(window.document.body.children).forEach((el) => el.addEventListener(\"click\", noop));\n window.document.documentElement.addEventListener(\"click\", noop);\n }\n let shouldListen = true;\n const shouldIgnore = (event) => {\n return ignore.some((target2) => {\n if (typeof target2 === \"string\") {\n return Array.from(window.document.querySelectorAll(target2)).some((el) => el === event.target || event.composedPath().includes(el));\n } else {\n const el = unrefElement(target2);\n return el && (event.target === el || event.composedPath().includes(el));\n }\n });\n };\n const listener = (event) => {\n const el = unrefElement(target);\n if (!el || el === event.target || event.composedPath().includes(el))\n return;\n if (event.detail === 0)\n shouldListen = !shouldIgnore(event);\n if (!shouldListen) {\n shouldListen = true;\n return;\n }\n handler(event);\n };\n const cleanup = [\n useEventListener(window, \"click\", listener, { passive: true, capture }),\n useEventListener(window, \"pointerdown\", (e) => {\n const el = unrefElement(target);\n shouldListen = !shouldIgnore(e) && !!(el && !e.composedPath().includes(el));\n }, { passive: true }),\n detectIframe && useEventListener(window, \"blur\", (event) => {\n setTimeout(() => {\n var _a;\n const el = unrefElement(target);\n if (((_a = window.document.activeElement) == null ? void 0 : _a.tagName) === \"IFRAME\" && !(el == null ? void 0 : el.contains(window.document.activeElement))) {\n handler(event);\n }\n }, 0);\n })\n ].filter(Boolean);\n const stop = () => cleanup.forEach((fn) => fn());\n return stop;\n}\n\nfunction createKeyPredicate(keyFilter) {\n if (typeof keyFilter === \"function\")\n return keyFilter;\n else if (typeof keyFilter === \"string\")\n return (event) => event.key === keyFilter;\n else if (Array.isArray(keyFilter))\n return (event) => keyFilter.includes(event.key);\n return () => true;\n}\nfunction onKeyStroke(...args) {\n let key;\n let handler;\n let options = {};\n if (args.length === 3) {\n key = args[0];\n handler = args[1];\n options = args[2];\n } else if (args.length === 2) {\n if (typeof args[1] === \"object\") {\n key = true;\n handler = args[0];\n options = args[1];\n } else {\n key = args[0];\n handler = args[1];\n }\n } else {\n key = true;\n handler = args[0];\n }\n const {\n target = defaultWindow,\n eventName = \"keydown\",\n passive = false,\n dedupe = false\n } = options;\n const predicate = createKeyPredicate(key);\n const listener = (e) => {\n if (e.repeat && toValue(dedupe))\n return;\n if (predicate(e))\n handler(e);\n };\n return useEventListener(target, eventName, listener, passive);\n}\nfunction onKeyDown(key, handler, options = {}) {\n return onKeyStroke(key, handler, { ...options, eventName: \"keydown\" });\n}\nfunction onKeyPressed(key, handler, options = {}) {\n return onKeyStroke(key, handler, { ...options, eventName: \"keypress\" });\n}\nfunction onKeyUp(key, handler, options = {}) {\n return onKeyStroke(key, handler, { ...options, eventName: \"keyup\" });\n}\n\nconst DEFAULT_DELAY = 500;\nconst DEFAULT_THRESHOLD = 10;\nfunction onLongPress(target, handler, options) {\n var _a, _b;\n const elementRef = computed(() => unrefElement(target));\n let timeout;\n let posStart;\n let startTimestamp;\n let hasLongPressed = false;\n function clear() {\n if (timeout) {\n clearTimeout(timeout);\n timeout = void 0;\n }\n posStart = void 0;\n startTimestamp = void 0;\n hasLongPressed = false;\n }\n function onRelease(ev) {\n var _a2, _b2, _c;\n const [_startTimestamp, _posStart, _hasLongPressed] = [startTimestamp, posStart, hasLongPressed];\n clear();\n if (!(options == null ? void 0 : options.onMouseUp) || !_posStart || !_startTimestamp)\n return;\n if (((_a2 = options == null ? void 0 : options.modifiers) == null ? void 0 : _a2.self) && ev.target !== elementRef.value)\n return;\n if ((_b2 = options == null ? void 0 : options.modifiers) == null ? void 0 : _b2.prevent)\n ev.preventDefault();\n if ((_c = options == null ? void 0 : options.modifiers) == null ? void 0 : _c.stop)\n ev.stopPropagation();\n const dx = ev.x - _posStart.x;\n const dy = ev.y - _posStart.y;\n const distance = Math.sqrt(dx * dx + dy * dy);\n options.onMouseUp(ev.timeStamp - _startTimestamp, distance, _hasLongPressed);\n }\n function onDown(ev) {\n var _a2, _b2, _c, _d;\n if (((_a2 = options == null ? void 0 : options.modifiers) == null ? void 0 : _a2.self) && ev.target !== elementRef.value)\n return;\n clear();\n if ((_b2 = options == null ? void 0 : options.modifiers) == null ? void 0 : _b2.prevent)\n ev.preventDefault();\n if ((_c = options == null ? void 0 : options.modifiers) == null ? void 0 : _c.stop)\n ev.stopPropagation();\n posStart = {\n x: ev.x,\n y: ev.y\n };\n startTimestamp = ev.timeStamp;\n timeout = setTimeout(\n () => {\n hasLongPressed = true;\n handler(ev);\n },\n (_d = options == null ? void 0 : options.delay) != null ? _d : DEFAULT_DELAY\n );\n }\n function onMove(ev) {\n var _a2, _b2, _c, _d;\n if (((_a2 = options == null ? void 0 : options.modifiers) == null ? void 0 : _a2.self) && ev.target !== elementRef.value)\n return;\n if (!posStart || (options == null ? void 0 : options.distanceThreshold) === false)\n return;\n if ((_b2 = options == null ? void 0 : options.modifiers) == null ? void 0 : _b2.prevent)\n ev.preventDefault();\n if ((_c = options == null ? void 0 : options.modifiers) == null ? void 0 : _c.stop)\n ev.stopPropagation();\n const dx = ev.x - posStart.x;\n const dy = ev.y - posStart.y;\n const distance = Math.sqrt(dx * dx + dy * dy);\n if (distance >= ((_d = options == null ? void 0 : options.distanceThreshold) != null ? _d : DEFAULT_THRESHOLD))\n clear();\n }\n const listenerOptions = {\n capture: (_a = options == null ? void 0 : options.modifiers) == null ? void 0 : _a.capture,\n once: (_b = options == null ? void 0 : options.modifiers) == null ? void 0 : _b.once\n };\n const cleanup = [\n useEventListener(elementRef, \"pointerdown\", onDown, listenerOptions),\n useEventListener(elementRef, \"pointermove\", onMove, listenerOptions),\n useEventListener(elementRef, [\"pointerup\", \"pointerleave\"], onRelease, listenerOptions)\n ];\n const stop = () => cleanup.forEach((fn) => fn());\n return stop;\n}\n\nfunction isFocusedElementEditable() {\n const { activeElement, body } = document;\n if (!activeElement)\n return false;\n if (activeElement === body)\n return false;\n switch (activeElement.tagName) {\n case \"INPUT\":\n case \"TEXTAREA\":\n return true;\n }\n return activeElement.hasAttribute(\"contenteditable\");\n}\nfunction isTypedCharValid({\n keyCode,\n metaKey,\n ctrlKey,\n altKey\n}) {\n if (metaKey || ctrlKey || altKey)\n return false;\n if (keyCode >= 48 && keyCode <= 57)\n return true;\n if (keyCode >= 65 && keyCode <= 90)\n return true;\n if (keyCode >= 97 && keyCode <= 122)\n return true;\n return false;\n}\nfunction onStartTyping(callback, options = {}) {\n const { document: document2 = defaultDocument } = options;\n const keydown = (event) => {\n if (!isFocusedElementEditable() && isTypedCharValid(event)) {\n callback(event);\n }\n };\n if (document2)\n useEventListener(document2, \"keydown\", keydown, { passive: true });\n}\n\nfunction templateRef(key, initialValue = null) {\n const instance = getCurrentInstance();\n let _trigger = () => {\n };\n const element = customRef((track, trigger) => {\n _trigger = trigger;\n return {\n get() {\n var _a, _b;\n track();\n return (_b = (_a = instance == null ? void 0 : instance.proxy) == null ? void 0 : _a.$refs[key]) != null ? _b : initialValue;\n },\n set() {\n }\n };\n });\n tryOnMounted(_trigger);\n onUpdated(_trigger);\n return element;\n}\n\nfunction useMounted() {\n const isMounted = ref(false);\n const instance = getCurrentInstance();\n if (instance) {\n onMounted(() => {\n isMounted.value = true;\n }, isVue2 ? void 0 : instance);\n }\n return isMounted;\n}\n\nfunction useSupported(callback) {\n const isMounted = useMounted();\n return computed(() => {\n isMounted.value;\n return Boolean(callback());\n });\n}\n\nfunction useMutationObserver(target, callback, options = {}) {\n const { window = defaultWindow, ...mutationOptions } = options;\n let observer;\n const isSupported = useSupported(() => window && \"MutationObserver\" in window);\n const cleanup = () => {\n if (observer) {\n observer.disconnect();\n observer = void 0;\n }\n };\n const targets = computed(() => {\n const value = toValue(target);\n const items = (Array.isArray(value) ? value : [value]).map(unrefElement).filter(notNullish);\n return new Set(items);\n });\n const stopWatch = watch(\n () => targets.value,\n (targets2) => {\n cleanup();\n if (isSupported.value && targets2.size) {\n observer = new MutationObserver(callback);\n targets2.forEach((el) => observer.observe(el, mutationOptions));\n }\n },\n { immediate: true, flush: \"post\" }\n );\n const takeRecords = () => {\n return observer == null ? void 0 : observer.takeRecords();\n };\n const stop = () => {\n stopWatch();\n cleanup();\n };\n tryOnScopeDispose(stop);\n return {\n isSupported,\n stop,\n takeRecords\n };\n}\n\nfunction useActiveElement(options = {}) {\n var _a;\n const {\n window = defaultWindow,\n deep = true,\n triggerOnRemoval = false\n } = options;\n const document = (_a = options.document) != null ? _a : window == null ? void 0 : window.document;\n const getDeepActiveElement = () => {\n var _a2;\n let element = document == null ? void 0 : document.activeElement;\n if (deep) {\n while (element == null ? void 0 : element.shadowRoot)\n element = (_a2 = element == null ? void 0 : element.shadowRoot) == null ? void 0 : _a2.activeElement;\n }\n return element;\n };\n const activeElement = ref();\n const trigger = () => {\n activeElement.value = getDeepActiveElement();\n };\n if (window) {\n useEventListener(window, \"blur\", (event) => {\n if (event.relatedTarget !== null)\n return;\n trigger();\n }, true);\n useEventListener(window, \"focus\", trigger, true);\n }\n if (triggerOnRemoval) {\n useMutationObserver(document, (mutations) => {\n mutations.filter((m) => m.removedNodes.length).map((n) => Array.from(n.removedNodes)).flat().forEach((node) => {\n if (node === activeElement.value)\n trigger();\n });\n }, {\n childList: true,\n subtree: true\n });\n }\n trigger();\n return activeElement;\n}\n\nfunction useRafFn(fn, options = {}) {\n const {\n immediate = true,\n fpsLimit = void 0,\n window = defaultWindow\n } = options;\n const isActive = ref(false);\n const intervalLimit = fpsLimit ? 1e3 / fpsLimit : null;\n let previousFrameTimestamp = 0;\n let rafId = null;\n function loop(timestamp) {\n if (!isActive.value || !window)\n return;\n if (!previousFrameTimestamp)\n previousFrameTimestamp = timestamp;\n const delta = timestamp - previousFrameTimestamp;\n if (intervalLimit && delta < intervalLimit) {\n rafId = window.requestAnimationFrame(loop);\n return;\n }\n previousFrameTimestamp = timestamp;\n fn({ delta, timestamp });\n rafId = window.requestAnimationFrame(loop);\n }\n function resume() {\n if (!isActive.value && window) {\n isActive.value = true;\n previousFrameTimestamp = 0;\n rafId = window.requestAnimationFrame(loop);\n }\n }\n function pause() {\n isActive.value = false;\n if (rafId != null && window) {\n window.cancelAnimationFrame(rafId);\n rafId = null;\n }\n }\n if (immediate)\n resume();\n tryOnScopeDispose(pause);\n return {\n isActive: readonly(isActive),\n pause,\n resume\n };\n}\n\nfunction useAnimate(target, keyframes, options) {\n let config;\n let animateOptions;\n if (isObject(options)) {\n config = options;\n animateOptions = objectOmit(options, [\"window\", \"immediate\", \"commitStyles\", \"persist\", \"onReady\", \"onError\"]);\n } else {\n config = { duration: options };\n animateOptions = options;\n }\n const {\n window = defaultWindow,\n immediate = true,\n commitStyles,\n persist,\n playbackRate: _playbackRate = 1,\n onReady,\n onError = (e) => {\n console.error(e);\n }\n } = config;\n const isSupported = useSupported(() => window && HTMLElement && \"animate\" in HTMLElement.prototype);\n const animate = shallowRef(void 0);\n const store = shallowReactive({\n startTime: null,\n currentTime: null,\n timeline: null,\n playbackRate: _playbackRate,\n pending: false,\n playState: immediate ? \"idle\" : \"paused\",\n replaceState: \"active\"\n });\n const pending = computed(() => store.pending);\n const playState = computed(() => store.playState);\n const replaceState = computed(() => store.replaceState);\n const startTime = computed({\n get() {\n return store.startTime;\n },\n set(value) {\n store.startTime = value;\n if (animate.value)\n animate.value.startTime = value;\n }\n });\n const currentTime = computed({\n get() {\n return store.currentTime;\n },\n set(value) {\n store.currentTime = value;\n if (animate.value) {\n animate.value.currentTime = value;\n syncResume();\n }\n }\n });\n const timeline = computed({\n get() {\n return store.timeline;\n },\n set(value) {\n store.timeline = value;\n if (animate.value)\n animate.value.timeline = value;\n }\n });\n const playbackRate = computed({\n get() {\n return store.playbackRate;\n },\n set(value) {\n store.playbackRate = value;\n if (animate.value)\n animate.value.playbackRate = value;\n }\n });\n const play = () => {\n if (animate.value) {\n try {\n animate.value.play();\n syncResume();\n } catch (e) {\n syncPause();\n onError(e);\n }\n } else {\n update();\n }\n };\n const pause = () => {\n var _a;\n try {\n (_a = animate.value) == null ? void 0 : _a.pause();\n syncPause();\n } catch (e) {\n onError(e);\n }\n };\n const reverse = () => {\n var _a;\n if (!animate.value)\n update();\n try {\n (_a = animate.value) == null ? void 0 : _a.reverse();\n syncResume();\n } catch (e) {\n syncPause();\n onError(e);\n }\n };\n const finish = () => {\n var _a;\n try {\n (_a = animate.value) == null ? void 0 : _a.finish();\n syncPause();\n } catch (e) {\n onError(e);\n }\n };\n const cancel = () => {\n var _a;\n try {\n (_a = animate.value) == null ? void 0 : _a.cancel();\n syncPause();\n } catch (e) {\n onError(e);\n }\n };\n watch(() => unrefElement(target), (el) => {\n if (el)\n update();\n });\n watch(() => keyframes, (value) => {\n if (animate.value)\n update();\n if (!unrefElement(target) && animate.value) {\n animate.value.effect = new KeyframeEffect(\n unrefElement(target),\n toValue(value),\n animateOptions\n );\n }\n }, { deep: true });\n tryOnMounted(() => update(true), false);\n tryOnScopeDispose(cancel);\n function update(init) {\n const el = unrefElement(target);\n if (!isSupported.value || !el)\n return;\n if (!animate.value)\n animate.value = el.animate(toValue(keyframes), animateOptions);\n if (persist)\n animate.value.persist();\n if (_playbackRate !== 1)\n animate.value.playbackRate = _playbackRate;\n if (init && !immediate)\n animate.value.pause();\n else\n syncResume();\n onReady == null ? void 0 : onReady(animate.value);\n }\n useEventListener(animate, [\"cancel\", \"finish\", \"remove\"], syncPause);\n useEventListener(animate, \"finish\", () => {\n var _a;\n if (commitStyles)\n (_a = animate.value) == null ? void 0 : _a.commitStyles();\n });\n const { resume: resumeRef, pause: pauseRef } = useRafFn(() => {\n if (!animate.value)\n return;\n store.pending = animate.value.pending;\n store.playState = animate.value.playState;\n store.replaceState = animate.value.replaceState;\n store.startTime = animate.value.startTime;\n store.currentTime = animate.value.currentTime;\n store.timeline = animate.value.timeline;\n store.playbackRate = animate.value.playbackRate;\n }, { immediate: false });\n function syncResume() {\n if (isSupported.value)\n resumeRef();\n }\n function syncPause() {\n if (isSupported.value && window)\n window.requestAnimationFrame(pauseRef);\n }\n return {\n isSupported,\n animate,\n // actions\n play,\n pause,\n reverse,\n finish,\n cancel,\n // state\n pending,\n playState,\n replaceState,\n startTime,\n currentTime,\n timeline,\n playbackRate\n };\n}\n\nfunction useAsyncQueue(tasks, options) {\n const {\n interrupt = true,\n onError = noop,\n onFinished = noop,\n signal\n } = options || {};\n const promiseState = {\n aborted: \"aborted\",\n fulfilled: \"fulfilled\",\n pending: \"pending\",\n rejected: \"rejected\"\n };\n const initialResult = Array.from(Array.from({ length: tasks.length }), () => ({ state: promiseState.pending, data: null }));\n const result = reactive(initialResult);\n const activeIndex = ref(-1);\n if (!tasks || tasks.length === 0) {\n onFinished();\n return {\n activeIndex,\n result\n };\n }\n function updateResult(state, res) {\n activeIndex.value++;\n result[activeIndex.value].data = res;\n result[activeIndex.value].state = state;\n }\n tasks.reduce((prev, curr) => {\n return prev.then((prevRes) => {\n var _a;\n if (signal == null ? void 0 : signal.aborted) {\n updateResult(promiseState.aborted, new Error(\"aborted\"));\n return;\n }\n if (((_a = result[activeIndex.value]) == null ? void 0 : _a.state) === promiseState.rejected && interrupt) {\n onFinished();\n return;\n }\n const done = curr(prevRes).then((currentRes) => {\n updateResult(promiseState.fulfilled, currentRes);\n if (activeIndex.value === tasks.length - 1)\n onFinished();\n return currentRes;\n });\n if (!signal)\n return done;\n return Promise.race([done, whenAborted(signal)]);\n }).catch((e) => {\n if (signal == null ? void 0 : signal.aborted) {\n updateResult(promiseState.aborted, e);\n return e;\n }\n updateResult(promiseState.rejected, e);\n onError();\n return e;\n });\n }, Promise.resolve());\n return {\n activeIndex,\n result\n };\n}\nfunction whenAborted(signal) {\n return new Promise((resolve, reject) => {\n const error = new Error(\"aborted\");\n if (signal.aborted)\n reject(error);\n else\n signal.addEventListener(\"abort\", () => reject(error), { once: true });\n });\n}\n\nfunction useAsyncState(promise, initialState, options) {\n const {\n immediate = true,\n delay = 0,\n onError = noop,\n onSuccess = noop,\n resetOnExecute = true,\n shallow = true,\n throwError\n } = options != null ? options : {};\n const state = shallow ? shallowRef(initialState) : ref(initialState);\n const isReady = ref(false);\n const isLoading = ref(false);\n const error = shallowRef(void 0);\n async function execute(delay2 = 0, ...args) {\n if (resetOnExecute)\n state.value = initialState;\n error.value = void 0;\n isReady.value = false;\n isLoading.value = true;\n if (delay2 > 0)\n await promiseTimeout(delay2);\n const _promise = typeof promise === \"function\" ? promise(...args) : promise;\n try {\n const data = await _promise;\n state.value = data;\n isReady.value = true;\n onSuccess(data);\n } catch (e) {\n error.value = e;\n onError(e);\n if (throwError)\n throw e;\n } finally {\n isLoading.value = false;\n }\n return state.value;\n }\n if (immediate)\n execute(delay);\n const shell = {\n state,\n isReady,\n isLoading,\n error,\n execute\n };\n function waitUntilIsLoaded() {\n return new Promise((resolve, reject) => {\n until(isLoading).toBe(false).then(() => resolve(shell)).catch(reject);\n });\n }\n return {\n ...shell,\n then(onFulfilled, onRejected) {\n return waitUntilIsLoaded().then(onFulfilled, onRejected);\n }\n };\n}\n\nconst defaults = {\n array: (v) => JSON.stringify(v),\n object: (v) => JSON.stringify(v),\n set: (v) => JSON.stringify(Array.from(v)),\n map: (v) => JSON.stringify(Object.fromEntries(v)),\n null: () => \"\"\n};\nfunction getDefaultSerialization(target) {\n if (!target)\n return defaults.null;\n if (target instanceof Map)\n return defaults.map;\n else if (target instanceof Set)\n return defaults.set;\n else if (Array.isArray(target))\n return defaults.array;\n else\n return defaults.object;\n}\n\nfunction useBase64(target, options) {\n const base64 = ref(\"\");\n const promise = ref();\n function execute() {\n if (!isClient)\n return;\n promise.value = new Promise((resolve, reject) => {\n try {\n const _target = toValue(target);\n if (_target == null) {\n resolve(\"\");\n } else if (typeof _target === \"string\") {\n resolve(blobToBase64(new Blob([_target], { type: \"text/plain\" })));\n } else if (_target instanceof Blob) {\n resolve(blobToBase64(_target));\n } else if (_target instanceof ArrayBuffer) {\n resolve(window.btoa(String.fromCharCode(...new Uint8Array(_target))));\n } else if (_target instanceof HTMLCanvasElement) {\n resolve(_target.toDataURL(options == null ? void 0 : options.type, options == null ? void 0 : options.quality));\n } else if (_target instanceof HTMLImageElement) {\n const img = _target.cloneNode(false);\n img.crossOrigin = \"Anonymous\";\n imgLoaded(img).then(() => {\n const canvas = document.createElement(\"canvas\");\n const ctx = canvas.getContext(\"2d\");\n canvas.width = img.width;\n canvas.height = img.height;\n ctx.drawImage(img, 0, 0, canvas.width, canvas.height);\n resolve(canvas.toDataURL(options == null ? void 0 : options.type, options == null ? void 0 : options.quality));\n }).catch(reject);\n } else if (typeof _target === \"object\") {\n const _serializeFn = (options == null ? void 0 : options.serializer) || getDefaultSerialization(_target);\n const serialized = _serializeFn(_target);\n return resolve(blobToBase64(new Blob([serialized], { type: \"application/json\" })));\n } else {\n reject(new Error(\"target is unsupported types\"));\n }\n } catch (error) {\n reject(error);\n }\n });\n promise.value.then((res) => base64.value = res);\n return promise.value;\n }\n if (isRef(target) || typeof target === \"function\")\n watch(target, execute, { immediate: true });\n else\n execute();\n return {\n base64,\n promise,\n execute\n };\n}\nfunction imgLoaded(img) {\n return new Promise((resolve, reject) => {\n if (!img.complete) {\n img.onload = () => {\n resolve();\n };\n img.onerror = reject;\n } else {\n resolve();\n }\n });\n}\nfunction blobToBase64(blob) {\n return new Promise((resolve, reject) => {\n const fr = new FileReader();\n fr.onload = (e) => {\n resolve(e.target.result);\n };\n fr.onerror = reject;\n fr.readAsDataURL(blob);\n });\n}\n\nfunction useBattery(options = {}) {\n const { navigator = defaultNavigator } = options;\n const events = [\"chargingchange\", \"chargingtimechange\", \"dischargingtimechange\", \"levelchange\"];\n const isSupported = useSupported(() => navigator && \"getBattery\" in navigator && typeof navigator.getBattery === \"function\");\n const charging = ref(false);\n const chargingTime = ref(0);\n const dischargingTime = ref(0);\n const level = ref(1);\n let battery;\n function updateBatteryInfo() {\n charging.value = this.charging;\n chargingTime.value = this.chargingTime || 0;\n dischargingTime.value = this.dischargingTime || 0;\n level.value = this.level;\n }\n if (isSupported.value) {\n navigator.getBattery().then((_battery) => {\n battery = _battery;\n updateBatteryInfo.call(battery);\n useEventListener(battery, events, updateBatteryInfo, { passive: true });\n });\n }\n return {\n isSupported,\n charging,\n chargingTime,\n dischargingTime,\n level\n };\n}\n\nfunction useBluetooth(options) {\n let {\n acceptAllDevices = false\n } = options || {};\n const {\n filters = void 0,\n optionalServices = void 0,\n navigator = defaultNavigator\n } = options || {};\n const isSupported = useSupported(() => navigator && \"bluetooth\" in navigator);\n const device = shallowRef(void 0);\n const error = shallowRef(null);\n watch(device, () => {\n connectToBluetoothGATTServer();\n });\n async function requestDevice() {\n if (!isSupported.value)\n return;\n error.value = null;\n if (filters && filters.length > 0)\n acceptAllDevices = false;\n try {\n device.value = await (navigator == null ? void 0 : navigator.bluetooth.requestDevice({\n acceptAllDevices,\n filters,\n optionalServices\n }));\n } catch (err) {\n error.value = err;\n }\n }\n const server = ref();\n const isConnected = computed(() => {\n var _a;\n return ((_a = server.value) == null ? void 0 : _a.connected) || false;\n });\n async function connectToBluetoothGATTServer() {\n error.value = null;\n if (device.value && device.value.gatt) {\n device.value.addEventListener(\"gattserverdisconnected\", () => {\n });\n try {\n server.value = await device.value.gatt.connect();\n } catch (err) {\n error.value = err;\n }\n }\n }\n tryOnMounted(() => {\n var _a;\n if (device.value)\n (_a = device.value.gatt) == null ? void 0 : _a.connect();\n });\n tryOnScopeDispose(() => {\n var _a;\n if (device.value)\n (_a = device.value.gatt) == null ? void 0 : _a.disconnect();\n });\n return {\n isSupported,\n isConnected,\n // Device:\n device,\n requestDevice,\n // Server:\n server,\n // Errors:\n error\n };\n}\n\nfunction useMediaQuery(query, options = {}) {\n const { window = defaultWindow } = options;\n const isSupported = useSupported(() => window && \"matchMedia\" in window && typeof window.matchMedia === \"function\");\n let mediaQuery;\n const matches = ref(false);\n const handler = (event) => {\n matches.value = event.matches;\n };\n const cleanup = () => {\n if (!mediaQuery)\n return;\n if (\"removeEventListener\" in mediaQuery)\n mediaQuery.removeEventListener(\"change\", handler);\n else\n mediaQuery.removeListener(handler);\n };\n const stopWatch = watchEffect(() => {\n if (!isSupported.value)\n return;\n cleanup();\n mediaQuery = window.matchMedia(toValue(query));\n if (\"addEventListener\" in mediaQuery)\n mediaQuery.addEventListener(\"change\", handler);\n else\n mediaQuery.addListener(handler);\n matches.value = mediaQuery.matches;\n });\n tryOnScopeDispose(() => {\n stopWatch();\n cleanup();\n mediaQuery = void 0;\n });\n return matches;\n}\n\nconst breakpointsTailwind = {\n \"sm\": 640,\n \"md\": 768,\n \"lg\": 1024,\n \"xl\": 1280,\n \"2xl\": 1536\n};\nconst breakpointsBootstrapV5 = {\n xs: 0,\n sm: 576,\n md: 768,\n lg: 992,\n xl: 1200,\n xxl: 1400\n};\nconst breakpointsVuetifyV2 = {\n xs: 0,\n sm: 600,\n md: 960,\n lg: 1264,\n xl: 1904\n};\nconst breakpointsVuetifyV3 = {\n xs: 0,\n sm: 600,\n md: 960,\n lg: 1280,\n xl: 1920,\n xxl: 2560\n};\nconst breakpointsVuetify = breakpointsVuetifyV2;\nconst breakpointsAntDesign = {\n xs: 480,\n sm: 576,\n md: 768,\n lg: 992,\n xl: 1200,\n xxl: 1600\n};\nconst breakpointsQuasar = {\n xs: 0,\n sm: 600,\n md: 1024,\n lg: 1440,\n xl: 1920\n};\nconst breakpointsSematic = {\n mobileS: 320,\n mobileM: 375,\n mobileL: 425,\n tablet: 768,\n laptop: 1024,\n laptopL: 1440,\n desktop4K: 2560\n};\nconst breakpointsMasterCss = {\n \"3xs\": 360,\n \"2xs\": 480,\n \"xs\": 600,\n \"sm\": 768,\n \"md\": 1024,\n \"lg\": 1280,\n \"xl\": 1440,\n \"2xl\": 1600,\n \"3xl\": 1920,\n \"4xl\": 2560\n};\nconst breakpointsPrimeFlex = {\n sm: 576,\n md: 768,\n lg: 992,\n xl: 1200\n};\n\nfunction useBreakpoints(breakpoints, options = {}) {\n function getValue(k, delta) {\n let v = toValue(breakpoints[toValue(k)]);\n if (delta != null)\n v = increaseWithUnit(v, delta);\n if (typeof v === \"number\")\n v = `${v}px`;\n return v;\n }\n const { window = defaultWindow, strategy = \"min-width\" } = options;\n function match(query) {\n if (!window)\n return false;\n return window.matchMedia(query).matches;\n }\n const greaterOrEqual = (k) => {\n return useMediaQuery(() => `(min-width: ${getValue(k)})`, options);\n };\n const smallerOrEqual = (k) => {\n return useMediaQuery(() => `(max-width: ${getValue(k)})`, options);\n };\n const shortcutMethods = Object.keys(breakpoints).reduce((shortcuts, k) => {\n Object.defineProperty(shortcuts, k, {\n get: () => strategy === \"min-width\" ? greaterOrEqual(k) : smallerOrEqual(k),\n enumerable: true,\n configurable: true\n });\n return shortcuts;\n }, {});\n function current() {\n const points = Object.keys(breakpoints).map((i) => [i, greaterOrEqual(i)]);\n return computed(() => points.filter(([, v]) => v.value).map(([k]) => k));\n }\n return Object.assign(shortcutMethods, {\n greaterOrEqual,\n smallerOrEqual,\n greater(k) {\n return useMediaQuery(() => `(min-width: ${getValue(k, 0.1)})`, options);\n },\n smaller(k) {\n return useMediaQuery(() => `(max-width: ${getValue(k, -0.1)})`, options);\n },\n between(a, b) {\n return useMediaQuery(() => `(min-width: ${getValue(a)}) and (max-width: ${getValue(b, -0.1)})`, options);\n },\n isGreater(k) {\n return match(`(min-width: ${getValue(k, 0.1)})`);\n },\n isGreaterOrEqual(k) {\n return match(`(min-width: ${getValue(k)})`);\n },\n isSmaller(k) {\n return match(`(max-width: ${getValue(k, -0.1)})`);\n },\n isSmallerOrEqual(k) {\n return match(`(max-width: ${getValue(k)})`);\n },\n isInBetween(a, b) {\n return match(`(min-width: ${getValue(a)}) and (max-width: ${getValue(b, -0.1)})`);\n },\n current,\n active() {\n const bps = current();\n return computed(() => bps.value.length === 0 ? \"\" : bps.value.at(-1));\n }\n });\n}\n\nfunction useBroadcastChannel(options) {\n const {\n name,\n window = defaultWindow\n } = options;\n const isSupported = useSupported(() => window && \"BroadcastChannel\" in window);\n const isClosed = ref(false);\n const channel = ref();\n const data = ref();\n const error = shallowRef(null);\n const post = (data2) => {\n if (channel.value)\n channel.value.postMessage(data2);\n };\n const close = () => {\n if (channel.value)\n channel.value.close();\n isClosed.value = true;\n };\n if (isSupported.value) {\n tryOnMounted(() => {\n error.value = null;\n channel.value = new BroadcastChannel(name);\n channel.value.addEventListener(\"message\", (e) => {\n data.value = e.data;\n }, { passive: true });\n channel.value.addEventListener(\"messageerror\", (e) => {\n error.value = e;\n }, { passive: true });\n channel.value.addEventListener(\"close\", () => {\n isClosed.value = true;\n });\n });\n }\n tryOnScopeDispose(() => {\n close();\n });\n return {\n isSupported,\n channel,\n data,\n post,\n close,\n error,\n isClosed\n };\n}\n\nconst WRITABLE_PROPERTIES = [\n \"hash\",\n \"host\",\n \"hostname\",\n \"href\",\n \"pathname\",\n \"port\",\n \"protocol\",\n \"search\"\n];\nfunction useBrowserLocation(options = {}) {\n const { window = defaultWindow } = options;\n const refs = Object.fromEntries(\n WRITABLE_PROPERTIES.map((key) => [key, ref()])\n );\n for (const [key, ref2] of objectEntries(refs)) {\n watch(ref2, (value) => {\n if (!(window == null ? void 0 : window.location) || window.location[key] === value)\n return;\n window.location[key] = value;\n });\n }\n const buildState = (trigger) => {\n var _a;\n const { state: state2, length } = (window == null ? void 0 : window.history) || {};\n const { origin } = (window == null ? void 0 : window.location) || {};\n for (const key of WRITABLE_PROPERTIES)\n refs[key].value = (_a = window == null ? void 0 : window.location) == null ? void 0 : _a[key];\n return reactive({\n trigger,\n state: state2,\n length,\n origin,\n ...refs\n });\n };\n const state = ref(buildState(\"load\"));\n if (window) {\n useEventListener(window, \"popstate\", () => state.value = buildState(\"popstate\"), { passive: true });\n useEventListener(window, \"hashchange\", () => state.value = buildState(\"hashchange\"), { passive: true });\n }\n return state;\n}\n\nfunction useCached(refValue, comparator = (a, b) => a === b, watchOptions) {\n const cachedValue = ref(refValue.value);\n watch(() => refValue.value, (value) => {\n if (!comparator(value, cachedValue.value))\n cachedValue.value = value;\n }, watchOptions);\n return cachedValue;\n}\n\nfunction usePermission(permissionDesc, options = {}) {\n const {\n controls = false,\n navigator = defaultNavigator\n } = options;\n const isSupported = useSupported(() => navigator && \"permissions\" in navigator);\n let permissionStatus;\n const desc = typeof permissionDesc === \"string\" ? { name: permissionDesc } : permissionDesc;\n const state = ref();\n const onChange = () => {\n if (permissionStatus)\n state.value = permissionStatus.state;\n };\n const query = createSingletonPromise(async () => {\n if (!isSupported.value)\n return;\n if (!permissionStatus) {\n try {\n permissionStatus = await navigator.permissions.query(desc);\n useEventListener(permissionStatus, \"change\", onChange);\n onChange();\n } catch (e) {\n state.value = \"prompt\";\n }\n }\n return permissionStatus;\n });\n query();\n if (controls) {\n return {\n state,\n isSupported,\n query\n };\n } else {\n return state;\n }\n}\n\nfunction useClipboard(options = {}) {\n const {\n navigator = defaultNavigator,\n read = false,\n source,\n copiedDuring = 1500,\n legacy = false\n } = options;\n const isClipboardApiSupported = useSupported(() => navigator && \"clipboard\" in navigator);\n const permissionRead = usePermission(\"clipboard-read\");\n const permissionWrite = usePermission(\"clipboard-write\");\n const isSupported = computed(() => isClipboardApiSupported.value || legacy);\n const text = ref(\"\");\n const copied = ref(false);\n const timeout = useTimeoutFn(() => copied.value = false, copiedDuring);\n function updateText() {\n if (isClipboardApiSupported.value && isAllowed(permissionRead.value)) {\n navigator.clipboard.readText().then((value) => {\n text.value = value;\n });\n } else {\n text.value = legacyRead();\n }\n }\n if (isSupported.value && read)\n useEventListener([\"copy\", \"cut\"], updateText);\n async function copy(value = toValue(source)) {\n if (isSupported.value && value != null) {\n if (isClipboardApiSupported.value && isAllowed(permissionWrite.value))\n await navigator.clipboard.writeText(value);\n else\n legacyCopy(value);\n text.value = value;\n copied.value = true;\n timeout.start();\n }\n }\n function legacyCopy(value) {\n const ta = document.createElement(\"textarea\");\n ta.value = value != null ? value : \"\";\n ta.style.position = \"absolute\";\n ta.style.opacity = \"0\";\n document.body.appendChild(ta);\n ta.select();\n document.execCommand(\"copy\");\n ta.remove();\n }\n function legacyRead() {\n var _a, _b, _c;\n return (_c = (_b = (_a = document == null ? void 0 : document.getSelection) == null ? void 0 : _a.call(document)) == null ? void 0 : _b.toString()) != null ? _c : \"\";\n }\n function isAllowed(status) {\n return status === \"granted\" || status === \"prompt\";\n }\n return {\n isSupported,\n text,\n copied,\n copy\n };\n}\n\nfunction useClipboardItems(options = {}) {\n const {\n navigator = defaultNavigator,\n read = false,\n source,\n copiedDuring = 1500\n } = options;\n const isSupported = useSupported(() => navigator && \"clipboard\" in navigator);\n const content = ref([]);\n const copied = ref(false);\n const timeout = useTimeoutFn(() => copied.value = false, copiedDuring);\n function updateContent() {\n if (isSupported.value) {\n navigator.clipboard.read().then((items) => {\n content.value = items;\n });\n }\n }\n if (isSupported.value && read)\n useEventListener([\"copy\", \"cut\"], updateContent);\n async function copy(value = toValue(source)) {\n if (isSupported.value && value != null) {\n await navigator.clipboard.write(value);\n content.value = value;\n copied.value = true;\n timeout.start();\n }\n }\n return {\n isSupported,\n content,\n copied,\n copy\n };\n}\n\nfunction cloneFnJSON(source) {\n return JSON.parse(JSON.stringify(source));\n}\nfunction useCloned(source, options = {}) {\n const cloned = ref({});\n const {\n manual,\n clone = cloneFnJSON,\n // watch options\n deep = true,\n immediate = true\n } = options;\n function sync() {\n cloned.value = clone(toValue(source));\n }\n if (!manual && (isRef(source) || typeof source === \"function\")) {\n watch(source, sync, {\n ...options,\n deep,\n immediate\n });\n } else {\n sync();\n }\n return { cloned, sync };\n}\n\nconst _global = typeof globalThis !== \"undefined\" ? globalThis : typeof window !== \"undefined\" ? window : typeof global !== \"undefined\" ? global : typeof self !== \"undefined\" ? self : {};\nconst globalKey = \"__vueuse_ssr_handlers__\";\nconst handlers = /* @__PURE__ */ getHandlers();\nfunction getHandlers() {\n if (!(globalKey in _global))\n _global[globalKey] = _global[globalKey] || {};\n return _global[globalKey];\n}\nfunction getSSRHandler(key, fallback) {\n return handlers[key] || fallback;\n}\nfunction setSSRHandler(key, fn) {\n handlers[key] = fn;\n}\n\nfunction guessSerializerType(rawInit) {\n return rawInit == null ? \"any\" : rawInit instanceof Set ? \"set\" : rawInit instanceof Map ? \"map\" : rawInit instanceof Date ? \"date\" : typeof rawInit === \"boolean\" ? \"boolean\" : typeof rawInit === \"string\" ? \"string\" : typeof rawInit === \"object\" ? \"object\" : !Number.isNaN(rawInit) ? \"number\" : \"any\";\n}\n\nconst StorageSerializers = {\n boolean: {\n read: (v) => v === \"true\",\n write: (v) => String(v)\n },\n object: {\n read: (v) => JSON.parse(v),\n write: (v) => JSON.stringify(v)\n },\n number: {\n read: (v) => Number.parseFloat(v),\n write: (v) => String(v)\n },\n any: {\n read: (v) => v,\n write: (v) => String(v)\n },\n string: {\n read: (v) => v,\n write: (v) => String(v)\n },\n map: {\n read: (v) => new Map(JSON.parse(v)),\n write: (v) => JSON.stringify(Array.from(v.entries()))\n },\n set: {\n read: (v) => new Set(JSON.parse(v)),\n write: (v) => JSON.stringify(Array.from(v))\n },\n date: {\n read: (v) => new Date(v),\n write: (v) => v.toISOString()\n }\n};\nconst customStorageEventName = \"vueuse-storage\";\nfunction useStorage(key, defaults, storage, options = {}) {\n var _a;\n const {\n flush = \"pre\",\n deep = true,\n listenToStorageChanges = true,\n writeDefaults = true,\n mergeDefaults = false,\n shallow,\n window = defaultWindow,\n eventFilter,\n onError = (e) => {\n console.error(e);\n },\n initOnMounted\n } = options;\n const data = (shallow ? shallowRef : ref)(typeof defaults === \"function\" ? defaults() : defaults);\n if (!storage) {\n try {\n storage = getSSRHandler(\"getDefaultStorage\", () => {\n var _a2;\n return (_a2 = defaultWindow) == null ? void 0 : _a2.localStorage;\n })();\n } catch (e) {\n onError(e);\n }\n }\n if (!storage)\n return data;\n const rawInit = toValue(defaults);\n const type = guessSerializerType(rawInit);\n const serializer = (_a = options.serializer) != null ? _a : StorageSerializers[type];\n const { pause: pauseWatch, resume: resumeWatch } = pausableWatch(\n data,\n () => write(data.value),\n { flush, deep, eventFilter }\n );\n if (window && listenToStorageChanges) {\n tryOnMounted(() => {\n if (storage instanceof Storage)\n useEventListener(window, \"storage\", update);\n else\n useEventListener(window, customStorageEventName, updateFromCustomEvent);\n if (initOnMounted)\n update();\n });\n }\n if (!initOnMounted)\n update();\n function dispatchWriteEvent(oldValue, newValue) {\n if (window && !(storage instanceof Storage)) {\n window.dispatchEvent(new CustomEvent(customStorageEventName, {\n detail: {\n key,\n oldValue,\n newValue,\n storageArea: storage\n }\n }));\n }\n }\n function write(v) {\n try {\n const oldValue = storage.getItem(key);\n if (v == null) {\n dispatchWriteEvent(oldValue, null);\n storage.removeItem(key);\n } else {\n const serialized = serializer.write(v);\n if (oldValue !== serialized) {\n storage.setItem(key, serialized);\n dispatchWriteEvent(oldValue, serialized);\n }\n }\n } catch (e) {\n onError(e);\n }\n }\n function read(event) {\n const rawValue = event ? event.newValue : storage.getItem(key);\n if (rawValue == null) {\n if (writeDefaults && rawInit != null)\n storage.setItem(key, serializer.write(rawInit));\n return rawInit;\n } else if (!event && mergeDefaults) {\n const value = serializer.read(rawValue);\n if (typeof mergeDefaults === \"function\")\n return mergeDefaults(value, rawInit);\n else if (type === \"object\" && !Array.isArray(value))\n return { ...rawInit, ...value };\n return value;\n } else if (typeof rawValue !== \"string\") {\n return rawValue;\n } else {\n return serializer.read(rawValue);\n }\n }\n function update(event) {\n if (event && event.storageArea !== storage)\n return;\n if (event && event.key == null) {\n data.value = rawInit;\n return;\n }\n if (event && event.key !== key)\n return;\n pauseWatch();\n try {\n if ((event == null ? void 0 : event.newValue) !== serializer.write(data.value))\n data.value = read(event);\n } catch (e) {\n onError(e);\n } finally {\n if (event)\n nextTick(resumeWatch);\n else\n resumeWatch();\n }\n }\n function updateFromCustomEvent(event) {\n update(event.detail);\n }\n return data;\n}\n\nfunction usePreferredDark(options) {\n return useMediaQuery(\"(prefers-color-scheme: dark)\", options);\n}\n\nconst CSS_DISABLE_TRANS = \"*,*::before,*::after{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}\";\nfunction useColorMode(options = {}) {\n const {\n selector = \"html\",\n attribute = \"class\",\n initialValue = \"auto\",\n window = defaultWindow,\n storage,\n storageKey = \"vueuse-color-scheme\",\n listenToStorageChanges = true,\n storageRef,\n emitAuto,\n disableTransition = true\n } = options;\n const modes = {\n auto: \"\",\n light: \"light\",\n dark: \"dark\",\n ...options.modes || {}\n };\n const preferredDark = usePreferredDark({ window });\n const system = computed(() => preferredDark.value ? \"dark\" : \"light\");\n const store = storageRef || (storageKey == null ? toRef(initialValue) : useStorage(storageKey, initialValue, storage, { window, listenToStorageChanges }));\n const state = computed(() => store.value === \"auto\" ? system.value : store.value);\n const updateHTMLAttrs = getSSRHandler(\n \"updateHTMLAttrs\",\n (selector2, attribute2, value) => {\n const el = typeof selector2 === \"string\" ? window == null ? void 0 : window.document.querySelector(selector2) : unrefElement(selector2);\n if (!el)\n return;\n const classesToAdd = /* @__PURE__ */ new Set();\n const classesToRemove = /* @__PURE__ */ new Set();\n let attributeToChange = null;\n if (attribute2 === \"class\") {\n const current = value.split(/\\s/g);\n Object.values(modes).flatMap((i) => (i || \"\").split(/\\s/g)).filter(Boolean).forEach((v) => {\n if (current.includes(v))\n classesToAdd.add(v);\n else\n classesToRemove.add(v);\n });\n } else {\n attributeToChange = { key: attribute2, value };\n }\n if (classesToAdd.size === 0 && classesToRemove.size === 0 && attributeToChange === null)\n return;\n let style;\n if (disableTransition) {\n style = window.document.createElement(\"style\");\n style.appendChild(document.createTextNode(CSS_DISABLE_TRANS));\n window.document.head.appendChild(style);\n }\n for (const c of classesToAdd) {\n el.classList.add(c);\n }\n for (const c of classesToRemove) {\n el.classList.remove(c);\n }\n if (attributeToChange) {\n el.setAttribute(attributeToChange.key, attributeToChange.value);\n }\n if (disableTransition) {\n window.getComputedStyle(style).opacity;\n document.head.removeChild(style);\n }\n }\n );\n function defaultOnChanged(mode) {\n var _a;\n updateHTMLAttrs(selector, attribute, (_a = modes[mode]) != null ? _a : mode);\n }\n function onChanged(mode) {\n if (options.onChanged)\n options.onChanged(mode, defaultOnChanged);\n else\n defaultOnChanged(mode);\n }\n watch(state, onChanged, { flush: \"post\", immediate: true });\n tryOnMounted(() => onChanged(state.value));\n const auto = computed({\n get() {\n return emitAuto ? store.value : state.value;\n },\n set(v) {\n store.value = v;\n }\n });\n try {\n return Object.assign(auto, { store, system, state });\n } catch (e) {\n return auto;\n }\n}\n\nfunction useConfirmDialog(revealed = ref(false)) {\n const confirmHook = createEventHook();\n const cancelHook = createEventHook();\n const revealHook = createEventHook();\n let _resolve = noop;\n const reveal = (data) => {\n revealHook.trigger(data);\n revealed.value = true;\n return new Promise((resolve) => {\n _resolve = resolve;\n });\n };\n const confirm = (data) => {\n revealed.value = false;\n confirmHook.trigger(data);\n _resolve({ data, isCanceled: false });\n };\n const cancel = (data) => {\n revealed.value = false;\n cancelHook.trigger(data);\n _resolve({ data, isCanceled: true });\n };\n return {\n isRevealed: computed(() => revealed.value),\n reveal,\n confirm,\n cancel,\n onReveal: revealHook.on,\n onConfirm: confirmHook.on,\n onCancel: cancelHook.on\n };\n}\n\nfunction useCssVar(prop, target, options = {}) {\n const { window = defaultWindow, initialValue, observe = false } = options;\n const variable = ref(initialValue);\n const elRef = computed(() => {\n var _a;\n return unrefElement(target) || ((_a = window == null ? void 0 : window.document) == null ? void 0 : _a.documentElement);\n });\n function updateCssVar() {\n var _a;\n const key = toValue(prop);\n const el = toValue(elRef);\n if (el && window && key) {\n const value = (_a = window.getComputedStyle(el).getPropertyValue(key)) == null ? void 0 : _a.trim();\n variable.value = value || initialValue;\n }\n }\n if (observe) {\n useMutationObserver(elRef, updateCssVar, {\n attributeFilter: [\"style\", \"class\"],\n window\n });\n }\n watch(\n [elRef, () => toValue(prop)],\n (_, old) => {\n if (old[0] && old[1] && window)\n window.getComputedStyle(old[0]).removeProperty(old[1]);\n updateCssVar();\n },\n { immediate: true }\n );\n watch(\n variable,\n (val) => {\n var _a;\n const raw_prop = toValue(prop);\n if (((_a = elRef.value) == null ? void 0 : _a.style) && raw_prop) {\n if (val == null)\n elRef.value.style.removeProperty(raw_prop);\n else\n elRef.value.style.setProperty(raw_prop, val);\n }\n }\n );\n return variable;\n}\n\nfunction useCurrentElement(rootComponent) {\n const vm = getCurrentInstance();\n const currentElement = computedWithControl(\n () => null,\n () => rootComponent ? unrefElement(rootComponent) : vm.proxy.$el\n );\n onUpdated(currentElement.trigger);\n onMounted(currentElement.trigger);\n return currentElement;\n}\n\nfunction useCycleList(list, options) {\n const state = shallowRef(getInitialValue());\n const listRef = toRef(list);\n const index = computed({\n get() {\n var _a;\n const targetList = listRef.value;\n let index2 = (options == null ? void 0 : options.getIndexOf) ? options.getIndexOf(state.value, targetList) : targetList.indexOf(state.value);\n if (index2 < 0)\n index2 = (_a = options == null ? void 0 : options.fallbackIndex) != null ? _a : 0;\n return index2;\n },\n set(v) {\n set(v);\n }\n });\n function set(i) {\n const targetList = listRef.value;\n const length = targetList.length;\n const index2 = (i % length + length) % length;\n const value = targetList[index2];\n state.value = value;\n return value;\n }\n function shift(delta = 1) {\n return set(index.value + delta);\n }\n function next(n = 1) {\n return shift(n);\n }\n function prev(n = 1) {\n return shift(-n);\n }\n function getInitialValue() {\n var _a, _b;\n return (_b = toValue((_a = options == null ? void 0 : options.initialValue) != null ? _a : toValue(list)[0])) != null ? _b : void 0;\n }\n watch(listRef, () => set(index.value));\n return {\n state,\n index,\n next,\n prev,\n go: set\n };\n}\n\nfunction useDark(options = {}) {\n const {\n valueDark = \"dark\",\n valueLight = \"\",\n window = defaultWindow\n } = options;\n const mode = useColorMode({\n ...options,\n onChanged: (mode2, defaultHandler) => {\n var _a;\n if (options.onChanged)\n (_a = options.onChanged) == null ? void 0 : _a.call(options, mode2 === \"dark\", defaultHandler, mode2);\n else\n defaultHandler(mode2);\n },\n modes: {\n dark: valueDark,\n light: valueLight\n }\n });\n const system = computed(() => {\n if (mode.system) {\n return mode.system.value;\n } else {\n const preferredDark = usePreferredDark({ window });\n return preferredDark.value ? \"dark\" : \"light\";\n }\n });\n const isDark = computed({\n get() {\n return mode.value === \"dark\";\n },\n set(v) {\n const modeVal = v ? \"dark\" : \"light\";\n if (system.value === modeVal)\n mode.value = \"auto\";\n else\n mode.value = modeVal;\n }\n });\n return isDark;\n}\n\nfunction fnBypass(v) {\n return v;\n}\nfunction fnSetSource(source, value) {\n return source.value = value;\n}\nfunction defaultDump(clone) {\n return clone ? typeof clone === \"function\" ? clone : cloneFnJSON : fnBypass;\n}\nfunction defaultParse(clone) {\n return clone ? typeof clone === \"function\" ? clone : cloneFnJSON : fnBypass;\n}\nfunction useManualRefHistory(source, options = {}) {\n const {\n clone = false,\n dump = defaultDump(clone),\n parse = defaultParse(clone),\n setSource = fnSetSource\n } = options;\n function _createHistoryRecord() {\n return markRaw({\n snapshot: dump(source.value),\n timestamp: timestamp()\n });\n }\n const last = ref(_createHistoryRecord());\n const undoStack = ref([]);\n const redoStack = ref([]);\n const _setSource = (record) => {\n setSource(source, parse(record.snapshot));\n last.value = record;\n };\n const commit = () => {\n undoStack.value.unshift(last.value);\n last.value = _createHistoryRecord();\n if (options.capacity && undoStack.value.length > options.capacity)\n undoStack.value.splice(options.capacity, Number.POSITIVE_INFINITY);\n if (redoStack.value.length)\n redoStack.value.splice(0, redoStack.value.length);\n };\n const clear = () => {\n undoStack.value.splice(0, undoStack.value.length);\n redoStack.value.splice(0, redoStack.value.length);\n };\n const undo = () => {\n const state = undoStack.value.shift();\n if (state) {\n redoStack.value.unshift(last.value);\n _setSource(state);\n }\n };\n const redo = () => {\n const state = redoStack.value.shift();\n if (state) {\n undoStack.value.unshift(last.value);\n _setSource(state);\n }\n };\n const reset = () => {\n _setSource(last.value);\n };\n const history = computed(() => [last.value, ...undoStack.value]);\n const canUndo = computed(() => undoStack.value.length > 0);\n const canRedo = computed(() => redoStack.value.length > 0);\n return {\n source,\n undoStack,\n redoStack,\n last,\n history,\n canUndo,\n canRedo,\n clear,\n commit,\n reset,\n undo,\n redo\n };\n}\n\nfunction useRefHistory(source, options = {}) {\n const {\n deep = false,\n flush = \"pre\",\n eventFilter\n } = options;\n const {\n eventFilter: composedFilter,\n pause,\n resume: resumeTracking,\n isActive: isTracking\n } = pausableFilter(eventFilter);\n const {\n ignoreUpdates,\n ignorePrevAsyncUpdates,\n stop\n } = watchIgnorable(\n source,\n commit,\n { deep, flush, eventFilter: composedFilter }\n );\n function setSource(source2, value) {\n ignorePrevAsyncUpdates();\n ignoreUpdates(() => {\n source2.value = value;\n });\n }\n const manualHistory = useManualRefHistory(source, { ...options, clone: options.clone || deep, setSource });\n const { clear, commit: manualCommit } = manualHistory;\n function commit() {\n ignorePrevAsyncUpdates();\n manualCommit();\n }\n function resume(commitNow) {\n resumeTracking();\n if (commitNow)\n commit();\n }\n function batch(fn) {\n let canceled = false;\n const cancel = () => canceled = true;\n ignoreUpdates(() => {\n fn(cancel);\n });\n if (!canceled)\n commit();\n }\n function dispose() {\n stop();\n clear();\n }\n return {\n ...manualHistory,\n isTracking,\n pause,\n resume,\n commit,\n batch,\n dispose\n };\n}\n\nfunction useDebouncedRefHistory(source, options = {}) {\n const filter = options.debounce ? debounceFilter(options.debounce) : void 0;\n const history = useRefHistory(source, { ...options, eventFilter: filter });\n return {\n ...history\n };\n}\n\nfunction useDeviceMotion(options = {}) {\n const {\n window = defaultWindow,\n eventFilter = bypassFilter\n } = options;\n const acceleration = ref({ x: null, y: null, z: null });\n const rotationRate = ref({ alpha: null, beta: null, gamma: null });\n const interval = ref(0);\n const accelerationIncludingGravity = ref({\n x: null,\n y: null,\n z: null\n });\n if (window) {\n const onDeviceMotion = createFilterWrapper(\n eventFilter,\n (event) => {\n acceleration.value = event.acceleration;\n accelerationIncludingGravity.value = event.accelerationIncludingGravity;\n rotationRate.value = event.rotationRate;\n interval.value = event.interval;\n }\n );\n useEventListener(window, \"devicemotion\", onDeviceMotion);\n }\n return {\n acceleration,\n accelerationIncludingGravity,\n rotationRate,\n interval\n };\n}\n\nfunction useDeviceOrientation(options = {}) {\n const { window = defaultWindow } = options;\n const isSupported = useSupported(() => window && \"DeviceOrientationEvent\" in window);\n const isAbsolute = ref(false);\n const alpha = ref(null);\n const beta = ref(null);\n const gamma = ref(null);\n if (window && isSupported.value) {\n useEventListener(window, \"deviceorientation\", (event) => {\n isAbsolute.value = event.absolute;\n alpha.value = event.alpha;\n beta.value = event.beta;\n gamma.value = event.gamma;\n });\n }\n return {\n isSupported,\n isAbsolute,\n alpha,\n beta,\n gamma\n };\n}\n\nfunction useDevicePixelRatio(options = {}) {\n const {\n window = defaultWindow\n } = options;\n const pixelRatio = ref(1);\n if (window) {\n let observe2 = function() {\n pixelRatio.value = window.devicePixelRatio;\n cleanup2();\n media = window.matchMedia(`(resolution: ${pixelRatio.value}dppx)`);\n media.addEventListener(\"change\", observe2, { once: true });\n }, cleanup2 = function() {\n media == null ? void 0 : media.removeEventListener(\"change\", observe2);\n };\n let media;\n observe2();\n tryOnScopeDispose(cleanup2);\n }\n return { pixelRatio };\n}\n\nfunction useDevicesList(options = {}) {\n const {\n navigator = defaultNavigator,\n requestPermissions = false,\n constraints = { audio: true, video: true },\n onUpdated\n } = options;\n const devices = ref([]);\n const videoInputs = computed(() => devices.value.filter((i) => i.kind === \"videoinput\"));\n const audioInputs = computed(() => devices.value.filter((i) => i.kind === \"audioinput\"));\n const audioOutputs = computed(() => devices.value.filter((i) => i.kind === \"audiooutput\"));\n const isSupported = useSupported(() => navigator && navigator.mediaDevices && navigator.mediaDevices.enumerateDevices);\n const permissionGranted = ref(false);\n let stream;\n async function update() {\n if (!isSupported.value)\n return;\n devices.value = await navigator.mediaDevices.enumerateDevices();\n onUpdated == null ? void 0 : onUpdated(devices.value);\n if (stream) {\n stream.getTracks().forEach((t) => t.stop());\n stream = null;\n }\n }\n async function ensurePermissions() {\n if (!isSupported.value)\n return false;\n if (permissionGranted.value)\n return true;\n const { state, query } = usePermission(\"camera\", { controls: true });\n await query();\n if (state.value !== \"granted\") {\n stream = await navigator.mediaDevices.getUserMedia(constraints);\n update();\n permissionGranted.value = true;\n } else {\n permissionGranted.value = true;\n }\n return permissionGranted.value;\n }\n if (isSupported.value) {\n if (requestPermissions)\n ensurePermissions();\n useEventListener(navigator.mediaDevices, \"devicechange\", update);\n update();\n }\n return {\n devices,\n ensurePermissions,\n permissionGranted,\n videoInputs,\n audioInputs,\n audioOutputs,\n isSupported\n };\n}\n\nfunction useDisplayMedia(options = {}) {\n var _a;\n const enabled = ref((_a = options.enabled) != null ? _a : false);\n const video = options.video;\n const audio = options.audio;\n const { navigator = defaultNavigator } = options;\n const isSupported = useSupported(() => {\n var _a2;\n return (_a2 = navigator == null ? void 0 : navigator.mediaDevices) == null ? void 0 : _a2.getDisplayMedia;\n });\n const constraint = { audio, video };\n const stream = shallowRef();\n async function _start() {\n var _a2;\n if (!isSupported.value || stream.value)\n return;\n stream.value = await navigator.mediaDevices.getDisplayMedia(constraint);\n (_a2 = stream.value) == null ? void 0 : _a2.getTracks().forEach((t) => t.addEventListener(\"ended\", stop));\n return stream.value;\n }\n async function _stop() {\n var _a2;\n (_a2 = stream.value) == null ? void 0 : _a2.getTracks().forEach((t) => t.stop());\n stream.value = void 0;\n }\n function stop() {\n _stop();\n enabled.value = false;\n }\n async function start() {\n await _start();\n if (stream.value)\n enabled.value = true;\n return stream.value;\n }\n watch(\n enabled,\n (v) => {\n if (v)\n _start();\n else\n _stop();\n },\n { immediate: true }\n );\n return {\n isSupported,\n stream,\n start,\n stop,\n enabled\n };\n}\n\nfunction useDocumentVisibility(options = {}) {\n const { document = defaultDocument } = options;\n if (!document)\n return ref(\"visible\");\n const visibility = ref(document.visibilityState);\n useEventListener(document, \"visibilitychange\", () => {\n visibility.value = document.visibilityState;\n });\n return visibility;\n}\n\nfunction useDraggable(target, options = {}) {\n var _a, _b;\n const {\n pointerTypes,\n preventDefault,\n stopPropagation,\n exact,\n onMove,\n onEnd,\n onStart,\n initialValue,\n axis = \"both\",\n draggingElement = defaultWindow,\n containerElement,\n handle: draggingHandle = target,\n buttons = [0]\n } = options;\n const position = ref(\n (_a = toValue(initialValue)) != null ? _a : { x: 0, y: 0 }\n );\n const pressedDelta = ref();\n const filterEvent = (e) => {\n if (pointerTypes)\n return pointerTypes.includes(e.pointerType);\n return true;\n };\n const handleEvent = (e) => {\n if (toValue(preventDefault))\n e.preventDefault();\n if (toValue(stopPropagation))\n e.stopPropagation();\n };\n const start = (e) => {\n var _a2;\n if (!toValue(buttons).includes(e.button))\n return;\n if (toValue(options.disabled) || !filterEvent(e))\n return;\n if (toValue(exact) && e.target !== toValue(target))\n return;\n const container = toValue(containerElement);\n const containerRect = (_a2 = container == null ? void 0 : container.getBoundingClientRect) == null ? void 0 : _a2.call(container);\n const targetRect = toValue(target).getBoundingClientRect();\n const pos = {\n x: e.clientX - (container ? targetRect.left - containerRect.left + container.scrollLeft : targetRect.left),\n y: e.clientY - (container ? targetRect.top - containerRect.top + container.scrollTop : targetRect.top)\n };\n if ((onStart == null ? void 0 : onStart(pos, e)) === false)\n return;\n pressedDelta.value = pos;\n handleEvent(e);\n };\n const move = (e) => {\n if (toValue(options.disabled) || !filterEvent(e))\n return;\n if (!pressedDelta.value)\n return;\n const container = toValue(containerElement);\n const targetRect = toValue(target).getBoundingClientRect();\n let { x, y } = position.value;\n if (axis === \"x\" || axis === \"both\") {\n x = e.clientX - pressedDelta.value.x;\n if (container)\n x = Math.min(Math.max(0, x), container.scrollWidth - targetRect.width);\n }\n if (axis === \"y\" || axis === \"both\") {\n y = e.clientY - pressedDelta.value.y;\n if (container)\n y = Math.min(Math.max(0, y), container.scrollHeight - targetRect.height);\n }\n position.value = {\n x,\n y\n };\n onMove == null ? void 0 : onMove(position.value, e);\n handleEvent(e);\n };\n const end = (e) => {\n if (toValue(options.disabled) || !filterEvent(e))\n return;\n if (!pressedDelta.value)\n return;\n pressedDelta.value = void 0;\n onEnd == null ? void 0 : onEnd(position.value, e);\n handleEvent(e);\n };\n if (isClient) {\n const config = { capture: (_b = options.capture) != null ? _b : true };\n useEventListener(draggingHandle, \"pointerdown\", start, config);\n useEventListener(draggingElement, \"pointermove\", move, config);\n useEventListener(draggingElement, \"pointerup\", end, config);\n }\n return {\n ...toRefs(position),\n position,\n isDragging: computed(() => !!pressedDelta.value),\n style: computed(\n () => `left:${position.value.x}px;top:${position.value.y}px;`\n )\n };\n}\n\nfunction useDropZone(target, options = {}) {\n const isOverDropZone = ref(false);\n const files = shallowRef(null);\n let counter = 0;\n let isDataTypeIncluded = true;\n if (isClient) {\n const _options = typeof options === \"function\" ? { onDrop: options } : options;\n const getFiles = (event) => {\n var _a, _b;\n const list = Array.from((_b = (_a = event.dataTransfer) == null ? void 0 : _a.files) != null ? _b : []);\n return files.value = list.length === 0 ? null : list;\n };\n useEventListener(target, \"dragenter\", (event) => {\n var _a, _b;\n const types = Array.from(((_a = event == null ? void 0 : event.dataTransfer) == null ? void 0 : _a.items) || []).map((i) => i.kind === \"file\" ? i.type : null).filter(notNullish);\n if (_options.dataTypes && event.dataTransfer) {\n const dataTypes = unref(_options.dataTypes);\n isDataTypeIncluded = typeof dataTypes === \"function\" ? dataTypes(types) : dataTypes ? dataTypes.some((item) => types.includes(item)) : true;\n if (!isDataTypeIncluded)\n return;\n }\n event.preventDefault();\n counter += 1;\n isOverDropZone.value = true;\n (_b = _options.onEnter) == null ? void 0 : _b.call(_options, getFiles(event), event);\n });\n useEventListener(target, \"dragover\", (event) => {\n var _a;\n if (!isDataTypeIncluded)\n return;\n event.preventDefault();\n (_a = _options.onOver) == null ? void 0 : _a.call(_options, getFiles(event), event);\n });\n useEventListener(target, \"dragleave\", (event) => {\n var _a;\n if (!isDataTypeIncluded)\n return;\n event.preventDefault();\n counter -= 1;\n if (counter === 0)\n isOverDropZone.value = false;\n (_a = _options.onLeave) == null ? void 0 : _a.call(_options, getFiles(event), event);\n });\n useEventListener(target, \"drop\", (event) => {\n var _a;\n event.preventDefault();\n counter = 0;\n isOverDropZone.value = false;\n (_a = _options.onDrop) == null ? void 0 : _a.call(_options, getFiles(event), event);\n });\n }\n return {\n files,\n isOverDropZone\n };\n}\n\nfunction useResizeObserver(target, callback, options = {}) {\n const { window = defaultWindow, ...observerOptions } = options;\n let observer;\n const isSupported = useSupported(() => window && \"ResizeObserver\" in window);\n const cleanup = () => {\n if (observer) {\n observer.disconnect();\n observer = void 0;\n }\n };\n const targets = computed(() => {\n const _targets = toValue(target);\n return Array.isArray(_targets) ? _targets.map((el) => unrefElement(el)) : [unrefElement(_targets)];\n });\n const stopWatch = watch(\n targets,\n (els) => {\n cleanup();\n if (isSupported.value && window) {\n observer = new ResizeObserver(callback);\n for (const _el of els) {\n if (_el)\n observer.observe(_el, observerOptions);\n }\n }\n },\n { immediate: true, flush: \"post\" }\n );\n const stop = () => {\n cleanup();\n stopWatch();\n };\n tryOnScopeDispose(stop);\n return {\n isSupported,\n stop\n };\n}\n\nfunction useElementBounding(target, options = {}) {\n const {\n reset = true,\n windowResize = true,\n windowScroll = true,\n immediate = true,\n updateTiming = \"sync\"\n } = options;\n const height = ref(0);\n const bottom = ref(0);\n const left = ref(0);\n const right = ref(0);\n const top = ref(0);\n const width = ref(0);\n const x = ref(0);\n const y = ref(0);\n function recalculate() {\n const el = unrefElement(target);\n if (!el) {\n if (reset) {\n height.value = 0;\n bottom.value = 0;\n left.value = 0;\n right.value = 0;\n top.value = 0;\n width.value = 0;\n x.value = 0;\n y.value = 0;\n }\n return;\n }\n const rect = el.getBoundingClientRect();\n height.value = rect.height;\n bottom.value = rect.bottom;\n left.value = rect.left;\n right.value = rect.right;\n top.value = rect.top;\n width.value = rect.width;\n x.value = rect.x;\n y.value = rect.y;\n }\n function update() {\n if (updateTiming === \"sync\")\n recalculate();\n else if (updateTiming === \"next-frame\")\n requestAnimationFrame(() => recalculate());\n }\n useResizeObserver(target, update);\n watch(() => unrefElement(target), (ele) => !ele && update());\n useMutationObserver(target, update, {\n attributeFilter: [\"style\", \"class\"]\n });\n if (windowScroll)\n useEventListener(\"scroll\", update, { capture: true, passive: true });\n if (windowResize)\n useEventListener(\"resize\", update, { passive: true });\n tryOnMounted(() => {\n if (immediate)\n update();\n });\n return {\n height,\n bottom,\n left,\n right,\n top,\n width,\n x,\n y,\n update\n };\n}\n\nfunction useElementByPoint(options) {\n const {\n x,\n y,\n document = defaultDocument,\n multiple,\n interval = \"requestAnimationFrame\",\n immediate = true\n } = options;\n const isSupported = useSupported(() => {\n if (toValue(multiple))\n return document && \"elementsFromPoint\" in document;\n return document && \"elementFromPoint\" in document;\n });\n const element = ref(null);\n const cb = () => {\n var _a, _b;\n element.value = toValue(multiple) ? (_a = document == null ? void 0 : document.elementsFromPoint(toValue(x), toValue(y))) != null ? _a : [] : (_b = document == null ? void 0 : document.elementFromPoint(toValue(x), toValue(y))) != null ? _b : null;\n };\n const controls = interval === \"requestAnimationFrame\" ? useRafFn(cb, { immediate }) : useIntervalFn(cb, interval, { immediate });\n return {\n isSupported,\n element,\n ...controls\n };\n}\n\nfunction useElementHover(el, options = {}) {\n const {\n delayEnter = 0,\n delayLeave = 0,\n window = defaultWindow\n } = options;\n const isHovered = ref(false);\n let timer;\n const toggle = (entering) => {\n const delay = entering ? delayEnter : delayLeave;\n if (timer) {\n clearTimeout(timer);\n timer = void 0;\n }\n if (delay)\n timer = setTimeout(() => isHovered.value = entering, delay);\n else\n isHovered.value = entering;\n };\n if (!window)\n return isHovered;\n useEventListener(el, \"mouseenter\", () => toggle(true), { passive: true });\n useEventListener(el, \"mouseleave\", () => toggle(false), { passive: true });\n return isHovered;\n}\n\nfunction useElementSize(target, initialSize = { width: 0, height: 0 }, options = {}) {\n const { window = defaultWindow, box = \"content-box\" } = options;\n const isSVG = computed(() => {\n var _a, _b;\n return (_b = (_a = unrefElement(target)) == null ? void 0 : _a.namespaceURI) == null ? void 0 : _b.includes(\"svg\");\n });\n const width = ref(initialSize.width);\n const height = ref(initialSize.height);\n const { stop: stop1 } = useResizeObserver(\n target,\n ([entry]) => {\n const boxSize = box === \"border-box\" ? entry.borderBoxSize : box === \"content-box\" ? entry.contentBoxSize : entry.devicePixelContentBoxSize;\n if (window && isSVG.value) {\n const $elem = unrefElement(target);\n if ($elem) {\n const rect = $elem.getBoundingClientRect();\n width.value = rect.width;\n height.value = rect.height;\n }\n } else {\n if (boxSize) {\n const formatBoxSize = Array.isArray(boxSize) ? boxSize : [boxSize];\n width.value = formatBoxSize.reduce((acc, { inlineSize }) => acc + inlineSize, 0);\n height.value = formatBoxSize.reduce((acc, { blockSize }) => acc + blockSize, 0);\n } else {\n width.value = entry.contentRect.width;\n height.value = entry.contentRect.height;\n }\n }\n },\n options\n );\n tryOnMounted(() => {\n const ele = unrefElement(target);\n if (ele) {\n width.value = \"offsetWidth\" in ele ? ele.offsetWidth : initialSize.width;\n height.value = \"offsetHeight\" in ele ? ele.offsetHeight : initialSize.height;\n }\n });\n const stop2 = watch(\n () => unrefElement(target),\n (ele) => {\n width.value = ele ? initialSize.width : 0;\n height.value = ele ? initialSize.height : 0;\n }\n );\n function stop() {\n stop1();\n stop2();\n }\n return {\n width,\n height,\n stop\n };\n}\n\nfunction useIntersectionObserver(target, callback, options = {}) {\n const {\n root,\n rootMargin = \"0px\",\n threshold = 0,\n window = defaultWindow,\n immediate = true\n } = options;\n const isSupported = useSupported(() => window && \"IntersectionObserver\" in window);\n const targets = computed(() => {\n const _target = toValue(target);\n return (Array.isArray(_target) ? _target : [_target]).map(unrefElement).filter(notNullish);\n });\n let cleanup = noop;\n const isActive = ref(immediate);\n const stopWatch = isSupported.value ? watch(\n () => [targets.value, unrefElement(root), isActive.value],\n ([targets2, root2]) => {\n cleanup();\n if (!isActive.value)\n return;\n if (!targets2.length)\n return;\n const observer = new IntersectionObserver(\n callback,\n {\n root: unrefElement(root2),\n rootMargin,\n threshold\n }\n );\n targets2.forEach((el) => el && observer.observe(el));\n cleanup = () => {\n observer.disconnect();\n cleanup = noop;\n };\n },\n { immediate, flush: \"post\" }\n ) : noop;\n const stop = () => {\n cleanup();\n stopWatch();\n isActive.value = false;\n };\n tryOnScopeDispose(stop);\n return {\n isSupported,\n isActive,\n pause() {\n cleanup();\n isActive.value = false;\n },\n resume() {\n isActive.value = true;\n },\n stop\n };\n}\n\nfunction useElementVisibility(element, options = {}) {\n const { window = defaultWindow, scrollTarget, threshold = 0 } = options;\n const elementIsVisible = ref(false);\n useIntersectionObserver(\n element,\n (intersectionObserverEntries) => {\n let isIntersecting = elementIsVisible.value;\n let latestTime = 0;\n for (const entry of intersectionObserverEntries) {\n if (entry.time >= latestTime) {\n latestTime = entry.time;\n isIntersecting = entry.isIntersecting;\n }\n }\n elementIsVisible.value = isIntersecting;\n },\n {\n root: scrollTarget,\n window,\n threshold\n }\n );\n return elementIsVisible;\n}\n\nconst events = /* @__PURE__ */ new Map();\n\nfunction useEventBus(key) {\n const scope = getCurrentScope();\n function on(listener) {\n var _a;\n const listeners = events.get(key) || /* @__PURE__ */ new Set();\n listeners.add(listener);\n events.set(key, listeners);\n const _off = () => off(listener);\n (_a = scope == null ? void 0 : scope.cleanups) == null ? void 0 : _a.push(_off);\n return _off;\n }\n function once(listener) {\n function _listener(...args) {\n off(_listener);\n listener(...args);\n }\n return on(_listener);\n }\n function off(listener) {\n const listeners = events.get(key);\n if (!listeners)\n return;\n listeners.delete(listener);\n if (!listeners.size)\n reset();\n }\n function reset() {\n events.delete(key);\n }\n function emit(event, payload) {\n var _a;\n (_a = events.get(key)) == null ? void 0 : _a.forEach((v) => v(event, payload));\n }\n return { on, once, off, emit, reset };\n}\n\nfunction resolveNestedOptions$1(options) {\n if (options === true)\n return {};\n return options;\n}\nfunction useEventSource(url, events = [], options = {}) {\n const event = ref(null);\n const data = ref(null);\n const status = ref(\"CONNECTING\");\n const eventSource = ref(null);\n const error = shallowRef(null);\n const urlRef = toRef(url);\n const lastEventId = shallowRef(null);\n let explicitlyClosed = false;\n let retried = 0;\n const {\n withCredentials = false,\n immediate = true\n } = options;\n const close = () => {\n if (isClient && eventSource.value) {\n eventSource.value.close();\n eventSource.value = null;\n status.value = \"CLOSED\";\n explicitlyClosed = true;\n }\n };\n const _init = () => {\n if (explicitlyClosed || typeof urlRef.value === \"undefined\")\n return;\n const es = new EventSource(urlRef.value, { withCredentials });\n status.value = \"CONNECTING\";\n eventSource.value = es;\n es.onopen = () => {\n status.value = \"OPEN\";\n error.value = null;\n };\n es.onerror = (e) => {\n status.value = \"CLOSED\";\n error.value = e;\n if (es.readyState === 2 && !explicitlyClosed && options.autoReconnect) {\n es.close();\n const {\n retries = -1,\n delay = 1e3,\n onFailed\n } = resolveNestedOptions$1(options.autoReconnect);\n retried += 1;\n if (typeof retries === \"number\" && (retries < 0 || retried < retries))\n setTimeout(_init, delay);\n else if (typeof retries === \"function\" && retries())\n setTimeout(_init, delay);\n else\n onFailed == null ? void 0 : onFailed();\n }\n };\n es.onmessage = (e) => {\n event.value = null;\n data.value = e.data;\n lastEventId.value = e.lastEventId;\n };\n for (const event_name of events) {\n useEventListener(es, event_name, (e) => {\n event.value = event_name;\n data.value = e.data || null;\n });\n }\n };\n const open = () => {\n if (!isClient)\n return;\n close();\n explicitlyClosed = false;\n retried = 0;\n _init();\n };\n if (immediate)\n watch(urlRef, open, { immediate: true });\n tryOnScopeDispose(close);\n return {\n eventSource,\n event,\n data,\n status,\n error,\n open,\n close,\n lastEventId\n };\n}\n\nfunction useEyeDropper(options = {}) {\n const { initialValue = \"\" } = options;\n const isSupported = useSupported(() => typeof window !== \"undefined\" && \"EyeDropper\" in window);\n const sRGBHex = ref(initialValue);\n async function open(openOptions) {\n if (!isSupported.value)\n return;\n const eyeDropper = new window.EyeDropper();\n const result = await eyeDropper.open(openOptions);\n sRGBHex.value = result.sRGBHex;\n return result;\n }\n return { isSupported, sRGBHex, open };\n}\n\nfunction useFavicon(newIcon = null, options = {}) {\n const {\n baseUrl = \"\",\n rel = \"icon\",\n document = defaultDocument\n } = options;\n const favicon = toRef(newIcon);\n const applyIcon = (icon) => {\n const elements = document == null ? void 0 : document.head.querySelectorAll(`link[rel*=\"${rel}\"]`);\n if (!elements || elements.length === 0) {\n const link = document == null ? void 0 : document.createElement(\"link\");\n if (link) {\n link.rel = rel;\n link.href = `${baseUrl}${icon}`;\n link.type = `image/${icon.split(\".\").pop()}`;\n document == null ? void 0 : document.head.append(link);\n }\n return;\n }\n elements == null ? void 0 : elements.forEach((el) => el.href = `${baseUrl}${icon}`);\n };\n watch(\n favicon,\n (i, o) => {\n if (typeof i === \"string\" && i !== o)\n applyIcon(i);\n },\n { immediate: true }\n );\n return favicon;\n}\n\nconst payloadMapping = {\n json: \"application/json\",\n text: \"text/plain\"\n};\nfunction isFetchOptions(obj) {\n return obj && containsProp(obj, \"immediate\", \"refetch\", \"initialData\", \"timeout\", \"beforeFetch\", \"afterFetch\", \"onFetchError\", \"fetch\", \"updateDataOnError\");\n}\nconst reAbsolute = /^(?:[a-z][a-z\\d+\\-.]*:)?\\/\\//i;\nfunction isAbsoluteURL(url) {\n return reAbsolute.test(url);\n}\nfunction headersToObject(headers) {\n if (typeof Headers !== \"undefined\" && headers instanceof Headers)\n return Object.fromEntries(headers.entries());\n return headers;\n}\nfunction combineCallbacks(combination, ...callbacks) {\n if (combination === \"overwrite\") {\n return async (ctx) => {\n const callback = callbacks[callbacks.length - 1];\n if (callback)\n return { ...ctx, ...await callback(ctx) };\n return ctx;\n };\n } else {\n return async (ctx) => {\n for (const callback of callbacks) {\n if (callback)\n ctx = { ...ctx, ...await callback(ctx) };\n }\n return ctx;\n };\n }\n}\nfunction createFetch(config = {}) {\n const _combination = config.combination || \"chain\";\n const _options = config.options || {};\n const _fetchOptions = config.fetchOptions || {};\n function useFactoryFetch(url, ...args) {\n const computedUrl = computed(() => {\n const baseUrl = toValue(config.baseUrl);\n const targetUrl = toValue(url);\n return baseUrl && !isAbsoluteURL(targetUrl) ? joinPaths(baseUrl, targetUrl) : targetUrl;\n });\n let options = _options;\n let fetchOptions = _fetchOptions;\n if (args.length > 0) {\n if (isFetchOptions(args[0])) {\n options = {\n ...options,\n ...args[0],\n beforeFetch: combineCallbacks(_combination, _options.beforeFetch, args[0].beforeFetch),\n afterFetch: combineCallbacks(_combination, _options.afterFetch, args[0].afterFetch),\n onFetchError: combineCallbacks(_combination, _options.onFetchError, args[0].onFetchError)\n };\n } else {\n fetchOptions = {\n ...fetchOptions,\n ...args[0],\n headers: {\n ...headersToObject(fetchOptions.headers) || {},\n ...headersToObject(args[0].headers) || {}\n }\n };\n }\n }\n if (args.length > 1 && isFetchOptions(args[1])) {\n options = {\n ...options,\n ...args[1],\n beforeFetch: combineCallbacks(_combination, _options.beforeFetch, args[1].beforeFetch),\n afterFetch: combineCallbacks(_combination, _options.afterFetch, args[1].afterFetch),\n onFetchError: combineCallbacks(_combination, _options.onFetchError, args[1].onFetchError)\n };\n }\n return useFetch(computedUrl, fetchOptions, options);\n }\n return useFactoryFetch;\n}\nfunction useFetch(url, ...args) {\n var _a;\n const supportsAbort = typeof AbortController === \"function\";\n let fetchOptions = {};\n let options = {\n immediate: true,\n refetch: false,\n timeout: 0,\n updateDataOnError: false\n };\n const config = {\n method: \"GET\",\n type: \"text\",\n payload: void 0\n };\n if (args.length > 0) {\n if (isFetchOptions(args[0]))\n options = { ...options, ...args[0] };\n else\n fetchOptions = args[0];\n }\n if (args.length > 1) {\n if (isFetchOptions(args[1]))\n options = { ...options, ...args[1] };\n }\n const {\n fetch = (_a = defaultWindow) == null ? void 0 : _a.fetch,\n initialData,\n timeout\n } = options;\n const responseEvent = createEventHook();\n const errorEvent = createEventHook();\n const finallyEvent = createEventHook();\n const isFinished = ref(false);\n const isFetching = ref(false);\n const aborted = ref(false);\n const statusCode = ref(null);\n const response = shallowRef(null);\n const error = shallowRef(null);\n const data = shallowRef(initialData || null);\n const canAbort = computed(() => supportsAbort && isFetching.value);\n let controller;\n let timer;\n const abort = () => {\n if (supportsAbort) {\n controller == null ? void 0 : controller.abort();\n controller = new AbortController();\n controller.signal.onabort = () => aborted.value = true;\n fetchOptions = {\n ...fetchOptions,\n signal: controller.signal\n };\n }\n };\n const loading = (isLoading) => {\n isFetching.value = isLoading;\n isFinished.value = !isLoading;\n };\n if (timeout)\n timer = useTimeoutFn(abort, timeout, { immediate: false });\n let executeCounter = 0;\n const execute = async (throwOnFailed = false) => {\n var _a2, _b;\n abort();\n loading(true);\n error.value = null;\n statusCode.value = null;\n aborted.value = false;\n executeCounter += 1;\n const currentExecuteCounter = executeCounter;\n const defaultFetchOptions = {\n method: config.method,\n headers: {}\n };\n if (config.payload) {\n const headers = headersToObject(defaultFetchOptions.headers);\n const payload = toValue(config.payload);\n if (!config.payloadType && payload && Object.getPrototypeOf(payload) === Object.prototype && !(payload instanceof FormData))\n config.payloadType = \"json\";\n if (config.payloadType)\n headers[\"Content-Type\"] = (_a2 = payloadMapping[config.payloadType]) != null ? _a2 : config.payloadType;\n defaultFetchOptions.body = config.payloadType === \"json\" ? JSON.stringify(payload) : payload;\n }\n let isCanceled = false;\n const context = {\n url: toValue(url),\n options: {\n ...defaultFetchOptions,\n ...fetchOptions\n },\n cancel: () => {\n isCanceled = true;\n }\n };\n if (options.beforeFetch)\n Object.assign(context, await options.beforeFetch(context));\n if (isCanceled || !fetch) {\n loading(false);\n return Promise.resolve(null);\n }\n let responseData = null;\n if (timer)\n timer.start();\n return fetch(\n context.url,\n {\n ...defaultFetchOptions,\n ...context.options,\n headers: {\n ...headersToObject(defaultFetchOptions.headers),\n ...headersToObject((_b = context.options) == null ? void 0 : _b.headers)\n }\n }\n ).then(async (fetchResponse) => {\n response.value = fetchResponse;\n statusCode.value = fetchResponse.status;\n responseData = await fetchResponse.clone()[config.type]();\n if (!fetchResponse.ok) {\n data.value = initialData || null;\n throw new Error(fetchResponse.statusText);\n }\n if (options.afterFetch) {\n ({ data: responseData } = await options.afterFetch({\n data: responseData,\n response: fetchResponse\n }));\n }\n data.value = responseData;\n responseEvent.trigger(fetchResponse);\n return fetchResponse;\n }).catch(async (fetchError) => {\n let errorData = fetchError.message || fetchError.name;\n if (options.onFetchError) {\n ({ error: errorData, data: responseData } = await options.onFetchError({\n data: responseData,\n error: fetchError,\n response: response.value\n }));\n }\n error.value = errorData;\n if (options.updateDataOnError)\n data.value = responseData;\n errorEvent.trigger(fetchError);\n if (throwOnFailed)\n throw fetchError;\n return null;\n }).finally(() => {\n if (currentExecuteCounter === executeCounter)\n loading(false);\n if (timer)\n timer.stop();\n finallyEvent.trigger(null);\n });\n };\n const refetch = toRef(options.refetch);\n watch(\n [\n refetch,\n toRef(url)\n ],\n ([refetch2]) => refetch2 && execute(),\n { deep: true }\n );\n const shell = {\n isFinished: readonly(isFinished),\n isFetching: readonly(isFetching),\n statusCode,\n response,\n error,\n data,\n canAbort,\n aborted,\n abort,\n execute,\n onFetchResponse: responseEvent.on,\n onFetchError: errorEvent.on,\n onFetchFinally: finallyEvent.on,\n // method\n get: setMethod(\"GET\"),\n put: setMethod(\"PUT\"),\n post: setMethod(\"POST\"),\n delete: setMethod(\"DELETE\"),\n patch: setMethod(\"PATCH\"),\n head: setMethod(\"HEAD\"),\n options: setMethod(\"OPTIONS\"),\n // type\n json: setType(\"json\"),\n text: setType(\"text\"),\n blob: setType(\"blob\"),\n arrayBuffer: setType(\"arrayBuffer\"),\n formData: setType(\"formData\")\n };\n function setMethod(method) {\n return (payload, payloadType) => {\n if (!isFetching.value) {\n config.method = method;\n config.payload = payload;\n config.payloadType = payloadType;\n if (isRef(config.payload)) {\n watch(\n [\n refetch,\n toRef(config.payload)\n ],\n ([refetch2]) => refetch2 && execute(),\n { deep: true }\n );\n }\n return {\n ...shell,\n then(onFulfilled, onRejected) {\n return waitUntilFinished().then(onFulfilled, onRejected);\n }\n };\n }\n return void 0;\n };\n }\n function waitUntilFinished() {\n return new Promise((resolve, reject) => {\n until(isFinished).toBe(true).then(() => resolve(shell)).catch((error2) => reject(error2));\n });\n }\n function setType(type) {\n return () => {\n if (!isFetching.value) {\n config.type = type;\n return {\n ...shell,\n then(onFulfilled, onRejected) {\n return waitUntilFinished().then(onFulfilled, onRejected);\n }\n };\n }\n return void 0;\n };\n }\n if (options.immediate)\n Promise.resolve().then(() => execute());\n return {\n ...shell,\n then(onFulfilled, onRejected) {\n return waitUntilFinished().then(onFulfilled, onRejected);\n }\n };\n}\nfunction joinPaths(start, end) {\n if (!start.endsWith(\"/\") && !end.startsWith(\"/\"))\n return `${start}/${end}`;\n return `${start}${end}`;\n}\n\nconst DEFAULT_OPTIONS = {\n multiple: true,\n accept: \"*\",\n reset: false,\n directory: false\n};\nfunction useFileDialog(options = {}) {\n const {\n document = defaultDocument\n } = options;\n const files = ref(null);\n const { on: onChange, trigger } = createEventHook();\n let input;\n if (document) {\n input = document.createElement(\"input\");\n input.type = \"file\";\n input.onchange = (event) => {\n const result = event.target;\n files.value = result.files;\n trigger(files.value);\n };\n }\n const reset = () => {\n files.value = null;\n if (input && input.value) {\n input.value = \"\";\n trigger(null);\n }\n };\n const open = (localOptions) => {\n if (!input)\n return;\n const _options = {\n ...DEFAULT_OPTIONS,\n ...options,\n ...localOptions\n };\n input.multiple = _options.multiple;\n input.accept = _options.accept;\n input.webkitdirectory = _options.directory;\n if (hasOwn(_options, \"capture\"))\n input.capture = _options.capture;\n if (_options.reset)\n reset();\n input.click();\n };\n return {\n files: readonly(files),\n open,\n reset,\n onChange\n };\n}\n\nfunction useFileSystemAccess(options = {}) {\n const {\n window: _window = defaultWindow,\n dataType = \"Text\"\n } = options;\n const window = _window;\n const isSupported = useSupported(() => window && \"showSaveFilePicker\" in window && \"showOpenFilePicker\" in window);\n const fileHandle = ref();\n const data = ref();\n const file = ref();\n const fileName = computed(() => {\n var _a, _b;\n return (_b = (_a = file.value) == null ? void 0 : _a.name) != null ? _b : \"\";\n });\n const fileMIME = computed(() => {\n var _a, _b;\n return (_b = (_a = file.value) == null ? void 0 : _a.type) != null ? _b : \"\";\n });\n const fileSize = computed(() => {\n var _a, _b;\n return (_b = (_a = file.value) == null ? void 0 : _a.size) != null ? _b : 0;\n });\n const fileLastModified = computed(() => {\n var _a, _b;\n return (_b = (_a = file.value) == null ? void 0 : _a.lastModified) != null ? _b : 0;\n });\n async function open(_options = {}) {\n if (!isSupported.value)\n return;\n const [handle] = await window.showOpenFilePicker({ ...toValue(options), ..._options });\n fileHandle.value = handle;\n await updateData();\n }\n async function create(_options = {}) {\n if (!isSupported.value)\n return;\n fileHandle.value = await window.showSaveFilePicker({ ...options, ..._options });\n data.value = void 0;\n await updateData();\n }\n async function save(_options = {}) {\n if (!isSupported.value)\n return;\n if (!fileHandle.value)\n return saveAs(_options);\n if (data.value) {\n const writableStream = await fileHandle.value.createWritable();\n await writableStream.write(data.value);\n await writableStream.close();\n }\n await updateFile();\n }\n async function saveAs(_options = {}) {\n if (!isSupported.value)\n return;\n fileHandle.value = await window.showSaveFilePicker({ ...options, ..._options });\n if (data.value) {\n const writableStream = await fileHandle.value.createWritable();\n await writableStream.write(data.value);\n await writableStream.close();\n }\n await updateFile();\n }\n async function updateFile() {\n var _a;\n file.value = await ((_a = fileHandle.value) == null ? void 0 : _a.getFile());\n }\n async function updateData() {\n var _a, _b;\n await updateFile();\n const type = toValue(dataType);\n if (type === \"Text\")\n data.value = await ((_a = file.value) == null ? void 0 : _a.text());\n else if (type === \"ArrayBuffer\")\n data.value = await ((_b = file.value) == null ? void 0 : _b.arrayBuffer());\n else if (type === \"Blob\")\n data.value = file.value;\n }\n watch(() => toValue(dataType), updateData);\n return {\n isSupported,\n data,\n file,\n fileName,\n fileMIME,\n fileSize,\n fileLastModified,\n open,\n create,\n save,\n saveAs,\n updateData\n };\n}\n\nfunction useFocus(target, options = {}) {\n const { initialValue = false, focusVisible = false, preventScroll = false } = options;\n const innerFocused = ref(false);\n const targetElement = computed(() => unrefElement(target));\n useEventListener(targetElement, \"focus\", (event) => {\n var _a, _b;\n if (!focusVisible || ((_b = (_a = event.target).matches) == null ? void 0 : _b.call(_a, \":focus-visible\")))\n innerFocused.value = true;\n });\n useEventListener(targetElement, \"blur\", () => innerFocused.value = false);\n const focused = computed({\n get: () => innerFocused.value,\n set(value) {\n var _a, _b;\n if (!value && innerFocused.value)\n (_a = targetElement.value) == null ? void 0 : _a.blur();\n else if (value && !innerFocused.value)\n (_b = targetElement.value) == null ? void 0 : _b.focus({ preventScroll });\n }\n });\n watch(\n targetElement,\n () => {\n focused.value = initialValue;\n },\n { immediate: true, flush: \"post\" }\n );\n return { focused };\n}\n\nfunction useFocusWithin(target, options = {}) {\n const activeElement = useActiveElement(options);\n const targetElement = computed(() => unrefElement(target));\n const focused = computed(() => targetElement.value && activeElement.value ? targetElement.value.contains(activeElement.value) : false);\n return { focused };\n}\n\nfunction useFps(options) {\n var _a;\n const fps = ref(0);\n if (typeof performance === \"undefined\")\n return fps;\n const every = (_a = options == null ? void 0 : options.every) != null ? _a : 10;\n let last = performance.now();\n let ticks = 0;\n useRafFn(() => {\n ticks += 1;\n if (ticks >= every) {\n const now = performance.now();\n const diff = now - last;\n fps.value = Math.round(1e3 / (diff / ticks));\n last = now;\n ticks = 0;\n }\n });\n return fps;\n}\n\nconst eventHandlers = [\n \"fullscreenchange\",\n \"webkitfullscreenchange\",\n \"webkitendfullscreen\",\n \"mozfullscreenchange\",\n \"MSFullscreenChange\"\n];\nfunction useFullscreen(target, options = {}) {\n const {\n document = defaultDocument,\n autoExit = false\n } = options;\n const targetRef = computed(() => {\n var _a;\n return (_a = unrefElement(target)) != null ? _a : document == null ? void 0 : document.querySelector(\"html\");\n });\n const isFullscreen = ref(false);\n const requestMethod = computed(() => {\n return [\n \"requestFullscreen\",\n \"webkitRequestFullscreen\",\n \"webkitEnterFullscreen\",\n \"webkitEnterFullScreen\",\n \"webkitRequestFullScreen\",\n \"mozRequestFullScreen\",\n \"msRequestFullscreen\"\n ].find((m) => document && m in document || targetRef.value && m in targetRef.value);\n });\n const exitMethod = computed(() => {\n return [\n \"exitFullscreen\",\n \"webkitExitFullscreen\",\n \"webkitExitFullScreen\",\n \"webkitCancelFullScreen\",\n \"mozCancelFullScreen\",\n \"msExitFullscreen\"\n ].find((m) => document && m in document || targetRef.value && m in targetRef.value);\n });\n const fullscreenEnabled = computed(() => {\n return [\n \"fullScreen\",\n \"webkitIsFullScreen\",\n \"webkitDisplayingFullscreen\",\n \"mozFullScreen\",\n \"msFullscreenElement\"\n ].find((m) => document && m in document || targetRef.value && m in targetRef.value);\n });\n const fullscreenElementMethod = [\n \"fullscreenElement\",\n \"webkitFullscreenElement\",\n \"mozFullScreenElement\",\n \"msFullscreenElement\"\n ].find((m) => document && m in document);\n const isSupported = useSupported(() => targetRef.value && document && requestMethod.value !== void 0 && exitMethod.value !== void 0 && fullscreenEnabled.value !== void 0);\n const isCurrentElementFullScreen = () => {\n if (fullscreenElementMethod)\n return (document == null ? void 0 : document[fullscreenElementMethod]) === targetRef.value;\n return false;\n };\n const isElementFullScreen = () => {\n if (fullscreenEnabled.value) {\n if (document && document[fullscreenEnabled.value] != null) {\n return document[fullscreenEnabled.value];\n } else {\n const target2 = targetRef.value;\n if ((target2 == null ? void 0 : target2[fullscreenEnabled.value]) != null) {\n return Boolean(target2[fullscreenEnabled.value]);\n }\n }\n }\n return false;\n };\n async function exit() {\n if (!isSupported.value || !isFullscreen.value)\n return;\n if (exitMethod.value) {\n if ((document == null ? void 0 : document[exitMethod.value]) != null) {\n await document[exitMethod.value]();\n } else {\n const target2 = targetRef.value;\n if ((target2 == null ? void 0 : target2[exitMethod.value]) != null)\n await target2[exitMethod.value]();\n }\n }\n isFullscreen.value = false;\n }\n async function enter() {\n if (!isSupported.value || isFullscreen.value)\n return;\n if (isElementFullScreen())\n await exit();\n const target2 = targetRef.value;\n if (requestMethod.value && (target2 == null ? void 0 : target2[requestMethod.value]) != null) {\n await target2[requestMethod.value]();\n isFullscreen.value = true;\n }\n }\n async function toggle() {\n await (isFullscreen.value ? exit() : enter());\n }\n const handlerCallback = () => {\n const isElementFullScreenValue = isElementFullScreen();\n if (!isElementFullScreenValue || isElementFullScreenValue && isCurrentElementFullScreen())\n isFullscreen.value = isElementFullScreenValue;\n };\n useEventListener(document, eventHandlers, handlerCallback, false);\n useEventListener(() => unrefElement(targetRef), eventHandlers, handlerCallback, false);\n if (autoExit)\n tryOnScopeDispose(exit);\n return {\n isSupported,\n isFullscreen,\n enter,\n exit,\n toggle\n };\n}\n\nfunction mapGamepadToXbox360Controller(gamepad) {\n return computed(() => {\n if (gamepad.value) {\n return {\n buttons: {\n a: gamepad.value.buttons[0],\n b: gamepad.value.buttons[1],\n x: gamepad.value.buttons[2],\n y: gamepad.value.buttons[3]\n },\n bumper: {\n left: gamepad.value.buttons[4],\n right: gamepad.value.buttons[5]\n },\n triggers: {\n left: gamepad.value.buttons[6],\n right: gamepad.value.buttons[7]\n },\n stick: {\n left: {\n horizontal: gamepad.value.axes[0],\n vertical: gamepad.value.axes[1],\n button: gamepad.value.buttons[10]\n },\n right: {\n horizontal: gamepad.value.axes[2],\n vertical: gamepad.value.axes[3],\n button: gamepad.value.buttons[11]\n }\n },\n dpad: {\n up: gamepad.value.buttons[12],\n down: gamepad.value.buttons[13],\n left: gamepad.value.buttons[14],\n right: gamepad.value.buttons[15]\n },\n back: gamepad.value.buttons[8],\n start: gamepad.value.buttons[9]\n };\n }\n return null;\n });\n}\nfunction useGamepad(options = {}) {\n const {\n navigator = defaultNavigator\n } = options;\n const isSupported = useSupported(() => navigator && \"getGamepads\" in navigator);\n const gamepads = ref([]);\n const onConnectedHook = createEventHook();\n const onDisconnectedHook = createEventHook();\n const stateFromGamepad = (gamepad) => {\n const hapticActuators = [];\n const vibrationActuator = \"vibrationActuator\" in gamepad ? gamepad.vibrationActuator : null;\n if (vibrationActuator)\n hapticActuators.push(vibrationActuator);\n if (gamepad.hapticActuators)\n hapticActuators.push(...gamepad.hapticActuators);\n return {\n id: gamepad.id,\n index: gamepad.index,\n connected: gamepad.connected,\n mapping: gamepad.mapping,\n timestamp: gamepad.timestamp,\n vibrationActuator: gamepad.vibrationActuator,\n hapticActuators,\n axes: gamepad.axes.map((axes) => axes),\n buttons: gamepad.buttons.map((button) => ({ pressed: button.pressed, touched: button.touched, value: button.value }))\n };\n };\n const updateGamepadState = () => {\n const _gamepads = (navigator == null ? void 0 : navigator.getGamepads()) || [];\n for (const gamepad of _gamepads) {\n if (gamepad && gamepads.value[gamepad.index])\n gamepads.value[gamepad.index] = stateFromGamepad(gamepad);\n }\n };\n const { isActive, pause, resume } = useRafFn(updateGamepadState);\n const onGamepadConnected = (gamepad) => {\n if (!gamepads.value.some(({ index }) => index === gamepad.index)) {\n gamepads.value.push(stateFromGamepad(gamepad));\n onConnectedHook.trigger(gamepad.index);\n }\n resume();\n };\n const onGamepadDisconnected = (gamepad) => {\n gamepads.value = gamepads.value.filter((x) => x.index !== gamepad.index);\n onDisconnectedHook.trigger(gamepad.index);\n };\n useEventListener(\"gamepadconnected\", (e) => onGamepadConnected(e.gamepad));\n useEventListener(\"gamepaddisconnected\", (e) => onGamepadDisconnected(e.gamepad));\n tryOnMounted(() => {\n const _gamepads = (navigator == null ? void 0 : navigator.getGamepads()) || [];\n for (const gamepad of _gamepads) {\n if (gamepad && gamepads.value[gamepad.index])\n onGamepadConnected(gamepad);\n }\n });\n pause();\n return {\n isSupported,\n onConnected: onConnectedHook.on,\n onDisconnected: onDisconnectedHook.on,\n gamepads,\n pause,\n resume,\n isActive\n };\n}\n\nfunction useGeolocation(options = {}) {\n const {\n enableHighAccuracy = true,\n maximumAge = 3e4,\n timeout = 27e3,\n navigator = defaultNavigator,\n immediate = true\n } = options;\n const isSupported = useSupported(() => navigator && \"geolocation\" in navigator);\n const locatedAt = ref(null);\n const error = shallowRef(null);\n const coords = ref({\n accuracy: 0,\n latitude: Number.POSITIVE_INFINITY,\n longitude: Number.POSITIVE_INFINITY,\n altitude: null,\n altitudeAccuracy: null,\n heading: null,\n speed: null\n });\n function updatePosition(position) {\n locatedAt.value = position.timestamp;\n coords.value = position.coords;\n error.value = null;\n }\n let watcher;\n function resume() {\n if (isSupported.value) {\n watcher = navigator.geolocation.watchPosition(\n updatePosition,\n (err) => error.value = err,\n {\n enableHighAccuracy,\n maximumAge,\n timeout\n }\n );\n }\n }\n if (immediate)\n resume();\n function pause() {\n if (watcher && navigator)\n navigator.geolocation.clearWatch(watcher);\n }\n tryOnScopeDispose(() => {\n pause();\n });\n return {\n isSupported,\n coords,\n locatedAt,\n error,\n resume,\n pause\n };\n}\n\nconst defaultEvents$1 = [\"mousemove\", \"mousedown\", \"resize\", \"keydown\", \"touchstart\", \"wheel\"];\nconst oneMinute = 6e4;\nfunction useIdle(timeout = oneMinute, options = {}) {\n const {\n initialState = false,\n listenForVisibilityChange = true,\n events = defaultEvents$1,\n window = defaultWindow,\n eventFilter = throttleFilter(50)\n } = options;\n const idle = ref(initialState);\n const lastActive = ref(timestamp());\n let timer;\n const reset = () => {\n idle.value = false;\n clearTimeout(timer);\n timer = setTimeout(() => idle.value = true, timeout);\n };\n const onEvent = createFilterWrapper(\n eventFilter,\n () => {\n lastActive.value = timestamp();\n reset();\n }\n );\n if (window) {\n const document = window.document;\n for (const event of events)\n useEventListener(window, event, onEvent, { passive: true });\n if (listenForVisibilityChange) {\n useEventListener(document, \"visibilitychange\", () => {\n if (!document.hidden)\n onEvent();\n });\n }\n reset();\n }\n return {\n idle,\n lastActive,\n reset\n };\n}\n\nasync function loadImage(options) {\n return new Promise((resolve, reject) => {\n const img = new Image();\n const { src, srcset, sizes, class: clazz, loading, crossorigin, referrerPolicy } = options;\n img.src = src;\n if (srcset)\n img.srcset = srcset;\n if (sizes)\n img.sizes = sizes;\n if (clazz)\n img.className = clazz;\n if (loading)\n img.loading = loading;\n if (crossorigin)\n img.crossOrigin = crossorigin;\n if (referrerPolicy)\n img.referrerPolicy = referrerPolicy;\n img.onload = () => resolve(img);\n img.onerror = reject;\n });\n}\nfunction useImage(options, asyncStateOptions = {}) {\n const state = useAsyncState(\n () => loadImage(toValue(options)),\n void 0,\n {\n resetOnExecute: true,\n ...asyncStateOptions\n }\n );\n watch(\n () => toValue(options),\n () => state.execute(asyncStateOptions.delay),\n { deep: true }\n );\n return state;\n}\n\nconst ARRIVED_STATE_THRESHOLD_PIXELS = 1;\nfunction useScroll(element, options = {}) {\n const {\n throttle = 0,\n idle = 200,\n onStop = noop,\n onScroll = noop,\n offset = {\n left: 0,\n right: 0,\n top: 0,\n bottom: 0\n },\n eventListenerOptions = {\n capture: false,\n passive: true\n },\n behavior = \"auto\",\n window = defaultWindow,\n onError = (e) => {\n console.error(e);\n }\n } = options;\n const internalX = ref(0);\n const internalY = ref(0);\n const x = computed({\n get() {\n return internalX.value;\n },\n set(x2) {\n scrollTo(x2, void 0);\n }\n });\n const y = computed({\n get() {\n return internalY.value;\n },\n set(y2) {\n scrollTo(void 0, y2);\n }\n });\n function scrollTo(_x, _y) {\n var _a, _b, _c, _d;\n if (!window)\n return;\n const _element = toValue(element);\n if (!_element)\n return;\n (_c = _element instanceof Document ? window.document.body : _element) == null ? void 0 : _c.scrollTo({\n top: (_a = toValue(_y)) != null ? _a : y.value,\n left: (_b = toValue(_x)) != null ? _b : x.value,\n behavior: toValue(behavior)\n });\n const scrollContainer = ((_d = _element == null ? void 0 : _element.document) == null ? void 0 : _d.documentElement) || (_element == null ? void 0 : _element.documentElement) || _element;\n if (x != null)\n internalX.value = scrollContainer.scrollLeft;\n if (y != null)\n internalY.value = scrollContainer.scrollTop;\n }\n const isScrolling = ref(false);\n const arrivedState = reactive({\n left: true,\n right: false,\n top: true,\n bottom: false\n });\n const directions = reactive({\n left: false,\n right: false,\n top: false,\n bottom: false\n });\n const onScrollEnd = (e) => {\n if (!isScrolling.value)\n return;\n isScrolling.value = false;\n directions.left = false;\n directions.right = false;\n directions.top = false;\n directions.bottom = false;\n onStop(e);\n };\n const onScrollEndDebounced = useDebounceFn(onScrollEnd, throttle + idle);\n const setArrivedState = (target) => {\n var _a;\n if (!window)\n return;\n const el = ((_a = target == null ? void 0 : target.document) == null ? void 0 : _a.documentElement) || (target == null ? void 0 : target.documentElement) || unrefElement(target);\n const { display, flexDirection } = getComputedStyle(el);\n const scrollLeft = el.scrollLeft;\n directions.left = scrollLeft < internalX.value;\n directions.right = scrollLeft > internalX.value;\n const left = Math.abs(scrollLeft) <= (offset.left || 0);\n const right = Math.abs(scrollLeft) + el.clientWidth >= el.scrollWidth - (offset.right || 0) - ARRIVED_STATE_THRESHOLD_PIXELS;\n if (display === \"flex\" && flexDirection === \"row-reverse\") {\n arrivedState.left = right;\n arrivedState.right = left;\n } else {\n arrivedState.left = left;\n arrivedState.right = right;\n }\n internalX.value = scrollLeft;\n let scrollTop = el.scrollTop;\n if (target === window.document && !scrollTop)\n scrollTop = window.document.body.scrollTop;\n directions.top = scrollTop < internalY.value;\n directions.bottom = scrollTop > internalY.value;\n const top = Math.abs(scrollTop) <= (offset.top || 0);\n const bottom = Math.abs(scrollTop) + el.clientHeight >= el.scrollHeight - (offset.bottom || 0) - ARRIVED_STATE_THRESHOLD_PIXELS;\n if (display === \"flex\" && flexDirection === \"column-reverse\") {\n arrivedState.top = bottom;\n arrivedState.bottom = top;\n } else {\n arrivedState.top = top;\n arrivedState.bottom = bottom;\n }\n internalY.value = scrollTop;\n };\n const onScrollHandler = (e) => {\n var _a;\n if (!window)\n return;\n const eventTarget = (_a = e.target.documentElement) != null ? _a : e.target;\n setArrivedState(eventTarget);\n isScrolling.value = true;\n onScrollEndDebounced(e);\n onScroll(e);\n };\n useEventListener(\n element,\n \"scroll\",\n throttle ? useThrottleFn(onScrollHandler, throttle, true, false) : onScrollHandler,\n eventListenerOptions\n );\n tryOnMounted(() => {\n try {\n const _element = toValue(element);\n if (!_element)\n return;\n setArrivedState(_element);\n } catch (e) {\n onError(e);\n }\n });\n useEventListener(\n element,\n \"scrollend\",\n onScrollEnd,\n eventListenerOptions\n );\n return {\n x,\n y,\n isScrolling,\n arrivedState,\n directions,\n measure() {\n const _element = toValue(element);\n if (window && _element)\n setArrivedState(_element);\n }\n };\n}\n\nfunction resolveElement(el) {\n if (typeof Window !== \"undefined\" && el instanceof Window)\n return el.document.documentElement;\n if (typeof Document !== \"undefined\" && el instanceof Document)\n return el.documentElement;\n return el;\n}\n\nfunction useInfiniteScroll(element, onLoadMore, options = {}) {\n var _a;\n const {\n direction = \"bottom\",\n interval = 100,\n canLoadMore = () => true\n } = options;\n const state = reactive(useScroll(\n element,\n {\n ...options,\n offset: {\n [direction]: (_a = options.distance) != null ? _a : 0,\n ...options.offset\n }\n }\n ));\n const promise = ref();\n const isLoading = computed(() => !!promise.value);\n const observedElement = computed(() => {\n return resolveElement(toValue(element));\n });\n const isElementVisible = useElementVisibility(observedElement);\n function checkAndLoad() {\n state.measure();\n if (!observedElement.value || !isElementVisible.value || !canLoadMore(observedElement.value))\n return;\n const { scrollHeight, clientHeight, scrollWidth, clientWidth } = observedElement.value;\n const isNarrower = direction === \"bottom\" || direction === \"top\" ? scrollHeight <= clientHeight : scrollWidth <= clientWidth;\n if (state.arrivedState[direction] || isNarrower) {\n if (!promise.value) {\n promise.value = Promise.all([\n onLoadMore(state),\n new Promise((resolve) => setTimeout(resolve, interval))\n ]).finally(() => {\n promise.value = null;\n nextTick(() => checkAndLoad());\n });\n }\n }\n }\n watch(\n () => [state.arrivedState[direction], isElementVisible.value],\n checkAndLoad,\n { immediate: true }\n );\n return {\n isLoading,\n reset() {\n nextTick(() => checkAndLoad());\n }\n };\n}\n\nconst defaultEvents = [\"mousedown\", \"mouseup\", \"keydown\", \"keyup\"];\nfunction useKeyModifier(modifier, options = {}) {\n const {\n events = defaultEvents,\n document = defaultDocument,\n initial = null\n } = options;\n const state = ref(initial);\n if (document) {\n events.forEach((listenerEvent) => {\n useEventListener(document, listenerEvent, (evt) => {\n if (typeof evt.getModifierState === \"function\")\n state.value = evt.getModifierState(modifier);\n });\n });\n }\n return state;\n}\n\nfunction useLocalStorage(key, initialValue, options = {}) {\n const { window = defaultWindow } = options;\n return useStorage(key, initialValue, window == null ? void 0 : window.localStorage, options);\n}\n\nconst DefaultMagicKeysAliasMap = {\n ctrl: \"control\",\n command: \"meta\",\n cmd: \"meta\",\n option: \"alt\",\n up: \"arrowup\",\n down: \"arrowdown\",\n left: \"arrowleft\",\n right: \"arrowright\"\n};\n\nfunction useMagicKeys(options = {}) {\n const {\n reactive: useReactive = false,\n target = defaultWindow,\n aliasMap = DefaultMagicKeysAliasMap,\n passive = true,\n onEventFired = noop\n } = options;\n const current = reactive(/* @__PURE__ */ new Set());\n const obj = {\n toJSON() {\n return {};\n },\n current\n };\n const refs = useReactive ? reactive(obj) : obj;\n const metaDeps = /* @__PURE__ */ new Set();\n const usedKeys = /* @__PURE__ */ new Set();\n function setRefs(key, value) {\n if (key in refs) {\n if (useReactive)\n refs[key] = value;\n else\n refs[key].value = value;\n }\n }\n function reset() {\n current.clear();\n for (const key of usedKeys)\n setRefs(key, false);\n }\n function updateRefs(e, value) {\n var _a, _b;\n const key = (_a = e.key) == null ? void 0 : _a.toLowerCase();\n const code = (_b = e.code) == null ? void 0 : _b.toLowerCase();\n const values = [code, key].filter(Boolean);\n if (key) {\n if (value)\n current.add(key);\n else\n current.delete(key);\n }\n for (const key2 of values) {\n usedKeys.add(key2);\n setRefs(key2, value);\n }\n if (key === \"meta\" && !value) {\n metaDeps.forEach((key2) => {\n current.delete(key2);\n setRefs(key2, false);\n });\n metaDeps.clear();\n } else if (typeof e.getModifierState === \"function\" && e.getModifierState(\"Meta\") && value) {\n [...current, ...values].forEach((key2) => metaDeps.add(key2));\n }\n }\n useEventListener(target, \"keydown\", (e) => {\n updateRefs(e, true);\n return onEventFired(e);\n }, { passive });\n useEventListener(target, \"keyup\", (e) => {\n updateRefs(e, false);\n return onEventFired(e);\n }, { passive });\n useEventListener(\"blur\", reset, { passive: true });\n useEventListener(\"focus\", reset, { passive: true });\n const proxy = new Proxy(\n refs,\n {\n get(target2, prop, rec) {\n if (typeof prop !== \"string\")\n return Reflect.get(target2, prop, rec);\n prop = prop.toLowerCase();\n if (prop in aliasMap)\n prop = aliasMap[prop];\n if (!(prop in refs)) {\n if (/[+_-]/.test(prop)) {\n const keys = prop.split(/[+_-]/g).map((i) => i.trim());\n refs[prop] = computed(() => keys.every((key) => toValue(proxy[key])));\n } else {\n refs[prop] = ref(false);\n }\n }\n const r = Reflect.get(target2, prop, rec);\n return useReactive ? toValue(r) : r;\n }\n }\n );\n return proxy;\n}\n\nfunction usingElRef(source, cb) {\n if (toValue(source))\n cb(toValue(source));\n}\nfunction timeRangeToArray(timeRanges) {\n let ranges = [];\n for (let i = 0; i < timeRanges.length; ++i)\n ranges = [...ranges, [timeRanges.start(i), timeRanges.end(i)]];\n return ranges;\n}\nfunction tracksToArray(tracks) {\n return Array.from(tracks).map(({ label, kind, language, mode, activeCues, cues, inBandMetadataTrackDispatchType }, id) => ({ id, label, kind, language, mode, activeCues, cues, inBandMetadataTrackDispatchType }));\n}\nconst defaultOptions = {\n src: \"\",\n tracks: []\n};\nfunction useMediaControls(target, options = {}) {\n target = toRef(target);\n options = {\n ...defaultOptions,\n ...options\n };\n const {\n document = defaultDocument\n } = options;\n const currentTime = ref(0);\n const duration = ref(0);\n const seeking = ref(false);\n const volume = ref(1);\n const waiting = ref(false);\n const ended = ref(false);\n const playing = ref(false);\n const rate = ref(1);\n const stalled = ref(false);\n const buffered = ref([]);\n const tracks = ref([]);\n const selectedTrack = ref(-1);\n const isPictureInPicture = ref(false);\n const muted = ref(false);\n const supportsPictureInPicture = document && \"pictureInPictureEnabled\" in document;\n const sourceErrorEvent = createEventHook();\n const disableTrack = (track) => {\n usingElRef(target, (el) => {\n if (track) {\n const id = typeof track === \"number\" ? track : track.id;\n el.textTracks[id].mode = \"disabled\";\n } else {\n for (let i = 0; i < el.textTracks.length; ++i)\n el.textTracks[i].mode = \"disabled\";\n }\n selectedTrack.value = -1;\n });\n };\n const enableTrack = (track, disableTracks = true) => {\n usingElRef(target, (el) => {\n const id = typeof track === \"number\" ? track : track.id;\n if (disableTracks)\n disableTrack();\n el.textTracks[id].mode = \"showing\";\n selectedTrack.value = id;\n });\n };\n const togglePictureInPicture = () => {\n return new Promise((resolve, reject) => {\n usingElRef(target, async (el) => {\n if (supportsPictureInPicture) {\n if (!isPictureInPicture.value) {\n el.requestPictureInPicture().then(resolve).catch(reject);\n } else {\n document.exitPictureInPicture().then(resolve).catch(reject);\n }\n }\n });\n });\n };\n watchEffect(() => {\n if (!document)\n return;\n const el = toValue(target);\n if (!el)\n return;\n const src = toValue(options.src);\n let sources = [];\n if (!src)\n return;\n if (typeof src === \"string\")\n sources = [{ src }];\n else if (Array.isArray(src))\n sources = src;\n else if (isObject(src))\n sources = [src];\n el.querySelectorAll(\"source\").forEach((e) => {\n e.removeEventListener(\"error\", sourceErrorEvent.trigger);\n e.remove();\n });\n sources.forEach(({ src: src2, type }) => {\n const source = document.createElement(\"source\");\n source.setAttribute(\"src\", src2);\n source.setAttribute(\"type\", type || \"\");\n source.addEventListener(\"error\", sourceErrorEvent.trigger);\n el.appendChild(source);\n });\n el.load();\n });\n tryOnScopeDispose(() => {\n const el = toValue(target);\n if (!el)\n return;\n el.querySelectorAll(\"source\").forEach((e) => e.removeEventListener(\"error\", sourceErrorEvent.trigger));\n });\n watch([target, volume], () => {\n const el = toValue(target);\n if (!el)\n return;\n el.volume = volume.value;\n });\n watch([target, muted], () => {\n const el = toValue(target);\n if (!el)\n return;\n el.muted = muted.value;\n });\n watch([target, rate], () => {\n const el = toValue(target);\n if (!el)\n return;\n el.playbackRate = rate.value;\n });\n watchEffect(() => {\n if (!document)\n return;\n const textTracks = toValue(options.tracks);\n const el = toValue(target);\n if (!textTracks || !textTracks.length || !el)\n return;\n el.querySelectorAll(\"track\").forEach((e) => e.remove());\n textTracks.forEach(({ default: isDefault, kind, label, src, srcLang }, i) => {\n const track = document.createElement(\"track\");\n track.default = isDefault || false;\n track.kind = kind;\n track.label = label;\n track.src = src;\n track.srclang = srcLang;\n if (track.default)\n selectedTrack.value = i;\n el.appendChild(track);\n });\n });\n const { ignoreUpdates: ignoreCurrentTimeUpdates } = watchIgnorable(currentTime, (time) => {\n const el = toValue(target);\n if (!el)\n return;\n el.currentTime = time;\n });\n const { ignoreUpdates: ignorePlayingUpdates } = watchIgnorable(playing, (isPlaying) => {\n const el = toValue(target);\n if (!el)\n return;\n if (isPlaying)\n el.play();\n else\n el.pause();\n });\n useEventListener(target, \"timeupdate\", () => ignoreCurrentTimeUpdates(() => currentTime.value = toValue(target).currentTime));\n useEventListener(target, \"durationchange\", () => duration.value = toValue(target).duration);\n useEventListener(target, \"progress\", () => buffered.value = timeRangeToArray(toValue(target).buffered));\n useEventListener(target, \"seeking\", () => seeking.value = true);\n useEventListener(target, \"seeked\", () => seeking.value = false);\n useEventListener(target, [\"waiting\", \"loadstart\"], () => {\n waiting.value = true;\n ignorePlayingUpdates(() => playing.value = false);\n });\n useEventListener(target, \"loadeddata\", () => waiting.value = false);\n useEventListener(target, \"playing\", () => {\n waiting.value = false;\n ended.value = false;\n ignorePlayingUpdates(() => playing.value = true);\n });\n useEventListener(target, \"ratechange\", () => rate.value = toValue(target).playbackRate);\n useEventListener(target, \"stalled\", () => stalled.value = true);\n useEventListener(target, \"ended\", () => ended.value = true);\n useEventListener(target, \"pause\", () => ignorePlayingUpdates(() => playing.value = false));\n useEventListener(target, \"play\", () => ignorePlayingUpdates(() => playing.value = true));\n useEventListener(target, \"enterpictureinpicture\", () => isPictureInPicture.value = true);\n useEventListener(target, \"leavepictureinpicture\", () => isPictureInPicture.value = false);\n useEventListener(target, \"volumechange\", () => {\n const el = toValue(target);\n if (!el)\n return;\n volume.value = el.volume;\n muted.value = el.muted;\n });\n const listeners = [];\n const stop = watch([target], () => {\n const el = toValue(target);\n if (!el)\n return;\n stop();\n listeners[0] = useEventListener(el.textTracks, \"addtrack\", () => tracks.value = tracksToArray(el.textTracks));\n listeners[1] = useEventListener(el.textTracks, \"removetrack\", () => tracks.value = tracksToArray(el.textTracks));\n listeners[2] = useEventListener(el.textTracks, \"change\", () => tracks.value = tracksToArray(el.textTracks));\n });\n tryOnScopeDispose(() => listeners.forEach((listener) => listener()));\n return {\n currentTime,\n duration,\n waiting,\n seeking,\n ended,\n stalled,\n buffered,\n playing,\n rate,\n // Volume\n volume,\n muted,\n // Tracks\n tracks,\n selectedTrack,\n enableTrack,\n disableTrack,\n // Picture in Picture\n supportsPictureInPicture,\n togglePictureInPicture,\n isPictureInPicture,\n // Events\n onSourceError: sourceErrorEvent.on\n };\n}\n\nfunction getMapVue2Compat() {\n const data = shallowReactive({});\n return {\n get: (key) => data[key],\n set: (key, value) => set(data, key, value),\n has: (key) => hasOwn(data, key),\n delete: (key) => del(data, key),\n clear: () => {\n Object.keys(data).forEach((key) => {\n del(data, key);\n });\n }\n };\n}\nfunction useMemoize(resolver, options) {\n const initCache = () => {\n if (options == null ? void 0 : options.cache)\n return shallowReactive(options.cache);\n if (isVue2)\n return getMapVue2Compat();\n return shallowReactive(/* @__PURE__ */ new Map());\n };\n const cache = initCache();\n const generateKey = (...args) => (options == null ? void 0 : options.getKey) ? options.getKey(...args) : JSON.stringify(args);\n const _loadData = (key, ...args) => {\n cache.set(key, resolver(...args));\n return cache.get(key);\n };\n const loadData = (...args) => _loadData(generateKey(...args), ...args);\n const deleteData = (...args) => {\n cache.delete(generateKey(...args));\n };\n const clearData = () => {\n cache.clear();\n };\n const memoized = (...args) => {\n const key = generateKey(...args);\n if (cache.has(key))\n return cache.get(key);\n return _loadData(key, ...args);\n };\n memoized.load = loadData;\n memoized.delete = deleteData;\n memoized.clear = clearData;\n memoized.generateKey = generateKey;\n memoized.cache = cache;\n return memoized;\n}\n\nfunction useMemory(options = {}) {\n const memory = ref();\n const isSupported = useSupported(() => typeof performance !== \"undefined\" && \"memory\" in performance);\n if (isSupported.value) {\n const { interval = 1e3 } = options;\n useIntervalFn(() => {\n memory.value = performance.memory;\n }, interval, { immediate: options.immediate, immediateCallback: options.immediateCallback });\n }\n return { isSupported, memory };\n}\n\nconst UseMouseBuiltinExtractors = {\n page: (event) => [event.pageX, event.pageY],\n client: (event) => [event.clientX, event.clientY],\n screen: (event) => [event.screenX, event.screenY],\n movement: (event) => event instanceof Touch ? null : [event.movementX, event.movementY]\n};\nfunction useMouse(options = {}) {\n const {\n type = \"page\",\n touch = true,\n resetOnTouchEnds = false,\n initialValue = { x: 0, y: 0 },\n window = defaultWindow,\n target = window,\n scroll = true,\n eventFilter\n } = options;\n let _prevMouseEvent = null;\n const x = ref(initialValue.x);\n const y = ref(initialValue.y);\n const sourceType = ref(null);\n const extractor = typeof type === \"function\" ? type : UseMouseBuiltinExtractors[type];\n const mouseHandler = (event) => {\n const result = extractor(event);\n _prevMouseEvent = event;\n if (result) {\n [x.value, y.value] = result;\n sourceType.value = \"mouse\";\n }\n };\n const touchHandler = (event) => {\n if (event.touches.length > 0) {\n const result = extractor(event.touches[0]);\n if (result) {\n [x.value, y.value] = result;\n sourceType.value = \"touch\";\n }\n }\n };\n const scrollHandler = () => {\n if (!_prevMouseEvent || !window)\n return;\n const pos = extractor(_prevMouseEvent);\n if (_prevMouseEvent instanceof MouseEvent && pos) {\n x.value = pos[0] + window.scrollX;\n y.value = pos[1] + window.scrollY;\n }\n };\n const reset = () => {\n x.value = initialValue.x;\n y.value = initialValue.y;\n };\n const mouseHandlerWrapper = eventFilter ? (event) => eventFilter(() => mouseHandler(event), {}) : (event) => mouseHandler(event);\n const touchHandlerWrapper = eventFilter ? (event) => eventFilter(() => touchHandler(event), {}) : (event) => touchHandler(event);\n const scrollHandlerWrapper = eventFilter ? () => eventFilter(() => scrollHandler(), {}) : () => scrollHandler();\n if (target) {\n const listenerOptions = { passive: true };\n useEventListener(target, [\"mousemove\", \"dragover\"], mouseHandlerWrapper, listenerOptions);\n if (touch && type !== \"movement\") {\n useEventListener(target, [\"touchstart\", \"touchmove\"], touchHandlerWrapper, listenerOptions);\n if (resetOnTouchEnds)\n useEventListener(target, \"touchend\", reset, listenerOptions);\n }\n if (scroll && type === \"page\")\n useEventListener(window, \"scroll\", scrollHandlerWrapper, { passive: true });\n }\n return {\n x,\n y,\n sourceType\n };\n}\n\nfunction useMouseInElement(target, options = {}) {\n const {\n handleOutside = true,\n window = defaultWindow\n } = options;\n const type = options.type || \"page\";\n const { x, y, sourceType } = useMouse(options);\n const targetRef = ref(target != null ? target : window == null ? void 0 : window.document.body);\n const elementX = ref(0);\n const elementY = ref(0);\n const elementPositionX = ref(0);\n const elementPositionY = ref(0);\n const elementHeight = ref(0);\n const elementWidth = ref(0);\n const isOutside = ref(true);\n let stop = () => {\n };\n if (window) {\n stop = watch(\n [targetRef, x, y],\n () => {\n const el = unrefElement(targetRef);\n if (!el || !(el instanceof HTMLElement))\n return;\n const {\n left,\n top,\n width,\n height\n } = el.getBoundingClientRect();\n elementPositionX.value = left + (type === \"page\" ? window.pageXOffset : 0);\n elementPositionY.value = top + (type === \"page\" ? window.pageYOffset : 0);\n elementHeight.value = height;\n elementWidth.value = width;\n const elX = x.value - elementPositionX.value;\n const elY = y.value - elementPositionY.value;\n isOutside.value = width === 0 || height === 0 || elX < 0 || elY < 0 || elX > width || elY > height;\n if (handleOutside || !isOutside.value) {\n elementX.value = elX;\n elementY.value = elY;\n }\n },\n { immediate: true }\n );\n useEventListener(document, \"mouseleave\", () => {\n isOutside.value = true;\n });\n }\n return {\n x,\n y,\n sourceType,\n elementX,\n elementY,\n elementPositionX,\n elementPositionY,\n elementHeight,\n elementWidth,\n isOutside,\n stop\n };\n}\n\nfunction useMousePressed(options = {}) {\n const {\n touch = true,\n drag = true,\n capture = false,\n initialValue = false,\n window = defaultWindow\n } = options;\n const pressed = ref(initialValue);\n const sourceType = ref(null);\n if (!window) {\n return {\n pressed,\n sourceType\n };\n }\n const onPressed = (srcType) => () => {\n pressed.value = true;\n sourceType.value = srcType;\n };\n const onReleased = () => {\n pressed.value = false;\n sourceType.value = null;\n };\n const target = computed(() => unrefElement(options.target) || window);\n useEventListener(target, \"mousedown\", onPressed(\"mouse\"), { passive: true, capture });\n useEventListener(window, \"mouseleave\", onReleased, { passive: true, capture });\n useEventListener(window, \"mouseup\", onReleased, { passive: true, capture });\n if (drag) {\n useEventListener(target, \"dragstart\", onPressed(\"mouse\"), { passive: true, capture });\n useEventListener(window, \"drop\", onReleased, { passive: true, capture });\n useEventListener(window, \"dragend\", onReleased, { passive: true, capture });\n }\n if (touch) {\n useEventListener(target, \"touchstart\", onPressed(\"touch\"), { passive: true, capture });\n useEventListener(window, \"touchend\", onReleased, { passive: true, capture });\n useEventListener(window, \"touchcancel\", onReleased, { passive: true, capture });\n }\n return {\n pressed,\n sourceType\n };\n}\n\nfunction useNavigatorLanguage(options = {}) {\n const { window = defaultWindow } = options;\n const navigator = window == null ? void 0 : window.navigator;\n const isSupported = useSupported(() => navigator && \"language\" in navigator);\n const language = ref(navigator == null ? void 0 : navigator.language);\n useEventListener(window, \"languagechange\", () => {\n if (navigator)\n language.value = navigator.language;\n });\n return {\n isSupported,\n language\n };\n}\n\nfunction useNetwork(options = {}) {\n const { window = defaultWindow } = options;\n const navigator = window == null ? void 0 : window.navigator;\n const isSupported = useSupported(() => navigator && \"connection\" in navigator);\n const isOnline = ref(true);\n const saveData = ref(false);\n const offlineAt = ref(void 0);\n const onlineAt = ref(void 0);\n const downlink = ref(void 0);\n const downlinkMax = ref(void 0);\n const rtt = ref(void 0);\n const effectiveType = ref(void 0);\n const type = ref(\"unknown\");\n const connection = isSupported.value && navigator.connection;\n function updateNetworkInformation() {\n if (!navigator)\n return;\n isOnline.value = navigator.onLine;\n offlineAt.value = isOnline.value ? void 0 : Date.now();\n onlineAt.value = isOnline.value ? Date.now() : void 0;\n if (connection) {\n downlink.value = connection.downlink;\n downlinkMax.value = connection.downlinkMax;\n effectiveType.value = connection.effectiveType;\n rtt.value = connection.rtt;\n saveData.value = connection.saveData;\n type.value = connection.type;\n }\n }\n if (window) {\n useEventListener(window, \"offline\", () => {\n isOnline.value = false;\n offlineAt.value = Date.now();\n });\n useEventListener(window, \"online\", () => {\n isOnline.value = true;\n onlineAt.value = Date.now();\n });\n }\n if (connection)\n useEventListener(connection, \"change\", updateNetworkInformation, false);\n updateNetworkInformation();\n return {\n isSupported,\n isOnline,\n saveData,\n offlineAt,\n onlineAt,\n downlink,\n downlinkMax,\n effectiveType,\n rtt,\n type\n };\n}\n\nfunction useNow(options = {}) {\n const {\n controls: exposeControls = false,\n interval = \"requestAnimationFrame\"\n } = options;\n const now = ref(/* @__PURE__ */ new Date());\n const update = () => now.value = /* @__PURE__ */ new Date();\n const controls = interval === \"requestAnimationFrame\" ? useRafFn(update, { immediate: true }) : useIntervalFn(update, interval, { immediate: true });\n if (exposeControls) {\n return {\n now,\n ...controls\n };\n } else {\n return now;\n }\n}\n\nfunction useObjectUrl(object) {\n const url = ref();\n const release = () => {\n if (url.value)\n URL.revokeObjectURL(url.value);\n url.value = void 0;\n };\n watch(\n () => toValue(object),\n (newObject) => {\n release();\n if (newObject)\n url.value = URL.createObjectURL(newObject);\n },\n { immediate: true }\n );\n tryOnScopeDispose(release);\n return readonly(url);\n}\n\nfunction useClamp(value, min, max) {\n if (typeof value === \"function\" || isReadonly(value))\n return computed(() => clamp(toValue(value), toValue(min), toValue(max)));\n const _value = ref(value);\n return computed({\n get() {\n return _value.value = clamp(_value.value, toValue(min), toValue(max));\n },\n set(value2) {\n _value.value = clamp(value2, toValue(min), toValue(max));\n }\n });\n}\n\nfunction useOffsetPagination(options) {\n const {\n total = Number.POSITIVE_INFINITY,\n pageSize = 10,\n page = 1,\n onPageChange = noop,\n onPageSizeChange = noop,\n onPageCountChange = noop\n } = options;\n const currentPageSize = useClamp(pageSize, 1, Number.POSITIVE_INFINITY);\n const pageCount = computed(() => Math.max(\n 1,\n Math.ceil(toValue(total) / toValue(currentPageSize))\n ));\n const currentPage = useClamp(page, 1, pageCount);\n const isFirstPage = computed(() => currentPage.value === 1);\n const isLastPage = computed(() => currentPage.value === pageCount.value);\n if (isRef(page)) {\n syncRef(page, currentPage, {\n direction: isReadonly(page) ? \"ltr\" : \"both\"\n });\n }\n if (isRef(pageSize)) {\n syncRef(pageSize, currentPageSize, {\n direction: isReadonly(pageSize) ? \"ltr\" : \"both\"\n });\n }\n function prev() {\n currentPage.value--;\n }\n function next() {\n currentPage.value++;\n }\n const returnValue = {\n currentPage,\n currentPageSize,\n pageCount,\n isFirstPage,\n isLastPage,\n prev,\n next\n };\n watch(currentPage, () => {\n onPageChange(reactive(returnValue));\n });\n watch(currentPageSize, () => {\n onPageSizeChange(reactive(returnValue));\n });\n watch(pageCount, () => {\n onPageCountChange(reactive(returnValue));\n });\n return returnValue;\n}\n\nfunction useOnline(options = {}) {\n const { isOnline } = useNetwork(options);\n return isOnline;\n}\n\nfunction usePageLeave(options = {}) {\n const { window = defaultWindow } = options;\n const isLeft = ref(false);\n const handler = (event) => {\n if (!window)\n return;\n event = event || window.event;\n const from = event.relatedTarget || event.toElement;\n isLeft.value = !from;\n };\n if (window) {\n useEventListener(window, \"mouseout\", handler, { passive: true });\n useEventListener(window.document, \"mouseleave\", handler, { passive: true });\n useEventListener(window.document, \"mouseenter\", handler, { passive: true });\n }\n return isLeft;\n}\n\nfunction useScreenOrientation(options = {}) {\n const {\n window = defaultWindow\n } = options;\n const isSupported = useSupported(() => window && \"screen\" in window && \"orientation\" in window.screen);\n const screenOrientation = isSupported.value ? window.screen.orientation : {};\n const orientation = ref(screenOrientation.type);\n const angle = ref(screenOrientation.angle || 0);\n if (isSupported.value) {\n useEventListener(window, \"orientationchange\", () => {\n orientation.value = screenOrientation.type;\n angle.value = screenOrientation.angle;\n });\n }\n const lockOrientation = (type) => {\n if (isSupported.value && typeof screenOrientation.lock === \"function\")\n return screenOrientation.lock(type);\n return Promise.reject(new Error(\"Not supported\"));\n };\n const unlockOrientation = () => {\n if (isSupported.value && typeof screenOrientation.unlock === \"function\")\n screenOrientation.unlock();\n };\n return {\n isSupported,\n orientation,\n angle,\n lockOrientation,\n unlockOrientation\n };\n}\n\nfunction useParallax(target, options = {}) {\n const {\n deviceOrientationTiltAdjust = (i) => i,\n deviceOrientationRollAdjust = (i) => i,\n mouseTiltAdjust = (i) => i,\n mouseRollAdjust = (i) => i,\n window = defaultWindow\n } = options;\n const orientation = reactive(useDeviceOrientation({ window }));\n const screenOrientation = reactive(useScreenOrientation({ window }));\n const {\n elementX: x,\n elementY: y,\n elementWidth: width,\n elementHeight: height\n } = useMouseInElement(target, { handleOutside: false, window });\n const source = computed(() => {\n if (orientation.isSupported && (orientation.alpha != null && orientation.alpha !== 0 || orientation.gamma != null && orientation.gamma !== 0)) {\n return \"deviceOrientation\";\n }\n return \"mouse\";\n });\n const roll = computed(() => {\n if (source.value === \"deviceOrientation\") {\n let value;\n switch (screenOrientation.orientation) {\n case \"landscape-primary\":\n value = orientation.gamma / 90;\n break;\n case \"landscape-secondary\":\n value = -orientation.gamma / 90;\n break;\n case \"portrait-primary\":\n value = -orientation.beta / 90;\n break;\n case \"portrait-secondary\":\n value = orientation.beta / 90;\n break;\n default:\n value = -orientation.beta / 90;\n }\n return deviceOrientationRollAdjust(value);\n } else {\n const value = -(y.value - height.value / 2) / height.value;\n return mouseRollAdjust(value);\n }\n });\n const tilt = computed(() => {\n if (source.value === \"deviceOrientation\") {\n let value;\n switch (screenOrientation.orientation) {\n case \"landscape-primary\":\n value = orientation.beta / 90;\n break;\n case \"landscape-secondary\":\n value = -orientation.beta / 90;\n break;\n case \"portrait-primary\":\n value = orientation.gamma / 90;\n break;\n case \"portrait-secondary\":\n value = -orientation.gamma / 90;\n break;\n default:\n value = orientation.gamma / 90;\n }\n return deviceOrientationTiltAdjust(value);\n } else {\n const value = (x.value - width.value / 2) / width.value;\n return mouseTiltAdjust(value);\n }\n });\n return { roll, tilt, source };\n}\n\nfunction useParentElement(element = useCurrentElement()) {\n const parentElement = shallowRef();\n const update = () => {\n const el = unrefElement(element);\n if (el)\n parentElement.value = el.parentElement;\n };\n tryOnMounted(update);\n watch(() => toValue(element), update);\n return parentElement;\n}\n\nfunction usePerformanceObserver(options, callback) {\n const {\n window = defaultWindow,\n immediate = true,\n ...performanceOptions\n } = options;\n const isSupported = useSupported(() => window && \"PerformanceObserver\" in window);\n let observer;\n const stop = () => {\n observer == null ? void 0 : observer.disconnect();\n };\n const start = () => {\n if (isSupported.value) {\n stop();\n observer = new PerformanceObserver(callback);\n observer.observe(performanceOptions);\n }\n };\n tryOnScopeDispose(stop);\n if (immediate)\n start();\n return {\n isSupported,\n start,\n stop\n };\n}\n\nconst defaultState = {\n x: 0,\n y: 0,\n pointerId: 0,\n pressure: 0,\n tiltX: 0,\n tiltY: 0,\n width: 0,\n height: 0,\n twist: 0,\n pointerType: null\n};\nconst keys = /* @__PURE__ */ Object.keys(defaultState);\nfunction usePointer(options = {}) {\n const {\n target = defaultWindow\n } = options;\n const isInside = ref(false);\n const state = ref(options.initialValue || {});\n Object.assign(state.value, defaultState, state.value);\n const handler = (event) => {\n isInside.value = true;\n if (options.pointerTypes && !options.pointerTypes.includes(event.pointerType))\n return;\n state.value = objectPick(event, keys, false);\n };\n if (target) {\n const listenerOptions = { passive: true };\n useEventListener(target, [\"pointerdown\", \"pointermove\", \"pointerup\"], handler, listenerOptions);\n useEventListener(target, \"pointerleave\", () => isInside.value = false, listenerOptions);\n }\n return {\n ...toRefs(state),\n isInside\n };\n}\n\nfunction usePointerLock(target, options = {}) {\n const { document = defaultDocument } = options;\n const isSupported = useSupported(() => document && \"pointerLockElement\" in document);\n const element = ref();\n const triggerElement = ref();\n let targetElement;\n if (isSupported.value) {\n useEventListener(document, \"pointerlockchange\", () => {\n var _a;\n const currentElement = (_a = document.pointerLockElement) != null ? _a : element.value;\n if (targetElement && currentElement === targetElement) {\n element.value = document.pointerLockElement;\n if (!element.value)\n targetElement = triggerElement.value = null;\n }\n });\n useEventListener(document, \"pointerlockerror\", () => {\n var _a;\n const currentElement = (_a = document.pointerLockElement) != null ? _a : element.value;\n if (targetElement && currentElement === targetElement) {\n const action = document.pointerLockElement ? \"release\" : \"acquire\";\n throw new Error(`Failed to ${action} pointer lock.`);\n }\n });\n }\n async function lock(e) {\n var _a;\n if (!isSupported.value)\n throw new Error(\"Pointer Lock API is not supported by your browser.\");\n triggerElement.value = e instanceof Event ? e.currentTarget : null;\n targetElement = e instanceof Event ? (_a = unrefElement(target)) != null ? _a : triggerElement.value : unrefElement(e);\n if (!targetElement)\n throw new Error(\"Target element undefined.\");\n targetElement.requestPointerLock();\n return await until(element).toBe(targetElement);\n }\n async function unlock() {\n if (!element.value)\n return false;\n document.exitPointerLock();\n await until(element).toBeNull();\n return true;\n }\n return {\n isSupported,\n element,\n triggerElement,\n lock,\n unlock\n };\n}\n\nfunction usePointerSwipe(target, options = {}) {\n const targetRef = toRef(target);\n const {\n threshold = 50,\n onSwipe,\n onSwipeEnd,\n onSwipeStart,\n disableTextSelect = false\n } = options;\n const posStart = reactive({ x: 0, y: 0 });\n const updatePosStart = (x, y) => {\n posStart.x = x;\n posStart.y = y;\n };\n const posEnd = reactive({ x: 0, y: 0 });\n const updatePosEnd = (x, y) => {\n posEnd.x = x;\n posEnd.y = y;\n };\n const distanceX = computed(() => posStart.x - posEnd.x);\n const distanceY = computed(() => posStart.y - posEnd.y);\n const { max, abs } = Math;\n const isThresholdExceeded = computed(() => max(abs(distanceX.value), abs(distanceY.value)) >= threshold);\n const isSwiping = ref(false);\n const isPointerDown = ref(false);\n const direction = computed(() => {\n if (!isThresholdExceeded.value)\n return \"none\";\n if (abs(distanceX.value) > abs(distanceY.value)) {\n return distanceX.value > 0 ? \"left\" : \"right\";\n } else {\n return distanceY.value > 0 ? \"up\" : \"down\";\n }\n });\n const eventIsAllowed = (e) => {\n var _a, _b, _c;\n const isReleasingButton = e.buttons === 0;\n const isPrimaryButton = e.buttons === 1;\n return (_c = (_b = (_a = options.pointerTypes) == null ? void 0 : _a.includes(e.pointerType)) != null ? _b : isReleasingButton || isPrimaryButton) != null ? _c : true;\n };\n const stops = [\n useEventListener(target, \"pointerdown\", (e) => {\n if (!eventIsAllowed(e))\n return;\n isPointerDown.value = true;\n const eventTarget = e.target;\n eventTarget == null ? void 0 : eventTarget.setPointerCapture(e.pointerId);\n const { clientX: x, clientY: y } = e;\n updatePosStart(x, y);\n updatePosEnd(x, y);\n onSwipeStart == null ? void 0 : onSwipeStart(e);\n }),\n useEventListener(target, \"pointermove\", (e) => {\n if (!eventIsAllowed(e))\n return;\n if (!isPointerDown.value)\n return;\n const { clientX: x, clientY: y } = e;\n updatePosEnd(x, y);\n if (!isSwiping.value && isThresholdExceeded.value)\n isSwiping.value = true;\n if (isSwiping.value)\n onSwipe == null ? void 0 : onSwipe(e);\n }),\n useEventListener(target, \"pointerup\", (e) => {\n if (!eventIsAllowed(e))\n return;\n if (isSwiping.value)\n onSwipeEnd == null ? void 0 : onSwipeEnd(e, direction.value);\n isPointerDown.value = false;\n isSwiping.value = false;\n })\n ];\n tryOnMounted(() => {\n var _a, _b, _c, _d, _e, _f, _g, _h;\n (_b = (_a = targetRef.value) == null ? void 0 : _a.style) == null ? void 0 : _b.setProperty(\"touch-action\", \"none\");\n if (disableTextSelect) {\n (_d = (_c = targetRef.value) == null ? void 0 : _c.style) == null ? void 0 : _d.setProperty(\"-webkit-user-select\", \"none\");\n (_f = (_e = targetRef.value) == null ? void 0 : _e.style) == null ? void 0 : _f.setProperty(\"-ms-user-select\", \"none\");\n (_h = (_g = targetRef.value) == null ? void 0 : _g.style) == null ? void 0 : _h.setProperty(\"user-select\", \"none\");\n }\n });\n const stop = () => stops.forEach((s) => s());\n return {\n isSwiping: readonly(isSwiping),\n direction: readonly(direction),\n posStart: readonly(posStart),\n posEnd: readonly(posEnd),\n distanceX,\n distanceY,\n stop\n };\n}\n\nfunction usePreferredColorScheme(options) {\n const isLight = useMediaQuery(\"(prefers-color-scheme: light)\", options);\n const isDark = useMediaQuery(\"(prefers-color-scheme: dark)\", options);\n return computed(() => {\n if (isDark.value)\n return \"dark\";\n if (isLight.value)\n return \"light\";\n return \"no-preference\";\n });\n}\n\nfunction usePreferredContrast(options) {\n const isMore = useMediaQuery(\"(prefers-contrast: more)\", options);\n const isLess = useMediaQuery(\"(prefers-contrast: less)\", options);\n const isCustom = useMediaQuery(\"(prefers-contrast: custom)\", options);\n return computed(() => {\n if (isMore.value)\n return \"more\";\n if (isLess.value)\n return \"less\";\n if (isCustom.value)\n return \"custom\";\n return \"no-preference\";\n });\n}\n\nfunction usePreferredLanguages(options = {}) {\n const { window = defaultWindow } = options;\n if (!window)\n return ref([\"en\"]);\n const navigator = window.navigator;\n const value = ref(navigator.languages);\n useEventListener(window, \"languagechange\", () => {\n value.value = navigator.languages;\n });\n return value;\n}\n\nfunction usePreferredReducedMotion(options) {\n const isReduced = useMediaQuery(\"(prefers-reduced-motion: reduce)\", options);\n return computed(() => {\n if (isReduced.value)\n return \"reduce\";\n return \"no-preference\";\n });\n}\n\nfunction usePrevious(value, initialValue) {\n const previous = shallowRef(initialValue);\n watch(\n toRef(value),\n (_, oldValue) => {\n previous.value = oldValue;\n },\n { flush: \"sync\" }\n );\n return readonly(previous);\n}\n\nconst topVarName = \"--vueuse-safe-area-top\";\nconst rightVarName = \"--vueuse-safe-area-right\";\nconst bottomVarName = \"--vueuse-safe-area-bottom\";\nconst leftVarName = \"--vueuse-safe-area-left\";\nfunction useScreenSafeArea() {\n const top = ref(\"\");\n const right = ref(\"\");\n const bottom = ref(\"\");\n const left = ref(\"\");\n if (isClient) {\n const topCssVar = useCssVar(topVarName);\n const rightCssVar = useCssVar(rightVarName);\n const bottomCssVar = useCssVar(bottomVarName);\n const leftCssVar = useCssVar(leftVarName);\n topCssVar.value = \"env(safe-area-inset-top, 0px)\";\n rightCssVar.value = \"env(safe-area-inset-right, 0px)\";\n bottomCssVar.value = \"env(safe-area-inset-bottom, 0px)\";\n leftCssVar.value = \"env(safe-area-inset-left, 0px)\";\n update();\n useEventListener(\"resize\", useDebounceFn(update));\n }\n function update() {\n top.value = getValue(topVarName);\n right.value = getValue(rightVarName);\n bottom.value = getValue(bottomVarName);\n left.value = getValue(leftVarName);\n }\n return {\n top,\n right,\n bottom,\n left,\n update\n };\n}\nfunction getValue(position) {\n return getComputedStyle(document.documentElement).getPropertyValue(position);\n}\n\nfunction useScriptTag(src, onLoaded = noop, options = {}) {\n const {\n immediate = true,\n manual = false,\n type = \"text/javascript\",\n async = true,\n crossOrigin,\n referrerPolicy,\n noModule,\n defer,\n document = defaultDocument,\n attrs = {}\n } = options;\n const scriptTag = ref(null);\n let _promise = null;\n const loadScript = (waitForScriptLoad) => new Promise((resolve, reject) => {\n const resolveWithElement = (el2) => {\n scriptTag.value = el2;\n resolve(el2);\n return el2;\n };\n if (!document) {\n resolve(false);\n return;\n }\n let shouldAppend = false;\n let el = document.querySelector(`script[src=\"${toValue(src)}\"]`);\n if (!el) {\n el = document.createElement(\"script\");\n el.type = type;\n el.async = async;\n el.src = toValue(src);\n if (defer)\n el.defer = defer;\n if (crossOrigin)\n el.crossOrigin = crossOrigin;\n if (noModule)\n el.noModule = noModule;\n if (referrerPolicy)\n el.referrerPolicy = referrerPolicy;\n Object.entries(attrs).forEach(([name, value]) => el == null ? void 0 : el.setAttribute(name, value));\n shouldAppend = true;\n } else if (el.hasAttribute(\"data-loaded\")) {\n resolveWithElement(el);\n }\n el.addEventListener(\"error\", (event) => reject(event));\n el.addEventListener(\"abort\", (event) => reject(event));\n el.addEventListener(\"load\", () => {\n el.setAttribute(\"data-loaded\", \"true\");\n onLoaded(el);\n resolveWithElement(el);\n });\n if (shouldAppend)\n el = document.head.appendChild(el);\n if (!waitForScriptLoad)\n resolveWithElement(el);\n });\n const load = (waitForScriptLoad = true) => {\n if (!_promise)\n _promise = loadScript(waitForScriptLoad);\n return _promise;\n };\n const unload = () => {\n if (!document)\n return;\n _promise = null;\n if (scriptTag.value)\n scriptTag.value = null;\n const el = document.querySelector(`script[src=\"${toValue(src)}\"]`);\n if (el)\n document.head.removeChild(el);\n };\n if (immediate && !manual)\n tryOnMounted(load);\n if (!manual)\n tryOnUnmounted(unload);\n return { scriptTag, load, unload };\n}\n\nfunction checkOverflowScroll(ele) {\n const style = window.getComputedStyle(ele);\n if (style.overflowX === \"scroll\" || style.overflowY === \"scroll\" || style.overflowX === \"auto\" && ele.clientWidth < ele.scrollWidth || style.overflowY === \"auto\" && ele.clientHeight < ele.scrollHeight) {\n return true;\n } else {\n const parent = ele.parentNode;\n if (!parent || parent.tagName === \"BODY\")\n return false;\n return checkOverflowScroll(parent);\n }\n}\nfunction preventDefault(rawEvent) {\n const e = rawEvent || window.event;\n const _target = e.target;\n if (checkOverflowScroll(_target))\n return false;\n if (e.touches.length > 1)\n return true;\n if (e.preventDefault)\n e.preventDefault();\n return false;\n}\nconst elInitialOverflow = /* @__PURE__ */ new WeakMap();\nfunction useScrollLock(element, initialState = false) {\n const isLocked = ref(initialState);\n let stopTouchMoveListener = null;\n let initialOverflow = \"\";\n watch(toRef(element), (el) => {\n const target = resolveElement(toValue(el));\n if (target) {\n const ele = target;\n if (!elInitialOverflow.get(ele))\n elInitialOverflow.set(ele, ele.style.overflow);\n if (ele.style.overflow !== \"hidden\")\n initialOverflow = ele.style.overflow;\n if (ele.style.overflow === \"hidden\")\n return isLocked.value = true;\n if (isLocked.value)\n return ele.style.overflow = \"hidden\";\n }\n }, {\n immediate: true\n });\n const lock = () => {\n const el = resolveElement(toValue(element));\n if (!el || isLocked.value)\n return;\n if (isIOS) {\n stopTouchMoveListener = useEventListener(\n el,\n \"touchmove\",\n (e) => {\n preventDefault(e);\n },\n { passive: false }\n );\n }\n el.style.overflow = \"hidden\";\n isLocked.value = true;\n };\n const unlock = () => {\n const el = resolveElement(toValue(element));\n if (!el || !isLocked.value)\n return;\n if (isIOS)\n stopTouchMoveListener == null ? void 0 : stopTouchMoveListener();\n el.style.overflow = initialOverflow;\n elInitialOverflow.delete(el);\n isLocked.value = false;\n };\n tryOnScopeDispose(unlock);\n return computed({\n get() {\n return isLocked.value;\n },\n set(v) {\n if (v)\n lock();\n else unlock();\n }\n });\n}\n\nfunction useSessionStorage(key, initialValue, options = {}) {\n const { window = defaultWindow } = options;\n return useStorage(key, initialValue, window == null ? void 0 : window.sessionStorage, options);\n}\n\nfunction useShare(shareOptions = {}, options = {}) {\n const { navigator = defaultNavigator } = options;\n const _navigator = navigator;\n const isSupported = useSupported(() => _navigator && \"canShare\" in _navigator);\n const share = async (overrideOptions = {}) => {\n if (isSupported.value) {\n const data = {\n ...toValue(shareOptions),\n ...toValue(overrideOptions)\n };\n let granted = true;\n if (data.files && _navigator.canShare)\n granted = _navigator.canShare({ files: data.files });\n if (granted)\n return _navigator.share(data);\n }\n };\n return {\n isSupported,\n share\n };\n}\n\nconst defaultSortFn = (source, compareFn) => source.sort(compareFn);\nconst defaultCompare = (a, b) => a - b;\nfunction useSorted(...args) {\n var _a, _b, _c, _d;\n const [source] = args;\n let compareFn = defaultCompare;\n let options = {};\n if (args.length === 2) {\n if (typeof args[1] === \"object\") {\n options = args[1];\n compareFn = (_a = options.compareFn) != null ? _a : defaultCompare;\n } else {\n compareFn = (_b = args[1]) != null ? _b : defaultCompare;\n }\n } else if (args.length > 2) {\n compareFn = (_c = args[1]) != null ? _c : defaultCompare;\n options = (_d = args[2]) != null ? _d : {};\n }\n const {\n dirty = false,\n sortFn = defaultSortFn\n } = options;\n if (!dirty)\n return computed(() => sortFn([...toValue(source)], compareFn));\n watchEffect(() => {\n const result = sortFn(toValue(source), compareFn);\n if (isRef(source))\n source.value = result;\n else\n source.splice(0, source.length, ...result);\n });\n return source;\n}\n\nfunction useSpeechRecognition(options = {}) {\n const {\n interimResults = true,\n continuous = true,\n maxAlternatives = 1,\n window = defaultWindow\n } = options;\n const lang = toRef(options.lang || \"en-US\");\n const isListening = ref(false);\n const isFinal = ref(false);\n const result = ref(\"\");\n const error = shallowRef(void 0);\n const toggle = (value = !isListening.value) => {\n isListening.value = value;\n };\n const start = () => {\n isListening.value = true;\n };\n const stop = () => {\n isListening.value = false;\n };\n const SpeechRecognition = window && (window.SpeechRecognition || window.webkitSpeechRecognition);\n const isSupported = useSupported(() => SpeechRecognition);\n let recognition;\n if (isSupported.value) {\n recognition = new SpeechRecognition();\n recognition.continuous = continuous;\n recognition.interimResults = interimResults;\n recognition.lang = toValue(lang);\n recognition.maxAlternatives = maxAlternatives;\n recognition.onstart = () => {\n isFinal.value = false;\n };\n watch(lang, (lang2) => {\n if (recognition && !isListening.value)\n recognition.lang = lang2;\n });\n recognition.onresult = (event) => {\n const currentResult = event.results[event.resultIndex];\n const { transcript } = currentResult[0];\n isFinal.value = currentResult.isFinal;\n result.value = transcript;\n error.value = void 0;\n };\n recognition.onerror = (event) => {\n error.value = event;\n };\n recognition.onend = () => {\n isListening.value = false;\n recognition.lang = toValue(lang);\n };\n watch(isListening, () => {\n if (isListening.value)\n recognition.start();\n else\n recognition.stop();\n });\n }\n tryOnScopeDispose(() => {\n isListening.value = false;\n });\n return {\n isSupported,\n isListening,\n isFinal,\n recognition,\n result,\n error,\n toggle,\n start,\n stop\n };\n}\n\nfunction useSpeechSynthesis(text, options = {}) {\n const {\n pitch = 1,\n rate = 1,\n volume = 1,\n window = defaultWindow\n } = options;\n const synth = window && window.speechSynthesis;\n const isSupported = useSupported(() => synth);\n const isPlaying = ref(false);\n const status = ref(\"init\");\n const spokenText = toRef(text || \"\");\n const lang = toRef(options.lang || \"en-US\");\n const error = shallowRef(void 0);\n const toggle = (value = !isPlaying.value) => {\n isPlaying.value = value;\n };\n const bindEventsForUtterance = (utterance2) => {\n utterance2.lang = toValue(lang);\n utterance2.voice = toValue(options.voice) || null;\n utterance2.pitch = toValue(pitch);\n utterance2.rate = toValue(rate);\n utterance2.volume = volume;\n utterance2.onstart = () => {\n isPlaying.value = true;\n status.value = \"play\";\n };\n utterance2.onpause = () => {\n isPlaying.value = false;\n status.value = \"pause\";\n };\n utterance2.onresume = () => {\n isPlaying.value = true;\n status.value = \"play\";\n };\n utterance2.onend = () => {\n isPlaying.value = false;\n status.value = \"end\";\n };\n utterance2.onerror = (event) => {\n error.value = event;\n };\n };\n const utterance = computed(() => {\n isPlaying.value = false;\n status.value = \"init\";\n const newUtterance = new SpeechSynthesisUtterance(spokenText.value);\n bindEventsForUtterance(newUtterance);\n return newUtterance;\n });\n const speak = () => {\n synth.cancel();\n if (utterance)\n synth.speak(utterance.value);\n };\n const stop = () => {\n synth.cancel();\n isPlaying.value = false;\n };\n if (isSupported.value) {\n bindEventsForUtterance(utterance.value);\n watch(lang, (lang2) => {\n if (utterance.value && !isPlaying.value)\n utterance.value.lang = lang2;\n });\n if (options.voice) {\n watch(options.voice, () => {\n synth.cancel();\n });\n }\n watch(isPlaying, () => {\n if (isPlaying.value)\n synth.resume();\n else\n synth.pause();\n });\n }\n tryOnScopeDispose(() => {\n isPlaying.value = false;\n });\n return {\n isSupported,\n isPlaying,\n status,\n utterance,\n error,\n stop,\n toggle,\n speak\n };\n}\n\nfunction useStepper(steps, initialStep) {\n const stepsRef = ref(steps);\n const stepNames = computed(() => Array.isArray(stepsRef.value) ? stepsRef.value : Object.keys(stepsRef.value));\n const index = ref(stepNames.value.indexOf(initialStep != null ? initialStep : stepNames.value[0]));\n const current = computed(() => at(index.value));\n const isFirst = computed(() => index.value === 0);\n const isLast = computed(() => index.value === stepNames.value.length - 1);\n const next = computed(() => stepNames.value[index.value + 1]);\n const previous = computed(() => stepNames.value[index.value - 1]);\n function at(index2) {\n if (Array.isArray(stepsRef.value))\n return stepsRef.value[index2];\n return stepsRef.value[stepNames.value[index2]];\n }\n function get(step) {\n if (!stepNames.value.includes(step))\n return;\n return at(stepNames.value.indexOf(step));\n }\n function goTo(step) {\n if (stepNames.value.includes(step))\n index.value = stepNames.value.indexOf(step);\n }\n function goToNext() {\n if (isLast.value)\n return;\n index.value++;\n }\n function goToPrevious() {\n if (isFirst.value)\n return;\n index.value--;\n }\n function goBackTo(step) {\n if (isAfter(step))\n goTo(step);\n }\n function isNext(step) {\n return stepNames.value.indexOf(step) === index.value + 1;\n }\n function isPrevious(step) {\n return stepNames.value.indexOf(step) === index.value - 1;\n }\n function isCurrent(step) {\n return stepNames.value.indexOf(step) === index.value;\n }\n function isBefore(step) {\n return index.value < stepNames.value.indexOf(step);\n }\n function isAfter(step) {\n return index.value > stepNames.value.indexOf(step);\n }\n return {\n steps: stepsRef,\n stepNames,\n index,\n current,\n next,\n previous,\n isFirst,\n isLast,\n at,\n get,\n goTo,\n goToNext,\n goToPrevious,\n goBackTo,\n isNext,\n isPrevious,\n isCurrent,\n isBefore,\n isAfter\n };\n}\n\nfunction useStorageAsync(key, initialValue, storage, options = {}) {\n var _a;\n const {\n flush = \"pre\",\n deep = true,\n listenToStorageChanges = true,\n writeDefaults = true,\n mergeDefaults = false,\n shallow,\n window = defaultWindow,\n eventFilter,\n onError = (e) => {\n console.error(e);\n }\n } = options;\n const rawInit = toValue(initialValue);\n const type = guessSerializerType(rawInit);\n const data = (shallow ? shallowRef : ref)(initialValue);\n const serializer = (_a = options.serializer) != null ? _a : StorageSerializers[type];\n if (!storage) {\n try {\n storage = getSSRHandler(\"getDefaultStorageAsync\", () => {\n var _a2;\n return (_a2 = defaultWindow) == null ? void 0 : _a2.localStorage;\n })();\n } catch (e) {\n onError(e);\n }\n }\n async function read(event) {\n if (!storage || event && event.key !== key)\n return;\n try {\n const rawValue = event ? event.newValue : await storage.getItem(key);\n if (rawValue == null) {\n data.value = rawInit;\n if (writeDefaults && rawInit !== null)\n await storage.setItem(key, await serializer.write(rawInit));\n } else if (mergeDefaults) {\n const value = await serializer.read(rawValue);\n if (typeof mergeDefaults === \"function\")\n data.value = mergeDefaults(value, rawInit);\n else if (type === \"object\" && !Array.isArray(value))\n data.value = { ...rawInit, ...value };\n else data.value = value;\n } else {\n data.value = await serializer.read(rawValue);\n }\n } catch (e) {\n onError(e);\n }\n }\n read();\n if (window && listenToStorageChanges)\n useEventListener(window, \"storage\", (e) => Promise.resolve().then(() => read(e)));\n if (storage) {\n watchWithFilter(\n data,\n async () => {\n try {\n if (data.value == null)\n await storage.removeItem(key);\n else\n await storage.setItem(key, await serializer.write(data.value));\n } catch (e) {\n onError(e);\n }\n },\n {\n flush,\n deep,\n eventFilter\n }\n );\n }\n return data;\n}\n\nlet _id = 0;\nfunction useStyleTag(css, options = {}) {\n const isLoaded = ref(false);\n const {\n document = defaultDocument,\n immediate = true,\n manual = false,\n id = `vueuse_styletag_${++_id}`\n } = options;\n const cssRef = ref(css);\n let stop = () => {\n };\n const load = () => {\n if (!document)\n return;\n const el = document.getElementById(id) || document.createElement(\"style\");\n if (!el.isConnected) {\n el.id = id;\n if (options.media)\n el.media = options.media;\n document.head.appendChild(el);\n }\n if (isLoaded.value)\n return;\n stop = watch(\n cssRef,\n (value) => {\n el.textContent = value;\n },\n { immediate: true }\n );\n isLoaded.value = true;\n };\n const unload = () => {\n if (!document || !isLoaded.value)\n return;\n stop();\n document.head.removeChild(document.getElementById(id));\n isLoaded.value = false;\n };\n if (immediate && !manual)\n tryOnMounted(load);\n if (!manual)\n tryOnScopeDispose(unload);\n return {\n id,\n css: cssRef,\n unload,\n load,\n isLoaded: readonly(isLoaded)\n };\n}\n\nfunction useSwipe(target, options = {}) {\n const {\n threshold = 50,\n onSwipe,\n onSwipeEnd,\n onSwipeStart,\n passive = true,\n window = defaultWindow\n } = options;\n const coordsStart = reactive({ x: 0, y: 0 });\n const coordsEnd = reactive({ x: 0, y: 0 });\n const diffX = computed(() => coordsStart.x - coordsEnd.x);\n const diffY = computed(() => coordsStart.y - coordsEnd.y);\n const { max, abs } = Math;\n const isThresholdExceeded = computed(() => max(abs(diffX.value), abs(diffY.value)) >= threshold);\n const isSwiping = ref(false);\n const direction = computed(() => {\n if (!isThresholdExceeded.value)\n return \"none\";\n if (abs(diffX.value) > abs(diffY.value)) {\n return diffX.value > 0 ? \"left\" : \"right\";\n } else {\n return diffY.value > 0 ? \"up\" : \"down\";\n }\n });\n const getTouchEventCoords = (e) => [e.touches[0].clientX, e.touches[0].clientY];\n const updateCoordsStart = (x, y) => {\n coordsStart.x = x;\n coordsStart.y = y;\n };\n const updateCoordsEnd = (x, y) => {\n coordsEnd.x = x;\n coordsEnd.y = y;\n };\n let listenerOptions;\n const isPassiveEventSupported = checkPassiveEventSupport(window == null ? void 0 : window.document);\n if (!passive)\n listenerOptions = isPassiveEventSupported ? { passive: false, capture: true } : { capture: true };\n else\n listenerOptions = isPassiveEventSupported ? { passive: true } : { capture: false };\n const onTouchEnd = (e) => {\n if (isSwiping.value)\n onSwipeEnd == null ? void 0 : onSwipeEnd(e, direction.value);\n isSwiping.value = false;\n };\n const stops = [\n useEventListener(target, \"touchstart\", (e) => {\n if (e.touches.length !== 1)\n return;\n if (listenerOptions.capture && !listenerOptions.passive)\n e.preventDefault();\n const [x, y] = getTouchEventCoords(e);\n updateCoordsStart(x, y);\n updateCoordsEnd(x, y);\n onSwipeStart == null ? void 0 : onSwipeStart(e);\n }, listenerOptions),\n useEventListener(target, \"touchmove\", (e) => {\n if (e.touches.length !== 1)\n return;\n const [x, y] = getTouchEventCoords(e);\n updateCoordsEnd(x, y);\n if (!isSwiping.value && isThresholdExceeded.value)\n isSwiping.value = true;\n if (isSwiping.value)\n onSwipe == null ? void 0 : onSwipe(e);\n }, listenerOptions),\n useEventListener(target, [\"touchend\", \"touchcancel\"], onTouchEnd, listenerOptions)\n ];\n const stop = () => stops.forEach((s) => s());\n return {\n isPassiveEventSupported,\n isSwiping,\n direction,\n coordsStart,\n coordsEnd,\n lengthX: diffX,\n lengthY: diffY,\n stop\n };\n}\nfunction checkPassiveEventSupport(document) {\n if (!document)\n return false;\n let supportsPassive = false;\n const optionsBlock = {\n get passive() {\n supportsPassive = true;\n return false;\n }\n };\n document.addEventListener(\"x\", noop, optionsBlock);\n document.removeEventListener(\"x\", noop);\n return supportsPassive;\n}\n\nfunction useTemplateRefsList() {\n const refs = ref([]);\n refs.value.set = (el) => {\n if (el)\n refs.value.push(el);\n };\n onBeforeUpdate(() => {\n refs.value.length = 0;\n });\n return refs;\n}\n\nfunction useTextDirection(options = {}) {\n const {\n document = defaultDocument,\n selector = \"html\",\n observe = false,\n initialValue = \"ltr\"\n } = options;\n function getValue() {\n var _a, _b;\n return (_b = (_a = document == null ? void 0 : document.querySelector(selector)) == null ? void 0 : _a.getAttribute(\"dir\")) != null ? _b : initialValue;\n }\n const dir = ref(getValue());\n tryOnMounted(() => dir.value = getValue());\n if (observe && document) {\n useMutationObserver(\n document.querySelector(selector),\n () => dir.value = getValue(),\n { attributes: true }\n );\n }\n return computed({\n get() {\n return dir.value;\n },\n set(v) {\n var _a, _b;\n dir.value = v;\n if (!document)\n return;\n if (dir.value)\n (_a = document.querySelector(selector)) == null ? void 0 : _a.setAttribute(\"dir\", dir.value);\n else\n (_b = document.querySelector(selector)) == null ? void 0 : _b.removeAttribute(\"dir\");\n }\n });\n}\n\nfunction getRangesFromSelection(selection) {\n var _a;\n const rangeCount = (_a = selection.rangeCount) != null ? _a : 0;\n return Array.from({ length: rangeCount }, (_, i) => selection.getRangeAt(i));\n}\nfunction useTextSelection(options = {}) {\n const {\n window = defaultWindow\n } = options;\n const selection = ref(null);\n const text = computed(() => {\n var _a, _b;\n return (_b = (_a = selection.value) == null ? void 0 : _a.toString()) != null ? _b : \"\";\n });\n const ranges = computed(() => selection.value ? getRangesFromSelection(selection.value) : []);\n const rects = computed(() => ranges.value.map((range) => range.getBoundingClientRect()));\n function onSelectionChange() {\n selection.value = null;\n if (window)\n selection.value = window.getSelection();\n }\n if (window)\n useEventListener(window.document, \"selectionchange\", onSelectionChange);\n return {\n text,\n rects,\n ranges,\n selection\n };\n}\n\nfunction useTextareaAutosize(options) {\n var _a;\n const textarea = ref(options == null ? void 0 : options.element);\n const input = ref(options == null ? void 0 : options.input);\n const styleProp = (_a = options == null ? void 0 : options.styleProp) != null ? _a : \"height\";\n const textareaScrollHeight = ref(1);\n const textareaOldWidth = ref(0);\n function triggerResize() {\n var _a2;\n if (!textarea.value)\n return;\n let height = \"\";\n textarea.value.style[styleProp] = \"1px\";\n textareaScrollHeight.value = (_a2 = textarea.value) == null ? void 0 : _a2.scrollHeight;\n const _styleTarget = toValue(options == null ? void 0 : options.styleTarget);\n if (_styleTarget)\n _styleTarget.style[styleProp] = `${textareaScrollHeight.value}px`;\n else\n height = `${textareaScrollHeight.value}px`;\n textarea.value.style[styleProp] = height;\n }\n watch([input, textarea], () => nextTick(triggerResize), { immediate: true });\n watch(textareaScrollHeight, () => {\n var _a2;\n return (_a2 = options == null ? void 0 : options.onResize) == null ? void 0 : _a2.call(options);\n });\n useResizeObserver(textarea, ([{ contentRect }]) => {\n if (textareaOldWidth.value === contentRect.width)\n return;\n textareaOldWidth.value = contentRect.width;\n triggerResize();\n });\n if (options == null ? void 0 : options.watch)\n watch(options.watch, triggerResize, { immediate: true, deep: true });\n return {\n textarea,\n input,\n triggerResize\n };\n}\n\nfunction useThrottledRefHistory(source, options = {}) {\n const { throttle = 200, trailing = true } = options;\n const filter = throttleFilter(throttle, trailing);\n const history = useRefHistory(source, { ...options, eventFilter: filter });\n return {\n ...history\n };\n}\n\nconst DEFAULT_UNITS = [\n { max: 6e4, value: 1e3, name: \"second\" },\n { max: 276e4, value: 6e4, name: \"minute\" },\n { max: 72e6, value: 36e5, name: \"hour\" },\n { max: 5184e5, value: 864e5, name: \"day\" },\n { max: 24192e5, value: 6048e5, name: \"week\" },\n { max: 28512e6, value: 2592e6, name: \"month\" },\n { max: Number.POSITIVE_INFINITY, value: 31536e6, name: \"year\" }\n];\nconst DEFAULT_MESSAGES = {\n justNow: \"just now\",\n past: (n) => n.match(/\\d/) ? `${n} ago` : n,\n future: (n) => n.match(/\\d/) ? `in ${n}` : n,\n month: (n, past) => n === 1 ? past ? \"last month\" : \"next month\" : `${n} month${n > 1 ? \"s\" : \"\"}`,\n year: (n, past) => n === 1 ? past ? \"last year\" : \"next year\" : `${n} year${n > 1 ? \"s\" : \"\"}`,\n day: (n, past) => n === 1 ? past ? \"yesterday\" : \"tomorrow\" : `${n} day${n > 1 ? \"s\" : \"\"}`,\n week: (n, past) => n === 1 ? past ? \"last week\" : \"next week\" : `${n} week${n > 1 ? \"s\" : \"\"}`,\n hour: (n) => `${n} hour${n > 1 ? \"s\" : \"\"}`,\n minute: (n) => `${n} minute${n > 1 ? \"s\" : \"\"}`,\n second: (n) => `${n} second${n > 1 ? \"s\" : \"\"}`,\n invalid: \"\"\n};\nfunction DEFAULT_FORMATTER(date) {\n return date.toISOString().slice(0, 10);\n}\nfunction useTimeAgo(time, options = {}) {\n const {\n controls: exposeControls = false,\n updateInterval = 3e4\n } = options;\n const { now, ...controls } = useNow({ interval: updateInterval, controls: true });\n const timeAgo = computed(() => formatTimeAgo(new Date(toValue(time)), options, toValue(now)));\n if (exposeControls) {\n return {\n timeAgo,\n ...controls\n };\n } else {\n return timeAgo;\n }\n}\nfunction formatTimeAgo(from, options = {}, now = Date.now()) {\n var _a;\n const {\n max,\n messages = DEFAULT_MESSAGES,\n fullDateFormatter = DEFAULT_FORMATTER,\n units = DEFAULT_UNITS,\n showSecond = false,\n rounding = \"round\"\n } = options;\n const roundFn = typeof rounding === \"number\" ? (n) => +n.toFixed(rounding) : Math[rounding];\n const diff = +now - +from;\n const absDiff = Math.abs(diff);\n function getValue(diff2, unit) {\n return roundFn(Math.abs(diff2) / unit.value);\n }\n function format(diff2, unit) {\n const val = getValue(diff2, unit);\n const past = diff2 > 0;\n const str = applyFormat(unit.name, val, past);\n return applyFormat(past ? \"past\" : \"future\", str, past);\n }\n function applyFormat(name, val, isPast) {\n const formatter = messages[name];\n if (typeof formatter === \"function\")\n return formatter(val, isPast);\n return formatter.replace(\"{0}\", val.toString());\n }\n if (absDiff < 6e4 && !showSecond)\n return messages.justNow;\n if (typeof max === \"number\" && absDiff > max)\n return fullDateFormatter(new Date(from));\n if (typeof max === \"string\") {\n const unitMax = (_a = units.find((i) => i.name === max)) == null ? void 0 : _a.max;\n if (unitMax && absDiff > unitMax)\n return fullDateFormatter(new Date(from));\n }\n for (const [idx, unit] of units.entries()) {\n const val = getValue(diff, unit);\n if (val <= 0 && units[idx - 1])\n return format(diff, units[idx - 1]);\n if (absDiff < unit.max)\n return format(diff, unit);\n }\n return messages.invalid;\n}\n\nfunction useTimeoutPoll(fn, interval, timeoutPollOptions) {\n const { start } = useTimeoutFn(loop, interval, { immediate: false });\n const isActive = ref(false);\n async function loop() {\n if (!isActive.value)\n return;\n await fn();\n start();\n }\n function resume() {\n if (!isActive.value) {\n isActive.value = true;\n loop();\n }\n }\n function pause() {\n isActive.value = false;\n }\n if (timeoutPollOptions == null ? void 0 : timeoutPollOptions.immediate)\n resume();\n tryOnScopeDispose(pause);\n return {\n isActive,\n pause,\n resume\n };\n}\n\nfunction useTimestamp(options = {}) {\n const {\n controls: exposeControls = false,\n offset = 0,\n immediate = true,\n interval = \"requestAnimationFrame\",\n callback\n } = options;\n const ts = ref(timestamp() + offset);\n const update = () => ts.value = timestamp() + offset;\n const cb = callback ? () => {\n update();\n callback(ts.value);\n } : update;\n const controls = interval === \"requestAnimationFrame\" ? useRafFn(cb, { immediate }) : useIntervalFn(cb, interval, { immediate });\n if (exposeControls) {\n return {\n timestamp: ts,\n ...controls\n };\n } else {\n return ts;\n }\n}\n\nfunction useTitle(newTitle = null, options = {}) {\n var _a, _b, _c;\n const {\n document = defaultDocument,\n restoreOnUnmount = (t) => t\n } = options;\n const originalTitle = (_a = document == null ? void 0 : document.title) != null ? _a : \"\";\n const title = toRef((_b = newTitle != null ? newTitle : document == null ? void 0 : document.title) != null ? _b : null);\n const isReadonly = newTitle && typeof newTitle === \"function\";\n function format(t) {\n if (!(\"titleTemplate\" in options))\n return t;\n const template = options.titleTemplate || \"%s\";\n return typeof template === \"function\" ? template(t) : toValue(template).replace(/%s/g, t);\n }\n watch(\n title,\n (t, o) => {\n if (t !== o && document)\n document.title = format(typeof t === \"string\" ? t : \"\");\n },\n { immediate: true }\n );\n if (options.observe && !options.titleTemplate && document && !isReadonly) {\n useMutationObserver(\n (_c = document.head) == null ? void 0 : _c.querySelector(\"title\"),\n () => {\n if (document && document.title !== title.value)\n title.value = format(document.title);\n },\n { childList: true }\n );\n }\n tryOnBeforeUnmount(() => {\n if (restoreOnUnmount) {\n const restoredTitle = restoreOnUnmount(originalTitle, title.value || \"\");\n if (restoredTitle != null && document)\n document.title = restoredTitle;\n }\n });\n return title;\n}\n\nconst _TransitionPresets = {\n easeInSine: [0.12, 0, 0.39, 0],\n easeOutSine: [0.61, 1, 0.88, 1],\n easeInOutSine: [0.37, 0, 0.63, 1],\n easeInQuad: [0.11, 0, 0.5, 0],\n easeOutQuad: [0.5, 1, 0.89, 1],\n easeInOutQuad: [0.45, 0, 0.55, 1],\n easeInCubic: [0.32, 0, 0.67, 0],\n easeOutCubic: [0.33, 1, 0.68, 1],\n easeInOutCubic: [0.65, 0, 0.35, 1],\n easeInQuart: [0.5, 0, 0.75, 0],\n easeOutQuart: [0.25, 1, 0.5, 1],\n easeInOutQuart: [0.76, 0, 0.24, 1],\n easeInQuint: [0.64, 0, 0.78, 0],\n easeOutQuint: [0.22, 1, 0.36, 1],\n easeInOutQuint: [0.83, 0, 0.17, 1],\n easeInExpo: [0.7, 0, 0.84, 0],\n easeOutExpo: [0.16, 1, 0.3, 1],\n easeInOutExpo: [0.87, 0, 0.13, 1],\n easeInCirc: [0.55, 0, 1, 0.45],\n easeOutCirc: [0, 0.55, 0.45, 1],\n easeInOutCirc: [0.85, 0, 0.15, 1],\n easeInBack: [0.36, 0, 0.66, -0.56],\n easeOutBack: [0.34, 1.56, 0.64, 1],\n easeInOutBack: [0.68, -0.6, 0.32, 1.6]\n};\nconst TransitionPresets = /* @__PURE__ */ Object.assign({}, { linear: identity }, _TransitionPresets);\nfunction createEasingFunction([p0, p1, p2, p3]) {\n const a = (a1, a2) => 1 - 3 * a2 + 3 * a1;\n const b = (a1, a2) => 3 * a2 - 6 * a1;\n const c = (a1) => 3 * a1;\n const calcBezier = (t, a1, a2) => ((a(a1, a2) * t + b(a1, a2)) * t + c(a1)) * t;\n const getSlope = (t, a1, a2) => 3 * a(a1, a2) * t * t + 2 * b(a1, a2) * t + c(a1);\n const getTforX = (x) => {\n let aGuessT = x;\n for (let i = 0; i < 4; ++i) {\n const currentSlope = getSlope(aGuessT, p0, p2);\n if (currentSlope === 0)\n return aGuessT;\n const currentX = calcBezier(aGuessT, p0, p2) - x;\n aGuessT -= currentX / currentSlope;\n }\n return aGuessT;\n };\n return (x) => p0 === p1 && p2 === p3 ? x : calcBezier(getTforX(x), p1, p3);\n}\nfunction lerp(a, b, alpha) {\n return a + alpha * (b - a);\n}\nfunction toVec(t) {\n return (typeof t === \"number\" ? [t] : t) || [];\n}\nfunction executeTransition(source, from, to, options = {}) {\n var _a, _b;\n const fromVal = toValue(from);\n const toVal = toValue(to);\n const v1 = toVec(fromVal);\n const v2 = toVec(toVal);\n const duration = (_a = toValue(options.duration)) != null ? _a : 1e3;\n const startedAt = Date.now();\n const endAt = Date.now() + duration;\n const trans = typeof options.transition === \"function\" ? options.transition : (_b = toValue(options.transition)) != null ? _b : identity;\n const ease = typeof trans === \"function\" ? trans : createEasingFunction(trans);\n return new Promise((resolve) => {\n source.value = fromVal;\n const tick = () => {\n var _a2;\n if ((_a2 = options.abort) == null ? void 0 : _a2.call(options)) {\n resolve();\n return;\n }\n const now = Date.now();\n const alpha = ease((now - startedAt) / duration);\n const arr = toVec(source.value).map((n, i) => lerp(v1[i], v2[i], alpha));\n if (Array.isArray(source.value))\n source.value = arr.map((n, i) => {\n var _a3, _b2;\n return lerp((_a3 = v1[i]) != null ? _a3 : 0, (_b2 = v2[i]) != null ? _b2 : 0, alpha);\n });\n else if (typeof source.value === \"number\")\n source.value = arr[0];\n if (now < endAt) {\n requestAnimationFrame(tick);\n } else {\n source.value = toVal;\n resolve();\n }\n };\n tick();\n });\n}\nfunction useTransition(source, options = {}) {\n let currentId = 0;\n const sourceVal = () => {\n const v = toValue(source);\n return typeof v === \"number\" ? v : v.map(toValue);\n };\n const outputRef = ref(sourceVal());\n watch(sourceVal, async (to) => {\n var _a, _b;\n if (toValue(options.disabled))\n return;\n const id = ++currentId;\n if (options.delay)\n await promiseTimeout(toValue(options.delay));\n if (id !== currentId)\n return;\n const toVal = Array.isArray(to) ? to.map(toValue) : toValue(to);\n (_a = options.onStarted) == null ? void 0 : _a.call(options);\n await executeTransition(outputRef, outputRef.value, toVal, {\n ...options,\n abort: () => {\n var _a2;\n return id !== currentId || ((_a2 = options.abort) == null ? void 0 : _a2.call(options));\n }\n });\n (_b = options.onFinished) == null ? void 0 : _b.call(options);\n }, { deep: true });\n watch(() => toValue(options.disabled), (disabled) => {\n if (disabled) {\n currentId++;\n outputRef.value = sourceVal();\n }\n });\n tryOnScopeDispose(() => {\n currentId++;\n });\n return computed(() => toValue(options.disabled) ? sourceVal() : outputRef.value);\n}\n\nfunction useUrlSearchParams(mode = \"history\", options = {}) {\n const {\n initialValue = {},\n removeNullishValues = true,\n removeFalsyValues = false,\n write: enableWrite = true,\n window = defaultWindow\n } = options;\n if (!window)\n return reactive(initialValue);\n const state = reactive({});\n function getRawParams() {\n if (mode === \"history\") {\n return window.location.search || \"\";\n } else if (mode === \"hash\") {\n const hash = window.location.hash || \"\";\n const index = hash.indexOf(\"?\");\n return index > 0 ? hash.slice(index) : \"\";\n } else {\n return (window.location.hash || \"\").replace(/^#/, \"\");\n }\n }\n function constructQuery(params) {\n const stringified = params.toString();\n if (mode === \"history\")\n return `${stringified ? `?${stringified}` : \"\"}${window.location.hash || \"\"}`;\n if (mode === \"hash-params\")\n return `${window.location.search || \"\"}${stringified ? `#${stringified}` : \"\"}`;\n const hash = window.location.hash || \"#\";\n const index = hash.indexOf(\"?\");\n if (index > 0)\n return `${hash.slice(0, index)}${stringified ? `?${stringified}` : \"\"}`;\n return `${hash}${stringified ? `?${stringified}` : \"\"}`;\n }\n function read() {\n return new URLSearchParams(getRawParams());\n }\n function updateState(params) {\n const unusedKeys = new Set(Object.keys(state));\n for (const key of params.keys()) {\n const paramsForKey = params.getAll(key);\n state[key] = paramsForKey.length > 1 ? paramsForKey : params.get(key) || \"\";\n unusedKeys.delete(key);\n }\n Array.from(unusedKeys).forEach((key) => delete state[key]);\n }\n const { pause, resume } = pausableWatch(\n state,\n () => {\n const params = new URLSearchParams(\"\");\n Object.keys(state).forEach((key) => {\n const mapEntry = state[key];\n if (Array.isArray(mapEntry))\n mapEntry.forEach((value) => params.append(key, value));\n else if (removeNullishValues && mapEntry == null)\n params.delete(key);\n else if (removeFalsyValues && !mapEntry)\n params.delete(key);\n else\n params.set(key, mapEntry);\n });\n write(params);\n },\n { deep: true }\n );\n function write(params, shouldUpdate) {\n pause();\n if (shouldUpdate)\n updateState(params);\n window.history.replaceState(\n window.history.state,\n window.document.title,\n window.location.pathname + constructQuery(params)\n );\n resume();\n }\n function onChanged() {\n if (!enableWrite)\n return;\n write(read(), true);\n }\n useEventListener(window, \"popstate\", onChanged, false);\n if (mode !== \"history\")\n useEventListener(window, \"hashchange\", onChanged, false);\n const initial = read();\n if (initial.keys().next().value)\n updateState(initial);\n else\n Object.assign(state, initialValue);\n return state;\n}\n\nfunction useUserMedia(options = {}) {\n var _a, _b;\n const enabled = ref((_a = options.enabled) != null ? _a : false);\n const autoSwitch = ref((_b = options.autoSwitch) != null ? _b : true);\n const constraints = ref(options.constraints);\n const { navigator = defaultNavigator } = options;\n const isSupported = useSupported(() => {\n var _a2;\n return (_a2 = navigator == null ? void 0 : navigator.mediaDevices) == null ? void 0 : _a2.getUserMedia;\n });\n const stream = shallowRef();\n function getDeviceOptions(type) {\n switch (type) {\n case \"video\": {\n if (constraints.value)\n return constraints.value.video || false;\n break;\n }\n case \"audio\": {\n if (constraints.value)\n return constraints.value.audio || false;\n break;\n }\n }\n }\n async function _start() {\n if (!isSupported.value || stream.value)\n return;\n stream.value = await navigator.mediaDevices.getUserMedia({\n video: getDeviceOptions(\"video\"),\n audio: getDeviceOptions(\"audio\")\n });\n return stream.value;\n }\n function _stop() {\n var _a2;\n (_a2 = stream.value) == null ? void 0 : _a2.getTracks().forEach((t) => t.stop());\n stream.value = void 0;\n }\n function stop() {\n _stop();\n enabled.value = false;\n }\n async function start() {\n await _start();\n if (stream.value)\n enabled.value = true;\n return stream.value;\n }\n async function restart() {\n _stop();\n return await start();\n }\n watch(\n enabled,\n (v) => {\n if (v)\n _start();\n else _stop();\n },\n { immediate: true }\n );\n watch(\n constraints,\n () => {\n if (autoSwitch.value && stream.value)\n restart();\n },\n { immediate: true }\n );\n tryOnScopeDispose(() => {\n stop();\n });\n return {\n isSupported,\n stream,\n start,\n stop,\n restart,\n constraints,\n enabled,\n autoSwitch\n };\n}\n\nfunction useVModel(props, key, emit, options = {}) {\n var _a, _b, _c, _d, _e;\n const {\n clone = false,\n passive = false,\n eventName,\n deep = false,\n defaultValue,\n shouldEmit\n } = options;\n const vm = getCurrentInstance();\n const _emit = emit || (vm == null ? void 0 : vm.emit) || ((_a = vm == null ? void 0 : vm.$emit) == null ? void 0 : _a.bind(vm)) || ((_c = (_b = vm == null ? void 0 : vm.proxy) == null ? void 0 : _b.$emit) == null ? void 0 : _c.bind(vm == null ? void 0 : vm.proxy));\n let event = eventName;\n if (!key) {\n if (isVue2) {\n const modelOptions = (_e = (_d = vm == null ? void 0 : vm.proxy) == null ? void 0 : _d.$options) == null ? void 0 : _e.model;\n key = (modelOptions == null ? void 0 : modelOptions.value) || \"value\";\n if (!eventName)\n event = (modelOptions == null ? void 0 : modelOptions.event) || \"input\";\n } else {\n key = \"modelValue\";\n }\n }\n event = event || `update:${key.toString()}`;\n const cloneFn = (val) => !clone ? val : typeof clone === \"function\" ? clone(val) : cloneFnJSON(val);\n const getValue = () => isDef(props[key]) ? cloneFn(props[key]) : defaultValue;\n const triggerEmit = (value) => {\n if (shouldEmit) {\n if (shouldEmit(value))\n _emit(event, value);\n } else {\n _emit(event, value);\n }\n };\n if (passive) {\n const initialValue = getValue();\n const proxy = ref(initialValue);\n let isUpdating = false;\n watch(\n () => props[key],\n (v) => {\n if (!isUpdating) {\n isUpdating = true;\n proxy.value = cloneFn(v);\n nextTick(() => isUpdating = false);\n }\n }\n );\n watch(\n proxy,\n (v) => {\n if (!isUpdating && (v !== props[key] || deep))\n triggerEmit(v);\n },\n { deep }\n );\n return proxy;\n } else {\n return computed({\n get() {\n return getValue();\n },\n set(value) {\n triggerEmit(value);\n }\n });\n }\n}\n\nfunction useVModels(props, emit, options = {}) {\n const ret = {};\n for (const key in props) {\n ret[key] = useVModel(\n props,\n key,\n emit,\n options\n );\n }\n return ret;\n}\n\nfunction useVibrate(options) {\n const {\n pattern = [],\n interval = 0,\n navigator = defaultNavigator\n } = options || {};\n const isSupported = useSupported(() => typeof navigator !== \"undefined\" && \"vibrate\" in navigator);\n const patternRef = toRef(pattern);\n let intervalControls;\n const vibrate = (pattern2 = patternRef.value) => {\n if (isSupported.value)\n navigator.vibrate(pattern2);\n };\n const stop = () => {\n if (isSupported.value)\n navigator.vibrate(0);\n intervalControls == null ? void 0 : intervalControls.pause();\n };\n if (interval > 0) {\n intervalControls = useIntervalFn(\n vibrate,\n interval,\n {\n immediate: false,\n immediateCallback: false\n }\n );\n }\n return {\n isSupported,\n pattern,\n intervalControls,\n vibrate,\n stop\n };\n}\n\nfunction useVirtualList(list, options) {\n const { containerStyle, wrapperProps, scrollTo, calculateRange, currentList, containerRef } = \"itemHeight\" in options ? useVerticalVirtualList(options, list) : useHorizontalVirtualList(options, list);\n return {\n list: currentList,\n scrollTo,\n containerProps: {\n ref: containerRef,\n onScroll: () => {\n calculateRange();\n },\n style: containerStyle\n },\n wrapperProps\n };\n}\nfunction useVirtualListResources(list) {\n const containerRef = ref(null);\n const size = useElementSize(containerRef);\n const currentList = ref([]);\n const source = shallowRef(list);\n const state = ref({ start: 0, end: 10 });\n return { state, source, currentList, size, containerRef };\n}\nfunction createGetViewCapacity(state, source, itemSize) {\n return (containerSize) => {\n if (typeof itemSize === \"number\")\n return Math.ceil(containerSize / itemSize);\n const { start = 0 } = state.value;\n let sum = 0;\n let capacity = 0;\n for (let i = start; i < source.value.length; i++) {\n const size = itemSize(i);\n sum += size;\n capacity = i;\n if (sum > containerSize)\n break;\n }\n return capacity - start;\n };\n}\nfunction createGetOffset(source, itemSize) {\n return (scrollDirection) => {\n if (typeof itemSize === \"number\")\n return Math.floor(scrollDirection / itemSize) + 1;\n let sum = 0;\n let offset = 0;\n for (let i = 0; i < source.value.length; i++) {\n const size = itemSize(i);\n sum += size;\n if (sum >= scrollDirection) {\n offset = i;\n break;\n }\n }\n return offset + 1;\n };\n}\nfunction createCalculateRange(type, overscan, getOffset, getViewCapacity, { containerRef, state, currentList, source }) {\n return () => {\n const element = containerRef.value;\n if (element) {\n const offset = getOffset(type === \"vertical\" ? element.scrollTop : element.scrollLeft);\n const viewCapacity = getViewCapacity(type === \"vertical\" ? element.clientHeight : element.clientWidth);\n const from = offset - overscan;\n const to = offset + viewCapacity + overscan;\n state.value = {\n start: from < 0 ? 0 : from,\n end: to > source.value.length ? source.value.length : to\n };\n currentList.value = source.value.slice(state.value.start, state.value.end).map((ele, index) => ({\n data: ele,\n index: index + state.value.start\n }));\n }\n };\n}\nfunction createGetDistance(itemSize, source) {\n return (index) => {\n if (typeof itemSize === \"number\") {\n const size2 = index * itemSize;\n return size2;\n }\n const size = source.value.slice(0, index).reduce((sum, _, i) => sum + itemSize(i), 0);\n return size;\n };\n}\nfunction useWatchForSizes(size, list, containerRef, calculateRange) {\n watch([size.width, size.height, list, containerRef], () => {\n calculateRange();\n });\n}\nfunction createComputedTotalSize(itemSize, source) {\n return computed(() => {\n if (typeof itemSize === \"number\")\n return source.value.length * itemSize;\n return source.value.reduce((sum, _, index) => sum + itemSize(index), 0);\n });\n}\nconst scrollToDictionaryForElementScrollKey = {\n horizontal: \"scrollLeft\",\n vertical: \"scrollTop\"\n};\nfunction createScrollTo(type, calculateRange, getDistance, containerRef) {\n return (index) => {\n if (containerRef.value) {\n containerRef.value[scrollToDictionaryForElementScrollKey[type]] = getDistance(index);\n calculateRange();\n }\n };\n}\nfunction useHorizontalVirtualList(options, list) {\n const resources = useVirtualListResources(list);\n const { state, source, currentList, size, containerRef } = resources;\n const containerStyle = { overflowX: \"auto\" };\n const { itemWidth, overscan = 5 } = options;\n const getViewCapacity = createGetViewCapacity(state, source, itemWidth);\n const getOffset = createGetOffset(source, itemWidth);\n const calculateRange = createCalculateRange(\"horizontal\", overscan, getOffset, getViewCapacity, resources);\n const getDistanceLeft = createGetDistance(itemWidth, source);\n const offsetLeft = computed(() => getDistanceLeft(state.value.start));\n const totalWidth = createComputedTotalSize(itemWidth, source);\n useWatchForSizes(size, list, containerRef, calculateRange);\n const scrollTo = createScrollTo(\"horizontal\", calculateRange, getDistanceLeft, containerRef);\n const wrapperProps = computed(() => {\n return {\n style: {\n height: \"100%\",\n width: `${totalWidth.value - offsetLeft.value}px`,\n marginLeft: `${offsetLeft.value}px`,\n display: \"flex\"\n }\n };\n });\n return {\n scrollTo,\n calculateRange,\n wrapperProps,\n containerStyle,\n currentList,\n containerRef\n };\n}\nfunction useVerticalVirtualList(options, list) {\n const resources = useVirtualListResources(list);\n const { state, source, currentList, size, containerRef } = resources;\n const containerStyle = { overflowY: \"auto\" };\n const { itemHeight, overscan = 5 } = options;\n const getViewCapacity = createGetViewCapacity(state, source, itemHeight);\n const getOffset = createGetOffset(source, itemHeight);\n const calculateRange = createCalculateRange(\"vertical\", overscan, getOffset, getViewCapacity, resources);\n const getDistanceTop = createGetDistance(itemHeight, source);\n const offsetTop = computed(() => getDistanceTop(state.value.start));\n const totalHeight = createComputedTotalSize(itemHeight, source);\n useWatchForSizes(size, list, containerRef, calculateRange);\n const scrollTo = createScrollTo(\"vertical\", calculateRange, getDistanceTop, containerRef);\n const wrapperProps = computed(() => {\n return {\n style: {\n width: \"100%\",\n height: `${totalHeight.value - offsetTop.value}px`,\n marginTop: `${offsetTop.value}px`\n }\n };\n });\n return {\n calculateRange,\n scrollTo,\n containerStyle,\n wrapperProps,\n currentList,\n containerRef\n };\n}\n\nfunction useWakeLock(options = {}) {\n const {\n navigator = defaultNavigator,\n document = defaultDocument\n } = options;\n const requestedType = ref(false);\n const sentinel = shallowRef(null);\n const documentVisibility = useDocumentVisibility({ document });\n const isSupported = useSupported(() => navigator && \"wakeLock\" in navigator);\n const isActive = computed(() => !!sentinel.value && documentVisibility.value === \"visible\");\n if (isSupported.value) {\n useEventListener(sentinel, \"release\", () => {\n var _a, _b;\n requestedType.value = (_b = (_a = sentinel.value) == null ? void 0 : _a.type) != null ? _b : false;\n });\n whenever(\n () => documentVisibility.value === \"visible\" && (document == null ? void 0 : document.visibilityState) === \"visible\" && requestedType.value,\n (type) => {\n requestedType.value = false;\n forceRequest(type);\n }\n );\n }\n async function forceRequest(type) {\n var _a;\n await ((_a = sentinel.value) == null ? void 0 : _a.release());\n sentinel.value = isSupported.value ? await navigator.wakeLock.request(type) : null;\n }\n async function request(type) {\n if (documentVisibility.value === \"visible\")\n await forceRequest(type);\n else\n requestedType.value = type;\n }\n async function release() {\n requestedType.value = false;\n const s = sentinel.value;\n sentinel.value = null;\n await (s == null ? void 0 : s.release());\n }\n return {\n sentinel,\n isSupported,\n isActive,\n request,\n forceRequest,\n release\n };\n}\n\nfunction useWebNotification(options = {}) {\n const {\n window = defaultWindow,\n requestPermissions: _requestForPermissions = true\n } = options;\n const defaultWebNotificationOptions = options;\n const isSupported = useSupported(() => {\n if (!window || !(\"Notification\" in window))\n return false;\n try {\n new Notification(\"\");\n } catch (e) {\n return false;\n }\n return true;\n });\n const permissionGranted = ref(isSupported.value && \"permission\" in Notification && Notification.permission === \"granted\");\n const notification = ref(null);\n const ensurePermissions = async () => {\n if (!isSupported.value)\n return;\n if (!permissionGranted.value && Notification.permission !== \"denied\") {\n const result = await Notification.requestPermission();\n if (result === \"granted\")\n permissionGranted.value = true;\n }\n return permissionGranted.value;\n };\n const { on: onClick, trigger: clickTrigger } = createEventHook();\n const { on: onShow, trigger: showTrigger } = createEventHook();\n const { on: onError, trigger: errorTrigger } = createEventHook();\n const { on: onClose, trigger: closeTrigger } = createEventHook();\n const show = async (overrides) => {\n if (!isSupported.value || !permissionGranted.value)\n return;\n const options2 = Object.assign({}, defaultWebNotificationOptions, overrides);\n notification.value = new Notification(options2.title || \"\", options2);\n notification.value.onclick = clickTrigger;\n notification.value.onshow = showTrigger;\n notification.value.onerror = errorTrigger;\n notification.value.onclose = closeTrigger;\n return notification.value;\n };\n const close = () => {\n if (notification.value)\n notification.value.close();\n notification.value = null;\n };\n if (_requestForPermissions)\n tryOnMounted(ensurePermissions);\n tryOnScopeDispose(close);\n if (isSupported.value && window) {\n const document = window.document;\n useEventListener(document, \"visibilitychange\", (e) => {\n e.preventDefault();\n if (document.visibilityState === \"visible\") {\n close();\n }\n });\n }\n return {\n isSupported,\n notification,\n ensurePermissions,\n permissionGranted,\n show,\n close,\n onClick,\n onShow,\n onError,\n onClose\n };\n}\n\nconst DEFAULT_PING_MESSAGE = \"ping\";\nfunction resolveNestedOptions(options) {\n if (options === true)\n return {};\n return options;\n}\nfunction useWebSocket(url, options = {}) {\n const {\n onConnected,\n onDisconnected,\n onError,\n onMessage,\n immediate = true,\n autoClose = true,\n protocols = []\n } = options;\n const data = ref(null);\n const status = ref(\"CLOSED\");\n const wsRef = ref();\n const urlRef = toRef(url);\n let heartbeatPause;\n let heartbeatResume;\n let explicitlyClosed = false;\n let retried = 0;\n let bufferedData = [];\n let pongTimeoutWait;\n const _sendBuffer = () => {\n if (bufferedData.length && wsRef.value && status.value === \"OPEN\") {\n for (const buffer of bufferedData)\n wsRef.value.send(buffer);\n bufferedData = [];\n }\n };\n const resetHeartbeat = () => {\n clearTimeout(pongTimeoutWait);\n pongTimeoutWait = void 0;\n };\n const close = (code = 1e3, reason) => {\n if (!isClient || !wsRef.value)\n return;\n explicitlyClosed = true;\n resetHeartbeat();\n heartbeatPause == null ? void 0 : heartbeatPause();\n wsRef.value.close(code, reason);\n wsRef.value = void 0;\n };\n const send = (data2, useBuffer = true) => {\n if (!wsRef.value || status.value !== \"OPEN\") {\n if (useBuffer)\n bufferedData.push(data2);\n return false;\n }\n _sendBuffer();\n wsRef.value.send(data2);\n return true;\n };\n const _init = () => {\n if (explicitlyClosed || typeof urlRef.value === \"undefined\")\n return;\n const ws = new WebSocket(urlRef.value, protocols);\n wsRef.value = ws;\n status.value = \"CONNECTING\";\n ws.onopen = () => {\n status.value = \"OPEN\";\n onConnected == null ? void 0 : onConnected(ws);\n heartbeatResume == null ? void 0 : heartbeatResume();\n _sendBuffer();\n };\n ws.onclose = (ev) => {\n status.value = \"CLOSED\";\n onDisconnected == null ? void 0 : onDisconnected(ws, ev);\n if (!explicitlyClosed && options.autoReconnect) {\n const {\n retries = -1,\n delay = 1e3,\n onFailed\n } = resolveNestedOptions(options.autoReconnect);\n retried += 1;\n if (typeof retries === \"number\" && (retries < 0 || retried < retries))\n setTimeout(_init, delay);\n else if (typeof retries === \"function\" && retries())\n setTimeout(_init, delay);\n else\n onFailed == null ? void 0 : onFailed();\n }\n };\n ws.onerror = (e) => {\n onError == null ? void 0 : onError(ws, e);\n };\n ws.onmessage = (e) => {\n if (options.heartbeat) {\n resetHeartbeat();\n const {\n message = DEFAULT_PING_MESSAGE,\n responseMessage = message\n } = resolveNestedOptions(options.heartbeat);\n if (e.data === responseMessage)\n return;\n }\n data.value = e.data;\n onMessage == null ? void 0 : onMessage(ws, e);\n };\n };\n if (options.heartbeat) {\n const {\n message = DEFAULT_PING_MESSAGE,\n interval = 1e3,\n pongTimeout = 1e3\n } = resolveNestedOptions(options.heartbeat);\n const { pause, resume } = useIntervalFn(\n () => {\n send(message, false);\n if (pongTimeoutWait != null)\n return;\n pongTimeoutWait = setTimeout(() => {\n close();\n explicitlyClosed = false;\n }, pongTimeout);\n },\n interval,\n { immediate: false }\n );\n heartbeatPause = pause;\n heartbeatResume = resume;\n }\n if (autoClose) {\n if (isClient)\n useEventListener(\"beforeunload\", () => close());\n tryOnScopeDispose(close);\n }\n const open = () => {\n if (!isClient && !isWorker)\n return;\n close();\n explicitlyClosed = false;\n retried = 0;\n _init();\n };\n if (immediate)\n open();\n watch(urlRef, open);\n return {\n data,\n status,\n close,\n send,\n open,\n ws: wsRef\n };\n}\n\nfunction useWebWorker(arg0, workerOptions, options) {\n const {\n window = defaultWindow\n } = options != null ? options : {};\n const data = ref(null);\n const worker = shallowRef();\n const post = (...args) => {\n if (!worker.value)\n return;\n worker.value.postMessage(...args);\n };\n const terminate = function terminate2() {\n if (!worker.value)\n return;\n worker.value.terminate();\n };\n if (window) {\n if (typeof arg0 === \"string\")\n worker.value = new Worker(arg0, workerOptions);\n else if (typeof arg0 === \"function\")\n worker.value = arg0();\n else\n worker.value = arg0;\n worker.value.onmessage = (e) => {\n data.value = e.data;\n };\n tryOnScopeDispose(() => {\n if (worker.value)\n worker.value.terminate();\n });\n }\n return {\n data,\n post,\n terminate,\n worker\n };\n}\n\nfunction jobRunner(userFunc) {\n return (e) => {\n const userFuncArgs = e.data[0];\n return Promise.resolve(userFunc.apply(void 0, userFuncArgs)).then((result) => {\n postMessage([\"SUCCESS\", result]);\n }).catch((error) => {\n postMessage([\"ERROR\", error]);\n });\n };\n}\n\nfunction depsParser(deps, localDeps) {\n if (deps.length === 0 && localDeps.length === 0)\n return \"\";\n const depsString = deps.map((dep) => `'${dep}'`).toString();\n const depsFunctionString = localDeps.filter((dep) => typeof dep === \"function\").map((fn) => {\n const str = fn.toString();\n if (str.trim().startsWith(\"function\")) {\n return str;\n } else {\n const name = fn.name;\n return `const ${name} = ${str}`;\n }\n }).join(\";\");\n const importString = `importScripts(${depsString});`;\n return `${depsString.trim() === \"\" ? \"\" : importString} ${depsFunctionString}`;\n}\n\nfunction createWorkerBlobUrl(fn, deps, localDeps) {\n const blobCode = `${depsParser(deps, localDeps)}; onmessage=(${jobRunner})(${fn})`;\n const blob = new Blob([blobCode], { type: \"text/javascript\" });\n const url = URL.createObjectURL(blob);\n return url;\n}\n\nfunction useWebWorkerFn(fn, options = {}) {\n const {\n dependencies = [],\n localDependencies = [],\n timeout,\n window = defaultWindow\n } = options;\n const worker = ref();\n const workerStatus = ref(\"PENDING\");\n const promise = ref({});\n const timeoutId = ref();\n const workerTerminate = (status = \"PENDING\") => {\n if (worker.value && worker.value._url && window) {\n worker.value.terminate();\n URL.revokeObjectURL(worker.value._url);\n promise.value = {};\n worker.value = void 0;\n window.clearTimeout(timeoutId.value);\n workerStatus.value = status;\n }\n };\n workerTerminate();\n tryOnScopeDispose(workerTerminate);\n const generateWorker = () => {\n const blobUrl = createWorkerBlobUrl(fn, dependencies, localDependencies);\n const newWorker = new Worker(blobUrl);\n newWorker._url = blobUrl;\n newWorker.onmessage = (e) => {\n const { resolve = () => {\n }, reject = () => {\n } } = promise.value;\n const [status, result] = e.data;\n switch (status) {\n case \"SUCCESS\":\n resolve(result);\n workerTerminate(status);\n break;\n default:\n reject(result);\n workerTerminate(\"ERROR\");\n break;\n }\n };\n newWorker.onerror = (e) => {\n const { reject = () => {\n } } = promise.value;\n e.preventDefault();\n reject(e);\n workerTerminate(\"ERROR\");\n };\n if (timeout) {\n timeoutId.value = setTimeout(\n () => workerTerminate(\"TIMEOUT_EXPIRED\"),\n timeout\n );\n }\n return newWorker;\n };\n const callWorker = (...fnArgs) => new Promise((resolve, reject) => {\n var _a;\n promise.value = {\n resolve,\n reject\n };\n (_a = worker.value) == null ? void 0 : _a.postMessage([[...fnArgs]]);\n workerStatus.value = \"RUNNING\";\n });\n const workerFn = (...fnArgs) => {\n if (workerStatus.value === \"RUNNING\") {\n console.error(\n \"[useWebWorkerFn] You can only run one instance of the worker at a time.\"\n );\n return Promise.reject();\n }\n worker.value = generateWorker();\n return callWorker(...fnArgs);\n };\n return {\n workerFn,\n workerStatus,\n workerTerminate\n };\n}\n\nfunction useWindowFocus(options = {}) {\n const { window = defaultWindow } = options;\n if (!window)\n return ref(false);\n const focused = ref(window.document.hasFocus());\n useEventListener(window, \"blur\", () => {\n focused.value = false;\n });\n useEventListener(window, \"focus\", () => {\n focused.value = true;\n });\n return focused;\n}\n\nfunction useWindowScroll(options = {}) {\n const { window = defaultWindow, behavior = \"auto\" } = options;\n if (!window) {\n return {\n x: ref(0),\n y: ref(0)\n };\n }\n const internalX = ref(window.scrollX);\n const internalY = ref(window.scrollY);\n const x = computed({\n get() {\n return internalX.value;\n },\n set(x2) {\n scrollTo({ left: x2, behavior });\n }\n });\n const y = computed({\n get() {\n return internalY.value;\n },\n set(y2) {\n scrollTo({ top: y2, behavior });\n }\n });\n useEventListener(\n window,\n \"scroll\",\n () => {\n internalX.value = window.scrollX;\n internalY.value = window.scrollY;\n },\n {\n capture: false,\n passive: true\n }\n );\n return { x, y };\n}\n\nfunction useWindowSize(options = {}) {\n const {\n window = defaultWindow,\n initialWidth = Number.POSITIVE_INFINITY,\n initialHeight = Number.POSITIVE_INFINITY,\n listenOrientation = true,\n includeScrollbar = true,\n type = \"inner\"\n } = options;\n const width = ref(initialWidth);\n const height = ref(initialHeight);\n const update = () => {\n if (window) {\n if (type === \"outer\") {\n width.value = window.outerWidth;\n height.value = window.outerHeight;\n } else if (includeScrollbar) {\n width.value = window.innerWidth;\n height.value = window.innerHeight;\n } else {\n width.value = window.document.documentElement.clientWidth;\n height.value = window.document.documentElement.clientHeight;\n }\n }\n };\n update();\n tryOnMounted(update);\n useEventListener(\"resize\", update, { passive: true });\n if (listenOrientation) {\n const matches = useMediaQuery(\"(orientation: portrait)\");\n watch(matches, () => update());\n }\n return { width, height };\n}\n\nexport { DefaultMagicKeysAliasMap, StorageSerializers, TransitionPresets, computedAsync as asyncComputed, breakpointsAntDesign, breakpointsBootstrapV5, breakpointsMasterCss, breakpointsPrimeFlex, breakpointsQuasar, breakpointsSematic, breakpointsTailwind, breakpointsVuetify, breakpointsVuetifyV2, breakpointsVuetifyV3, cloneFnJSON, computedAsync, computedInject, createFetch, createReusableTemplate, createTemplatePromise, createUnrefFn, customStorageEventName, defaultDocument, defaultLocation, defaultNavigator, defaultWindow, executeTransition, formatTimeAgo, getSSRHandler, mapGamepadToXbox360Controller, onClickOutside, onKeyDown, onKeyPressed, onKeyStroke, onKeyUp, onLongPress, onStartTyping, setSSRHandler, templateRef, unrefElement, useActiveElement, useAnimate, useAsyncQueue, useAsyncState, useBase64, useBattery, useBluetooth, useBreakpoints, useBroadcastChannel, useBrowserLocation, useCached, useClipboard, useClipboardItems, useCloned, useColorMode, useConfirmDialog, useCssVar, useCurrentElement, useCycleList, useDark, useDebouncedRefHistory, useDeviceMotion, useDeviceOrientation, useDevicePixelRatio, useDevicesList, useDisplayMedia, useDocumentVisibility, useDraggable, useDropZone, useElementBounding, useElementByPoint, useElementHover, useElementSize, useElementVisibility, useEventBus, useEventListener, useEventSource, useEyeDropper, useFavicon, useFetch, useFileDialog, useFileSystemAccess, useFocus, useFocusWithin, useFps, useFullscreen, useGamepad, useGeolocation, useIdle, useImage, useInfiniteScroll, useIntersectionObserver, useKeyModifier, useLocalStorage, useMagicKeys, useManualRefHistory, useMediaControls, useMediaQuery, useMemoize, useMemory, useMounted, useMouse, useMouseInElement, useMousePressed, useMutationObserver, useNavigatorLanguage, useNetwork, useNow, useObjectUrl, useOffsetPagination, useOnline, usePageLeave, useParallax, useParentElement, usePerformanceObserver, usePermission, usePointer, usePointerLock, usePointerSwipe, usePreferredColorScheme, usePreferredContrast, usePreferredDark, usePreferredLanguages, usePreferredReducedMotion, usePrevious, useRafFn, useRefHistory, useResizeObserver, useScreenOrientation, useScreenSafeArea, useScriptTag, useScroll, useScrollLock, useSessionStorage, useShare, useSorted, useSpeechRecognition, useSpeechSynthesis, useStepper, useStorage, useStorageAsync, useStyleTag, useSupported, useSwipe, useTemplateRefsList, useTextDirection, useTextSelection, useTextareaAutosize, useThrottledRefHistory, useTimeAgo, useTimeoutPoll, useTimestamp, useTitle, useTransition, useUrlSearchParams, useUserMedia, useVModel, useVModels, useVibrate, useVirtualList, useWakeLock, useWebNotification, useWebSocket, useWebWorker, useWebWorkerFn, useWindowFocus, useWindowScroll, useWindowSize };\n","import { inject } from 'vue';\n\nvar PrimeVueToastSymbol = Symbol();\nfunction useToast() {\n var PrimeVueToast = inject(PrimeVueToastSymbol);\n if (!PrimeVueToast) {\n throw new Error('No PrimeVue Toast provided!');\n }\n return PrimeVueToast;\n}\n\nexport { PrimeVueToastSymbol, useToast };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n","\n\n\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-progressspinner {\\n position: relative;\\n margin: 0 auto;\\n width: 100px;\\n height: 100px;\\n display: inline-block;\\n}\\n\\n.p-progressspinner::before {\\n content: \\\"\\\";\\n display: block;\\n padding-top: 100%;\\n}\\n\\n.p-progressspinner-spin {\\n height: 100%;\\n transform-origin: center center;\\n width: 100%;\\n position: absolute;\\n top: 0;\\n bottom: 0;\\n left: 0;\\n right: 0;\\n margin: auto;\\n animation: p-progressspinner-rotate 2s linear infinite;\\n}\\n\\n.p-progressspinner-circle {\\n stroke-dasharray: 89, 200;\\n stroke-dashoffset: 0;\\n stroke: \".concat(dt('progressspinner.color.1'), \";\\n animation: p-progressspinner-dash 1.5s ease-in-out infinite, p-progressspinner-color 6s ease-in-out infinite;\\n stroke-linecap: round;\\n}\\n\\n@keyframes p-progressspinner-rotate {\\n 100% {\\n transform: rotate(360deg);\\n }\\n}\\n@keyframes p-progressspinner-dash {\\n 0% {\\n stroke-dasharray: 1, 200;\\n stroke-dashoffset: 0;\\n }\\n 50% {\\n stroke-dasharray: 89, 200;\\n stroke-dashoffset: -35px;\\n }\\n 100% {\\n stroke-dasharray: 89, 200;\\n stroke-dashoffset: -124px;\\n }\\n}\\n@keyframes p-progressspinner-color {\\n 100%,\\n 0% {\\n stroke: \").concat(dt('progressspinner.color.1'), \";\\n }\\n 40% {\\n stroke: \").concat(dt('progressspinner.color.2'), \";\\n }\\n 66% {\\n stroke: \").concat(dt('progressspinner.color.3'), \";\\n }\\n 80%,\\n 90% {\\n stroke: \").concat(dt('progressspinner.color.4'), \";\\n }\\n}\\n\");\n};\nvar classes = {\n root: 'p-progressspinner',\n spin: 'p-progressspinner-spin',\n circle: 'p-progressspinner-circle'\n};\nvar ProgressSpinnerStyle = BaseStyle.extend({\n name: 'progressspinner',\n theme: theme,\n classes: classes\n});\n\nexport { ProgressSpinnerStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseComponent from '@primevue/core/basecomponent';\nimport ProgressSpinnerStyle from 'primevue/progressspinner/style';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script$1 = {\n name: 'BaseProgressSpinner',\n \"extends\": BaseComponent,\n props: {\n strokeWidth: {\n type: String,\n \"default\": '2'\n },\n fill: {\n type: String,\n \"default\": 'none'\n },\n animationDuration: {\n type: String,\n \"default\": '2s'\n }\n },\n style: ProgressSpinnerStyle,\n provide: function provide() {\n return {\n $pcProgressSpinner: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'ProgressSpinner',\n \"extends\": script$1,\n inheritAttrs: false,\n computed: {\n svgStyle: function svgStyle() {\n return {\n 'animation-duration': this.animationDuration\n };\n }\n }\n};\n\nvar _hoisted_1 = [\"fill\", \"stroke-width\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root'),\n role: \"progressbar\"\n }, _ctx.ptmi('root')), [(openBlock(), createElementBlock(\"svg\", mergeProps({\n \"class\": _ctx.cx('spin'),\n viewBox: \"25 25 50 50\",\n style: $options.svgStyle\n }, _ctx.ptm('spin')), [createElementVNode(\"circle\", mergeProps({\n \"class\": _ctx.cx('circle'),\n cx: \"50\",\n cy: \"50\",\n r: \"20\",\n fill: _ctx.fill,\n \"stroke-width\": _ctx.strokeWidth,\n strokeMiterlimit: \"10\"\n }, _ctx.ptm('circle')), null, 16, _hoisted_1)], 16))], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","/*!\n * shared v9.13.1\n * (c) 2024 kazuya kawaguchi\n * Released under the MIT License.\n */\n/**\n * Original Utilities\n * written by kazuya kawaguchi\n */\nconst inBrowser = typeof window !== 'undefined';\nlet mark;\nlet measure;\nif ((process.env.NODE_ENV !== 'production')) {\n const perf = inBrowser && window.performance;\n if (perf &&\n perf.mark &&\n perf.measure &&\n perf.clearMarks &&\n // @ts-ignore browser compat\n perf.clearMeasures) {\n mark = (tag) => {\n perf.mark(tag);\n };\n measure = (name, startTag, endTag) => {\n perf.measure(name, startTag, endTag);\n perf.clearMarks(startTag);\n perf.clearMarks(endTag);\n };\n }\n}\nconst RE_ARGS = /\\{([0-9a-zA-Z]+)\\}/g;\n/* eslint-disable */\nfunction format(message, ...args) {\n if (args.length === 1 && isObject(args[0])) {\n args = args[0];\n }\n if (!args || !args.hasOwnProperty) {\n args = {};\n }\n return message.replace(RE_ARGS, (match, identifier) => {\n return args.hasOwnProperty(identifier) ? args[identifier] : '';\n });\n}\nconst makeSymbol = (name, shareable = false) => !shareable ? Symbol(name) : Symbol.for(name);\nconst generateFormatCacheKey = (locale, key, source) => friendlyJSONstringify({ l: locale, k: key, s: source });\nconst friendlyJSONstringify = (json) => JSON.stringify(json)\n .replace(/\\u2028/g, '\\\\u2028')\n .replace(/\\u2029/g, '\\\\u2029')\n .replace(/\\u0027/g, '\\\\u0027');\nconst isNumber = (val) => typeof val === 'number' && isFinite(val);\nconst isDate = (val) => toTypeString(val) === '[object Date]';\nconst isRegExp = (val) => toTypeString(val) === '[object RegExp]';\nconst isEmptyObject = (val) => isPlainObject(val) && Object.keys(val).length === 0;\nconst assign = Object.assign;\nlet _globalThis;\nconst getGlobalThis = () => {\n // prettier-ignore\n return (_globalThis ||\n (_globalThis =\n typeof globalThis !== 'undefined'\n ? globalThis\n : typeof self !== 'undefined'\n ? self\n : typeof window !== 'undefined'\n ? window\n : typeof global !== 'undefined'\n ? global\n : {}));\n};\nfunction escapeHtml(rawText) {\n return rawText\n .replace(//g, '>')\n .replace(/\"/g, '"')\n .replace(/'/g, ''');\n}\nconst hasOwnProperty = Object.prototype.hasOwnProperty;\nfunction hasOwn(obj, key) {\n return hasOwnProperty.call(obj, key);\n}\n/* eslint-enable */\n/**\n * Useful Utilities By Evan you\n * Modified by kazuya kawaguchi\n * MIT License\n * https://github.com/vuejs/vue-next/blob/master/packages/shared/src/index.ts\n * https://github.com/vuejs/vue-next/blob/master/packages/shared/src/codeframe.ts\n */\nconst isArray = Array.isArray;\nconst isFunction = (val) => typeof val === 'function';\nconst isString = (val) => typeof val === 'string';\nconst isBoolean = (val) => typeof val === 'boolean';\nconst isSymbol = (val) => typeof val === 'symbol';\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nconst isObject = (val) => val !== null && typeof val === 'object';\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nconst isPromise = (val) => {\n return isObject(val) && isFunction(val.then) && isFunction(val.catch);\n};\nconst objectToString = Object.prototype.toString;\nconst toTypeString = (value) => objectToString.call(value);\nconst isPlainObject = (val) => {\n if (!isObject(val))\n return false;\n const proto = Object.getPrototypeOf(val);\n return proto === null || proto.constructor === Object;\n};\n// for converting list and named values to displayed strings.\nconst toDisplayString = (val) => {\n return val == null\n ? ''\n : isArray(val) || (isPlainObject(val) && val.toString === objectToString)\n ? JSON.stringify(val, null, 2)\n : String(val);\n};\nfunction join(items, separator = '') {\n return items.reduce((str, item, index) => (index === 0 ? str + item : str + separator + item), '');\n}\nconst RANGE = 2;\nfunction generateCodeFrame(source, start = 0, end = source.length) {\n const lines = source.split(/\\r?\\n/);\n let count = 0;\n const res = [];\n for (let i = 0; i < lines.length; i++) {\n count += lines[i].length + 1;\n if (count >= start) {\n for (let j = i - RANGE; j <= i + RANGE || end > count; j++) {\n if (j < 0 || j >= lines.length)\n continue;\n const line = j + 1;\n res.push(`${line}${' '.repeat(3 - String(line).length)}| ${lines[j]}`);\n const lineLength = lines[j].length;\n if (j === i) {\n // push underline\n const pad = start - (count - lineLength) + 1;\n const length = Math.max(1, end > count ? lineLength - pad : end - start);\n res.push(` | ` + ' '.repeat(pad) + '^'.repeat(length));\n }\n else if (j > i) {\n if (end > count) {\n const length = Math.max(Math.min(end - count, lineLength), 1);\n res.push(` | ` + '^'.repeat(length));\n }\n count += lineLength + 1;\n }\n }\n break;\n }\n }\n return res.join('\\n');\n}\nfunction incrementer(code) {\n let current = code;\n return () => ++current;\n}\n\nfunction warn(msg, err) {\n if (typeof console !== 'undefined') {\n console.warn(`[intlify] ` + msg);\n /* istanbul ignore if */\n if (err) {\n console.warn(err.stack);\n }\n }\n}\nconst hasWarned = {};\nfunction warnOnce(msg) {\n if (!hasWarned[msg]) {\n hasWarned[msg] = true;\n warn(msg);\n }\n}\n\n/**\n * Event emitter, forked from the below:\n * - original repository url: https://github.com/developit/mitt\n * - code url: https://github.com/developit/mitt/blob/master/src/index.ts\n * - author: Jason Miller (https://github.com/developit)\n * - license: MIT\n */\n/**\n * Create a event emitter\n *\n * @returns An event emitter\n */\nfunction createEmitter() {\n const events = new Map();\n const emitter = {\n events,\n on(event, handler) {\n const handlers = events.get(event);\n const added = handlers && handlers.push(handler);\n if (!added) {\n events.set(event, [handler]);\n }\n },\n off(event, handler) {\n const handlers = events.get(event);\n if (handlers) {\n handlers.splice(handlers.indexOf(handler) >>> 0, 1);\n }\n },\n emit(event, payload) {\n (events.get(event) || [])\n .slice()\n .map(handler => handler(payload));\n (events.get('*') || [])\n .slice()\n .map(handler => handler(event, payload));\n }\n };\n return emitter;\n}\n\nconst isNotObjectOrIsArray = (val) => !isObject(val) || isArray(val);\n// eslint-disable-next-line @typescript-eslint/no-explicit-any, @typescript-eslint/explicit-module-boundary-types\nfunction deepCopy(src, des) {\n // src and des should both be objects, and none of them can be a array\n if (isNotObjectOrIsArray(src) || isNotObjectOrIsArray(des)) {\n throw new Error('Invalid value');\n }\n const stack = [{ src, des }];\n while (stack.length) {\n const { src, des } = stack.pop();\n Object.keys(src).forEach(key => {\n if (isNotObjectOrIsArray(src[key]) || isNotObjectOrIsArray(des[key])) {\n // replace with src[key] when:\n // src[key] or des[key] is not an object, or\n // src[key] or des[key] is an array\n des[key] = src[key];\n }\n else {\n // src[key] and des[key] are both objects, merge them\n stack.push({ src: src[key], des: des[key] });\n }\n });\n }\n}\n\nexport { assign, createEmitter, deepCopy, escapeHtml, format, friendlyJSONstringify, generateCodeFrame, generateFormatCacheKey, getGlobalThis, hasOwn, inBrowser, incrementer, isArray, isBoolean, isDate, isEmptyObject, isFunction, isNumber, isObject, isPlainObject, isPromise, isRegExp, isString, isSymbol, join, makeSymbol, mark, measure, objectToString, toDisplayString, toTypeString, warn, warnOnce };\n","/*!\n * message-compiler v9.13.1\n * (c) 2024 kazuya kawaguchi\n * Released under the MIT License.\n */\nconst LOCATION_STUB = {\n start: { line: 1, column: 1, offset: 0 },\n end: { line: 1, column: 1, offset: 0 }\n};\nfunction createPosition(line, column, offset) {\n return { line, column, offset };\n}\nfunction createLocation(start, end, source) {\n const loc = { start, end };\n if (source != null) {\n loc.source = source;\n }\n return loc;\n}\n\n/**\n * Original Utilities\n * written by kazuya kawaguchi\n */\nconst RE_ARGS = /\\{([0-9a-zA-Z]+)\\}/g;\n/* eslint-disable */\nfunction format(message, ...args) {\n if (args.length === 1 && isObject(args[0])) {\n args = args[0];\n }\n if (!args || !args.hasOwnProperty) {\n args = {};\n }\n return message.replace(RE_ARGS, (match, identifier) => {\n return args.hasOwnProperty(identifier) ? args[identifier] : '';\n });\n}\nconst assign = Object.assign;\nconst isString = (val) => typeof val === 'string';\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nconst isObject = (val) => val !== null && typeof val === 'object';\nfunction join(items, separator = '') {\n return items.reduce((str, item, index) => (index === 0 ? str + item : str + separator + item), '');\n}\n\nconst CompileWarnCodes = {\n USE_MODULO_SYNTAX: 1,\n __EXTEND_POINT__: 2\n};\n/** @internal */\nconst warnMessages = {\n [CompileWarnCodes.USE_MODULO_SYNTAX]: `Use modulo before '{{0}}'.`\n};\nfunction createCompileWarn(code, loc, ...args) {\n const msg = format(warnMessages[code] || '', ...(args || [])) ;\n const message = { message: String(msg), code };\n if (loc) {\n message.location = loc;\n }\n return message;\n}\n\nconst CompileErrorCodes = {\n // tokenizer error codes\n EXPECTED_TOKEN: 1,\n INVALID_TOKEN_IN_PLACEHOLDER: 2,\n UNTERMINATED_SINGLE_QUOTE_IN_PLACEHOLDER: 3,\n UNKNOWN_ESCAPE_SEQUENCE: 4,\n INVALID_UNICODE_ESCAPE_SEQUENCE: 5,\n UNBALANCED_CLOSING_BRACE: 6,\n UNTERMINATED_CLOSING_BRACE: 7,\n EMPTY_PLACEHOLDER: 8,\n NOT_ALLOW_NEST_PLACEHOLDER: 9,\n INVALID_LINKED_FORMAT: 10,\n // parser error codes\n MUST_HAVE_MESSAGES_IN_PLURAL: 11,\n UNEXPECTED_EMPTY_LINKED_MODIFIER: 12,\n UNEXPECTED_EMPTY_LINKED_KEY: 13,\n UNEXPECTED_LEXICAL_ANALYSIS: 14,\n // generator error codes\n UNHANDLED_CODEGEN_NODE_TYPE: 15,\n // minifier error codes\n UNHANDLED_MINIFIER_NODE_TYPE: 16,\n // Special value for higher-order compilers to pick up the last code\n // to avoid collision of error codes. This should always be kept as the last\n // item.\n __EXTEND_POINT__: 17\n};\n/** @internal */\nconst errorMessages = {\n // tokenizer error messages\n [CompileErrorCodes.EXPECTED_TOKEN]: `Expected token: '{0}'`,\n [CompileErrorCodes.INVALID_TOKEN_IN_PLACEHOLDER]: `Invalid token in placeholder: '{0}'`,\n [CompileErrorCodes.UNTERMINATED_SINGLE_QUOTE_IN_PLACEHOLDER]: `Unterminated single quote in placeholder`,\n [CompileErrorCodes.UNKNOWN_ESCAPE_SEQUENCE]: `Unknown escape sequence: \\\\{0}`,\n [CompileErrorCodes.INVALID_UNICODE_ESCAPE_SEQUENCE]: `Invalid unicode escape sequence: {0}`,\n [CompileErrorCodes.UNBALANCED_CLOSING_BRACE]: `Unbalanced closing brace`,\n [CompileErrorCodes.UNTERMINATED_CLOSING_BRACE]: `Unterminated closing brace`,\n [CompileErrorCodes.EMPTY_PLACEHOLDER]: `Empty placeholder`,\n [CompileErrorCodes.NOT_ALLOW_NEST_PLACEHOLDER]: `Not allowed nest placeholder`,\n [CompileErrorCodes.INVALID_LINKED_FORMAT]: `Invalid linked format`,\n // parser error messages\n [CompileErrorCodes.MUST_HAVE_MESSAGES_IN_PLURAL]: `Plural must have messages`,\n [CompileErrorCodes.UNEXPECTED_EMPTY_LINKED_MODIFIER]: `Unexpected empty linked modifier`,\n [CompileErrorCodes.UNEXPECTED_EMPTY_LINKED_KEY]: `Unexpected empty linked key`,\n [CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS]: `Unexpected lexical analysis in token: '{0}'`,\n // generator error messages\n [CompileErrorCodes.UNHANDLED_CODEGEN_NODE_TYPE]: `unhandled codegen node type: '{0}'`,\n // minimizer error messages\n [CompileErrorCodes.UNHANDLED_MINIFIER_NODE_TYPE]: `unhandled mimifier node type: '{0}'`\n};\nfunction createCompileError(code, loc, options = {}) {\n const { domain, messages, args } = options;\n const msg = format((messages || errorMessages)[code] || '', ...(args || []))\n ;\n const error = new SyntaxError(String(msg));\n error.code = code;\n if (loc) {\n error.location = loc;\n }\n error.domain = domain;\n return error;\n}\n/** @internal */\nfunction defaultOnError(error) {\n throw error;\n}\n\n// eslint-disable-next-line no-useless-escape\nconst RE_HTML_TAG = /<\\/?[\\w\\s=\"/.':;#-\\/]+>/;\nconst detectHtmlTag = (source) => RE_HTML_TAG.test(source);\n\nconst CHAR_SP = ' ';\nconst CHAR_CR = '\\r';\nconst CHAR_LF = '\\n';\nconst CHAR_LS = String.fromCharCode(0x2028);\nconst CHAR_PS = String.fromCharCode(0x2029);\nfunction createScanner(str) {\n const _buf = str;\n let _index = 0;\n let _line = 1;\n let _column = 1;\n let _peekOffset = 0;\n const isCRLF = (index) => _buf[index] === CHAR_CR && _buf[index + 1] === CHAR_LF;\n const isLF = (index) => _buf[index] === CHAR_LF;\n const isPS = (index) => _buf[index] === CHAR_PS;\n const isLS = (index) => _buf[index] === CHAR_LS;\n const isLineEnd = (index) => isCRLF(index) || isLF(index) || isPS(index) || isLS(index);\n const index = () => _index;\n const line = () => _line;\n const column = () => _column;\n const peekOffset = () => _peekOffset;\n const charAt = (offset) => isCRLF(offset) || isPS(offset) || isLS(offset) ? CHAR_LF : _buf[offset];\n const currentChar = () => charAt(_index);\n const currentPeek = () => charAt(_index + _peekOffset);\n function next() {\n _peekOffset = 0;\n if (isLineEnd(_index)) {\n _line++;\n _column = 0;\n }\n if (isCRLF(_index)) {\n _index++;\n }\n _index++;\n _column++;\n return _buf[_index];\n }\n function peek() {\n if (isCRLF(_index + _peekOffset)) {\n _peekOffset++;\n }\n _peekOffset++;\n return _buf[_index + _peekOffset];\n }\n function reset() {\n _index = 0;\n _line = 1;\n _column = 1;\n _peekOffset = 0;\n }\n function resetPeek(offset = 0) {\n _peekOffset = offset;\n }\n function skipToPeek() {\n const target = _index + _peekOffset;\n // eslint-disable-next-line no-unmodified-loop-condition\n while (target !== _index) {\n next();\n }\n _peekOffset = 0;\n }\n return {\n index,\n line,\n column,\n peekOffset,\n charAt,\n currentChar,\n currentPeek,\n next,\n peek,\n reset,\n resetPeek,\n skipToPeek\n };\n}\n\nconst EOF = undefined;\nconst DOT = '.';\nconst LITERAL_DELIMITER = \"'\";\nconst ERROR_DOMAIN$3 = 'tokenizer';\nfunction createTokenizer(source, options = {}) {\n const location = options.location !== false;\n const _scnr = createScanner(source);\n const currentOffset = () => _scnr.index();\n const currentPosition = () => createPosition(_scnr.line(), _scnr.column(), _scnr.index());\n const _initLoc = currentPosition();\n const _initOffset = currentOffset();\n const _context = {\n currentType: 14 /* TokenTypes.EOF */,\n offset: _initOffset,\n startLoc: _initLoc,\n endLoc: _initLoc,\n lastType: 14 /* TokenTypes.EOF */,\n lastOffset: _initOffset,\n lastStartLoc: _initLoc,\n lastEndLoc: _initLoc,\n braceNest: 0,\n inLinked: false,\n text: ''\n };\n const context = () => _context;\n const { onError } = options;\n function emitError(code, pos, offset, ...args) {\n const ctx = context();\n pos.column += offset;\n pos.offset += offset;\n if (onError) {\n const loc = location ? createLocation(ctx.startLoc, pos) : null;\n const err = createCompileError(code, loc, {\n domain: ERROR_DOMAIN$3,\n args\n });\n onError(err);\n }\n }\n function getToken(context, type, value) {\n context.endLoc = currentPosition();\n context.currentType = type;\n const token = { type };\n if (location) {\n token.loc = createLocation(context.startLoc, context.endLoc);\n }\n if (value != null) {\n token.value = value;\n }\n return token;\n }\n const getEndToken = (context) => getToken(context, 14 /* TokenTypes.EOF */);\n function eat(scnr, ch) {\n if (scnr.currentChar() === ch) {\n scnr.next();\n return ch;\n }\n else {\n emitError(CompileErrorCodes.EXPECTED_TOKEN, currentPosition(), 0, ch);\n return '';\n }\n }\n function peekSpaces(scnr) {\n let buf = '';\n while (scnr.currentPeek() === CHAR_SP || scnr.currentPeek() === CHAR_LF) {\n buf += scnr.currentPeek();\n scnr.peek();\n }\n return buf;\n }\n function skipSpaces(scnr) {\n const buf = peekSpaces(scnr);\n scnr.skipToPeek();\n return buf;\n }\n function isIdentifierStart(ch) {\n if (ch === EOF) {\n return false;\n }\n const cc = ch.charCodeAt(0);\n return ((cc >= 97 && cc <= 122) || // a-z\n (cc >= 65 && cc <= 90) || // A-Z\n cc === 95 // _\n );\n }\n function isNumberStart(ch) {\n if (ch === EOF) {\n return false;\n }\n const cc = ch.charCodeAt(0);\n return cc >= 48 && cc <= 57; // 0-9\n }\n function isNamedIdentifierStart(scnr, context) {\n const { currentType } = context;\n if (currentType !== 2 /* TokenTypes.BraceLeft */) {\n return false;\n }\n peekSpaces(scnr);\n const ret = isIdentifierStart(scnr.currentPeek());\n scnr.resetPeek();\n return ret;\n }\n function isListIdentifierStart(scnr, context) {\n const { currentType } = context;\n if (currentType !== 2 /* TokenTypes.BraceLeft */) {\n return false;\n }\n peekSpaces(scnr);\n const ch = scnr.currentPeek() === '-' ? scnr.peek() : scnr.currentPeek();\n const ret = isNumberStart(ch);\n scnr.resetPeek();\n return ret;\n }\n function isLiteralStart(scnr, context) {\n const { currentType } = context;\n if (currentType !== 2 /* TokenTypes.BraceLeft */) {\n return false;\n }\n peekSpaces(scnr);\n const ret = scnr.currentPeek() === LITERAL_DELIMITER;\n scnr.resetPeek();\n return ret;\n }\n function isLinkedDotStart(scnr, context) {\n const { currentType } = context;\n if (currentType !== 8 /* TokenTypes.LinkedAlias */) {\n return false;\n }\n peekSpaces(scnr);\n const ret = scnr.currentPeek() === \".\" /* TokenChars.LinkedDot */;\n scnr.resetPeek();\n return ret;\n }\n function isLinkedModifierStart(scnr, context) {\n const { currentType } = context;\n if (currentType !== 9 /* TokenTypes.LinkedDot */) {\n return false;\n }\n peekSpaces(scnr);\n const ret = isIdentifierStart(scnr.currentPeek());\n scnr.resetPeek();\n return ret;\n }\n function isLinkedDelimiterStart(scnr, context) {\n const { currentType } = context;\n if (!(currentType === 8 /* TokenTypes.LinkedAlias */ ||\n currentType === 12 /* TokenTypes.LinkedModifier */)) {\n return false;\n }\n peekSpaces(scnr);\n const ret = scnr.currentPeek() === \":\" /* TokenChars.LinkedDelimiter */;\n scnr.resetPeek();\n return ret;\n }\n function isLinkedReferStart(scnr, context) {\n const { currentType } = context;\n if (currentType !== 10 /* TokenTypes.LinkedDelimiter */) {\n return false;\n }\n const fn = () => {\n const ch = scnr.currentPeek();\n if (ch === \"{\" /* TokenChars.BraceLeft */) {\n return isIdentifierStart(scnr.peek());\n }\n else if (ch === \"@\" /* TokenChars.LinkedAlias */ ||\n ch === \"%\" /* TokenChars.Modulo */ ||\n ch === \"|\" /* TokenChars.Pipe */ ||\n ch === \":\" /* TokenChars.LinkedDelimiter */ ||\n ch === \".\" /* TokenChars.LinkedDot */ ||\n ch === CHAR_SP ||\n !ch) {\n return false;\n }\n else if (ch === CHAR_LF) {\n scnr.peek();\n return fn();\n }\n else {\n // other characters\n return isTextStart(scnr, false);\n }\n };\n const ret = fn();\n scnr.resetPeek();\n return ret;\n }\n function isPluralStart(scnr) {\n peekSpaces(scnr);\n const ret = scnr.currentPeek() === \"|\" /* TokenChars.Pipe */;\n scnr.resetPeek();\n return ret;\n }\n function detectModuloStart(scnr) {\n const spaces = peekSpaces(scnr);\n const ret = scnr.currentPeek() === \"%\" /* TokenChars.Modulo */ &&\n scnr.peek() === \"{\" /* TokenChars.BraceLeft */;\n scnr.resetPeek();\n return {\n isModulo: ret,\n hasSpace: spaces.length > 0\n };\n }\n function isTextStart(scnr, reset = true) {\n const fn = (hasSpace = false, prev = '', detectModulo = false) => {\n const ch = scnr.currentPeek();\n if (ch === \"{\" /* TokenChars.BraceLeft */) {\n return prev === \"%\" /* TokenChars.Modulo */ ? false : hasSpace;\n }\n else if (ch === \"@\" /* TokenChars.LinkedAlias */ || !ch) {\n return prev === \"%\" /* TokenChars.Modulo */ ? true : hasSpace;\n }\n else if (ch === \"%\" /* TokenChars.Modulo */) {\n scnr.peek();\n return fn(hasSpace, \"%\" /* TokenChars.Modulo */, true);\n }\n else if (ch === \"|\" /* TokenChars.Pipe */) {\n return prev === \"%\" /* TokenChars.Modulo */ || detectModulo\n ? true\n : !(prev === CHAR_SP || prev === CHAR_LF);\n }\n else if (ch === CHAR_SP) {\n scnr.peek();\n return fn(true, CHAR_SP, detectModulo);\n }\n else if (ch === CHAR_LF) {\n scnr.peek();\n return fn(true, CHAR_LF, detectModulo);\n }\n else {\n return true;\n }\n };\n const ret = fn();\n reset && scnr.resetPeek();\n return ret;\n }\n function takeChar(scnr, fn) {\n const ch = scnr.currentChar();\n if (ch === EOF) {\n return EOF;\n }\n if (fn(ch)) {\n scnr.next();\n return ch;\n }\n return null;\n }\n function isIdentifier(ch) {\n const cc = ch.charCodeAt(0);\n return ((cc >= 97 && cc <= 122) || // a-z\n (cc >= 65 && cc <= 90) || // A-Z\n (cc >= 48 && cc <= 57) || // 0-9\n cc === 95 || // _\n cc === 36 // $\n );\n }\n function takeIdentifierChar(scnr) {\n return takeChar(scnr, isIdentifier);\n }\n function isNamedIdentifier(ch) {\n const cc = ch.charCodeAt(0);\n return ((cc >= 97 && cc <= 122) || // a-z\n (cc >= 65 && cc <= 90) || // A-Z\n (cc >= 48 && cc <= 57) || // 0-9\n cc === 95 || // _\n cc === 36 || // $\n cc === 45 // -\n );\n }\n function takeNamedIdentifierChar(scnr) {\n return takeChar(scnr, isNamedIdentifier);\n }\n function isDigit(ch) {\n const cc = ch.charCodeAt(0);\n return cc >= 48 && cc <= 57; // 0-9\n }\n function takeDigit(scnr) {\n return takeChar(scnr, isDigit);\n }\n function isHexDigit(ch) {\n const cc = ch.charCodeAt(0);\n return ((cc >= 48 && cc <= 57) || // 0-9\n (cc >= 65 && cc <= 70) || // A-F\n (cc >= 97 && cc <= 102)); // a-f\n }\n function takeHexDigit(scnr) {\n return takeChar(scnr, isHexDigit);\n }\n function getDigits(scnr) {\n let ch = '';\n let num = '';\n while ((ch = takeDigit(scnr))) {\n num += ch;\n }\n return num;\n }\n function readModulo(scnr) {\n skipSpaces(scnr);\n const ch = scnr.currentChar();\n if (ch !== \"%\" /* TokenChars.Modulo */) {\n emitError(CompileErrorCodes.EXPECTED_TOKEN, currentPosition(), 0, ch);\n }\n scnr.next();\n return \"%\" /* TokenChars.Modulo */;\n }\n function readText(scnr) {\n let buf = '';\n // eslint-disable-next-line no-constant-condition\n while (true) {\n const ch = scnr.currentChar();\n if (ch === \"{\" /* TokenChars.BraceLeft */ ||\n ch === \"}\" /* TokenChars.BraceRight */ ||\n ch === \"@\" /* TokenChars.LinkedAlias */ ||\n ch === \"|\" /* TokenChars.Pipe */ ||\n !ch) {\n break;\n }\n else if (ch === \"%\" /* TokenChars.Modulo */) {\n if (isTextStart(scnr)) {\n buf += ch;\n scnr.next();\n }\n else {\n break;\n }\n }\n else if (ch === CHAR_SP || ch === CHAR_LF) {\n if (isTextStart(scnr)) {\n buf += ch;\n scnr.next();\n }\n else if (isPluralStart(scnr)) {\n break;\n }\n else {\n buf += ch;\n scnr.next();\n }\n }\n else {\n buf += ch;\n scnr.next();\n }\n }\n return buf;\n }\n function readNamedIdentifier(scnr) {\n skipSpaces(scnr);\n let ch = '';\n let name = '';\n while ((ch = takeNamedIdentifierChar(scnr))) {\n name += ch;\n }\n if (scnr.currentChar() === EOF) {\n emitError(CompileErrorCodes.UNTERMINATED_CLOSING_BRACE, currentPosition(), 0);\n }\n return name;\n }\n function readListIdentifier(scnr) {\n skipSpaces(scnr);\n let value = '';\n if (scnr.currentChar() === '-') {\n scnr.next();\n value += `-${getDigits(scnr)}`;\n }\n else {\n value += getDigits(scnr);\n }\n if (scnr.currentChar() === EOF) {\n emitError(CompileErrorCodes.UNTERMINATED_CLOSING_BRACE, currentPosition(), 0);\n }\n return value;\n }\n function isLiteral(ch) {\n return ch !== LITERAL_DELIMITER && ch !== CHAR_LF;\n }\n function readLiteral(scnr) {\n skipSpaces(scnr);\n // eslint-disable-next-line no-useless-escape\n eat(scnr, `\\'`);\n let ch = '';\n let literal = '';\n while ((ch = takeChar(scnr, isLiteral))) {\n if (ch === '\\\\') {\n literal += readEscapeSequence(scnr);\n }\n else {\n literal += ch;\n }\n }\n const current = scnr.currentChar();\n if (current === CHAR_LF || current === EOF) {\n emitError(CompileErrorCodes.UNTERMINATED_SINGLE_QUOTE_IN_PLACEHOLDER, currentPosition(), 0);\n // TODO: Is it correct really?\n if (current === CHAR_LF) {\n scnr.next();\n // eslint-disable-next-line no-useless-escape\n eat(scnr, `\\'`);\n }\n return literal;\n }\n // eslint-disable-next-line no-useless-escape\n eat(scnr, `\\'`);\n return literal;\n }\n function readEscapeSequence(scnr) {\n const ch = scnr.currentChar();\n switch (ch) {\n case '\\\\':\n case `\\'`: // eslint-disable-line no-useless-escape\n scnr.next();\n return `\\\\${ch}`;\n case 'u':\n return readUnicodeEscapeSequence(scnr, ch, 4);\n case 'U':\n return readUnicodeEscapeSequence(scnr, ch, 6);\n default:\n emitError(CompileErrorCodes.UNKNOWN_ESCAPE_SEQUENCE, currentPosition(), 0, ch);\n return '';\n }\n }\n function readUnicodeEscapeSequence(scnr, unicode, digits) {\n eat(scnr, unicode);\n let sequence = '';\n for (let i = 0; i < digits; i++) {\n const ch = takeHexDigit(scnr);\n if (!ch) {\n emitError(CompileErrorCodes.INVALID_UNICODE_ESCAPE_SEQUENCE, currentPosition(), 0, `\\\\${unicode}${sequence}${scnr.currentChar()}`);\n break;\n }\n sequence += ch;\n }\n return `\\\\${unicode}${sequence}`;\n }\n function isInvalidIdentifier(ch) {\n return (ch !== \"{\" /* TokenChars.BraceLeft */ &&\n ch !== \"}\" /* TokenChars.BraceRight */ &&\n ch !== CHAR_SP &&\n ch !== CHAR_LF);\n }\n function readInvalidIdentifier(scnr) {\n skipSpaces(scnr);\n let ch = '';\n let identifiers = '';\n while ((ch = takeChar(scnr, isInvalidIdentifier))) {\n identifiers += ch;\n }\n return identifiers;\n }\n function readLinkedModifier(scnr) {\n let ch = '';\n let name = '';\n while ((ch = takeIdentifierChar(scnr))) {\n name += ch;\n }\n return name;\n }\n function readLinkedRefer(scnr) {\n const fn = (buf) => {\n const ch = scnr.currentChar();\n if (ch === \"{\" /* TokenChars.BraceLeft */ ||\n ch === \"%\" /* TokenChars.Modulo */ ||\n ch === \"@\" /* TokenChars.LinkedAlias */ ||\n ch === \"|\" /* TokenChars.Pipe */ ||\n ch === \"(\" /* TokenChars.ParenLeft */ ||\n ch === \")\" /* TokenChars.ParenRight */ ||\n !ch) {\n return buf;\n }\n else if (ch === CHAR_SP) {\n return buf;\n }\n else if (ch === CHAR_LF || ch === DOT) {\n buf += ch;\n scnr.next();\n return fn(buf);\n }\n else {\n buf += ch;\n scnr.next();\n return fn(buf);\n }\n };\n return fn('');\n }\n function readPlural(scnr) {\n skipSpaces(scnr);\n const plural = eat(scnr, \"|\" /* TokenChars.Pipe */);\n skipSpaces(scnr);\n return plural;\n }\n // TODO: We need refactoring of token parsing ...\n function readTokenInPlaceholder(scnr, context) {\n let token = null;\n const ch = scnr.currentChar();\n switch (ch) {\n case \"{\" /* TokenChars.BraceLeft */:\n if (context.braceNest >= 1) {\n emitError(CompileErrorCodes.NOT_ALLOW_NEST_PLACEHOLDER, currentPosition(), 0);\n }\n scnr.next();\n token = getToken(context, 2 /* TokenTypes.BraceLeft */, \"{\" /* TokenChars.BraceLeft */);\n skipSpaces(scnr);\n context.braceNest++;\n return token;\n case \"}\" /* TokenChars.BraceRight */:\n if (context.braceNest > 0 &&\n context.currentType === 2 /* TokenTypes.BraceLeft */) {\n emitError(CompileErrorCodes.EMPTY_PLACEHOLDER, currentPosition(), 0);\n }\n scnr.next();\n token = getToken(context, 3 /* TokenTypes.BraceRight */, \"}\" /* TokenChars.BraceRight */);\n context.braceNest--;\n context.braceNest > 0 && skipSpaces(scnr);\n if (context.inLinked && context.braceNest === 0) {\n context.inLinked = false;\n }\n return token;\n case \"@\" /* TokenChars.LinkedAlias */:\n if (context.braceNest > 0) {\n emitError(CompileErrorCodes.UNTERMINATED_CLOSING_BRACE, currentPosition(), 0);\n }\n token = readTokenInLinked(scnr, context) || getEndToken(context);\n context.braceNest = 0;\n return token;\n default: {\n let validNamedIdentifier = true;\n let validListIdentifier = true;\n let validLiteral = true;\n if (isPluralStart(scnr)) {\n if (context.braceNest > 0) {\n emitError(CompileErrorCodes.UNTERMINATED_CLOSING_BRACE, currentPosition(), 0);\n }\n token = getToken(context, 1 /* TokenTypes.Pipe */, readPlural(scnr));\n // reset\n context.braceNest = 0;\n context.inLinked = false;\n return token;\n }\n if (context.braceNest > 0 &&\n (context.currentType === 5 /* TokenTypes.Named */ ||\n context.currentType === 6 /* TokenTypes.List */ ||\n context.currentType === 7 /* TokenTypes.Literal */)) {\n emitError(CompileErrorCodes.UNTERMINATED_CLOSING_BRACE, currentPosition(), 0);\n context.braceNest = 0;\n return readToken(scnr, context);\n }\n if ((validNamedIdentifier = isNamedIdentifierStart(scnr, context))) {\n token = getToken(context, 5 /* TokenTypes.Named */, readNamedIdentifier(scnr));\n skipSpaces(scnr);\n return token;\n }\n if ((validListIdentifier = isListIdentifierStart(scnr, context))) {\n token = getToken(context, 6 /* TokenTypes.List */, readListIdentifier(scnr));\n skipSpaces(scnr);\n return token;\n }\n if ((validLiteral = isLiteralStart(scnr, context))) {\n token = getToken(context, 7 /* TokenTypes.Literal */, readLiteral(scnr));\n skipSpaces(scnr);\n return token;\n }\n if (!validNamedIdentifier && !validListIdentifier && !validLiteral) {\n // TODO: we should be re-designed invalid cases, when we will extend message syntax near the future ...\n token = getToken(context, 13 /* TokenTypes.InvalidPlace */, readInvalidIdentifier(scnr));\n emitError(CompileErrorCodes.INVALID_TOKEN_IN_PLACEHOLDER, currentPosition(), 0, token.value);\n skipSpaces(scnr);\n return token;\n }\n break;\n }\n }\n return token;\n }\n // TODO: We need refactoring of token parsing ...\n function readTokenInLinked(scnr, context) {\n const { currentType } = context;\n let token = null;\n const ch = scnr.currentChar();\n if ((currentType === 8 /* TokenTypes.LinkedAlias */ ||\n currentType === 9 /* TokenTypes.LinkedDot */ ||\n currentType === 12 /* TokenTypes.LinkedModifier */ ||\n currentType === 10 /* TokenTypes.LinkedDelimiter */) &&\n (ch === CHAR_LF || ch === CHAR_SP)) {\n emitError(CompileErrorCodes.INVALID_LINKED_FORMAT, currentPosition(), 0);\n }\n switch (ch) {\n case \"@\" /* TokenChars.LinkedAlias */:\n scnr.next();\n token = getToken(context, 8 /* TokenTypes.LinkedAlias */, \"@\" /* TokenChars.LinkedAlias */);\n context.inLinked = true;\n return token;\n case \".\" /* TokenChars.LinkedDot */:\n skipSpaces(scnr);\n scnr.next();\n return getToken(context, 9 /* TokenTypes.LinkedDot */, \".\" /* TokenChars.LinkedDot */);\n case \":\" /* TokenChars.LinkedDelimiter */:\n skipSpaces(scnr);\n scnr.next();\n return getToken(context, 10 /* TokenTypes.LinkedDelimiter */, \":\" /* TokenChars.LinkedDelimiter */);\n default:\n if (isPluralStart(scnr)) {\n token = getToken(context, 1 /* TokenTypes.Pipe */, readPlural(scnr));\n // reset\n context.braceNest = 0;\n context.inLinked = false;\n return token;\n }\n if (isLinkedDotStart(scnr, context) ||\n isLinkedDelimiterStart(scnr, context)) {\n skipSpaces(scnr);\n return readTokenInLinked(scnr, context);\n }\n if (isLinkedModifierStart(scnr, context)) {\n skipSpaces(scnr);\n return getToken(context, 12 /* TokenTypes.LinkedModifier */, readLinkedModifier(scnr));\n }\n if (isLinkedReferStart(scnr, context)) {\n skipSpaces(scnr);\n if (ch === \"{\" /* TokenChars.BraceLeft */) {\n // scan the placeholder\n return readTokenInPlaceholder(scnr, context) || token;\n }\n else {\n return getToken(context, 11 /* TokenTypes.LinkedKey */, readLinkedRefer(scnr));\n }\n }\n if (currentType === 8 /* TokenTypes.LinkedAlias */) {\n emitError(CompileErrorCodes.INVALID_LINKED_FORMAT, currentPosition(), 0);\n }\n context.braceNest = 0;\n context.inLinked = false;\n return readToken(scnr, context);\n }\n }\n // TODO: We need refactoring of token parsing ...\n function readToken(scnr, context) {\n let token = { type: 14 /* TokenTypes.EOF */ };\n if (context.braceNest > 0) {\n return readTokenInPlaceholder(scnr, context) || getEndToken(context);\n }\n if (context.inLinked) {\n return readTokenInLinked(scnr, context) || getEndToken(context);\n }\n const ch = scnr.currentChar();\n switch (ch) {\n case \"{\" /* TokenChars.BraceLeft */:\n return readTokenInPlaceholder(scnr, context) || getEndToken(context);\n case \"}\" /* TokenChars.BraceRight */:\n emitError(CompileErrorCodes.UNBALANCED_CLOSING_BRACE, currentPosition(), 0);\n scnr.next();\n return getToken(context, 3 /* TokenTypes.BraceRight */, \"}\" /* TokenChars.BraceRight */);\n case \"@\" /* TokenChars.LinkedAlias */:\n return readTokenInLinked(scnr, context) || getEndToken(context);\n default: {\n if (isPluralStart(scnr)) {\n token = getToken(context, 1 /* TokenTypes.Pipe */, readPlural(scnr));\n // reset\n context.braceNest = 0;\n context.inLinked = false;\n return token;\n }\n const { isModulo, hasSpace } = detectModuloStart(scnr);\n if (isModulo) {\n return hasSpace\n ? getToken(context, 0 /* TokenTypes.Text */, readText(scnr))\n : getToken(context, 4 /* TokenTypes.Modulo */, readModulo(scnr));\n }\n if (isTextStart(scnr)) {\n return getToken(context, 0 /* TokenTypes.Text */, readText(scnr));\n }\n break;\n }\n }\n return token;\n }\n function nextToken() {\n const { currentType, offset, startLoc, endLoc } = _context;\n _context.lastType = currentType;\n _context.lastOffset = offset;\n _context.lastStartLoc = startLoc;\n _context.lastEndLoc = endLoc;\n _context.offset = currentOffset();\n _context.startLoc = currentPosition();\n if (_scnr.currentChar() === EOF) {\n return getToken(_context, 14 /* TokenTypes.EOF */);\n }\n return readToken(_scnr, _context);\n }\n return {\n nextToken,\n currentOffset,\n currentPosition,\n context\n };\n}\n\nconst ERROR_DOMAIN$2 = 'parser';\n// Backslash backslash, backslash quote, uHHHH, UHHHHHH.\nconst KNOWN_ESCAPES = /(?:\\\\\\\\|\\\\'|\\\\u([0-9a-fA-F]{4})|\\\\U([0-9a-fA-F]{6}))/g;\nfunction fromEscapeSequence(match, codePoint4, codePoint6) {\n switch (match) {\n case `\\\\\\\\`:\n return `\\\\`;\n // eslint-disable-next-line no-useless-escape\n case `\\\\\\'`:\n // eslint-disable-next-line no-useless-escape\n return `\\'`;\n default: {\n const codePoint = parseInt(codePoint4 || codePoint6, 16);\n if (codePoint <= 0xd7ff || codePoint >= 0xe000) {\n return String.fromCodePoint(codePoint);\n }\n // invalid ...\n // Replace them with U+FFFD REPLACEMENT CHARACTER.\n return '�';\n }\n }\n}\nfunction createParser(options = {}) {\n const location = options.location !== false;\n const { onError, onWarn } = options;\n function emitError(tokenzer, code, start, offset, ...args) {\n const end = tokenzer.currentPosition();\n end.offset += offset;\n end.column += offset;\n if (onError) {\n const loc = location ? createLocation(start, end) : null;\n const err = createCompileError(code, loc, {\n domain: ERROR_DOMAIN$2,\n args\n });\n onError(err);\n }\n }\n function emitWarn(tokenzer, code, start, offset, ...args) {\n const end = tokenzer.currentPosition();\n end.offset += offset;\n end.column += offset;\n if (onWarn) {\n const loc = location ? createLocation(start, end) : null;\n onWarn(createCompileWarn(code, loc, args));\n }\n }\n function startNode(type, offset, loc) {\n const node = { type };\n if (location) {\n node.start = offset;\n node.end = offset;\n node.loc = { start: loc, end: loc };\n }\n return node;\n }\n function endNode(node, offset, pos, type) {\n if (type) {\n node.type = type;\n }\n if (location) {\n node.end = offset;\n if (node.loc) {\n node.loc.end = pos;\n }\n }\n }\n function parseText(tokenizer, value) {\n const context = tokenizer.context();\n const node = startNode(3 /* NodeTypes.Text */, context.offset, context.startLoc);\n node.value = value;\n endNode(node, tokenizer.currentOffset(), tokenizer.currentPosition());\n return node;\n }\n function parseList(tokenizer, index) {\n const context = tokenizer.context();\n const { lastOffset: offset, lastStartLoc: loc } = context; // get brace left loc\n const node = startNode(5 /* NodeTypes.List */, offset, loc);\n node.index = parseInt(index, 10);\n tokenizer.nextToken(); // skip brach right\n endNode(node, tokenizer.currentOffset(), tokenizer.currentPosition());\n return node;\n }\n function parseNamed(tokenizer, key, modulo) {\n const context = tokenizer.context();\n const { lastOffset: offset, lastStartLoc: loc } = context; // get brace left loc\n const node = startNode(4 /* NodeTypes.Named */, offset, loc);\n node.key = key;\n if (modulo === true) {\n node.modulo = true;\n }\n tokenizer.nextToken(); // skip brach right\n endNode(node, tokenizer.currentOffset(), tokenizer.currentPosition());\n return node;\n }\n function parseLiteral(tokenizer, value) {\n const context = tokenizer.context();\n const { lastOffset: offset, lastStartLoc: loc } = context; // get brace left loc\n const node = startNode(9 /* NodeTypes.Literal */, offset, loc);\n node.value = value.replace(KNOWN_ESCAPES, fromEscapeSequence);\n tokenizer.nextToken(); // skip brach right\n endNode(node, tokenizer.currentOffset(), tokenizer.currentPosition());\n return node;\n }\n function parseLinkedModifier(tokenizer) {\n const token = tokenizer.nextToken();\n const context = tokenizer.context();\n const { lastOffset: offset, lastStartLoc: loc } = context; // get linked dot loc\n const node = startNode(8 /* NodeTypes.LinkedModifier */, offset, loc);\n if (token.type !== 12 /* TokenTypes.LinkedModifier */) {\n // empty modifier\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_EMPTY_LINKED_MODIFIER, context.lastStartLoc, 0);\n node.value = '';\n endNode(node, offset, loc);\n return {\n nextConsumeToken: token,\n node\n };\n }\n // check token\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n node.value = token.value || '';\n endNode(node, tokenizer.currentOffset(), tokenizer.currentPosition());\n return {\n node\n };\n }\n function parseLinkedKey(tokenizer, value) {\n const context = tokenizer.context();\n const node = startNode(7 /* NodeTypes.LinkedKey */, context.offset, context.startLoc);\n node.value = value;\n endNode(node, tokenizer.currentOffset(), tokenizer.currentPosition());\n return node;\n }\n function parseLinked(tokenizer) {\n const context = tokenizer.context();\n const linkedNode = startNode(6 /* NodeTypes.Linked */, context.offset, context.startLoc);\n let token = tokenizer.nextToken();\n if (token.type === 9 /* TokenTypes.LinkedDot */) {\n const parsed = parseLinkedModifier(tokenizer);\n linkedNode.modifier = parsed.node;\n token = parsed.nextConsumeToken || tokenizer.nextToken();\n }\n // asset check token\n if (token.type !== 10 /* TokenTypes.LinkedDelimiter */) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n token = tokenizer.nextToken();\n // skip brace left\n if (token.type === 2 /* TokenTypes.BraceLeft */) {\n token = tokenizer.nextToken();\n }\n switch (token.type) {\n case 11 /* TokenTypes.LinkedKey */:\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n linkedNode.key = parseLinkedKey(tokenizer, token.value || '');\n break;\n case 5 /* TokenTypes.Named */:\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n linkedNode.key = parseNamed(tokenizer, token.value || '');\n break;\n case 6 /* TokenTypes.List */:\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n linkedNode.key = parseList(tokenizer, token.value || '');\n break;\n case 7 /* TokenTypes.Literal */:\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n linkedNode.key = parseLiteral(tokenizer, token.value || '');\n break;\n default: {\n // empty key\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_EMPTY_LINKED_KEY, context.lastStartLoc, 0);\n const nextContext = tokenizer.context();\n const emptyLinkedKeyNode = startNode(7 /* NodeTypes.LinkedKey */, nextContext.offset, nextContext.startLoc);\n emptyLinkedKeyNode.value = '';\n endNode(emptyLinkedKeyNode, nextContext.offset, nextContext.startLoc);\n linkedNode.key = emptyLinkedKeyNode;\n endNode(linkedNode, nextContext.offset, nextContext.startLoc);\n return {\n nextConsumeToken: token,\n node: linkedNode\n };\n }\n }\n endNode(linkedNode, tokenizer.currentOffset(), tokenizer.currentPosition());\n return {\n node: linkedNode\n };\n }\n function parseMessage(tokenizer) {\n const context = tokenizer.context();\n const startOffset = context.currentType === 1 /* TokenTypes.Pipe */\n ? tokenizer.currentOffset()\n : context.offset;\n const startLoc = context.currentType === 1 /* TokenTypes.Pipe */\n ? context.endLoc\n : context.startLoc;\n const node = startNode(2 /* NodeTypes.Message */, startOffset, startLoc);\n node.items = [];\n let nextToken = null;\n let modulo = null;\n do {\n const token = nextToken || tokenizer.nextToken();\n nextToken = null;\n switch (token.type) {\n case 0 /* TokenTypes.Text */:\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n node.items.push(parseText(tokenizer, token.value || ''));\n break;\n case 6 /* TokenTypes.List */:\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n node.items.push(parseList(tokenizer, token.value || ''));\n break;\n case 4 /* TokenTypes.Modulo */:\n modulo = true;\n break;\n case 5 /* TokenTypes.Named */:\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n node.items.push(parseNamed(tokenizer, token.value || '', !!modulo));\n if (modulo) {\n emitWarn(tokenizer, CompileWarnCodes.USE_MODULO_SYNTAX, context.lastStartLoc, 0, getTokenCaption(token));\n modulo = null;\n }\n break;\n case 7 /* TokenTypes.Literal */:\n if (token.value == null) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, getTokenCaption(token));\n }\n node.items.push(parseLiteral(tokenizer, token.value || ''));\n break;\n case 8 /* TokenTypes.LinkedAlias */: {\n const parsed = parseLinked(tokenizer);\n node.items.push(parsed.node);\n nextToken = parsed.nextConsumeToken || null;\n break;\n }\n }\n } while (context.currentType !== 14 /* TokenTypes.EOF */ &&\n context.currentType !== 1 /* TokenTypes.Pipe */);\n // adjust message node loc\n const endOffset = context.currentType === 1 /* TokenTypes.Pipe */\n ? context.lastOffset\n : tokenizer.currentOffset();\n const endLoc = context.currentType === 1 /* TokenTypes.Pipe */\n ? context.lastEndLoc\n : tokenizer.currentPosition();\n endNode(node, endOffset, endLoc);\n return node;\n }\n function parsePlural(tokenizer, offset, loc, msgNode) {\n const context = tokenizer.context();\n let hasEmptyMessage = msgNode.items.length === 0;\n const node = startNode(1 /* NodeTypes.Plural */, offset, loc);\n node.cases = [];\n node.cases.push(msgNode);\n do {\n const msg = parseMessage(tokenizer);\n if (!hasEmptyMessage) {\n hasEmptyMessage = msg.items.length === 0;\n }\n node.cases.push(msg);\n } while (context.currentType !== 14 /* TokenTypes.EOF */);\n if (hasEmptyMessage) {\n emitError(tokenizer, CompileErrorCodes.MUST_HAVE_MESSAGES_IN_PLURAL, loc, 0);\n }\n endNode(node, tokenizer.currentOffset(), tokenizer.currentPosition());\n return node;\n }\n function parseResource(tokenizer) {\n const context = tokenizer.context();\n const { offset, startLoc } = context;\n const msgNode = parseMessage(tokenizer);\n if (context.currentType === 14 /* TokenTypes.EOF */) {\n return msgNode;\n }\n else {\n return parsePlural(tokenizer, offset, startLoc, msgNode);\n }\n }\n function parse(source) {\n const tokenizer = createTokenizer(source, assign({}, options));\n const context = tokenizer.context();\n const node = startNode(0 /* NodeTypes.Resource */, context.offset, context.startLoc);\n if (location && node.loc) {\n node.loc.source = source;\n }\n node.body = parseResource(tokenizer);\n if (options.onCacheKey) {\n node.cacheKey = options.onCacheKey(source);\n }\n // assert whether achieved to EOF\n if (context.currentType !== 14 /* TokenTypes.EOF */) {\n emitError(tokenizer, CompileErrorCodes.UNEXPECTED_LEXICAL_ANALYSIS, context.lastStartLoc, 0, source[context.offset] || '');\n }\n endNode(node, tokenizer.currentOffset(), tokenizer.currentPosition());\n return node;\n }\n return { parse };\n}\nfunction getTokenCaption(token) {\n if (token.type === 14 /* TokenTypes.EOF */) {\n return 'EOF';\n }\n const name = (token.value || '').replace(/\\r?\\n/gu, '\\\\n');\n return name.length > 10 ? name.slice(0, 9) + '…' : name;\n}\n\nfunction createTransformer(ast, options = {} // eslint-disable-line\n) {\n const _context = {\n ast,\n helpers: new Set()\n };\n const context = () => _context;\n const helper = (name) => {\n _context.helpers.add(name);\n return name;\n };\n return { context, helper };\n}\nfunction traverseNodes(nodes, transformer) {\n for (let i = 0; i < nodes.length; i++) {\n traverseNode(nodes[i], transformer);\n }\n}\nfunction traverseNode(node, transformer) {\n // TODO: if we need pre-hook of transform, should be implemented to here\n switch (node.type) {\n case 1 /* NodeTypes.Plural */:\n traverseNodes(node.cases, transformer);\n transformer.helper(\"plural\" /* HelperNameMap.PLURAL */);\n break;\n case 2 /* NodeTypes.Message */:\n traverseNodes(node.items, transformer);\n break;\n case 6 /* NodeTypes.Linked */: {\n const linked = node;\n traverseNode(linked.key, transformer);\n transformer.helper(\"linked\" /* HelperNameMap.LINKED */);\n transformer.helper(\"type\" /* HelperNameMap.TYPE */);\n break;\n }\n case 5 /* NodeTypes.List */:\n transformer.helper(\"interpolate\" /* HelperNameMap.INTERPOLATE */);\n transformer.helper(\"list\" /* HelperNameMap.LIST */);\n break;\n case 4 /* NodeTypes.Named */:\n transformer.helper(\"interpolate\" /* HelperNameMap.INTERPOLATE */);\n transformer.helper(\"named\" /* HelperNameMap.NAMED */);\n break;\n }\n // TODO: if we need post-hook of transform, should be implemented to here\n}\n// transform AST\nfunction transform(ast, options = {} // eslint-disable-line\n) {\n const transformer = createTransformer(ast);\n transformer.helper(\"normalize\" /* HelperNameMap.NORMALIZE */);\n // traverse\n ast.body && traverseNode(ast.body, transformer);\n // set meta information\n const context = transformer.context();\n ast.helpers = Array.from(context.helpers);\n}\n\nfunction optimize(ast) {\n const body = ast.body;\n if (body.type === 2 /* NodeTypes.Message */) {\n optimizeMessageNode(body);\n }\n else {\n body.cases.forEach(c => optimizeMessageNode(c));\n }\n return ast;\n}\nfunction optimizeMessageNode(message) {\n if (message.items.length === 1) {\n const item = message.items[0];\n if (item.type === 3 /* NodeTypes.Text */ || item.type === 9 /* NodeTypes.Literal */) {\n message.static = item.value;\n delete item.value; // optimization for size\n }\n }\n else {\n const values = [];\n for (let i = 0; i < message.items.length; i++) {\n const item = message.items[i];\n if (!(item.type === 3 /* NodeTypes.Text */ || item.type === 9 /* NodeTypes.Literal */)) {\n break;\n }\n if (item.value == null) {\n break;\n }\n values.push(item.value);\n }\n if (values.length === message.items.length) {\n message.static = join(values);\n for (let i = 0; i < message.items.length; i++) {\n const item = message.items[i];\n if (item.type === 3 /* NodeTypes.Text */ || item.type === 9 /* NodeTypes.Literal */) {\n delete item.value; // optimization for size\n }\n }\n }\n }\n}\n\nconst ERROR_DOMAIN$1 = 'minifier';\n/* eslint-disable @typescript-eslint/no-explicit-any */\nfunction minify(node) {\n node.t = node.type;\n switch (node.type) {\n case 0 /* NodeTypes.Resource */: {\n const resource = node;\n minify(resource.body);\n resource.b = resource.body;\n delete resource.body;\n break;\n }\n case 1 /* NodeTypes.Plural */: {\n const plural = node;\n const cases = plural.cases;\n for (let i = 0; i < cases.length; i++) {\n minify(cases[i]);\n }\n plural.c = cases;\n delete plural.cases;\n break;\n }\n case 2 /* NodeTypes.Message */: {\n const message = node;\n const items = message.items;\n for (let i = 0; i < items.length; i++) {\n minify(items[i]);\n }\n message.i = items;\n delete message.items;\n if (message.static) {\n message.s = message.static;\n delete message.static;\n }\n break;\n }\n case 3 /* NodeTypes.Text */:\n case 9 /* NodeTypes.Literal */:\n case 8 /* NodeTypes.LinkedModifier */:\n case 7 /* NodeTypes.LinkedKey */: {\n const valueNode = node;\n if (valueNode.value) {\n valueNode.v = valueNode.value;\n delete valueNode.value;\n }\n break;\n }\n case 6 /* NodeTypes.Linked */: {\n const linked = node;\n minify(linked.key);\n linked.k = linked.key;\n delete linked.key;\n if (linked.modifier) {\n minify(linked.modifier);\n linked.m = linked.modifier;\n delete linked.modifier;\n }\n break;\n }\n case 5 /* NodeTypes.List */: {\n const list = node;\n list.i = list.index;\n delete list.index;\n break;\n }\n case 4 /* NodeTypes.Named */: {\n const named = node;\n named.k = named.key;\n delete named.key;\n break;\n }\n default:\n {\n throw createCompileError(CompileErrorCodes.UNHANDLED_MINIFIER_NODE_TYPE, null, {\n domain: ERROR_DOMAIN$1,\n args: [node.type]\n });\n }\n }\n delete node.type;\n}\n/* eslint-enable @typescript-eslint/no-explicit-any */\n\n// eslint-disable-next-line @typescript-eslint/triple-slash-reference\n/// \nconst ERROR_DOMAIN = 'parser';\nfunction createCodeGenerator(ast, options) {\n const { sourceMap, filename, breakLineCode, needIndent: _needIndent } = options;\n const location = options.location !== false;\n const _context = {\n filename,\n code: '',\n column: 1,\n line: 1,\n offset: 0,\n map: undefined,\n breakLineCode,\n needIndent: _needIndent,\n indentLevel: 0\n };\n if (location && ast.loc) {\n _context.source = ast.loc.source;\n }\n const context = () => _context;\n function push(code, node) {\n _context.code += code;\n }\n function _newline(n, withBreakLine = true) {\n const _breakLineCode = withBreakLine ? breakLineCode : '';\n push(_needIndent ? _breakLineCode + ` `.repeat(n) : _breakLineCode);\n }\n function indent(withNewLine = true) {\n const level = ++_context.indentLevel;\n withNewLine && _newline(level);\n }\n function deindent(withNewLine = true) {\n const level = --_context.indentLevel;\n withNewLine && _newline(level);\n }\n function newline() {\n _newline(_context.indentLevel);\n }\n const helper = (key) => `_${key}`;\n const needIndent = () => _context.needIndent;\n return {\n context,\n push,\n indent,\n deindent,\n newline,\n helper,\n needIndent\n };\n}\nfunction generateLinkedNode(generator, node) {\n const { helper } = generator;\n generator.push(`${helper(\"linked\" /* HelperNameMap.LINKED */)}(`);\n generateNode(generator, node.key);\n if (node.modifier) {\n generator.push(`, `);\n generateNode(generator, node.modifier);\n generator.push(`, _type`);\n }\n else {\n generator.push(`, undefined, _type`);\n }\n generator.push(`)`);\n}\nfunction generateMessageNode(generator, node) {\n const { helper, needIndent } = generator;\n generator.push(`${helper(\"normalize\" /* HelperNameMap.NORMALIZE */)}([`);\n generator.indent(needIndent());\n const length = node.items.length;\n for (let i = 0; i < length; i++) {\n generateNode(generator, node.items[i]);\n if (i === length - 1) {\n break;\n }\n generator.push(', ');\n }\n generator.deindent(needIndent());\n generator.push('])');\n}\nfunction generatePluralNode(generator, node) {\n const { helper, needIndent } = generator;\n if (node.cases.length > 1) {\n generator.push(`${helper(\"plural\" /* HelperNameMap.PLURAL */)}([`);\n generator.indent(needIndent());\n const length = node.cases.length;\n for (let i = 0; i < length; i++) {\n generateNode(generator, node.cases[i]);\n if (i === length - 1) {\n break;\n }\n generator.push(', ');\n }\n generator.deindent(needIndent());\n generator.push(`])`);\n }\n}\nfunction generateResource(generator, node) {\n if (node.body) {\n generateNode(generator, node.body);\n }\n else {\n generator.push('null');\n }\n}\nfunction generateNode(generator, node) {\n const { helper } = generator;\n switch (node.type) {\n case 0 /* NodeTypes.Resource */:\n generateResource(generator, node);\n break;\n case 1 /* NodeTypes.Plural */:\n generatePluralNode(generator, node);\n break;\n case 2 /* NodeTypes.Message */:\n generateMessageNode(generator, node);\n break;\n case 6 /* NodeTypes.Linked */:\n generateLinkedNode(generator, node);\n break;\n case 8 /* NodeTypes.LinkedModifier */:\n generator.push(JSON.stringify(node.value), node);\n break;\n case 7 /* NodeTypes.LinkedKey */:\n generator.push(JSON.stringify(node.value), node);\n break;\n case 5 /* NodeTypes.List */:\n generator.push(`${helper(\"interpolate\" /* HelperNameMap.INTERPOLATE */)}(${helper(\"list\" /* HelperNameMap.LIST */)}(${node.index}))`, node);\n break;\n case 4 /* NodeTypes.Named */:\n generator.push(`${helper(\"interpolate\" /* HelperNameMap.INTERPOLATE */)}(${helper(\"named\" /* HelperNameMap.NAMED */)}(${JSON.stringify(node.key)}))`, node);\n break;\n case 9 /* NodeTypes.Literal */:\n generator.push(JSON.stringify(node.value), node);\n break;\n case 3 /* NodeTypes.Text */:\n generator.push(JSON.stringify(node.value), node);\n break;\n default:\n {\n throw createCompileError(CompileErrorCodes.UNHANDLED_CODEGEN_NODE_TYPE, null, {\n domain: ERROR_DOMAIN,\n args: [node.type]\n });\n }\n }\n}\n// generate code from AST\nconst generate = (ast, options = {} // eslint-disable-line\n) => {\n const mode = isString(options.mode) ? options.mode : 'normal';\n const filename = isString(options.filename)\n ? options.filename\n : 'message.intl';\n const sourceMap = !!options.sourceMap;\n // prettier-ignore\n const breakLineCode = options.breakLineCode != null\n ? options.breakLineCode\n : mode === 'arrow'\n ? ';'\n : '\\n';\n const needIndent = options.needIndent ? options.needIndent : mode !== 'arrow';\n const helpers = ast.helpers || [];\n const generator = createCodeGenerator(ast, {\n mode,\n filename,\n sourceMap,\n breakLineCode,\n needIndent\n });\n generator.push(mode === 'normal' ? `function __msg__ (ctx) {` : `(ctx) => {`);\n generator.indent(needIndent);\n if (helpers.length > 0) {\n generator.push(`const { ${join(helpers.map(s => `${s}: _${s}`), ', ')} } = ctx`);\n generator.newline();\n }\n generator.push(`return `);\n generateNode(generator, ast);\n generator.deindent(needIndent);\n generator.push(`}`);\n delete ast.helpers;\n const { code, map } = generator.context();\n return {\n ast,\n code,\n map: map ? map.toJSON() : undefined // eslint-disable-line @typescript-eslint/no-explicit-any\n };\n};\n\nfunction baseCompile(source, options = {}) {\n const assignedOptions = assign({}, options);\n const jit = !!assignedOptions.jit;\n const enalbeMinify = !!assignedOptions.minify;\n const enambeOptimize = assignedOptions.optimize == null ? true : assignedOptions.optimize;\n // parse source codes\n const parser = createParser(assignedOptions);\n const ast = parser.parse(source);\n if (!jit) {\n // transform ASTs\n transform(ast, assignedOptions);\n // generate javascript codes\n return generate(ast, assignedOptions);\n }\n else {\n // optimize ASTs\n enambeOptimize && optimize(ast);\n // minimize ASTs\n enalbeMinify && minify(ast);\n // In JIT mode, no ast transform, no code generation.\n return { ast, code: '' };\n }\n}\n\nexport { CompileErrorCodes, CompileWarnCodes, ERROR_DOMAIN$2 as ERROR_DOMAIN, LOCATION_STUB, baseCompile, createCompileError, createCompileWarn, createLocation, createParser, createPosition, defaultOnError, detectHtmlTag, errorMessages, warnMessages };\n","/*!\n * core-base v9.13.1\n * (c) 2024 kazuya kawaguchi\n * Released under the MIT License.\n */\nimport { getGlobalThis, isObject, isFunction, isString, isNumber, isPlainObject, assign, join, toDisplayString, isArray, incrementer, format as format$1, isPromise, isBoolean, warn, isRegExp, warnOnce, escapeHtml, inBrowser, mark, measure, isEmptyObject, generateCodeFrame, generateFormatCacheKey, isDate } from '@intlify/shared';\nimport { CompileWarnCodes, CompileErrorCodes, createCompileError, detectHtmlTag, defaultOnError, baseCompile as baseCompile$1 } from '@intlify/message-compiler';\nexport { CompileErrorCodes, createCompileError } from '@intlify/message-compiler';\n\n/**\n * This is only called in esm-bundler builds.\n * istanbul-ignore-next\n */\nfunction initFeatureFlags() {\n if (typeof __INTLIFY_PROD_DEVTOOLS__ !== 'boolean') {\n getGlobalThis().__INTLIFY_PROD_DEVTOOLS__ = false;\n }\n if (typeof __INTLIFY_JIT_COMPILATION__ !== 'boolean') {\n getGlobalThis().__INTLIFY_JIT_COMPILATION__ = false;\n }\n if (typeof __INTLIFY_DROP_MESSAGE_COMPILER__ !== 'boolean') {\n getGlobalThis().__INTLIFY_DROP_MESSAGE_COMPILER__ = false;\n }\n}\n\nconst pathStateMachine = [];\npathStateMachine[0 /* States.BEFORE_PATH */] = {\n [\"w\" /* PathCharTypes.WORKSPACE */]: [0 /* States.BEFORE_PATH */],\n [\"i\" /* PathCharTypes.IDENT */]: [3 /* States.IN_IDENT */, 0 /* Actions.APPEND */],\n [\"[\" /* PathCharTypes.LEFT_BRACKET */]: [4 /* States.IN_SUB_PATH */],\n [\"o\" /* PathCharTypes.END_OF_FAIL */]: [7 /* States.AFTER_PATH */]\n};\npathStateMachine[1 /* States.IN_PATH */] = {\n [\"w\" /* PathCharTypes.WORKSPACE */]: [1 /* States.IN_PATH */],\n [\".\" /* PathCharTypes.DOT */]: [2 /* States.BEFORE_IDENT */],\n [\"[\" /* PathCharTypes.LEFT_BRACKET */]: [4 /* States.IN_SUB_PATH */],\n [\"o\" /* PathCharTypes.END_OF_FAIL */]: [7 /* States.AFTER_PATH */]\n};\npathStateMachine[2 /* States.BEFORE_IDENT */] = {\n [\"w\" /* PathCharTypes.WORKSPACE */]: [2 /* States.BEFORE_IDENT */],\n [\"i\" /* PathCharTypes.IDENT */]: [3 /* States.IN_IDENT */, 0 /* Actions.APPEND */],\n [\"0\" /* PathCharTypes.ZERO */]: [3 /* States.IN_IDENT */, 0 /* Actions.APPEND */]\n};\npathStateMachine[3 /* States.IN_IDENT */] = {\n [\"i\" /* PathCharTypes.IDENT */]: [3 /* States.IN_IDENT */, 0 /* Actions.APPEND */],\n [\"0\" /* PathCharTypes.ZERO */]: [3 /* States.IN_IDENT */, 0 /* Actions.APPEND */],\n [\"w\" /* PathCharTypes.WORKSPACE */]: [1 /* States.IN_PATH */, 1 /* Actions.PUSH */],\n [\".\" /* PathCharTypes.DOT */]: [2 /* States.BEFORE_IDENT */, 1 /* Actions.PUSH */],\n [\"[\" /* PathCharTypes.LEFT_BRACKET */]: [4 /* States.IN_SUB_PATH */, 1 /* Actions.PUSH */],\n [\"o\" /* PathCharTypes.END_OF_FAIL */]: [7 /* States.AFTER_PATH */, 1 /* Actions.PUSH */]\n};\npathStateMachine[4 /* States.IN_SUB_PATH */] = {\n [\"'\" /* PathCharTypes.SINGLE_QUOTE */]: [5 /* States.IN_SINGLE_QUOTE */, 0 /* Actions.APPEND */],\n [\"\\\"\" /* PathCharTypes.DOUBLE_QUOTE */]: [6 /* States.IN_DOUBLE_QUOTE */, 0 /* Actions.APPEND */],\n [\"[\" /* PathCharTypes.LEFT_BRACKET */]: [\n 4 /* States.IN_SUB_PATH */,\n 2 /* Actions.INC_SUB_PATH_DEPTH */\n ],\n [\"]\" /* PathCharTypes.RIGHT_BRACKET */]: [1 /* States.IN_PATH */, 3 /* Actions.PUSH_SUB_PATH */],\n [\"o\" /* PathCharTypes.END_OF_FAIL */]: 8 /* States.ERROR */,\n [\"l\" /* PathCharTypes.ELSE */]: [4 /* States.IN_SUB_PATH */, 0 /* Actions.APPEND */]\n};\npathStateMachine[5 /* States.IN_SINGLE_QUOTE */] = {\n [\"'\" /* PathCharTypes.SINGLE_QUOTE */]: [4 /* States.IN_SUB_PATH */, 0 /* Actions.APPEND */],\n [\"o\" /* PathCharTypes.END_OF_FAIL */]: 8 /* States.ERROR */,\n [\"l\" /* PathCharTypes.ELSE */]: [5 /* States.IN_SINGLE_QUOTE */, 0 /* Actions.APPEND */]\n};\npathStateMachine[6 /* States.IN_DOUBLE_QUOTE */] = {\n [\"\\\"\" /* PathCharTypes.DOUBLE_QUOTE */]: [4 /* States.IN_SUB_PATH */, 0 /* Actions.APPEND */],\n [\"o\" /* PathCharTypes.END_OF_FAIL */]: 8 /* States.ERROR */,\n [\"l\" /* PathCharTypes.ELSE */]: [6 /* States.IN_DOUBLE_QUOTE */, 0 /* Actions.APPEND */]\n};\n/**\n * Check if an expression is a literal value.\n */\nconst literalValueRE = /^\\s?(?:true|false|-?[\\d.]+|'[^']*'|\"[^\"]*\")\\s?$/;\nfunction isLiteral(exp) {\n return literalValueRE.test(exp);\n}\n/**\n * Strip quotes from a string\n */\nfunction stripQuotes(str) {\n const a = str.charCodeAt(0);\n const b = str.charCodeAt(str.length - 1);\n return a === b && (a === 0x22 || a === 0x27) ? str.slice(1, -1) : str;\n}\n/**\n * Determine the type of a character in a keypath.\n */\nfunction getPathCharType(ch) {\n if (ch === undefined || ch === null) {\n return \"o\" /* PathCharTypes.END_OF_FAIL */;\n }\n const code = ch.charCodeAt(0);\n switch (code) {\n case 0x5b: // [\n case 0x5d: // ]\n case 0x2e: // .\n case 0x22: // \"\n case 0x27: // '\n return ch;\n case 0x5f: // _\n case 0x24: // $\n case 0x2d: // -\n return \"i\" /* PathCharTypes.IDENT */;\n case 0x09: // Tab (HT)\n case 0x0a: // Newline (LF)\n case 0x0d: // Return (CR)\n case 0xa0: // No-break space (NBSP)\n case 0xfeff: // Byte Order Mark (BOM)\n case 0x2028: // Line Separator (LS)\n case 0x2029: // Paragraph Separator (PS)\n return \"w\" /* PathCharTypes.WORKSPACE */;\n }\n return \"i\" /* PathCharTypes.IDENT */;\n}\n/**\n * Format a subPath, return its plain form if it is\n * a literal string or number. Otherwise prepend the\n * dynamic indicator (*).\n */\nfunction formatSubPath(path) {\n const trimmed = path.trim();\n // invalid leading 0\n if (path.charAt(0) === '0' && isNaN(parseInt(path))) {\n return false;\n }\n return isLiteral(trimmed)\n ? stripQuotes(trimmed)\n : \"*\" /* PathCharTypes.ASTARISK */ + trimmed;\n}\n/**\n * Parse a string path into an array of segments\n */\nfunction parse(path) {\n const keys = [];\n let index = -1;\n let mode = 0 /* States.BEFORE_PATH */;\n let subPathDepth = 0;\n let c;\n let key; // eslint-disable-line\n let newChar;\n let type;\n let transition;\n let action;\n let typeMap;\n const actions = [];\n actions[0 /* Actions.APPEND */] = () => {\n if (key === undefined) {\n key = newChar;\n }\n else {\n key += newChar;\n }\n };\n actions[1 /* Actions.PUSH */] = () => {\n if (key !== undefined) {\n keys.push(key);\n key = undefined;\n }\n };\n actions[2 /* Actions.INC_SUB_PATH_DEPTH */] = () => {\n actions[0 /* Actions.APPEND */]();\n subPathDepth++;\n };\n actions[3 /* Actions.PUSH_SUB_PATH */] = () => {\n if (subPathDepth > 0) {\n subPathDepth--;\n mode = 4 /* States.IN_SUB_PATH */;\n actions[0 /* Actions.APPEND */]();\n }\n else {\n subPathDepth = 0;\n if (key === undefined) {\n return false;\n }\n key = formatSubPath(key);\n if (key === false) {\n return false;\n }\n else {\n actions[1 /* Actions.PUSH */]();\n }\n }\n };\n function maybeUnescapeQuote() {\n const nextChar = path[index + 1];\n if ((mode === 5 /* States.IN_SINGLE_QUOTE */ &&\n nextChar === \"'\" /* PathCharTypes.SINGLE_QUOTE */) ||\n (mode === 6 /* States.IN_DOUBLE_QUOTE */ &&\n nextChar === \"\\\"\" /* PathCharTypes.DOUBLE_QUOTE */)) {\n index++;\n newChar = '\\\\' + nextChar;\n actions[0 /* Actions.APPEND */]();\n return true;\n }\n }\n while (mode !== null) {\n index++;\n c = path[index];\n if (c === '\\\\' && maybeUnescapeQuote()) {\n continue;\n }\n type = getPathCharType(c);\n typeMap = pathStateMachine[mode];\n transition = typeMap[type] || typeMap[\"l\" /* PathCharTypes.ELSE */] || 8 /* States.ERROR */;\n // check parse error\n if (transition === 8 /* States.ERROR */) {\n return;\n }\n mode = transition[0];\n if (transition[1] !== undefined) {\n action = actions[transition[1]];\n if (action) {\n newChar = c;\n if (action() === false) {\n return;\n }\n }\n }\n // check parse finish\n if (mode === 7 /* States.AFTER_PATH */) {\n return keys;\n }\n }\n}\n// path token cache\nconst cache = new Map();\n/**\n * key-value message resolver\n *\n * @remarks\n * Resolves messages with the key-value structure. Note that messages with a hierarchical structure such as objects cannot be resolved\n *\n * @param obj - A target object to be resolved with path\n * @param path - A {@link Path | path} to resolve the value of message\n *\n * @returns A resolved {@link PathValue | path value}\n *\n * @VueI18nGeneral\n */\nfunction resolveWithKeyValue(obj, path) {\n return isObject(obj) ? obj[path] : null;\n}\n/**\n * message resolver\n *\n * @remarks\n * Resolves messages. messages with a hierarchical structure such as objects can be resolved. This resolver is used in VueI18n as default.\n *\n * @param obj - A target object to be resolved with path\n * @param path - A {@link Path | path} to resolve the value of message\n *\n * @returns A resolved {@link PathValue | path value}\n *\n * @VueI18nGeneral\n */\nfunction resolveValue(obj, path) {\n // check object\n if (!isObject(obj)) {\n return null;\n }\n // parse path\n let hit = cache.get(path);\n if (!hit) {\n hit = parse(path);\n if (hit) {\n cache.set(path, hit);\n }\n }\n // check hit\n if (!hit) {\n return null;\n }\n // resolve path value\n const len = hit.length;\n let last = obj;\n let i = 0;\n while (i < len) {\n const val = last[hit[i]];\n if (val === undefined) {\n return null;\n }\n if (isFunction(last)) {\n return null;\n }\n last = val;\n i++;\n }\n return last;\n}\n\nconst DEFAULT_MODIFIER = (str) => str;\nconst DEFAULT_MESSAGE = (ctx) => ''; // eslint-disable-line\nconst DEFAULT_MESSAGE_DATA_TYPE = 'text';\nconst DEFAULT_NORMALIZE = (values) => values.length === 0 ? '' : join(values);\nconst DEFAULT_INTERPOLATE = toDisplayString;\nfunction pluralDefault(choice, choicesLength) {\n choice = Math.abs(choice);\n if (choicesLength === 2) {\n // prettier-ignore\n return choice\n ? choice > 1\n ? 1\n : 0\n : 1;\n }\n return choice ? Math.min(choice, 2) : 0;\n}\nfunction getPluralIndex(options) {\n // prettier-ignore\n const index = isNumber(options.pluralIndex)\n ? options.pluralIndex\n : -1;\n // prettier-ignore\n return options.named && (isNumber(options.named.count) || isNumber(options.named.n))\n ? isNumber(options.named.count)\n ? options.named.count\n : isNumber(options.named.n)\n ? options.named.n\n : index\n : index;\n}\nfunction normalizeNamed(pluralIndex, props) {\n if (!props.count) {\n props.count = pluralIndex;\n }\n if (!props.n) {\n props.n = pluralIndex;\n }\n}\nfunction createMessageContext(options = {}) {\n const locale = options.locale;\n const pluralIndex = getPluralIndex(options);\n const pluralRule = isObject(options.pluralRules) &&\n isString(locale) &&\n isFunction(options.pluralRules[locale])\n ? options.pluralRules[locale]\n : pluralDefault;\n const orgPluralRule = isObject(options.pluralRules) &&\n isString(locale) &&\n isFunction(options.pluralRules[locale])\n ? pluralDefault\n : undefined;\n const plural = (messages) => {\n return messages[pluralRule(pluralIndex, messages.length, orgPluralRule)];\n };\n const _list = options.list || [];\n const list = (index) => _list[index];\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const _named = options.named || {};\n isNumber(options.pluralIndex) && normalizeNamed(pluralIndex, _named);\n const named = (key) => _named[key];\n function message(key) {\n // prettier-ignore\n const msg = isFunction(options.messages)\n ? options.messages(key)\n : isObject(options.messages)\n ? options.messages[key]\n : false;\n return !msg\n ? options.parent\n ? options.parent.message(key) // resolve from parent messages\n : DEFAULT_MESSAGE\n : msg;\n }\n const _modifier = (name) => options.modifiers\n ? options.modifiers[name]\n : DEFAULT_MODIFIER;\n const normalize = isPlainObject(options.processor) && isFunction(options.processor.normalize)\n ? options.processor.normalize\n : DEFAULT_NORMALIZE;\n const interpolate = isPlainObject(options.processor) &&\n isFunction(options.processor.interpolate)\n ? options.processor.interpolate\n : DEFAULT_INTERPOLATE;\n const type = isPlainObject(options.processor) && isString(options.processor.type)\n ? options.processor.type\n : DEFAULT_MESSAGE_DATA_TYPE;\n const linked = (key, ...args) => {\n const [arg1, arg2] = args;\n let type = 'text';\n let modifier = '';\n if (args.length === 1) {\n if (isObject(arg1)) {\n modifier = arg1.modifier || modifier;\n type = arg1.type || type;\n }\n else if (isString(arg1)) {\n modifier = arg1 || modifier;\n }\n }\n else if (args.length === 2) {\n if (isString(arg1)) {\n modifier = arg1 || modifier;\n }\n if (isString(arg2)) {\n type = arg2 || type;\n }\n }\n const ret = message(key)(ctx);\n const msg = \n // The message in vnode resolved with linked are returned as an array by processor.nomalize\n type === 'vnode' && isArray(ret) && modifier\n ? ret[0]\n : ret;\n return modifier ? _modifier(modifier)(msg, type) : msg;\n };\n const ctx = {\n [\"list\" /* HelperNameMap.LIST */]: list,\n [\"named\" /* HelperNameMap.NAMED */]: named,\n [\"plural\" /* HelperNameMap.PLURAL */]: plural,\n [\"linked\" /* HelperNameMap.LINKED */]: linked,\n [\"message\" /* HelperNameMap.MESSAGE */]: message,\n [\"type\" /* HelperNameMap.TYPE */]: type,\n [\"interpolate\" /* HelperNameMap.INTERPOLATE */]: interpolate,\n [\"normalize\" /* HelperNameMap.NORMALIZE */]: normalize,\n [\"values\" /* HelperNameMap.VALUES */]: assign({}, _list, _named)\n };\n return ctx;\n}\n\nlet devtools = null;\nfunction setDevToolsHook(hook) {\n devtools = hook;\n}\nfunction getDevToolsHook() {\n return devtools;\n}\nfunction initI18nDevTools(i18n, version, meta) {\n // TODO: queue if devtools is undefined\n devtools &&\n devtools.emit(\"i18n:init\" /* IntlifyDevToolsHooks.I18nInit */, {\n timestamp: Date.now(),\n i18n,\n version,\n meta\n });\n}\nconst translateDevTools = /* #__PURE__*/ createDevToolsHook(\"function:translate\" /* IntlifyDevToolsHooks.FunctionTranslate */);\nfunction createDevToolsHook(hook) {\n return (payloads) => devtools && devtools.emit(hook, payloads);\n}\n\nconst code$1 = CompileWarnCodes.__EXTEND_POINT__;\nconst inc$1 = incrementer(code$1);\nconst CoreWarnCodes = {\n NOT_FOUND_KEY: code$1, // 2\n FALLBACK_TO_TRANSLATE: inc$1(), // 3\n CANNOT_FORMAT_NUMBER: inc$1(), // 4\n FALLBACK_TO_NUMBER_FORMAT: inc$1(), // 5\n CANNOT_FORMAT_DATE: inc$1(), // 6\n FALLBACK_TO_DATE_FORMAT: inc$1(), // 7\n EXPERIMENTAL_CUSTOM_MESSAGE_COMPILER: inc$1(), // 8\n __EXTEND_POINT__: inc$1() // 9\n};\n/** @internal */\nconst warnMessages = {\n [CoreWarnCodes.NOT_FOUND_KEY]: `Not found '{key}' key in '{locale}' locale messages.`,\n [CoreWarnCodes.FALLBACK_TO_TRANSLATE]: `Fall back to translate '{key}' key with '{target}' locale.`,\n [CoreWarnCodes.CANNOT_FORMAT_NUMBER]: `Cannot format a number value due to not supported Intl.NumberFormat.`,\n [CoreWarnCodes.FALLBACK_TO_NUMBER_FORMAT]: `Fall back to number format '{key}' key with '{target}' locale.`,\n [CoreWarnCodes.CANNOT_FORMAT_DATE]: `Cannot format a date value due to not supported Intl.DateTimeFormat.`,\n [CoreWarnCodes.FALLBACK_TO_DATE_FORMAT]: `Fall back to datetime format '{key}' key with '{target}' locale.`,\n [CoreWarnCodes.EXPERIMENTAL_CUSTOM_MESSAGE_COMPILER]: `This project is using Custom Message Compiler, which is an experimental feature. It may receive breaking changes or be removed in the future.`\n};\nfunction getWarnMessage(code, ...args) {\n return format$1(warnMessages[code], ...args);\n}\n\nconst code = CompileErrorCodes.__EXTEND_POINT__;\nconst inc = incrementer(code);\nconst CoreErrorCodes = {\n INVALID_ARGUMENT: code, // 17\n INVALID_DATE_ARGUMENT: inc(), // 18\n INVALID_ISO_DATE_ARGUMENT: inc(), // 19\n NOT_SUPPORT_NON_STRING_MESSAGE: inc(), // 20\n NOT_SUPPORT_LOCALE_PROMISE_VALUE: inc(), // 21\n NOT_SUPPORT_LOCALE_ASYNC_FUNCTION: inc(), // 22\n NOT_SUPPORT_LOCALE_TYPE: inc(), // 23\n __EXTEND_POINT__: inc() // 24\n};\nfunction createCoreError(code) {\n return createCompileError(code, null, (process.env.NODE_ENV !== 'production') ? { messages: errorMessages } : undefined);\n}\n/** @internal */\nconst errorMessages = {\n [CoreErrorCodes.INVALID_ARGUMENT]: 'Invalid arguments',\n [CoreErrorCodes.INVALID_DATE_ARGUMENT]: 'The date provided is an invalid Date object.' +\n 'Make sure your Date represents a valid date.',\n [CoreErrorCodes.INVALID_ISO_DATE_ARGUMENT]: 'The argument provided is not a valid ISO date string',\n [CoreErrorCodes.NOT_SUPPORT_NON_STRING_MESSAGE]: 'Not support non-string message',\n [CoreErrorCodes.NOT_SUPPORT_LOCALE_PROMISE_VALUE]: 'cannot support promise value',\n [CoreErrorCodes.NOT_SUPPORT_LOCALE_ASYNC_FUNCTION]: 'cannot support async function',\n [CoreErrorCodes.NOT_SUPPORT_LOCALE_TYPE]: 'cannot support locale type'\n};\n\n/** @internal */\nfunction getLocale(context, options) {\n return options.locale != null\n ? resolveLocale(options.locale)\n : resolveLocale(context.locale);\n}\nlet _resolveLocale;\n/** @internal */\nfunction resolveLocale(locale) {\n if (isString(locale)) {\n return locale;\n }\n else {\n if (isFunction(locale)) {\n if (locale.resolvedOnce && _resolveLocale != null) {\n return _resolveLocale;\n }\n else if (locale.constructor.name === 'Function') {\n const resolve = locale();\n if (isPromise(resolve)) {\n throw createCoreError(CoreErrorCodes.NOT_SUPPORT_LOCALE_PROMISE_VALUE);\n }\n return (_resolveLocale = resolve);\n }\n else {\n throw createCoreError(CoreErrorCodes.NOT_SUPPORT_LOCALE_ASYNC_FUNCTION);\n }\n }\n else {\n throw createCoreError(CoreErrorCodes.NOT_SUPPORT_LOCALE_TYPE);\n }\n }\n}\n/**\n * Fallback with simple implemenation\n *\n * @remarks\n * A fallback locale function implemented with a simple fallback algorithm.\n *\n * Basically, it returns the value as specified in the `fallbackLocale` props, and is processed with the fallback inside intlify.\n *\n * @param ctx - A {@link CoreContext | context}\n * @param fallback - A {@link FallbackLocale | fallback locale}\n * @param start - A starting {@link Locale | locale}\n *\n * @returns Fallback locales\n *\n * @VueI18nGeneral\n */\nfunction fallbackWithSimple(ctx, fallback, start // eslint-disable-line @typescript-eslint/no-unused-vars\n) {\n // prettier-ignore\n return [...new Set([\n start,\n ...(isArray(fallback)\n ? fallback\n : isObject(fallback)\n ? Object.keys(fallback)\n : isString(fallback)\n ? [fallback]\n : [start])\n ])];\n}\n/**\n * Fallback with locale chain\n *\n * @remarks\n * A fallback locale function implemented with a fallback chain algorithm. It's used in VueI18n as default.\n *\n * @param ctx - A {@link CoreContext | context}\n * @param fallback - A {@link FallbackLocale | fallback locale}\n * @param start - A starting {@link Locale | locale}\n *\n * @returns Fallback locales\n *\n * @VueI18nSee [Fallbacking](../guide/essentials/fallback)\n *\n * @VueI18nGeneral\n */\nfunction fallbackWithLocaleChain(ctx, fallback, start) {\n const startLocale = isString(start) ? start : DEFAULT_LOCALE;\n const context = ctx;\n if (!context.__localeChainCache) {\n context.__localeChainCache = new Map();\n }\n let chain = context.__localeChainCache.get(startLocale);\n if (!chain) {\n chain = [];\n // first block defined by start\n let block = [start];\n // while any intervening block found\n while (isArray(block)) {\n block = appendBlockToChain(chain, block, fallback);\n }\n // prettier-ignore\n // last block defined by default\n const defaults = isArray(fallback) || !isPlainObject(fallback)\n ? fallback\n : fallback['default']\n ? fallback['default']\n : null;\n // convert defaults to array\n block = isString(defaults) ? [defaults] : defaults;\n if (isArray(block)) {\n appendBlockToChain(chain, block, false);\n }\n context.__localeChainCache.set(startLocale, chain);\n }\n return chain;\n}\nfunction appendBlockToChain(chain, block, blocks) {\n let follow = true;\n for (let i = 0; i < block.length && isBoolean(follow); i++) {\n const locale = block[i];\n if (isString(locale)) {\n follow = appendLocaleToChain(chain, block[i], blocks);\n }\n }\n return follow;\n}\nfunction appendLocaleToChain(chain, locale, blocks) {\n let follow;\n const tokens = locale.split('-');\n do {\n const target = tokens.join('-');\n follow = appendItemToChain(chain, target, blocks);\n tokens.splice(-1, 1);\n } while (tokens.length && follow === true);\n return follow;\n}\nfunction appendItemToChain(chain, target, blocks) {\n let follow = false;\n if (!chain.includes(target)) {\n follow = true;\n if (target) {\n follow = target[target.length - 1] !== '!';\n const locale = target.replace(/!/g, '');\n chain.push(locale);\n if ((isArray(blocks) || isPlainObject(blocks)) &&\n blocks[locale] // eslint-disable-line @typescript-eslint/no-explicit-any\n ) {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n follow = blocks[locale];\n }\n }\n }\n return follow;\n}\n\n/* eslint-disable @typescript-eslint/no-explicit-any */\n/**\n * Intlify core-base version\n * @internal\n */\nconst VERSION = '9.13.1';\nconst NOT_REOSLVED = -1;\nconst DEFAULT_LOCALE = 'en-US';\nconst MISSING_RESOLVE_VALUE = '';\nconst capitalize = (str) => `${str.charAt(0).toLocaleUpperCase()}${str.substr(1)}`;\nfunction getDefaultLinkedModifiers() {\n return {\n upper: (val, type) => {\n // prettier-ignore\n return type === 'text' && isString(val)\n ? val.toUpperCase()\n : type === 'vnode' && isObject(val) && '__v_isVNode' in val\n ? val.children.toUpperCase()\n : val;\n },\n lower: (val, type) => {\n // prettier-ignore\n return type === 'text' && isString(val)\n ? val.toLowerCase()\n : type === 'vnode' && isObject(val) && '__v_isVNode' in val\n ? val.children.toLowerCase()\n : val;\n },\n capitalize: (val, type) => {\n // prettier-ignore\n return (type === 'text' && isString(val)\n ? capitalize(val)\n : type === 'vnode' && isObject(val) && '__v_isVNode' in val\n ? capitalize(val.children)\n : val);\n }\n };\n}\nlet _compiler;\nfunction registerMessageCompiler(compiler) {\n _compiler = compiler;\n}\nlet _resolver;\n/**\n * Register the message resolver\n *\n * @param resolver - A {@link MessageResolver} function\n *\n * @VueI18nGeneral\n */\nfunction registerMessageResolver(resolver) {\n _resolver = resolver;\n}\nlet _fallbacker;\n/**\n * Register the locale fallbacker\n *\n * @param fallbacker - A {@link LocaleFallbacker} function\n *\n * @VueI18nGeneral\n */\nfunction registerLocaleFallbacker(fallbacker) {\n _fallbacker = fallbacker;\n}\n// Additional Meta for Intlify DevTools\nlet _additionalMeta = null;\n/* #__NO_SIDE_EFFECTS__ */\nconst setAdditionalMeta = (meta) => {\n _additionalMeta = meta;\n};\n/* #__NO_SIDE_EFFECTS__ */\nconst getAdditionalMeta = () => _additionalMeta;\nlet _fallbackContext = null;\nconst setFallbackContext = (context) => {\n _fallbackContext = context;\n};\nconst getFallbackContext = () => _fallbackContext;\n// ID for CoreContext\nlet _cid = 0;\nfunction createCoreContext(options = {}) {\n // setup options\n const onWarn = isFunction(options.onWarn) ? options.onWarn : warn;\n const version = isString(options.version) ? options.version : VERSION;\n const locale = isString(options.locale) || isFunction(options.locale)\n ? options.locale\n : DEFAULT_LOCALE;\n const _locale = isFunction(locale) ? DEFAULT_LOCALE : locale;\n const fallbackLocale = isArray(options.fallbackLocale) ||\n isPlainObject(options.fallbackLocale) ||\n isString(options.fallbackLocale) ||\n options.fallbackLocale === false\n ? options.fallbackLocale\n : _locale;\n const messages = isPlainObject(options.messages)\n ? options.messages\n : { [_locale]: {} };\n const datetimeFormats = isPlainObject(options.datetimeFormats)\n ? options.datetimeFormats\n : { [_locale]: {} }\n ;\n const numberFormats = isPlainObject(options.numberFormats)\n ? options.numberFormats\n : { [_locale]: {} }\n ;\n const modifiers = assign({}, options.modifiers || {}, getDefaultLinkedModifiers());\n const pluralRules = options.pluralRules || {};\n const missing = isFunction(options.missing) ? options.missing : null;\n const missingWarn = isBoolean(options.missingWarn) || isRegExp(options.missingWarn)\n ? options.missingWarn\n : true;\n const fallbackWarn = isBoolean(options.fallbackWarn) || isRegExp(options.fallbackWarn)\n ? options.fallbackWarn\n : true;\n const fallbackFormat = !!options.fallbackFormat;\n const unresolving = !!options.unresolving;\n const postTranslation = isFunction(options.postTranslation)\n ? options.postTranslation\n : null;\n const processor = isPlainObject(options.processor) ? options.processor : null;\n const warnHtmlMessage = isBoolean(options.warnHtmlMessage)\n ? options.warnHtmlMessage\n : true;\n const escapeParameter = !!options.escapeParameter;\n const messageCompiler = isFunction(options.messageCompiler)\n ? options.messageCompiler\n : _compiler;\n if ((process.env.NODE_ENV !== 'production') &&\n !false &&\n !false &&\n isFunction(options.messageCompiler)) {\n warnOnce(getWarnMessage(CoreWarnCodes.EXPERIMENTAL_CUSTOM_MESSAGE_COMPILER));\n }\n const messageResolver = isFunction(options.messageResolver)\n ? options.messageResolver\n : _resolver || resolveWithKeyValue;\n const localeFallbacker = isFunction(options.localeFallbacker)\n ? options.localeFallbacker\n : _fallbacker || fallbackWithSimple;\n const fallbackContext = isObject(options.fallbackContext)\n ? options.fallbackContext\n : undefined;\n // setup internal options\n const internalOptions = options;\n const __datetimeFormatters = isObject(internalOptions.__datetimeFormatters)\n ? internalOptions.__datetimeFormatters\n : new Map()\n ;\n const __numberFormatters = isObject(internalOptions.__numberFormatters)\n ? internalOptions.__numberFormatters\n : new Map()\n ;\n const __meta = isObject(internalOptions.__meta) ? internalOptions.__meta : {};\n _cid++;\n const context = {\n version,\n cid: _cid,\n locale,\n fallbackLocale,\n messages,\n modifiers,\n pluralRules,\n missing,\n missingWarn,\n fallbackWarn,\n fallbackFormat,\n unresolving,\n postTranslation,\n processor,\n warnHtmlMessage,\n escapeParameter,\n messageCompiler,\n messageResolver,\n localeFallbacker,\n fallbackContext,\n onWarn,\n __meta\n };\n {\n context.datetimeFormats = datetimeFormats;\n context.numberFormats = numberFormats;\n context.__datetimeFormatters = __datetimeFormatters;\n context.__numberFormatters = __numberFormatters;\n }\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production')) {\n context.__v_emitter =\n internalOptions.__v_emitter != null\n ? internalOptions.__v_emitter\n : undefined;\n }\n // NOTE: experimental !!\n if ((process.env.NODE_ENV !== 'production') || __INTLIFY_PROD_DEVTOOLS__) {\n initI18nDevTools(context, version, __meta);\n }\n return context;\n}\n/** @internal */\nfunction isTranslateFallbackWarn(fallback, key) {\n return fallback instanceof RegExp ? fallback.test(key) : fallback;\n}\n/** @internal */\nfunction isTranslateMissingWarn(missing, key) {\n return missing instanceof RegExp ? missing.test(key) : missing;\n}\n/** @internal */\nfunction handleMissing(context, key, locale, missingWarn, type) {\n const { missing, onWarn } = context;\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production')) {\n const emitter = context.__v_emitter;\n if (emitter) {\n emitter.emit(\"missing\" /* VueDevToolsTimelineEvents.MISSING */, {\n locale,\n key,\n type,\n groupId: `${type}:${key}`\n });\n }\n }\n if (missing !== null) {\n const ret = missing(context, locale, key, type);\n return isString(ret) ? ret : key;\n }\n else {\n if ((process.env.NODE_ENV !== 'production') && isTranslateMissingWarn(missingWarn, key)) {\n onWarn(getWarnMessage(CoreWarnCodes.NOT_FOUND_KEY, { key, locale }));\n }\n return key;\n }\n}\n/** @internal */\nfunction updateFallbackLocale(ctx, locale, fallback) {\n const context = ctx;\n context.__localeChainCache = new Map();\n ctx.localeFallbacker(ctx, fallback, locale);\n}\n/** @internal */\nfunction isAlmostSameLocale(locale, compareLocale) {\n if (locale === compareLocale)\n return false;\n return locale.split('-')[0] === compareLocale.split('-')[0];\n}\n/** @internal */\nfunction isImplicitFallback(targetLocale, locales) {\n const index = locales.indexOf(targetLocale);\n if (index === -1) {\n return false;\n }\n for (let i = index + 1; i < locales.length; i++) {\n if (isAlmostSameLocale(targetLocale, locales[i])) {\n return true;\n }\n }\n return false;\n}\n/* eslint-enable @typescript-eslint/no-explicit-any */\n\nfunction format(ast) {\n const msg = (ctx) => formatParts(ctx, ast);\n return msg;\n}\nfunction formatParts(ctx, ast) {\n const body = ast.b || ast.body;\n if ((body.t || body.type) === 1 /* NodeTypes.Plural */) {\n const plural = body;\n const cases = plural.c || plural.cases;\n return ctx.plural(cases.reduce((messages, c) => [\n ...messages,\n formatMessageParts(ctx, c)\n ], []));\n }\n else {\n return formatMessageParts(ctx, body);\n }\n}\nfunction formatMessageParts(ctx, node) {\n const _static = node.s || node.static;\n if (_static) {\n return ctx.type === 'text'\n ? _static\n : ctx.normalize([_static]);\n }\n else {\n const messages = (node.i || node.items).reduce((acm, c) => [...acm, formatMessagePart(ctx, c)], []);\n return ctx.normalize(messages);\n }\n}\nfunction formatMessagePart(ctx, node) {\n const type = node.t || node.type;\n switch (type) {\n case 3 /* NodeTypes.Text */: {\n const text = node;\n return (text.v || text.value);\n }\n case 9 /* NodeTypes.Literal */: {\n const literal = node;\n return (literal.v || literal.value);\n }\n case 4 /* NodeTypes.Named */: {\n const named = node;\n return ctx.interpolate(ctx.named(named.k || named.key));\n }\n case 5 /* NodeTypes.List */: {\n const list = node;\n return ctx.interpolate(ctx.list(list.i != null ? list.i : list.index));\n }\n case 6 /* NodeTypes.Linked */: {\n const linked = node;\n const modifier = linked.m || linked.modifier;\n return ctx.linked(formatMessagePart(ctx, linked.k || linked.key), modifier ? formatMessagePart(ctx, modifier) : undefined, ctx.type);\n }\n case 7 /* NodeTypes.LinkedKey */: {\n const linkedKey = node;\n return (linkedKey.v || linkedKey.value);\n }\n case 8 /* NodeTypes.LinkedModifier */: {\n const linkedModifier = node;\n return (linkedModifier.v || linkedModifier.value);\n }\n default:\n throw new Error(`unhandled node type on format message part: ${type}`);\n }\n}\n\nconst WARN_MESSAGE = `Detected HTML in '{source}' message. Recommend not using HTML messages to avoid XSS.`;\nfunction checkHtmlMessage(source, warnHtmlMessage) {\n if (warnHtmlMessage && detectHtmlTag(source)) {\n warn(format$1(WARN_MESSAGE, { source }));\n }\n}\nconst defaultOnCacheKey = (message) => message;\nlet compileCache = Object.create(null);\nfunction onCompileWarn(_warn) {\n if (_warn.code === CompileWarnCodes.USE_MODULO_SYNTAX) {\n warn(`The use of named interpolation with modulo syntax is deprecated. ` +\n `It will be removed in v10.\\n` +\n `reference: https://vue-i18n.intlify.dev/guide/essentials/syntax#rails-i18n-format \\n` +\n `(message compiler warning message: ${_warn.message})`);\n }\n}\nfunction clearCompileCache() {\n compileCache = Object.create(null);\n}\nconst isMessageAST = (val) => isObject(val) &&\n (val.t === 0 || val.type === 0) &&\n ('b' in val || 'body' in val);\nfunction baseCompile(message, options = {}) {\n // error detecting on compile\n let detectError = false;\n const onError = options.onError || defaultOnError;\n options.onError = (err) => {\n detectError = true;\n onError(err);\n };\n // compile with mesasge-compiler\n return { ...baseCompile$1(message, options), detectError };\n}\n/* #__NO_SIDE_EFFECTS__ */\nconst compileToFunction = (message, context) => {\n if (!isString(message)) {\n throw createCoreError(CoreErrorCodes.NOT_SUPPORT_NON_STRING_MESSAGE);\n }\n // set onWarn\n if ((process.env.NODE_ENV !== 'production')) {\n context.onWarn = onCompileWarn;\n }\n {\n // check HTML message\n const warnHtmlMessage = isBoolean(context.warnHtmlMessage)\n ? context.warnHtmlMessage\n : true;\n (process.env.NODE_ENV !== 'production') && checkHtmlMessage(message, warnHtmlMessage);\n // check caches\n const onCacheKey = context.onCacheKey || defaultOnCacheKey;\n const cacheKey = onCacheKey(message);\n const cached = compileCache[cacheKey];\n if (cached) {\n return cached;\n }\n // compile\n const { code, detectError } = baseCompile(message, context);\n // evaluate function\n const msg = new Function(`return ${code}`)();\n // if occurred compile error, don't cache\n return !detectError\n ? (compileCache[cacheKey] = msg)\n : msg;\n }\n};\nfunction compile(message, context) {\n // set onWarn\n if ((process.env.NODE_ENV !== 'production')) {\n context.onWarn = onCompileWarn;\n }\n if (((__INTLIFY_JIT_COMPILATION__ && !__INTLIFY_DROP_MESSAGE_COMPILER__)) &&\n isString(message)) {\n // check HTML message\n const warnHtmlMessage = isBoolean(context.warnHtmlMessage)\n ? context.warnHtmlMessage\n : true;\n (process.env.NODE_ENV !== 'production') && checkHtmlMessage(message, warnHtmlMessage);\n // check caches\n const onCacheKey = context.onCacheKey || defaultOnCacheKey;\n const cacheKey = onCacheKey(message);\n const cached = compileCache[cacheKey];\n if (cached) {\n return cached;\n }\n // compile with JIT mode\n const { ast, detectError } = baseCompile(message, {\n ...context,\n location: (process.env.NODE_ENV !== 'production'),\n jit: true\n });\n // compose message function from AST\n const msg = format(ast);\n // if occurred compile error, don't cache\n return !detectError\n ? (compileCache[cacheKey] = msg)\n : msg;\n }\n else {\n if ((process.env.NODE_ENV !== 'production') && !isMessageAST(message)) {\n warn(`the message that is resolve with key '${context.key}' is not supported for jit compilation`);\n return (() => message);\n }\n // AST case (passed from bundler)\n const cacheKey = message.cacheKey;\n if (cacheKey) {\n const cached = compileCache[cacheKey];\n if (cached) {\n return cached;\n }\n // compose message function from message (AST)\n return (compileCache[cacheKey] =\n format(message));\n }\n else {\n return format(message);\n }\n }\n}\n\nconst NOOP_MESSAGE_FUNCTION = () => '';\nconst isMessageFunction = (val) => isFunction(val);\n// implementation of `translate` function\nfunction translate(context, ...args) {\n const { fallbackFormat, postTranslation, unresolving, messageCompiler, fallbackLocale, messages } = context;\n const [key, options] = parseTranslateArgs(...args);\n const missingWarn = isBoolean(options.missingWarn)\n ? options.missingWarn\n : context.missingWarn;\n const fallbackWarn = isBoolean(options.fallbackWarn)\n ? options.fallbackWarn\n : context.fallbackWarn;\n const escapeParameter = isBoolean(options.escapeParameter)\n ? options.escapeParameter\n : context.escapeParameter;\n const resolvedMessage = !!options.resolvedMessage;\n // prettier-ignore\n const defaultMsgOrKey = isString(options.default) || isBoolean(options.default) // default by function option\n ? !isBoolean(options.default)\n ? options.default\n : (!messageCompiler ? () => key : key)\n : fallbackFormat // default by `fallbackFormat` option\n ? (!messageCompiler ? () => key : key)\n : '';\n const enableDefaultMsg = fallbackFormat || defaultMsgOrKey !== '';\n const locale = getLocale(context, options);\n // escape params\n escapeParameter && escapeParams(options);\n // resolve message format\n // eslint-disable-next-line prefer-const\n let [formatScope, targetLocale, message] = !resolvedMessage\n ? resolveMessageFormat(context, key, locale, fallbackLocale, fallbackWarn, missingWarn)\n : [\n key,\n locale,\n messages[locale] || {}\n ];\n // NOTE:\n // Fix to work around `ssrTransfrom` bug in Vite.\n // https://github.com/vitejs/vite/issues/4306\n // To get around this, use temporary variables.\n // https://github.com/nuxt/framework/issues/1461#issuecomment-954606243\n let format = formatScope;\n // if you use default message, set it as message format!\n let cacheBaseKey = key;\n if (!resolvedMessage &&\n !(isString(format) ||\n isMessageAST(format) ||\n isMessageFunction(format))) {\n if (enableDefaultMsg) {\n format = defaultMsgOrKey;\n cacheBaseKey = format;\n }\n }\n // checking message format and target locale\n if (!resolvedMessage &&\n (!(isString(format) ||\n isMessageAST(format) ||\n isMessageFunction(format)) ||\n !isString(targetLocale))) {\n return unresolving ? NOT_REOSLVED : key;\n }\n // TODO: refactor\n if ((process.env.NODE_ENV !== 'production') && isString(format) && context.messageCompiler == null) {\n warn(`The message format compilation is not supported in this build. ` +\n `Because message compiler isn't included. ` +\n `You need to pre-compilation all message format. ` +\n `So translate function return '${key}'.`);\n return key;\n }\n // setup compile error detecting\n let occurred = false;\n const onError = () => {\n occurred = true;\n };\n // compile message format\n const msg = !isMessageFunction(format)\n ? compileMessageFormat(context, key, targetLocale, format, cacheBaseKey, onError)\n : format;\n // if occurred compile error, return the message format\n if (occurred) {\n return format;\n }\n // evaluate message with context\n const ctxOptions = getMessageContextOptions(context, targetLocale, message, options);\n const msgContext = createMessageContext(ctxOptions);\n const messaged = evaluateMessage(context, msg, msgContext);\n // if use post translation option, proceed it with handler\n const ret = postTranslation\n ? postTranslation(messaged, key)\n : messaged;\n // NOTE: experimental !!\n if ((process.env.NODE_ENV !== 'production') || __INTLIFY_PROD_DEVTOOLS__) {\n // prettier-ignore\n const payloads = {\n timestamp: Date.now(),\n key: isString(key)\n ? key\n : isMessageFunction(format)\n ? format.key\n : '',\n locale: targetLocale || (isMessageFunction(format)\n ? format.locale\n : ''),\n format: isString(format)\n ? format\n : isMessageFunction(format)\n ? format.source\n : '',\n message: ret\n };\n payloads.meta = assign({}, context.__meta, getAdditionalMeta() || {});\n translateDevTools(payloads);\n }\n return ret;\n}\nfunction escapeParams(options) {\n if (isArray(options.list)) {\n options.list = options.list.map(item => isString(item) ? escapeHtml(item) : item);\n }\n else if (isObject(options.named)) {\n Object.keys(options.named).forEach(key => {\n if (isString(options.named[key])) {\n options.named[key] = escapeHtml(options.named[key]);\n }\n });\n }\n}\nfunction resolveMessageFormat(context, key, locale, fallbackLocale, fallbackWarn, missingWarn) {\n const { messages, onWarn, messageResolver: resolveValue, localeFallbacker } = context;\n const locales = localeFallbacker(context, fallbackLocale, locale); // eslint-disable-line @typescript-eslint/no-explicit-any\n let message = {};\n let targetLocale;\n let format = null;\n let from = locale;\n let to = null;\n const type = 'translate';\n for (let i = 0; i < locales.length; i++) {\n targetLocale = to = locales[i];\n if ((process.env.NODE_ENV !== 'production') &&\n locale !== targetLocale &&\n !isAlmostSameLocale(locale, targetLocale) &&\n isTranslateFallbackWarn(fallbackWarn, key)) {\n onWarn(getWarnMessage(CoreWarnCodes.FALLBACK_TO_TRANSLATE, {\n key,\n target: targetLocale\n }));\n }\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production') && locale !== targetLocale) {\n const emitter = context.__v_emitter;\n if (emitter) {\n emitter.emit(\"fallback\" /* VueDevToolsTimelineEvents.FALBACK */, {\n type,\n key,\n from,\n to,\n groupId: `${type}:${key}`\n });\n }\n }\n message =\n messages[targetLocale] || {};\n // for vue-devtools timeline event\n let start = null;\n let startTag;\n let endTag;\n if ((process.env.NODE_ENV !== 'production') && inBrowser) {\n start = window.performance.now();\n startTag = 'intlify-message-resolve-start';\n endTag = 'intlify-message-resolve-end';\n mark && mark(startTag);\n }\n if ((format = resolveValue(message, key)) === null) {\n // if null, resolve with object key path\n format = message[key]; // eslint-disable-line @typescript-eslint/no-explicit-any\n }\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production') && inBrowser) {\n const end = window.performance.now();\n const emitter = context.__v_emitter;\n if (emitter && start && format) {\n emitter.emit(\"message-resolve\" /* VueDevToolsTimelineEvents.MESSAGE_RESOLVE */, {\n type: \"message-resolve\" /* VueDevToolsTimelineEvents.MESSAGE_RESOLVE */,\n key,\n message: format,\n time: end - start,\n groupId: `${type}:${key}`\n });\n }\n if (startTag && endTag && mark && measure) {\n mark(endTag);\n measure('intlify message resolve', startTag, endTag);\n }\n }\n if (isString(format) || isMessageAST(format) || isMessageFunction(format)) {\n break;\n }\n if (!isImplicitFallback(targetLocale, locales)) {\n const missingRet = handleMissing(context, // eslint-disable-line @typescript-eslint/no-explicit-any\n key, targetLocale, missingWarn, type);\n if (missingRet !== key) {\n format = missingRet;\n }\n }\n from = to;\n }\n return [format, targetLocale, message];\n}\nfunction compileMessageFormat(context, key, targetLocale, format, cacheBaseKey, onError) {\n const { messageCompiler, warnHtmlMessage } = context;\n if (isMessageFunction(format)) {\n const msg = format;\n msg.locale = msg.locale || targetLocale;\n msg.key = msg.key || key;\n return msg;\n }\n if (messageCompiler == null) {\n const msg = (() => format);\n msg.locale = targetLocale;\n msg.key = key;\n return msg;\n }\n // for vue-devtools timeline event\n let start = null;\n let startTag;\n let endTag;\n if ((process.env.NODE_ENV !== 'production') && inBrowser) {\n start = window.performance.now();\n startTag = 'intlify-message-compilation-start';\n endTag = 'intlify-message-compilation-end';\n mark && mark(startTag);\n }\n const msg = messageCompiler(format, getCompileContext(context, targetLocale, cacheBaseKey, format, warnHtmlMessage, onError));\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production') && inBrowser) {\n const end = window.performance.now();\n const emitter = context.__v_emitter;\n if (emitter && start) {\n emitter.emit(\"message-compilation\" /* VueDevToolsTimelineEvents.MESSAGE_COMPILATION */, {\n type: \"message-compilation\" /* VueDevToolsTimelineEvents.MESSAGE_COMPILATION */,\n message: format,\n time: end - start,\n groupId: `${'translate'}:${key}`\n });\n }\n if (startTag && endTag && mark && measure) {\n mark(endTag);\n measure('intlify message compilation', startTag, endTag);\n }\n }\n msg.locale = targetLocale;\n msg.key = key;\n msg.source = format;\n return msg;\n}\nfunction evaluateMessage(context, msg, msgCtx) {\n // for vue-devtools timeline event\n let start = null;\n let startTag;\n let endTag;\n if ((process.env.NODE_ENV !== 'production') && inBrowser) {\n start = window.performance.now();\n startTag = 'intlify-message-evaluation-start';\n endTag = 'intlify-message-evaluation-end';\n mark && mark(startTag);\n }\n const messaged = msg(msgCtx);\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production') && inBrowser) {\n const end = window.performance.now();\n const emitter = context.__v_emitter;\n if (emitter && start) {\n emitter.emit(\"message-evaluation\" /* VueDevToolsTimelineEvents.MESSAGE_EVALUATION */, {\n type: \"message-evaluation\" /* VueDevToolsTimelineEvents.MESSAGE_EVALUATION */,\n value: messaged,\n time: end - start,\n groupId: `${'translate'}:${msg.key}`\n });\n }\n if (startTag && endTag && mark && measure) {\n mark(endTag);\n measure('intlify message evaluation', startTag, endTag);\n }\n }\n return messaged;\n}\n/** @internal */\nfunction parseTranslateArgs(...args) {\n const [arg1, arg2, arg3] = args;\n const options = {};\n if (!isString(arg1) &&\n !isNumber(arg1) &&\n !isMessageFunction(arg1) &&\n !isMessageAST(arg1)) {\n throw createCoreError(CoreErrorCodes.INVALID_ARGUMENT);\n }\n // prettier-ignore\n const key = isNumber(arg1)\n ? String(arg1)\n : isMessageFunction(arg1)\n ? arg1\n : arg1;\n if (isNumber(arg2)) {\n options.plural = arg2;\n }\n else if (isString(arg2)) {\n options.default = arg2;\n }\n else if (isPlainObject(arg2) && !isEmptyObject(arg2)) {\n options.named = arg2;\n }\n else if (isArray(arg2)) {\n options.list = arg2;\n }\n if (isNumber(arg3)) {\n options.plural = arg3;\n }\n else if (isString(arg3)) {\n options.default = arg3;\n }\n else if (isPlainObject(arg3)) {\n assign(options, arg3);\n }\n return [key, options];\n}\nfunction getCompileContext(context, locale, key, source, warnHtmlMessage, onError) {\n return {\n locale,\n key,\n warnHtmlMessage,\n onError: (err) => {\n onError && onError(err);\n if ((process.env.NODE_ENV !== 'production')) {\n const _source = getSourceForCodeFrame(source);\n const message = `Message compilation error: ${err.message}`;\n const codeFrame = err.location &&\n _source &&\n generateCodeFrame(_source, err.location.start.offset, err.location.end.offset);\n const emitter = context.__v_emitter;\n if (emitter && _source) {\n emitter.emit(\"compile-error\" /* VueDevToolsTimelineEvents.COMPILE_ERROR */, {\n message: _source,\n error: err.message,\n start: err.location && err.location.start.offset,\n end: err.location && err.location.end.offset,\n groupId: `${'translate'}:${key}`\n });\n }\n console.error(codeFrame ? `${message}\\n${codeFrame}` : message);\n }\n else {\n throw err;\n }\n },\n onCacheKey: (source) => generateFormatCacheKey(locale, key, source)\n };\n}\nfunction getSourceForCodeFrame(source) {\n if (isString(source)) {\n return source;\n }\n else {\n if (source.loc && source.loc.source) {\n return source.loc.source;\n }\n }\n}\nfunction getMessageContextOptions(context, locale, message, options) {\n const { modifiers, pluralRules, messageResolver: resolveValue, fallbackLocale, fallbackWarn, missingWarn, fallbackContext } = context;\n const resolveMessage = (key) => {\n let val = resolveValue(message, key);\n // fallback to root context\n if (val == null && fallbackContext) {\n const [, , message] = resolveMessageFormat(fallbackContext, key, locale, fallbackLocale, fallbackWarn, missingWarn);\n val = resolveValue(message, key);\n }\n if (isString(val) || isMessageAST(val)) {\n let occurred = false;\n const onError = () => {\n occurred = true;\n };\n const msg = compileMessageFormat(context, key, locale, val, key, onError);\n return !occurred\n ? msg\n : NOOP_MESSAGE_FUNCTION;\n }\n else if (isMessageFunction(val)) {\n return val;\n }\n else {\n // TODO: should be implemented warning message\n return NOOP_MESSAGE_FUNCTION;\n }\n };\n const ctxOptions = {\n locale,\n modifiers,\n pluralRules,\n messages: resolveMessage\n };\n if (context.processor) {\n ctxOptions.processor = context.processor;\n }\n if (options.list) {\n ctxOptions.list = options.list;\n }\n if (options.named) {\n ctxOptions.named = options.named;\n }\n if (isNumber(options.plural)) {\n ctxOptions.pluralIndex = options.plural;\n }\n return ctxOptions;\n}\n\nconst intlDefined = typeof Intl !== 'undefined';\nconst Availabilities = {\n dateTimeFormat: intlDefined && typeof Intl.DateTimeFormat !== 'undefined',\n numberFormat: intlDefined && typeof Intl.NumberFormat !== 'undefined'\n};\n\n// implementation of `datetime` function\nfunction datetime(context, ...args) {\n const { datetimeFormats, unresolving, fallbackLocale, onWarn, localeFallbacker } = context;\n const { __datetimeFormatters } = context;\n if ((process.env.NODE_ENV !== 'production') && !Availabilities.dateTimeFormat) {\n onWarn(getWarnMessage(CoreWarnCodes.CANNOT_FORMAT_DATE));\n return MISSING_RESOLVE_VALUE;\n }\n const [key, value, options, overrides] = parseDateTimeArgs(...args);\n const missingWarn = isBoolean(options.missingWarn)\n ? options.missingWarn\n : context.missingWarn;\n const fallbackWarn = isBoolean(options.fallbackWarn)\n ? options.fallbackWarn\n : context.fallbackWarn;\n const part = !!options.part;\n const locale = getLocale(context, options);\n const locales = localeFallbacker(context, // eslint-disable-line @typescript-eslint/no-explicit-any\n fallbackLocale, locale);\n if (!isString(key) || key === '') {\n return new Intl.DateTimeFormat(locale, overrides).format(value);\n }\n // resolve format\n let datetimeFormat = {};\n let targetLocale;\n let format = null;\n let from = locale;\n let to = null;\n const type = 'datetime format';\n for (let i = 0; i < locales.length; i++) {\n targetLocale = to = locales[i];\n if ((process.env.NODE_ENV !== 'production') &&\n locale !== targetLocale &&\n isTranslateFallbackWarn(fallbackWarn, key)) {\n onWarn(getWarnMessage(CoreWarnCodes.FALLBACK_TO_DATE_FORMAT, {\n key,\n target: targetLocale\n }));\n }\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production') && locale !== targetLocale) {\n const emitter = context.__v_emitter;\n if (emitter) {\n emitter.emit(\"fallback\" /* VueDevToolsTimelineEvents.FALBACK */, {\n type,\n key,\n from,\n to,\n groupId: `${type}:${key}`\n });\n }\n }\n datetimeFormat =\n datetimeFormats[targetLocale] || {};\n format = datetimeFormat[key];\n if (isPlainObject(format))\n break;\n handleMissing(context, key, targetLocale, missingWarn, type); // eslint-disable-line @typescript-eslint/no-explicit-any\n from = to;\n }\n // checking format and target locale\n if (!isPlainObject(format) || !isString(targetLocale)) {\n return unresolving ? NOT_REOSLVED : key;\n }\n let id = `${targetLocale}__${key}`;\n if (!isEmptyObject(overrides)) {\n id = `${id}__${JSON.stringify(overrides)}`;\n }\n let formatter = __datetimeFormatters.get(id);\n if (!formatter) {\n formatter = new Intl.DateTimeFormat(targetLocale, assign({}, format, overrides));\n __datetimeFormatters.set(id, formatter);\n }\n return !part ? formatter.format(value) : formatter.formatToParts(value);\n}\n/** @internal */\nconst DATETIME_FORMAT_OPTIONS_KEYS = [\n 'localeMatcher',\n 'weekday',\n 'era',\n 'year',\n 'month',\n 'day',\n 'hour',\n 'minute',\n 'second',\n 'timeZoneName',\n 'formatMatcher',\n 'hour12',\n 'timeZone',\n 'dateStyle',\n 'timeStyle',\n 'calendar',\n 'dayPeriod',\n 'numberingSystem',\n 'hourCycle',\n 'fractionalSecondDigits'\n];\n/** @internal */\nfunction parseDateTimeArgs(...args) {\n const [arg1, arg2, arg3, arg4] = args;\n const options = {};\n let overrides = {};\n let value;\n if (isString(arg1)) {\n // Only allow ISO strings - other date formats are often supported,\n // but may cause different results in different browsers.\n const matches = arg1.match(/(\\d{4}-\\d{2}-\\d{2})(T|\\s)?(.*)/);\n if (!matches) {\n throw createCoreError(CoreErrorCodes.INVALID_ISO_DATE_ARGUMENT);\n }\n // Some browsers can not parse the iso datetime separated by space,\n // this is a compromise solution by replace the 'T'/' ' with 'T'\n const dateTime = matches[3]\n ? matches[3].trim().startsWith('T')\n ? `${matches[1].trim()}${matches[3].trim()}`\n : `${matches[1].trim()}T${matches[3].trim()}`\n : matches[1].trim();\n value = new Date(dateTime);\n try {\n // This will fail if the date is not valid\n value.toISOString();\n }\n catch (e) {\n throw createCoreError(CoreErrorCodes.INVALID_ISO_DATE_ARGUMENT);\n }\n }\n else if (isDate(arg1)) {\n if (isNaN(arg1.getTime())) {\n throw createCoreError(CoreErrorCodes.INVALID_DATE_ARGUMENT);\n }\n value = arg1;\n }\n else if (isNumber(arg1)) {\n value = arg1;\n }\n else {\n throw createCoreError(CoreErrorCodes.INVALID_ARGUMENT);\n }\n if (isString(arg2)) {\n options.key = arg2;\n }\n else if (isPlainObject(arg2)) {\n Object.keys(arg2).forEach(key => {\n if (DATETIME_FORMAT_OPTIONS_KEYS.includes(key)) {\n overrides[key] = arg2[key];\n }\n else {\n options[key] = arg2[key];\n }\n });\n }\n if (isString(arg3)) {\n options.locale = arg3;\n }\n else if (isPlainObject(arg3)) {\n overrides = arg3;\n }\n if (isPlainObject(arg4)) {\n overrides = arg4;\n }\n return [options.key || '', value, options, overrides];\n}\n/** @internal */\nfunction clearDateTimeFormat(ctx, locale, format) {\n const context = ctx;\n for (const key in format) {\n const id = `${locale}__${key}`;\n if (!context.__datetimeFormatters.has(id)) {\n continue;\n }\n context.__datetimeFormatters.delete(id);\n }\n}\n\n// implementation of `number` function\nfunction number(context, ...args) {\n const { numberFormats, unresolving, fallbackLocale, onWarn, localeFallbacker } = context;\n const { __numberFormatters } = context;\n if ((process.env.NODE_ENV !== 'production') && !Availabilities.numberFormat) {\n onWarn(getWarnMessage(CoreWarnCodes.CANNOT_FORMAT_NUMBER));\n return MISSING_RESOLVE_VALUE;\n }\n const [key, value, options, overrides] = parseNumberArgs(...args);\n const missingWarn = isBoolean(options.missingWarn)\n ? options.missingWarn\n : context.missingWarn;\n const fallbackWarn = isBoolean(options.fallbackWarn)\n ? options.fallbackWarn\n : context.fallbackWarn;\n const part = !!options.part;\n const locale = getLocale(context, options);\n const locales = localeFallbacker(context, // eslint-disable-line @typescript-eslint/no-explicit-any\n fallbackLocale, locale);\n if (!isString(key) || key === '') {\n return new Intl.NumberFormat(locale, overrides).format(value);\n }\n // resolve format\n let numberFormat = {};\n let targetLocale;\n let format = null;\n let from = locale;\n let to = null;\n const type = 'number format';\n for (let i = 0; i < locales.length; i++) {\n targetLocale = to = locales[i];\n if ((process.env.NODE_ENV !== 'production') &&\n locale !== targetLocale &&\n isTranslateFallbackWarn(fallbackWarn, key)) {\n onWarn(getWarnMessage(CoreWarnCodes.FALLBACK_TO_NUMBER_FORMAT, {\n key,\n target: targetLocale\n }));\n }\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production') && locale !== targetLocale) {\n const emitter = context.__v_emitter;\n if (emitter) {\n emitter.emit(\"fallback\" /* VueDevToolsTimelineEvents.FALBACK */, {\n type,\n key,\n from,\n to,\n groupId: `${type}:${key}`\n });\n }\n }\n numberFormat =\n numberFormats[targetLocale] || {};\n format = numberFormat[key];\n if (isPlainObject(format))\n break;\n handleMissing(context, key, targetLocale, missingWarn, type); // eslint-disable-line @typescript-eslint/no-explicit-any\n from = to;\n }\n // checking format and target locale\n if (!isPlainObject(format) || !isString(targetLocale)) {\n return unresolving ? NOT_REOSLVED : key;\n }\n let id = `${targetLocale}__${key}`;\n if (!isEmptyObject(overrides)) {\n id = `${id}__${JSON.stringify(overrides)}`;\n }\n let formatter = __numberFormatters.get(id);\n if (!formatter) {\n formatter = new Intl.NumberFormat(targetLocale, assign({}, format, overrides));\n __numberFormatters.set(id, formatter);\n }\n return !part ? formatter.format(value) : formatter.formatToParts(value);\n}\n/** @internal */\nconst NUMBER_FORMAT_OPTIONS_KEYS = [\n 'localeMatcher',\n 'style',\n 'currency',\n 'currencyDisplay',\n 'currencySign',\n 'useGrouping',\n 'minimumIntegerDigits',\n 'minimumFractionDigits',\n 'maximumFractionDigits',\n 'minimumSignificantDigits',\n 'maximumSignificantDigits',\n 'compactDisplay',\n 'notation',\n 'signDisplay',\n 'unit',\n 'unitDisplay',\n 'roundingMode',\n 'roundingPriority',\n 'roundingIncrement',\n 'trailingZeroDisplay'\n];\n/** @internal */\nfunction parseNumberArgs(...args) {\n const [arg1, arg2, arg3, arg4] = args;\n const options = {};\n let overrides = {};\n if (!isNumber(arg1)) {\n throw createCoreError(CoreErrorCodes.INVALID_ARGUMENT);\n }\n const value = arg1;\n if (isString(arg2)) {\n options.key = arg2;\n }\n else if (isPlainObject(arg2)) {\n Object.keys(arg2).forEach(key => {\n if (NUMBER_FORMAT_OPTIONS_KEYS.includes(key)) {\n overrides[key] = arg2[key];\n }\n else {\n options[key] = arg2[key];\n }\n });\n }\n if (isString(arg3)) {\n options.locale = arg3;\n }\n else if (isPlainObject(arg3)) {\n overrides = arg3;\n }\n if (isPlainObject(arg4)) {\n overrides = arg4;\n }\n return [options.key || '', value, options, overrides];\n}\n/** @internal */\nfunction clearNumberFormat(ctx, locale, format) {\n const context = ctx;\n for (const key in format) {\n const id = `${locale}__${key}`;\n if (!context.__numberFormatters.has(id)) {\n continue;\n }\n context.__numberFormatters.delete(id);\n }\n}\n\n{\n initFeatureFlags();\n}\n\nexport { CoreErrorCodes, CoreWarnCodes, DATETIME_FORMAT_OPTIONS_KEYS, DEFAULT_LOCALE, DEFAULT_MESSAGE_DATA_TYPE, MISSING_RESOLVE_VALUE, NOT_REOSLVED, NUMBER_FORMAT_OPTIONS_KEYS, VERSION, clearCompileCache, clearDateTimeFormat, clearNumberFormat, compile, compileToFunction, createCoreContext, createCoreError, createMessageContext, datetime, fallbackWithLocaleChain, fallbackWithSimple, getAdditionalMeta, getDevToolsHook, getFallbackContext, getLocale, getWarnMessage, handleMissing, initI18nDevTools, isAlmostSameLocale, isImplicitFallback, isMessageAST, isMessageFunction, isTranslateFallbackWarn, isTranslateMissingWarn, number, parse, parseDateTimeArgs, parseNumberArgs, parseTranslateArgs, registerLocaleFallbacker, registerMessageCompiler, registerMessageResolver, resolveLocale, resolveValue, resolveWithKeyValue, setAdditionalMeta, setDevToolsHook, setFallbackContext, translate, translateDevTools, updateFallbackLocale };\n","/*!\n * vue-i18n v9.13.1\n * (c) 2024 kazuya kawaguchi\n * Released under the MIT License.\n */\nimport { getGlobalThis, incrementer, format, makeSymbol, isPlainObject, isArray, deepCopy, isString, hasOwn, isObject, warn, warnOnce, isBoolean, isRegExp, isFunction, inBrowser, assign, isNumber, createEmitter, isEmptyObject } from '@intlify/shared';\nimport { CoreWarnCodes, CoreErrorCodes, createCompileError, DEFAULT_LOCALE, updateFallbackLocale, setFallbackContext, createCoreContext, clearDateTimeFormat, clearNumberFormat, setAdditionalMeta, getFallbackContext, NOT_REOSLVED, isTranslateFallbackWarn, isTranslateMissingWarn, parseTranslateArgs, translate, MISSING_RESOLVE_VALUE, parseDateTimeArgs, datetime, parseNumberArgs, number, isMessageAST, isMessageFunction, fallbackWithLocaleChain, NUMBER_FORMAT_OPTIONS_KEYS, DATETIME_FORMAT_OPTIONS_KEYS, registerMessageCompiler, compile, compileToFunction, registerMessageResolver, resolveValue, registerLocaleFallbacker, setDevToolsHook } from '@intlify/core-base';\nimport { createVNode, Text, computed, watch, getCurrentInstance, ref, shallowRef, Fragment, defineComponent, h, effectScope, inject, onMounted, onUnmounted, onBeforeMount, isRef } from 'vue';\nimport { setupDevtoolsPlugin } from '@vue/devtools-api';\n\n/**\n * Vue I18n Version\n *\n * @remarks\n * Semver format. Same format as the package.json `version` field.\n *\n * @VueI18nGeneral\n */\nconst VERSION = '9.13.1';\n/**\n * This is only called in esm-bundler builds.\n * istanbul-ignore-next\n */\nfunction initFeatureFlags() {\n if (typeof __VUE_I18N_FULL_INSTALL__ !== 'boolean') {\n getGlobalThis().__VUE_I18N_FULL_INSTALL__ = true;\n }\n if (typeof __VUE_I18N_LEGACY_API__ !== 'boolean') {\n getGlobalThis().__VUE_I18N_LEGACY_API__ = true;\n }\n if (typeof __INTLIFY_JIT_COMPILATION__ !== 'boolean') {\n getGlobalThis().__INTLIFY_JIT_COMPILATION__ = false;\n }\n if (typeof __INTLIFY_DROP_MESSAGE_COMPILER__ !== 'boolean') {\n getGlobalThis().__INTLIFY_DROP_MESSAGE_COMPILER__ = false;\n }\n if (typeof __INTLIFY_PROD_DEVTOOLS__ !== 'boolean') {\n getGlobalThis().__INTLIFY_PROD_DEVTOOLS__ = false;\n }\n}\n\nconst code$1 = CoreWarnCodes.__EXTEND_POINT__;\nconst inc$1 = incrementer(code$1);\nconst I18nWarnCodes = {\n FALLBACK_TO_ROOT: code$1, // 9\n NOT_SUPPORTED_PRESERVE: inc$1(), // 10\n NOT_SUPPORTED_FORMATTER: inc$1(), // 11\n NOT_SUPPORTED_PRESERVE_DIRECTIVE: inc$1(), // 12\n NOT_SUPPORTED_GET_CHOICE_INDEX: inc$1(), // 13\n COMPONENT_NAME_LEGACY_COMPATIBLE: inc$1(), // 14\n NOT_FOUND_PARENT_SCOPE: inc$1(), // 15\n IGNORE_OBJ_FLATTEN: inc$1(), // 16\n NOTICE_DROP_ALLOW_COMPOSITION: inc$1(), // 17\n NOTICE_DROP_TRANSLATE_EXIST_COMPATIBLE_FLAG: inc$1() // 18\n};\nconst warnMessages = {\n [I18nWarnCodes.FALLBACK_TO_ROOT]: `Fall back to {type} '{key}' with root locale.`,\n [I18nWarnCodes.NOT_SUPPORTED_PRESERVE]: `Not supported 'preserve'.`,\n [I18nWarnCodes.NOT_SUPPORTED_FORMATTER]: `Not supported 'formatter'.`,\n [I18nWarnCodes.NOT_SUPPORTED_PRESERVE_DIRECTIVE]: `Not supported 'preserveDirectiveContent'.`,\n [I18nWarnCodes.NOT_SUPPORTED_GET_CHOICE_INDEX]: `Not supported 'getChoiceIndex'.`,\n [I18nWarnCodes.COMPONENT_NAME_LEGACY_COMPATIBLE]: `Component name legacy compatible: '{name}' -> 'i18n'`,\n [I18nWarnCodes.NOT_FOUND_PARENT_SCOPE]: `Not found parent scope. use the global scope.`,\n [I18nWarnCodes.IGNORE_OBJ_FLATTEN]: `Ignore object flatten: '{key}' key has an string value`,\n [I18nWarnCodes.NOTICE_DROP_ALLOW_COMPOSITION]: `'allowComposition' option will be dropped in the next major version. For more information, please see 👉 https://tinyurl.com/2p97mcze`,\n [I18nWarnCodes.NOTICE_DROP_TRANSLATE_EXIST_COMPATIBLE_FLAG]: `'translateExistCompatible' option will be dropped in the next major version.`\n};\nfunction getWarnMessage(code, ...args) {\n return format(warnMessages[code], ...args);\n}\n\nconst code = CoreErrorCodes.__EXTEND_POINT__;\nconst inc = incrementer(code);\nconst I18nErrorCodes = {\n // composer module errors\n UNEXPECTED_RETURN_TYPE: code, // 24\n // legacy module errors\n INVALID_ARGUMENT: inc(), // 25\n // i18n module errors\n MUST_BE_CALL_SETUP_TOP: inc(), // 26\n NOT_INSTALLED: inc(), // 27\n NOT_AVAILABLE_IN_LEGACY_MODE: inc(), // 28\n // directive module errors\n REQUIRED_VALUE: inc(), // 29\n INVALID_VALUE: inc(), // 30\n // vue-devtools errors\n CANNOT_SETUP_VUE_DEVTOOLS_PLUGIN: inc(), // 31\n NOT_INSTALLED_WITH_PROVIDE: inc(), // 32\n // unexpected error\n UNEXPECTED_ERROR: inc(), // 33\n // not compatible legacy vue-i18n constructor\n NOT_COMPATIBLE_LEGACY_VUE_I18N: inc(), // 34\n // bridge support vue 2.x only\n BRIDGE_SUPPORT_VUE_2_ONLY: inc(), // 35\n // need to define `i18n` option in `allowComposition: true` and `useScope: 'local' at `useI18n``\n MUST_DEFINE_I18N_OPTION_IN_ALLOW_COMPOSITION: inc(), // 36\n // Not available Compostion API in Legacy API mode. Please make sure that the legacy API mode is working properly\n NOT_AVAILABLE_COMPOSITION_IN_LEGACY: inc(), // 37\n // for enhancement\n __EXTEND_POINT__: inc() // 38\n};\nfunction createI18nError(code, ...args) {\n return createCompileError(code, null, (process.env.NODE_ENV !== 'production') ? { messages: errorMessages, args } : undefined);\n}\nconst errorMessages = {\n [I18nErrorCodes.UNEXPECTED_RETURN_TYPE]: 'Unexpected return type in composer',\n [I18nErrorCodes.INVALID_ARGUMENT]: 'Invalid argument',\n [I18nErrorCodes.MUST_BE_CALL_SETUP_TOP]: 'Must be called at the top of a `setup` function',\n [I18nErrorCodes.NOT_INSTALLED]: 'Need to install with `app.use` function',\n [I18nErrorCodes.UNEXPECTED_ERROR]: 'Unexpected error',\n [I18nErrorCodes.NOT_AVAILABLE_IN_LEGACY_MODE]: 'Not available in legacy mode',\n [I18nErrorCodes.REQUIRED_VALUE]: `Required in value: {0}`,\n [I18nErrorCodes.INVALID_VALUE]: `Invalid value`,\n [I18nErrorCodes.CANNOT_SETUP_VUE_DEVTOOLS_PLUGIN]: `Cannot setup vue-devtools plugin`,\n [I18nErrorCodes.NOT_INSTALLED_WITH_PROVIDE]: 'Need to install with `provide` function',\n [I18nErrorCodes.NOT_COMPATIBLE_LEGACY_VUE_I18N]: 'Not compatible legacy VueI18n.',\n [I18nErrorCodes.BRIDGE_SUPPORT_VUE_2_ONLY]: 'vue-i18n-bridge support Vue 2.x only',\n [I18nErrorCodes.MUST_DEFINE_I18N_OPTION_IN_ALLOW_COMPOSITION]: 'Must define ‘i18n’ option or custom block in Composition API with using local scope in Legacy API mode',\n [I18nErrorCodes.NOT_AVAILABLE_COMPOSITION_IN_LEGACY]: 'Not available Compostion API in Legacy API mode. Please make sure that the legacy API mode is working properly'\n};\n\nconst TranslateVNodeSymbol = \n/* #__PURE__*/ makeSymbol('__translateVNode');\nconst DatetimePartsSymbol = /* #__PURE__*/ makeSymbol('__datetimeParts');\nconst NumberPartsSymbol = /* #__PURE__*/ makeSymbol('__numberParts');\nconst EnableEmitter = /* #__PURE__*/ makeSymbol('__enableEmitter');\nconst DisableEmitter = /* #__PURE__*/ makeSymbol('__disableEmitter');\nconst SetPluralRulesSymbol = makeSymbol('__setPluralRules');\nmakeSymbol('__intlifyMeta');\nconst InejctWithOptionSymbol = \n/* #__PURE__*/ makeSymbol('__injectWithOption');\nconst DisposeSymbol = /* #__PURE__*/ makeSymbol('__dispose');\nconst __VUE_I18N_BRIDGE__ = '__VUE_I18N_BRIDGE__';\n\n/* eslint-disable @typescript-eslint/no-explicit-any */\n/**\n * Transform flat json in obj to normal json in obj\n */\nfunction handleFlatJson(obj) {\n // check obj\n if (!isObject(obj)) {\n return obj;\n }\n for (const key in obj) {\n // check key\n if (!hasOwn(obj, key)) {\n continue;\n }\n // handle for normal json\n if (!key.includes('.')) {\n // recursive process value if value is also a object\n if (isObject(obj[key])) {\n handleFlatJson(obj[key]);\n }\n }\n // handle for flat json, transform to normal json\n else {\n // go to the last object\n const subKeys = key.split('.');\n const lastIndex = subKeys.length - 1;\n let currentObj = obj;\n let hasStringValue = false;\n for (let i = 0; i < lastIndex; i++) {\n if (!(subKeys[i] in currentObj)) {\n currentObj[subKeys[i]] = {};\n }\n if (!isObject(currentObj[subKeys[i]])) {\n (process.env.NODE_ENV !== 'production') &&\n warn(getWarnMessage(I18nWarnCodes.IGNORE_OBJ_FLATTEN, {\n key: subKeys[i]\n }));\n hasStringValue = true;\n break;\n }\n currentObj = currentObj[subKeys[i]];\n }\n // update last object value, delete old property\n if (!hasStringValue) {\n currentObj[subKeys[lastIndex]] = obj[key];\n delete obj[key];\n }\n // recursive process value if value is also a object\n if (isObject(currentObj[subKeys[lastIndex]])) {\n handleFlatJson(currentObj[subKeys[lastIndex]]);\n }\n }\n }\n return obj;\n}\nfunction getLocaleMessages(locale, options) {\n const { messages, __i18n, messageResolver, flatJson } = options;\n // prettier-ignore\n const ret = (isPlainObject(messages)\n ? messages\n : isArray(__i18n)\n ? {}\n : { [locale]: {} });\n // merge locale messages of i18n custom block\n if (isArray(__i18n)) {\n __i18n.forEach(custom => {\n if ('locale' in custom && 'resource' in custom) {\n const { locale, resource } = custom;\n if (locale) {\n ret[locale] = ret[locale] || {};\n deepCopy(resource, ret[locale]);\n }\n else {\n deepCopy(resource, ret);\n }\n }\n else {\n isString(custom) && deepCopy(JSON.parse(custom), ret);\n }\n });\n }\n // handle messages for flat json\n if (messageResolver == null && flatJson) {\n for (const key in ret) {\n if (hasOwn(ret, key)) {\n handleFlatJson(ret[key]);\n }\n }\n }\n return ret;\n}\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nfunction getComponentOptions(instance) {\n return instance.type ;\n}\nfunction adjustI18nResources(gl, options, componentOptions // eslint-disable-line @typescript-eslint/no-explicit-any\n) {\n let messages = isObject(options.messages) ? options.messages : {};\n if ('__i18nGlobal' in componentOptions) {\n messages = getLocaleMessages(gl.locale.value, {\n messages,\n __i18n: componentOptions.__i18nGlobal\n });\n }\n // merge locale messages\n const locales = Object.keys(messages);\n if (locales.length) {\n locales.forEach(locale => {\n gl.mergeLocaleMessage(locale, messages[locale]);\n });\n }\n {\n // merge datetime formats\n if (isObject(options.datetimeFormats)) {\n const locales = Object.keys(options.datetimeFormats);\n if (locales.length) {\n locales.forEach(locale => {\n gl.mergeDateTimeFormat(locale, options.datetimeFormats[locale]);\n });\n }\n }\n // merge number formats\n if (isObject(options.numberFormats)) {\n const locales = Object.keys(options.numberFormats);\n if (locales.length) {\n locales.forEach(locale => {\n gl.mergeNumberFormat(locale, options.numberFormats[locale]);\n });\n }\n }\n }\n}\nfunction createTextNode(key) {\n return createVNode(Text, null, key, 0)\n ;\n}\n/* eslint-enable @typescript-eslint/no-explicit-any */\n\n/* eslint-disable @typescript-eslint/no-explicit-any */\n// extend VNode interface\nconst DEVTOOLS_META = '__INTLIFY_META__';\nconst NOOP_RETURN_ARRAY = () => [];\nconst NOOP_RETURN_FALSE = () => false;\nlet composerID = 0;\nfunction defineCoreMissingHandler(missing) {\n return ((ctx, locale, key, type) => {\n return missing(locale, key, getCurrentInstance() || undefined, type);\n });\n}\n// for Intlify DevTools\n/* #__NO_SIDE_EFFECTS__ */\nconst getMetaInfo = () => {\n const instance = getCurrentInstance();\n let meta = null; // eslint-disable-line @typescript-eslint/no-explicit-any\n return instance && (meta = getComponentOptions(instance)[DEVTOOLS_META])\n ? { [DEVTOOLS_META]: meta } // eslint-disable-line @typescript-eslint/no-explicit-any\n : null;\n};\n/**\n * Create composer interface factory\n *\n * @internal\n */\n// eslint-disable-next-line @typescript-eslint/explicit-module-boundary-types\nfunction createComposer(options = {}, VueI18nLegacy) {\n const { __root, __injectWithOption } = options;\n const _isGlobal = __root === undefined;\n const flatJson = options.flatJson;\n const _ref = inBrowser ? ref : shallowRef;\n const translateExistCompatible = !!options.translateExistCompatible;\n if ((process.env.NODE_ENV !== 'production')) {\n if (translateExistCompatible && !false) {\n warnOnce(getWarnMessage(I18nWarnCodes.NOTICE_DROP_TRANSLATE_EXIST_COMPATIBLE_FLAG));\n }\n }\n let _inheritLocale = isBoolean(options.inheritLocale)\n ? options.inheritLocale\n : true;\n const _locale = _ref(\n // prettier-ignore\n __root && _inheritLocale\n ? __root.locale.value\n : isString(options.locale)\n ? options.locale\n : DEFAULT_LOCALE);\n const _fallbackLocale = _ref(\n // prettier-ignore\n __root && _inheritLocale\n ? __root.fallbackLocale.value\n : isString(options.fallbackLocale) ||\n isArray(options.fallbackLocale) ||\n isPlainObject(options.fallbackLocale) ||\n options.fallbackLocale === false\n ? options.fallbackLocale\n : _locale.value);\n const _messages = _ref(getLocaleMessages(_locale.value, options));\n // prettier-ignore\n const _datetimeFormats = _ref(isPlainObject(options.datetimeFormats)\n ? options.datetimeFormats\n : { [_locale.value]: {} })\n ;\n // prettier-ignore\n const _numberFormats = _ref(isPlainObject(options.numberFormats)\n ? options.numberFormats\n : { [_locale.value]: {} })\n ;\n // warning suppress options\n // prettier-ignore\n let _missingWarn = __root\n ? __root.missingWarn\n : isBoolean(options.missingWarn) || isRegExp(options.missingWarn)\n ? options.missingWarn\n : true;\n // prettier-ignore\n let _fallbackWarn = __root\n ? __root.fallbackWarn\n : isBoolean(options.fallbackWarn) || isRegExp(options.fallbackWarn)\n ? options.fallbackWarn\n : true;\n // prettier-ignore\n let _fallbackRoot = __root\n ? __root.fallbackRoot\n : isBoolean(options.fallbackRoot)\n ? options.fallbackRoot\n : true;\n // configure fall back to root\n let _fallbackFormat = !!options.fallbackFormat;\n // runtime missing\n let _missing = isFunction(options.missing) ? options.missing : null;\n let _runtimeMissing = isFunction(options.missing)\n ? defineCoreMissingHandler(options.missing)\n : null;\n // postTranslation handler\n let _postTranslation = isFunction(options.postTranslation)\n ? options.postTranslation\n : null;\n // prettier-ignore\n let _warnHtmlMessage = __root\n ? __root.warnHtmlMessage\n : isBoolean(options.warnHtmlMessage)\n ? options.warnHtmlMessage\n : true;\n let _escapeParameter = !!options.escapeParameter;\n // custom linked modifiers\n // prettier-ignore\n const _modifiers = __root\n ? __root.modifiers\n : isPlainObject(options.modifiers)\n ? options.modifiers\n : {};\n // pluralRules\n let _pluralRules = options.pluralRules || (__root && __root.pluralRules);\n // runtime context\n // eslint-disable-next-line prefer-const\n let _context;\n const getCoreContext = () => {\n _isGlobal && setFallbackContext(null);\n const ctxOptions = {\n version: VERSION,\n locale: _locale.value,\n fallbackLocale: _fallbackLocale.value,\n messages: _messages.value,\n modifiers: _modifiers,\n pluralRules: _pluralRules,\n missing: _runtimeMissing === null ? undefined : _runtimeMissing,\n missingWarn: _missingWarn,\n fallbackWarn: _fallbackWarn,\n fallbackFormat: _fallbackFormat,\n unresolving: true,\n postTranslation: _postTranslation === null ? undefined : _postTranslation,\n warnHtmlMessage: _warnHtmlMessage,\n escapeParameter: _escapeParameter,\n messageResolver: options.messageResolver,\n messageCompiler: options.messageCompiler,\n __meta: { framework: 'vue' }\n };\n {\n ctxOptions.datetimeFormats = _datetimeFormats.value;\n ctxOptions.numberFormats = _numberFormats.value;\n ctxOptions.__datetimeFormatters = isPlainObject(_context)\n ? _context.__datetimeFormatters\n : undefined;\n ctxOptions.__numberFormatters = isPlainObject(_context)\n ? _context.__numberFormatters\n : undefined;\n }\n if ((process.env.NODE_ENV !== 'production')) {\n ctxOptions.__v_emitter = isPlainObject(_context)\n ? _context.__v_emitter\n : undefined;\n }\n const ctx = createCoreContext(ctxOptions);\n _isGlobal && setFallbackContext(ctx);\n return ctx;\n };\n _context = getCoreContext();\n updateFallbackLocale(_context, _locale.value, _fallbackLocale.value);\n // track reactivity\n function trackReactivityValues() {\n return [\n _locale.value,\n _fallbackLocale.value,\n _messages.value,\n _datetimeFormats.value,\n _numberFormats.value\n ]\n ;\n }\n // locale\n const locale = computed({\n get: () => _locale.value,\n set: val => {\n _locale.value = val;\n _context.locale = _locale.value;\n }\n });\n // fallbackLocale\n const fallbackLocale = computed({\n get: () => _fallbackLocale.value,\n set: val => {\n _fallbackLocale.value = val;\n _context.fallbackLocale = _fallbackLocale.value;\n updateFallbackLocale(_context, _locale.value, val);\n }\n });\n // messages\n const messages = computed(() => _messages.value);\n // datetimeFormats\n const datetimeFormats = /* #__PURE__*/ computed(() => _datetimeFormats.value);\n // numberFormats\n const numberFormats = /* #__PURE__*/ computed(() => _numberFormats.value);\n // getPostTranslationHandler\n function getPostTranslationHandler() {\n return isFunction(_postTranslation) ? _postTranslation : null;\n }\n // setPostTranslationHandler\n function setPostTranslationHandler(handler) {\n _postTranslation = handler;\n _context.postTranslation = handler;\n }\n // getMissingHandler\n function getMissingHandler() {\n return _missing;\n }\n // setMissingHandler\n function setMissingHandler(handler) {\n if (handler !== null) {\n _runtimeMissing = defineCoreMissingHandler(handler);\n }\n _missing = handler;\n _context.missing = _runtimeMissing;\n }\n function isResolvedTranslateMessage(type, arg // eslint-disable-line @typescript-eslint/no-explicit-any\n ) {\n return type !== 'translate' || !arg.resolvedMessage;\n }\n const wrapWithDeps = (fn, argumentParser, warnType, fallbackSuccess, fallbackFail, successCondition) => {\n trackReactivityValues(); // track reactive dependency\n // NOTE: experimental !!\n let ret;\n try {\n if ((process.env.NODE_ENV !== 'production') || __INTLIFY_PROD_DEVTOOLS__) {\n setAdditionalMeta(getMetaInfo());\n }\n if (!_isGlobal) {\n _context.fallbackContext = __root\n ? getFallbackContext()\n : undefined;\n }\n ret = fn(_context);\n }\n finally {\n if ((process.env.NODE_ENV !== 'production') || __INTLIFY_PROD_DEVTOOLS__) {\n setAdditionalMeta(null);\n }\n if (!_isGlobal) {\n _context.fallbackContext = undefined;\n }\n }\n if ((warnType !== 'translate exists' && // for not `te` (e.g `t`)\n isNumber(ret) &&\n ret === NOT_REOSLVED) ||\n (warnType === 'translate exists' && !ret) // for `te`\n ) {\n const [key, arg2] = argumentParser();\n if ((process.env.NODE_ENV !== 'production') &&\n __root &&\n isString(key) &&\n isResolvedTranslateMessage(warnType, arg2)) {\n if (_fallbackRoot &&\n (isTranslateFallbackWarn(_fallbackWarn, key) ||\n isTranslateMissingWarn(_missingWarn, key))) {\n warn(getWarnMessage(I18nWarnCodes.FALLBACK_TO_ROOT, {\n key,\n type: warnType\n }));\n }\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production')) {\n const { __v_emitter: emitter } = _context;\n if (emitter && _fallbackRoot) {\n emitter.emit(\"fallback\" /* VueDevToolsTimelineEvents.FALBACK */, {\n type: warnType,\n key,\n to: 'global',\n groupId: `${warnType}:${key}`\n });\n }\n }\n }\n return __root && _fallbackRoot\n ? fallbackSuccess(__root)\n : fallbackFail(key);\n }\n else if (successCondition(ret)) {\n return ret;\n }\n else {\n /* istanbul ignore next */\n throw createI18nError(I18nErrorCodes.UNEXPECTED_RETURN_TYPE);\n }\n };\n // t\n function t(...args) {\n return wrapWithDeps(context => Reflect.apply(translate, null, [context, ...args]), () => parseTranslateArgs(...args), 'translate', root => Reflect.apply(root.t, root, [...args]), key => key, val => isString(val));\n }\n // rt\n function rt(...args) {\n const [arg1, arg2, arg3] = args;\n if (arg3 && !isObject(arg3)) {\n throw createI18nError(I18nErrorCodes.INVALID_ARGUMENT);\n }\n return t(...[arg1, arg2, assign({ resolvedMessage: true }, arg3 || {})]);\n }\n // d\n function d(...args) {\n return wrapWithDeps(context => Reflect.apply(datetime, null, [context, ...args]), () => parseDateTimeArgs(...args), 'datetime format', root => Reflect.apply(root.d, root, [...args]), () => MISSING_RESOLVE_VALUE, val => isString(val));\n }\n // n\n function n(...args) {\n return wrapWithDeps(context => Reflect.apply(number, null, [context, ...args]), () => parseNumberArgs(...args), 'number format', root => Reflect.apply(root.n, root, [...args]), () => MISSING_RESOLVE_VALUE, val => isString(val));\n }\n // for custom processor\n function normalize(values) {\n return values.map(val => isString(val) || isNumber(val) || isBoolean(val)\n ? createTextNode(String(val))\n : val);\n }\n const interpolate = (val) => val;\n const processor = {\n normalize,\n interpolate,\n type: 'vnode'\n };\n // translateVNode, using for `i18n-t` component\n function translateVNode(...args) {\n return wrapWithDeps(context => {\n let ret;\n const _context = context;\n try {\n _context.processor = processor;\n ret = Reflect.apply(translate, null, [_context, ...args]);\n }\n finally {\n _context.processor = null;\n }\n return ret;\n }, () => parseTranslateArgs(...args), 'translate', \n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n root => root[TranslateVNodeSymbol](...args), key => [createTextNode(key)], val => isArray(val));\n }\n // numberParts, using for `i18n-n` component\n function numberParts(...args) {\n return wrapWithDeps(context => Reflect.apply(number, null, [context, ...args]), () => parseNumberArgs(...args), 'number format', \n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n root => root[NumberPartsSymbol](...args), NOOP_RETURN_ARRAY, val => isString(val) || isArray(val));\n }\n // datetimeParts, using for `i18n-d` component\n function datetimeParts(...args) {\n return wrapWithDeps(context => Reflect.apply(datetime, null, [context, ...args]), () => parseDateTimeArgs(...args), 'datetime format', \n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n root => root[DatetimePartsSymbol](...args), NOOP_RETURN_ARRAY, val => isString(val) || isArray(val));\n }\n function setPluralRules(rules) {\n _pluralRules = rules;\n _context.pluralRules = _pluralRules;\n }\n // te\n function te(key, locale) {\n return wrapWithDeps(() => {\n if (!key) {\n return false;\n }\n const targetLocale = isString(locale) ? locale : _locale.value;\n const message = getLocaleMessage(targetLocale);\n const resolved = _context.messageResolver(message, key);\n return !translateExistCompatible\n ? isMessageAST(resolved) ||\n isMessageFunction(resolved) ||\n isString(resolved)\n : resolved != null;\n }, () => [key], 'translate exists', root => {\n return Reflect.apply(root.te, root, [key, locale]);\n }, NOOP_RETURN_FALSE, val => isBoolean(val));\n }\n function resolveMessages(key) {\n let messages = null;\n const locales = fallbackWithLocaleChain(_context, _fallbackLocale.value, _locale.value);\n for (let i = 0; i < locales.length; i++) {\n const targetLocaleMessages = _messages.value[locales[i]] || {};\n const messageValue = _context.messageResolver(targetLocaleMessages, key);\n if (messageValue != null) {\n messages = messageValue;\n break;\n }\n }\n return messages;\n }\n // tm\n function tm(key) {\n const messages = resolveMessages(key);\n // prettier-ignore\n return messages != null\n ? messages\n : __root\n ? __root.tm(key) || {}\n : {};\n }\n // getLocaleMessage\n function getLocaleMessage(locale) {\n return (_messages.value[locale] || {});\n }\n // setLocaleMessage\n function setLocaleMessage(locale, message) {\n if (flatJson) {\n const _message = { [locale]: message };\n for (const key in _message) {\n if (hasOwn(_message, key)) {\n handleFlatJson(_message[key]);\n }\n }\n message = _message[locale];\n }\n _messages.value[locale] = message;\n _context.messages = _messages.value;\n }\n // mergeLocaleMessage\n function mergeLocaleMessage(locale, message) {\n _messages.value[locale] = _messages.value[locale] || {};\n const _message = { [locale]: message };\n if (flatJson) {\n for (const key in _message) {\n if (hasOwn(_message, key)) {\n handleFlatJson(_message[key]);\n }\n }\n }\n message = _message[locale];\n deepCopy(message, _messages.value[locale]);\n _context.messages = _messages.value;\n }\n // getDateTimeFormat\n function getDateTimeFormat(locale) {\n return _datetimeFormats.value[locale] || {};\n }\n // setDateTimeFormat\n function setDateTimeFormat(locale, format) {\n _datetimeFormats.value[locale] = format;\n _context.datetimeFormats = _datetimeFormats.value;\n clearDateTimeFormat(_context, locale, format);\n }\n // mergeDateTimeFormat\n function mergeDateTimeFormat(locale, format) {\n _datetimeFormats.value[locale] = assign(_datetimeFormats.value[locale] || {}, format);\n _context.datetimeFormats = _datetimeFormats.value;\n clearDateTimeFormat(_context, locale, format);\n }\n // getNumberFormat\n function getNumberFormat(locale) {\n return _numberFormats.value[locale] || {};\n }\n // setNumberFormat\n function setNumberFormat(locale, format) {\n _numberFormats.value[locale] = format;\n _context.numberFormats = _numberFormats.value;\n clearNumberFormat(_context, locale, format);\n }\n // mergeNumberFormat\n function mergeNumberFormat(locale, format) {\n _numberFormats.value[locale] = assign(_numberFormats.value[locale] || {}, format);\n _context.numberFormats = _numberFormats.value;\n clearNumberFormat(_context, locale, format);\n }\n // for debug\n composerID++;\n // watch root locale & fallbackLocale\n if (__root && inBrowser) {\n watch(__root.locale, (val) => {\n if (_inheritLocale) {\n _locale.value = val;\n _context.locale = val;\n updateFallbackLocale(_context, _locale.value, _fallbackLocale.value);\n }\n });\n watch(__root.fallbackLocale, (val) => {\n if (_inheritLocale) {\n _fallbackLocale.value = val;\n _context.fallbackLocale = val;\n updateFallbackLocale(_context, _locale.value, _fallbackLocale.value);\n }\n });\n }\n // define basic composition API!\n const composer = {\n id: composerID,\n locale,\n fallbackLocale,\n get inheritLocale() {\n return _inheritLocale;\n },\n set inheritLocale(val) {\n _inheritLocale = val;\n if (val && __root) {\n _locale.value = __root.locale.value;\n _fallbackLocale.value = __root.fallbackLocale.value;\n updateFallbackLocale(_context, _locale.value, _fallbackLocale.value);\n }\n },\n get availableLocales() {\n return Object.keys(_messages.value).sort();\n },\n messages,\n get modifiers() {\n return _modifiers;\n },\n get pluralRules() {\n return _pluralRules || {};\n },\n get isGlobal() {\n return _isGlobal;\n },\n get missingWarn() {\n return _missingWarn;\n },\n set missingWarn(val) {\n _missingWarn = val;\n _context.missingWarn = _missingWarn;\n },\n get fallbackWarn() {\n return _fallbackWarn;\n },\n set fallbackWarn(val) {\n _fallbackWarn = val;\n _context.fallbackWarn = _fallbackWarn;\n },\n get fallbackRoot() {\n return _fallbackRoot;\n },\n set fallbackRoot(val) {\n _fallbackRoot = val;\n },\n get fallbackFormat() {\n return _fallbackFormat;\n },\n set fallbackFormat(val) {\n _fallbackFormat = val;\n _context.fallbackFormat = _fallbackFormat;\n },\n get warnHtmlMessage() {\n return _warnHtmlMessage;\n },\n set warnHtmlMessage(val) {\n _warnHtmlMessage = val;\n _context.warnHtmlMessage = val;\n },\n get escapeParameter() {\n return _escapeParameter;\n },\n set escapeParameter(val) {\n _escapeParameter = val;\n _context.escapeParameter = val;\n },\n t,\n getLocaleMessage,\n setLocaleMessage,\n mergeLocaleMessage,\n getPostTranslationHandler,\n setPostTranslationHandler,\n getMissingHandler,\n setMissingHandler,\n [SetPluralRulesSymbol]: setPluralRules\n };\n {\n composer.datetimeFormats = datetimeFormats;\n composer.numberFormats = numberFormats;\n composer.rt = rt;\n composer.te = te;\n composer.tm = tm;\n composer.d = d;\n composer.n = n;\n composer.getDateTimeFormat = getDateTimeFormat;\n composer.setDateTimeFormat = setDateTimeFormat;\n composer.mergeDateTimeFormat = mergeDateTimeFormat;\n composer.getNumberFormat = getNumberFormat;\n composer.setNumberFormat = setNumberFormat;\n composer.mergeNumberFormat = mergeNumberFormat;\n composer[InejctWithOptionSymbol] = __injectWithOption;\n composer[TranslateVNodeSymbol] = translateVNode;\n composer[DatetimePartsSymbol] = datetimeParts;\n composer[NumberPartsSymbol] = numberParts;\n }\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production')) {\n composer[EnableEmitter] = (emitter) => {\n _context.__v_emitter = emitter;\n };\n composer[DisableEmitter] = () => {\n _context.__v_emitter = undefined;\n };\n }\n return composer;\n}\n/* eslint-enable @typescript-eslint/no-explicit-any */\n\n/* eslint-disable @typescript-eslint/no-explicit-any */\n/**\n * Convert to I18n Composer Options from VueI18n Options\n *\n * @internal\n */\nfunction convertComposerOptions(options) {\n const locale = isString(options.locale) ? options.locale : DEFAULT_LOCALE;\n const fallbackLocale = isString(options.fallbackLocale) ||\n isArray(options.fallbackLocale) ||\n isPlainObject(options.fallbackLocale) ||\n options.fallbackLocale === false\n ? options.fallbackLocale\n : locale;\n const missing = isFunction(options.missing) ? options.missing : undefined;\n const missingWarn = isBoolean(options.silentTranslationWarn) ||\n isRegExp(options.silentTranslationWarn)\n ? !options.silentTranslationWarn\n : true;\n const fallbackWarn = isBoolean(options.silentFallbackWarn) ||\n isRegExp(options.silentFallbackWarn)\n ? !options.silentFallbackWarn\n : true;\n const fallbackRoot = isBoolean(options.fallbackRoot)\n ? options.fallbackRoot\n : true;\n const fallbackFormat = !!options.formatFallbackMessages;\n const modifiers = isPlainObject(options.modifiers) ? options.modifiers : {};\n const pluralizationRules = options.pluralizationRules;\n const postTranslation = isFunction(options.postTranslation)\n ? options.postTranslation\n : undefined;\n const warnHtmlMessage = isString(options.warnHtmlInMessage)\n ? options.warnHtmlInMessage !== 'off'\n : true;\n const escapeParameter = !!options.escapeParameterHtml;\n const inheritLocale = isBoolean(options.sync) ? options.sync : true;\n if ((process.env.NODE_ENV !== 'production') && options.formatter) {\n warn(getWarnMessage(I18nWarnCodes.NOT_SUPPORTED_FORMATTER));\n }\n if ((process.env.NODE_ENV !== 'production') && options.preserveDirectiveContent) {\n warn(getWarnMessage(I18nWarnCodes.NOT_SUPPORTED_PRESERVE_DIRECTIVE));\n }\n let messages = options.messages;\n if (isPlainObject(options.sharedMessages)) {\n const sharedMessages = options.sharedMessages;\n const locales = Object.keys(sharedMessages);\n messages = locales.reduce((messages, locale) => {\n const message = messages[locale] || (messages[locale] = {});\n assign(message, sharedMessages[locale]);\n return messages;\n }, (messages || {}));\n }\n const { __i18n, __root, __injectWithOption } = options;\n const datetimeFormats = options.datetimeFormats;\n const numberFormats = options.numberFormats;\n const flatJson = options.flatJson;\n const translateExistCompatible = options\n .translateExistCompatible;\n return {\n locale,\n fallbackLocale,\n messages,\n flatJson,\n datetimeFormats,\n numberFormats,\n missing,\n missingWarn,\n fallbackWarn,\n fallbackRoot,\n fallbackFormat,\n modifiers,\n pluralRules: pluralizationRules,\n postTranslation,\n warnHtmlMessage,\n escapeParameter,\n messageResolver: options.messageResolver,\n inheritLocale,\n translateExistCompatible,\n __i18n,\n __root,\n __injectWithOption\n };\n}\n/**\n * create VueI18n interface factory\n *\n * @internal\n */\n// eslint-disable-next-line @typescript-eslint/explicit-module-boundary-types\nfunction createVueI18n(options = {}, VueI18nLegacy) {\n {\n const composer = createComposer(convertComposerOptions(options));\n const { __extender } = options;\n // defines VueI18n\n const vueI18n = {\n // id\n id: composer.id,\n // locale\n get locale() {\n return composer.locale.value;\n },\n set locale(val) {\n composer.locale.value = val;\n },\n // fallbackLocale\n get fallbackLocale() {\n return composer.fallbackLocale.value;\n },\n set fallbackLocale(val) {\n composer.fallbackLocale.value = val;\n },\n // messages\n get messages() {\n return composer.messages.value;\n },\n // datetimeFormats\n get datetimeFormats() {\n return composer.datetimeFormats.value;\n },\n // numberFormats\n get numberFormats() {\n return composer.numberFormats.value;\n },\n // availableLocales\n get availableLocales() {\n return composer.availableLocales;\n },\n // formatter\n get formatter() {\n (process.env.NODE_ENV !== 'production') && warn(getWarnMessage(I18nWarnCodes.NOT_SUPPORTED_FORMATTER));\n // dummy\n return {\n interpolate() {\n return [];\n }\n };\n },\n set formatter(val) {\n (process.env.NODE_ENV !== 'production') && warn(getWarnMessage(I18nWarnCodes.NOT_SUPPORTED_FORMATTER));\n },\n // missing\n get missing() {\n return composer.getMissingHandler();\n },\n set missing(handler) {\n composer.setMissingHandler(handler);\n },\n // silentTranslationWarn\n get silentTranslationWarn() {\n return isBoolean(composer.missingWarn)\n ? !composer.missingWarn\n : composer.missingWarn;\n },\n set silentTranslationWarn(val) {\n composer.missingWarn = isBoolean(val) ? !val : val;\n },\n // silentFallbackWarn\n get silentFallbackWarn() {\n return isBoolean(composer.fallbackWarn)\n ? !composer.fallbackWarn\n : composer.fallbackWarn;\n },\n set silentFallbackWarn(val) {\n composer.fallbackWarn = isBoolean(val) ? !val : val;\n },\n // modifiers\n get modifiers() {\n return composer.modifiers;\n },\n // formatFallbackMessages\n get formatFallbackMessages() {\n return composer.fallbackFormat;\n },\n set formatFallbackMessages(val) {\n composer.fallbackFormat = val;\n },\n // postTranslation\n get postTranslation() {\n return composer.getPostTranslationHandler();\n },\n set postTranslation(handler) {\n composer.setPostTranslationHandler(handler);\n },\n // sync\n get sync() {\n return composer.inheritLocale;\n },\n set sync(val) {\n composer.inheritLocale = val;\n },\n // warnInHtmlMessage\n get warnHtmlInMessage() {\n return composer.warnHtmlMessage ? 'warn' : 'off';\n },\n set warnHtmlInMessage(val) {\n composer.warnHtmlMessage = val !== 'off';\n },\n // escapeParameterHtml\n get escapeParameterHtml() {\n return composer.escapeParameter;\n },\n set escapeParameterHtml(val) {\n composer.escapeParameter = val;\n },\n // preserveDirectiveContent\n get preserveDirectiveContent() {\n (process.env.NODE_ENV !== 'production') &&\n warn(getWarnMessage(I18nWarnCodes.NOT_SUPPORTED_PRESERVE_DIRECTIVE));\n return true;\n },\n set preserveDirectiveContent(val) {\n (process.env.NODE_ENV !== 'production') &&\n warn(getWarnMessage(I18nWarnCodes.NOT_SUPPORTED_PRESERVE_DIRECTIVE));\n },\n // pluralizationRules\n get pluralizationRules() {\n return composer.pluralRules || {};\n },\n // for internal\n __composer: composer,\n // t\n t(...args) {\n const [arg1, arg2, arg3] = args;\n const options = {};\n let list = null;\n let named = null;\n if (!isString(arg1)) {\n throw createI18nError(I18nErrorCodes.INVALID_ARGUMENT);\n }\n const key = arg1;\n if (isString(arg2)) {\n options.locale = arg2;\n }\n else if (isArray(arg2)) {\n list = arg2;\n }\n else if (isPlainObject(arg2)) {\n named = arg2;\n }\n if (isArray(arg3)) {\n list = arg3;\n }\n else if (isPlainObject(arg3)) {\n named = arg3;\n }\n // return composer.t(key, (list || named || {}) as any, options)\n return Reflect.apply(composer.t, composer, [\n key,\n (list || named || {}),\n options\n ]);\n },\n rt(...args) {\n return Reflect.apply(composer.rt, composer, [...args]);\n },\n // tc\n tc(...args) {\n const [arg1, arg2, arg3] = args;\n const options = { plural: 1 };\n let list = null;\n let named = null;\n if (!isString(arg1)) {\n throw createI18nError(I18nErrorCodes.INVALID_ARGUMENT);\n }\n const key = arg1;\n if (isString(arg2)) {\n options.locale = arg2;\n }\n else if (isNumber(arg2)) {\n options.plural = arg2;\n }\n else if (isArray(arg2)) {\n list = arg2;\n }\n else if (isPlainObject(arg2)) {\n named = arg2;\n }\n if (isString(arg3)) {\n options.locale = arg3;\n }\n else if (isArray(arg3)) {\n list = arg3;\n }\n else if (isPlainObject(arg3)) {\n named = arg3;\n }\n // return composer.t(key, (list || named || {}) as any, options)\n return Reflect.apply(composer.t, composer, [\n key,\n (list || named || {}),\n options\n ]);\n },\n // te\n te(key, locale) {\n return composer.te(key, locale);\n },\n // tm\n tm(key) {\n return composer.tm(key);\n },\n // getLocaleMessage\n getLocaleMessage(locale) {\n return composer.getLocaleMessage(locale);\n },\n // setLocaleMessage\n setLocaleMessage(locale, message) {\n composer.setLocaleMessage(locale, message);\n },\n // mergeLocaleMessage\n mergeLocaleMessage(locale, message) {\n composer.mergeLocaleMessage(locale, message);\n },\n // d\n d(...args) {\n return Reflect.apply(composer.d, composer, [...args]);\n },\n // getDateTimeFormat\n getDateTimeFormat(locale) {\n return composer.getDateTimeFormat(locale);\n },\n // setDateTimeFormat\n setDateTimeFormat(locale, format) {\n composer.setDateTimeFormat(locale, format);\n },\n // mergeDateTimeFormat\n mergeDateTimeFormat(locale, format) {\n composer.mergeDateTimeFormat(locale, format);\n },\n // n\n n(...args) {\n return Reflect.apply(composer.n, composer, [...args]);\n },\n // getNumberFormat\n getNumberFormat(locale) {\n return composer.getNumberFormat(locale);\n },\n // setNumberFormat\n setNumberFormat(locale, format) {\n composer.setNumberFormat(locale, format);\n },\n // mergeNumberFormat\n mergeNumberFormat(locale, format) {\n composer.mergeNumberFormat(locale, format);\n },\n // getChoiceIndex\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n getChoiceIndex(choice, choicesLength) {\n (process.env.NODE_ENV !== 'production') &&\n warn(getWarnMessage(I18nWarnCodes.NOT_SUPPORTED_GET_CHOICE_INDEX));\n return -1;\n }\n };\n vueI18n.__extender = __extender;\n // for vue-devtools timeline event\n if ((process.env.NODE_ENV !== 'production')) {\n vueI18n.__enableEmitter = (emitter) => {\n const __composer = composer;\n __composer[EnableEmitter] && __composer[EnableEmitter](emitter);\n };\n vueI18n.__disableEmitter = () => {\n const __composer = composer;\n __composer[DisableEmitter] && __composer[DisableEmitter]();\n };\n }\n return vueI18n;\n }\n}\n/* eslint-enable @typescript-eslint/no-explicit-any */\n\nconst baseFormatProps = {\n tag: {\n type: [String, Object]\n },\n locale: {\n type: String\n },\n scope: {\n type: String,\n // NOTE: avoid https://github.com/microsoft/rushstack/issues/1050\n validator: (val /* ComponentI18nScope */) => val === 'parent' || val === 'global',\n default: 'parent' /* ComponentI18nScope */\n },\n i18n: {\n type: Object\n }\n};\n\nfunction getInterpolateArg(\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\n{ slots }, // SetupContext,\nkeys) {\n if (keys.length === 1 && keys[0] === 'default') {\n // default slot with list\n const ret = slots.default ? slots.default() : [];\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n return ret.reduce((slot, current) => {\n return [\n ...slot,\n // prettier-ignore\n ...(current.type === Fragment ? current.children : [current]\n )\n ];\n }, []);\n }\n else {\n // named slots\n return keys.reduce((arg, key) => {\n const slot = slots[key];\n if (slot) {\n arg[key] = slot();\n }\n return arg;\n }, {});\n }\n}\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nfunction getFragmentableTag(tag) {\n return Fragment ;\n}\n\nconst TranslationImpl = /*#__PURE__*/ defineComponent({\n /* eslint-disable */\n name: 'i18n-t',\n props: assign({\n keypath: {\n type: String,\n required: true\n },\n plural: {\n type: [Number, String],\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n validator: (val) => isNumber(val) || !isNaN(val)\n }\n }, baseFormatProps),\n /* eslint-enable */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n setup(props, context) {\n const { slots, attrs } = context;\n // NOTE: avoid https://github.com/microsoft/rushstack/issues/1050\n const i18n = props.i18n ||\n useI18n({\n useScope: props.scope,\n __useComponent: true\n });\n return () => {\n const keys = Object.keys(slots).filter(key => key !== '_');\n const options = {};\n if (props.locale) {\n options.locale = props.locale;\n }\n if (props.plural !== undefined) {\n options.plural = isString(props.plural) ? +props.plural : props.plural;\n }\n const arg = getInterpolateArg(context, keys);\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const children = i18n[TranslateVNodeSymbol](props.keypath, arg, options);\n const assignedAttrs = assign({}, attrs);\n const tag = isString(props.tag) || isObject(props.tag)\n ? props.tag\n : getFragmentableTag();\n return h(tag, assignedAttrs, children);\n };\n }\n});\n/**\n * export the public type for h/tsx inference\n * also to avoid inline import() in generated d.ts files\n */\n/**\n * Translation Component\n *\n * @remarks\n * See the following items for property about details\n *\n * @VueI18nSee [TranslationProps](component#translationprops)\n * @VueI18nSee [BaseFormatProps](component#baseformatprops)\n * @VueI18nSee [Component Interpolation](../guide/advanced/component)\n *\n * @example\n * ```html\n * \n * ```\n * ```js\n * import { createApp } from 'vue'\n * import { createI18n } from 'vue-i18n'\n *\n * const messages = {\n * en: {\n * tos: 'Term of Service',\n * term: 'I accept xxx {0}.'\n * },\n * ja: {\n * tos: '利用規約',\n * term: '私は xxx の{0}に同意します。'\n * }\n * }\n *\n * const i18n = createI18n({\n * locale: 'en',\n * messages\n * })\n *\n * const app = createApp({\n * data: {\n * url: '/term'\n * }\n * }).use(i18n).mount('#app')\n * ```\n *\n * @VueI18nComponent\n */\nconst Translation = TranslationImpl;\nconst I18nT = Translation;\n\nfunction isVNode(target) {\n return isArray(target) && !isString(target[0]);\n}\nfunction renderFormatter(props, context, slotKeys, partFormatter) {\n const { slots, attrs } = context;\n return () => {\n const options = { part: true };\n let overrides = {};\n if (props.locale) {\n options.locale = props.locale;\n }\n if (isString(props.format)) {\n options.key = props.format;\n }\n else if (isObject(props.format)) {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n if (isString(props.format.key)) {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n options.key = props.format.key;\n }\n // Filter out number format options only\n overrides = Object.keys(props.format).reduce((options, prop) => {\n return slotKeys.includes(prop)\n ? assign({}, options, { [prop]: props.format[prop] }) // eslint-disable-line @typescript-eslint/no-explicit-any\n : options;\n }, {});\n }\n const parts = partFormatter(...[props.value, options, overrides]);\n let children = [options.key];\n if (isArray(parts)) {\n children = parts.map((part, index) => {\n const slot = slots[part.type];\n const node = slot\n ? slot({ [part.type]: part.value, index, parts })\n : [part.value];\n if (isVNode(node)) {\n node[0].key = `${part.type}-${index}`;\n }\n return node;\n });\n }\n else if (isString(parts)) {\n children = [parts];\n }\n const assignedAttrs = assign({}, attrs);\n const tag = isString(props.tag) || isObject(props.tag)\n ? props.tag\n : getFragmentableTag();\n return h(tag, assignedAttrs, children);\n };\n}\n\nconst NumberFormatImpl = /*#__PURE__*/ defineComponent({\n /* eslint-disable */\n name: 'i18n-n',\n props: assign({\n value: {\n type: Number,\n required: true\n },\n format: {\n type: [String, Object]\n }\n }, baseFormatProps),\n /* eslint-enable */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n setup(props, context) {\n const i18n = props.i18n ||\n useI18n({\n useScope: props.scope,\n __useComponent: true\n });\n return renderFormatter(props, context, NUMBER_FORMAT_OPTIONS_KEYS, (...args) => \n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n i18n[NumberPartsSymbol](...args));\n }\n});\n/**\n * export the public type for h/tsx inference\n * also to avoid inline import() in generated d.ts files\n */\n/**\n * Number Format Component\n *\n * @remarks\n * See the following items for property about details\n *\n * @VueI18nSee [FormattableProps](component#formattableprops)\n * @VueI18nSee [BaseFormatProps](component#baseformatprops)\n * @VueI18nSee [Custom Formatting](../guide/essentials/number#custom-formatting)\n *\n * @VueI18nDanger\n * Not supported IE, due to no support `Intl.NumberFormat#formatToParts` in [IE](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Intl/NumberFormat/formatToParts)\n *\n * If you want to use it, you need to use [polyfill](https://github.com/formatjs/formatjs/tree/main/packages/intl-numberformat)\n *\n * @VueI18nComponent\n */\nconst NumberFormat = NumberFormatImpl;\nconst I18nN = NumberFormat;\n\nconst DatetimeFormatImpl = /* #__PURE__*/ defineComponent({\n /* eslint-disable */\n name: 'i18n-d',\n props: assign({\n value: {\n type: [Number, Date],\n required: true\n },\n format: {\n type: [String, Object]\n }\n }, baseFormatProps),\n /* eslint-enable */\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n setup(props, context) {\n const i18n = props.i18n ||\n useI18n({\n useScope: props.scope,\n __useComponent: true\n });\n return renderFormatter(props, context, DATETIME_FORMAT_OPTIONS_KEYS, (...args) => \n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n i18n[DatetimePartsSymbol](...args));\n }\n});\n/**\n * Datetime Format Component\n *\n * @remarks\n * See the following items for property about details\n *\n * @VueI18nSee [FormattableProps](component#formattableprops)\n * @VueI18nSee [BaseFormatProps](component#baseformatprops)\n * @VueI18nSee [Custom Formatting](../guide/essentials/datetime#custom-formatting)\n *\n * @VueI18nDanger\n * Not supported IE, due to no support `Intl.DateTimeFormat#formatToParts` in [IE](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Intl/DateTimeFormat/formatToParts)\n *\n * If you want to use it, you need to use [polyfill](https://github.com/formatjs/formatjs/tree/main/packages/intl-datetimeformat)\n *\n * @VueI18nComponent\n */\nconst DatetimeFormat = DatetimeFormatImpl;\nconst I18nD = DatetimeFormat;\n\nfunction getComposer$2(i18n, instance) {\n const i18nInternal = i18n;\n if (i18n.mode === 'composition') {\n return (i18nInternal.__getInstance(instance) || i18n.global);\n }\n else {\n const vueI18n = i18nInternal.__getInstance(instance);\n return vueI18n != null\n ? vueI18n.__composer\n : i18n.global.__composer;\n }\n}\nfunction vTDirective(i18n) {\n const _process = (binding) => {\n const { instance, modifiers, value } = binding;\n /* istanbul ignore if */\n if (!instance || !instance.$) {\n throw createI18nError(I18nErrorCodes.UNEXPECTED_ERROR);\n }\n const composer = getComposer$2(i18n, instance.$);\n if ((process.env.NODE_ENV !== 'production') && modifiers.preserve) {\n warn(getWarnMessage(I18nWarnCodes.NOT_SUPPORTED_PRESERVE));\n }\n const parsedValue = parseValue(value);\n return [\n Reflect.apply(composer.t, composer, [...makeParams(parsedValue)]),\n composer\n ];\n };\n const register = (el, binding) => {\n const [textContent, composer] = _process(binding);\n if (inBrowser && i18n.global === composer) {\n // global scope only\n el.__i18nWatcher = watch(composer.locale, () => {\n binding.instance && binding.instance.$forceUpdate();\n });\n }\n el.__composer = composer;\n el.textContent = textContent;\n };\n const unregister = (el) => {\n if (inBrowser && el.__i18nWatcher) {\n el.__i18nWatcher();\n el.__i18nWatcher = undefined;\n delete el.__i18nWatcher;\n }\n if (el.__composer) {\n el.__composer = undefined;\n delete el.__composer;\n }\n };\n const update = (el, { value }) => {\n if (el.__composer) {\n const composer = el.__composer;\n const parsedValue = parseValue(value);\n el.textContent = Reflect.apply(composer.t, composer, [\n ...makeParams(parsedValue)\n ]);\n }\n };\n const getSSRProps = (binding) => {\n const [textContent] = _process(binding);\n return { textContent };\n };\n return {\n created: register,\n unmounted: unregister,\n beforeUpdate: update,\n getSSRProps\n };\n}\nfunction parseValue(value) {\n if (isString(value)) {\n return { path: value };\n }\n else if (isPlainObject(value)) {\n if (!('path' in value)) {\n throw createI18nError(I18nErrorCodes.REQUIRED_VALUE, 'path');\n }\n return value;\n }\n else {\n throw createI18nError(I18nErrorCodes.INVALID_VALUE);\n }\n}\nfunction makeParams(value) {\n const { path, locale, args, choice, plural } = value;\n const options = {};\n const named = args || {};\n if (isString(locale)) {\n options.locale = locale;\n }\n if (isNumber(choice)) {\n options.plural = choice;\n }\n if (isNumber(plural)) {\n options.plural = plural;\n }\n return [path, named, options];\n}\n\nfunction apply(app, i18n, ...options) {\n const pluginOptions = isPlainObject(options[0])\n ? options[0]\n : {};\n const useI18nComponentName = !!pluginOptions.useI18nComponentName;\n const globalInstall = isBoolean(pluginOptions.globalInstall)\n ? pluginOptions.globalInstall\n : true;\n if ((process.env.NODE_ENV !== 'production') && globalInstall && useI18nComponentName) {\n warn(getWarnMessage(I18nWarnCodes.COMPONENT_NAME_LEGACY_COMPATIBLE, {\n name: Translation.name\n }));\n }\n if (globalInstall) {\n [!useI18nComponentName ? Translation.name : 'i18n', 'I18nT'].forEach(name => app.component(name, Translation));\n [NumberFormat.name, 'I18nN'].forEach(name => app.component(name, NumberFormat));\n [DatetimeFormat.name, 'I18nD'].forEach(name => app.component(name, DatetimeFormat));\n }\n // install directive\n {\n app.directive('t', vTDirective(i18n));\n }\n}\n\nconst VueDevToolsLabels = {\n [\"vue-devtools-plugin-vue-i18n\" /* VueDevToolsIDs.PLUGIN */]: 'Vue I18n devtools',\n [\"vue-i18n-resource-inspector\" /* VueDevToolsIDs.CUSTOM_INSPECTOR */]: 'I18n Resources',\n [\"vue-i18n-timeline\" /* VueDevToolsIDs.TIMELINE */]: 'Vue I18n'\n};\nconst VueDevToolsPlaceholders = {\n [\"vue-i18n-resource-inspector\" /* VueDevToolsIDs.CUSTOM_INSPECTOR */]: 'Search for scopes ...'\n};\nconst VueDevToolsTimelineColors = {\n [\"vue-i18n-timeline\" /* VueDevToolsIDs.TIMELINE */]: 0xffcd19\n};\n\nconst VUE_I18N_COMPONENT_TYPES = 'vue-i18n: composer properties';\nlet devtoolsApi;\nasync function enableDevTools(app, i18n) {\n return new Promise((resolve, reject) => {\n try {\n setupDevtoolsPlugin({\n id: \"vue-devtools-plugin-vue-i18n\" /* VueDevToolsIDs.PLUGIN */,\n label: VueDevToolsLabels[\"vue-devtools-plugin-vue-i18n\" /* VueDevToolsIDs.PLUGIN */],\n packageName: 'vue-i18n',\n homepage: 'https://vue-i18n.intlify.dev',\n logo: 'https://vue-i18n.intlify.dev/vue-i18n-devtools-logo.png',\n componentStateTypes: [VUE_I18N_COMPONENT_TYPES],\n app: app // eslint-disable-line @typescript-eslint/no-explicit-any\n }, api => {\n devtoolsApi = api;\n api.on.visitComponentTree(({ componentInstance, treeNode }) => {\n updateComponentTreeTags(componentInstance, treeNode, i18n);\n });\n api.on.inspectComponent(({ componentInstance, instanceData }) => {\n if (componentInstance.vnode.el &&\n componentInstance.vnode.el.__VUE_I18N__ &&\n instanceData) {\n if (i18n.mode === 'legacy') {\n // ignore global scope on legacy mode\n if (componentInstance.vnode.el.__VUE_I18N__ !==\n i18n.global.__composer) {\n inspectComposer(instanceData, componentInstance.vnode.el.__VUE_I18N__);\n }\n }\n else {\n inspectComposer(instanceData, componentInstance.vnode.el.__VUE_I18N__);\n }\n }\n });\n api.addInspector({\n id: \"vue-i18n-resource-inspector\" /* VueDevToolsIDs.CUSTOM_INSPECTOR */,\n label: VueDevToolsLabels[\"vue-i18n-resource-inspector\" /* VueDevToolsIDs.CUSTOM_INSPECTOR */],\n icon: 'language',\n treeFilterPlaceholder: VueDevToolsPlaceholders[\"vue-i18n-resource-inspector\" /* VueDevToolsIDs.CUSTOM_INSPECTOR */]\n });\n api.on.getInspectorTree(payload => {\n if (payload.app === app &&\n payload.inspectorId === \"vue-i18n-resource-inspector\" /* VueDevToolsIDs.CUSTOM_INSPECTOR */) {\n registerScope(payload, i18n);\n }\n });\n const roots = new Map();\n api.on.getInspectorState(async (payload) => {\n if (payload.app === app &&\n payload.inspectorId === \"vue-i18n-resource-inspector\" /* VueDevToolsIDs.CUSTOM_INSPECTOR */) {\n api.unhighlightElement();\n inspectScope(payload, i18n);\n if (payload.nodeId === 'global') {\n if (!roots.has(payload.app)) {\n const [root] = await api.getComponentInstances(payload.app);\n roots.set(payload.app, root);\n }\n api.highlightElement(roots.get(payload.app));\n }\n else {\n const instance = getComponentInstance(payload.nodeId, i18n);\n instance && api.highlightElement(instance);\n }\n }\n });\n api.on.editInspectorState(payload => {\n if (payload.app === app &&\n payload.inspectorId === \"vue-i18n-resource-inspector\" /* VueDevToolsIDs.CUSTOM_INSPECTOR */) {\n editScope(payload, i18n);\n }\n });\n api.addTimelineLayer({\n id: \"vue-i18n-timeline\" /* VueDevToolsIDs.TIMELINE */,\n label: VueDevToolsLabels[\"vue-i18n-timeline\" /* VueDevToolsIDs.TIMELINE */],\n color: VueDevToolsTimelineColors[\"vue-i18n-timeline\" /* VueDevToolsIDs.TIMELINE */]\n });\n resolve(true);\n });\n }\n catch (e) {\n console.error(e);\n reject(false);\n }\n });\n}\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nfunction getI18nScopeLable(instance) {\n return (instance.type.name ||\n instance.type.displayName ||\n instance.type.__file ||\n 'Anonymous');\n}\nfunction updateComponentTreeTags(instance, // eslint-disable-line @typescript-eslint/no-explicit-any\ntreeNode, i18n) {\n // prettier-ignore\n const global = i18n.mode === 'composition'\n ? i18n.global\n : i18n.global.__composer;\n if (instance && instance.vnode.el && instance.vnode.el.__VUE_I18N__) {\n // add custom tags local scope only\n if (instance.vnode.el.__VUE_I18N__ !== global) {\n const tag = {\n label: `i18n (${getI18nScopeLable(instance)} Scope)`,\n textColor: 0x000000,\n backgroundColor: 0xffcd19\n };\n treeNode.tags.push(tag);\n }\n }\n}\nfunction inspectComposer(instanceData, composer) {\n const type = VUE_I18N_COMPONENT_TYPES;\n instanceData.state.push({\n type,\n key: 'locale',\n editable: true,\n value: composer.locale.value\n });\n instanceData.state.push({\n type,\n key: 'availableLocales',\n editable: false,\n value: composer.availableLocales\n });\n instanceData.state.push({\n type,\n key: 'fallbackLocale',\n editable: true,\n value: composer.fallbackLocale.value\n });\n instanceData.state.push({\n type,\n key: 'inheritLocale',\n editable: true,\n value: composer.inheritLocale\n });\n instanceData.state.push({\n type,\n key: 'messages',\n editable: false,\n value: getLocaleMessageValue(composer.messages.value)\n });\n {\n instanceData.state.push({\n type,\n key: 'datetimeFormats',\n editable: false,\n value: composer.datetimeFormats.value\n });\n instanceData.state.push({\n type,\n key: 'numberFormats',\n editable: false,\n value: composer.numberFormats.value\n });\n }\n}\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nfunction getLocaleMessageValue(messages) {\n const value = {};\n Object.keys(messages).forEach((key) => {\n const v = messages[key];\n if (isFunction(v) && 'source' in v) {\n value[key] = getMessageFunctionDetails(v);\n }\n else if (isMessageAST(v) && v.loc && v.loc.source) {\n value[key] = v.loc.source;\n }\n else if (isObject(v)) {\n value[key] = getLocaleMessageValue(v);\n }\n else {\n value[key] = v;\n }\n });\n return value;\n}\nconst ESC = {\n '<': '<',\n '>': '>',\n '\"': '"',\n '&': '&'\n};\nfunction escape(s) {\n return s.replace(/[<>\"&]/g, escapeChar);\n}\nfunction escapeChar(a) {\n return ESC[a] || a;\n}\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nfunction getMessageFunctionDetails(func) {\n const argString = func.source ? `(\"${escape(func.source)}\")` : `(?)`;\n return {\n _custom: {\n type: 'function',\n display: `ƒ ${argString}`\n }\n };\n}\nfunction registerScope(payload, i18n) {\n payload.rootNodes.push({\n id: 'global',\n label: 'Global Scope'\n });\n // prettier-ignore\n const global = i18n.mode === 'composition'\n ? i18n.global\n : i18n.global.__composer;\n for (const [keyInstance, instance] of i18n.__instances) {\n // prettier-ignore\n const composer = i18n.mode === 'composition'\n ? instance\n : instance.__composer;\n if (global === composer) {\n continue;\n }\n payload.rootNodes.push({\n id: composer.id.toString(),\n label: `${getI18nScopeLable(keyInstance)} Scope`\n });\n }\n}\nfunction getComponentInstance(nodeId, i18n) {\n let instance = null;\n if (nodeId !== 'global') {\n for (const [component, composer] of i18n.__instances.entries()) {\n if (composer.id.toString() === nodeId) {\n instance = component;\n break;\n }\n }\n }\n return instance;\n}\nfunction getComposer$1(nodeId, i18n) {\n if (nodeId === 'global') {\n return i18n.mode === 'composition'\n ? i18n.global\n : i18n.global.__composer;\n }\n else {\n const instance = Array.from(i18n.__instances.values()).find(item => item.id.toString() === nodeId);\n if (instance) {\n return i18n.mode === 'composition'\n ? instance\n : instance.__composer;\n }\n else {\n return null;\n }\n }\n}\nfunction inspectScope(payload, i18n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\n) {\n const composer = getComposer$1(payload.nodeId, i18n);\n if (composer) {\n // TODO:\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n payload.state = makeScopeInspectState(composer);\n }\n return null;\n}\nfunction makeScopeInspectState(composer) {\n const state = {};\n const localeType = 'Locale related info';\n const localeStates = [\n {\n type: localeType,\n key: 'locale',\n editable: true,\n value: composer.locale.value\n },\n {\n type: localeType,\n key: 'fallbackLocale',\n editable: true,\n value: composer.fallbackLocale.value\n },\n {\n type: localeType,\n key: 'availableLocales',\n editable: false,\n value: composer.availableLocales\n },\n {\n type: localeType,\n key: 'inheritLocale',\n editable: true,\n value: composer.inheritLocale\n }\n ];\n state[localeType] = localeStates;\n const localeMessagesType = 'Locale messages info';\n const localeMessagesStates = [\n {\n type: localeMessagesType,\n key: 'messages',\n editable: false,\n value: getLocaleMessageValue(composer.messages.value)\n }\n ];\n state[localeMessagesType] = localeMessagesStates;\n {\n const datetimeFormatsType = 'Datetime formats info';\n const datetimeFormatsStates = [\n {\n type: datetimeFormatsType,\n key: 'datetimeFormats',\n editable: false,\n value: composer.datetimeFormats.value\n }\n ];\n state[datetimeFormatsType] = datetimeFormatsStates;\n const numberFormatsType = 'Datetime formats info';\n const numberFormatsStates = [\n {\n type: numberFormatsType,\n key: 'numberFormats',\n editable: false,\n value: composer.numberFormats.value\n }\n ];\n state[numberFormatsType] = numberFormatsStates;\n }\n return state;\n}\nfunction addTimelineEvent(event, payload) {\n if (devtoolsApi) {\n let groupId;\n if (payload && 'groupId' in payload) {\n groupId = payload.groupId;\n delete payload.groupId;\n }\n devtoolsApi.addTimelineEvent({\n layerId: \"vue-i18n-timeline\" /* VueDevToolsIDs.TIMELINE */,\n event: {\n title: event,\n groupId,\n time: Date.now(),\n meta: {},\n data: payload || {},\n logType: event === \"compile-error\" /* VueDevToolsTimelineEvents.COMPILE_ERROR */\n ? 'error'\n : event === \"fallback\" /* VueDevToolsTimelineEvents.FALBACK */ ||\n event === \"missing\" /* VueDevToolsTimelineEvents.MISSING */\n ? 'warning'\n : 'default'\n }\n });\n }\n}\nfunction editScope(payload, i18n) {\n const composer = getComposer$1(payload.nodeId, i18n);\n if (composer) {\n const [field] = payload.path;\n if (field === 'locale' && isString(payload.state.value)) {\n composer.locale.value = payload.state.value;\n }\n else if (field === 'fallbackLocale' &&\n (isString(payload.state.value) ||\n isArray(payload.state.value) ||\n isObject(payload.state.value))) {\n composer.fallbackLocale.value = payload.state.value;\n }\n else if (field === 'inheritLocale' && isBoolean(payload.state.value)) {\n composer.inheritLocale = payload.state.value;\n }\n }\n}\n\n/**\n * Supports compatibility for legacy vue-i18n APIs\n * This mixin is used when we use vue-i18n@v9.x or later\n */\nfunction defineMixin(vuei18n, composer, i18n) {\n return {\n beforeCreate() {\n const instance = getCurrentInstance();\n /* istanbul ignore if */\n if (!instance) {\n throw createI18nError(I18nErrorCodes.UNEXPECTED_ERROR);\n }\n const options = this.$options;\n if (options.i18n) {\n const optionsI18n = options.i18n;\n if (options.__i18n) {\n optionsI18n.__i18n = options.__i18n;\n }\n optionsI18n.__root = composer;\n if (this === this.$root) {\n // merge option and gttach global\n this.$i18n = mergeToGlobal(vuei18n, optionsI18n);\n }\n else {\n optionsI18n.__injectWithOption = true;\n optionsI18n.__extender = i18n.__vueI18nExtend;\n // atttach local VueI18n instance\n this.$i18n = createVueI18n(optionsI18n);\n // extend VueI18n instance\n const _vueI18n = this.$i18n;\n if (_vueI18n.__extender) {\n _vueI18n.__disposer = _vueI18n.__extender(this.$i18n);\n }\n }\n }\n else if (options.__i18n) {\n if (this === this.$root) {\n // merge option and gttach global\n this.$i18n = mergeToGlobal(vuei18n, options);\n }\n else {\n // atttach local VueI18n instance\n this.$i18n = createVueI18n({\n __i18n: options.__i18n,\n __injectWithOption: true,\n __extender: i18n.__vueI18nExtend,\n __root: composer\n });\n // extend VueI18n instance\n const _vueI18n = this.$i18n;\n if (_vueI18n.__extender) {\n _vueI18n.__disposer = _vueI18n.__extender(this.$i18n);\n }\n }\n }\n else {\n // attach global VueI18n instance\n this.$i18n = vuei18n;\n }\n if (options.__i18nGlobal) {\n adjustI18nResources(composer, options, options);\n }\n // defines vue-i18n legacy APIs\n this.$t = (...args) => this.$i18n.t(...args);\n this.$rt = (...args) => this.$i18n.rt(...args);\n this.$tc = (...args) => this.$i18n.tc(...args);\n this.$te = (key, locale) => this.$i18n.te(key, locale);\n this.$d = (...args) => this.$i18n.d(...args);\n this.$n = (...args) => this.$i18n.n(...args);\n this.$tm = (key) => this.$i18n.tm(key);\n i18n.__setInstance(instance, this.$i18n);\n },\n mounted() {\n /* istanbul ignore if */\n if (((process.env.NODE_ENV !== 'production') || __VUE_PROD_DEVTOOLS__) &&\n !false &&\n this.$el &&\n this.$i18n) {\n const _vueI18n = this.$i18n;\n this.$el.__VUE_I18N__ = _vueI18n.__composer;\n const emitter = (this.__v_emitter =\n createEmitter());\n _vueI18n.__enableEmitter && _vueI18n.__enableEmitter(emitter);\n emitter.on('*', addTimelineEvent);\n }\n },\n unmounted() {\n const instance = getCurrentInstance();\n /* istanbul ignore if */\n if (!instance) {\n throw createI18nError(I18nErrorCodes.UNEXPECTED_ERROR);\n }\n const _vueI18n = this.$i18n;\n /* istanbul ignore if */\n if (((process.env.NODE_ENV !== 'production') || __VUE_PROD_DEVTOOLS__) &&\n !false &&\n this.$el &&\n this.$el.__VUE_I18N__) {\n if (this.__v_emitter) {\n this.__v_emitter.off('*', addTimelineEvent);\n delete this.__v_emitter;\n }\n if (this.$i18n) {\n _vueI18n.__disableEmitter && _vueI18n.__disableEmitter();\n delete this.$el.__VUE_I18N__;\n }\n }\n delete this.$t;\n delete this.$rt;\n delete this.$tc;\n delete this.$te;\n delete this.$d;\n delete this.$n;\n delete this.$tm;\n if (_vueI18n.__disposer) {\n _vueI18n.__disposer();\n delete _vueI18n.__disposer;\n delete _vueI18n.__extender;\n }\n i18n.__deleteInstance(instance);\n delete this.$i18n;\n }\n };\n}\nfunction mergeToGlobal(g, options) {\n g.locale = options.locale || g.locale;\n g.fallbackLocale = options.fallbackLocale || g.fallbackLocale;\n g.missing = options.missing || g.missing;\n g.silentTranslationWarn =\n options.silentTranslationWarn || g.silentFallbackWarn;\n g.silentFallbackWarn = options.silentFallbackWarn || g.silentFallbackWarn;\n g.formatFallbackMessages =\n options.formatFallbackMessages || g.formatFallbackMessages;\n g.postTranslation = options.postTranslation || g.postTranslation;\n g.warnHtmlInMessage = options.warnHtmlInMessage || g.warnHtmlInMessage;\n g.escapeParameterHtml = options.escapeParameterHtml || g.escapeParameterHtml;\n g.sync = options.sync || g.sync;\n g.__composer[SetPluralRulesSymbol](options.pluralizationRules || g.pluralizationRules);\n const messages = getLocaleMessages(g.locale, {\n messages: options.messages,\n __i18n: options.__i18n\n });\n Object.keys(messages).forEach(locale => g.mergeLocaleMessage(locale, messages[locale]));\n if (options.datetimeFormats) {\n Object.keys(options.datetimeFormats).forEach(locale => g.mergeDateTimeFormat(locale, options.datetimeFormats[locale]));\n }\n if (options.numberFormats) {\n Object.keys(options.numberFormats).forEach(locale => g.mergeNumberFormat(locale, options.numberFormats[locale]));\n }\n return g;\n}\n\n/**\n * Injection key for {@link useI18n}\n *\n * @remarks\n * The global injection key for I18n instances with `useI18n`. this injection key is used in Web Components.\n * Specify the i18n instance created by {@link createI18n} together with `provide` function.\n *\n * @VueI18nGeneral\n */\nconst I18nInjectionKey = \n/* #__PURE__*/ makeSymbol('global-vue-i18n');\n// eslint-disable-next-line @typescript-eslint/no-explicit-any, @typescript-eslint/explicit-module-boundary-types\nfunction createI18n(options = {}, VueI18nLegacy) {\n // prettier-ignore\n const __legacyMode = __VUE_I18N_LEGACY_API__ && isBoolean(options.legacy)\n ? options.legacy\n : __VUE_I18N_LEGACY_API__;\n // prettier-ignore\n const __globalInjection = isBoolean(options.globalInjection)\n ? options.globalInjection\n : true;\n // prettier-ignore\n const __allowComposition = __VUE_I18N_LEGACY_API__ && __legacyMode\n ? !!options.allowComposition\n : true;\n const __instances = new Map();\n const [globalScope, __global] = createGlobal(options, __legacyMode);\n const symbol = /* #__PURE__*/ makeSymbol((process.env.NODE_ENV !== 'production') ? 'vue-i18n' : '');\n if ((process.env.NODE_ENV !== 'production')) {\n if (__legacyMode && __allowComposition && !false) {\n warn(getWarnMessage(I18nWarnCodes.NOTICE_DROP_ALLOW_COMPOSITION));\n }\n }\n function __getInstance(component) {\n return __instances.get(component) || null;\n }\n function __setInstance(component, instance) {\n __instances.set(component, instance);\n }\n function __deleteInstance(component) {\n __instances.delete(component);\n }\n {\n const i18n = {\n // mode\n get mode() {\n return __VUE_I18N_LEGACY_API__ && __legacyMode\n ? 'legacy'\n : 'composition';\n },\n // allowComposition\n get allowComposition() {\n return __allowComposition;\n },\n // install plugin\n async install(app, ...options) {\n if (((process.env.NODE_ENV !== 'production') || __VUE_PROD_DEVTOOLS__) &&\n !false) {\n app.__VUE_I18N__ = i18n;\n }\n // setup global provider\n app.__VUE_I18N_SYMBOL__ = symbol;\n app.provide(app.__VUE_I18N_SYMBOL__, i18n);\n // set composer & vuei18n extend hook options from plugin options\n if (isPlainObject(options[0])) {\n const opts = options[0];\n i18n.__composerExtend =\n opts.__composerExtend;\n i18n.__vueI18nExtend =\n opts.__vueI18nExtend;\n }\n // global method and properties injection for Composition API\n let globalReleaseHandler = null;\n if (!__legacyMode && __globalInjection) {\n globalReleaseHandler = injectGlobalFields(app, i18n.global);\n }\n // install built-in components and directive\n if (__VUE_I18N_FULL_INSTALL__) {\n apply(app, i18n, ...options);\n }\n // setup mixin for Legacy API\n if (__VUE_I18N_LEGACY_API__ && __legacyMode) {\n app.mixin(defineMixin(__global, __global.__composer, i18n));\n }\n // release global scope\n const unmountApp = app.unmount;\n app.unmount = () => {\n globalReleaseHandler && globalReleaseHandler();\n i18n.dispose();\n unmountApp();\n };\n // setup vue-devtools plugin\n if (((process.env.NODE_ENV !== 'production') || __VUE_PROD_DEVTOOLS__) && !false) {\n const ret = await enableDevTools(app, i18n);\n if (!ret) {\n throw createI18nError(I18nErrorCodes.CANNOT_SETUP_VUE_DEVTOOLS_PLUGIN);\n }\n const emitter = createEmitter();\n if (__legacyMode) {\n const _vueI18n = __global;\n _vueI18n.__enableEmitter && _vueI18n.__enableEmitter(emitter);\n }\n else {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const _composer = __global;\n _composer[EnableEmitter] && _composer[EnableEmitter](emitter);\n }\n emitter.on('*', addTimelineEvent);\n }\n },\n // global accessor\n get global() {\n return __global;\n },\n dispose() {\n globalScope.stop();\n },\n // @internal\n __instances,\n // @internal\n __getInstance,\n // @internal\n __setInstance,\n // @internal\n __deleteInstance\n };\n return i18n;\n }\n}\n// eslint-disable-next-line @typescript-eslint/explicit-module-boundary-types\nfunction useI18n(options = {}) {\n const instance = getCurrentInstance();\n if (instance == null) {\n throw createI18nError(I18nErrorCodes.MUST_BE_CALL_SETUP_TOP);\n }\n if (!instance.isCE &&\n instance.appContext.app != null &&\n !instance.appContext.app.__VUE_I18N_SYMBOL__) {\n throw createI18nError(I18nErrorCodes.NOT_INSTALLED);\n }\n const i18n = getI18nInstance(instance);\n const gl = getGlobalComposer(i18n);\n const componentOptions = getComponentOptions(instance);\n const scope = getScope(options, componentOptions);\n if (__VUE_I18N_LEGACY_API__) {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n if (i18n.mode === 'legacy' && !options.__useComponent) {\n if (!i18n.allowComposition) {\n throw createI18nError(I18nErrorCodes.NOT_AVAILABLE_IN_LEGACY_MODE);\n }\n return useI18nForLegacy(instance, scope, gl, options);\n }\n }\n if (scope === 'global') {\n adjustI18nResources(gl, options, componentOptions);\n return gl;\n }\n if (scope === 'parent') {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n let composer = getComposer(i18n, instance, options.__useComponent);\n if (composer == null) {\n if ((process.env.NODE_ENV !== 'production')) {\n warn(getWarnMessage(I18nWarnCodes.NOT_FOUND_PARENT_SCOPE));\n }\n composer = gl;\n }\n return composer;\n }\n const i18nInternal = i18n;\n let composer = i18nInternal.__getInstance(instance);\n if (composer == null) {\n const composerOptions = assign({}, options);\n if ('__i18n' in componentOptions) {\n composerOptions.__i18n = componentOptions.__i18n;\n }\n if (gl) {\n composerOptions.__root = gl;\n }\n composer = createComposer(composerOptions);\n if (i18nInternal.__composerExtend) {\n composer[DisposeSymbol] =\n i18nInternal.__composerExtend(composer);\n }\n setupLifeCycle(i18nInternal, instance, composer);\n i18nInternal.__setInstance(instance, composer);\n }\n return composer;\n}\n/**\n * Cast to VueI18n legacy compatible type\n *\n * @remarks\n * This API is provided only with [vue-i18n-bridge](https://vue-i18n.intlify.dev/guide/migration/ways.html#what-is-vue-i18n-bridge).\n *\n * The purpose of this function is to convert an {@link I18n} instance created with {@link createI18n | createI18n(legacy: true)} into a `vue-i18n@v8.x` compatible instance of `new VueI18n` in a TypeScript environment.\n *\n * @param i18n - An instance of {@link I18n}\n * @returns A i18n instance which is casted to {@link VueI18n} type\n *\n * @VueI18nTip\n * :new: provided by **vue-i18n-bridge only**\n *\n * @VueI18nGeneral\n */\n/* #__NO_SIDE_EFFECTS__ */\nconst castToVueI18n = (i18n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\n) => {\n if (!(__VUE_I18N_BRIDGE__ in i18n)) {\n throw createI18nError(I18nErrorCodes.NOT_COMPATIBLE_LEGACY_VUE_I18N);\n }\n return i18n;\n};\nfunction createGlobal(options, legacyMode, VueI18nLegacy // eslint-disable-line @typescript-eslint/no-explicit-any\n) {\n const scope = effectScope();\n {\n const obj = __VUE_I18N_LEGACY_API__ && legacyMode\n ? scope.run(() => createVueI18n(options))\n : scope.run(() => createComposer(options));\n if (obj == null) {\n throw createI18nError(I18nErrorCodes.UNEXPECTED_ERROR);\n }\n return [scope, obj];\n }\n}\nfunction getI18nInstance(instance) {\n {\n const i18n = inject(!instance.isCE\n ? instance.appContext.app.__VUE_I18N_SYMBOL__\n : I18nInjectionKey);\n /* istanbul ignore if */\n if (!i18n) {\n throw createI18nError(!instance.isCE\n ? I18nErrorCodes.UNEXPECTED_ERROR\n : I18nErrorCodes.NOT_INSTALLED_WITH_PROVIDE);\n }\n return i18n;\n }\n}\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nfunction getScope(options, componentOptions) {\n // prettier-ignore\n return isEmptyObject(options)\n ? ('__i18n' in componentOptions)\n ? 'local'\n : 'global'\n : !options.useScope\n ? 'local'\n : options.useScope;\n}\nfunction getGlobalComposer(i18n) {\n // prettier-ignore\n return i18n.mode === 'composition'\n ? i18n.global\n : i18n.global.__composer\n ;\n}\nfunction getComposer(i18n, target, useComponent = false) {\n let composer = null;\n const root = target.root;\n let current = getParentComponentInstance(target, useComponent);\n while (current != null) {\n const i18nInternal = i18n;\n if (i18n.mode === 'composition') {\n composer = i18nInternal.__getInstance(current);\n }\n else {\n if (__VUE_I18N_LEGACY_API__) {\n const vueI18n = i18nInternal.__getInstance(current);\n if (vueI18n != null) {\n composer = vueI18n\n .__composer;\n if (useComponent &&\n composer &&\n !composer[InejctWithOptionSymbol] // eslint-disable-line @typescript-eslint/no-explicit-any\n ) {\n composer = null;\n }\n }\n }\n }\n if (composer != null) {\n break;\n }\n if (root === current) {\n break;\n }\n current = current.parent;\n }\n return composer;\n}\nfunction getParentComponentInstance(target, useComponent = false) {\n if (target == null) {\n return null;\n }\n {\n // if `useComponent: true` will be specified, we get lexical scope owner instance for use-case slots\n return !useComponent\n ? target.parent\n : target.vnode.ctx || target.parent; // eslint-disable-line @typescript-eslint/no-explicit-any\n }\n}\nfunction setupLifeCycle(i18n, target, composer) {\n let emitter = null;\n {\n onMounted(() => {\n // inject composer instance to DOM for intlify-devtools\n if (((process.env.NODE_ENV !== 'production') || __VUE_PROD_DEVTOOLS__) &&\n !false &&\n target.vnode.el) {\n target.vnode.el.__VUE_I18N__ = composer;\n emitter = createEmitter();\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const _composer = composer;\n _composer[EnableEmitter] && _composer[EnableEmitter](emitter);\n emitter.on('*', addTimelineEvent);\n }\n }, target);\n onUnmounted(() => {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const _composer = composer;\n // remove composer instance from DOM for intlify-devtools\n if (((process.env.NODE_ENV !== 'production') || __VUE_PROD_DEVTOOLS__) &&\n !false &&\n target.vnode.el &&\n target.vnode.el.__VUE_I18N__) {\n emitter && emitter.off('*', addTimelineEvent);\n _composer[DisableEmitter] && _composer[DisableEmitter]();\n delete target.vnode.el.__VUE_I18N__;\n }\n i18n.__deleteInstance(target);\n // dispose extended resources\n const dispose = _composer[DisposeSymbol];\n if (dispose) {\n dispose();\n delete _composer[DisposeSymbol];\n }\n }, target);\n }\n}\nfunction useI18nForLegacy(instance, scope, root, options = {} // eslint-disable-line @typescript-eslint/no-explicit-any\n) {\n const isLocalScope = scope === 'local';\n const _composer = shallowRef(null);\n if (isLocalScope &&\n instance.proxy &&\n !(instance.proxy.$options.i18n || instance.proxy.$options.__i18n)) {\n throw createI18nError(I18nErrorCodes.MUST_DEFINE_I18N_OPTION_IN_ALLOW_COMPOSITION);\n }\n const _inheritLocale = isBoolean(options.inheritLocale)\n ? options.inheritLocale\n : !isString(options.locale);\n const _locale = ref(\n // prettier-ignore\n !isLocalScope || _inheritLocale\n ? root.locale.value\n : isString(options.locale)\n ? options.locale\n : DEFAULT_LOCALE);\n const _fallbackLocale = ref(\n // prettier-ignore\n !isLocalScope || _inheritLocale\n ? root.fallbackLocale.value\n : isString(options.fallbackLocale) ||\n isArray(options.fallbackLocale) ||\n isPlainObject(options.fallbackLocale) ||\n options.fallbackLocale === false\n ? options.fallbackLocale\n : _locale.value);\n const _messages = ref(getLocaleMessages(_locale.value, options));\n // prettier-ignore\n const _datetimeFormats = ref(isPlainObject(options.datetimeFormats)\n ? options.datetimeFormats\n : { [_locale.value]: {} });\n // prettier-ignore\n const _numberFormats = ref(isPlainObject(options.numberFormats)\n ? options.numberFormats\n : { [_locale.value]: {} });\n // prettier-ignore\n const _missingWarn = isLocalScope\n ? root.missingWarn\n : isBoolean(options.missingWarn) || isRegExp(options.missingWarn)\n ? options.missingWarn\n : true;\n // prettier-ignore\n const _fallbackWarn = isLocalScope\n ? root.fallbackWarn\n : isBoolean(options.fallbackWarn) || isRegExp(options.fallbackWarn)\n ? options.fallbackWarn\n : true;\n // prettier-ignore\n const _fallbackRoot = isLocalScope\n ? root.fallbackRoot\n : isBoolean(options.fallbackRoot)\n ? options.fallbackRoot\n : true;\n // configure fall back to root\n const _fallbackFormat = !!options.fallbackFormat;\n // runtime missing\n const _missing = isFunction(options.missing) ? options.missing : null;\n // postTranslation handler\n const _postTranslation = isFunction(options.postTranslation)\n ? options.postTranslation\n : null;\n // prettier-ignore\n const _warnHtmlMessage = isLocalScope\n ? root.warnHtmlMessage\n : isBoolean(options.warnHtmlMessage)\n ? options.warnHtmlMessage\n : true;\n const _escapeParameter = !!options.escapeParameter;\n // prettier-ignore\n const _modifiers = isLocalScope\n ? root.modifiers\n : isPlainObject(options.modifiers)\n ? options.modifiers\n : {};\n // pluralRules\n const _pluralRules = options.pluralRules || (isLocalScope && root.pluralRules);\n // track reactivity\n function trackReactivityValues() {\n return [\n _locale.value,\n _fallbackLocale.value,\n _messages.value,\n _datetimeFormats.value,\n _numberFormats.value\n ];\n }\n // locale\n const locale = computed({\n get: () => {\n return _composer.value ? _composer.value.locale.value : _locale.value;\n },\n set: val => {\n if (_composer.value) {\n _composer.value.locale.value = val;\n }\n _locale.value = val;\n }\n });\n // fallbackLocale\n const fallbackLocale = computed({\n get: () => {\n return _composer.value\n ? _composer.value.fallbackLocale.value\n : _fallbackLocale.value;\n },\n set: val => {\n if (_composer.value) {\n _composer.value.fallbackLocale.value = val;\n }\n _fallbackLocale.value = val;\n }\n });\n // messages\n const messages = computed(() => {\n if (_composer.value) {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n return _composer.value.messages.value;\n }\n else {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n return _messages.value;\n }\n });\n const datetimeFormats = computed(() => _datetimeFormats.value);\n const numberFormats = computed(() => _numberFormats.value);\n function getPostTranslationHandler() {\n return _composer.value\n ? _composer.value.getPostTranslationHandler()\n : _postTranslation;\n }\n function setPostTranslationHandler(handler) {\n if (_composer.value) {\n _composer.value.setPostTranslationHandler(handler);\n }\n }\n function getMissingHandler() {\n return _composer.value ? _composer.value.getMissingHandler() : _missing;\n }\n function setMissingHandler(handler) {\n if (_composer.value) {\n _composer.value.setMissingHandler(handler);\n }\n }\n function warpWithDeps(fn) {\n trackReactivityValues();\n return fn();\n }\n function t(...args) {\n return _composer.value\n ? warpWithDeps(() => Reflect.apply(_composer.value.t, null, [...args]))\n : warpWithDeps(() => '');\n }\n function rt(...args) {\n return _composer.value\n ? Reflect.apply(_composer.value.rt, null, [...args])\n : '';\n }\n function d(...args) {\n return _composer.value\n ? warpWithDeps(() => Reflect.apply(_composer.value.d, null, [...args]))\n : warpWithDeps(() => '');\n }\n function n(...args) {\n return _composer.value\n ? warpWithDeps(() => Reflect.apply(_composer.value.n, null, [...args]))\n : warpWithDeps(() => '');\n }\n function tm(key) {\n return _composer.value ? _composer.value.tm(key) : {};\n }\n function te(key, locale) {\n return _composer.value ? _composer.value.te(key, locale) : false;\n }\n function getLocaleMessage(locale) {\n return _composer.value ? _composer.value.getLocaleMessage(locale) : {};\n }\n function setLocaleMessage(locale, message) {\n if (_composer.value) {\n _composer.value.setLocaleMessage(locale, message);\n _messages.value[locale] = message;\n }\n }\n function mergeLocaleMessage(locale, message) {\n if (_composer.value) {\n _composer.value.mergeLocaleMessage(locale, message);\n }\n }\n function getDateTimeFormat(locale) {\n return _composer.value ? _composer.value.getDateTimeFormat(locale) : {};\n }\n function setDateTimeFormat(locale, format) {\n if (_composer.value) {\n _composer.value.setDateTimeFormat(locale, format);\n _datetimeFormats.value[locale] = format;\n }\n }\n function mergeDateTimeFormat(locale, format) {\n if (_composer.value) {\n _composer.value.mergeDateTimeFormat(locale, format);\n }\n }\n function getNumberFormat(locale) {\n return _composer.value ? _composer.value.getNumberFormat(locale) : {};\n }\n function setNumberFormat(locale, format) {\n if (_composer.value) {\n _composer.value.setNumberFormat(locale, format);\n _numberFormats.value[locale] = format;\n }\n }\n function mergeNumberFormat(locale, format) {\n if (_composer.value) {\n _composer.value.mergeNumberFormat(locale, format);\n }\n }\n const wrapper = {\n get id() {\n return _composer.value ? _composer.value.id : -1;\n },\n locale,\n fallbackLocale,\n messages,\n datetimeFormats,\n numberFormats,\n get inheritLocale() {\n return _composer.value ? _composer.value.inheritLocale : _inheritLocale;\n },\n set inheritLocale(val) {\n if (_composer.value) {\n _composer.value.inheritLocale = val;\n }\n },\n get availableLocales() {\n return _composer.value\n ? _composer.value.availableLocales\n : Object.keys(_messages.value);\n },\n get modifiers() {\n return (_composer.value ? _composer.value.modifiers : _modifiers);\n },\n get pluralRules() {\n return (_composer.value ? _composer.value.pluralRules : _pluralRules);\n },\n get isGlobal() {\n return _composer.value ? _composer.value.isGlobal : false;\n },\n get missingWarn() {\n return _composer.value ? _composer.value.missingWarn : _missingWarn;\n },\n set missingWarn(val) {\n if (_composer.value) {\n _composer.value.missingWarn = val;\n }\n },\n get fallbackWarn() {\n return _composer.value ? _composer.value.fallbackWarn : _fallbackWarn;\n },\n set fallbackWarn(val) {\n if (_composer.value) {\n _composer.value.missingWarn = val;\n }\n },\n get fallbackRoot() {\n return _composer.value ? _composer.value.fallbackRoot : _fallbackRoot;\n },\n set fallbackRoot(val) {\n if (_composer.value) {\n _composer.value.fallbackRoot = val;\n }\n },\n get fallbackFormat() {\n return _composer.value ? _composer.value.fallbackFormat : _fallbackFormat;\n },\n set fallbackFormat(val) {\n if (_composer.value) {\n _composer.value.fallbackFormat = val;\n }\n },\n get warnHtmlMessage() {\n return _composer.value\n ? _composer.value.warnHtmlMessage\n : _warnHtmlMessage;\n },\n set warnHtmlMessage(val) {\n if (_composer.value) {\n _composer.value.warnHtmlMessage = val;\n }\n },\n get escapeParameter() {\n return _composer.value\n ? _composer.value.escapeParameter\n : _escapeParameter;\n },\n set escapeParameter(val) {\n if (_composer.value) {\n _composer.value.escapeParameter = val;\n }\n },\n t,\n getPostTranslationHandler,\n setPostTranslationHandler,\n getMissingHandler,\n setMissingHandler,\n rt,\n d,\n n,\n tm,\n te,\n getLocaleMessage,\n setLocaleMessage,\n mergeLocaleMessage,\n getDateTimeFormat,\n setDateTimeFormat,\n mergeDateTimeFormat,\n getNumberFormat,\n setNumberFormat,\n mergeNumberFormat\n };\n function sync(composer) {\n composer.locale.value = _locale.value;\n composer.fallbackLocale.value = _fallbackLocale.value;\n Object.keys(_messages.value).forEach(locale => {\n composer.mergeLocaleMessage(locale, _messages.value[locale]);\n });\n Object.keys(_datetimeFormats.value).forEach(locale => {\n composer.mergeDateTimeFormat(locale, _datetimeFormats.value[locale]);\n });\n Object.keys(_numberFormats.value).forEach(locale => {\n composer.mergeNumberFormat(locale, _numberFormats.value[locale]);\n });\n composer.escapeParameter = _escapeParameter;\n composer.fallbackFormat = _fallbackFormat;\n composer.fallbackRoot = _fallbackRoot;\n composer.fallbackWarn = _fallbackWarn;\n composer.missingWarn = _missingWarn;\n composer.warnHtmlMessage = _warnHtmlMessage;\n }\n onBeforeMount(() => {\n if (instance.proxy == null || instance.proxy.$i18n == null) {\n throw createI18nError(I18nErrorCodes.NOT_AVAILABLE_COMPOSITION_IN_LEGACY);\n }\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n const composer = (_composer.value = instance.proxy.$i18n\n .__composer);\n if (scope === 'global') {\n _locale.value = composer.locale.value;\n _fallbackLocale.value = composer.fallbackLocale.value;\n _messages.value = composer.messages.value;\n _datetimeFormats.value = composer.datetimeFormats.value;\n _numberFormats.value = composer.numberFormats.value;\n }\n else if (isLocalScope) {\n sync(composer);\n }\n });\n return wrapper;\n}\nconst globalExportProps = [\n 'locale',\n 'fallbackLocale',\n 'availableLocales'\n];\nconst globalExportMethods = ['t', 'rt', 'd', 'n', 'tm', 'te']\n ;\nfunction injectGlobalFields(app, composer) {\n const i18n = Object.create(null);\n globalExportProps.forEach(prop => {\n const desc = Object.getOwnPropertyDescriptor(composer, prop);\n if (!desc) {\n throw createI18nError(I18nErrorCodes.UNEXPECTED_ERROR);\n }\n const wrap = isRef(desc.value) // check computed props\n ? {\n get() {\n return desc.value.value;\n },\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n set(val) {\n desc.value.value = val;\n }\n }\n : {\n get() {\n return desc.get && desc.get();\n }\n };\n Object.defineProperty(i18n, prop, wrap);\n });\n app.config.globalProperties.$i18n = i18n;\n globalExportMethods.forEach(method => {\n const desc = Object.getOwnPropertyDescriptor(composer, method);\n if (!desc || !desc.value) {\n throw createI18nError(I18nErrorCodes.UNEXPECTED_ERROR);\n }\n Object.defineProperty(app.config.globalProperties, `$${method}`, desc);\n });\n const dispose = () => {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n delete app.config.globalProperties.$i18n;\n globalExportMethods.forEach(method => {\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n delete app.config.globalProperties[`$${method}`];\n });\n };\n return dispose;\n}\n\n{\n initFeatureFlags();\n}\n// register message compiler at vue-i18n\nif (__INTLIFY_JIT_COMPILATION__) {\n registerMessageCompiler(compile);\n}\nelse {\n registerMessageCompiler(compileToFunction);\n}\n// register message resolver at vue-i18n\nregisterMessageResolver(resolveValue);\n// register fallback locale at vue-i18n\nregisterLocaleFallbacker(fallbackWithLocaleChain);\n// NOTE: experimental !!\nif ((process.env.NODE_ENV !== 'production') || __INTLIFY_PROD_DEVTOOLS__) {\n const target = getGlobalThis();\n target.__INTLIFY__ = true;\n setDevToolsHook(target.__INTLIFY_DEVTOOLS_GLOBAL_HOOK__);\n}\nif ((process.env.NODE_ENV !== 'production')) ;\n\nexport { DatetimeFormat, I18nD, I18nInjectionKey, I18nN, I18nT, NumberFormat, Translation, VERSION, castToVueI18n, createI18n, useI18n, vTDirective };\n","\n\n\n\n\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-floatlabel {\\n display: block;\\n position: relative;\\n}\\n\\n.p-floatlabel label {\\n position: absolute;\\n pointer-events: none;\\n top: 50%;\\n margin-top: -.5rem;\\n transition-property: all;\\n transition-timing-function: ease;\\n line-height: 1;\\n left: 0.75rem;\\n color: \".concat(dt('floatlabel.color'), \";\\n transition-duration: \").concat(dt('floatlabel.transition.duration'), \";\\n}\\n\\n.p-floatlabel:has(textarea) label {\\n top: 1rem;\\n}\\n\\n.p-floatlabel:has(input:focus) label,\\n.p-floatlabel:has(input.p-filled) label,\\n.p-floatlabel:has(input:-webkit-autofill) label,\\n.p-floatlabel:has(textarea:focus) label,\\n.p-floatlabel:has(textarea.p-filled) label,\\n.p-floatlabel:has(.p-inputwrapper-focus) label,\\n.p-floatlabel:has(.p-inputwrapper-filled) label {\\n top: -.75rem;\\n font-size: 12px;\\n color: \").concat(dt('floatlabel.focus.color'), \";\\n}\\n\\n.p-floatlabel .p-placeholder,\\n.p-floatlabel input::placeholder,\\n.p-floatlabel .p-inputtext::placeholder {\\n opacity: 0;\\n transition-property: all;\\n transition-timing-function: ease;\\n}\\n\\n.p-floatlabel .p-focus .p-placeholder,\\n.p-floatlabel input:focus::placeholder,\\n.p-floatlabel .p-inputtext:focus::placeholder {\\n opacity: 1;\\n transition-property: all;\\n transition-timing-function: ease;\\n}\\n\\n.p-floatlabel > .p-invalid + label {\\n color: \").concat(dt('floatlabel.invalid.color'), \";\\n}\\n\");\n};\nvar classes = {\n root: 'p-floatlabel'\n};\nvar FloatLabelStyle = BaseStyle.extend({\n name: 'floatlabel',\n theme: theme,\n classes: classes\n});\n\nexport { FloatLabelStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseComponent from '@primevue/core/basecomponent';\nimport FloatLabelStyle from 'primevue/floatlabel/style';\nimport { openBlock, createElementBlock, mergeProps, renderSlot } from 'vue';\n\nvar script$1 = {\n name: 'BaseFloatLabel',\n \"extends\": BaseComponent,\n props: {},\n style: FloatLabelStyle,\n provide: function provide() {\n return {\n $pcFloatLabel: this,\n $parentInstance: this\n };\n }\n};\n\nvar script = {\n name: 'FloatLabel',\n \"extends\": script$1,\n inheritAttrs: false\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"span\", mergeProps({\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [renderSlot(_ctx.$slots, \"default\")], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n","import { createI18n } from 'vue-i18n'\n\nconst messages = {\n en: {\n terminal: 'Terminal',\n videoFailedToLoad: 'Video failed to load',\n extensionName: 'Extension Name',\n reloadToApplyChanges: 'Reload to apply changes',\n insert: 'Insert',\n systemInfo: 'System Info',\n devices: 'Devices',\n about: 'About',\n add: 'Add',\n confirm: 'Confirm',\n reset: 'Reset',\n resetKeybindingsTooltip: 'Reset keybindings to default',\n customizeFolder: 'Customize Folder',\n icon: 'Icon',\n color: 'Color',\n bookmark: 'Bookmark',\n folder: 'Folder',\n star: 'Star',\n heart: 'Heart',\n file: 'File',\n inbox: 'Inbox',\n box: 'Box',\n briefcase: 'Briefcase',\n error: 'Error',\n loading: 'Loading',\n findIssues: 'Find Issues',\n copyToClipboard: 'Copy to Clipboard',\n openNewIssue: 'Open New Issue',\n showReport: 'Show Report',\n imageFailedToLoad: 'Image failed to load',\n reconnecting: 'Reconnecting',\n reconnected: 'Reconnected',\n delete: 'Delete',\n rename: 'Rename',\n customize: 'Customize',\n experimental: 'BETA',\n deprecated: 'DEPR',\n loadWorkflow: 'Load Workflow',\n goToNode: 'Go to Node',\n settings: 'Settings',\n searchWorkflows: 'Search Workflows',\n searchSettings: 'Search Settings',\n searchNodes: 'Search Nodes',\n searchModels: 'Search Models',\n searchKeybindings: 'Search Keybindings',\n noResultsFound: 'No Results Found',\n searchFailedMessage:\n \"We couldn't find any settings matching your search. Try adjusting your search terms.\",\n noTasksFound: 'No Tasks Found',\n noTasksFoundMessage: 'There are no tasks in the queue.',\n newFolder: 'New Folder',\n sideToolbar: {\n themeToggle: 'Toggle Theme',\n queue: 'Queue',\n nodeLibrary: 'Node Library',\n workflows: 'Workflows',\n browseTemplates: 'Browse example templates',\n openWorkflow: 'Open workflow in local file system',\n newBlankWorkflow: 'Create a new blank workflow',\n nodeLibraryTab: {\n sortOrder: 'Sort Order'\n },\n modelLibrary: 'Model Library',\n queueTab: {\n showFlatList: 'Show Flat List',\n backToAllTasks: 'Back to All Tasks',\n containImagePreview: 'Fill Image Preview',\n coverImagePreview: 'Fit Image Preview',\n clearPendingTasks: 'Clear Pending Tasks'\n }\n },\n menu: {\n batchCount: 'Batch Count',\n batchCountTooltip:\n 'The number of times the workflow generation should be queued',\n autoQueue: 'Auto Queue',\n disabled: 'Disabled',\n disabledTooltip: 'The workflow will not be automatically queued',\n instant: 'Instant',\n instantTooltip:\n 'The workflow will be queued instantly after a generation finishes',\n change: 'On Change',\n changeTooltip: 'The workflow will be queued once a change is made',\n queueWorkflow: 'Queue workflow',\n queueWorkflowFront: 'Queue workflow (Insert at Front)',\n queue: 'Queue',\n interrupt: 'Cancel current run',\n refresh: 'Refresh node definitions',\n clipspace: 'Open Clipspace',\n resetView: 'Reset canvas view',\n clear: 'Clear workflow',\n toggleBottomPanel: 'Toggle Bottom Panel'\n },\n templateWorkflows: {\n title: 'Get Started with a Template',\n template: {\n default: 'Image Generation',\n image2image: 'Image to Image',\n upscale: '2 Pass Upscale',\n flux_schnell: 'Flux Schnell'\n }\n },\n graphCanvasMenu: {\n zoomIn: 'Zoom In',\n zoomOut: 'Zoom Out',\n resetView: 'Reset View',\n selectMode: 'Select Mode',\n panMode: 'Pan Mode',\n toggleLinkVisibility: 'Toggle Link Visibility'\n }\n },\n zh: {\n terminal: '终端',\n videoFailedToLoad: '视频加载失败',\n extensionName: '扩展名称',\n reloadToApplyChanges: '重新加载以应用更改',\n insert: '插入',\n systemInfo: '系统信息',\n devices: '设备',\n about: '关于',\n add: '添加',\n confirm: '确认',\n reset: '重置',\n resetKeybindingsTooltip: '重置键位',\n customizeFolder: '定制文件夹',\n icon: '图标',\n color: '颜色',\n bookmark: '书签',\n folder: '文件夹',\n star: '星星',\n heart: '心',\n file: '文件',\n inbox: '收件箱',\n box: '盒子',\n briefcase: '公文包',\n error: '错误',\n loading: '加载中',\n findIssues: '查找 Issue',\n copyToClipboard: '复制到剪贴板',\n openNewIssue: '开启新 Issue',\n showReport: '显示报告',\n imageFailedToLoad: '图像加载失败',\n reconnecting: '重新连接中',\n reconnected: '已重新连接',\n delete: '删除',\n rename: '重命名',\n customize: '定制',\n experimental: 'BETA',\n deprecated: '弃用',\n loadWorkflow: '加载工作流',\n goToNode: '前往节点',\n settings: '设置',\n searchWorkflows: '搜索工作流',\n searchSettings: '搜索设置',\n searchNodes: '搜索节点',\n searchModels: '搜索模型',\n searchKeybindings: '搜索键位',\n noResultsFound: '未找到结果',\n searchFailedMessage:\n '我们找不到与您的搜索匹配的任何设置。请尝试调整搜索条件。',\n noContent: '(无内容)',\n noTasksFound: '未找到任务',\n noTasksFoundMessage: '队列中没有任务。',\n newFolder: '新建文件夹',\n sideToolbar: {\n themeToggle: '主题切换',\n queue: '队列',\n nodeLibrary: '节点库',\n workflows: '工作流',\n browseTemplates: '浏览示例模板',\n openWorkflow: '在本地文件系统中打开工作流',\n newBlankWorkflow: '创建一个新空白工作流',\n nodeLibraryTab: {\n sortOrder: '排序顺序'\n },\n modelLibrary: '模型库',\n queueTab: {\n showFlatList: '平铺结果',\n backToAllTasks: '返回',\n containImagePreview: '填充图像预览',\n coverImagePreview: '适应图像预览',\n clearPendingTasks: '清除待处理任务'\n }\n },\n menu: {\n batchCount: '批次数量',\n batchCountTooltip: '工作流生成次数',\n autoQueue: '自动执行',\n disabled: '禁用',\n disabledTooltip: '工作流将不会自动执行',\n instant: '实时',\n instantTooltip: '工作流将会在生成完成后立即执行',\n change: '变动',\n changeTooltip: '工作流将会在改变后执行',\n queueWorkflow: '执行工作流',\n queueWorkflowFront: '执行工作流 (队列首)',\n queue: '队列',\n interrupt: '取消当前任务',\n refresh: '刷新节点',\n clipspace: '打开剪贴板',\n resetView: '重置画布视图',\n clear: '清空工作流',\n toggleBottomPanel: '底部面板'\n },\n templateWorkflows: {\n title: '从模板开始',\n template: {\n default: 'Image Generation',\n image2image: 'Image to Image',\n upscale: '2 Pass Upscale',\n flux_schnell: 'Flux Schnell'\n }\n },\n graphCanvasMenu: {\n zoomIn: '放大',\n zoomOut: '缩小',\n resetView: '重置视图',\n selectMode: '选择模式',\n panMode: '平移模式',\n toggleLinkVisibility: '切换链接可见性'\n }\n },\n ru: {\n terminal: 'Терминал',\n videoFailedToLoad: 'Видео не удалось загрузить',\n extensionName: 'Название расширения',\n reloadToApplyChanges: 'Перезагрузите, чтобы применить изменения',\n insert: 'Вставить',\n systemInfo: 'Информация о системе',\n devices: 'Устройства',\n about: 'О',\n add: 'Добавить',\n confirm: 'Подтвердить',\n reset: 'Сбросить',\n resetKeybindingsTooltip: 'Сбросить сочетания клавиш по умолчанию',\n customizeFolder: 'Настроить папку',\n icon: 'Иконка',\n color: 'Цвет',\n bookmark: 'Закладка',\n folder: 'Папка',\n star: 'Звёздочка',\n heart: 'Сердце',\n file: 'Файл',\n inbox: 'Входящие',\n box: 'Ящик',\n briefcase: 'Чемодан',\n error: 'Ошибка',\n loading: 'Загрузка',\n findIssues: 'Найти Issue',\n copyToClipboard: 'Копировать в буфер обмена',\n openNewIssue: 'Открыть новый Issue',\n showReport: 'Показать отчёт',\n imageFailedToLoad: 'Изображение не удалось загрузить',\n reconnecting: 'Переподключение',\n reconnected: 'Переподключено',\n delete: 'Удалить',\n rename: 'Переименовать',\n customize: 'Настроить',\n experimental: 'БЕТА',\n deprecated: 'УСТАР',\n loadWorkflow: 'Загрузить рабочий процесс',\n goToNode: 'Перейти к узлу',\n settings: 'Настройки',\n searchWorkflows: 'Поиск рабочих процессов',\n searchSettings: 'Поиск настроек',\n searchNodes: 'Поиск узлов',\n searchModels: 'Поиск моделей',\n searchKeybindings: 'Поиск сочетаний клавиш',\n noResultsFound: 'Ничего не найдено',\n searchFailedMessage:\n 'Не удалось найти ни одной настройки, соответствующей вашему запросу. Попробуйте скорректировать поисковый запрос.',\n noContent: '(Нет контента)',\n noTasksFound: 'Задачи не найдены',\n noTasksFoundMessage: 'В очереди нет задач.',\n newFolder: 'Новая папка',\n sideToolbar: {\n themeToggle: 'Переключить тему',\n queue: 'Очередь',\n nodeLibrary: 'Библиотека узлов',\n workflows: 'Рабочие процессы',\n browseTemplates: 'Просмотреть примеры шаблонов',\n openWorkflow: 'Открыть рабочий процесс в локальной файловой системе',\n newBlankWorkflow: 'Создайте новый пустой рабочий процесс',\n nodeLibraryTab: {\n sortOrder: 'Порядок сортировки'\n },\n modelLibrary: 'Библиотека моделей',\n queueTab: {\n showFlatList: 'Показать плоский список',\n backToAllTasks: 'Вернуться ко всем задачам',\n containImagePreview: 'Предпросмотр заливающего изображения',\n coverImagePreview: 'Предпросмотр подходящего изображения',\n clearPendingTasks: 'Очистить отложенные задачи'\n }\n },\n menu: {\n batchCount: 'Количество пакетов',\n batchCountTooltip:\n 'Количество раз, когда генерация рабочего процесса должна быть помещена в очередь',\n autoQueue: 'Автоочередь',\n disabled: 'Отключено',\n disabledTooltip:\n 'Рабочий процесс не будет автоматически помещён в очередь',\n instant: 'Мгновенно',\n instantTooltip:\n 'Рабочий процесс будет помещён в очередь сразу же после завершения генерации',\n change: 'При изменении',\n changeTooltip:\n 'Рабочий процесс будет поставлен в очередь после внесения изменений',\n queueWorkflow: 'Очередь рабочего процесса',\n queueWorkflowFront: 'Очередь рабочего процесса (Вставка спереди)',\n queue: 'Очередь',\n interrupt: 'Отменить текущее выполнение',\n refresh: 'Обновить определения узлов',\n clipspace: 'Открыть Clipspace',\n resetView: 'Сбросить вид холста',\n clear: 'Очистить рабочий процесс'\n },\n templateWorkflows: {\n title: 'Начните работу с шаблона',\n template: {\n default: 'Image Generation',\n image2image: 'Image to Image',\n upscale: '2 Pass Upscale',\n flux_schnell: 'Flux Schnell'\n }\n },\n graphCanvasMenu: {\n zoomIn: 'Увеличить',\n zoomOut: 'Уменьшить',\n resetView: 'Сбросить вид',\n selectMode: 'Выбрать режим',\n panMode: 'Режим панорамирования',\n toggleLinkVisibility: 'Переключить видимость ссылок'\n }\n }\n // TODO: Add more languages\n}\n\nexport const i18n = createI18n({\n // Must set `false`, as Vue I18n Legacy API is for Vue 2\n legacy: false,\n locale: navigator.language.split('-')[0] || 'en',\n fallbackLocale: 'en',\n messages\n})\n","// This module is mocked in tests-ui/\n// Import vue components here to avoid tests-ui/ reporting errors\n// about importing primevue components.\nimport { useDialogStore } from '@/stores/dialogStore'\nimport LoadWorkflowWarning from '@/components/dialog/content/LoadWorkflowWarning.vue'\nimport MissingModelsWarning from '@/components/dialog/content/MissingModelsWarning.vue'\nimport SettingDialogContent from '@/components/dialog/content/SettingDialogContent.vue'\nimport SettingDialogHeader from '@/components/dialog/header/SettingDialogHeader.vue'\nimport type { ExecutionErrorWsMessage } from '@/types/apiTypes'\nimport ExecutionErrorDialogContent from '@/components/dialog/content/ExecutionErrorDialogContent.vue'\nimport TemplateWorkflowsContent from '@/components/templates/TemplateWorkflowsContent.vue'\nimport PromptDialogContent from '@/components/dialog/content/PromptDialogContent.vue'\nimport { i18n } from '@/i18n'\nimport type { MissingNodeType } from '@/types/comfy'\n\nexport function showLoadWorkflowWarning(props: {\n missingNodeTypes: MissingNodeType[]\n [key: string]: any\n}) {\n const dialogStore = useDialogStore()\n dialogStore.showDialog({\n component: LoadWorkflowWarning,\n props\n })\n}\n\nexport function showMissingModelsWarning(props: {\n missingModels: any[]\n paths: Record\n [key: string]: any\n}) {\n const dialogStore = useDialogStore()\n dialogStore.showDialog({\n component: MissingModelsWarning,\n props,\n dialogComponentProps: {\n maximizable: true\n }\n })\n}\n\nexport function showSettingsDialog() {\n useDialogStore().showDialog({\n headerComponent: SettingDialogHeader,\n component: SettingDialogContent\n })\n}\n\nexport function showExecutionErrorDialog(error: ExecutionErrorWsMessage) {\n useDialogStore().showDialog({\n component: ExecutionErrorDialogContent,\n props: {\n error\n }\n })\n}\n\nexport function showTemplateWorkflowsDialog() {\n useDialogStore().showDialog({\n title: i18n.global.t('templateWorkflows.title'),\n component: TemplateWorkflowsContent\n })\n}\n\nexport async function showPromptDialog({\n title,\n message,\n defaultValue = ''\n}: {\n title: string\n message: string\n defaultValue?: string\n}): Promise {\n const dialogStore = useDialogStore()\n\n return new Promise((resolve) => {\n dialogStore.showDialog({\n title,\n component: PromptDialogContent,\n props: {\n message,\n defaultValue,\n onConfirm: (value: string) => {\n resolve(value)\n }\n },\n dialogComponentProps: {\n onClose: () => {\n resolve(null)\n }\n }\n })\n })\n}\n","// @ts-strict-ignore\nimport { api } from './api'\nimport { ComfyDialog as _ComfyDialog } from './ui/dialog'\nimport { toggleSwitch } from './ui/toggleSwitch'\nimport { ComfySettingsDialog } from './ui/settings'\nimport { ComfyApp, app } from './app'\nimport { TaskItem } from '@/types/apiTypes'\nimport { showSettingsDialog } from '@/services/dialogService'\nimport { useSettingStore } from '@/stores/settingStore'\nimport { useCommandStore } from '@/stores/commandStore'\n\nexport const ComfyDialog = _ComfyDialog\n\ntype Position2D = {\n x: number\n y: number\n}\n\ntype Props = {\n parent?: HTMLElement\n $?: (el: HTMLElement) => void\n dataset?: DOMStringMap\n style?: Partial\n for?: string\n textContent?: string\n [key: string]: any\n}\n\ntype Children = Element[] | Element | string | string[]\n\ntype ElementType = K extends keyof HTMLElementTagNameMap\n ? HTMLElementTagNameMap[K]\n : HTMLElement\n\nexport function $el(\n tag: TTag,\n propsOrChildren?: Children | Props,\n children?: Children\n): ElementType {\n const split = tag.split('.')\n const element = document.createElement(split.shift() as string)\n if (split.length > 0) {\n element.classList.add(...split)\n }\n\n if (propsOrChildren) {\n if (typeof propsOrChildren === 'string') {\n propsOrChildren = { textContent: propsOrChildren }\n } else if (propsOrChildren instanceof Element) {\n propsOrChildren = [propsOrChildren]\n }\n if (Array.isArray(propsOrChildren)) {\n element.append(...propsOrChildren)\n } else {\n const {\n parent,\n $: cb,\n dataset,\n style,\n ...rest\n } = propsOrChildren as Props\n\n if (rest.for) {\n element.setAttribute('for', rest.for)\n }\n\n if (style) {\n Object.assign(element.style, style)\n }\n\n if (dataset) {\n Object.assign(element.dataset, dataset)\n }\n\n Object.assign(element, rest)\n if (children) {\n element.append(...(Array.isArray(children) ? children : [children]))\n }\n\n if (parent) {\n parent.append(element)\n }\n\n if (cb) {\n cb(element)\n }\n }\n }\n return element as ElementType\n}\n\nfunction dragElement(dragEl, settings): () => void {\n var posDiffX = 0,\n posDiffY = 0,\n posStartX = 0,\n posStartY = 0,\n newPosX = 0,\n newPosY = 0\n if (dragEl.getElementsByClassName('drag-handle')[0]) {\n // if present, the handle is where you move the DIV from:\n dragEl.getElementsByClassName('drag-handle')[0].onmousedown = dragMouseDown\n } else {\n // otherwise, move the DIV from anywhere inside the DIV:\n dragEl.onmousedown = dragMouseDown\n }\n\n // When the element resizes (e.g. view queue) ensure it is still in the windows bounds\n const resizeObserver = new ResizeObserver(() => {\n ensureInBounds()\n }).observe(dragEl)\n\n function ensureInBounds() {\n try {\n newPosX = Math.min(\n document.body.clientWidth - dragEl.clientWidth,\n Math.max(0, dragEl.offsetLeft)\n )\n newPosY = Math.min(\n document.body.clientHeight - dragEl.clientHeight,\n Math.max(0, dragEl.offsetTop)\n )\n\n positionElement()\n } catch (exception) {\n // robust\n }\n }\n\n function positionElement() {\n if (dragEl.style.display === 'none') return\n\n const halfWidth = document.body.clientWidth / 2\n const anchorRight = newPosX + dragEl.clientWidth / 2 > halfWidth\n\n // set the element's new position:\n if (anchorRight) {\n dragEl.style.left = 'unset'\n dragEl.style.right =\n document.body.clientWidth - newPosX - dragEl.clientWidth + 'px'\n } else {\n dragEl.style.left = newPosX + 'px'\n dragEl.style.right = 'unset'\n }\n\n dragEl.style.top = newPosY + 'px'\n dragEl.style.bottom = 'unset'\n\n if (savePos) {\n localStorage.setItem(\n 'Comfy.MenuPosition',\n JSON.stringify({\n x: dragEl.offsetLeft,\n y: dragEl.offsetTop\n })\n )\n }\n }\n\n function restorePos() {\n let posString = localStorage.getItem('Comfy.MenuPosition')\n if (posString) {\n const pos = JSON.parse(posString) as Position2D\n newPosX = pos.x\n newPosY = pos.y\n positionElement()\n ensureInBounds()\n }\n }\n\n let savePos = undefined\n settings.addSetting({\n id: 'Comfy.MenuPosition',\n category: ['Comfy', 'Menu', 'MenuPosition'],\n name: \"Save legacy menu's position\",\n type: 'boolean',\n defaultValue: savePos,\n onChange(value) {\n if (savePos === undefined && value) {\n restorePos()\n }\n savePos = value\n }\n })\n\n function dragMouseDown(e) {\n e = e || window.event\n e.preventDefault()\n // get the mouse cursor position at startup:\n posStartX = e.clientX\n posStartY = e.clientY\n document.onmouseup = closeDragElement\n // call a function whenever the cursor moves:\n document.onmousemove = elementDrag\n }\n\n function elementDrag(e) {\n e = e || window.event\n e.preventDefault()\n\n dragEl.classList.add('comfy-menu-manual-pos')\n\n // calculate the new cursor position:\n posDiffX = e.clientX - posStartX\n posDiffY = e.clientY - posStartY\n posStartX = e.clientX\n posStartY = e.clientY\n\n newPosX = Math.min(\n document.body.clientWidth - dragEl.clientWidth,\n Math.max(0, dragEl.offsetLeft + posDiffX)\n )\n newPosY = Math.min(\n document.body.clientHeight - dragEl.clientHeight,\n Math.max(0, dragEl.offsetTop + posDiffY)\n )\n\n positionElement()\n }\n\n window.addEventListener('resize', () => {\n ensureInBounds()\n })\n\n function closeDragElement() {\n // stop moving when mouse button is released:\n document.onmouseup = null\n document.onmousemove = null\n }\n\n return restorePos\n}\n\nclass ComfyList {\n #type\n #text\n #reverse\n element: HTMLDivElement\n button?: HTMLButtonElement\n\n constructor(text, type?, reverse?) {\n this.#text = text\n this.#type = type || text.toLowerCase()\n this.#reverse = reverse || false\n this.element = $el('div.comfy-list') as HTMLDivElement\n this.element.style.display = 'none'\n }\n\n get visible() {\n return this.element.style.display !== 'none'\n }\n\n async load() {\n const items = await api.getItems(this.#type)\n this.element.replaceChildren(\n ...Object.keys(items).flatMap((section) => [\n $el('h4', {\n textContent: section\n }),\n $el('div.comfy-list-items', [\n ...(this.#reverse ? items[section].reverse() : items[section]).map(\n (item: TaskItem) => {\n // Allow items to specify a custom remove action (e.g. for interrupt current prompt)\n const removeAction =\n 'remove' in item\n ? item.remove\n : {\n name: 'Delete',\n cb: () => api.deleteItem(this.#type, item.prompt[1])\n }\n return $el('div', { textContent: item.prompt[0] + ': ' }, [\n $el('button', {\n textContent: 'Load',\n onclick: async () => {\n await app.loadGraphData(\n item.prompt[3].extra_pnginfo.workflow,\n true,\n false\n )\n if ('outputs' in item) {\n app.nodeOutputs = {}\n for (const [key, value] of Object.entries(item.outputs)) {\n const realKey = item['meta']?.[key]?.display_node ?? key\n app.nodeOutputs[realKey] = value\n }\n }\n }\n }),\n $el('button', {\n textContent: removeAction.name,\n onclick: async () => {\n await removeAction.cb()\n await this.update()\n }\n })\n ])\n }\n )\n ])\n ]),\n $el('div.comfy-list-actions', [\n $el('button', {\n textContent: 'Clear ' + this.#text,\n onclick: async () => {\n await api.clearItems(this.#type)\n await this.load()\n }\n }),\n $el('button', { textContent: 'Refresh', onclick: () => this.load() })\n ])\n )\n }\n\n async update() {\n if (this.visible) {\n await this.load()\n }\n }\n\n async show() {\n this.element.style.display = 'block'\n this.button.textContent = 'Close'\n\n await this.load()\n }\n\n hide() {\n this.element.style.display = 'none'\n this.button.textContent = 'View ' + this.#text\n }\n\n toggle() {\n if (this.visible) {\n this.hide()\n return false\n } else {\n this.show()\n return true\n }\n }\n}\n\nexport class ComfyUI {\n app: ComfyApp\n dialog: _ComfyDialog\n settings: ComfySettingsDialog\n batchCount: number\n lastQueueSize: number\n queue: ComfyList\n history: ComfyList\n autoQueueMode: string\n graphHasChanged: boolean\n autoQueueEnabled: boolean\n menuHamburger: HTMLDivElement\n menuContainer: HTMLDivElement\n queueSize: Element\n restoreMenuPosition: () => void\n loadFile: () => void\n\n constructor(app) {\n this.app = app\n this.dialog = new ComfyDialog()\n this.settings = new ComfySettingsDialog(app)\n\n this.batchCount = 1\n this.lastQueueSize = 0\n this.queue = new ComfyList('Queue')\n this.history = new ComfyList('History', 'history', true)\n\n api.addEventListener('status', () => {\n this.queue.update()\n this.history.update()\n })\n\n this.setup(document.body)\n }\n\n setup(containerElement: HTMLElement) {\n const fileInput = $el('input', {\n id: 'comfy-file-input',\n type: 'file',\n accept: '.json,image/png,.latent,.safetensors,image/webp,audio/flac',\n style: { display: 'none' },\n parent: document.body,\n onchange: async () => {\n await app.handleFile(fileInput.files[0])\n fileInput.value = ''\n }\n })\n\n this.loadFile = () => fileInput.click()\n\n const autoQueueModeEl = toggleSwitch(\n 'autoQueueMode',\n [\n {\n text: 'instant',\n tooltip: 'A new prompt will be queued as soon as the queue reaches 0'\n },\n {\n text: 'change',\n tooltip:\n 'A new prompt will be queued when the queue is at 0 and the graph is/has changed'\n }\n ],\n {\n onChange: (value) => {\n this.autoQueueMode = value.item.value\n }\n }\n )\n autoQueueModeEl.style.display = 'none'\n\n api.addEventListener('graphChanged', () => {\n if (this.autoQueueMode === 'change' && this.autoQueueEnabled === true) {\n if (this.lastQueueSize === 0) {\n this.graphHasChanged = false\n app.queuePrompt(0, this.batchCount)\n } else {\n this.graphHasChanged = true\n }\n }\n })\n\n this.menuHamburger = $el(\n 'div.comfy-menu-hamburger',\n {\n parent: containerElement,\n onclick: () => {\n this.menuContainer.style.display = 'block'\n this.menuHamburger.style.display = 'none'\n }\n },\n [$el('div'), $el('div'), $el('div')]\n ) as HTMLDivElement\n\n this.menuContainer = $el('div.comfy-menu', { parent: containerElement }, [\n $el(\n 'div.drag-handle.comfy-menu-header',\n {\n style: {\n overflow: 'hidden',\n position: 'relative',\n width: '100%',\n cursor: 'default'\n }\n },\n [\n $el('span.drag-handle'),\n $el('span.comfy-menu-queue-size', { $: (q) => (this.queueSize = q) }),\n $el('div.comfy-menu-actions', [\n $el('button.comfy-settings-btn', {\n textContent: '⚙️',\n onclick: showSettingsDialog\n }),\n $el('button.comfy-close-menu-btn', {\n textContent: '\\u00d7',\n onclick: () => {\n this.menuContainer.style.display = 'none'\n this.menuHamburger.style.display = 'flex'\n }\n })\n ])\n ]\n ),\n $el('button.comfy-queue-btn', {\n id: 'queue-button',\n textContent: 'Queue Prompt',\n onclick: () => app.queuePrompt(0, this.batchCount)\n }),\n $el('div', {}, [\n $el('label', { innerHTML: 'Extra options' }, [\n $el('input', {\n type: 'checkbox',\n onchange: (i) => {\n document.getElementById('extraOptions').style.display = i\n .srcElement.checked\n ? 'block'\n : 'none'\n this.batchCount = i.srcElement.checked\n ? Number.parseInt(\n (\n document.getElementById(\n 'batchCountInputRange'\n ) as HTMLInputElement\n ).value\n )\n : 1\n ;(\n document.getElementById('autoQueueCheckbox') as HTMLInputElement\n ).checked = false\n this.autoQueueEnabled = false\n }\n })\n ])\n ]),\n $el(\n 'div',\n { id: 'extraOptions', style: { width: '100%', display: 'none' } },\n [\n $el('div', [\n $el('label', { innerHTML: 'Batch count' }),\n $el('input', {\n id: 'batchCountInputNumber',\n type: 'number',\n value: this.batchCount,\n min: '1',\n style: { width: '35%', marginLeft: '0.4em' },\n oninput: (i) => {\n this.batchCount = i.target.value\n /* Even though an element with a type of range logically represents a number (since\n it's used for numeric input), the value it holds is still treated as a string in HTML and\n JavaScript. This behavior is consistent across all elements regardless of their type\n (like text, number, or range), where the .value property is always a string. */\n ;(\n document.getElementById(\n 'batchCountInputRange'\n ) as HTMLInputElement\n ).value = this.batchCount.toString()\n }\n }),\n $el('input', {\n id: 'batchCountInputRange',\n type: 'range',\n min: '1',\n max: '100',\n value: this.batchCount,\n oninput: (i) => {\n this.batchCount = i.srcElement.value\n // Note\n ;(\n document.getElementById(\n 'batchCountInputNumber'\n ) as HTMLInputElement\n ).value = i.srcElement.value\n }\n })\n ]),\n $el('div', [\n $el('label', {\n for: 'autoQueueCheckbox',\n innerHTML: 'Auto Queue'\n }),\n $el('input', {\n id: 'autoQueueCheckbox',\n type: 'checkbox',\n checked: false,\n title: 'Automatically queue prompt when the queue size hits 0',\n onchange: (e) => {\n this.autoQueueEnabled = e.target.checked\n autoQueueModeEl.style.display = this.autoQueueEnabled\n ? ''\n : 'none'\n }\n }),\n autoQueueModeEl\n ])\n ]\n ),\n $el('div.comfy-menu-btns', [\n $el('button', {\n id: 'queue-front-button',\n textContent: 'Queue Front',\n onclick: () => app.queuePrompt(-1, this.batchCount)\n }),\n $el('button', {\n $: (b) => (this.queue.button = b as HTMLButtonElement),\n id: 'comfy-view-queue-button',\n textContent: 'View Queue',\n onclick: () => {\n this.history.hide()\n this.queue.toggle()\n }\n }),\n $el('button', {\n $: (b) => (this.history.button = b as HTMLButtonElement),\n id: 'comfy-view-history-button',\n textContent: 'View History',\n onclick: () => {\n this.queue.hide()\n this.history.toggle()\n }\n })\n ]),\n this.queue.element,\n this.history.element,\n $el('button', {\n id: 'comfy-save-button',\n textContent: 'Save',\n onclick: () => {\n useCommandStore().execute('Comfy.ExportWorkflow')\n }\n }),\n $el('button', {\n id: 'comfy-dev-save-api-button',\n textContent: 'Save (API Format)',\n style: { width: '100%', display: 'none' },\n onclick: () => {\n useCommandStore().execute('Comfy.ExportWorkflowAPI')\n }\n }),\n $el('button', {\n id: 'comfy-load-button',\n textContent: 'Load',\n onclick: () => fileInput.click()\n }),\n $el('button', {\n id: 'comfy-refresh-button',\n textContent: 'Refresh',\n onclick: () => app.refreshComboInNodes()\n }),\n $el('button', {\n id: 'comfy-clipspace-button',\n textContent: 'Clipspace',\n onclick: () => app.openClipspace()\n }),\n $el('button', {\n id: 'comfy-clear-button',\n textContent: 'Clear',\n onclick: () => {\n if (\n !useSettingStore().get('Comfy.ConfirmClear') ||\n confirm('Clear workflow?')\n ) {\n app.clean()\n app.graph.clear()\n app.resetView()\n api.dispatchEvent(new CustomEvent('graphCleared'))\n }\n }\n }),\n $el('button', {\n id: 'comfy-load-default-button',\n textContent: 'Load Default',\n onclick: async () => {\n if (\n !useSettingStore().get('Comfy.ConfirmClear') ||\n confirm('Load default workflow?')\n ) {\n app.resetView()\n await app.loadGraphData()\n }\n }\n }),\n $el('button', {\n id: 'comfy-reset-view-button',\n textContent: 'Reset View',\n onclick: async () => {\n app.resetView()\n }\n })\n ]) as HTMLDivElement\n\n this.restoreMenuPosition = dragElement(this.menuContainer, this.settings)\n\n this.setStatus({ exec_info: { queue_remaining: 'X' } })\n }\n\n setStatus(status) {\n this.queueSize.textContent =\n 'Queue size: ' + (status ? status.exec_info.queue_remaining : 'ERR')\n if (status) {\n if (\n this.lastQueueSize != 0 &&\n status.exec_info.queue_remaining == 0 &&\n this.autoQueueEnabled &&\n (this.autoQueueMode === 'instant' || this.graphHasChanged) &&\n !app.lastExecutionError\n ) {\n app.queuePrompt(0, this.batchCount)\n status.exec_info.queue_remaining += this.batchCount\n this.graphHasChanged = false\n }\n this.lastQueueSize = status.exec_info.queue_remaining\n }\n }\n}\n","// @ts-strict-ignore\nimport { $el, ComfyDialog } from './ui'\nimport { api } from './api'\nimport type { ComfyApp } from './app'\nimport { useToastStore } from '@/stores/toastStore'\n\n$el('style', {\n textContent: `\n .comfy-logging-logs {\n display: grid;\n color: var(--fg-color);\n white-space: pre-wrap;\n }\n .comfy-logging-log {\n display: contents;\n }\n .comfy-logging-title {\n background: var(--tr-even-bg-color);\n font-weight: bold;\n margin-bottom: 5px;\n text-align: center;\n }\n .comfy-logging-log div {\n background: var(--row-bg);\n padding: 5px;\n }\n `,\n parent: document.body\n})\n\n// Stringify function supporting max depth and removal of circular references\n// https://stackoverflow.com/a/57193345\nfunction stringify(val, depth, replacer, space, onGetObjID?) {\n depth = isNaN(+depth) ? 1 : depth\n var recursMap = new WeakMap()\n function _build(val, depth, o?, a?, r?) {\n // (JSON.stringify() has it's own rules, which we respect here by using it for property iteration)\n return !val || typeof val != 'object'\n ? val\n : ((r = recursMap.has(val)),\n recursMap.set(val, true),\n (a = Array.isArray(val)),\n r\n ? (o = (onGetObjID && onGetObjID(val)) || null)\n : JSON.stringify(val, function (k, v) {\n if (a || depth > 0) {\n if (replacer) v = replacer(k, v)\n if (!k) return (a = Array.isArray(v)), (val = v)\n !o && (o = a ? [] : {})\n o[k] = _build(v, a ? depth : depth - 1)\n }\n }),\n o === void 0 ? (a ? [] : {}) : o)\n }\n return JSON.stringify(_build(val, depth), null, space)\n}\n\nconst jsonReplacer = (k, v, ui) => {\n if (v instanceof Array && v.length === 1) {\n v = v[0]\n }\n if (v instanceof Date) {\n v = v.toISOString()\n if (ui) {\n v = v.split('T')[1]\n }\n }\n if (v instanceof Error) {\n let err = ''\n if (v.name) err += v.name + '\\n'\n if (v.message) err += v.message + '\\n'\n if (v.stack) err += v.stack + '\\n'\n if (!err) {\n err = v.toString()\n }\n v = err\n }\n return v\n}\n\nconst fileInput: HTMLInputElement = $el('input', {\n type: 'file',\n accept: '.json',\n style: { display: 'none' },\n parent: document.body\n}) as HTMLInputElement\n\nclass ComfyLoggingDialog extends ComfyDialog {\n logging: any\n\n constructor(logging) {\n super()\n this.logging = logging\n }\n\n clear() {\n this.logging.clear()\n this.show()\n }\n\n export() {\n const blob = new Blob(\n [stringify([...this.logging.entries], 20, jsonReplacer, '\\t')],\n {\n type: 'application/json'\n }\n )\n const url = URL.createObjectURL(blob)\n const a = $el('a', {\n href: url,\n download: `comfyui-logs-${Date.now()}.json`,\n style: { display: 'none' },\n parent: document.body\n })\n a.click()\n setTimeout(function () {\n a.remove()\n window.URL.revokeObjectURL(url)\n }, 0)\n }\n\n import() {\n fileInput.onchange = () => {\n const reader = new FileReader()\n reader.onload = () => {\n fileInput.remove()\n try {\n const obj = JSON.parse(reader.result as string)\n if (obj instanceof Array) {\n this.show(obj)\n } else {\n throw new Error('Invalid file selected.')\n }\n } catch (error) {\n useToastStore().addAlert('Unable to load logs: ' + error.message)\n }\n }\n reader.readAsText(fileInput.files[0])\n }\n fileInput.click()\n }\n\n createButtons() {\n return [\n $el('button', {\n type: 'button',\n textContent: 'Clear',\n onclick: () => this.clear()\n }),\n $el('button', {\n type: 'button',\n textContent: 'Export logs...',\n onclick: () => this.export()\n }),\n $el('button', {\n type: 'button',\n textContent: 'View exported logs...',\n onclick: () => this.import()\n }),\n ...super.createButtons()\n ]\n }\n\n getTypeColor(type) {\n switch (type) {\n case 'error':\n return 'red'\n case 'warn':\n return 'orange'\n case 'debug':\n return 'dodgerblue'\n }\n }\n\n show(entries?: any[]) {\n if (!entries) entries = this.logging.entries\n this.element.style.width = '100%'\n const cols = {\n source: 'Source',\n type: 'Type',\n timestamp: 'Timestamp',\n message: 'Message'\n }\n const keys = Object.keys(cols)\n const headers = Object.values(cols).map((title) =>\n $el('div.comfy-logging-title', {\n textContent: title\n })\n )\n const rows = entries.map((entry, i) => {\n return $el(\n 'div.comfy-logging-log',\n {\n $: (el) =>\n el.style.setProperty(\n '--row-bg',\n `var(--tr-${i % 2 ? 'even' : 'odd'}-bg-color)`\n )\n },\n keys.map((key) => {\n let v = entry[key]\n let color\n if (key === 'type') {\n color = this.getTypeColor(v)\n } else {\n v = jsonReplacer(key, v, true)\n\n if (typeof v === 'object') {\n v = stringify(v, 5, jsonReplacer, ' ')\n }\n }\n\n return $el('div', {\n style: {\n color\n },\n textContent: v\n })\n })\n )\n })\n\n const grid = $el(\n 'div.comfy-logging-logs',\n {\n style: {\n gridTemplateColumns: `repeat(${headers.length}, 1fr)`\n }\n },\n [...headers, ...rows]\n )\n const els = [grid]\n if (!this.logging.enabled) {\n els.unshift(\n $el('h3', {\n style: { textAlign: 'center' },\n textContent: 'Logging is disabled'\n })\n )\n }\n super.show($el('div', els))\n }\n}\n\nexport class ComfyLogging {\n /**\n * @type Array<{ source: string, type: string, timestamp: Date, message: any }>\n */\n entries = []\n\n #enabled\n #console = {}\n\n app: ComfyApp\n dialog: ComfyLoggingDialog\n\n get enabled() {\n return this.#enabled\n }\n\n set enabled(value) {\n if (value === this.#enabled) return\n if (value) {\n this.patchConsole()\n } else {\n this.unpatchConsole()\n }\n this.#enabled = value\n }\n\n constructor(app) {\n this.app = app\n\n this.dialog = new ComfyLoggingDialog(this)\n this.addSetting()\n this.catchUnhandled()\n this.addInitData()\n }\n\n addSetting() {\n const settingId = 'Comfy.Logging.Enabled'\n const htmlSettingId = settingId.replaceAll('.', '-')\n const setting = this.app.ui.settings.addSetting({\n id: settingId,\n name: 'Enable logging',\n defaultValue: true,\n onChange: (value) => {\n this.enabled = value\n },\n type: (name, setter, value) => {\n return $el('tr', [\n $el('td', [\n $el('label', {\n textContent: 'Logging',\n for: htmlSettingId\n })\n ]),\n $el('td', [\n $el('input', {\n id: htmlSettingId,\n type: 'checkbox',\n checked: value,\n onchange: (event) => {\n setter(event.target.checked)\n }\n }),\n $el('button', {\n textContent: 'View Logs',\n onclick: () => {\n this.app.ui.settings.element.close()\n this.dialog.show()\n },\n style: {\n fontSize: '14px',\n display: 'block',\n marginTop: '5px'\n }\n })\n ])\n ])\n }\n })\n this.enabled = setting.value\n }\n\n patchConsole() {\n // Capture common console outputs\n const self = this\n for (const type of ['log', 'warn', 'error', 'debug']) {\n const orig = console[type]\n this.#console[type] = orig\n console[type] = function () {\n orig.apply(console, arguments)\n self.addEntry('console', type, ...arguments)\n }\n }\n }\n\n unpatchConsole() {\n // Restore original console functions\n for (const type of Object.keys(this.#console)) {\n console[type] = this.#console[type]\n }\n this.#console = {}\n }\n\n catchUnhandled() {\n // Capture uncaught errors\n window.addEventListener('error', (e) => {\n this.addEntry('window', 'error', e.error ?? 'Unknown error')\n return false\n })\n\n window.addEventListener('unhandledrejection', (e) => {\n this.addEntry('unhandledrejection', 'error', e.reason ?? 'Unknown error')\n })\n }\n\n clear() {\n this.entries = []\n }\n\n addEntry(source, type, ...args) {\n if (this.enabled) {\n this.entries.push({\n source,\n type,\n timestamp: new Date(),\n message: args\n })\n }\n }\n\n log(source, ...args) {\n this.addEntry(source, 'log', ...args)\n }\n\n async addInitData() {\n if (!this.enabled) return\n const source = 'ComfyUI.Logging'\n this.addEntry(source, 'debug', { UserAgent: navigator.userAgent })\n }\n}\n","var NodeSlotType = /* @__PURE__ */ ((NodeSlotType2) => {\n NodeSlotType2[NodeSlotType2[\"INPUT\"] = 1] = \"INPUT\";\n NodeSlotType2[NodeSlotType2[\"OUTPUT\"] = 2] = \"OUTPUT\";\n return NodeSlotType2;\n})(NodeSlotType || {});\nvar RenderShape = /* @__PURE__ */ ((RenderShape2) => {\n RenderShape2[RenderShape2[\"BOX\"] = 1] = \"BOX\";\n RenderShape2[RenderShape2[\"ROUND\"] = 2] = \"ROUND\";\n RenderShape2[RenderShape2[\"CIRCLE\"] = 3] = \"CIRCLE\";\n RenderShape2[RenderShape2[\"CARD\"] = 4] = \"CARD\";\n RenderShape2[RenderShape2[\"ARROW\"] = 5] = \"ARROW\";\n RenderShape2[RenderShape2[\"GRID\"] = 6] = \"GRID\";\n RenderShape2[RenderShape2[\"HollowCircle\"] = 7] = \"HollowCircle\";\n return RenderShape2;\n})(RenderShape || {});\nvar LinkDirection = /* @__PURE__ */ ((LinkDirection2) => {\n LinkDirection2[LinkDirection2[\"NONE\"] = 0] = \"NONE\";\n LinkDirection2[LinkDirection2[\"UP\"] = 1] = \"UP\";\n LinkDirection2[LinkDirection2[\"DOWN\"] = 2] = \"DOWN\";\n LinkDirection2[LinkDirection2[\"LEFT\"] = 3] = \"LEFT\";\n LinkDirection2[LinkDirection2[\"RIGHT\"] = 4] = \"RIGHT\";\n LinkDirection2[LinkDirection2[\"CENTER\"] = 5] = \"CENTER\";\n return LinkDirection2;\n})(LinkDirection || {});\nvar LinkRenderType = /* @__PURE__ */ ((LinkRenderType2) => {\n LinkRenderType2[LinkRenderType2[\"HIDDEN_LINK\"] = -1] = \"HIDDEN_LINK\";\n LinkRenderType2[LinkRenderType2[\"STRAIGHT_LINK\"] = 0] = \"STRAIGHT_LINK\";\n LinkRenderType2[LinkRenderType2[\"LINEAR_LINK\"] = 1] = \"LINEAR_LINK\";\n LinkRenderType2[LinkRenderType2[\"SPLINE_LINK\"] = 2] = \"SPLINE_LINK\";\n return LinkRenderType2;\n})(LinkRenderType || {});\nvar TitleMode = /* @__PURE__ */ ((TitleMode2) => {\n TitleMode2[TitleMode2[\"NORMAL_TITLE\"] = 0] = \"NORMAL_TITLE\";\n TitleMode2[TitleMode2[\"NO_TITLE\"] = 1] = \"NO_TITLE\";\n TitleMode2[TitleMode2[\"TRANSPARENT_TITLE\"] = 2] = \"TRANSPARENT_TITLE\";\n TitleMode2[TitleMode2[\"AUTOHIDE_TITLE\"] = 3] = \"AUTOHIDE_TITLE\";\n return TitleMode2;\n})(TitleMode || {});\nvar LGraphEventMode = /* @__PURE__ */ ((LGraphEventMode2) => {\n LGraphEventMode2[LGraphEventMode2[\"ALWAYS\"] = 0] = \"ALWAYS\";\n LGraphEventMode2[LGraphEventMode2[\"ON_EVENT\"] = 1] = \"ON_EVENT\";\n LGraphEventMode2[LGraphEventMode2[\"NEVER\"] = 2] = \"NEVER\";\n LGraphEventMode2[LGraphEventMode2[\"ON_TRIGGER\"] = 3] = \"ON_TRIGGER\";\n LGraphEventMode2[LGraphEventMode2[\"BYPASS\"] = 4] = \"BYPASS\";\n return LGraphEventMode2;\n})(LGraphEventMode || {});\nfunction distance(a, b) {\n return Math.sqrt(\n (b[0] - a[0]) * (b[0] - a[0]) + (b[1] - a[1]) * (b[1] - a[1])\n );\n}\nfunction isPointInRectangle(point, rect) {\n return rect[0] < point[0] && rect[0] + rect[2] > point[0] && rect[1] < point[1] && rect[1] + rect[3] > point[1];\n}\nfunction isInsideRectangle(x2, y2, left, top, width2, height) {\n return left < x2 && left + width2 > x2 && top < y2 && top + height > y2;\n}\nfunction overlapBounding(a, b) {\n const aRight = a[0] + a[2];\n const aBottom = a[1] + a[3];\n const bRight = b[0] + b[2];\n const bBottom = b[1] + b[3];\n return a[0] > bRight || a[1] > bBottom || aRight < b[0] || aBottom < b[1] ? false : true;\n}\nvar BadgePosition = /* @__PURE__ */ ((BadgePosition2) => {\n BadgePosition2[\"TopLeft\"] = \"top-left\";\n BadgePosition2[\"TopRight\"] = \"top-right\";\n return BadgePosition2;\n})(BadgePosition || {});\nclass LGraphBadge {\n text;\n fgColor;\n bgColor;\n fontSize;\n padding;\n height;\n cornerRadius;\n constructor({\n text,\n fgColor = \"white\",\n bgColor = \"#0F1F0F\",\n fontSize = 12,\n padding = 6,\n height = 20,\n cornerRadius = 5\n }) {\n this.text = text;\n this.fgColor = fgColor;\n this.bgColor = bgColor;\n this.fontSize = fontSize;\n this.padding = padding;\n this.height = height;\n this.cornerRadius = cornerRadius;\n }\n get visible() {\n return this.text.length > 0;\n }\n getWidth(ctx) {\n if (!this.visible) return 0;\n ctx.save();\n ctx.font = `${this.fontSize}px sans-serif`;\n const textWidth = ctx.measureText(this.text).width;\n ctx.restore();\n return textWidth + this.padding * 2;\n }\n draw(ctx, x2, y2) {\n if (!this.visible) return;\n ctx.save();\n ctx.font = `${this.fontSize}px sans-serif`;\n const badgeWidth = this.getWidth(ctx);\n const badgeX = 0;\n ctx.fillStyle = this.bgColor;\n ctx.beginPath();\n if (ctx.roundRect) {\n ctx.roundRect(x2 + badgeX, y2, badgeWidth, this.height, this.cornerRadius);\n } else {\n ctx.rect(x2 + badgeX, y2, badgeWidth, this.height);\n }\n ctx.fill();\n ctx.fillStyle = this.fgColor;\n ctx.fillText(\n this.text,\n x2 + badgeX + this.padding,\n y2 + this.height - this.padding\n );\n ctx.restore();\n }\n}\nclass LLink {\n /** Link ID */\n id;\n type;\n /** Output node ID */\n origin_id;\n /** Output slot index */\n origin_slot;\n /** Input node ID */\n target_id;\n /** Input slot index */\n target_slot;\n data;\n _data;\n _pos;\n _last_time;\n path;\n #color;\n /** Custom colour for this link only */\n get color() {\n return this.#color;\n }\n set color(value) {\n this.#color = value === \"\" ? null : value;\n }\n constructor(id, type, origin_id, origin_slot, target_id, target_slot) {\n this.id = id;\n this.type = type;\n this.origin_id = origin_id;\n this.origin_slot = origin_slot;\n this.target_id = target_id;\n this.target_slot = target_slot;\n this._data = null;\n this._pos = new Float32Array(2);\n }\n configure(o) {\n if (Array.isArray(o)) {\n this.id = o[0];\n this.origin_id = o[1];\n this.origin_slot = o[2];\n this.target_id = o[3];\n this.target_slot = o[4];\n this.type = o[5];\n } else {\n this.id = o.id;\n this.type = o.type;\n this.origin_id = o.origin_id;\n this.origin_slot = o.origin_slot;\n this.target_id = o.target_id;\n this.target_slot = o.target_slot;\n }\n }\n serialize() {\n return [\n this.id,\n this.origin_id,\n this.origin_slot,\n this.target_id,\n this.target_slot,\n this.type\n ];\n }\n}\nclass LGraphNode {\n // Static properties used by dynamic child classes\n static title;\n static MAX_CONSOLE;\n static type;\n static category;\n static supported_extensions;\n static filter;\n static skip_list;\n title;\n graph;\n id;\n type;\n inputs;\n outputs;\n // Not used\n connections;\n properties;\n properties_info;\n flags;\n widgets;\n size;\n pos;\n _pos;\n locked;\n // Execution order, automatically computed during run\n order;\n mode;\n last_serialization;\n serialize_widgets;\n color;\n bgcolor;\n boxcolor;\n exec_version;\n action_call;\n execute_triggered;\n action_triggered;\n widgets_up;\n widgets_start_y;\n lostFocusAt;\n gotFocusAt;\n badges;\n badgePosition;\n _collapsed_width;\n horizontal;\n console;\n _level;\n _shape;\n subgraph;\n skip_subgraph_button;\n mouseOver;\n is_selected;\n redraw_on_mouse;\n // Appears unused\n optional_inputs;\n // Appears unused\n optional_outputs;\n resizable;\n clonable;\n _relative_id;\n clip_area;\n ignore_remove;\n has_errors;\n removable;\n block_delete;\n get shape() {\n return this._shape;\n }\n set shape(v2) {\n switch (v2) {\n case \"default\":\n delete this._shape;\n break;\n case \"box\":\n this._shape = RenderShape.BOX;\n break;\n case \"round\":\n this._shape = RenderShape.ROUND;\n break;\n case \"circle\":\n this._shape = RenderShape.CIRCLE;\n break;\n case \"card\":\n this._shape = RenderShape.CARD;\n break;\n default:\n this._shape = v2;\n }\n }\n constructor(title) {\n this._ctor(title);\n }\n _ctor(title) {\n this.title = title || \"Unnamed\";\n this.size = [LiteGraph.NODE_WIDTH, 60];\n this.graph = null;\n this._pos = new Float32Array([10, 10]);\n Object.defineProperty(this, \"pos\", {\n set: function(v2) {\n if (!v2 || v2.length < 2) {\n return;\n }\n this._pos[0] = v2[0];\n this._pos[1] = v2[1];\n },\n get: function() {\n return this._pos;\n },\n enumerable: true\n });\n if (LiteGraph.use_uuids) {\n this.id = LiteGraph.uuidv4();\n } else {\n this.id = -1;\n }\n this.type = null;\n this.inputs = [];\n this.outputs = [];\n this.connections = [];\n this.badges = [];\n this.badgePosition = BadgePosition.TopLeft;\n this.properties = {};\n this.properties_info = [];\n this.flags = {};\n }\n /**\n * configure a node from an object containing the serialized info\n */\n configure(info) {\n if (this.graph) {\n this.graph._version++;\n }\n for (const j in info) {\n if (j == \"properties\") {\n for (const k in info.properties) {\n this.properties[k] = info.properties[k];\n this.onPropertyChanged?.(k, info.properties[k]);\n }\n continue;\n }\n if (info[j] == null) {\n continue;\n } else if (typeof info[j] == \"object\") {\n if (this[j]?.configure) {\n this[j]?.configure(info[j]);\n } else {\n this[j] = LiteGraph.cloneObject(info[j], this[j]);\n }\n } else {\n this[j] = info[j];\n }\n }\n if (!info.title) {\n this.title = this.constructor.title;\n }\n if (this.inputs) {\n for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n const input = this.inputs[i2];\n const link = this.graph ? this.graph.links[input.link] : null;\n this.onConnectionsChange?.(LiteGraph.INPUT, i2, true, link, input);\n this.onInputAdded?.(input);\n }\n }\n if (this.outputs) {\n for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n const output = this.outputs[i2];\n if (!output.links) {\n continue;\n }\n for (let j = 0; j < output.links.length; ++j) {\n const link = this.graph ? this.graph.links[output.links[j]] : null;\n this.onConnectionsChange?.(LiteGraph.OUTPUT, i2, true, link, output);\n }\n this.onOutputAdded?.(output);\n }\n }\n if (this.widgets) {\n for (let i2 = 0; i2 < this.widgets.length; ++i2) {\n const w2 = this.widgets[i2];\n if (!w2)\n continue;\n if (w2.options?.property && this.properties[w2.options.property] != void 0)\n w2.value = JSON.parse(JSON.stringify(this.properties[w2.options.property]));\n }\n if (info.widgets_values) {\n for (let i2 = 0; i2 < info.widgets_values.length; ++i2) {\n if (this.widgets[i2]) {\n this.widgets[i2].value = info.widgets_values[i2];\n }\n }\n }\n }\n if (this.pinned) this.pin(true);\n this.onConfigure?.(info);\n }\n /**\n * serialize the content\n */\n serialize() {\n const o = {\n id: this.id,\n type: this.type,\n pos: this.pos,\n size: this.size,\n flags: LiteGraph.cloneObject(this.flags),\n order: this.order,\n mode: this.mode\n };\n if (this.constructor === LGraphNode && this.last_serialization)\n return this.last_serialization;\n if (this.inputs) o.inputs = this.inputs;\n if (this.outputs) {\n for (let i2 = 0; i2 < this.outputs.length; i2++) {\n delete this.outputs[i2]._data;\n }\n o.outputs = this.outputs;\n }\n if (this.title && this.title != this.constructor.title) o.title = this.title;\n if (this.properties) o.properties = LiteGraph.cloneObject(this.properties);\n if (this.widgets && this.serialize_widgets) {\n o.widgets_values = [];\n for (let i2 = 0; i2 < this.widgets.length; ++i2) {\n if (this.widgets[i2])\n o.widgets_values[i2] = this.widgets[i2].value;\n else\n o.widgets_values[i2] = null;\n }\n }\n if (!o.type) o.type = this.constructor.type;\n if (this.color) o.color = this.color;\n if (this.bgcolor) o.bgcolor = this.bgcolor;\n if (this.boxcolor) o.boxcolor = this.boxcolor;\n if (this.shape) o.shape = this.shape;\n if (this.onSerialize?.(o)) console.warn(\"node onSerialize shouldnt return anything, data should be stored in the object pass in the first parameter\");\n return o;\n }\n /* Creates a clone of this node */\n clone() {\n const node2 = LiteGraph.createNode(this.type);\n if (!node2) return null;\n const data = LiteGraph.cloneObject(this.serialize());\n if (data.inputs) {\n for (let i2 = 0; i2 < data.inputs.length; ++i2) {\n data.inputs[i2].link = null;\n }\n }\n if (data.outputs) {\n for (let i2 = 0; i2 < data.outputs.length; ++i2) {\n if (data.outputs[i2].links) {\n data.outputs[i2].links.length = 0;\n }\n }\n }\n delete data.id;\n if (LiteGraph.use_uuids) data.id = LiteGraph.uuidv4();\n node2.configure(data);\n return node2;\n }\n /**\n * serialize and stringify\n */\n toString() {\n return JSON.stringify(this.serialize());\n }\n /**\n * get the title string\n */\n getTitle() {\n return this.title || this.constructor.title;\n }\n /**\n * sets the value of a property\n * @param {String} name\n * @param {*} value\n */\n setProperty(name, value) {\n this.properties ||= {};\n if (value === this.properties[name])\n return;\n const prev_value = this.properties[name];\n this.properties[name] = value;\n if (this.onPropertyChanged?.(name, value, prev_value) === false)\n this.properties[name] = prev_value;\n if (this.widgets)\n for (let i2 = 0; i2 < this.widgets.length; ++i2) {\n const w2 = this.widgets[i2];\n if (!w2)\n continue;\n if (w2.options.property == name) {\n w2.value = value;\n break;\n }\n }\n }\n /**\n * sets the output data\n * @param {number} slot\n * @param {*} data\n */\n setOutputData(slot, data) {\n if (!this.outputs) return;\n if (slot == -1 || slot >= this.outputs.length) return;\n const output_info = this.outputs[slot];\n if (!output_info) return;\n output_info._data = data;\n if (this.outputs[slot].links) {\n for (let i2 = 0; i2 < this.outputs[slot].links.length; i2++) {\n const link_id = this.outputs[slot].links[i2];\n const link = this.graph.links[link_id];\n if (link)\n link.data = data;\n }\n }\n }\n /**\n * sets the output data type, useful when you want to be able to overwrite the data type\n * @param {number} slot\n * @param {String} datatype\n */\n setOutputDataType(slot, type) {\n if (!this.outputs) return;\n if (slot == -1 || slot >= this.outputs.length) return;\n const output_info = this.outputs[slot];\n if (!output_info) return;\n output_info.type = type;\n if (this.outputs[slot].links) {\n for (let i2 = 0; i2 < this.outputs[slot].links.length; i2++) {\n const link_id = this.outputs[slot].links[i2];\n this.graph.links[link_id].type = type;\n }\n }\n }\n /**\n * Retrieves the input data (data traveling through the connection) from one slot\n * @param {number} slot\n * @param {boolean} force_update if set to true it will force the connected node of this slot to output data into this link\n * @return {*} data or if it is not connected returns undefined\n */\n getInputData(slot, force_update) {\n if (!this.inputs) return;\n if (slot >= this.inputs.length || this.inputs[slot].link == null) return;\n const link_id = this.inputs[slot].link;\n const link = this.graph.links[link_id];\n if (!link) return null;\n if (!force_update) return link.data;\n const node2 = this.graph.getNodeById(link.origin_id);\n if (!node2) return link.data;\n if (node2.updateOutputData) {\n node2.updateOutputData(link.origin_slot);\n } else {\n node2.onExecute?.();\n }\n return link.data;\n }\n /**\n * Retrieves the input data type (in case this supports multiple input types)\n * @param {number} slot\n * @return {String} datatype in string format\n */\n getInputDataType(slot) {\n if (!this.inputs) return null;\n if (slot >= this.inputs.length || this.inputs[slot].link == null) return null;\n const link_id = this.inputs[slot].link;\n const link = this.graph.links[link_id];\n if (!link) return null;\n const node2 = this.graph.getNodeById(link.origin_id);\n if (!node2) return link.type;\n const output_info = node2.outputs[link.origin_slot];\n return output_info ? output_info.type : null;\n }\n /**\n * Retrieves the input data from one slot using its name instead of slot number\n * @param {String} slot_name\n * @param {boolean} force_update if set to true it will force the connected node of this slot to output data into this link\n * @return {*} data or if it is not connected returns null\n */\n getInputDataByName(slot_name, force_update) {\n const slot = this.findInputSlot(slot_name);\n return slot == -1 ? null : this.getInputData(slot, force_update);\n }\n /**\n * tells you if there is a connection in one input slot\n * @param {number} slot\n * @return {boolean}\n */\n isInputConnected(slot) {\n if (!this.inputs) return false;\n return slot < this.inputs.length && this.inputs[slot].link != null;\n }\n /**\n * tells you info about an input connection (which node, type, etc)\n * @param {number} slot\n * @return {Object} object or null { link: id, name: string, type: string or 0 }\n */\n getInputInfo(slot) {\n return !this.inputs || !(slot < this.inputs.length) ? null : this.inputs[slot];\n }\n /**\n * Returns the link info in the connection of an input slot\n * @param {number} slot\n * @return {LLink} object or null\n */\n getInputLink(slot) {\n if (!this.inputs) return null;\n if (slot < this.inputs.length) {\n const slot_info = this.inputs[slot];\n return this.graph.links[slot_info.link];\n }\n return null;\n }\n /**\n * returns the node connected in the input slot\n * @param {number} slot\n * @return {LGraphNode} node or null\n */\n getInputNode(slot) {\n if (!this.inputs) return null;\n if (slot >= this.inputs.length) return null;\n const input = this.inputs[slot];\n if (!input || input.link === null) return null;\n const link_info = this.graph.links[input.link];\n if (!link_info) return null;\n return this.graph.getNodeById(link_info.origin_id);\n }\n /**\n * returns the value of an input with this name, otherwise checks if there is a property with that name\n * @param {string} name\n * @return {*} value\n */\n getInputOrProperty(name) {\n if (!this.inputs || !this.inputs.length) {\n return this.properties ? this.properties[name] : null;\n }\n for (let i2 = 0, l = this.inputs.length; i2 < l; ++i2) {\n const input_info = this.inputs[i2];\n if (name == input_info.name && input_info.link != null) {\n const link = this.graph.links[input_info.link];\n if (link) return link.data;\n }\n }\n return this.properties[name];\n }\n /**\n * tells you the last output data that went in that slot\n * @param {number} slot\n * @return {Object} object or null\n */\n getOutputData(slot) {\n if (!this.outputs) return null;\n if (slot >= this.outputs.length) return null;\n const info = this.outputs[slot];\n return info._data;\n }\n /**\n * tells you info about an output connection (which node, type, etc)\n * @param {number} slot\n * @return {Object} object or null { name: string, type: string, links: [ ids of links in number ] }\n */\n getOutputInfo(slot) {\n return !this.outputs || !(slot < this.outputs.length) ? null : this.outputs[slot];\n }\n /**\n * tells you if there is a connection in one output slot\n * @param {number} slot\n * @return {boolean}\n */\n isOutputConnected(slot) {\n if (!this.outputs) return false;\n return slot < this.outputs.length && this.outputs[slot].links?.length > 0;\n }\n /**\n * tells you if there is any connection in the output slots\n * @return {boolean}\n */\n isAnyOutputConnected() {\n if (!this.outputs) return false;\n for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n if (this.outputs[i2].links && this.outputs[i2].links.length) {\n return true;\n }\n }\n return false;\n }\n /**\n * retrieves all the nodes connected to this output slot\n * @param {number} slot\n * @return {array}\n */\n getOutputNodes(slot) {\n if (!this.outputs || this.outputs.length == 0) return null;\n if (slot >= this.outputs.length) return null;\n const output = this.outputs[slot];\n if (!output.links || output.links.length == 0) return null;\n const r = [];\n for (let i2 = 0; i2 < output.links.length; i2++) {\n const link_id = output.links[i2];\n const link = this.graph.links[link_id];\n if (link) {\n const target_node = this.graph.getNodeById(link.target_id);\n if (target_node) {\n r.push(target_node);\n }\n }\n }\n return r;\n }\n addOnTriggerInput() {\n const trigS = this.findInputSlot(\"onTrigger\");\n if (trigS == -1) {\n //!trigS || \n this.addInput(\"onTrigger\", LiteGraph.EVENT, { optional: true, nameLocked: true });\n return this.findInputSlot(\"onTrigger\");\n }\n return trigS;\n }\n addOnExecutedOutput() {\n const trigS = this.findOutputSlot(\"onExecuted\");\n if (trigS == -1) {\n //!trigS || \n this.addOutput(\"onExecuted\", LiteGraph.ACTION, { optional: true, nameLocked: true });\n return this.findOutputSlot(\"onExecuted\");\n }\n return trigS;\n }\n onAfterExecuteNode(param, options) {\n const trigS = this.findOutputSlot(\"onExecuted\");\n if (trigS != -1) {\n this.triggerSlot(trigS, param, null, options);\n }\n }\n changeMode(modeTo) {\n switch (modeTo) {\n case LiteGraph.ON_EVENT:\n break;\n case LiteGraph.ON_TRIGGER:\n this.addOnTriggerInput();\n this.addOnExecutedOutput();\n break;\n case LiteGraph.NEVER:\n break;\n case LiteGraph.ALWAYS:\n break;\n case LiteGraph.ON_REQUEST:\n break;\n default:\n return false;\n }\n this.mode = modeTo;\n return true;\n }\n /**\n * Triggers the node code execution, place a boolean/counter to mark the node as being executed\n * @param {*} param\n * @param {*} options\n */\n doExecute(param, options) {\n options = options || {};\n if (this.onExecute) {\n options.action_call ||= this.id + \"_exec_\" + Math.floor(Math.random() * 9999);\n this.graph.nodes_executing[this.id] = true;\n this.onExecute(param, options);\n this.graph.nodes_executing[this.id] = false;\n this.exec_version = this.graph.iteration;\n if (options?.action_call) {\n this.action_call = options.action_call;\n this.graph.nodes_executedAction[this.id] = options.action_call;\n }\n }\n this.execute_triggered = 2;\n this.onAfterExecuteNode?.(param, options);\n }\n /**\n * Triggers an action, wrapped by logics to control execution flow\n * @param {String} action name\n * @param {*} param\n */\n actionDo(action, param, options) {\n options = options || {};\n if (this.onAction) {\n options.action_call ||= this.id + \"_\" + (action ? action : \"action\") + \"_\" + Math.floor(Math.random() * 9999);\n this.graph.nodes_actioning[this.id] = action ? action : \"actioning\";\n this.onAction(action, param, options);\n this.graph.nodes_actioning[this.id] = false;\n if (options?.action_call) {\n this.action_call = options.action_call;\n this.graph.nodes_executedAction[this.id] = options.action_call;\n }\n }\n this.action_triggered = 2;\n this.onAfterExecuteNode?.(param, options);\n }\n /**\n * Triggers an event in this node, this will trigger any output with the same name\n * @param {String} event name ( \"on_play\", ... ) if action is equivalent to false then the event is send to all\n * @param {*} param\n */\n trigger(action, param, options) {\n if (!this.outputs || !this.outputs.length) {\n return;\n }\n if (this.graph)\n this.graph._last_trigger_time = LiteGraph.getTime();\n for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n const output = this.outputs[i2];\n if (!output || output.type !== LiteGraph.EVENT || action && output.name != action)\n continue;\n this.triggerSlot(i2, param, null, options);\n }\n }\n /**\n * Triggers a slot event in this node: cycle output slots and launch execute/action on connected nodes\n * @param {Number} slot the index of the output slot\n * @param {*} param\n * @param {Number} link_id [optional] in case you want to trigger and specific output link in a slot\n */\n triggerSlot(slot, param, link_id, options) {\n options = options || {};\n if (!this.outputs) return;\n if (slot == null) {\n console.error(\"slot must be a number\");\n return;\n }\n if (typeof slot !== \"number\")\n console.warn(\"slot must be a number, use node.trigger('name') if you want to use a string\");\n const output = this.outputs[slot];\n if (!output) return;\n const links = output.links;\n if (!links || !links.length) return;\n if (this.graph)\n this.graph._last_trigger_time = LiteGraph.getTime();\n for (let k = 0; k < links.length; ++k) {\n const id = links[k];\n if (link_id != null && link_id != id) continue;\n const link_info = this.graph.links[links[k]];\n if (!link_info) continue;\n link_info._last_time = LiteGraph.getTime();\n const node2 = this.graph.getNodeById(link_info.target_id);\n if (!node2) continue;\n if (node2.mode === LiteGraph.ON_TRIGGER) {\n if (!options.action_call) options.action_call = this.id + \"_trigg_\" + Math.floor(Math.random() * 9999);\n node2.doExecute?.(param, options);\n } else if (node2.onAction) {\n if (!options.action_call) options.action_call = this.id + \"_act_\" + Math.floor(Math.random() * 9999);\n const target_connection = node2.inputs[link_info.target_slot];\n node2.actionDo(target_connection.name, param, options);\n }\n }\n }\n /**\n * clears the trigger slot animation\n * @param {Number} slot the index of the output slot\n * @param {Number} link_id [optional] in case you want to trigger and specific output link in a slot\n */\n clearTriggeredSlot(slot, link_id) {\n if (!this.outputs) return;\n const output = this.outputs[slot];\n if (!output) return;\n const links = output.links;\n if (!links || !links.length) return;\n for (let k = 0; k < links.length; ++k) {\n const id = links[k];\n if (link_id != null && link_id != id) continue;\n const link_info = this.graph.links[links[k]];\n if (!link_info) continue;\n link_info._last_time = 0;\n }\n }\n /**\n * changes node size and triggers callback\n * @param {vec2} size\n */\n setSize(size) {\n this.size = size;\n this.onResize?.(this.size);\n }\n /**\n * add a new property to this node\n * @param {string} name\n * @param {*} default_value\n * @param {string} type string defining the output type (\"vec3\",\"number\",...)\n * @param {Object} extra_info this can be used to have special properties of the property (like values, etc)\n */\n addProperty(name, default_value, type, extra_info) {\n const o = { name, type, default_value };\n if (extra_info) {\n for (const i2 in extra_info) {\n o[i2] = extra_info[i2];\n }\n }\n this.properties_info ||= [];\n this.properties_info.push(o);\n this.properties ||= {};\n this.properties[name] = default_value;\n return o;\n }\n /**\n * add a new output slot to use in this node\n * @param {string} name\n * @param {string} type string defining the output type (\"vec3\",\"number\",...)\n * @param {Object} extra_info this can be used to have special properties of an output (label, special color, position, etc)\n */\n addOutput(name, type, extra_info) {\n const output = { name, type, links: null };\n if (extra_info) {\n for (const i2 in extra_info) {\n output[i2] = extra_info[i2];\n }\n }\n this.outputs ||= [];\n this.outputs.push(output);\n this.onOutputAdded?.(output);\n if (LiteGraph.auto_load_slot_types) LiteGraph.registerNodeAndSlotType(this, type, true);\n this.setSize(this.computeSize());\n this.setDirtyCanvas(true, true);\n return output;\n }\n /**\n * add a new output slot to use in this node\n * @param {Array} array of triplets like [[name,type,extra_info],[...]]\n */\n addOutputs(array) {\n for (let i2 = 0; i2 < array.length; ++i2) {\n const info = array[i2];\n const o = { name: info[0], type: info[1], links: null };\n if (array[2]) {\n for (const j in info[2]) {\n o[j] = info[2][j];\n }\n }\n this.outputs ||= [];\n this.outputs.push(o);\n this.onOutputAdded?.(o);\n if (LiteGraph.auto_load_slot_types) LiteGraph.registerNodeAndSlotType(this, info[1], true);\n }\n this.setSize(this.computeSize());\n this.setDirtyCanvas(true, true);\n }\n /**\n * remove an existing output slot\n * @param {number} slot\n */\n removeOutput(slot) {\n this.disconnectOutput(slot);\n this.outputs.splice(slot, 1);\n for (let i2 = slot; i2 < this.outputs.length; ++i2) {\n if (!this.outputs[i2] || !this.outputs[i2].links)\n continue;\n const links = this.outputs[i2].links;\n for (let j = 0; j < links.length; ++j) {\n const link = this.graph.links[links[j]];\n if (!link) continue;\n link.origin_slot -= 1;\n }\n }\n this.setSize(this.computeSize());\n this.onOutputRemoved?.(slot);\n this.setDirtyCanvas(true, true);\n }\n /**\n * add a new input slot to use in this node\n * @param {string} name\n * @param {string} type string defining the input type (\"vec3\",\"number\",...), it its a generic one use 0\n * @param {Object} extra_info this can be used to have special properties of an input (label, color, position, etc)\n */\n addInput(name, type, extra_info) {\n type = type || 0;\n const input = { name, type, link: null };\n if (extra_info) {\n for (const i2 in extra_info) {\n input[i2] = extra_info[i2];\n }\n }\n this.inputs ||= [];\n this.inputs.push(input);\n this.setSize(this.computeSize());\n this.onInputAdded?.(input);\n LiteGraph.registerNodeAndSlotType(this, type);\n this.setDirtyCanvas(true, true);\n return input;\n }\n /**\n * add several new input slots in this node\n * @param {Array} array of triplets like [[name,type,extra_info],[...]]\n */\n addInputs(array) {\n for (let i2 = 0; i2 < array.length; ++i2) {\n const info = array[i2];\n const o = { name: info[0], type: info[1], link: null };\n if (array[2]) {\n for (const j in info[2]) {\n o[j] = info[2][j];\n }\n }\n this.inputs ||= [];\n this.inputs.push(o);\n this.onInputAdded?.(o);\n LiteGraph.registerNodeAndSlotType(this, info[1]);\n }\n this.setSize(this.computeSize());\n this.setDirtyCanvas(true, true);\n }\n /**\n * remove an existing input slot\n * @param {number} slot\n */\n removeInput(slot) {\n this.disconnectInput(slot);\n const slot_info = this.inputs.splice(slot, 1);\n for (let i2 = slot; i2 < this.inputs.length; ++i2) {\n if (!this.inputs[i2]) continue;\n const link = this.graph.links[this.inputs[i2].link];\n if (!link) continue;\n link.target_slot -= 1;\n }\n this.setSize(this.computeSize());\n this.onInputRemoved?.(slot, slot_info[0]);\n this.setDirtyCanvas(true, true);\n }\n /**\n * add an special connection to this node (used for special kinds of graphs)\n * @param {string} name\n * @param {string} type string defining the input type (\"vec3\",\"number\",...)\n * @param {[x,y]} pos position of the connection inside the node\n * @param {string} direction if is input or output\n */\n addConnection(name, type, pos2, direction) {\n const o = {\n name,\n type,\n pos: pos2,\n direction,\n links: null\n };\n this.connections.push(o);\n return o;\n }\n /**\n * computes the minimum size of a node according to its inputs and output slots\n * @param out\n * @return the total size\n */\n computeSize(out) {\n const ctorSize = this.constructor.size;\n if (ctorSize) return [ctorSize[0], ctorSize[1]];\n let rows = Math.max(\n this.inputs ? this.inputs.length : 1,\n this.outputs ? this.outputs.length : 1\n );\n const size = out || new Float32Array([0, 0]);\n rows = Math.max(rows, 1);\n const font_size = LiteGraph.NODE_TEXT_SIZE;\n const title_width = compute_text_size(this.title);\n let input_width = 0;\n let output_width = 0;\n if (this.inputs) {\n for (let i2 = 0, l = this.inputs.length; i2 < l; ++i2) {\n const input = this.inputs[i2];\n const text = input.label || input.name || \"\";\n const text_width = compute_text_size(text);\n if (input_width < text_width)\n input_width = text_width;\n }\n }\n if (this.outputs) {\n for (let i2 = 0, l = this.outputs.length; i2 < l; ++i2) {\n const output = this.outputs[i2];\n const text = output.label || output.name || \"\";\n const text_width = compute_text_size(text);\n if (output_width < text_width)\n output_width = text_width;\n }\n }\n size[0] = Math.max(input_width + output_width + 10, title_width);\n size[0] = Math.max(size[0], LiteGraph.NODE_WIDTH);\n if (this.widgets?.length)\n size[0] = Math.max(size[0], LiteGraph.NODE_WIDTH * 1.5);\n size[1] = (this.constructor.slot_start_y || 0) + rows * LiteGraph.NODE_SLOT_HEIGHT;\n let widgets_height = 0;\n if (this.widgets?.length) {\n for (let i2 = 0, l = this.widgets.length; i2 < l; ++i2) {\n const widget = this.widgets[i2];\n widgets_height += widget.computeSize ? widget.computeSize(size[0])[1] + 4 : LiteGraph.NODE_WIDGET_HEIGHT + 4;\n }\n widgets_height += 8;\n }\n if (this.widgets_up)\n size[1] = Math.max(size[1], widgets_height);\n else if (this.widgets_start_y != null)\n size[1] = Math.max(size[1], widgets_height + this.widgets_start_y);\n else\n size[1] += widgets_height;\n function compute_text_size(text) {\n return text ? font_size * text.length * 0.6 : 0;\n }\n if (this.constructor.min_height && size[1] < this.constructor.min_height) {\n size[1] = this.constructor.min_height;\n }\n size[1] += 6;\n return size;\n }\n inResizeCorner(canvasX, canvasY) {\n const rows = this.outputs ? this.outputs.length : 1;\n const outputs_offset = (this.constructor.slot_start_y || 0) + rows * LiteGraph.NODE_SLOT_HEIGHT;\n return isInsideRectangle(\n canvasX,\n canvasY,\n this.pos[0] + this.size[0] - 15,\n this.pos[1] + Math.max(this.size[1] - 15, outputs_offset),\n 20,\n 20\n );\n }\n /**\n * returns all the info available about a property of this node.\n *\n * @param {String} property name of the property\n * @return {Object} the object with all the available info\n */\n getPropertyInfo(property) {\n let info = null;\n if (this.properties_info) {\n for (let i2 = 0; i2 < this.properties_info.length; ++i2) {\n if (this.properties_info[i2].name == property) {\n info = this.properties_info[i2];\n break;\n }\n }\n }\n if (this.constructor[\"@\" + property])\n info = this.constructor[\"@\" + property];\n if (this.constructor.widgets_info?.[property])\n info = this.constructor.widgets_info[property];\n if (!info && this.onGetPropertyInfo) {\n info = this.onGetPropertyInfo(property);\n }\n info ||= {};\n info.type ||= typeof this.properties[property];\n if (info.widget == \"combo\")\n info.type = \"enum\";\n return info;\n }\n /**\n * Defines a widget inside the node, it will be rendered on top of the node, you can control lots of properties\n *\n * @param {String} type the widget type (could be \"number\",\"string\",\"combo\"\n * @param {String} name the text to show on the widget\n * @param {String} value the default value\n * @param {Function|String} callback function to call when it changes (optionally, it can be the name of the property to modify)\n * @param {Object} options the object that contains special properties of this widget\n * @return {Object} the created widget object\n */\n addWidget(type, name, value, callback, options) {\n this.widgets ||= [];\n if (!options && callback && typeof callback === \"object\") {\n options = callback;\n callback = null;\n }\n if (options && typeof options === \"string\")\n options = { property: options };\n if (callback && typeof callback === \"string\") {\n options ||= {};\n options.property = callback;\n callback = null;\n }\n if (callback && typeof callback !== \"function\") {\n console.warn(\"addWidget: callback must be a function\");\n callback = null;\n }\n const w2 = {\n // @ts-expect-error Type check or just assert?\n type: type.toLowerCase(),\n name,\n value,\n callback,\n options: options || {}\n };\n if (w2.options.y !== void 0) {\n w2.y = w2.options.y;\n }\n if (!callback && !w2.options.callback && !w2.options.property) {\n console.warn(\"LiteGraph addWidget(...) without a callback or property assigned\");\n }\n if (type == \"combo\" && !w2.options.values) {\n throw \"LiteGraph addWidget('combo',...) requires to pass values in options: { values:['red','blue'] }\";\n }\n this.widgets.push(w2);\n this.setSize(this.computeSize());\n return w2;\n }\n addCustomWidget(custom_widget) {\n this.widgets ||= [];\n this.widgets.push(custom_widget);\n return custom_widget;\n }\n /**\n * returns the bounding of the object, used for rendering purposes\n * @param out {Float32Array[4]?} [optional] a place to store the output, to free garbage\n * @param compute_outer {boolean?} [optional] set to true to include the shadow and connection points in the bounding calculation\n * @return {Float32Array[4]} the bounding box in format of [topleft_cornerx, topleft_cornery, width, height]\n */\n getBounding(out, compute_outer) {\n out = out || new Float32Array(4);\n const nodePos = this.pos;\n const isCollapsed = this.flags.collapsed;\n const nodeSize = this.size;\n let left_offset = 0;\n let right_offset = 1;\n let top_offset = 0;\n let bottom_offset = 0;\n if (compute_outer) {\n left_offset = 4;\n right_offset = 6 + left_offset;\n top_offset = 4;\n bottom_offset = 5 + top_offset;\n }\n out[0] = nodePos[0] - left_offset;\n out[1] = nodePos[1] - LiteGraph.NODE_TITLE_HEIGHT - top_offset;\n out[2] = isCollapsed ? (this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH) + right_offset : nodeSize[0] + right_offset;\n out[3] = isCollapsed ? LiteGraph.NODE_TITLE_HEIGHT + bottom_offset : nodeSize[1] + LiteGraph.NODE_TITLE_HEIGHT + bottom_offset;\n this.onBounding?.(out);\n return out;\n }\n /**\n * checks if a point is inside the shape of a node\n * @param {number} x\n * @param {number} y\n * @return {boolean}\n */\n isPointInside(x2, y2, margin, skip_title) {\n margin ||= 0;\n const margin_top = skip_title || this.graph?.isLive() ? 0 : LiteGraph.NODE_TITLE_HEIGHT;\n if (this.flags?.collapsed) {\n if (isInsideRectangle(\n x2,\n y2,\n this.pos[0] - margin,\n this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT - margin,\n (this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH) + 2 * margin,\n LiteGraph.NODE_TITLE_HEIGHT + 2 * margin\n )) {\n return true;\n }\n } else if (this.pos[0] - 4 - margin < x2 && this.pos[0] + this.size[0] + 4 + margin > x2 && this.pos[1] - margin_top - margin < y2 && this.pos[1] + this.size[1] + margin > y2) {\n return true;\n }\n return false;\n }\n /**\n * checks if a point is inside a node slot, and returns info about which slot\n * @param x\n * @param y\n * @returns if found the object contains { input|output: slot object, slot: number, link_pos: [x,y] }\n */\n getSlotInPosition(x2, y2) {\n const link_pos = new Float32Array(2);\n if (this.inputs) {\n for (let i2 = 0, l = this.inputs.length; i2 < l; ++i2) {\n const input = this.inputs[i2];\n this.getConnectionPos(true, i2, link_pos);\n if (isInsideRectangle(x2, y2, link_pos[0] - 10, link_pos[1] - 5, 20, 10)) {\n return { input, slot: i2, link_pos };\n }\n }\n }\n if (this.outputs) {\n for (let i2 = 0, l = this.outputs.length; i2 < l; ++i2) {\n const output = this.outputs[i2];\n this.getConnectionPos(false, i2, link_pos);\n if (isInsideRectangle(x2, y2, link_pos[0] - 10, link_pos[1] - 5, 20, 10)) {\n return { output, slot: i2, link_pos };\n }\n }\n }\n return null;\n }\n findInputSlot(name, returnObj = false) {\n if (!this.inputs) return -1;\n for (let i2 = 0, l = this.inputs.length; i2 < l; ++i2) {\n if (name == this.inputs[i2].name) {\n return !returnObj ? i2 : this.inputs[i2];\n }\n }\n return -1;\n }\n findOutputSlot(name, returnObj = false) {\n if (!this.outputs) return -1;\n for (let i2 = 0, l = this.outputs.length; i2 < l; ++i2) {\n if (name == this.outputs[i2].name) {\n return !returnObj ? i2 : this.outputs[i2];\n }\n }\n return -1;\n }\n findInputSlotFree(optsIn) {\n return this.#findFreeSlot(this.inputs, optsIn);\n }\n findOutputSlotFree(optsIn) {\n return this.#findFreeSlot(this.outputs, optsIn);\n }\n /**\n * Finds the next free slot\n * @param slots The slots to search, i.e. this.inputs or this.outputs\n * @param options Options\n */\n #findFreeSlot(slots, options) {\n const defaults = {\n returnObj: false,\n typesNotAccepted: []\n };\n const opts = Object.assign(defaults, options || {});\n const length = slots?.length;\n if (!(length > 0)) return -1;\n for (let i2 = 0; i2 < length; ++i2) {\n const slot = slots[i2];\n if (!slot || slot.link || slot.links?.length) continue;\n if (opts.typesNotAccepted?.includes?.(slot.type)) continue;\n return !opts.returnObj ? i2 : slot;\n }\n return -1;\n }\n findInputSlotByType(type, returnObj, preferFreeSlot, doNotUseOccupied) {\n return this.#findSlotByType(this.inputs, type, returnObj, preferFreeSlot, doNotUseOccupied);\n }\n findOutputSlotByType(type, returnObj, preferFreeSlot, doNotUseOccupied) {\n return this.#findSlotByType(this.outputs, type, returnObj, preferFreeSlot, doNotUseOccupied);\n }\n findSlotByType(input, type, returnObj, preferFreeSlot, doNotUseOccupied) {\n return input ? this.#findSlotByType(this.inputs, type, returnObj, preferFreeSlot, doNotUseOccupied) : this.#findSlotByType(this.outputs, type, returnObj, preferFreeSlot, doNotUseOccupied);\n }\n /**\n * Finds a matching slot from those provided, returning the slot itself or its index in {@link slots}.\n * @param slots Slots to search (this.inputs or this.outputs)\n * @param type Type of slot to look for\n * @param returnObj If true, returns the slot itself. Otherwise, the index.\n * @param preferFreeSlot Prefer a free slot, but if none are found, fall back to an occupied slot.\n * @param doNotUseOccupied Do not fall back to occupied slots.\n * @see {findSlotByType}\n * @see {findOutputSlotByType}\n * @see {findInputSlotByType}\n * @returns If a match is found, the slot if returnObj is true, otherwise the index. If no matches are found, -1\n */\n #findSlotByType(slots, type, returnObj, preferFreeSlot, doNotUseOccupied) {\n const length = slots?.length;\n if (!length) return -1;\n if (type == \"\" || type == \"*\") type = 0;\n const sourceTypes = String(type).toLowerCase().split(\",\");\n let occupiedSlot = null;\n for (let i2 = 0; i2 < length; ++i2) {\n const slot = slots[i2];\n const destTypes = slot.type == \"0\" || slot.type == \"*\" ? [\"0\"] : String(slot.type).toLowerCase().split(\",\");\n for (const sourceType of sourceTypes) {\n const source = sourceType == \"_event_\" ? LiteGraph.EVENT : sourceType;\n for (const destType of destTypes) {\n const dest = destType == \"_event_\" ? LiteGraph.EVENT : destType;\n if (source == dest || source === \"*\" || dest === \"*\") {\n if (preferFreeSlot && (slot.links?.length || slot.link != null)) {\n occupiedSlot ??= returnObj ? slot : i2;\n continue;\n }\n return returnObj ? slot : i2;\n }\n }\n }\n }\n return doNotUseOccupied ? -1 : occupiedSlot ?? -1;\n }\n /**\n * Determines the slot index to connect to when attempting to connect by type.\n * \n * @param findInputs If true, searches for an input. Otherwise, an output.\n * @param node The node at the other end of the connection.\n * @param slotType The type of slot at the other end of the connection.\n * @param options Search restrictions to adhere to.\n * @see {connectByType}\n * @see {connectByTypeOutput}\n */\n findConnectByTypeSlot(findInputs, node2, slotType, options) {\n if (options && typeof options === \"object\") {\n if (\"firstFreeIfInputGeneralInCase\" in options) options.wildcardToTyped = !!options.firstFreeIfInputGeneralInCase;\n if (\"firstFreeIfOutputGeneralInCase\" in options) options.wildcardToTyped = !!options.firstFreeIfOutputGeneralInCase;\n if (\"generalTypeInCase\" in options) options.typedToWildcard = !!options.generalTypeInCase;\n }\n const optsDef = {\n createEventInCase: true,\n wildcardToTyped: true,\n typedToWildcard: true\n };\n const opts = Object.assign(optsDef, options);\n if (node2 && typeof node2 === \"number\") {\n node2 = this.graph.getNodeById(node2);\n }\n const slot = node2.findSlotByType(findInputs, slotType, false, true);\n if (slot >= 0 && slot !== null) return slot;\n if (opts.createEventInCase && slotType == LiteGraph.EVENT) {\n if (findInputs) return -1;\n if (LiteGraph.do_add_triggers_slots) return node2.addOnExecutedOutput();\n }\n if (opts.typedToWildcard) {\n const generalSlot = node2.findSlotByType(findInputs, 0, false, true, true);\n if (generalSlot >= 0) return generalSlot;\n }\n if (opts.wildcardToTyped && (slotType == 0 || slotType == \"*\" || slotType == \"\")) {\n const find = findInputs ? node2.findInputSlotFree : node2.findOutputSlotFree;\n const nonEventSlot = find({ typesNotAccepted: [LiteGraph.EVENT] });\n if (nonEventSlot >= 0) return nonEventSlot;\n }\n console.debug(\"no way to connect type: \", slotType, \" to node: \", node2);\n return null;\n }\n /**\n * connect this node output to the input of another node BY TYPE\n * @param {number} slot (could be the number of the slot or the string with the name of the slot)\n * @param {LGraphNode} target_node the target node\n * @param {string} target_slotType the input slot type of the target node\n * @return {Object} the link_info is created, otherwise null\n */\n connectByType(slot, target_node, target_slotType, optsIn) {\n const slotIndex = this.findConnectByTypeSlot(true, target_node, target_slotType, optsIn);\n if (slotIndex !== null) return this.connect(slot, target_node, slotIndex);\n return null;\n }\n /**\n * connect this node input to the output of another node BY TYPE\n * @method connectByType\n * @param {number | string} slot (could be the number of the slot or the string with the name of the slot)\n * @param {LGraphNode} source_node the target node\n * @param {string} source_slotType the output slot type of the target node\n * @return {Object} the link_info is created, otherwise null\n */\n connectByTypeOutput(slot, source_node, source_slotType, optsIn) {\n if (typeof optsIn === \"object\") {\n if (\"firstFreeIfInputGeneralInCase\" in optsIn) optsIn.wildcardToTyped = !!optsIn.firstFreeIfInputGeneralInCase;\n if (\"generalTypeInCase\" in optsIn) optsIn.typedToWildcard = !!optsIn.generalTypeInCase;\n }\n const slotIndex = this.findConnectByTypeSlot(false, source_node, source_slotType, optsIn);\n if (slotIndex !== null) return source_node.connect(slotIndex, this, slot);\n return null;\n }\n /**\n * Connect an output of this node to an input of another node\n * @param {number | string} slot (could be the number of the slot or the string with the name of the slot)\n * @param {LGraphNode} target_node the target node\n * @param {number | string} target_slot the input slot of the target node (could be the number of the slot or the string with the name of the slot, or -1 to connect a trigger)\n * @return {Object} the link_info is created, otherwise null\n */\n connect(slot, target_node, target_slot) {\n let targetIndex;\n if (!this.graph) {\n console.log(\"Connect: Error, node doesn't belong to any graph. Nodes must be added first to a graph before connecting them.\");\n return null;\n }\n if (typeof slot === \"string\") {\n slot = this.findOutputSlot(slot);\n if (slot == -1) {\n if (LiteGraph.debug) console.log(\"Connect: Error, no slot of name \" + slot);\n return null;\n }\n } else if (!this.outputs || slot >= this.outputs.length) {\n if (LiteGraph.debug) console.log(\"Connect: Error, slot number not found\");\n return null;\n }\n if (target_node && typeof target_node === \"number\") {\n target_node = this.graph.getNodeById(target_node);\n }\n if (!target_node) throw \"target node is null\";\n if (target_node == this) return null;\n if (typeof target_slot === \"string\") {\n targetIndex = target_node.findInputSlot(target_slot);\n if (targetIndex == -1) {\n if (LiteGraph.debug) console.log(\"Connect: Error, no slot of name \" + targetIndex);\n return null;\n }\n } else if (target_slot === LiteGraph.EVENT) {\n if (LiteGraph.do_add_triggers_slots) {\n target_node.changeMode(LiteGraph.ON_TRIGGER);\n targetIndex = target_node.findInputSlot(\"onTrigger\");\n } else {\n return null;\n }\n } else if (typeof target_slot === \"number\") {\n targetIndex = target_slot;\n } else {\n targetIndex = 0;\n }\n if (target_node.onBeforeConnectInput) {\n const requestedIndex = target_node.onBeforeConnectInput(targetIndex, target_slot);\n targetIndex = typeof requestedIndex === \"number\" ? requestedIndex : null;\n }\n if (targetIndex === null || !target_node.inputs || targetIndex >= target_node.inputs.length) {\n if (LiteGraph.debug) console.log(\"Connect: Error, slot number not found\");\n return null;\n }\n let changed = false;\n const input = target_node.inputs[targetIndex];\n let link_info = null;\n const output = this.outputs[slot];\n if (!this.outputs[slot]) return null;\n if (!LiteGraph.isValidConnection(output.type, input.type)) {\n this.setDirtyCanvas(false, true);\n if (changed) this.graph.connectionChange(this, link_info);\n return null;\n }\n if (target_node.onConnectInput?.(targetIndex, output.type, output, this, slot) === false)\n return null;\n if (this.onConnectOutput?.(slot, input.type, input, target_node, targetIndex) === false)\n return null;\n if (target_node.inputs[targetIndex]?.link != null) {\n this.graph.beforeChange();\n target_node.disconnectInput(targetIndex);\n changed = true;\n }\n if (output.links?.length) {\n if (output.type === LiteGraph.EVENT && !LiteGraph.allow_multi_output_for_events) {\n this.graph.beforeChange();\n this.disconnectOutput(slot, false, { doProcessChange: false });\n changed = true;\n }\n }\n const nextId = LiteGraph.use_uuids ? LiteGraph.uuidv4() : ++this.graph.last_link_id;\n link_info = new LLink(\n nextId,\n input.type || output.type,\n this.id,\n slot,\n target_node.id,\n targetIndex\n );\n this.graph.links[link_info.id] = link_info;\n output.links ??= [];\n output.links.push(link_info.id);\n target_node.inputs[targetIndex].link = link_info.id;\n if (this.graph) this.graph._version++;\n this.onConnectionsChange?.(\n LiteGraph.OUTPUT,\n slot,\n true,\n link_info,\n output\n );\n target_node.onConnectionsChange?.(\n LiteGraph.INPUT,\n targetIndex,\n true,\n link_info,\n input\n );\n this.graph?.onNodeConnectionChange?.(\n LiteGraph.INPUT,\n target_node,\n targetIndex,\n this,\n slot\n );\n this.graph?.onNodeConnectionChange?.(\n LiteGraph.OUTPUT,\n this,\n slot,\n target_node,\n targetIndex\n );\n this.setDirtyCanvas(false, true);\n this.graph.afterChange();\n this.graph.connectionChange(this);\n return link_info;\n }\n /**\n * disconnect one output to an specific node\n * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot)\n * @param {LGraphNode} target_node the target node to which this slot is connected [Optional, if not target_node is specified all nodes will be disconnected]\n * @return {boolean} if it was disconnected successfully\n */\n disconnectOutput(slot, target_node) {\n if (typeof slot === \"string\") {\n slot = this.findOutputSlot(slot);\n if (slot == -1) {\n if (LiteGraph.debug) console.log(\"Connect: Error, no slot of name \" + slot);\n return false;\n }\n } else if (!this.outputs || slot >= this.outputs.length) {\n if (LiteGraph.debug) console.log(\"Connect: Error, slot number not found\");\n return false;\n }\n const output = this.outputs[slot];\n if (!output || !output.links || output.links.length == 0)\n return false;\n const graph = this.graph;\n if (target_node) {\n if (typeof target_node === \"number\")\n target_node = graph.getNodeById(target_node);\n if (!target_node)\n throw \"Target Node not found\";\n for (let i2 = 0, l = output.links.length; i2 < l; i2++) {\n const link_id = output.links[i2];\n const link_info = graph.links[link_id];\n if (link_info.target_id == target_node.id) {\n output.links.splice(i2, 1);\n const input = target_node.inputs[link_info.target_slot];\n input.link = null;\n delete graph.links[link_id];\n if (graph) graph._version++;\n target_node.onConnectionsChange?.(\n LiteGraph.INPUT,\n link_info.target_slot,\n false,\n link_info,\n input\n );\n this.onConnectionsChange?.(\n LiteGraph.OUTPUT,\n slot,\n false,\n link_info,\n output\n );\n graph?.onNodeConnectionChange?.(LiteGraph.OUTPUT, this, slot);\n graph?.onNodeConnectionChange?.(LiteGraph.OUTPUT, this, slot);\n graph?.onNodeConnectionChange?.(LiteGraph.INPUT, target_node, link_info.target_slot);\n break;\n }\n }\n } else {\n for (let i2 = 0, l = output.links.length; i2 < l; i2++) {\n const link_id = output.links[i2];\n const link_info = graph.links[link_id];\n if (!link_info) continue;\n target_node = graph.getNodeById(link_info.target_id);\n if (graph) graph._version++;\n if (target_node) {\n const input = target_node.inputs[link_info.target_slot];\n input.link = null;\n target_node.onConnectionsChange?.(\n LiteGraph.INPUT,\n link_info.target_slot,\n false,\n link_info,\n input\n );\n graph?.onNodeConnectionChange?.(LiteGraph.INPUT, target_node, link_info.target_slot);\n }\n delete graph.links[link_id];\n this.onConnectionsChange?.(\n LiteGraph.OUTPUT,\n slot,\n false,\n link_info,\n output\n );\n graph?.onNodeConnectionChange?.(LiteGraph.OUTPUT, this, slot);\n graph?.onNodeConnectionChange?.(LiteGraph.INPUT, target_node, link_info.target_slot);\n }\n output.links = null;\n }\n this.setDirtyCanvas(false, true);\n graph.connectionChange(this);\n return true;\n }\n /**\n * Disconnect one input\n * @param slot Input slot index, or the name of the slot\n * @return true if disconnected successfully or already disconnected, otherwise false\n */\n disconnectInput(slot) {\n if (typeof slot === \"string\") {\n slot = this.findInputSlot(slot);\n if (slot == -1) {\n if (LiteGraph.debug) console.log(\"Connect: Error, no slot of name \" + slot);\n return false;\n }\n } else if (!this.inputs || slot >= this.inputs.length) {\n if (LiteGraph.debug) {\n console.log(\"Connect: Error, slot number not found\");\n }\n return false;\n }\n const input = this.inputs[slot];\n if (!input) {\n return false;\n }\n const link_id = this.inputs[slot].link;\n if (link_id != null) {\n this.inputs[slot].link = null;\n const link_info = this.graph.links[link_id];\n if (link_info) {\n const target_node = this.graph.getNodeById(link_info.origin_id);\n if (!target_node) {\n return false;\n }\n const output = target_node.outputs[link_info.origin_slot];\n if (!(output?.links?.length > 0)) {\n return false;\n }\n let i2 = 0;\n for (const l = output.links.length; i2 < l; i2++) {\n if (output.links[i2] == link_id) {\n output.links.splice(i2, 1);\n break;\n }\n }\n delete this.graph.links[link_id];\n if (this.graph) this.graph._version++;\n this.onConnectionsChange?.(\n LiteGraph.INPUT,\n slot,\n false,\n link_info,\n input\n );\n target_node.onConnectionsChange?.(\n LiteGraph.OUTPUT,\n i2,\n false,\n link_info,\n output\n );\n this.graph?.onNodeConnectionChange?.(LiteGraph.OUTPUT, target_node, i2);\n this.graph?.onNodeConnectionChange?.(LiteGraph.INPUT, this, slot);\n }\n }\n this.setDirtyCanvas(false, true);\n this.graph?.connectionChange(this);\n return true;\n }\n /**\n * returns the center of a connection point in canvas coords\n * @param {boolean} is_input true if if a input slot, false if it is an output\n * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot)\n * @param {vec2} out [optional] a place to store the output, to free garbage\n * @return {[x,y]} the position\n **/\n getConnectionPos(is_input, slot_number, out) {\n out ||= new Float32Array(2);\n const num_slots = is_input ? this.inputs?.length ?? 0 : this.outputs?.length ?? 0;\n const offset = LiteGraph.NODE_SLOT_HEIGHT * 0.5;\n if (this.flags.collapsed) {\n const w2 = this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH;\n if (this.horizontal) {\n out[0] = this.pos[0] + w2 * 0.5;\n out[1] = is_input ? this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT : this.pos[1];\n } else {\n out[0] = is_input ? this.pos[0] : this.pos[0] + w2;\n out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5;\n }\n return out;\n }\n if (is_input && slot_number == -1) {\n out[0] = this.pos[0] + LiteGraph.NODE_TITLE_HEIGHT * 0.5;\n out[1] = this.pos[1] + LiteGraph.NODE_TITLE_HEIGHT * 0.5;\n return out;\n }\n if (is_input && num_slots > slot_number && this.inputs[slot_number].pos) {\n out[0] = this.pos[0] + this.inputs[slot_number].pos[0];\n out[1] = this.pos[1] + this.inputs[slot_number].pos[1];\n return out;\n } else if (!is_input && num_slots > slot_number && this.outputs[slot_number].pos) {\n out[0] = this.pos[0] + this.outputs[slot_number].pos[0];\n out[1] = this.pos[1] + this.outputs[slot_number].pos[1];\n return out;\n }\n if (this.horizontal) {\n out[0] = this.pos[0] + (slot_number + 0.5) * (this.size[0] / num_slots);\n out[1] = is_input ? this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT : this.pos[1] + this.size[1];\n return out;\n }\n out[0] = is_input ? this.pos[0] + offset : this.pos[0] + this.size[0] + 1 - offset;\n out[1] = this.pos[1] + (slot_number + 0.7) * LiteGraph.NODE_SLOT_HEIGHT + (this.constructor.slot_start_y || 0);\n return out;\n }\n /* Force align to grid */\n alignToGrid() {\n this.pos[0] = LiteGraph.CANVAS_GRID_SIZE * Math.round(this.pos[0] / LiteGraph.CANVAS_GRID_SIZE);\n this.pos[1] = LiteGraph.CANVAS_GRID_SIZE * Math.round(this.pos[1] / LiteGraph.CANVAS_GRID_SIZE);\n }\n /* Console output */\n trace(msg) {\n this.console ||= [];\n this.console.push(msg);\n if (this.console.length > LGraphNode.MAX_CONSOLE)\n this.console.shift();\n this.graph.onNodeTrace?.(this, msg);\n }\n /* Forces to redraw or the main canvas (LGraphNode) or the bg canvas (links) */\n setDirtyCanvas(dirty_foreground, dirty_background) {\n this.graph?.sendActionToCanvas(\"setDirty\", [\n dirty_foreground,\n dirty_background\n ]);\n }\n loadImage(url) {\n const img = new Image();\n img.src = LiteGraph.node_images_path + url;\n img.ready = false;\n const that2 = this;\n img.onload = function() {\n this.ready = true;\n that2.setDirtyCanvas(true);\n };\n return img;\n }\n /* Allows to get onMouseMove and onMouseUp events even if the mouse is out of focus */\n captureInput(v2) {\n if (!this.graph || !this.graph.list_of_graphcanvas)\n return;\n const list = this.graph.list_of_graphcanvas;\n for (let i2 = 0; i2 < list.length; ++i2) {\n const c = list[i2];\n if (!v2 && c.node_capturing_input != this)\n continue;\n c.node_capturing_input = v2 ? this : null;\n }\n }\n get collapsed() {\n return !!this.flags.collapsed;\n }\n get collapsible() {\n return !this.pinned && this.constructor.collapsable !== false;\n }\n /**\n * Collapse the node to make it smaller on the canvas\n **/\n collapse(force) {\n this.graph._version++;\n if (!this.collapsible && !force) return;\n this.flags.collapsed = !this.flags.collapsed;\n this.setDirtyCanvas(true, true);\n }\n get pinned() {\n return !!this.flags.pinned;\n }\n /**\n * Prevents the node being accidentally moved or resized by mouse interaction.\n **/\n pin(v2) {\n this.graph._version++;\n this.flags.pinned = v2 === void 0 ? !this.flags.pinned : v2;\n this.resizable = !this.pinned;\n if (!this.pinned)\n delete this.flags.pinned;\n }\n localToScreen(x2, y2, dragAndScale) {\n return [\n (x2 + this.pos[0]) * dragAndScale.scale + dragAndScale.offset[0],\n (y2 + this.pos[1]) * dragAndScale.scale + dragAndScale.offset[1]\n ];\n }\n get width() {\n return this.collapsed ? this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH : this.size[0];\n }\n get height() {\n return this.collapsed ? LiteGraph.NODE_COLLAPSED_HEIGHT : this.size[1];\n }\n drawBadges(ctx, { gap = 2 } = {}) {\n const badgeInstances = this.badges.map((badge) => badge instanceof LGraphBadge ? badge : badge());\n const isLeftAligned = this.badgePosition === BadgePosition.TopLeft;\n let currentX = isLeftAligned ? 0 : this.width - badgeInstances.reduce((acc, badge) => acc + badge.getWidth(ctx) + gap, 0);\n const y2 = -(LiteGraph.NODE_TITLE_HEIGHT + gap);\n for (const badge of badgeInstances) {\n badge.draw(ctx, currentX, y2 - badge.height);\n currentX += badge.getWidth(ctx) + gap;\n }\n }\n}\nclass LGraphGroup {\n color;\n title;\n font;\n font_size;\n _bounding;\n _pos;\n _size;\n _nodes;\n graph;\n flags;\n selected;\n constructor(title) {\n this._ctor(title);\n }\n _ctor(title) {\n this.title = title || \"Group\";\n this.font_size = LiteGraph.DEFAULT_GROUP_FONT || 24;\n this.color = LGraphCanvas.node_colors.pale_blue ? LGraphCanvas.node_colors.pale_blue.groupcolor : \"#AAA\";\n this._bounding = new Float32Array([10, 10, 140, 80]);\n this._pos = this._bounding.subarray(0, 2);\n this._size = this._bounding.subarray(2, 4);\n this._nodes = [];\n this.graph = null;\n this.flags = {};\n }\n /** Position of the group, as x,y co-ordinates in graph space */\n get pos() {\n return this._pos;\n }\n set pos(v2) {\n if (!v2 || v2.length < 2) return;\n this._pos[0] = v2[0];\n this._pos[1] = v2[1];\n }\n /** Size of the group, as width,height in graph units */\n get size() {\n return this._size;\n }\n set size(v2) {\n if (!v2 || v2.length < 2) return;\n this._size[0] = Math.max(140, v2[0]);\n this._size[1] = Math.max(80, v2[1]);\n }\n get nodes() {\n return this._nodes;\n }\n get titleHeight() {\n return this.font_size * 1.4;\n }\n get pinned() {\n return !!this.flags.pinned;\n }\n pin() {\n this.flags.pinned = true;\n }\n unpin() {\n delete this.flags.pinned;\n }\n configure(o) {\n this.title = o.title;\n this._bounding.set(o.bounding);\n this.color = o.color;\n this.flags = o.flags || this.flags;\n if (o.font_size) this.font_size = o.font_size;\n }\n serialize() {\n const b = this._bounding;\n return {\n title: this.title,\n bounding: [\n Math.round(b[0]),\n Math.round(b[1]),\n Math.round(b[2]),\n Math.round(b[3])\n ],\n color: this.color,\n font_size: this.font_size,\n flags: this.flags\n };\n }\n /**\n * Draws the group on the canvas\n * @param {LGraphCanvas} graphCanvas\n * @param {CanvasRenderingContext2D} ctx\n */\n draw(graphCanvas, ctx) {\n const padding = 4;\n ctx.fillStyle = this.color;\n ctx.strokeStyle = this.color;\n const [x2, y2] = this._pos;\n const [width2, height] = this._size;\n ctx.globalAlpha = 0.25 * graphCanvas.editor_alpha;\n ctx.beginPath();\n ctx.rect(x2 + 0.5, y2 + 0.5, width2, height);\n ctx.fill();\n ctx.globalAlpha = graphCanvas.editor_alpha;\n ctx.stroke();\n ctx.beginPath();\n ctx.moveTo(x2 + width2, y2 + height);\n ctx.lineTo(x2 + width2 - 10, y2 + height);\n ctx.lineTo(x2 + width2, y2 + height - 10);\n ctx.fill();\n const font_size = this.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE;\n ctx.font = font_size + \"px Arial\";\n ctx.textAlign = \"left\";\n ctx.fillText(this.title + (this.pinned ? \"📌\" : \"\"), x2 + padding, y2 + font_size);\n if (LiteGraph.highlight_selected_group && this.selected) {\n graphCanvas.drawSelectionBounding(ctx, this._bounding, {\n shape: LiteGraph.BOX_SHAPE,\n title_height: this.titleHeight,\n title_mode: LiteGraph.NORMAL_TITLE,\n fgcolor: this.color,\n padding\n });\n }\n }\n resize(width2, height) {\n if (this.pinned) return;\n this._size[0] = width2;\n this._size[1] = height;\n }\n move(deltax, deltay, ignore_nodes = false) {\n if (this.pinned) return;\n this._pos[0] += deltax;\n this._pos[1] += deltay;\n if (ignore_nodes) return;\n for (let i2 = 0; i2 < this._nodes.length; ++i2) {\n const node2 = this._nodes[i2];\n node2.pos[0] += deltax;\n node2.pos[1] += deltay;\n }\n }\n recomputeInsideNodes() {\n this._nodes.length = 0;\n const nodes = this.graph._nodes;\n const node_bounding = new Float32Array(4);\n for (let i2 = 0; i2 < nodes.length; ++i2) {\n const node2 = nodes[i2];\n node2.getBounding(node_bounding);\n if (!overlapBounding(this._bounding, node_bounding))\n continue;\n this._nodes.push(node2);\n }\n }\n /**\n * Add nodes to the group and adjust the group's position and size accordingly\n * @param {LGraphNode[]} nodes - The nodes to add to the group\n * @param {number} [padding=10] - The padding around the group\n * @returns {void}\n */\n addNodes(nodes, padding = 10) {\n if (!this._nodes && nodes.length === 0) return;\n const allNodes = [...this._nodes || [], ...nodes];\n const bounds = allNodes.reduce((acc, node2) => {\n const [x2, y2] = node2.pos;\n const [width2, height] = node2.size;\n const isReroute = node2.type === \"Reroute\";\n const isCollapsed = node2.flags?.collapsed;\n const top = y2 - (isReroute ? 0 : LiteGraph.NODE_TITLE_HEIGHT);\n const bottom = isCollapsed ? top + LiteGraph.NODE_TITLE_HEIGHT : y2 + height;\n const right = isCollapsed && node2._collapsed_width ? x2 + Math.round(node2._collapsed_width) : x2 + width2;\n return {\n left: Math.min(acc.left, x2),\n top: Math.min(acc.top, top),\n right: Math.max(acc.right, right),\n bottom: Math.max(acc.bottom, bottom)\n };\n }, { left: Infinity, top: Infinity, right: -Infinity, bottom: -Infinity });\n this.pos = [\n bounds.left - padding,\n bounds.top - padding - this.titleHeight\n ];\n this.size = [\n bounds.right - bounds.left + padding * 2,\n bounds.bottom - bounds.top + padding * 2 + this.titleHeight\n ];\n }\n getMenuOptions() {\n return [\n {\n content: this.pinned ? \"Unpin\" : \"Pin\",\n callback: () => {\n if (this.pinned) this.unpin();\n else this.pin();\n this.setDirtyCanvas(false, true);\n }\n },\n null,\n { content: \"Title\", callback: LGraphCanvas.onShowPropertyEditor },\n {\n content: \"Color\",\n has_submenu: true,\n callback: LGraphCanvas.onMenuNodeColors\n },\n {\n content: \"Font size\",\n property: \"font_size\",\n type: \"Number\",\n callback: LGraphCanvas.onShowPropertyEditor\n },\n null,\n { content: \"Remove\", callback: LGraphCanvas.onMenuNodeRemove }\n ];\n }\n isPointInTitlebar(x2, y2) {\n const b = this._bounding;\n return isInsideRectangle(x2, y2, b[0], b[1], b[2], this.titleHeight);\n }\n isPointInside = LGraphNode.prototype.isPointInside;\n setDirtyCanvas = LGraphNode.prototype.setDirtyCanvas;\n}\nvar SlotType = /* @__PURE__ */ ((SlotType2) => {\n SlotType2[\"Array\"] = \"array\";\n SlotType2[SlotType2[\"Event\"] = -1] = \"Event\";\n return SlotType2;\n})(SlotType || {});\nvar SlotShape = ((SlotShape2) => {\n SlotShape2[SlotShape2[\"Box\"] = RenderShape.BOX] = \"Box\";\n SlotShape2[SlotShape2[\"Arrow\"] = RenderShape.ARROW] = \"Arrow\";\n SlotShape2[SlotShape2[\"Grid\"] = RenderShape.GRID] = \"Grid\";\n SlotShape2[SlotShape2[\"Circle\"] = RenderShape.CIRCLE] = \"Circle\";\n SlotShape2[SlotShape2[\"HollowCircle\"] = RenderShape.HollowCircle] = \"HollowCircle\";\n return SlotShape2;\n})(SlotShape || {});\nvar SlotDirection = ((SlotDirection2) => {\n SlotDirection2[SlotDirection2[\"Up\"] = LinkDirection.UP] = \"Up\";\n SlotDirection2[SlotDirection2[\"Right\"] = LinkDirection.RIGHT] = \"Right\";\n SlotDirection2[SlotDirection2[\"Down\"] = LinkDirection.DOWN] = \"Down\";\n SlotDirection2[SlotDirection2[\"Left\"] = LinkDirection.LEFT] = \"Left\";\n return SlotDirection2;\n})(SlotDirection || {});\nvar LabelPosition = /* @__PURE__ */ ((LabelPosition2) => {\n LabelPosition2[\"Left\"] = \"left\";\n LabelPosition2[\"Right\"] = \"right\";\n return LabelPosition2;\n})(LabelPosition || {});\nfunction drawSlot(ctx, slot, pos2, {\n label_color = \"#AAA\",\n label_position = \"right\",\n horizontal = false,\n low_quality = false,\n render_text = true,\n do_stroke = false,\n highlight = false\n} = {}) {\n const originalFillStyle = ctx.fillStyle;\n const originalStrokeStyle = ctx.strokeStyle;\n const originalLineWidth = ctx.lineWidth;\n const slot_type = slot.type;\n const slot_shape = slot_type === \"array\" ? SlotShape.Grid : slot.shape;\n ctx.beginPath();\n let doStroke = do_stroke;\n let doFill = true;\n if (slot_type === -1 || slot_shape === SlotShape.Box) {\n if (horizontal) {\n ctx.rect(pos2[0] - 5 + 0.5, pos2[1] - 8 + 0.5, 10, 14);\n } else {\n ctx.rect(pos2[0] - 6 + 0.5, pos2[1] - 5 + 0.5, 14, 10);\n }\n } else if (slot_shape === SlotShape.Arrow) {\n ctx.moveTo(pos2[0] + 8, pos2[1] + 0.5);\n ctx.lineTo(pos2[0] - 4, pos2[1] + 6 + 0.5);\n ctx.lineTo(pos2[0] - 4, pos2[1] - 6 + 0.5);\n ctx.closePath();\n } else if (slot_shape === SlotShape.Grid) {\n const gridSize = 3;\n const cellSize = 2;\n const spacing = 3;\n for (let x2 = 0; x2 < gridSize; x2++) {\n for (let y2 = 0; y2 < gridSize; y2++) {\n ctx.rect(\n pos2[0] - 4 + x2 * spacing,\n pos2[1] - 4 + y2 * spacing,\n cellSize,\n cellSize\n );\n }\n }\n doStroke = false;\n } else {\n if (low_quality) {\n ctx.rect(pos2[0] - 4, pos2[1] - 4, 8, 8);\n } else {\n let radius;\n if (slot_shape === SlotShape.HollowCircle) {\n doFill = false;\n doStroke = true;\n ctx.lineWidth = 3;\n ctx.strokeStyle = ctx.fillStyle;\n radius = highlight ? 4 : 3;\n } else {\n radius = highlight ? 5 : 4;\n }\n ctx.arc(pos2[0], pos2[1], radius, 0, Math.PI * 2);\n }\n }\n if (doFill) ctx.fill();\n if (!low_quality && doStroke) ctx.stroke();\n if (render_text) {\n const text = slot.label != null ? slot.label : slot.name;\n if (text) {\n ctx.fillStyle = label_color;\n if (label_position === \"right\") {\n if (horizontal || slot.dir == LinkDirection.UP) {\n ctx.fillText(text, pos2[0], pos2[1] - 10);\n } else {\n ctx.fillText(text, pos2[0] + 10, pos2[1] + 5);\n }\n } else {\n if (horizontal || slot.dir == LinkDirection.DOWN) {\n ctx.fillText(text, pos2[0], pos2[1] - 8);\n } else {\n ctx.fillText(text, pos2[0] - 10, pos2[1] + 5);\n }\n }\n }\n }\n ctx.fillStyle = originalFillStyle;\n ctx.strokeStyle = originalStrokeStyle;\n ctx.lineWidth = originalLineWidth;\n}\nclass DragAndScale {\n /** Maximum scale (zoom in) */\n max_scale;\n /** Minimum scale (zoom out) */\n min_scale;\n offset;\n scale;\n enabled;\n last_mouse;\n element;\n visible_area;\n _binded_mouse_callback;\n dragging;\n viewport;\n constructor(element, skip_events) {\n this.offset = new Float32Array([0, 0]);\n this.scale = 1;\n this.max_scale = 10;\n this.min_scale = 0.1;\n this.onredraw = null;\n this.enabled = true;\n this.last_mouse = [0, 0];\n this.element = null;\n this.visible_area = new Float32Array(4);\n if (element) {\n this.element = element;\n if (!skip_events) {\n this.bindEvents(element);\n }\n }\n }\n /** @deprecated Has not been kept up to date */\n bindEvents(element) {\n this.last_mouse = new Float32Array(2);\n this._binded_mouse_callback = this.onMouse.bind(this);\n LiteGraph.pointerListenerAdd(element, \"down\", this._binded_mouse_callback);\n LiteGraph.pointerListenerAdd(element, \"move\", this._binded_mouse_callback);\n LiteGraph.pointerListenerAdd(element, \"up\", this._binded_mouse_callback);\n element.addEventListener(\n \"mousewheel\",\n this._binded_mouse_callback,\n false\n );\n element.addEventListener(\"wheel\", this._binded_mouse_callback, false);\n }\n computeVisibleArea(viewport) {\n if (!this.element) {\n this.visible_area[0] = this.visible_area[1] = this.visible_area[2] = this.visible_area[3] = 0;\n return;\n }\n let width2 = this.element.width;\n let height = this.element.height;\n let startx = -this.offset[0];\n let starty = -this.offset[1];\n if (viewport) {\n startx += viewport[0] / this.scale;\n starty += viewport[1] / this.scale;\n width2 = viewport[2];\n height = viewport[3];\n }\n const endx = startx + width2 / this.scale;\n const endy = starty + height / this.scale;\n this.visible_area[0] = startx;\n this.visible_area[1] = starty;\n this.visible_area[2] = endx - startx;\n this.visible_area[3] = endy - starty;\n }\n /** @deprecated Has not been kept up to date */\n onMouse(e) {\n if (!this.enabled) {\n return;\n }\n const canvas = this.element;\n const rect = canvas.getBoundingClientRect();\n const x2 = e.clientX - rect.left;\n const y2 = e.clientY - rect.top;\n e.canvasx = x2;\n e.canvasy = y2;\n e.dragging = this.dragging;\n const is_inside = !this.viewport || this.viewport && x2 >= this.viewport[0] && x2 < this.viewport[0] + this.viewport[2] && y2 >= this.viewport[1] && y2 < this.viewport[1] + this.viewport[3];\n let ignore = false;\n if (this.onmouse) {\n ignore = this.onmouse(e);\n }\n if (e.type == LiteGraph.pointerevents_method + \"down\" && is_inside) {\n this.dragging = true;\n LiteGraph.pointerListenerRemove(canvas, \"move\", this._binded_mouse_callback);\n LiteGraph.pointerListenerAdd(document, \"move\", this._binded_mouse_callback);\n LiteGraph.pointerListenerAdd(document, \"up\", this._binded_mouse_callback);\n } else if (e.type == LiteGraph.pointerevents_method + \"move\") {\n if (!ignore) {\n const deltax = x2 - this.last_mouse[0];\n const deltay = y2 - this.last_mouse[1];\n if (this.dragging) {\n this.mouseDrag(deltax, deltay);\n }\n }\n } else if (e.type == LiteGraph.pointerevents_method + \"up\") {\n this.dragging = false;\n LiteGraph.pointerListenerRemove(document, \"move\", this._binded_mouse_callback);\n LiteGraph.pointerListenerRemove(document, \"up\", this._binded_mouse_callback);\n LiteGraph.pointerListenerAdd(canvas, \"move\", this._binded_mouse_callback);\n } else if (is_inside && (e.type == \"mousewheel\" || e.type == \"wheel\" || e.type == \"DOMMouseScroll\")) {\n e.eventType = \"mousewheel\";\n if (e.type == \"wheel\") e.wheel = -e.deltaY;\n else e.wheel = e.wheelDeltaY != null ? e.wheelDeltaY : e.detail * -60;\n e.delta = e.wheelDelta ? e.wheelDelta / 40 : e.deltaY ? -e.deltaY / 3 : 0;\n this.changeDeltaScale(1 + e.delta * 0.05);\n }\n this.last_mouse[0] = x2;\n this.last_mouse[1] = y2;\n if (is_inside) {\n e.preventDefault();\n e.stopPropagation();\n return false;\n }\n }\n toCanvasContext(ctx) {\n ctx.scale(this.scale, this.scale);\n ctx.translate(this.offset[0], this.offset[1]);\n }\n convertOffsetToCanvas(pos2) {\n return [\n (pos2[0] + this.offset[0]) * this.scale,\n (pos2[1] + this.offset[1]) * this.scale\n ];\n }\n convertCanvasToOffset(pos2, out) {\n out = out || [0, 0];\n out[0] = pos2[0] / this.scale - this.offset[0];\n out[1] = pos2[1] / this.scale - this.offset[1];\n return out;\n }\n /** @deprecated Has not been kept up to date */\n mouseDrag(x2, y2) {\n this.offset[0] += x2 / this.scale;\n this.offset[1] += y2 / this.scale;\n this.onredraw?.(this);\n }\n changeScale(value, zooming_center) {\n if (value < this.min_scale) {\n value = this.min_scale;\n } else if (value > this.max_scale) {\n value = this.max_scale;\n }\n if (value == this.scale) return;\n if (!this.element) return;\n const rect = this.element.getBoundingClientRect();\n if (!rect) return;\n zooming_center = zooming_center || [\n rect.width * 0.5,\n rect.height * 0.5\n ];\n const center = this.convertCanvasToOffset(zooming_center);\n this.scale = value;\n if (Math.abs(this.scale - 1) < 0.01) this.scale = 1;\n const new_center = this.convertCanvasToOffset(zooming_center);\n const delta_offset = [\n new_center[0] - center[0],\n new_center[1] - center[1]\n ];\n this.offset[0] += delta_offset[0];\n this.offset[1] += delta_offset[1];\n this.onredraw?.(this);\n }\n changeDeltaScale(value, zooming_center) {\n this.changeScale(this.scale * value, zooming_center);\n }\n reset() {\n this.scale = 1;\n this.offset[0] = 0;\n this.offset[1] = 0;\n }\n}\nfunction stringOrNull(value) {\n return value == null ? null : String(value);\n}\nfunction stringOrEmpty(value) {\n return value == null ? \"\" : String(value);\n}\nfunction distributeNodes(nodes, horizontal) {\n const nodeCount = nodes?.length;\n if (!(nodeCount > 1)) return;\n const index = horizontal ? 0 : 1;\n let total = 0;\n let highest = -Infinity;\n for (const node2 of nodes) {\n total += node2.size[index];\n const high = node2.pos[index] + node2.size[index];\n if (high > highest) highest = high;\n }\n const sorted = [...nodes].sort((a, b) => a.pos[index] - b.pos[index]);\n const lowest = sorted[0].pos[index];\n const gap = (highest - lowest - total) / (nodeCount - 1);\n let startAt = lowest;\n for (let i2 = 0; i2 < nodeCount; i2++) {\n const node2 = sorted[i2];\n node2.pos[index] = startAt + gap * i2;\n startAt += node2.size[index];\n }\n}\nclass LGraphCanvas {\n /* Interaction */\n static #temp = new Float32Array(4);\n static #temp_vec2 = new Float32Array(2);\n static #tmp_area = new Float32Array(4);\n static #margin_area = new Float32Array(4);\n static #link_bounding = new Float32Array(4);\n static #tempA = new Float32Array(2);\n static #tempB = new Float32Array(2);\n static DEFAULT_BACKGROUND_IMAGE = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQBJREFUeNrs1rEKwjAUhlETUkj3vP9rdmr1Ysammk2w5wdxuLgcMHyptfawuZX4pJSWZTnfnu/lnIe/jNNxHHGNn//HNbbv+4dr6V+11uF527arU7+u63qfa/bnmh8sWLBgwYJlqRf8MEptXPBXJXa37BSl3ixYsGDBMliwFLyCV/DeLIMFCxYsWLBMwSt4Be/NggXLYMGCBUvBK3iNruC9WbBgwYJlsGApeAWv4L1ZBgsWLFiwYJmCV/AK3psFC5bBggULloJX8BpdwXuzYMGCBctgwVLwCl7Be7MMFixYsGDBsu8FH1FaSmExVfAxBa/gvVmwYMGCZbBg/W4vAQYA5tRF9QYlv/QAAAAASUVORK5CYII=\";\n /** Initialised from LiteGraphGlobal static block to avoid circular dependency. */\n static link_type_colors;\n static gradients = {};\n //cache of gradients\n static search_limit = -1;\n static node_colors = {\n red: { color: \"#322\", bgcolor: \"#533\", groupcolor: \"#A88\" },\n brown: { color: \"#332922\", bgcolor: \"#593930\", groupcolor: \"#b06634\" },\n green: { color: \"#232\", bgcolor: \"#353\", groupcolor: \"#8A8\" },\n blue: { color: \"#223\", bgcolor: \"#335\", groupcolor: \"#88A\" },\n pale_blue: {\n color: \"#2a363b\",\n bgcolor: \"#3f5159\",\n groupcolor: \"#3f789e\"\n },\n cyan: { color: \"#233\", bgcolor: \"#355\", groupcolor: \"#8AA\" },\n purple: { color: \"#323\", bgcolor: \"#535\", groupcolor: \"#a1309b\" },\n yellow: { color: \"#432\", bgcolor: \"#653\", groupcolor: \"#b58b2a\" },\n black: { color: \"#222\", bgcolor: \"#000\", groupcolor: \"#444\" }\n };\n _dragging_canvas = false;\n get dragging_canvas() {\n return this._dragging_canvas;\n }\n set dragging_canvas(value) {\n if (value !== this._dragging_canvas) {\n this._dragging_canvas = value;\n this.emitEvent({\n subType: \"dragging-canvas\",\n draggingCanvas: value\n });\n }\n }\n // Whether the canvas was previously being dragged prior to pressing space key.\n // null if space key is not pressed.\n _previously_dragging_canvas = null;\n // if set to true users cannot modify the graph\n _read_only = false;\n get read_only() {\n return this._read_only;\n }\n set read_only(value) {\n if (value !== this._read_only) {\n this._read_only = value;\n this.emitEvent({\n subType: \"read-only\",\n readOnly: value\n });\n }\n }\n options;\n background_image;\n ds;\n zoom_modify_alpha;\n zoom_speed;\n title_text_font;\n inner_text_font;\n node_title_color;\n default_link_color;\n default_connection_color;\n default_connection_color_byType;\n default_connection_color_byTypeOff;\n highquality_render;\n use_gradients;\n editor_alpha;\n pause_rendering;\n clear_background;\n clear_background_color;\n render_only_selected;\n live_mode;\n show_info;\n allow_dragcanvas;\n allow_dragnodes;\n allow_interaction;\n multi_select;\n allow_searchbox;\n allow_reconnect_links;\n align_to_grid;\n drag_mode;\n dragging_rectangle;\n filter;\n set_canvas_dirty_on_mouse_event;\n always_render_background;\n render_shadows;\n render_canvas_border;\n render_connections_shadows;\n render_connections_border;\n render_curved_connections;\n render_connection_arrows;\n render_collapsed_slots;\n render_execution_order;\n render_title_colored;\n render_link_tooltip;\n links_render_mode;\n mouse;\n graph_mouse;\n canvas_mouse;\n onSearchBox;\n onSearchBoxSelection;\n onMouse;\n onDrawBackground;\n onDrawForeground;\n connections_width;\n round_radius;\n current_node;\n node_widget;\n over_link_center;\n last_mouse_position;\n visible_area;\n visible_links;\n connecting_links;\n viewport;\n autoresize;\n static active_canvas;\n frame;\n last_draw_time;\n render_time;\n fps;\n selected_nodes;\n /** @deprecated Temporary implementation only - will be replaced with `selectedItems: Set`. */\n selectedGroups;\n isDragging;\n selected_group;\n visible_nodes;\n node_dragged;\n node_over;\n node_capturing_input;\n highlighted_links;\n link_over_widget;\n link_over_widget_type;\n dirty_canvas;\n dirty_bgcanvas;\n /** A map of nodes that require selective-redraw */\n dirty_nodes = /* @__PURE__ */ new Map();\n dirty_area;\n // Unused\n node_in_panel;\n last_mouse;\n last_mouseclick;\n pointer_is_down;\n pointer_is_double;\n graph;\n _graph_stack;\n canvas;\n bgcanvas;\n ctx;\n _events_binded;\n gl;\n bgctx;\n is_rendering;\n block_click;\n last_click_position;\n resizing_node;\n selected_group_resizing;\n last_mouse_dragging;\n onMouseDown;\n _highlight_pos;\n _highlight_input;\n _highlight_input_slot;\n _highlight_output;\n // TODO: Check if panels are used\n node_panel;\n options_panel;\n onDropItem;\n _bg_img;\n _pattern;\n _pattern_img;\n // TODO: This looks like another panel thing\n prompt_box;\n search_box;\n SELECTED_NODE;\n NODEPANEL_IS_OPEN;\n static active_node;\n onClear;\n onNodeMoved;\n onSelectionChange;\n onDrawLinkTooltip;\n onDrawOverlay;\n onRenderBackground;\n onNodeDblClicked;\n onShowNodePanel;\n onNodeSelected;\n onNodeDeselected;\n onRender;\n /** Implement this function to allow conversion of widget types to input types, e.g. number -> INT or FLOAT for widget link validation checks */\n getWidgetLinkType;\n // FIXME: Has never worked - undefined\n visible_rect;\n constructor(canvas, graph, options) {\n this.options = options = options || {};\n this.background_image = LGraphCanvas.DEFAULT_BACKGROUND_IMAGE;\n if (canvas && typeof canvas === \"string\") {\n canvas = document.querySelector(canvas);\n }\n this.ds = new DragAndScale();\n this.zoom_modify_alpha = true;\n this.zoom_speed = 1.1;\n this.title_text_font = \"\" + LiteGraph.NODE_TEXT_SIZE + \"px Arial\";\n this.inner_text_font = \"normal \" + LiteGraph.NODE_SUBTEXT_SIZE + \"px Arial\";\n this.node_title_color = LiteGraph.NODE_TITLE_COLOR;\n this.default_link_color = LiteGraph.LINK_COLOR;\n this.default_connection_color = {\n input_off: \"#778\",\n input_on: \"#7F7\",\n //\"#BBD\"\n output_off: \"#778\",\n output_on: \"#7F7\"\n //\"#BBD\"\n };\n this.default_connection_color_byType = {\n /*number: \"#7F7\",\n string: \"#77F\",\n boolean: \"#F77\",*/\n };\n this.default_connection_color_byTypeOff = {\n /*number: \"#474\",\n string: \"#447\",\n boolean: \"#744\",*/\n };\n this.highquality_render = true;\n this.use_gradients = false;\n this.editor_alpha = 1;\n this.pause_rendering = false;\n this.clear_background = true;\n this.clear_background_color = \"#222\";\n this.render_only_selected = true;\n this.live_mode = false;\n this.show_info = true;\n this.allow_dragcanvas = true;\n this.allow_dragnodes = true;\n this.allow_interaction = true;\n this.multi_select = false;\n this.allow_searchbox = true;\n this.allow_reconnect_links = true;\n this.align_to_grid = false;\n this.drag_mode = false;\n this.dragging_rectangle = null;\n this.filter = null;\n this.set_canvas_dirty_on_mouse_event = true;\n this.always_render_background = false;\n this.render_shadows = true;\n this.render_canvas_border = true;\n this.render_connections_shadows = false;\n this.render_connections_border = true;\n this.render_curved_connections = false;\n this.render_connection_arrows = false;\n this.render_collapsed_slots = true;\n this.render_execution_order = false;\n this.render_title_colored = true;\n this.render_link_tooltip = true;\n this.links_render_mode = LiteGraph.SPLINE_LINK;\n this.mouse = [0, 0];\n this.graph_mouse = [0, 0];\n this.canvas_mouse = this.graph_mouse;\n this.onSearchBox = null;\n this.onSearchBoxSelection = null;\n this.onMouse = null;\n this.onDrawBackground = null;\n this.onDrawForeground = null;\n this.onDrawOverlay = null;\n this.onDrawLinkTooltip = null;\n this.onNodeMoved = null;\n this.onSelectionChange = null;\n this.onConnectingChange = null;\n this.onBeforeChange = null;\n this.onAfterChange = null;\n this.connections_width = 3;\n this.round_radius = 8;\n this.current_node = null;\n this.node_widget = null;\n this.over_link_center = null;\n this.last_mouse_position = [0, 0];\n this.visible_area = this.ds.visible_area;\n this.visible_links = [];\n this.connecting_links = null;\n this.viewport = options.viewport || null;\n graph?.attachCanvas(this);\n this.setCanvas(canvas, options.skip_events);\n this.clear();\n if (!options.skip_render) {\n this.startRendering();\n }\n this.autoresize = options.autoresize;\n }\n static getFileExtension(url) {\n const question = url.indexOf(\"?\");\n if (question != -1) {\n url = url.substr(0, question);\n }\n const point = url.lastIndexOf(\".\");\n if (point == -1) {\n return \"\";\n }\n return url.substr(point + 1).toLowerCase();\n }\n /* this is an implementation for touch not in production and not ready\n */\n /*LGraphCanvas.prototype.touchHandler = function(event) {\n //alert(\"foo\");\n var touches = event.changedTouches,\n first = touches[0],\n type = \"\";\n \n switch (event.type) {\n case \"touchstart\":\n type = \"mousedown\";\n break;\n case \"touchmove\":\n type = \"mousemove\";\n break;\n case \"touchend\":\n type = \"mouseup\";\n break;\n default:\n return;\n }\n \n //initMouseEvent(type, canBubble, cancelable, view, clickCount,\n // screenX, screenY, clientX, clientY, ctrlKey,\n // altKey, shiftKey, metaKey, button, relatedTarget);\n \n // this is eventually a Dom object, get the LGraphCanvas back\n if(typeof this.getCanvasWindow == \"undefined\"){\n var window = this.lgraphcanvas.getCanvasWindow();\n }else{\n var window = this.getCanvasWindow();\n }\n \n var document = window.document;\n \n var simulatedEvent = document.createEvent(\"MouseEvent\");\n simulatedEvent.initMouseEvent(\n type,\n true,\n true,\n window,\n 1,\n first.screenX,\n first.screenY,\n first.clientX,\n first.clientY,\n false,\n false,\n false,\n false,\n 0, //left\n null\n );\n first.target.dispatchEvent(simulatedEvent);\n event.preventDefault();\n };*/\n /* CONTEXT MENU ********************/\n static onGroupAdd(info, entry, mouse_event) {\n const canvas = LGraphCanvas.active_canvas;\n const group = new LiteGraph.LGraphGroup();\n group.pos = canvas.convertEventToCanvasOffset(mouse_event);\n canvas.graph.add(group);\n }\n /**\n * Determines the furthest nodes in each direction\n * @param {Dictionary} nodes the nodes to from which boundary nodes will be extracted\n * @return {{left: LGraphNode, top: LGraphNode, right: LGraphNode, bottom: LGraphNode}}\n */\n static getBoundaryNodes(nodes) {\n let top = null;\n let right = null;\n let bottom = null;\n let left = null;\n for (const nID in nodes) {\n const node2 = nodes[nID];\n const [x2, y2] = node2.pos;\n const [width2, height] = node2.size;\n if (top === null || y2 < top.pos[1]) {\n top = node2;\n }\n if (right === null || x2 + width2 > right.pos[0] + right.size[0]) {\n right = node2;\n }\n if (bottom === null || y2 + height > bottom.pos[1] + bottom.size[1]) {\n bottom = node2;\n }\n if (left === null || x2 < left.pos[0]) {\n left = node2;\n }\n }\n return {\n \"top\": top,\n \"right\": right,\n \"bottom\": bottom,\n \"left\": left\n };\n }\n /**\n *\n * @param {Dictionary} nodes a list of nodes\n * @param {\"top\"|\"bottom\"|\"left\"|\"right\"} direction Direction to align the nodes\n * @param {LGraphNode?} align_to Node to align to (if null, align to the furthest node in the given direction)\n */\n static alignNodes(nodes, direction, align_to) {\n if (!nodes) {\n return;\n }\n const canvas = LGraphCanvas.active_canvas;\n let boundaryNodes;\n if (align_to === void 0) {\n boundaryNodes = LGraphCanvas.getBoundaryNodes(nodes);\n } else {\n boundaryNodes = {\n \"top\": align_to,\n \"right\": align_to,\n \"bottom\": align_to,\n \"left\": align_to\n };\n }\n for (const node2 of Object.values(canvas.selected_nodes)) {\n switch (direction) {\n case \"right\":\n node2.pos[0] = boundaryNodes[\"right\"].pos[0] + boundaryNodes[\"right\"].size[0] - node2.size[0];\n break;\n case \"left\":\n node2.pos[0] = boundaryNodes[\"left\"].pos[0];\n break;\n case \"top\":\n node2.pos[1] = boundaryNodes[\"top\"].pos[1];\n break;\n case \"bottom\":\n node2.pos[1] = boundaryNodes[\"bottom\"].pos[1] + boundaryNodes[\"bottom\"].size[1] - node2.size[1];\n break;\n }\n }\n canvas.dirty_canvas = true;\n canvas.dirty_bgcanvas = true;\n }\n static onNodeAlign(value, options, event2, prev_menu, node2) {\n new LiteGraph.ContextMenu([\"Top\", \"Bottom\", \"Left\", \"Right\"], {\n event: event2,\n callback: inner_clicked,\n parentMenu: prev_menu\n });\n function inner_clicked(value2) {\n LGraphCanvas.alignNodes(LGraphCanvas.active_canvas.selected_nodes, value2.toLowerCase(), node2);\n }\n }\n static onGroupAlign(value, options, event2, prev_menu) {\n new LiteGraph.ContextMenu([\"Top\", \"Bottom\", \"Left\", \"Right\"], {\n event: event2,\n callback: inner_clicked,\n parentMenu: prev_menu\n });\n function inner_clicked(value2) {\n LGraphCanvas.alignNodes(LGraphCanvas.active_canvas.selected_nodes, value2.toLowerCase());\n }\n }\n static createDistributeMenu(value, options, event2, prev_menu, node2) {\n new LiteGraph.ContextMenu([\"Vertically\", \"Horizontally\"], {\n event: event2,\n callback: inner_clicked,\n parentMenu: prev_menu\n });\n function inner_clicked(value2) {\n const canvas = LGraphCanvas.active_canvas;\n distributeNodes(Object.values(canvas.selected_nodes), value2 === \"Horizontally\");\n canvas.setDirty(true, true);\n }\n }\n static onMenuAdd(node2, options, e, prev_menu, callback) {\n const canvas = LGraphCanvas.active_canvas;\n const ref_window2 = canvas.getCanvasWindow();\n const graph = canvas.graph;\n if (!graph)\n return;\n function inner_onMenuAdded(base_category, prev_menu2) {\n const categories = LiteGraph.getNodeTypesCategories(canvas.filter || graph.filter).filter(function(category) {\n return category.startsWith(base_category);\n });\n const entries = [];\n categories.map(function(category) {\n if (!category)\n return;\n const base_category_regex = new RegExp(\"^(\" + base_category + \")\");\n const category_name = category.replace(base_category_regex, \"\").split(\"/\")[0];\n const category_path = base_category === \"\" ? category_name + \"/\" : base_category + category_name + \"/\";\n let name = category_name;\n if (name.indexOf(\"::\") != -1)\n name = name.split(\"::\")[1];\n const index = entries.findIndex(function(entry) {\n return entry.value === category_path;\n });\n if (index === -1) {\n entries.push({\n value: category_path,\n content: name,\n has_submenu: true,\n callback: function(value, event2, mouseEvent, contextMenu) {\n inner_onMenuAdded(value.value, contextMenu);\n }\n });\n }\n });\n const nodes = LiteGraph.getNodeTypesInCategory(base_category.slice(0, -1), canvas.filter || graph.filter);\n nodes.map(function(node3) {\n if (node3.skip_list)\n return;\n const entry = {\n value: node3.type,\n content: node3.title,\n has_submenu: false,\n callback: function(value, event2, mouseEvent, contextMenu) {\n const first_event = contextMenu.getFirstEvent();\n canvas.graph.beforeChange();\n const node4 = LiteGraph.createNode(value.value);\n if (node4) {\n node4.pos = canvas.convertEventToCanvasOffset(first_event);\n canvas.graph.add(node4);\n }\n callback?.(node4);\n canvas.graph.afterChange();\n }\n };\n entries.push(entry);\n });\n new LiteGraph.ContextMenu(entries, { event: e, parentMenu: prev_menu2 }, ref_window2);\n }\n inner_onMenuAdded(\"\", prev_menu);\n return false;\n }\n static onMenuCollapseAll() {\n }\n static onMenuNodeEdit() {\n }\n /** @param options Parameter is never used */\n static showMenuNodeOptionalInputs(v2, options, e, prev_menu, node2) {\n if (!node2) return;\n const that2 = this;\n const canvas = LGraphCanvas.active_canvas;\n const ref_window2 = canvas.getCanvasWindow();\n options = node2.onGetInputs ? node2.onGetInputs() : node2.optional_inputs;\n let entries = [];\n if (options) {\n for (let i2 = 0; i2 < options.length; i2++) {\n const entry = options[i2];\n if (!entry) {\n entries.push(null);\n continue;\n }\n let label = entry[0];\n entry[2] ||= {};\n if (entry[2].label) {\n label = entry[2].label;\n }\n entry[2].removable = true;\n const data = { content: label, value: entry };\n if (entry[1] == LiteGraph.ACTION) {\n data.className = \"event\";\n }\n entries.push(data);\n }\n }\n const retEntries = node2.onMenuNodeInputs?.(entries);\n if (retEntries) entries = retEntries;\n if (!entries.length) {\n console.log(\"no input entries\");\n return;\n }\n new LiteGraph.ContextMenu(\n entries,\n {\n event: e,\n callback: inner_clicked,\n parentMenu: prev_menu,\n node: node2\n },\n // @ts-expect-error Unused param\n ref_window2\n );\n function inner_clicked(v3, e2, prev) {\n if (!node2) {\n return;\n }\n v3.callback?.call(that2, node2, v3, e2, prev);\n if (v3.value) {\n node2.graph.beforeChange();\n node2.addInput(v3.value[0], v3.value[1], v3.value[2]);\n node2.onNodeInputAdd?.(v3.value);\n node2.setDirtyCanvas(true, true);\n node2.graph.afterChange();\n }\n }\n return false;\n }\n /** @param options Parameter is never used */\n static showMenuNodeOptionalOutputs(v2, options, e, prev_menu, node2) {\n if (!node2) return;\n const that2 = this;\n const canvas = LGraphCanvas.active_canvas;\n const ref_window2 = canvas.getCanvasWindow();\n options = node2.onGetOutputs ? node2.onGetOutputs() : node2.optional_outputs;\n let entries = [];\n if (options) {\n for (let i2 = 0; i2 < options.length; i2++) {\n const entry = options[i2];\n if (!entry) {\n entries.push(null);\n continue;\n }\n if (node2.flags && node2.flags.skip_repeated_outputs && node2.findOutputSlot(entry[0]) != -1) {\n continue;\n }\n let label = entry[0];\n entry[2] ||= {};\n if (entry[2].label) {\n label = entry[2].label;\n }\n entry[2].removable = true;\n const data = { content: label, value: entry };\n if (entry[1] == LiteGraph.EVENT) {\n data.className = \"event\";\n }\n entries.push(data);\n }\n }\n if (this.onMenuNodeOutputs) entries = this.onMenuNodeOutputs(entries);\n if (LiteGraph.do_add_triggers_slots) {\n if (node2.findOutputSlot(\"onExecuted\") == -1) {\n entries.push({ content: \"On Executed\", value: [\"onExecuted\", LiteGraph.EVENT, { nameLocked: true }], className: \"event\" });\n }\n }\n const retEntries = node2.onMenuNodeOutputs?.(entries);\n if (retEntries) entries = retEntries;\n if (!entries.length) {\n return;\n }\n new LiteGraph.ContextMenu(\n entries,\n {\n event: e,\n callback: inner_clicked,\n parentMenu: prev_menu,\n node: node2\n },\n // @ts-expect-error Unused\n ref_window2\n );\n function inner_clicked(v3, e2, prev) {\n if (!node2) {\n return;\n }\n if (v3.callback) {\n v3.callback.call(that2, node2, v3, e2, prev);\n }\n if (!v3.value) {\n return;\n }\n const value = v3.value[1];\n if (value && (typeof value === \"object\" || Array.isArray(value))) {\n const entries2 = [];\n for (const i2 in value) {\n entries2.push({ content: i2, value: value[i2] });\n }\n new LiteGraph.ContextMenu(entries2, {\n event: e2,\n callback: inner_clicked,\n parentMenu: prev_menu,\n node: node2\n });\n return false;\n } else {\n node2.graph.beforeChange();\n node2.addOutput(v3.value[0], v3.value[1], v3.value[2]);\n node2.onNodeOutputAdd?.(v3.value);\n node2.setDirtyCanvas(true, true);\n node2.graph.afterChange();\n }\n }\n return false;\n }\n /** @param value Parameter is never used */\n static onShowMenuNodeProperties(value, options, e, prev_menu, node2) {\n if (!node2 || !node2.properties) return;\n const canvas = LGraphCanvas.active_canvas;\n const ref_window2 = canvas.getCanvasWindow();\n const entries = [];\n for (const i2 in node2.properties) {\n value = node2.properties[i2] !== void 0 ? node2.properties[i2] : \" \";\n if (typeof value == \"object\")\n value = JSON.stringify(value);\n const info = node2.getPropertyInfo(i2);\n if (info.type == \"enum\" || info.type == \"combo\")\n value = LGraphCanvas.getPropertyPrintableValue(value, info.values);\n value = LGraphCanvas.decodeHTML(stringOrNull(value));\n entries.push({\n content: \"\" + (info.label || i2) + \"\" + value + \"\",\n value: i2\n });\n }\n if (!entries.length) {\n return;\n }\n new LiteGraph.ContextMenu(\n entries,\n {\n event: e,\n callback: inner_clicked,\n parentMenu: prev_menu,\n allow_html: true,\n node: node2\n },\n // @ts-expect-error Unused\n ref_window2\n );\n function inner_clicked(v2) {\n if (!node2) return;\n const rect = this.getBoundingClientRect();\n canvas.showEditPropertyValue(node2, v2.value, {\n position: [rect.left, rect.top]\n });\n }\n return false;\n }\n static decodeHTML(str) {\n const e = document.createElement(\"div\");\n e.innerText = str;\n return e.innerHTML;\n }\n static onMenuResizeNode(value, options, e, menu, node2) {\n if (!node2) return;\n const fApplyMultiNode = function(node3) {\n node3.size = node3.computeSize();\n node3.onResize?.(node3.size);\n };\n const graphcanvas = LGraphCanvas.active_canvas;\n if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) {\n fApplyMultiNode(node2);\n } else {\n for (const i2 in graphcanvas.selected_nodes) {\n fApplyMultiNode(graphcanvas.selected_nodes[i2]);\n }\n }\n node2.setDirtyCanvas(true, true);\n }\n // TODO refactor :: this is used fot title but not for properties!\n static onShowPropertyEditor(item, options, e, menu, node2) {\n const property = item.property || \"title\";\n const value = node2[property];\n const dialog = document.createElement(\"div\");\n dialog.is_modified = false;\n dialog.className = \"graphdialog\";\n dialog.innerHTML = \"\";\n dialog.close = function() {\n dialog.parentNode?.removeChild(dialog);\n };\n const title = dialog.querySelector(\".name\");\n title.innerText = property;\n const input = dialog.querySelector(\".value\");\n if (input) {\n input.value = value;\n input.addEventListener(\"blur\", function() {\n this.focus();\n });\n input.addEventListener(\"keydown\", function(e2) {\n dialog.is_modified = true;\n if (e2.keyCode == 27) {\n dialog.close();\n } else if (e2.keyCode == 13) {\n inner();\n } else if (e2.keyCode != 13 && e2.target.localName != \"textarea\") {\n return;\n }\n e2.preventDefault();\n e2.stopPropagation();\n });\n }\n const graphcanvas = LGraphCanvas.active_canvas;\n const canvas = graphcanvas.canvas;\n const rect = canvas.getBoundingClientRect();\n let offsetx = -20;\n let offsety = -20;\n if (rect) {\n offsetx -= rect.left;\n offsety -= rect.top;\n }\n if (e) {\n dialog.style.left = e.clientX + offsetx + \"px\";\n dialog.style.top = e.clientY + offsety + \"px\";\n } else {\n dialog.style.left = canvas.width * 0.5 + offsetx + \"px\";\n dialog.style.top = canvas.height * 0.5 + offsety + \"px\";\n }\n const button = dialog.querySelector(\"button\");\n button.addEventListener(\"click\", inner);\n canvas.parentNode.appendChild(dialog);\n input?.focus();\n let dialogCloseTimer = null;\n dialog.addEventListener(\"mouseleave\", function() {\n if (LiteGraph.dialog_close_on_mouse_leave) {\n if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave)\n dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay);\n }\n });\n dialog.addEventListener(\"mouseenter\", function() {\n if (LiteGraph.dialog_close_on_mouse_leave) {\n if (dialogCloseTimer) clearTimeout(dialogCloseTimer);\n }\n });\n function inner() {\n if (input) setValue(input.value);\n }\n function setValue(value2) {\n if (item.type == \"Number\") {\n value2 = Number(value2);\n } else if (item.type == \"Boolean\") {\n value2 = Boolean(value2);\n }\n node2[property] = value2;\n dialog.parentNode?.removeChild(dialog);\n node2.setDirtyCanvas(true, true);\n }\n }\n static getPropertyPrintableValue(value, values2) {\n if (!values2)\n return String(value);\n if (Array.isArray(values2)) {\n return String(value);\n }\n if (typeof values2 === \"object\") {\n let desc_value = \"\";\n for (const k in values2) {\n if (values2[k] != value)\n continue;\n desc_value = k;\n break;\n }\n return String(value) + \" (\" + desc_value + \")\";\n }\n }\n static onMenuNodeCollapse(value, options, e, menu, node2) {\n node2.graph.beforeChange(\n /*?*/\n );\n const fApplyMultiNode = function(node3) {\n node3.collapse();\n };\n const graphcanvas = LGraphCanvas.active_canvas;\n if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) {\n fApplyMultiNode(node2);\n } else {\n for (const i2 in graphcanvas.selected_nodes) {\n fApplyMultiNode(graphcanvas.selected_nodes[i2]);\n }\n }\n node2.graph.afterChange(\n /*?*/\n );\n }\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n static onMenuNodePin(value, options, e, menu, node2) {\n }\n static onMenuNodeMode(value, options, e, menu, node2) {\n new LiteGraph.ContextMenu(\n LiteGraph.NODE_MODES,\n { event: e, callback: inner_clicked, parentMenu: menu, node: node2 }\n );\n function inner_clicked(v2) {\n if (!node2) return;\n const kV = Object.values(LiteGraph.NODE_MODES).indexOf(v2);\n const fApplyMultiNode = function(node3) {\n if (kV >= 0 && LiteGraph.NODE_MODES[kV])\n node3.changeMode(kV);\n else {\n console.warn(\"unexpected mode: \" + v2);\n node3.changeMode(LiteGraph.ALWAYS);\n }\n };\n const graphcanvas = LGraphCanvas.active_canvas;\n if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) {\n fApplyMultiNode(node2);\n } else {\n for (const i2 in graphcanvas.selected_nodes) {\n fApplyMultiNode(graphcanvas.selected_nodes[i2]);\n }\n }\n }\n return false;\n }\n /** @param value Parameter is never used */\n static onMenuNodeColors(value, options, e, menu, node2) {\n if (!node2) throw \"no node for color\";\n const values2 = [];\n values2.push({\n value: null,\n content: \"No color\"\n });\n for (const i2 in LGraphCanvas.node_colors) {\n const color = LGraphCanvas.node_colors[i2];\n value = {\n value: i2,\n content: \"\" + i2 + \"\"\n };\n values2.push(value);\n }\n new LiteGraph.ContextMenu(values2, {\n event: e,\n callback: inner_clicked,\n parentMenu: menu,\n node: node2\n });\n function inner_clicked(v2) {\n if (!node2) return;\n const color = v2.value ? LGraphCanvas.node_colors[v2.value] : null;\n const fApplyColor = function(node3) {\n if (color) {\n if (node3 instanceof LGraphGroup) {\n node3.color = color.groupcolor;\n } else {\n node3.color = color.color;\n node3.bgcolor = color.bgcolor;\n }\n } else {\n delete node3.color;\n delete node3.bgcolor;\n }\n };\n const graphcanvas = LGraphCanvas.active_canvas;\n if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) {\n fApplyColor(node2);\n } else {\n for (const i2 in graphcanvas.selected_nodes) {\n fApplyColor(graphcanvas.selected_nodes[i2]);\n }\n }\n node2.setDirtyCanvas(true, true);\n }\n return false;\n }\n static onMenuNodeShapes(value, options, e, menu, node2) {\n if (!node2) throw \"no node passed\";\n new LiteGraph.ContextMenu(LiteGraph.VALID_SHAPES, {\n event: e,\n callback: inner_clicked,\n parentMenu: menu,\n node: node2\n });\n function inner_clicked(v2) {\n if (!node2) return;\n node2.graph.beforeChange(\n /*?*/\n );\n const fApplyMultiNode = function(node3) {\n node3.shape = v2;\n };\n const graphcanvas = LGraphCanvas.active_canvas;\n if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) {\n fApplyMultiNode(node2);\n } else {\n for (const i2 in graphcanvas.selected_nodes) {\n fApplyMultiNode(graphcanvas.selected_nodes[i2]);\n }\n }\n node2.graph.afterChange(\n /*?*/\n );\n node2.setDirtyCanvas(true);\n }\n return false;\n }\n static onMenuNodeRemove(value, options, e, menu, node2) {\n if (!node2) throw \"no node passed\";\n const graph = node2.graph;\n graph.beforeChange();\n const fApplyMultiNode = function(node3) {\n if (node3.removable === false) return;\n graph.remove(node3);\n };\n const graphcanvas = LGraphCanvas.active_canvas;\n if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) {\n fApplyMultiNode(node2);\n } else {\n for (const i2 in graphcanvas.selected_nodes) {\n fApplyMultiNode(graphcanvas.selected_nodes[i2]);\n }\n }\n graph.afterChange();\n node2.setDirtyCanvas(true, true);\n }\n static onMenuNodeToSubgraph(value, options, e, menu, node2) {\n const graph = node2.graph;\n const graphcanvas = LGraphCanvas.active_canvas;\n if (!graphcanvas) return;\n let nodes_list = Object.values(graphcanvas.selected_nodes || {});\n if (!nodes_list.length)\n nodes_list = [node2];\n const subgraph_node = LiteGraph.createNode(\"graph/subgraph\");\n subgraph_node.pos = node2.pos.concat();\n graph.add(subgraph_node);\n subgraph_node.buildFromNodes(nodes_list);\n graphcanvas.deselectAllNodes();\n node2.setDirtyCanvas(true, true);\n }\n static onMenuNodeClone(value, options, e, menu, node2) {\n node2.graph.beforeChange();\n const newSelected = {};\n const fApplyMultiNode = function(node3) {\n if (node3.clonable === false) return;\n const newnode = node3.clone();\n if (!newnode) return;\n newnode.pos = [node3.pos[0] + 5, node3.pos[1] + 5];\n node3.graph.add(newnode);\n newSelected[newnode.id] = newnode;\n };\n const graphcanvas = LGraphCanvas.active_canvas;\n if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1) {\n fApplyMultiNode(node2);\n } else {\n for (const i2 in graphcanvas.selected_nodes) {\n fApplyMultiNode(graphcanvas.selected_nodes[i2]);\n }\n }\n if (Object.keys(newSelected).length) {\n graphcanvas.selectNodes(newSelected);\n }\n node2.graph.afterChange();\n node2.setDirtyCanvas(true, true);\n }\n /**\n * clears all the data inside\n *\n */\n clear() {\n this.frame = 0;\n this.last_draw_time = 0;\n this.render_time = 0;\n this.fps = 0;\n this.dragging_rectangle = null;\n this.selected_nodes = {};\n this.selectedGroups = null;\n this.selected_group = null;\n this.visible_nodes = [];\n this.node_dragged = null;\n this.node_over = null;\n this.node_capturing_input = null;\n this.connecting_links = null;\n this.highlighted_links = {};\n this.dragging_canvas = false;\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n this.dirty_area = null;\n this.node_in_panel = null;\n this.node_widget = null;\n this.last_mouse = [0, 0];\n this.last_mouseclick = 0;\n this.pointer_is_down = false;\n this.pointer_is_double = false;\n this.visible_area.set([0, 0, 0, 0]);\n this.onClear?.();\n }\n /**\n * assigns a graph, you can reassign graphs to the same canvas\n *\n * @param {LGraph} graph\n */\n setGraph(graph, skip_clear) {\n if (this.graph == graph) return;\n if (!skip_clear) this.clear();\n if (!graph && this.graph) {\n this.graph.detachCanvas(this);\n return;\n }\n graph.attachCanvas(this);\n this._graph_stack &&= null;\n this.setDirty(true, true);\n }\n /**\n * returns the top level graph (in case there are subgraphs open on the canvas)\n *\n * @return {LGraph} graph\n */\n getTopGraph() {\n return this._graph_stack.length ? this._graph_stack[0] : this.graph;\n }\n /**\n * opens a graph contained inside a node in the current graph\n *\n * @param {LGraph} graph\n */\n openSubgraph(graph) {\n if (!graph) throw \"graph cannot be null\";\n if (this.graph == graph) throw \"graph cannot be the same\";\n this.clear();\n if (this.graph) {\n this._graph_stack ||= [];\n this._graph_stack.push(this.graph);\n }\n graph.attachCanvas(this);\n this.checkPanels();\n this.setDirty(true, true);\n }\n /**\n * closes a subgraph contained inside a node\n *\n * @param {LGraph} assigns a graph\n */\n closeSubgraph() {\n if (!this._graph_stack || this._graph_stack.length == 0) return;\n const subgraph_node = this.graph._subgraph_node;\n const graph = this._graph_stack.pop();\n this.selected_nodes = {};\n this.highlighted_links = {};\n graph.attachCanvas(this);\n this.setDirty(true, true);\n if (subgraph_node) {\n this.centerOnNode(subgraph_node);\n this.selectNodes([subgraph_node]);\n }\n this.ds.offset = [0, 0];\n this.ds.scale = 1;\n }\n /**\n * returns the visually active graph (in case there are more in the stack)\n * @return {LGraph} the active graph\n */\n getCurrentGraph() {\n return this.graph;\n }\n /**\n * Sets the current HTML canvas element.\n * Calls bindEvents to add input event listeners, and (re)creates the background canvas.\n *\n * @param canvas The canvas element to assign, or its HTML element ID. If null or undefined, the current reference is cleared.\n * @param skip_events If true, events on the previous canvas will not be removed. Has no effect on the first invocation.\n */\n setCanvas(canvas, skip_events) {\n let element;\n if (typeof canvas === \"string\") {\n const el = document.getElementById(canvas);\n if (!(el instanceof HTMLCanvasElement)) throw \"Error creating LiteGraph canvas: Canvas not found\";\n element = el;\n } else {\n element = canvas;\n }\n if (element === this.canvas) return;\n if (!element && this.canvas && !skip_events) this.unbindEvents();\n this.canvas = element;\n this.ds.element = element;\n if (!element) return;\n element.className += \" lgraphcanvas\";\n element.data = this;\n element.tabindex = \"1\";\n this.bgcanvas = null;\n if (!this.bgcanvas) {\n this.bgcanvas = document.createElement(\"canvas\");\n this.bgcanvas.width = this.canvas.width;\n this.bgcanvas.height = this.canvas.height;\n }\n if (element.getContext == null) {\n if (element.localName != \"canvas\") {\n throw \"Element supplied for LGraphCanvas must be a element, you passed a \" + element.localName;\n }\n throw \"This browser doesn't support Canvas\";\n }\n const ctx = this.ctx = element.getContext(\"2d\");\n if (ctx == null) {\n if (!element.webgl_enabled) {\n console.warn(\n \"This canvas seems to be WebGL, enabling WebGL renderer\"\n );\n }\n this.enableWebGL();\n }\n if (!skip_events) this.bindEvents();\n }\n //used in some events to capture them\n _doNothing(e) {\n e.preventDefault();\n return false;\n }\n _doReturnTrue(e) {\n e.preventDefault();\n return true;\n }\n /**\n * binds mouse, keyboard, touch and drag events to the canvas\n **/\n bindEvents() {\n if (this._events_binded) {\n console.warn(\"LGraphCanvas: events already binded\");\n return;\n }\n const canvas = this.canvas;\n const ref_window2 = this.getCanvasWindow();\n const document2 = ref_window2.document;\n this._mousedown_callback = this.processMouseDown.bind(this);\n this._mousewheel_callback = this.processMouseWheel.bind(this);\n this._mousemove_callback = this.processMouseMove.bind(this);\n this._mouseup_callback = this.processMouseUp.bind(this);\n this._mouseout_callback = this.processMouseOut.bind(this);\n LiteGraph.pointerListenerAdd(canvas, \"down\", this._mousedown_callback, true);\n canvas.addEventListener(\"mousewheel\", this._mousewheel_callback, false);\n LiteGraph.pointerListenerAdd(canvas, \"up\", this._mouseup_callback, true);\n LiteGraph.pointerListenerAdd(canvas, \"move\", this._mousemove_callback);\n canvas.addEventListener(\"pointerout\", this._mouseout_callback);\n canvas.addEventListener(\"contextmenu\", this._doNothing);\n canvas.addEventListener(\n \"DOMMouseScroll\",\n this._mousewheel_callback,\n false\n );\n this._key_callback = this.processKey.bind(this);\n canvas.addEventListener(\"keydown\", this._key_callback, true);\n document2.addEventListener(\"keyup\", this._key_callback, true);\n this._ondrop_callback = this.processDrop.bind(this);\n canvas.addEventListener(\"dragover\", this._doNothing, false);\n canvas.addEventListener(\"dragend\", this._doNothing, false);\n canvas.addEventListener(\"drop\", this._ondrop_callback, false);\n canvas.addEventListener(\"dragenter\", this._doReturnTrue, false);\n this._events_binded = true;\n }\n /**\n * unbinds mouse events from the canvas\n **/\n unbindEvents() {\n if (!this._events_binded) {\n console.warn(\"LGraphCanvas: no events binded\");\n return;\n }\n const ref_window2 = this.getCanvasWindow();\n const document2 = ref_window2.document;\n this.canvas.removeEventListener(\"pointerout\", this._mouseout_callback);\n LiteGraph.pointerListenerRemove(this.canvas, \"move\", this._mousemove_callback);\n LiteGraph.pointerListenerRemove(this.canvas, \"up\", this._mouseup_callback);\n LiteGraph.pointerListenerRemove(this.canvas, \"down\", this._mousedown_callback);\n this.canvas.removeEventListener(\n \"mousewheel\",\n this._mousewheel_callback\n );\n this.canvas.removeEventListener(\n \"DOMMouseScroll\",\n this._mousewheel_callback\n );\n this.canvas.removeEventListener(\"keydown\", this._key_callback);\n document2.removeEventListener(\"keyup\", this._key_callback);\n this.canvas.removeEventListener(\"contextmenu\", this._doNothing);\n this.canvas.removeEventListener(\"drop\", this._ondrop_callback);\n this.canvas.removeEventListener(\"dragenter\", this._doReturnTrue);\n this._mousedown_callback = null;\n this._mousewheel_callback = null;\n this._key_callback = null;\n this._ondrop_callback = null;\n this._events_binded = false;\n }\n /**\n * this function allows to render the canvas using WebGL instead of Canvas2D\n * this is useful if you plant to render 3D objects inside your nodes, it uses litegl.js for webgl and canvas2DtoWebGL to emulate the Canvas2D calls in webGL\n **/\n enableWebGL() {\n if (typeof GL === \"undefined\") {\n throw \"litegl.js must be included to use a WebGL canvas\";\n }\n if (typeof enableWebGLCanvas === \"undefined\") {\n throw \"webglCanvas.js must be included to use this feature\";\n }\n this.gl = this.ctx = enableWebGLCanvas(this.canvas);\n this.ctx.webgl = true;\n this.bgcanvas = this.canvas;\n this.bgctx = this.gl;\n this.canvas.webgl_enabled = true;\n }\n /**\n * marks as dirty the canvas, this way it will be rendered again\n *\n * @class LGraphCanvas\n * @param {bool} fgcanvas if the foreground canvas is dirty (the one containing the nodes)\n * @param {bool} bgcanvas if the background canvas is dirty (the one containing the wires)\n */\n setDirty(fgcanvas, bgcanvas) {\n if (fgcanvas) this.dirty_canvas = true;\n if (bgcanvas) this.dirty_bgcanvas = true;\n }\n /**\n * Used to attach the canvas in a popup\n *\n * @return {window} returns the window where the canvas is attached (the DOM root node)\n */\n getCanvasWindow() {\n if (!this.canvas) return window;\n const doc = this.canvas.ownerDocument;\n return doc.defaultView || doc.parentWindow;\n }\n /**\n * starts rendering the content of the canvas when needed\n *\n */\n startRendering() {\n if (this.is_rendering) return;\n this.is_rendering = true;\n renderFrame.call(this);\n function renderFrame() {\n if (!this.pause_rendering) {\n this.draw();\n }\n const window2 = this.getCanvasWindow();\n if (this.is_rendering) {\n window2.requestAnimationFrame(renderFrame.bind(this));\n }\n }\n }\n /**\n * stops rendering the content of the canvas (to save resources)\n *\n */\n stopRendering() {\n this.is_rendering = false;\n }\n /* LiteGraphCanvas input */\n //used to block future mouse events (because of im gui)\n blockClick() {\n this.block_click = true;\n this.last_mouseclick = 0;\n }\n /**\n * Gets the widget at the current cursor position\n * @param node Optional node to check for widgets under cursor\n * @returns The widget located at the current cursor position or null\n */\n getWidgetAtCursor(node2) {\n node2 ??= this.node_over;\n if (!node2.widgets) return null;\n const graphPos = this.graph_mouse;\n const x2 = graphPos[0] - node2.pos[0];\n const y2 = graphPos[1] - node2.pos[1];\n for (const widget of node2.widgets) {\n let widgetWidth, widgetHeight;\n if (widget.computeSize) {\n [widgetWidth, widgetHeight] = widget.computeSize(node2.size[0]);\n } else {\n widgetWidth = widget.width || node2.size[0];\n widgetHeight = LiteGraph.NODE_WIDGET_HEIGHT;\n }\n if (widget.last_y !== void 0 && x2 >= 6 && x2 <= widgetWidth - 12 && y2 >= widget.last_y && y2 <= widget.last_y + widgetHeight) {\n return widget;\n }\n }\n return null;\n }\n /**\n * Clears highlight and mouse-over information from nodes that should not have it.\n * \n * Intended to be called when the pointer moves away from a node.\n * @param {LGraphNode} node The node that the mouse is now over\n * @param {MouseEvent} e MouseEvent that is triggering this\n */\n updateMouseOverNodes(node2, e) {\n const nodes = this.graph._nodes;\n const l = nodes.length;\n for (let i2 = 0; i2 < l; ++i2) {\n if (nodes[i2].mouseOver && node2 != nodes[i2]) {\n nodes[i2].mouseOver = null;\n this._highlight_input = null;\n this._highlight_pos = null;\n nodes[i2].lostFocusAt = LiteGraph.getTime();\n this.node_over?.onMouseLeave?.(e);\n this.node_over = null;\n this.dirty_canvas = true;\n }\n }\n }\n processMouseDown(e) {\n if (this.set_canvas_dirty_on_mouse_event)\n this.dirty_canvas = true;\n if (!this.graph) return;\n this.adjustMouseEvent(e);\n const ref_window2 = this.getCanvasWindow();\n LGraphCanvas.active_canvas = this;\n const x2 = e.clientX;\n const y2 = e.clientY;\n this.ds.viewport = this.viewport;\n const is_inside = !this.viewport || this.viewport && x2 >= this.viewport[0] && x2 < this.viewport[0] + this.viewport[2] && y2 >= this.viewport[1] && y2 < this.viewport[1] + this.viewport[3];\n if (!this.options.skip_events) {\n LiteGraph.pointerListenerRemove(this.canvas, \"move\", this._mousemove_callback);\n LiteGraph.pointerListenerAdd(ref_window2.document, \"move\", this._mousemove_callback, true);\n LiteGraph.pointerListenerAdd(ref_window2.document, \"up\", this._mouseup_callback, true);\n }\n if (!is_inside) return;\n let node2 = this.graph.getNodeOnPos(e.canvasX, e.canvasY, this.visible_nodes, 5);\n let skip_action = false;\n const now = LiteGraph.getTime();\n const is_primary = e.isPrimary === void 0 || !e.isPrimary;\n const is_double_click = now - this.last_mouseclick < 300;\n this.mouse[0] = e.clientX;\n this.mouse[1] = e.clientY;\n this.graph_mouse[0] = e.canvasX;\n this.graph_mouse[1] = e.canvasY;\n this.last_click_position = [this.mouse[0], this.mouse[1]];\n this.pointer_is_double = this.pointer_is_down && is_primary;\n this.pointer_is_down = true;\n this.canvas.focus();\n LiteGraph.closeAllContextMenus(ref_window2);\n if (this.onMouse?.(e) == true) return;\n if (e.which == 1 && !this.pointer_is_double) {\n if ((e.metaKey || e.ctrlKey) && !e.altKey) {\n this.dragging_rectangle = new Float32Array(4);\n this.dragging_rectangle[0] = e.canvasX;\n this.dragging_rectangle[1] = e.canvasY;\n this.dragging_rectangle[2] = 1;\n this.dragging_rectangle[3] = 1;\n skip_action = true;\n }\n if (LiteGraph.alt_drag_do_clone_nodes && e.altKey && !e.ctrlKey && node2 && this.allow_interaction && !skip_action && !this.read_only) {\n const node_data = node2.clone()?.serialize();\n const cloned = LiteGraph.createNode(node_data.type);\n if (cloned) {\n cloned.configure(node_data);\n cloned.pos[0] += 5;\n cloned.pos[1] += 5;\n this.graph.add(cloned, false);\n node2 = cloned;\n skip_action = true;\n if (this.allow_dragnodes) {\n this.graph.beforeChange();\n this.node_dragged = node2;\n this.isDragging = true;\n }\n if (!this.selected_nodes[node2.id]) {\n this.processNodeSelected(node2, e);\n }\n }\n }\n let clicking_canvas_bg = false;\n if (node2 && (this.allow_interaction || node2.flags.allow_interaction) && !skip_action && !this.read_only) {\n if (!this.live_mode && !node2.flags.pinned) {\n this.bringToFront(node2);\n }\n if (this.allow_interaction && !this.connecting_links && !node2.flags.collapsed && !this.live_mode) {\n if (!skip_action && node2.resizable !== false && node2.inResizeCorner(e.canvasX, e.canvasY)) {\n this.graph.beforeChange();\n this.resizing_node = node2;\n this.canvas.style.cursor = \"se-resize\";\n skip_action = true;\n } else {\n if (node2.outputs) {\n for (let i2 = 0, l = node2.outputs.length; i2 < l; ++i2) {\n const output = node2.outputs[i2];\n const link_pos = node2.getConnectionPos(false, i2);\n if (isInsideRectangle(\n e.canvasX,\n e.canvasY,\n link_pos[0] - 15,\n link_pos[1] - 10,\n 30,\n 20\n )) {\n if (e.shiftKey) {\n if (output.links?.length > 0) {\n this.connecting_links = [];\n for (const linkId of output.links) {\n const link = this.graph.links[linkId];\n const slot = link.target_slot;\n const linked_node = this.graph._nodes_by_id[link.target_id];\n const input = linked_node.inputs[slot];\n const pos2 = linked_node.getConnectionPos(true, slot);\n this.connecting_links.push({\n node: linked_node,\n slot,\n input,\n output: null,\n pos: pos2,\n direction: node2.horizontal !== true ? LinkDirection.RIGHT : LinkDirection.CENTER\n });\n }\n skip_action = true;\n break;\n }\n }\n output.slot_index = i2;\n this.connecting_links = [\n {\n node: node2,\n slot: i2,\n input: null,\n output,\n pos: link_pos\n }\n ];\n if (LiteGraph.shift_click_do_break_link_from) {\n if (e.shiftKey) {\n node2.disconnectOutput(i2);\n }\n } else if (LiteGraph.ctrl_alt_click_do_break_link) {\n if (e.ctrlKey && e.altKey && !e.shiftKey) {\n node2.disconnectOutput(i2);\n }\n }\n if (is_double_click) {\n node2.onOutputDblClick?.(i2, e);\n } else {\n node2.onOutputClick?.(i2, e);\n }\n skip_action = true;\n break;\n }\n }\n }\n if (node2.inputs) {\n for (let i2 = 0, l = node2.inputs.length; i2 < l; ++i2) {\n const input = node2.inputs[i2];\n const link_pos = node2.getConnectionPos(true, i2);\n if (isInsideRectangle(\n e.canvasX,\n e.canvasY,\n link_pos[0] - 15,\n link_pos[1] - 10,\n 30,\n 20\n )) {\n if (is_double_click) {\n node2.onInputDblClick?.(i2, e);\n } else {\n node2.onInputClick?.(i2, e);\n }\n if (input.link !== null) {\n const link_info = this.graph.links[input.link];\n const slot = link_info.origin_slot;\n const linked_node = this.graph._nodes_by_id[link_info.origin_id];\n if (LiteGraph.click_do_break_link_to || LiteGraph.ctrl_alt_click_do_break_link && e.ctrlKey && e.altKey && !e.shiftKey) {\n node2.disconnectInput(i2);\n } else if (e.shiftKey) {\n this.connecting_links = [{\n node: linked_node,\n slot,\n output: linked_node.outputs[slot],\n pos: linked_node.getConnectionPos(false, slot)\n }];\n this.dirty_bgcanvas = true;\n skip_action = true;\n } else if (this.allow_reconnect_links) {\n if (!LiteGraph.click_do_break_link_to) {\n node2.disconnectInput(i2);\n }\n this.connecting_links = [\n {\n node: linked_node,\n slot,\n input: null,\n output: linked_node.outputs[slot],\n pos: linked_node.getConnectionPos(false, slot)\n }\n ];\n this.dirty_bgcanvas = true;\n skip_action = true;\n } else ;\n }\n if (!skip_action) {\n this.connecting_links = [\n {\n node: node2,\n slot: i2,\n input,\n output: null,\n pos: link_pos\n }\n ];\n this.dirty_bgcanvas = true;\n skip_action = true;\n }\n break;\n }\n }\n }\n }\n }\n if (!skip_action) {\n let block_drag_node = node2?.pinned ? true : false;\n const pos2 = [e.canvasX - node2.pos[0], e.canvasY - node2.pos[1]];\n const widget = this.processNodeWidgets(node2, this.graph_mouse, e);\n if (widget) {\n block_drag_node = true;\n this.node_widget = [node2, widget];\n }\n if (this.allow_interaction && is_double_click && this.selected_nodes[node2.id]) {\n if (pos2[1] < 0) {\n node2.onNodeTitleDblClick?.(e, pos2, this);\n }\n node2.onDblClick?.(e, pos2, this);\n this.processNodeDblClicked(node2);\n block_drag_node = true;\n }\n if (node2.onMouseDown?.(e, pos2, this)) {\n block_drag_node = true;\n } else {\n if (node2.subgraph && !node2.skip_subgraph_button) {\n if (!node2.flags.collapsed && pos2[0] > node2.size[0] - LiteGraph.NODE_TITLE_HEIGHT && pos2[1] < 0) {\n const that2 = this;\n setTimeout(function() {\n that2.openSubgraph(node2.subgraph);\n }, 10);\n }\n }\n if (this.live_mode) {\n clicking_canvas_bg = true;\n block_drag_node = true;\n }\n }\n if (!block_drag_node) {\n if (this.allow_dragnodes) {\n this.graph.beforeChange();\n this.node_dragged = node2;\n this.isDragging = true;\n }\n if (!(e.shiftKey && !e.ctrlKey && !e.altKey) || !node2.is_selected) {\n this.processNodeSelected(node2, e);\n }\n } else {\n if (!node2.is_selected) this.processNodeSelected(node2, e);\n }\n this.dirty_canvas = true;\n }\n } else {\n if (!skip_action) {\n if (!this.read_only) {\n const lineWidth = this.ctx.lineWidth;\n this.ctx.lineWidth = this.connections_width + 7;\n for (let i2 = 0; i2 < this.visible_links.length; ++i2) {\n const link = this.visible_links[i2];\n const center = link._pos;\n let overLink = null;\n if (!center || e.canvasX < center[0] - 4 || e.canvasX > center[0] + 4 || e.canvasY < center[1] - 4 || e.canvasY > center[1] + 4) {\n if (e.shiftKey && link.path && this.ctx.isPointInStroke(link.path, e.canvasX, e.canvasY)) {\n overLink = link;\n } else {\n continue;\n }\n }\n if (overLink) {\n const slot = overLink.origin_slot;\n const originNode = this.graph._nodes_by_id[overLink.origin_id];\n this.connecting_links ??= [];\n this.connecting_links.push({\n node: originNode,\n slot,\n output: originNode.outputs[slot],\n pos: originNode.getConnectionPos(false, slot)\n });\n skip_action = true;\n } else {\n this.showLinkMenu(link, e);\n this.over_link_center = null;\n }\n break;\n }\n this.ctx.lineWidth = lineWidth;\n }\n this.selected_group = this.graph.getGroupOnPos(e.canvasX, e.canvasY);\n this.selected_group_resizing = false;\n const group = this.selected_group;\n if (this.selected_group && !this.read_only) {\n if (e.ctrlKey) {\n this.dragging_rectangle = null;\n }\n const dist = distance([e.canvasX, e.canvasY], [this.selected_group.pos[0] + this.selected_group.size[0], this.selected_group.pos[1] + this.selected_group.size[1]]);\n if (dist * this.ds.scale < 10) {\n this.selected_group_resizing = true;\n } else {\n const f = group.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE;\n const headerHeight = f * 1.4;\n if (isInsideRectangle(e.canvasX, e.canvasY, group.pos[0], group.pos[1], group.size[0], headerHeight)) {\n this.selected_group.recomputeInsideNodes();\n if (!e.shiftKey && !e.ctrlKey && !e.metaKey) this.deselectAllNodes();\n this.selectedGroups ??= /* @__PURE__ */ new Set();\n this.selectedGroups.add(group);\n group.selected = true;\n this.isDragging = true;\n skip_action = true;\n }\n }\n if (is_double_click) {\n this.emitEvent({\n subType: \"group-double-click\",\n originalEvent: e,\n group: this.selected_group\n });\n }\n } else if (is_double_click && !this.read_only) {\n if (this.allow_searchbox) {\n this.showSearchBox(e);\n e.preventDefault();\n e.stopPropagation();\n }\n this.emitEvent({\n subType: \"empty-double-click\",\n originalEvent: e\n });\n }\n clicking_canvas_bg = true;\n }\n }\n if (!skip_action && clicking_canvas_bg && this.allow_dragcanvas) {\n this.dragging_canvas = true;\n }\n } else if (e.which == 2) {\n if (LiteGraph.middle_click_slot_add_default_node) {\n if (node2 && this.allow_interaction && !skip_action && !this.read_only) {\n if (!this.connecting_links && !node2.flags.collapsed && !this.live_mode) {\n let mClikSlot = false;\n let mClikSlot_index = false;\n let mClikSlot_isOut = false;\n if (node2.outputs) {\n for (let i2 = 0, l = node2.outputs.length; i2 < l; ++i2) {\n const output = node2.outputs[i2];\n const link_pos = node2.getConnectionPos(false, i2);\n if (isInsideRectangle(e.canvasX, e.canvasY, link_pos[0] - 15, link_pos[1] - 10, 30, 20)) {\n mClikSlot = output;\n mClikSlot_index = i2;\n mClikSlot_isOut = true;\n break;\n }\n }\n }\n if (node2.inputs) {\n for (let i2 = 0, l = node2.inputs.length; i2 < l; ++i2) {\n const input = node2.inputs[i2];\n const link_pos = node2.getConnectionPos(true, i2);\n if (isInsideRectangle(e.canvasX, e.canvasY, link_pos[0] - 15, link_pos[1] - 10, 30, 20)) {\n mClikSlot = input;\n mClikSlot_index = i2;\n mClikSlot_isOut = false;\n break;\n }\n }\n }\n if (mClikSlot && mClikSlot_index !== false) {\n const alphaPosY = 0.5 - (mClikSlot_index + 1) / (mClikSlot_isOut ? node2.outputs.length : node2.inputs.length);\n const node_bounding = node2.getBounding();\n const posRef = [\n !mClikSlot_isOut ? node_bounding[0] : node_bounding[0] + node_bounding[2],\n e.canvasY - 80\n // + node_bounding[0]/this.canvas.width*66 // vertical \"derive\"\n ];\n this.createDefaultNodeForSlot({\n nodeFrom: !mClikSlot_isOut ? null : node2,\n slotFrom: !mClikSlot_isOut ? null : mClikSlot_index,\n nodeTo: !mClikSlot_isOut ? node2 : null,\n slotTo: !mClikSlot_isOut ? mClikSlot_index : null,\n //,e: e\n position: posRef,\n //nodeNewType\n nodeType: \"AUTO\",\n //-alphaPosY*30]\n posAdd: [!mClikSlot_isOut ? -30 : 30, -alphaPosY * 130],\n //-alphaPosY*2*/\n posSizeFix: [!mClikSlot_isOut ? -1 : 0, 0]\n });\n skip_action = true;\n }\n }\n }\n }\n if (!skip_action && this.allow_dragcanvas) {\n this.dragging_canvas = true;\n }\n } else if (e.which == 3 || this.pointer_is_double) {\n if (this.allow_interaction && !skip_action && !this.read_only) {\n if (node2) {\n if (Object.keys(this.selected_nodes).length && (this.selected_nodes[node2.id] || e.shiftKey || e.ctrlKey || e.metaKey)) {\n if (!this.selected_nodes[node2.id]) this.selectNodes([node2], true);\n } else {\n this.selectNodes([node2]);\n }\n }\n this.processContextMenu(node2, e);\n }\n }\n this.last_mouse[0] = e.clientX;\n this.last_mouse[1] = e.clientY;\n this.last_mouseclick = LiteGraph.getTime();\n this.last_mouse_dragging = true;\n this.graph.change();\n if (!ref_window2.document.activeElement || ref_window2.document.activeElement.nodeName.toLowerCase() != \"input\" && ref_window2.document.activeElement.nodeName.toLowerCase() != \"textarea\") {\n e.preventDefault();\n }\n e.stopPropagation();\n this.onMouseDown?.(e);\n return false;\n }\n /**\n * Called when a mouse move event has to be processed\n **/\n processMouseMove(e) {\n this.link_over_widget = null;\n if (this.autoresize) this.resize();\n if (this.set_canvas_dirty_on_mouse_event)\n this.dirty_canvas = true;\n if (!this.graph) return;\n LGraphCanvas.active_canvas = this;\n this.adjustMouseEvent(e);\n const mouse = [e.clientX, e.clientY];\n this.mouse[0] = mouse[0];\n this.mouse[1] = mouse[1];\n const delta2 = [\n mouse[0] - this.last_mouse[0],\n mouse[1] - this.last_mouse[1]\n ];\n this.last_mouse = mouse;\n this.graph_mouse[0] = e.canvasX;\n this.graph_mouse[1] = e.canvasY;\n if (this.block_click) {\n e.preventDefault();\n return false;\n }\n e.dragging = this.last_mouse_dragging;\n if (this.node_widget) {\n this.processNodeWidgets(\n this.node_widget[0],\n this.graph_mouse,\n e,\n this.node_widget[1]\n );\n this.dirty_canvas = true;\n }\n const node2 = this.graph.getNodeOnPos(e.canvasX, e.canvasY, this.visible_nodes);\n if (this.dragging_rectangle) {\n this.dragging_rectangle[2] = e.canvasX - this.dragging_rectangle[0];\n this.dragging_rectangle[3] = e.canvasY - this.dragging_rectangle[1];\n this.dirty_canvas = true;\n } else if (this.selected_group_resizing && !this.read_only) {\n this.selected_group.resize(\n e.canvasX - this.selected_group.pos[0],\n e.canvasY - this.selected_group.pos[1]\n );\n this.dirty_bgcanvas = true;\n } else if (this.dragging_canvas) {\n this.ds.offset[0] += delta2[0] / this.ds.scale;\n this.ds.offset[1] += delta2[1] / this.ds.scale;\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n } else if ((this.allow_interaction || node2 && node2.flags.allow_interaction) && !this.read_only) {\n if (this.connecting_links) {\n this.dirty_canvas = true;\n }\n this.updateMouseOverNodes(node2, e);\n if (node2) {\n if (node2.redraw_on_mouse)\n this.dirty_canvas = true;\n const pos2 = [0, 0];\n const inputId = this.isOverNodeInput(node2, e.canvasX, e.canvasY, pos2);\n const outputId = this.isOverNodeOutput(node2, e.canvasX, e.canvasY, pos2);\n if (!node2.mouseOver) {\n node2.mouseOver = {\n inputId,\n outputId\n };\n this.node_over = node2;\n this.dirty_canvas = true;\n node2.onMouseEnter?.(e);\n }\n if (node2.mouseOver.inputId !== inputId || node2.mouseOver.outputId !== outputId) {\n node2.mouseOver.inputId = inputId;\n node2.mouseOver.outputId = outputId;\n this.dirty_canvas = true;\n }\n node2.onMouseMove?.(e, [e.canvasX - node2.pos[0], e.canvasY - node2.pos[1]], this);\n if (this.connecting_links) {\n const firstLink = this.connecting_links[0];\n if (firstLink.output) {\n if (this.isOverNodeBox(node2, e.canvasX, e.canvasY)) ;\n else {\n if (inputId != -1 && node2.inputs[inputId] && LiteGraph.isValidConnection(firstLink.output.type, node2.inputs[inputId].type)) {\n this._highlight_input = pos2;\n this._highlight_input_slot = node2.inputs[inputId];\n } else {\n if (this.getWidgetLinkType) {\n const overWidget = this.getWidgetAtCursor(node2);\n if (overWidget) {\n const widgetLinkType = this.getWidgetLinkType(overWidget, node2);\n if (widgetLinkType && LiteGraph.isValidConnection(firstLink.output.type, widgetLinkType)) {\n if (firstLink.node.isValidWidgetLink?.(firstLink.output.slot_index, node2, overWidget) !== false) {\n this.link_over_widget = overWidget;\n this.link_over_widget_type = widgetLinkType;\n }\n }\n }\n }\n this._highlight_input = null;\n this._highlight_input_slot = null;\n }\n }\n } else if (firstLink.input) {\n if (this.isOverNodeBox(node2, e.canvasX, e.canvasY)) ;\n else {\n if (outputId != -1 && node2.outputs[outputId] && LiteGraph.isValidConnection(firstLink.input.type, node2.outputs[outputId].type)) {\n this._highlight_output = pos2;\n } else {\n this._highlight_output = null;\n }\n }\n }\n }\n if (this.canvas) {\n this.canvas.style.cursor = node2.inResizeCorner(e.canvasX, e.canvasY) ? \"se-resize\" : \"crosshair\";\n }\n } else {\n let over_link = null;\n for (let i2 = 0; i2 < this.visible_links.length; ++i2) {\n const link = this.visible_links[i2];\n const center = link._pos;\n if (!center || e.canvasX < center[0] - 4 || e.canvasX > center[0] + 4 || e.canvasY < center[1] - 4 || e.canvasY > center[1] + 4) {\n continue;\n }\n over_link = link;\n break;\n }\n if (over_link != this.over_link_center) {\n this.over_link_center = over_link;\n this.dirty_canvas = true;\n }\n if (this.canvas) {\n this.canvas.style.cursor = \"\";\n }\n }\n if (this.node_capturing_input && this.node_capturing_input != node2) {\n this.node_capturing_input.onMouseMove?.(e, [e.canvasX - this.node_capturing_input.pos[0], e.canvasY - this.node_capturing_input.pos[1]], this);\n }\n if (this.isDragging && !this.live_mode) {\n const nodes = /* @__PURE__ */ new Set();\n const deltax = delta2[0] / this.ds.scale;\n const deltay = delta2[1] / this.ds.scale;\n for (const i2 in this.selected_nodes) {\n const n = this.selected_nodes[i2];\n nodes.add(n);\n n.pos[0] += delta2[0] / this.ds.scale;\n n.pos[1] += delta2[1] / this.ds.scale;\n if (!n.is_selected) this.processNodeSelected(n, e);\n }\n if (this.selectedGroups) {\n for (const group of this.selectedGroups) {\n group.move(deltax, deltay, true);\n if (!e.ctrlKey) {\n for (const node3 of group._nodes) {\n if (!nodes.has(node3)) {\n node3.pos[0] += deltax;\n node3.pos[1] += deltay;\n }\n }\n }\n }\n }\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n }\n if (this.resizing_node && !this.live_mode) {\n const desired_size = [e.canvasX - this.resizing_node.pos[0], e.canvasY - this.resizing_node.pos[1]];\n const min_size = this.resizing_node.computeSize();\n desired_size[0] = Math.max(min_size[0], desired_size[0]);\n desired_size[1] = Math.max(min_size[1], desired_size[1]);\n this.resizing_node.setSize(desired_size);\n this.canvas.style.cursor = \"se-resize\";\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n }\n }\n e.preventDefault();\n return false;\n }\n /**\n * Called when a mouse up event has to be processed\n **/\n processMouseUp(e) {\n const is_primary = e.isPrimary === void 0 || e.isPrimary;\n if (!is_primary) return false;\n if (!this.graph) return;\n const window2 = this.getCanvasWindow();\n const document2 = window2.document;\n LGraphCanvas.active_canvas = this;\n if (!this.options.skip_events) {\n LiteGraph.pointerListenerRemove(document2, \"move\", this._mousemove_callback, true);\n LiteGraph.pointerListenerAdd(this.canvas, \"move\", this._mousemove_callback, true);\n LiteGraph.pointerListenerRemove(document2, \"up\", this._mouseup_callback, true);\n }\n this.adjustMouseEvent(e);\n const now = LiteGraph.getTime();\n e.click_time = now - this.last_mouseclick;\n this.last_mouse_dragging = false;\n this.last_click_position = null;\n this.block_click &&= false;\n if (e.which == 1) {\n if (this.node_widget) {\n this.processNodeWidgets(this.node_widget[0], this.graph_mouse, e);\n }\n this.node_widget = null;\n if (this.selected_group) {\n const diffx = this.selected_group.pos[0] - Math.round(this.selected_group.pos[0]);\n const diffy = this.selected_group.pos[1] - Math.round(this.selected_group.pos[1]);\n this.selected_group.move(diffx, diffy, e.ctrlKey);\n this.selected_group.pos[0] = Math.round(\n this.selected_group.pos[0]\n );\n this.selected_group.pos[1] = Math.round(\n this.selected_group.pos[1]\n );\n if (this.selected_group._nodes.length) {\n this.dirty_canvas = true;\n }\n this.selected_group = null;\n }\n this.selected_group_resizing = false;\n this.isDragging = false;\n let node2 = this.graph.getNodeOnPos(\n e.canvasX,\n e.canvasY,\n this.visible_nodes\n );\n if (this.dragging_rectangle) {\n if (this.graph) {\n const nodes = this.graph._nodes;\n const node_bounding = new Float32Array(4);\n const w2 = Math.abs(this.dragging_rectangle[2]);\n const h = Math.abs(this.dragging_rectangle[3]);\n const startx = this.dragging_rectangle[2] < 0 ? this.dragging_rectangle[0] - w2 : this.dragging_rectangle[0];\n const starty = this.dragging_rectangle[3] < 0 ? this.dragging_rectangle[1] - h : this.dragging_rectangle[1];\n this.dragging_rectangle[0] = startx;\n this.dragging_rectangle[1] = starty;\n this.dragging_rectangle[2] = w2;\n this.dragging_rectangle[3] = h;\n if (!node2 || w2 > 10 && h > 10) {\n const to_select = [];\n for (let i2 = 0; i2 < nodes.length; ++i2) {\n const nodeX = nodes[i2];\n nodeX.getBounding(node_bounding);\n if (!overlapBounding(\n this.dragging_rectangle,\n node_bounding\n )) {\n continue;\n }\n to_select.push(nodeX);\n }\n if (to_select.length) {\n this.selectNodes(to_select, e.shiftKey);\n }\n if (!e.shiftKey) this.deselectGroups();\n this.selectedGroups ??= /* @__PURE__ */ new Set();\n const groups = this.graph.groups;\n for (const group of groups) {\n const r = this.dragging_rectangle;\n const pos2 = group.pos;\n const size = group.size;\n if (!isInsideRectangle(pos2[0], pos2[1], r[0], r[1], r[2], r[3]) || !isInsideRectangle(pos2[0] + size[0], pos2[1] + size[1], r[0], r[1], r[2], r[3])) continue;\n this.selectedGroups.add(group);\n group.recomputeInsideNodes();\n group.selected = true;\n }\n } else {\n this.selectNodes([node2], e.shiftKey || e.ctrlKey || e.metaKey);\n }\n }\n this.dragging_rectangle = null;\n } else if (this.connecting_links) {\n if (node2) {\n for (const link of this.connecting_links) {\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n if (link.output) {\n const slot = this.isOverNodeInput(\n node2,\n e.canvasX,\n e.canvasY\n );\n if (slot != -1) {\n link.node.connect(link.slot, node2, slot);\n } else if (this.link_over_widget) {\n this.emitEvent({\n subType: \"connectingWidgetLink\",\n link,\n node: node2,\n widget: this.link_over_widget\n });\n this.link_over_widget = null;\n } else {\n link.node.connectByType(link.slot, node2, link.output.type);\n }\n } else if (link.input) {\n const slot = this.isOverNodeOutput(\n node2,\n e.canvasX,\n e.canvasY\n );\n if (slot != -1) {\n node2.connect(slot, link.node, link.slot);\n } else {\n link.node.connectByTypeOutput(link.slot, node2, link.input.type);\n }\n }\n }\n } else {\n const firstLink = this.connecting_links[0];\n const linkReleaseContext = firstLink.output ? {\n node_from: firstLink.node,\n slot_from: firstLink.output,\n type_filter_in: firstLink.output.type\n } : {\n node_to: firstLink.node,\n slot_from: firstLink.input,\n type_filter_out: firstLink.input.type\n };\n const linkReleaseContextExtended = {\n links: this.connecting_links\n };\n this.emitEvent({\n subType: \"empty-release\",\n originalEvent: e,\n linkReleaseContext: linkReleaseContextExtended\n });\n if (LiteGraph.release_link_on_empty_shows_menu) {\n if (e.shiftKey) {\n if (this.allow_searchbox) {\n this.showSearchBox(e, linkReleaseContext);\n }\n } else {\n if (firstLink.output) {\n this.showConnectionMenu({ nodeFrom: firstLink.node, slotFrom: firstLink.output, e });\n } else if (firstLink.input) {\n this.showConnectionMenu({ nodeTo: firstLink.node, slotTo: firstLink.input, e });\n }\n }\n }\n }\n this.connecting_links = null;\n } else if (this.resizing_node) {\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n this.graph.afterChange(this.resizing_node);\n this.resizing_node = null;\n } else if (this.node_dragged) {\n node2 = this.node_dragged;\n if (node2 && e.click_time < 300 && isInsideRectangle(e.canvasX, e.canvasY, node2.pos[0], node2.pos[1] - LiteGraph.NODE_TITLE_HEIGHT, LiteGraph.NODE_TITLE_HEIGHT, LiteGraph.NODE_TITLE_HEIGHT)) {\n node2.collapse();\n }\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n this.node_dragged.pos[0] = Math.round(this.node_dragged.pos[0]);\n this.node_dragged.pos[1] = Math.round(this.node_dragged.pos[1]);\n if (this.graph.config.align_to_grid || this.align_to_grid) {\n this.node_dragged.alignToGrid();\n }\n this.onNodeMoved?.(this.node_dragged);\n this.graph.afterChange(this.node_dragged);\n this.node_dragged = null;\n } else {\n node2 = this.graph.getNodeOnPos(\n e.canvasX,\n e.canvasY,\n this.visible_nodes\n );\n if (!node2 && e.click_time < 300 && !this.graph.groups.some((x2) => x2.isPointInTitlebar(e.canvasX, e.canvasY))) {\n this.deselectAllNodes();\n }\n this.dirty_canvas = true;\n this.dragging_canvas = false;\n this.node_over?.onMouseUp?.(e, [e.canvasX - this.node_over.pos[0], e.canvasY - this.node_over.pos[1]], this);\n this.node_capturing_input?.onMouseUp?.(e, [\n e.canvasX - this.node_capturing_input.pos[0],\n e.canvasY - this.node_capturing_input.pos[1]\n ]);\n }\n } else if (e.which == 2) {\n this.dirty_canvas = true;\n this.dragging_canvas = false;\n } else if (e.which == 3) {\n this.dirty_canvas = true;\n this.dragging_canvas = false;\n }\n if (is_primary) {\n this.pointer_is_down = false;\n this.pointer_is_double = false;\n }\n this.graph.change();\n e.stopPropagation();\n e.preventDefault();\n return false;\n }\n /**\n * Called when the mouse moves off the canvas. Clears all node hover states.\n * @param e\n */\n processMouseOut(e) {\n this.updateMouseOverNodes(null, e);\n }\n /**\n * Called when a mouse wheel event has to be processed\n **/\n processMouseWheel(e) {\n if (!this.graph || !this.allow_dragcanvas) return;\n const delta2 = e.wheelDeltaY ?? e.detail * -60;\n this.adjustMouseEvent(e);\n const pos2 = [e.clientX, e.clientY];\n if (this.viewport && !isPointInRectangle(pos2, this.viewport)) return;\n let scale = this.ds.scale;\n if (delta2 > 0) scale *= this.zoom_speed;\n else if (delta2 < 0) scale *= 1 / this.zoom_speed;\n this.ds.changeScale(scale, [e.clientX, e.clientY]);\n this.graph.change();\n e.preventDefault();\n return false;\n }\n /**\n * returns true if a position (in graph space) is on top of a node little corner box\n **/\n isOverNodeBox(node2, canvasx, canvasy) {\n const title_height = LiteGraph.NODE_TITLE_HEIGHT;\n return Boolean(isInsideRectangle(\n canvasx,\n canvasy,\n node2.pos[0] + 2,\n node2.pos[1] + 2 - title_height,\n title_height - 4,\n title_height - 4\n ));\n }\n /**\n * returns the INDEX if a position (in graph space) is on top of a node input slot\n **/\n isOverNodeInput(node2, canvasx, canvasy, slot_pos) {\n if (node2.inputs) {\n for (let i2 = 0, l = node2.inputs.length; i2 < l; ++i2) {\n const input = node2.inputs[i2];\n const link_pos = node2.getConnectionPos(true, i2);\n let is_inside = false;\n if (node2.horizontal) {\n is_inside = isInsideRectangle(\n canvasx,\n canvasy,\n link_pos[0] - 5,\n link_pos[1] - 10,\n 10,\n 20\n );\n } else {\n const width2 = 20 + ((input.label?.length ?? input.name?.length) || 3) * 7;\n is_inside = isInsideRectangle(\n canvasx,\n canvasy,\n link_pos[0] - 10,\n link_pos[1] - 10,\n width2,\n 20\n );\n }\n if (is_inside) {\n if (slot_pos) {\n slot_pos[0] = link_pos[0];\n slot_pos[1] = link_pos[1];\n }\n return i2;\n }\n }\n }\n return -1;\n }\n /**\n * returns the INDEX if a position (in graph space) is on top of a node output slot\n **/\n isOverNodeOutput(node2, canvasx, canvasy, slot_pos) {\n if (node2.outputs) {\n for (let i2 = 0, l = node2.outputs.length; i2 < l; ++i2) {\n const link_pos = node2.getConnectionPos(false, i2);\n let is_inside = false;\n if (node2.horizontal) {\n is_inside = isInsideRectangle(\n canvasx,\n canvasy,\n link_pos[0] - 5,\n link_pos[1] - 10,\n 10,\n 20\n );\n } else {\n is_inside = isInsideRectangle(\n canvasx,\n canvasy,\n link_pos[0] - 10,\n link_pos[1] - 10,\n 40,\n 20\n );\n }\n if (is_inside) {\n if (slot_pos) {\n slot_pos[0] = link_pos[0];\n slot_pos[1] = link_pos[1];\n }\n return i2;\n }\n }\n }\n return -1;\n }\n /**\n * process a key event\n **/\n processKey(e) {\n if (!this.graph) return;\n let block_default = false;\n if (e.target.localName == \"input\") return;\n if (e.type == \"keydown\") {\n if (e.keyCode == 32) {\n this.read_only = true;\n if (this._previously_dragging_canvas === null) {\n this._previously_dragging_canvas = this.dragging_canvas;\n }\n this.dragging_canvas = this.pointer_is_down;\n block_default = true;\n } else if (e.keyCode == 27) {\n this.node_panel?.close();\n this.options_panel?.close();\n block_default = true;\n } else if (e.keyCode == 65 && e.ctrlKey) {\n this.selectNodes();\n block_default = true;\n } else if (e.keyCode === 67 && (e.metaKey || e.ctrlKey) && !e.shiftKey) {\n if (this.selected_nodes) {\n this.copyToClipboard();\n block_default = true;\n }\n } else if (e.keyCode === 86 && (e.metaKey || e.ctrlKey)) {\n this.pasteFromClipboard(e.shiftKey);\n } else if (e.keyCode == 46 || e.keyCode == 8) {\n if (e.target.localName != \"input\" && e.target.localName != \"textarea\") {\n this.deleteSelectedNodes();\n block_default = true;\n }\n }\n if (this.selected_nodes) {\n for (const i2 in this.selected_nodes) {\n this.selected_nodes[i2].onKeyDown?.(e);\n }\n }\n } else if (e.type == \"keyup\") {\n if (e.keyCode == 32) {\n this.read_only = false;\n this.dragging_canvas = this._previously_dragging_canvas ?? false;\n this._previously_dragging_canvas = null;\n }\n if (this.selected_nodes) {\n for (const i2 in this.selected_nodes) {\n this.selected_nodes[i2].onKeyUp?.(e);\n }\n }\n }\n this.graph.change();\n if (block_default) {\n e.preventDefault();\n e.stopImmediatePropagation();\n return false;\n }\n }\n copyToClipboard(nodes) {\n const clipboard_info = {\n nodes: [],\n links: []\n };\n let index = 0;\n const selected_nodes_array = [];\n if (!nodes) nodes = this.selected_nodes;\n for (const i2 in nodes) {\n const node2 = nodes[i2];\n if (node2.clonable === false) continue;\n node2._relative_id = index;\n selected_nodes_array.push(node2);\n index += 1;\n }\n for (let i2 = 0; i2 < selected_nodes_array.length; ++i2) {\n const node2 = selected_nodes_array[i2];\n const cloned = node2.clone();\n if (!cloned) {\n console.warn(\"node type not found: \" + node2.type);\n continue;\n }\n clipboard_info.nodes.push(cloned.serialize());\n if (node2.inputs?.length) {\n for (let j = 0; j < node2.inputs.length; ++j) {\n const input = node2.inputs[j];\n if (!input || input.link == null) continue;\n const link_info = this.graph.links[input.link];\n if (!link_info) continue;\n const target_node = this.graph.getNodeById(link_info.origin_id);\n if (!target_node) continue;\n clipboard_info.links.push([\n target_node._relative_id,\n link_info.origin_slot,\n //j,\n node2._relative_id,\n link_info.target_slot,\n target_node.id\n ]);\n }\n }\n }\n localStorage.setItem(\n \"litegrapheditor_clipboard\",\n JSON.stringify(clipboard_info)\n );\n }\n emitEvent(detail) {\n this.canvas.dispatchEvent(new CustomEvent(\n \"litegraph:canvas\",\n {\n bubbles: true,\n detail\n }\n ));\n }\n emitBeforeChange() {\n this.emitEvent({\n subType: \"before-change\"\n });\n }\n emitAfterChange() {\n this.emitEvent({\n subType: \"after-change\"\n });\n }\n _pasteFromClipboard(isConnectUnselected = false) {\n if (!LiteGraph.ctrl_shift_v_paste_connect_unselected_outputs && isConnectUnselected) return;\n const data = localStorage.getItem(\"litegrapheditor_clipboard\");\n if (!data) return;\n this.graph.beforeChange();\n const clipboard_info = JSON.parse(data);\n let posMin = false;\n let posMinIndexes = false;\n for (let i2 = 0; i2 < clipboard_info.nodes.length; ++i2) {\n if (posMin) {\n if (posMin[0] > clipboard_info.nodes[i2].pos[0]) {\n posMin[0] = clipboard_info.nodes[i2].pos[0];\n posMinIndexes[0] = i2;\n }\n if (posMin[1] > clipboard_info.nodes[i2].pos[1]) {\n posMin[1] = clipboard_info.nodes[i2].pos[1];\n posMinIndexes[1] = i2;\n }\n } else {\n posMin = [clipboard_info.nodes[i2].pos[0], clipboard_info.nodes[i2].pos[1]];\n posMinIndexes = [i2, i2];\n }\n }\n const nodes = [];\n for (let i2 = 0; i2 < clipboard_info.nodes.length; ++i2) {\n const node_data = clipboard_info.nodes[i2];\n const node2 = LiteGraph.createNode(node_data.type);\n if (node2) {\n node2.configure(node_data);\n node2.pos[0] += this.graph_mouse[0] - posMin[0];\n node2.pos[1] += this.graph_mouse[1] - posMin[1];\n this.graph.add(node2, true);\n nodes.push(node2);\n }\n }\n for (let i2 = 0; i2 < clipboard_info.links.length; ++i2) {\n const link_info = clipboard_info.links[i2];\n let origin_node = void 0;\n const origin_node_relative_id = link_info[0];\n if (origin_node_relative_id != null) {\n origin_node = nodes[origin_node_relative_id];\n } else if (LiteGraph.ctrl_shift_v_paste_connect_unselected_outputs && isConnectUnselected) {\n const origin_node_id = link_info[4];\n if (origin_node_id) {\n origin_node = this.graph.getNodeById(origin_node_id);\n }\n }\n const target_node = nodes[link_info[2]];\n if (origin_node && target_node)\n origin_node.connect(link_info[1], target_node, link_info[3]);\n else\n console.warn(\"Warning, nodes missing on pasting\");\n }\n this.selectNodes(nodes);\n this.graph.afterChange();\n }\n pasteFromClipboard(isConnectUnselected = false) {\n this.emitBeforeChange();\n try {\n this._pasteFromClipboard(isConnectUnselected);\n } finally {\n this.emitAfterChange();\n }\n }\n /**\n * process a item drop event on top the canvas\n **/\n processDrop(e) {\n e.preventDefault();\n this.adjustMouseEvent(e);\n const x2 = e.clientX;\n const y2 = e.clientY;\n const is_inside = !this.viewport || this.viewport && x2 >= this.viewport[0] && x2 < this.viewport[0] + this.viewport[2] && y2 >= this.viewport[1] && y2 < this.viewport[1] + this.viewport[3];\n if (!is_inside) return;\n const pos2 = [e.canvasX, e.canvasY];\n const node2 = this.graph ? this.graph.getNodeOnPos(pos2[0], pos2[1]) : null;\n if (!node2) {\n const r = this.onDropItem?.(e);\n if (!r) this.checkDropItem(e);\n return;\n }\n if (node2.onDropFile || node2.onDropData) {\n const files = e.dataTransfer.files;\n if (files && files.length) {\n for (let i2 = 0; i2 < files.length; i2++) {\n const file = e.dataTransfer.files[0];\n const filename = file.name;\n node2.onDropFile?.(file);\n if (node2.onDropData) {\n const reader = new FileReader();\n reader.onload = function(event2) {\n const data = event2.target.result;\n node2.onDropData(data, filename, file);\n };\n const type = file.type.split(\"/\")[0];\n if (type == \"text\" || type == \"\") {\n reader.readAsText(file);\n } else if (type == \"image\") {\n reader.readAsDataURL(file);\n } else {\n reader.readAsArrayBuffer(file);\n }\n }\n }\n }\n }\n if (node2.onDropItem?.(e)) return true;\n return this.onDropItem ? this.onDropItem(e) : false;\n }\n //called if the graph doesn't have a default drop item behaviour\n checkDropItem(e) {\n if (!e.dataTransfer.files.length) return;\n const file = e.dataTransfer.files[0];\n const ext = LGraphCanvas.getFileExtension(file.name).toLowerCase();\n const nodetype = LiteGraph.node_types_by_file_extension[ext];\n if (!nodetype) return;\n this.graph.beforeChange();\n const node2 = LiteGraph.createNode(nodetype.type);\n node2.pos = [e.canvasX, e.canvasY];\n this.graph.add(node2);\n node2.onDropFile?.(file);\n this.graph.afterChange();\n }\n processNodeDblClicked(n) {\n this.onShowNodePanel?.(n);\n this.onNodeDblClicked?.(n);\n this.setDirty(true);\n }\n processNodeSelected(node2, e) {\n this.selectNode(node2, e && (e.shiftKey || e.metaKey || e.ctrlKey || this.multi_select));\n this.onNodeSelected?.(node2);\n }\n /**\n * selects a given node (or adds it to the current selection)\n **/\n selectNode(node2, add_to_current_selection) {\n if (node2 == null) {\n this.deselectAllNodes();\n } else {\n this.selectNodes([node2], add_to_current_selection);\n }\n }\n /**\n * selects several nodes (or adds them to the current selection)\n **/\n selectNodes(nodes, add_to_current_selection) {\n if (!add_to_current_selection) {\n this.deselectAllNodes();\n }\n nodes = nodes || this.graph._nodes;\n if (typeof nodes == \"string\") nodes = [nodes];\n for (const i2 in nodes) {\n const node2 = nodes[i2];\n if (node2.is_selected) {\n this.deselectNode(node2);\n continue;\n }\n if (!node2.is_selected) {\n node2.onSelected?.();\n }\n node2.is_selected = true;\n this.selected_nodes[node2.id] = node2;\n if (node2.inputs) {\n for (let j = 0; j < node2.inputs.length; ++j) {\n this.highlighted_links[node2.inputs[j].link] = true;\n }\n }\n if (node2.outputs) {\n for (let j = 0; j < node2.outputs.length; ++j) {\n const out = node2.outputs[j];\n if (out.links) {\n for (let k = 0; k < out.links.length; ++k) {\n this.highlighted_links[out.links[k]] = true;\n }\n }\n }\n }\n }\n this.onSelectionChange?.(this.selected_nodes);\n this.setDirty(true);\n }\n /**\n * removes a node from the current selection\n **/\n deselectNode(node2) {\n if (!node2.is_selected) return;\n node2.onDeselected?.();\n node2.is_selected = false;\n delete this.selected_nodes[node2.id];\n this.onNodeDeselected?.(node2);\n if (node2.inputs) {\n for (let i2 = 0; i2 < node2.inputs.length; ++i2) {\n delete this.highlighted_links[node2.inputs[i2].link];\n }\n }\n if (node2.outputs) {\n for (let i2 = 0; i2 < node2.outputs.length; ++i2) {\n const out = node2.outputs[i2];\n if (out.links) {\n for (let j = 0; j < out.links.length; ++j) {\n delete this.highlighted_links[out.links[j]];\n }\n }\n }\n }\n }\n /**\n * removes all nodes from the current selection\n **/\n deselectAllNodes() {\n if (!this.graph) return;\n const nodes = this.graph._nodes;\n for (let i2 = 0, l = nodes.length; i2 < l; ++i2) {\n const node2 = nodes[i2];\n if (!node2.is_selected) {\n continue;\n }\n node2.onDeselected?.();\n node2.is_selected = false;\n this.onNodeDeselected?.(node2);\n }\n this.selected_nodes = {};\n this.current_node = null;\n this.highlighted_links = {};\n this.deselectGroups();\n this.onSelectionChange?.(this.selected_nodes);\n this.setDirty(true);\n }\n deselectGroups() {\n if (!this.selectedGroups) return;\n for (const group of this.selectedGroups) {\n delete group.selected;\n }\n this.selectedGroups = null;\n }\n /**\n * deletes all nodes in the current selection from the graph\n **/\n deleteSelectedNodes() {\n this.graph.beforeChange();\n for (const i2 in this.selected_nodes) {\n const node2 = this.selected_nodes[i2];\n if (node2.block_delete) continue;\n if (node2.inputs?.length && node2.outputs && node2.outputs.length && LiteGraph.isValidConnection(node2.inputs[0].type, node2.outputs[0].type) && node2.inputs[0].link && node2.outputs[0].links && node2.outputs[0].links.length) {\n const input_link = node2.graph.links[node2.inputs[0].link];\n const output_link = node2.graph.links[node2.outputs[0].links[0]];\n const input_node = node2.getInputNode(0);\n const output_node = node2.getOutputNodes(0)[0];\n if (input_node && output_node)\n input_node.connect(input_link.origin_slot, output_node, output_link.target_slot);\n }\n this.graph.remove(node2);\n this.onNodeDeselected?.(node2);\n }\n this.selected_nodes = {};\n this.current_node = null;\n this.highlighted_links = {};\n this.setDirty(true);\n this.graph.afterChange();\n }\n /**\n * centers the camera on a given node\n **/\n centerOnNode(node2) {\n const dpi = window?.devicePixelRatio || 1;\n this.ds.offset[0] = -node2.pos[0] - node2.size[0] * 0.5 + this.canvas.width * 0.5 / (this.ds.scale * dpi);\n this.ds.offset[1] = -node2.pos[1] - node2.size[1] * 0.5 + this.canvas.height * 0.5 / (this.ds.scale * dpi);\n this.setDirty(true, true);\n }\n /**\n * adds some useful properties to a mouse event, like the position in graph coordinates\n **/\n adjustMouseEvent(e) {\n let clientX_rel = e.clientX;\n let clientY_rel = e.clientY;\n if (this.canvas) {\n const b = this.canvas.getBoundingClientRect();\n clientX_rel -= b.left;\n clientY_rel -= b.top;\n }\n if (e.deltaX === void 0) e.deltaX = clientX_rel - this.last_mouse_position[0];\n if (e.deltaY === void 0) e.deltaY = clientY_rel - this.last_mouse_position[1];\n this.last_mouse_position[0] = clientX_rel;\n this.last_mouse_position[1] = clientY_rel;\n e.canvasX = clientX_rel / this.ds.scale - this.ds.offset[0];\n e.canvasY = clientY_rel / this.ds.scale - this.ds.offset[1];\n }\n /**\n * changes the zoom level of the graph (default is 1), you can pass also a place used to pivot the zoom\n **/\n setZoom(value, zooming_center) {\n this.ds.changeScale(value, zooming_center);\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n }\n /**\n * converts a coordinate from graph coordinates to canvas2D coordinates\n **/\n convertOffsetToCanvas(pos2, out) {\n return this.ds.convertOffsetToCanvas(pos2, out);\n }\n /**\n * converts a coordinate from Canvas2D coordinates to graph space\n **/\n convertCanvasToOffset(pos2, out) {\n return this.ds.convertCanvasToOffset(pos2, out);\n }\n //converts event coordinates from canvas2D to graph coordinates\n convertEventToCanvasOffset(e) {\n const rect = this.canvas.getBoundingClientRect();\n return this.convertCanvasToOffset([\n e.clientX - rect.left,\n e.clientY - rect.top\n ]);\n }\n /**\n * brings a node to front (above all other nodes)\n **/\n bringToFront(node2) {\n const i2 = this.graph._nodes.indexOf(node2);\n if (i2 == -1) return;\n this.graph._nodes.splice(i2, 1);\n this.graph._nodes.push(node2);\n }\n /**\n * sends a node to the back (below all other nodes)\n **/\n sendToBack(node2) {\n const i2 = this.graph._nodes.indexOf(node2);\n if (i2 == -1) return;\n this.graph._nodes.splice(i2, 1);\n this.graph._nodes.unshift(node2);\n }\n /* LGraphCanvas render */\n /**\n * Determines which nodes are visible and populates {@link out} with the results.\n * @param nodes The list of nodes to check - if falsy, all nodes in the graph will be checked\n * @param out Array to write visible nodes into - if falsy, a new array is created instead\n * @returns {LGraphNode[]} Array passed ({@link out}), or a new array containing all visible nodes\n */\n computeVisibleNodes(nodes, out) {\n const visible_nodes = out || [];\n visible_nodes.length = 0;\n nodes = nodes || this.graph._nodes;\n for (let i2 = 0, l = nodes.length; i2 < l; ++i2) {\n const n = nodes[i2];\n if (this.live_mode && !n.onDrawBackground && !n.onDrawForeground) {\n continue;\n }\n if (!overlapBounding(this.visible_area, n.getBounding(LGraphCanvas.#temp, true))) {\n continue;\n }\n visible_nodes.push(n);\n }\n return visible_nodes;\n }\n /**\n * renders the whole canvas content, by rendering in two separated canvas, one containing the background grid and the connections, and one containing the nodes)\n **/\n draw(force_canvas, force_bgcanvas) {\n if (!this.canvas || this.canvas.width == 0 || this.canvas.height == 0) return;\n const now = LiteGraph.getTime();\n this.render_time = (now - this.last_draw_time) * 1e-3;\n this.last_draw_time = now;\n if (this.graph) {\n this.ds.computeVisibleArea(this.viewport);\n }\n if (this.dirty_bgcanvas || force_bgcanvas || this.always_render_background || this.graph && this.graph._last_trigger_time && now - this.graph._last_trigger_time < 1e3) {\n this.drawBackCanvas();\n }\n if (this.dirty_canvas || force_canvas) {\n this.drawFrontCanvas();\n }\n this.fps = this.render_time ? 1 / this.render_time : 0;\n this.frame += 1;\n }\n /**\n * draws the front canvas (the one containing all the nodes)\n **/\n drawFrontCanvas() {\n this.dirty_canvas = false;\n if (!this.ctx) {\n this.ctx = this.bgcanvas.getContext(\"2d\");\n }\n const ctx = this.ctx;\n if (!ctx) return;\n const canvas = this.canvas;\n if (ctx.start2D && !this.viewport) {\n ctx.start2D();\n ctx.restore();\n ctx.setTransform(1, 0, 0, 1, 0, 0);\n }\n const area = this.viewport || this.dirty_area;\n if (area) {\n ctx.save();\n ctx.beginPath();\n ctx.rect(area[0], area[1], area[2], area[3]);\n ctx.clip();\n }\n if (this.clear_background) {\n if (area)\n ctx.clearRect(area[0], area[1], area[2], area[3]);\n else\n ctx.clearRect(0, 0, canvas.width, canvas.height);\n }\n if (this.bgcanvas == this.canvas) {\n this.drawBackCanvas();\n } else {\n const scale = window.devicePixelRatio;\n ctx.drawImage(this.bgcanvas, 0, 0, this.bgcanvas.width / scale, this.bgcanvas.height / scale);\n }\n this.onRender?.(canvas, ctx);\n if (this.show_info) {\n this.renderInfo(ctx, area ? area[0] : 0, area ? area[1] : 0);\n }\n if (this.graph) {\n ctx.save();\n this.ds.toCanvasContext(ctx);\n const visible_nodes = this.computeVisibleNodes(\n null,\n this.visible_nodes\n );\n for (let i2 = 0; i2 < visible_nodes.length; ++i2) {\n const node2 = visible_nodes[i2];\n ctx.save();\n ctx.translate(node2.pos[0], node2.pos[1]);\n this.drawNode(node2, ctx);\n ctx.restore();\n }\n if (this.render_execution_order) {\n this.drawExecutionOrder(ctx);\n }\n if (this.graph.config.links_ontop) {\n if (!this.live_mode) {\n this.drawConnections(ctx);\n }\n }\n if (this.connecting_links) {\n for (const link of this.connecting_links) {\n ctx.lineWidth = this.connections_width;\n let link_color = null;\n const connInOrOut = link.output || link.input;\n const connType = connInOrOut.type;\n let connDir = connInOrOut.dir;\n if (connDir == null) {\n if (link.output)\n connDir = link.node.horizontal ? LiteGraph.DOWN : LiteGraph.RIGHT;\n else\n connDir = link.node.horizontal ? LiteGraph.UP : LiteGraph.LEFT;\n }\n const connShape = connInOrOut.shape;\n switch (connType) {\n case LiteGraph.EVENT:\n link_color = LiteGraph.EVENT_LINK_COLOR;\n break;\n default:\n link_color = LiteGraph.CONNECTING_LINK_COLOR;\n }\n this.renderLink(\n ctx,\n link.pos,\n [this.graph_mouse[0], this.graph_mouse[1]],\n null,\n false,\n null,\n link_color,\n connDir,\n link.direction ?? LinkDirection.CENTER\n );\n ctx.beginPath();\n if (connType === LiteGraph.EVENT || connShape === LiteGraph.BOX_SHAPE) {\n ctx.rect(\n link.pos[0] - 6 + 0.5,\n link.pos[1] - 5 + 0.5,\n 14,\n 10\n );\n ctx.fill();\n ctx.beginPath();\n ctx.rect(\n this.graph_mouse[0] - 6 + 0.5,\n this.graph_mouse[1] - 5 + 0.5,\n 14,\n 10\n );\n } else if (connShape === LiteGraph.ARROW_SHAPE) {\n ctx.moveTo(link.pos[0] + 8, link.pos[1] + 0.5);\n ctx.lineTo(link.pos[0] - 4, link.pos[1] + 6 + 0.5);\n ctx.lineTo(link.pos[0] - 4, link.pos[1] - 6 + 0.5);\n ctx.closePath();\n } else {\n ctx.arc(\n link.pos[0],\n link.pos[1],\n 4,\n 0,\n Math.PI * 2\n );\n ctx.fill();\n ctx.beginPath();\n ctx.arc(\n this.graph_mouse[0],\n this.graph_mouse[1],\n 4,\n 0,\n Math.PI * 2\n );\n }\n ctx.fill();\n ctx.fillStyle = \"#ffcc00\";\n if (this._highlight_input) {\n ctx.beginPath();\n if (this._highlight_input_slot?.shape === LiteGraph.ARROW_SHAPE) {\n ctx.moveTo(this._highlight_input[0] + 8, this._highlight_input[1] + 0.5);\n ctx.lineTo(this._highlight_input[0] - 4, this._highlight_input[1] + 6 + 0.5);\n ctx.lineTo(this._highlight_input[0] - 4, this._highlight_input[1] - 6 + 0.5);\n ctx.closePath();\n } else {\n ctx.arc(\n this._highlight_input[0],\n this._highlight_input[1],\n 6,\n 0,\n Math.PI * 2\n );\n }\n ctx.fill();\n }\n if (this._highlight_output) {\n ctx.beginPath();\n if (this._highlight_input_slot?.shape === LiteGraph.ARROW_SHAPE) {\n ctx.moveTo(this._highlight_output[0] + 8, this._highlight_output[1] + 0.5);\n ctx.lineTo(this._highlight_output[0] - 4, this._highlight_output[1] + 6 + 0.5);\n ctx.lineTo(this._highlight_output[0] - 4, this._highlight_output[1] - 6 + 0.5);\n ctx.closePath();\n } else {\n ctx.arc(\n this._highlight_output[0],\n this._highlight_output[1],\n 6,\n 0,\n Math.PI * 2\n );\n }\n ctx.fill();\n }\n }\n }\n if (this.dragging_rectangle) {\n ctx.strokeStyle = \"#FFF\";\n ctx.strokeRect(\n this.dragging_rectangle[0],\n this.dragging_rectangle[1],\n this.dragging_rectangle[2],\n this.dragging_rectangle[3]\n );\n }\n if (this.over_link_center && this.render_link_tooltip)\n this.drawLinkTooltip(ctx, this.over_link_center);\n else\n this.onDrawLinkTooltip?.(ctx, null);\n this.onDrawForeground?.(ctx, this.visible_rect);\n ctx.restore();\n }\n if (this._graph_stack?.length) {\n this.drawSubgraphPanel(ctx);\n }\n this.onDrawOverlay?.(ctx);\n if (area) ctx.restore();\n if (ctx.finish2D) ctx.finish2D();\n }\n /**\n * draws the panel in the corner that shows subgraph properties\n **/\n drawSubgraphPanel(ctx) {\n const subgraph = this.graph;\n const subnode = subgraph._subgraph_node;\n if (!subnode) {\n console.warn(\"subgraph without subnode\");\n return;\n }\n this.drawSubgraphPanelLeft(subgraph, subnode, ctx);\n this.drawSubgraphPanelRight(subgraph, subnode, ctx);\n }\n drawSubgraphPanelLeft(subgraph, subnode, ctx) {\n const num = subnode.inputs ? subnode.inputs.length : 0;\n const w2 = 200;\n const h = Math.floor(LiteGraph.NODE_SLOT_HEIGHT * 1.6);\n ctx.fillStyle = \"#111\";\n ctx.globalAlpha = 0.8;\n ctx.beginPath();\n ctx.roundRect(10, 10, w2, (num + 1) * h + 50, [8]);\n ctx.fill();\n ctx.globalAlpha = 1;\n ctx.fillStyle = \"#888\";\n ctx.font = \"14px Arial\";\n ctx.textAlign = \"left\";\n ctx.fillText(\"Graph Inputs\", 20, 34);\n if (this.drawButton(w2 - 20, 20, 20, 20, \"X\", \"#151515\")) {\n this.closeSubgraph();\n return;\n }\n let y2 = 50;\n ctx.font = \"14px Arial\";\n if (subnode.inputs)\n for (let i2 = 0; i2 < subnode.inputs.length; ++i2) {\n const input = subnode.inputs[i2];\n if (input.not_subgraph_input) continue;\n if (this.drawButton(20, y2 + 2, w2 - 20, h - 2)) {\n const type = subnode.constructor.input_node_type || \"graph/input\";\n this.graph.beforeChange();\n const newnode = LiteGraph.createNode(type);\n if (newnode) {\n subgraph.add(newnode);\n this.block_click = false;\n this.last_click_position = null;\n this.selectNodes([newnode]);\n this.node_dragged = newnode;\n this.dragging_canvas = false;\n newnode.setProperty(\"name\", input.name);\n newnode.setProperty(\"type\", input.type);\n this.node_dragged.pos[0] = this.graph_mouse[0] - 5;\n this.node_dragged.pos[1] = this.graph_mouse[1] - 5;\n this.graph.afterChange();\n } else\n console.error(\"graph input node not found:\", type);\n }\n ctx.fillStyle = \"#9C9\";\n ctx.beginPath();\n ctx.arc(w2 - 16, y2 + h * 0.5, 5, 0, 2 * Math.PI);\n ctx.fill();\n ctx.fillStyle = \"#AAA\";\n ctx.fillText(input.name, 30, y2 + h * 0.75);\n ctx.fillStyle = \"#777\";\n ctx.fillText(input.type, 130, y2 + h * 0.75);\n y2 += h;\n }\n if (this.drawButton(20, y2 + 2, w2 - 20, h - 2, \"+\", \"#151515\", \"#222\")) {\n this.showSubgraphPropertiesDialog(subnode);\n }\n }\n drawSubgraphPanelRight(subgraph, subnode, ctx) {\n const num = subnode.outputs ? subnode.outputs.length : 0;\n const canvas_w = this.bgcanvas.width;\n const w2 = 200;\n const h = Math.floor(LiteGraph.NODE_SLOT_HEIGHT * 1.6);\n ctx.fillStyle = \"#111\";\n ctx.globalAlpha = 0.8;\n ctx.beginPath();\n ctx.roundRect(canvas_w - w2 - 10, 10, w2, (num + 1) * h + 50, [8]);\n ctx.fill();\n ctx.globalAlpha = 1;\n ctx.fillStyle = \"#888\";\n ctx.font = \"14px Arial\";\n ctx.textAlign = \"left\";\n const title_text = \"Graph Outputs\";\n const tw = ctx.measureText(title_text).width;\n ctx.fillText(title_text, canvas_w - tw - 20, 34);\n if (this.drawButton(canvas_w - w2, 20, 20, 20, \"X\", \"#151515\")) {\n this.closeSubgraph();\n return;\n }\n let y2 = 50;\n ctx.font = \"14px Arial\";\n if (subnode.outputs)\n for (let i2 = 0; i2 < subnode.outputs.length; ++i2) {\n const output = subnode.outputs[i2];\n if (output.not_subgraph_input) continue;\n if (this.drawButton(canvas_w - w2, y2 + 2, w2 - 20, h - 2)) {\n const type = subnode.constructor.output_node_type || \"graph/output\";\n this.graph.beforeChange();\n const newnode = LiteGraph.createNode(type);\n if (newnode) {\n subgraph.add(newnode);\n this.block_click = false;\n this.last_click_position = null;\n this.selectNodes([newnode]);\n this.node_dragged = newnode;\n this.dragging_canvas = false;\n newnode.setProperty(\"name\", output.name);\n newnode.setProperty(\"type\", output.type);\n this.node_dragged.pos[0] = this.graph_mouse[0] - 5;\n this.node_dragged.pos[1] = this.graph_mouse[1] - 5;\n this.graph.afterChange();\n } else\n console.error(\"graph input node not found:\", type);\n }\n ctx.fillStyle = \"#9C9\";\n ctx.beginPath();\n ctx.arc(canvas_w - w2 + 16, y2 + h * 0.5, 5, 0, 2 * Math.PI);\n ctx.fill();\n ctx.fillStyle = \"#AAA\";\n ctx.fillText(output.name, canvas_w - w2 + 30, y2 + h * 0.75);\n ctx.fillStyle = \"#777\";\n ctx.fillText(output.type, canvas_w - w2 + 130, y2 + h * 0.75);\n y2 += h;\n }\n if (this.drawButton(canvas_w - w2, y2 + 2, w2 - 20, h - 2, \"+\", \"#151515\", \"#222\")) {\n this.showSubgraphPropertiesDialogRight(subnode);\n }\n }\n //Draws a button into the canvas overlay and computes if it was clicked using the immediate gui paradigm\n drawButton(x2, y2, w2, h, text, bgcolor, hovercolor, textcolor) {\n const ctx = this.ctx;\n bgcolor = bgcolor || LiteGraph.NODE_DEFAULT_COLOR;\n hovercolor = hovercolor || \"#555\";\n textcolor = textcolor || LiteGraph.NODE_TEXT_COLOR;\n let pos2 = this.ds.convertOffsetToCanvas(this.graph_mouse);\n const hover = LiteGraph.isInsideRectangle(pos2[0], pos2[1], x2, y2, w2, h);\n pos2 = this.last_click_position ? [this.last_click_position[0], this.last_click_position[1]] : null;\n if (pos2) {\n const rect = this.canvas.getBoundingClientRect();\n pos2[0] -= rect.left;\n pos2[1] -= rect.top;\n }\n const clicked = pos2 && LiteGraph.isInsideRectangle(pos2[0], pos2[1], x2, y2, w2, h);\n ctx.fillStyle = hover ? hovercolor : bgcolor;\n if (clicked) ctx.fillStyle = \"#AAA\";\n ctx.beginPath();\n ctx.roundRect(x2, y2, w2, h, [4]);\n ctx.fill();\n if (text != null) {\n if (text.constructor == String) {\n ctx.fillStyle = textcolor;\n ctx.textAlign = \"center\";\n ctx.font = (h * 0.65 | 0) + \"px Arial\";\n ctx.fillText(text, x2 + w2 * 0.5, y2 + h * 0.75);\n ctx.textAlign = \"left\";\n }\n }\n const was_clicked = clicked && !this.block_click;\n if (clicked) this.blockClick();\n return was_clicked;\n }\n isAreaClicked(x2, y2, w2, h, hold_click) {\n const clickPos = this.last_click_position;\n const clicked = clickPos && LiteGraph.isInsideRectangle(clickPos[0], clickPos[1], x2, y2, w2, h);\n const was_clicked = clicked && !this.block_click;\n if (clicked && hold_click) this.blockClick();\n return was_clicked;\n }\n /**\n * draws some useful stats in the corner of the canvas\n **/\n renderInfo(ctx, x2, y2) {\n x2 = x2 || 10;\n y2 = y2 || this.canvas.offsetHeight - 80;\n ctx.save();\n ctx.translate(x2, y2);\n ctx.font = \"10px Arial\";\n ctx.fillStyle = \"#888\";\n ctx.textAlign = \"left\";\n if (this.graph) {\n ctx.fillText(\"T: \" + this.graph.globaltime.toFixed(2) + \"s\", 5, 13 * 1);\n ctx.fillText(\"I: \" + this.graph.iteration, 5, 13 * 2);\n ctx.fillText(\"N: \" + this.graph._nodes.length + \" [\" + this.visible_nodes.length + \"]\", 5, 13 * 3);\n ctx.fillText(\"V: \" + this.graph._version, 5, 13 * 4);\n ctx.fillText(\"FPS:\" + this.fps.toFixed(2), 5, 13 * 5);\n } else {\n ctx.fillText(\"No graph selected\", 5, 13 * 1);\n }\n ctx.restore();\n }\n /**\n * draws the back canvas (the one containing the background and the connections)\n **/\n drawBackCanvas() {\n const canvas = this.bgcanvas;\n if (canvas.width != this.canvas.width || canvas.height != this.canvas.height) {\n canvas.width = this.canvas.width;\n canvas.height = this.canvas.height;\n }\n if (!this.bgctx) {\n this.bgctx = this.bgcanvas.getContext(\"2d\");\n }\n const ctx = this.bgctx;\n if (ctx.start) ctx.start();\n const viewport = this.viewport || [0, 0, ctx.canvas.width, ctx.canvas.height];\n if (this.clear_background) {\n ctx.clearRect(viewport[0], viewport[1], viewport[2], viewport[3]);\n }\n if (this._graph_stack?.length) {\n ctx.save();\n const subgraph_node = this.graph._subgraph_node;\n ctx.strokeStyle = subgraph_node.bgcolor;\n ctx.lineWidth = 10;\n ctx.strokeRect(1, 1, canvas.width - 2, canvas.height - 2);\n ctx.lineWidth = 1;\n ctx.font = \"40px Arial\";\n ctx.textAlign = \"center\";\n ctx.fillStyle = subgraph_node.bgcolor || \"#AAA\";\n let title = \"\";\n for (let i2 = 1; i2 < this._graph_stack.length; ++i2) {\n title += this._graph_stack[i2]._subgraph_node.getTitle() + \" >> \";\n }\n ctx.fillText(\n title + subgraph_node.getTitle(),\n canvas.width * 0.5,\n 40\n );\n ctx.restore();\n }\n const bg_already_painted = this.onRenderBackground ? this.onRenderBackground(canvas, ctx) : false;\n if (!this.viewport) {\n const scale = window.devicePixelRatio;\n ctx.restore();\n ctx.setTransform(scale, 0, 0, scale, 0, 0);\n }\n this.visible_links.length = 0;\n if (this.graph) {\n ctx.save();\n this.ds.toCanvasContext(ctx);\n if (this.ds.scale < 1.5 && !bg_already_painted && this.clear_background_color) {\n ctx.fillStyle = this.clear_background_color;\n ctx.fillRect(\n this.visible_area[0],\n this.visible_area[1],\n this.visible_area[2],\n this.visible_area[3]\n );\n }\n if (this.background_image && this.ds.scale > 0.5 && !bg_already_painted) {\n if (this.zoom_modify_alpha) {\n ctx.globalAlpha = (1 - 0.5 / this.ds.scale) * this.editor_alpha;\n } else {\n ctx.globalAlpha = this.editor_alpha;\n }\n ctx.imageSmoothingEnabled = false;\n if (!this._bg_img || this._bg_img.name != this.background_image) {\n this._bg_img = new Image();\n this._bg_img.name = this.background_image;\n this._bg_img.src = this.background_image;\n const that2 = this;\n this._bg_img.onload = function() {\n that2.draw(true, true);\n };\n }\n let pattern = this._pattern;\n if (pattern == null && this._bg_img.width > 0) {\n pattern = ctx.createPattern(this._bg_img, \"repeat\");\n this._pattern_img = this._bg_img;\n this._pattern = pattern;\n }\n if (pattern) {\n ctx.fillStyle = pattern;\n ctx.fillRect(\n this.visible_area[0],\n this.visible_area[1],\n this.visible_area[2],\n this.visible_area[3]\n );\n ctx.fillStyle = \"transparent\";\n }\n ctx.globalAlpha = 1;\n ctx.imageSmoothingEnabled = true;\n }\n if (this.graph._groups.length && !this.live_mode) {\n this.drawGroups(canvas, ctx);\n }\n this.onDrawBackground?.(ctx, this.visible_area);\n if (this.render_canvas_border) {\n ctx.strokeStyle = \"#235\";\n ctx.strokeRect(0, 0, canvas.width, canvas.height);\n }\n if (this.render_connections_shadows) {\n ctx.shadowColor = \"#000\";\n ctx.shadowOffsetX = 0;\n ctx.shadowOffsetY = 0;\n ctx.shadowBlur = 6;\n } else {\n ctx.shadowColor = \"rgba(0,0,0,0)\";\n }\n if (!this.live_mode) {\n this.drawConnections(ctx);\n }\n ctx.shadowColor = \"rgba(0,0,0,0)\";\n ctx.restore();\n }\n ctx.finish?.();\n this.dirty_bgcanvas = false;\n this.dirty_canvas = true;\n }\n /**\n * draws the given node inside the canvas\n **/\n drawNode(node2, ctx) {\n this.current_node = node2;\n const color = node2.color || node2.constructor.color || LiteGraph.NODE_DEFAULT_COLOR;\n let bgcolor = node2.bgcolor || node2.constructor.bgcolor || LiteGraph.NODE_DEFAULT_BGCOLOR;\n const low_quality = this.ds.scale < 0.6;\n if (this.live_mode) {\n if (!node2.flags.collapsed) {\n ctx.shadowColor = \"transparent\";\n node2.onDrawForeground?.(ctx, this, this.canvas);\n }\n return;\n }\n const editor_alpha = this.editor_alpha;\n ctx.globalAlpha = editor_alpha;\n if (this.render_shadows && !low_quality) {\n ctx.shadowColor = LiteGraph.DEFAULT_SHADOW_COLOR;\n ctx.shadowOffsetX = 2 * this.ds.scale;\n ctx.shadowOffsetY = 2 * this.ds.scale;\n ctx.shadowBlur = 3 * this.ds.scale;\n } else {\n ctx.shadowColor = \"transparent\";\n }\n if (node2.flags.collapsed && node2.onDrawCollapsed?.(ctx, this) == true)\n return;\n const shape = node2._shape || LiteGraph.BOX_SHAPE;\n const size = LGraphCanvas.#temp_vec2;\n LGraphCanvas.#temp_vec2.set(node2.size);\n const horizontal = node2.horizontal;\n if (node2.flags.collapsed) {\n ctx.font = this.inner_text_font;\n const title = node2.getTitle ? node2.getTitle() : node2.title;\n if (title != null) {\n node2._collapsed_width = Math.min(\n node2.size[0],\n ctx.measureText(title).width + LiteGraph.NODE_TITLE_HEIGHT * 2\n );\n size[0] = node2._collapsed_width;\n size[1] = 0;\n }\n }\n if (node2.clip_area) {\n ctx.save();\n ctx.beginPath();\n if (shape == LiteGraph.BOX_SHAPE) {\n ctx.rect(0, 0, size[0], size[1]);\n } else if (shape == LiteGraph.ROUND_SHAPE) {\n ctx.roundRect(0, 0, size[0], size[1], [10]);\n } else if (shape == LiteGraph.CIRCLE_SHAPE) {\n ctx.arc(\n size[0] * 0.5,\n size[1] * 0.5,\n size[0] * 0.5,\n 0,\n Math.PI * 2\n );\n }\n ctx.clip();\n }\n if (node2.has_errors) {\n bgcolor = \"red\";\n }\n this.drawNodeShape(\n node2,\n ctx,\n size,\n color,\n bgcolor,\n node2.is_selected\n );\n if (!low_quality) {\n node2.drawBadges(ctx);\n }\n ctx.shadowColor = \"transparent\";\n node2.onDrawForeground?.(ctx, this, this.canvas);\n ctx.textAlign = horizontal ? \"center\" : \"left\";\n ctx.font = this.inner_text_font;\n const render_text = !low_quality;\n const highlightColour = LiteGraph.NODE_TEXT_HIGHLIGHT_COLOR ?? LiteGraph.NODE_SELECTED_TITLE_COLOR ?? LiteGraph.NODE_TEXT_COLOR;\n const out_slot = this.connecting_links ? this.connecting_links[0].output : null;\n const in_slot = this.connecting_links ? this.connecting_links[0].input : null;\n ctx.lineWidth = 1;\n let max_y = 0;\n const slot_pos = new Float32Array(2);\n if (!node2.flags.collapsed) {\n if (node2.inputs) {\n for (let i2 = 0; i2 < node2.inputs.length; i2++) {\n const slot = node2.inputs[i2];\n const slot_type = slot.type;\n const isValid = !this.connecting_links || out_slot && LiteGraph.isValidConnection(slot.type, out_slot.type);\n const highlight = isValid && node2.mouseOver?.inputId === i2;\n const label_color = highlight ? highlightColour : LiteGraph.NODE_TEXT_COLOR;\n ctx.globalAlpha = isValid ? editor_alpha : 0.4 * editor_alpha;\n ctx.fillStyle = slot.link != null ? slot.color_on || this.default_connection_color_byType[slot_type] || this.default_connection_color.input_on : slot.color_off || this.default_connection_color_byTypeOff[slot_type] || this.default_connection_color_byType[slot_type] || this.default_connection_color.input_off;\n const pos2 = node2.getConnectionPos(true, i2, slot_pos);\n pos2[0] -= node2.pos[0];\n pos2[1] -= node2.pos[1];\n if (max_y < pos2[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5) {\n max_y = pos2[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5;\n }\n drawSlot(ctx, slot, pos2, {\n horizontal,\n low_quality,\n render_text,\n label_color,\n label_position: LabelPosition.Right,\n // Input slot is not stroked.\n do_stroke: false,\n highlight\n });\n }\n }\n ctx.textAlign = horizontal ? \"center\" : \"right\";\n ctx.strokeStyle = \"black\";\n if (node2.outputs) {\n for (let i2 = 0; i2 < node2.outputs.length; i2++) {\n const slot = node2.outputs[i2];\n const slot_type = slot.type;\n const isValid = !this.connecting_links || in_slot && LiteGraph.isValidConnection(slot_type, in_slot.type);\n const highlight = isValid && node2.mouseOver?.outputId === i2;\n const label_color = highlight ? highlightColour : LiteGraph.NODE_TEXT_COLOR;\n ctx.globalAlpha = isValid ? editor_alpha : 0.4 * editor_alpha;\n const pos2 = node2.getConnectionPos(false, i2, slot_pos);\n pos2[0] -= node2.pos[0];\n pos2[1] -= node2.pos[1];\n if (max_y < pos2[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5) {\n max_y = pos2[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5;\n }\n ctx.fillStyle = slot.links && slot.links.length ? slot.color_on || this.default_connection_color_byType[slot_type] || this.default_connection_color.output_on : slot.color_off || this.default_connection_color_byTypeOff[slot_type] || this.default_connection_color_byType[slot_type] || this.default_connection_color.output_off;\n drawSlot(ctx, slot, pos2, {\n horizontal,\n low_quality,\n render_text,\n label_color,\n label_position: LabelPosition.Left,\n do_stroke: true,\n highlight\n });\n }\n }\n ctx.textAlign = \"left\";\n ctx.globalAlpha = 1;\n if (node2.widgets) {\n let widgets_y = max_y;\n if (horizontal || node2.widgets_up) {\n widgets_y = 2;\n }\n if (node2.widgets_start_y != null)\n widgets_y = node2.widgets_start_y;\n this.drawNodeWidgets(\n node2,\n widgets_y,\n ctx,\n this.node_widget && this.node_widget[0] == node2 ? this.node_widget[1] : null\n );\n }\n } else if (this.render_collapsed_slots) {\n let input_slot = null;\n let output_slot = null;\n let slot;\n if (node2.inputs) {\n for (let i2 = 0; i2 < node2.inputs.length; i2++) {\n slot = node2.inputs[i2];\n if (slot.link == null) {\n continue;\n }\n input_slot = slot;\n break;\n }\n }\n if (node2.outputs) {\n for (let i2 = 0; i2 < node2.outputs.length; i2++) {\n slot = node2.outputs[i2];\n if (!slot.links || !slot.links.length) {\n continue;\n }\n output_slot = slot;\n }\n }\n if (input_slot) {\n let x2 = 0;\n let y2 = LiteGraph.NODE_TITLE_HEIGHT * -0.5;\n if (horizontal) {\n x2 = node2._collapsed_width * 0.5;\n y2 = -LiteGraph.NODE_TITLE_HEIGHT;\n }\n ctx.fillStyle = \"#686\";\n ctx.beginPath();\n if (slot.type === LiteGraph.EVENT || slot.shape === LiteGraph.BOX_SHAPE) {\n ctx.rect(x2 - 7 + 0.5, y2 - 4, 14, 8);\n } else if (slot.shape === LiteGraph.ARROW_SHAPE) {\n ctx.moveTo(x2 + 8, y2);\n ctx.lineTo(x2 + -4, y2 - 4);\n ctx.lineTo(x2 + -4, y2 + 4);\n ctx.closePath();\n } else {\n ctx.arc(x2, y2, 4, 0, Math.PI * 2);\n }\n ctx.fill();\n }\n if (output_slot) {\n let x2 = node2._collapsed_width;\n let y2 = LiteGraph.NODE_TITLE_HEIGHT * -0.5;\n if (horizontal) {\n x2 = node2._collapsed_width * 0.5;\n y2 = 0;\n }\n ctx.fillStyle = \"#686\";\n ctx.strokeStyle = \"black\";\n ctx.beginPath();\n if (slot.type === LiteGraph.EVENT || slot.shape === LiteGraph.BOX_SHAPE) {\n ctx.rect(x2 - 7 + 0.5, y2 - 4, 14, 8);\n } else if (slot.shape === LiteGraph.ARROW_SHAPE) {\n ctx.moveTo(x2 + 6, y2);\n ctx.lineTo(x2 - 6, y2 - 4);\n ctx.lineTo(x2 - 6, y2 + 4);\n ctx.closePath();\n } else {\n ctx.arc(x2, y2, 4, 0, Math.PI * 2);\n }\n ctx.fill();\n }\n }\n if (node2.clip_area) {\n ctx.restore();\n }\n ctx.globalAlpha = 1;\n }\n //used by this.over_link_center\n drawLinkTooltip(ctx, link) {\n const pos2 = link._pos;\n ctx.fillStyle = \"black\";\n ctx.beginPath();\n ctx.arc(pos2[0], pos2[1], 3, 0, Math.PI * 2);\n ctx.fill();\n if (link.data == null)\n return;\n if (this.onDrawLinkTooltip?.(ctx, link, this) == true)\n return;\n const data = link.data;\n let text = null;\n if (typeof data === \"number\")\n text = data.toFixed(2);\n else if (typeof data === \"string\")\n text = '\"' + data + '\"';\n else if (typeof data === \"boolean\")\n text = String(data);\n else if (data.toToolTip)\n text = data.toToolTip();\n else\n text = \"[\" + data.constructor.name + \"]\";\n if (text == null) return;\n text = text.substring(0, 30);\n ctx.font = \"14px Courier New\";\n const info = ctx.measureText(text);\n const w2 = info.width + 20;\n const h = 24;\n ctx.shadowColor = \"black\";\n ctx.shadowOffsetX = 2;\n ctx.shadowOffsetY = 2;\n ctx.shadowBlur = 3;\n ctx.fillStyle = \"#454\";\n ctx.beginPath();\n ctx.roundRect(pos2[0] - w2 * 0.5, pos2[1] - 15 - h, w2, h, [3]);\n ctx.moveTo(pos2[0] - 10, pos2[1] - 15);\n ctx.lineTo(pos2[0] + 10, pos2[1] - 15);\n ctx.lineTo(pos2[0], pos2[1] - 5);\n ctx.fill();\n ctx.shadowColor = \"transparent\";\n ctx.textAlign = \"center\";\n ctx.fillStyle = \"#CEC\";\n ctx.fillText(text, pos2[0], pos2[1] - 15 - h * 0.3);\n }\n /**\n * Draws the shape of the given node on the canvas\n * @param node The node to draw\n * @param ctx 2D canvas rendering context used to draw\n * @param size Size of the background to draw, in graph units. Differs from node size if collapsed, etc.\n * @param fgcolor Foreground colour - used for text\n * @param bgcolor Background colour of the node\n * @param selected Whether to render the node as selected. Likely to be removed in future, as current usage is simply the is_selected property of the node.\n * @param mouse_over Deprecated\n */\n drawNodeShape(node2, ctx, size, fgcolor, bgcolor, selected) {\n ctx.strokeStyle = fgcolor;\n ctx.fillStyle = bgcolor;\n const title_height = LiteGraph.NODE_TITLE_HEIGHT;\n const low_quality = this.ds.scale < 0.5;\n const shape = node2._shape || node2.constructor.shape || LiteGraph.ROUND_SHAPE;\n const title_mode = node2.constructor.title_mode;\n const render_title = title_mode == LiteGraph.TRANSPARENT_TITLE || title_mode == LiteGraph.NO_TITLE ? false : true;\n const area = LGraphCanvas.#tmp_area;\n area[0] = 0;\n area[1] = render_title ? -title_height : 0;\n area[2] = size[0] + 1;\n area[3] = render_title ? size[1] + title_height : size[1];\n const old_alpha = ctx.globalAlpha;\n {\n ctx.beginPath();\n if (shape == LiteGraph.BOX_SHAPE || low_quality) {\n ctx.fillRect(area[0], area[1], area[2], area[3]);\n } else if (shape == LiteGraph.ROUND_SHAPE || shape == LiteGraph.CARD_SHAPE) {\n ctx.roundRect(\n area[0],\n area[1],\n area[2],\n area[3],\n shape == LiteGraph.CARD_SHAPE ? [this.round_radius, this.round_radius, 0, 0] : [this.round_radius]\n );\n } else if (shape == LiteGraph.CIRCLE_SHAPE) {\n ctx.arc(\n size[0] * 0.5,\n size[1] * 0.5,\n size[0] * 0.5,\n 0,\n Math.PI * 2\n );\n }\n ctx.fill();\n if (!node2.flags.collapsed && render_title) {\n ctx.shadowColor = \"transparent\";\n ctx.fillStyle = \"rgba(0,0,0,0.2)\";\n ctx.fillRect(0, -1, area[2], 2);\n }\n }\n ctx.shadowColor = \"transparent\";\n node2.onDrawBackground?.(ctx, this, this.canvas, this.graph_mouse);\n if (render_title || title_mode == LiteGraph.TRANSPARENT_TITLE) {\n if (node2.onDrawTitleBar) {\n node2.onDrawTitleBar(ctx, title_height, size, this.ds.scale, fgcolor);\n } else if (title_mode != LiteGraph.TRANSPARENT_TITLE && (node2.constructor.title_color || this.render_title_colored)) {\n const title_color = node2.constructor.title_color || fgcolor;\n if (node2.flags.collapsed) {\n ctx.shadowColor = LiteGraph.DEFAULT_SHADOW_COLOR;\n }\n if (this.use_gradients) {\n let grad = LGraphCanvas.gradients[title_color];\n if (!grad) {\n grad = LGraphCanvas.gradients[title_color] = ctx.createLinearGradient(0, 0, 400, 0);\n grad.addColorStop(0, title_color);\n grad.addColorStop(1, \"#000\");\n }\n ctx.fillStyle = grad;\n } else {\n ctx.fillStyle = title_color;\n }\n ctx.beginPath();\n if (shape == LiteGraph.BOX_SHAPE || low_quality) {\n ctx.rect(0, -title_height, size[0] + 1, title_height);\n } else if (shape == LiteGraph.ROUND_SHAPE || shape == LiteGraph.CARD_SHAPE) {\n ctx.roundRect(\n 0,\n -title_height,\n size[0] + 1,\n title_height,\n node2.flags.collapsed ? [this.round_radius] : [this.round_radius, this.round_radius, 0, 0]\n );\n }\n ctx.fill();\n ctx.shadowColor = \"transparent\";\n }\n let colState = false;\n if (LiteGraph.node_box_coloured_by_mode) {\n if (LiteGraph.NODE_MODES_COLORS[node2.mode]) {\n colState = LiteGraph.NODE_MODES_COLORS[node2.mode];\n }\n }\n if (LiteGraph.node_box_coloured_when_on) {\n colState = node2.action_triggered ? \"#FFF\" : node2.execute_triggered ? \"#AAA\" : colState;\n }\n const box_size = 10;\n if (node2.onDrawTitleBox) {\n node2.onDrawTitleBox(ctx, title_height, size, this.ds.scale);\n } else if (shape == LiteGraph.ROUND_SHAPE || shape == LiteGraph.CIRCLE_SHAPE || shape == LiteGraph.CARD_SHAPE) {\n if (low_quality) {\n ctx.fillStyle = \"black\";\n ctx.beginPath();\n ctx.arc(\n title_height * 0.5,\n title_height * -0.5,\n box_size * 0.5 + 1,\n 0,\n Math.PI * 2\n );\n ctx.fill();\n }\n ctx.fillStyle = node2.boxcolor || colState || LiteGraph.NODE_DEFAULT_BOXCOLOR;\n if (low_quality)\n ctx.fillRect(title_height * 0.5 - box_size * 0.5, title_height * -0.5 - box_size * 0.5, box_size, box_size);\n else {\n ctx.beginPath();\n ctx.arc(\n title_height * 0.5,\n title_height * -0.5,\n box_size * 0.5,\n 0,\n Math.PI * 2\n );\n ctx.fill();\n }\n } else {\n if (low_quality) {\n ctx.fillStyle = \"black\";\n ctx.fillRect(\n (title_height - box_size) * 0.5 - 1,\n (title_height + box_size) * -0.5 - 1,\n box_size + 2,\n box_size + 2\n );\n }\n ctx.fillStyle = node2.boxcolor || colState || LiteGraph.NODE_DEFAULT_BOXCOLOR;\n ctx.fillRect(\n (title_height - box_size) * 0.5,\n (title_height + box_size) * -0.5,\n box_size,\n box_size\n );\n }\n ctx.globalAlpha = old_alpha;\n if (node2.onDrawTitleText) {\n node2.onDrawTitleText(\n ctx,\n title_height,\n size,\n this.ds.scale,\n this.title_text_font,\n selected\n );\n }\n if (!low_quality) {\n ctx.font = this.title_text_font;\n const title = String(node2.getTitle()) + (node2.pinned ? \"📌\" : \"\");\n if (title) {\n if (selected) {\n ctx.fillStyle = LiteGraph.NODE_SELECTED_TITLE_COLOR;\n } else {\n ctx.fillStyle = node2.constructor.title_text_color || this.node_title_color;\n }\n if (node2.flags.collapsed) {\n ctx.textAlign = \"left\";\n ctx.fillText(\n title.substr(0, 20),\n //avoid urls too long\n title_height,\n // + measure.width * 0.5,\n LiteGraph.NODE_TITLE_TEXT_Y - title_height\n );\n ctx.textAlign = \"left\";\n } else {\n ctx.textAlign = \"left\";\n ctx.fillText(\n title,\n title_height,\n LiteGraph.NODE_TITLE_TEXT_Y - title_height\n );\n }\n }\n }\n if (!node2.flags.collapsed && node2.subgraph && !node2.skip_subgraph_button) {\n const w2 = LiteGraph.NODE_TITLE_HEIGHT;\n const x2 = node2.size[0] - w2;\n const over = LiteGraph.isInsideRectangle(this.graph_mouse[0] - node2.pos[0], this.graph_mouse[1] - node2.pos[1], x2 + 2, -w2 + 2, w2 - 4, w2 - 4);\n ctx.fillStyle = over ? \"#888\" : \"#555\";\n if (shape == LiteGraph.BOX_SHAPE || low_quality) {\n ctx.fillRect(x2 + 2, -w2 + 2, w2 - 4, w2 - 4);\n } else {\n ctx.beginPath();\n ctx.roundRect(x2 + 2, -w2 + 2, w2 - 4, w2 - 4, [4]);\n ctx.fill();\n }\n ctx.fillStyle = \"#333\";\n ctx.beginPath();\n ctx.moveTo(x2 + w2 * 0.2, -w2 * 0.6);\n ctx.lineTo(x2 + w2 * 0.8, -w2 * 0.6);\n ctx.lineTo(x2 + w2 * 0.5, -w2 * 0.3);\n ctx.fill();\n }\n node2.onDrawTitle?.(ctx);\n }\n if (selected) {\n node2.onBounding?.(area);\n this.drawSelectionBounding(\n ctx,\n area,\n {\n shape,\n title_height,\n title_mode,\n fgcolor,\n collapsed: node2.flags?.collapsed\n }\n );\n }\n if (node2.execute_triggered > 0) node2.execute_triggered--;\n if (node2.action_triggered > 0) node2.action_triggered--;\n }\n /**\n * Draws the selection bounding of an area.\n * @param {CanvasRenderingContext2D} ctx\n * @param {Vector4} area\n * @param {{\n * shape: LiteGraph.Shape,\n * title_height: number,\n * title_mode: LiteGraph.TitleMode,\n * fgcolor: string,\n * padding: number,\n * }} options\n */\n drawSelectionBounding(ctx, area, {\n shape = LiteGraph.BOX_SHAPE,\n title_height = LiteGraph.NODE_TITLE_HEIGHT,\n title_mode = LiteGraph.NORMAL_TITLE,\n fgcolor = LiteGraph.NODE_BOX_OUTLINE_COLOR,\n padding = 6,\n collapsed = false\n } = {}) {\n if (title_mode === LiteGraph.TRANSPARENT_TITLE) {\n area[1] -= title_height;\n area[3] += title_height;\n }\n ctx.lineWidth = 1;\n ctx.globalAlpha = 0.8;\n ctx.beginPath();\n const [x2, y2, width2, height] = area;\n switch (shape) {\n case LiteGraph.BOX_SHAPE: {\n ctx.rect(x2 - padding, y2 - padding, width2 + 2 * padding, height + 2 * padding);\n break;\n }\n case LiteGraph.ROUND_SHAPE:\n case LiteGraph.CARD_SHAPE: {\n const radius = this.round_radius * 2;\n const isCollapsed = shape === LiteGraph.CARD_SHAPE && collapsed;\n const cornerRadii = isCollapsed || shape === LiteGraph.ROUND_SHAPE ? [radius] : [radius, 2, radius, 2];\n ctx.roundRect(x2 - padding, y2 - padding, width2 + 2 * padding, height + 2 * padding, cornerRadii);\n break;\n }\n case LiteGraph.CIRCLE_SHAPE: {\n const centerX = x2 + width2 / 2;\n const centerY = y2 + height / 2;\n const radius = Math.max(width2, height) / 2 + padding;\n ctx.arc(centerX, centerY, radius, 0, Math.PI * 2);\n break;\n }\n }\n ctx.strokeStyle = LiteGraph.NODE_BOX_OUTLINE_COLOR;\n ctx.stroke();\n ctx.strokeStyle = fgcolor;\n ctx.globalAlpha = 1;\n }\n drawConnections(ctx) {\n const now = LiteGraph.getTime();\n const visible_area = this.visible_area;\n LGraphCanvas.#margin_area[0] = visible_area[0] - 20;\n LGraphCanvas.#margin_area[1] = visible_area[1] - 20;\n LGraphCanvas.#margin_area[2] = visible_area[2] + 40;\n LGraphCanvas.#margin_area[3] = visible_area[3] + 40;\n ctx.lineWidth = this.connections_width;\n ctx.fillStyle = \"#AAA\";\n ctx.strokeStyle = \"#AAA\";\n ctx.globalAlpha = this.editor_alpha;\n const nodes = this.graph._nodes;\n for (let n = 0, l = nodes.length; n < l; ++n) {\n const node2 = nodes[n];\n if (!node2.inputs || !node2.inputs.length) continue;\n for (let i2 = 0; i2 < node2.inputs.length; ++i2) {\n const input = node2.inputs[i2];\n if (!input || input.link == null) continue;\n const link_id = input.link;\n const link = this.graph.links[link_id];\n if (!link) continue;\n const start_node = this.graph.getNodeById(link.origin_id);\n if (start_node == null) continue;\n const start_node_slot = link.origin_slot;\n let start_node_slotpos = null;\n if (start_node_slot == -1) {\n start_node_slotpos = [\n start_node.pos[0] + 10,\n start_node.pos[1] + 10\n ];\n } else {\n start_node_slotpos = start_node.getConnectionPos(\n false,\n start_node_slot,\n LGraphCanvas.#tempA\n );\n }\n const end_node_slotpos = node2.getConnectionPos(true, i2, LGraphCanvas.#tempB);\n LGraphCanvas.#link_bounding[0] = start_node_slotpos[0];\n LGraphCanvas.#link_bounding[1] = start_node_slotpos[1];\n LGraphCanvas.#link_bounding[2] = end_node_slotpos[0] - start_node_slotpos[0];\n LGraphCanvas.#link_bounding[3] = end_node_slotpos[1] - start_node_slotpos[1];\n if (LGraphCanvas.#link_bounding[2] < 0) {\n LGraphCanvas.#link_bounding[0] += LGraphCanvas.#link_bounding[2];\n LGraphCanvas.#link_bounding[2] = Math.abs(LGraphCanvas.#link_bounding[2]);\n }\n if (LGraphCanvas.#link_bounding[3] < 0) {\n LGraphCanvas.#link_bounding[1] += LGraphCanvas.#link_bounding[3];\n LGraphCanvas.#link_bounding[3] = Math.abs(LGraphCanvas.#link_bounding[3]);\n }\n if (!overlapBounding(LGraphCanvas.#link_bounding, LGraphCanvas.#margin_area))\n continue;\n const start_slot = start_node.outputs[start_node_slot];\n const end_slot = node2.inputs[i2];\n if (!start_slot || !end_slot)\n continue;\n const start_dir = start_slot.dir || (start_node.horizontal ? LiteGraph.DOWN : LiteGraph.RIGHT);\n const end_dir = end_slot.dir || (node2.horizontal ? LiteGraph.UP : LiteGraph.LEFT);\n this.renderLink(\n ctx,\n start_node_slotpos,\n end_node_slotpos,\n link,\n false,\n 0,\n null,\n start_dir,\n end_dir\n );\n if (link && link._last_time && now - link._last_time < 1e3) {\n const f = 2 - (now - link._last_time) * 2e-3;\n const tmp = ctx.globalAlpha;\n ctx.globalAlpha = tmp * f;\n this.renderLink(\n ctx,\n start_node_slotpos,\n end_node_slotpos,\n link,\n true,\n f,\n \"white\",\n start_dir,\n end_dir\n );\n ctx.globalAlpha = tmp;\n }\n }\n }\n ctx.globalAlpha = 1;\n }\n /**\n * draws a link between two points\n * @param {vec2} a start pos\n * @param {vec2} b end pos\n * @param {Object} link the link object with all the link info\n * @param {boolean} skip_border ignore the shadow of the link\n * @param {boolean} flow show flow animation (for events)\n * @param {string} color the color for the link\n * @param {LinkDirection} start_dir the direction enum\n * @param {LinkDirection} end_dir the direction enum\n * @param {number} num_sublines number of sublines (useful to represent vec3 or rgb)\n **/\n renderLink(ctx, a, b, link, skip_border, flow, color, start_dir, end_dir, num_sublines) {\n if (link) {\n this.visible_links.push(link);\n }\n if (!color && link) {\n color = link.color || LGraphCanvas.link_type_colors[link.type];\n }\n color ||= this.default_link_color;\n if (link != null && this.highlighted_links[link.id]) {\n color = \"#FFF\";\n }\n start_dir = start_dir || LiteGraph.RIGHT;\n end_dir = end_dir || LiteGraph.LEFT;\n const dist = distance(a, b);\n if (this.render_connections_border && this.ds.scale > 0.6) {\n ctx.lineWidth = this.connections_width + 4;\n }\n ctx.lineJoin = \"round\";\n num_sublines ||= 1;\n if (num_sublines > 1) {\n ctx.lineWidth = 0.5;\n }\n const path = new Path2D();\n if (link) {\n link.path = path;\n }\n for (let i2 = 0; i2 < num_sublines; i2 += 1) {\n const offsety = (i2 - (num_sublines - 1) * 0.5) * 5;\n if (this.links_render_mode == LiteGraph.SPLINE_LINK) {\n path.moveTo(a[0], a[1] + offsety);\n let start_offset_x = 0;\n let start_offset_y = 0;\n let end_offset_x = 0;\n let end_offset_y = 0;\n switch (start_dir) {\n case LiteGraph.LEFT:\n start_offset_x = dist * -0.25;\n break;\n case LiteGraph.RIGHT:\n start_offset_x = dist * 0.25;\n break;\n case LiteGraph.UP:\n start_offset_y = dist * -0.25;\n break;\n case LiteGraph.DOWN:\n start_offset_y = dist * 0.25;\n break;\n }\n switch (end_dir) {\n case LiteGraph.LEFT:\n end_offset_x = dist * -0.25;\n break;\n case LiteGraph.RIGHT:\n end_offset_x = dist * 0.25;\n break;\n case LiteGraph.UP:\n end_offset_y = dist * -0.25;\n break;\n case LiteGraph.DOWN:\n end_offset_y = dist * 0.25;\n break;\n }\n path.bezierCurveTo(\n a[0] + start_offset_x,\n a[1] + start_offset_y + offsety,\n b[0] + end_offset_x,\n b[1] + end_offset_y + offsety,\n b[0],\n b[1] + offsety\n );\n } else if (this.links_render_mode == LiteGraph.LINEAR_LINK) {\n path.moveTo(a[0], a[1] + offsety);\n let start_offset_x = 0;\n let start_offset_y = 0;\n let end_offset_x = 0;\n let end_offset_y = 0;\n switch (start_dir) {\n case LiteGraph.LEFT:\n start_offset_x = -1;\n break;\n case LiteGraph.RIGHT:\n start_offset_x = 1;\n break;\n case LiteGraph.UP:\n start_offset_y = -1;\n break;\n case LiteGraph.DOWN:\n start_offset_y = 1;\n break;\n }\n switch (end_dir) {\n case LiteGraph.LEFT:\n end_offset_x = -1;\n break;\n case LiteGraph.RIGHT:\n end_offset_x = 1;\n break;\n case LiteGraph.UP:\n end_offset_y = -1;\n break;\n case LiteGraph.DOWN:\n end_offset_y = 1;\n break;\n }\n const l = 15;\n path.lineTo(\n a[0] + start_offset_x * l,\n a[1] + start_offset_y * l + offsety\n );\n path.lineTo(\n b[0] + end_offset_x * l,\n b[1] + end_offset_y * l + offsety\n );\n path.lineTo(b[0], b[1] + offsety);\n } else if (this.links_render_mode == LiteGraph.STRAIGHT_LINK) {\n path.moveTo(a[0], a[1]);\n let start_x = a[0];\n let start_y = a[1];\n let end_x = b[0];\n let end_y = b[1];\n if (start_dir == LiteGraph.RIGHT) {\n start_x += 10;\n } else {\n start_y += 10;\n }\n if (end_dir == LiteGraph.LEFT) {\n end_x -= 10;\n } else {\n end_y -= 10;\n }\n path.lineTo(start_x, start_y);\n path.lineTo((start_x + end_x) * 0.5, start_y);\n path.lineTo((start_x + end_x) * 0.5, end_y);\n path.lineTo(end_x, end_y);\n path.lineTo(b[0], b[1]);\n } else {\n return;\n }\n }\n if (this.render_connections_border && this.ds.scale > 0.6 && !skip_border) {\n ctx.strokeStyle = \"rgba(0,0,0,0.5)\";\n ctx.stroke(path);\n }\n ctx.lineWidth = this.connections_width;\n ctx.fillStyle = ctx.strokeStyle = color;\n ctx.stroke(path);\n const pos2 = this.computeConnectionPoint(a, b, 0.5, start_dir, end_dir);\n if (link?._pos) {\n link._pos[0] = pos2[0];\n link._pos[1] = pos2[1];\n }\n if (this.ds.scale >= 0.6 && this.highquality_render && end_dir != LiteGraph.CENTER) {\n if (this.render_connection_arrows) {\n const posA = this.computeConnectionPoint(\n a,\n b,\n 0.25,\n start_dir,\n end_dir\n );\n const posB = this.computeConnectionPoint(\n a,\n b,\n 0.26,\n start_dir,\n end_dir\n );\n const posC = this.computeConnectionPoint(\n a,\n b,\n 0.75,\n start_dir,\n end_dir\n );\n const posD = this.computeConnectionPoint(\n a,\n b,\n 0.76,\n start_dir,\n end_dir\n );\n let angleA = 0;\n let angleB = 0;\n if (this.render_curved_connections) {\n angleA = -Math.atan2(posB[0] - posA[0], posB[1] - posA[1]);\n angleB = -Math.atan2(posD[0] - posC[0], posD[1] - posC[1]);\n } else {\n angleB = angleA = b[1] > a[1] ? 0 : Math.PI;\n }\n ctx.save();\n ctx.translate(posA[0], posA[1]);\n ctx.rotate(angleA);\n ctx.beginPath();\n ctx.moveTo(-5, -3);\n ctx.lineTo(0, 7);\n ctx.lineTo(5, -3);\n ctx.fill();\n ctx.restore();\n ctx.save();\n ctx.translate(posC[0], posC[1]);\n ctx.rotate(angleB);\n ctx.beginPath();\n ctx.moveTo(-5, -3);\n ctx.lineTo(0, 7);\n ctx.lineTo(5, -3);\n ctx.fill();\n ctx.restore();\n }\n ctx.beginPath();\n ctx.arc(pos2[0], pos2[1], 5, 0, Math.PI * 2);\n ctx.fill();\n }\n if (flow) {\n ctx.fillStyle = color;\n for (let i2 = 0; i2 < 5; ++i2) {\n const f = (LiteGraph.getTime() * 1e-3 + i2 * 0.2) % 1;\n const flowPos = this.computeConnectionPoint(\n a,\n b,\n f,\n start_dir,\n end_dir\n );\n ctx.beginPath();\n ctx.arc(flowPos[0], flowPos[1], 5, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n }\n //returns the link center point based on curvature\n computeConnectionPoint(a, b, t, start_dir, end_dir) {\n start_dir ||= LiteGraph.RIGHT;\n end_dir ||= LiteGraph.LEFT;\n const dist = distance(a, b);\n const p0 = a;\n const p1 = [a[0], a[1]];\n const p2 = [b[0], b[1]];\n const p3 = b;\n switch (start_dir) {\n case LiteGraph.LEFT:\n p1[0] += dist * -0.25;\n break;\n case LiteGraph.RIGHT:\n p1[0] += dist * 0.25;\n break;\n case LiteGraph.UP:\n p1[1] += dist * -0.25;\n break;\n case LiteGraph.DOWN:\n p1[1] += dist * 0.25;\n break;\n }\n switch (end_dir) {\n case LiteGraph.LEFT:\n p2[0] += dist * -0.25;\n break;\n case LiteGraph.RIGHT:\n p2[0] += dist * 0.25;\n break;\n case LiteGraph.UP:\n p2[1] += dist * -0.25;\n break;\n case LiteGraph.DOWN:\n p2[1] += dist * 0.25;\n break;\n }\n const c1 = (1 - t) * (1 - t) * (1 - t);\n const c2 = 3 * ((1 - t) * (1 - t)) * t;\n const c3 = 3 * (1 - t) * (t * t);\n const c4 = t * t * t;\n const x2 = c1 * p0[0] + c2 * p1[0] + c3 * p2[0] + c4 * p3[0];\n const y2 = c1 * p0[1] + c2 * p1[1] + c3 * p2[1] + c4 * p3[1];\n return [x2, y2];\n }\n drawExecutionOrder(ctx) {\n ctx.shadowColor = \"transparent\";\n ctx.globalAlpha = 0.25;\n ctx.textAlign = \"center\";\n ctx.strokeStyle = \"white\";\n ctx.globalAlpha = 0.75;\n const visible_nodes = this.visible_nodes;\n for (let i2 = 0; i2 < visible_nodes.length; ++i2) {\n const node2 = visible_nodes[i2];\n ctx.fillStyle = \"black\";\n ctx.fillRect(\n node2.pos[0] - LiteGraph.NODE_TITLE_HEIGHT,\n node2.pos[1] - LiteGraph.NODE_TITLE_HEIGHT,\n LiteGraph.NODE_TITLE_HEIGHT,\n LiteGraph.NODE_TITLE_HEIGHT\n );\n if (node2.order == 0) {\n ctx.strokeRect(\n node2.pos[0] - LiteGraph.NODE_TITLE_HEIGHT + 0.5,\n node2.pos[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5,\n LiteGraph.NODE_TITLE_HEIGHT,\n LiteGraph.NODE_TITLE_HEIGHT\n );\n }\n ctx.fillStyle = \"#FFF\";\n ctx.fillText(\n stringOrEmpty(node2.order),\n node2.pos[0] + LiteGraph.NODE_TITLE_HEIGHT * -0.5,\n node2.pos[1] - 6\n );\n }\n ctx.globalAlpha = 1;\n }\n /**\n * draws the widgets stored inside a node\n **/\n drawNodeWidgets(node2, posY, ctx, active_widget2) {\n if (!node2.widgets || !node2.widgets.length) return 0;\n const width2 = node2.size[0];\n const widgets = node2.widgets;\n posY += 2;\n const H = LiteGraph.NODE_WIDGET_HEIGHT;\n const show_text = this.ds.scale > 0.5;\n ctx.save();\n ctx.globalAlpha = this.editor_alpha;\n const outline_color = LiteGraph.WIDGET_OUTLINE_COLOR;\n const background_color = LiteGraph.WIDGET_BGCOLOR;\n const text_color = LiteGraph.WIDGET_TEXT_COLOR;\n const secondary_text_color = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR;\n const margin = 15;\n for (let i2 = 0; i2 < widgets.length; ++i2) {\n const w2 = widgets[i2];\n const y2 = w2.y || posY;\n if (w2 === this.link_over_widget) {\n ctx.fillStyle = this.default_connection_color_byType[this.link_over_widget_type] || this.default_connection_color.input_on;\n drawSlot(ctx, {}, [10, y2 + 10], {});\n }\n w2.last_y = y2;\n ctx.strokeStyle = outline_color;\n ctx.fillStyle = \"#222\";\n ctx.textAlign = \"left\";\n if (w2.disabled)\n ctx.globalAlpha *= 0.5;\n const widget_width2 = w2.width || width2;\n switch (w2.type) {\n case \"button\":\n ctx.fillStyle = background_color;\n if (w2.clicked) {\n ctx.fillStyle = \"#AAA\";\n w2.clicked = false;\n this.dirty_canvas = true;\n }\n ctx.fillRect(margin, y2, widget_width2 - margin * 2, H);\n if (show_text && !w2.disabled)\n ctx.strokeRect(margin, y2, widget_width2 - margin * 2, H);\n if (show_text) {\n ctx.textAlign = \"center\";\n ctx.fillStyle = text_color;\n ctx.fillText(w2.label || w2.name, widget_width2 * 0.5, y2 + H * 0.7);\n }\n break;\n case \"toggle\":\n ctx.textAlign = \"left\";\n ctx.strokeStyle = outline_color;\n ctx.fillStyle = background_color;\n ctx.beginPath();\n if (show_text)\n ctx.roundRect(margin, y2, widget_width2 - margin * 2, H, [H * 0.5]);\n else\n ctx.rect(margin, y2, widget_width2 - margin * 2, H);\n ctx.fill();\n if (show_text && !w2.disabled)\n ctx.stroke();\n ctx.fillStyle = w2.value ? \"#89A\" : \"#333\";\n ctx.beginPath();\n ctx.arc(widget_width2 - margin * 2, y2 + H * 0.5, H * 0.36, 0, Math.PI * 2);\n ctx.fill();\n if (show_text) {\n ctx.fillStyle = secondary_text_color;\n const label = w2.label || w2.name;\n if (label != null) {\n ctx.fillText(label, margin * 2, y2 + H * 0.7);\n }\n ctx.fillStyle = w2.value ? text_color : secondary_text_color;\n ctx.textAlign = \"right\";\n ctx.fillText(\n w2.value ? w2.options.on || \"true\" : w2.options.off || \"false\",\n widget_width2 - 40,\n y2 + H * 0.7\n );\n }\n break;\n case \"slider\": {\n ctx.fillStyle = background_color;\n ctx.fillRect(margin, y2, widget_width2 - margin * 2, H);\n const range = w2.options.max - w2.options.min;\n let nvalue = (w2.value - w2.options.min) / range;\n if (nvalue < 0) nvalue = 0;\n if (nvalue > 1) nvalue = 1;\n ctx.fillStyle = w2.options.hasOwnProperty(\"slider_color\") ? w2.options.slider_color : active_widget2 == w2 ? \"#89A\" : \"#678\";\n ctx.fillRect(margin, y2, nvalue * (widget_width2 - margin * 2), H);\n if (show_text && !w2.disabled)\n ctx.strokeRect(margin, y2, widget_width2 - margin * 2, H);\n if (w2.marker) {\n let marker_nvalue = (w2.marker - w2.options.min) / range;\n if (marker_nvalue < 0) marker_nvalue = 0;\n if (marker_nvalue > 1) marker_nvalue = 1;\n ctx.fillStyle = w2.options.hasOwnProperty(\"marker_color\") ? w2.options.marker_color : \"#AA9\";\n ctx.fillRect(margin + marker_nvalue * (widget_width2 - margin * 2), y2, 2, H);\n }\n if (show_text) {\n ctx.textAlign = \"center\";\n ctx.fillStyle = text_color;\n ctx.fillText(\n w2.label || w2.name + \" \" + Number(w2.value).toFixed(\n w2.options.precision != null ? w2.options.precision : 3\n ),\n widget_width2 * 0.5,\n y2 + H * 0.7\n );\n }\n break;\n }\n case \"number\":\n case \"combo\":\n ctx.textAlign = \"left\";\n ctx.strokeStyle = outline_color;\n ctx.fillStyle = background_color;\n ctx.beginPath();\n if (show_text)\n ctx.roundRect(margin, y2, widget_width2 - margin * 2, H, [H * 0.5]);\n else\n ctx.rect(margin, y2, widget_width2 - margin * 2, H);\n ctx.fill();\n if (show_text) {\n if (!w2.disabled)\n ctx.stroke();\n ctx.fillStyle = text_color;\n if (!w2.disabled) {\n ctx.beginPath();\n ctx.moveTo(margin + 16, y2 + 5);\n ctx.lineTo(margin + 6, y2 + H * 0.5);\n ctx.lineTo(margin + 16, y2 + H - 5);\n ctx.fill();\n ctx.beginPath();\n ctx.moveTo(widget_width2 - margin - 16, y2 + 5);\n ctx.lineTo(widget_width2 - margin - 6, y2 + H * 0.5);\n ctx.lineTo(widget_width2 - margin - 16, y2 + H - 5);\n ctx.fill();\n }\n ctx.fillStyle = secondary_text_color;\n ctx.fillText(w2.label || w2.name, margin * 2 + 5, y2 + H * 0.7);\n ctx.fillStyle = text_color;\n ctx.textAlign = \"right\";\n if (w2.type == \"number\") {\n ctx.fillText(\n Number(w2.value).toFixed(\n w2.options.precision !== void 0 ? w2.options.precision : 3\n ),\n widget_width2 - margin * 2 - 20,\n y2 + H * 0.7\n );\n } else {\n let v2 = typeof w2.value === \"number\" ? String(w2.value) : w2.value;\n if (w2.options.values) {\n let values2 = w2.options.values;\n if (typeof values2 === \"function\")\n values2 = values2();\n if (values2 && !Array.isArray(values2))\n v2 = values2[w2.value];\n }\n const labelWidth = ctx.measureText(w2.label || w2.name).width + margin * 2;\n const inputWidth = widget_width2 - margin * 4;\n const availableWidth = inputWidth - labelWidth;\n const textWidth = ctx.measureText(v2).width;\n if (textWidth > availableWidth) {\n const ELLIPSIS = \"…\";\n const ellipsisWidth = ctx.measureText(ELLIPSIS).width;\n const charWidthAvg = ctx.measureText(\"a\").width;\n if (availableWidth <= ellipsisWidth) {\n v2 = \"․\";\n } else {\n v2 = `${v2}`;\n const overflowWidth = textWidth + ellipsisWidth - availableWidth;\n if (overflowWidth + charWidthAvg * 3 > availableWidth) {\n const preciseRange = availableWidth + charWidthAvg * 3;\n const preTruncateCt = Math.floor((preciseRange - ellipsisWidth) / charWidthAvg);\n v2 = v2.substr(0, preTruncateCt);\n }\n while (ctx.measureText(v2).width + ellipsisWidth > availableWidth) {\n v2 = v2.substr(0, v2.length - 1);\n }\n v2 += ELLIPSIS;\n }\n }\n ctx.fillText(\n v2,\n widget_width2 - margin * 2 - 20,\n y2 + H * 0.7\n );\n }\n }\n break;\n case \"string\":\n case \"text\":\n ctx.textAlign = \"left\";\n ctx.strokeStyle = outline_color;\n ctx.fillStyle = background_color;\n ctx.beginPath();\n if (show_text)\n ctx.roundRect(margin, y2, widget_width2 - margin * 2, H, [H * 0.5]);\n else\n ctx.rect(margin, y2, widget_width2 - margin * 2, H);\n ctx.fill();\n if (show_text) {\n if (!w2.disabled)\n ctx.stroke();\n ctx.save();\n ctx.beginPath();\n ctx.rect(margin, y2, widget_width2 - margin * 2, H);\n ctx.clip();\n ctx.fillStyle = secondary_text_color;\n const label = w2.label || w2.name;\n if (label != null)\n ctx.fillText(label, margin * 2, y2 + H * 0.7);\n ctx.fillStyle = text_color;\n ctx.textAlign = \"right\";\n ctx.fillText(String(w2.value).substr(0, 30), widget_width2 - margin * 2, y2 + H * 0.7);\n ctx.restore();\n }\n break;\n default:\n w2.draw?.(ctx, node2, widget_width2, y2, H);\n break;\n }\n posY += (w2.computeSize ? w2.computeSize(widget_width2)[1] : H) + 4;\n ctx.globalAlpha = this.editor_alpha;\n }\n ctx.restore();\n ctx.textAlign = \"left\";\n }\n /**\n * process an event on widgets\n **/\n processNodeWidgets(node, pos, event, active_widget) {\n if (!node.widgets || !node.widgets.length || !this.allow_interaction && !node.flags.allow_interaction) {\n return null;\n }\n const x = pos[0] - node.pos[0];\n const y = pos[1] - node.pos[1];\n const width = node.size[0];\n const that = this;\n const ref_window = this.getCanvasWindow();\n let values;\n let values_list;\n for (let i = 0; i < node.widgets.length; ++i) {\n const w = node.widgets[i];\n if (!w || w.disabled)\n continue;\n const widget_height = w.computeSize ? w.computeSize(width)[1] : LiteGraph.NODE_WIDGET_HEIGHT;\n const widget_width = w.width || width;\n if (w != active_widget && (x < 6 || x > widget_width - 12 || y < w.last_y || y > w.last_y + widget_height || w.last_y === void 0))\n continue;\n const old_value = w.value;\n switch (w.type) {\n case \"button\": {\n if (event.type === LiteGraph.pointerevents_method + \"down\") {\n if (w.callback) {\n setTimeout(function() {\n w.callback(w, that, node, pos, event);\n }, 20);\n }\n w.clicked = true;\n this.dirty_canvas = true;\n }\n break;\n }\n case \"slider\": {\n const nvalue = clamp((x - 15) / (widget_width - 30), 0, 1);\n if (w.options.read_only) break;\n w.value = w.options.min + (w.options.max - w.options.min) * nvalue;\n if (old_value != w.value) {\n setTimeout(function() {\n inner_value_change(w, w.value);\n }, 20);\n }\n this.dirty_canvas = true;\n break;\n }\n case \"number\":\n case \"combo\": {\n let delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0;\n const allow_scroll = delta && (x > -3 && x < widget_width + 3) ? false : true;\n if (allow_scroll && event.type == LiteGraph.pointerevents_method + \"move\" && w.type == \"number\") {\n if (event.deltaX)\n w.value += event.deltaX * 0.1 * (w.options.step || 1);\n if (w.options.min != null && w.value < w.options.min) {\n w.value = w.options.min;\n }\n if (w.options.max != null && w.value > w.options.max) {\n w.value = w.options.max;\n }\n } else if (event.type == LiteGraph.pointerevents_method + \"down\") {\n values = w.options.values;\n if (typeof values === \"function\") {\n values = w.options.values(w, node);\n }\n values_list = null;\n if (w.type != \"number\")\n values_list = Array.isArray(values) ? values : Object.keys(values);\n delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0;\n if (w.type == \"number\") {\n w.value += delta * 0.1 * (w.options.step || 1);\n if (w.options.min != null && w.value < w.options.min) {\n w.value = w.options.min;\n }\n if (w.options.max != null && w.value > w.options.max) {\n w.value = w.options.max;\n }\n } else if (delta) {\n let index = -1;\n this.last_mouseclick = 0;\n index = typeof values === \"object\" ? values_list.indexOf(String(w.value)) + delta : values_list.indexOf(w.value) + delta;\n if (index >= values_list.length) index = values_list.length - 1;\n if (index < 0) index = 0;\n w.value = Array.isArray(values) ? values[index] : index;\n } else {\n let inner_clicked = function(v2) {\n if (values != values_list)\n v2 = text_values.indexOf(v2);\n this.value = v2;\n inner_value_change(this, v2);\n that.dirty_canvas = true;\n return false;\n };\n const text_values = values != values_list ? Object.values(values) : values;\n new LiteGraph.ContextMenu(\n text_values,\n {\n scale: Math.max(1, this.ds.scale),\n event,\n className: \"dark\",\n callback: inner_clicked.bind(w)\n },\n // @ts-expect-error Not impl - harmless\n ref_window\n );\n }\n } else if (event.type == LiteGraph.pointerevents_method + \"up\" && w.type == \"number\") {\n delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0;\n if (event.click_time < 200 && delta == 0) {\n this.prompt(\n \"Value\",\n w.value,\n function(v) {\n if (/^[0-9+\\-*/()\\s]+|\\d+\\.\\d+$/.test(v)) {\n try {\n v = eval(v);\n } catch {\n }\n }\n this.value = Number(v);\n inner_value_change(this, this.value);\n }.bind(w),\n event\n );\n }\n }\n if (old_value != w.value)\n setTimeout(\n function() {\n inner_value_change(this, this.value);\n }.bind(w),\n 20\n );\n this.dirty_canvas = true;\n break;\n }\n case \"toggle\":\n if (event.type == LiteGraph.pointerevents_method + \"down\") {\n w.value = !w.value;\n setTimeout(function() {\n inner_value_change(w, w.value);\n }, 20);\n }\n break;\n case \"string\":\n case \"text\":\n if (event.type == LiteGraph.pointerevents_method + \"down\") {\n this.prompt(\n \"Value\",\n w.value,\n function(v2) {\n inner_value_change(this, v2);\n }.bind(w),\n event,\n w.options ? w.options.multiline : false\n );\n }\n break;\n default:\n if (w.mouse) this.dirty_canvas = w.mouse(event, [x, y], node);\n break;\n }\n if (old_value != w.value) {\n node.onWidgetChanged?.(w.name, w.value, old_value, w);\n node.graph._version++;\n }\n return w;\n }\n function inner_value_change(widget, value) {\n const v2 = widget.type === \"number\" ? Number(value) : value;\n widget.value = v2;\n if (widget.options?.property && node.properties[widget.options.property] !== void 0) {\n node.setProperty(widget.options.property, v2);\n }\n widget.callback?.(widget.value, that, node, pos, event);\n }\n return null;\n }\n /**\n * draws every group area in the background\n **/\n drawGroups(canvas, ctx) {\n if (!this.graph) return;\n const groups = this.graph._groups;\n ctx.save();\n ctx.globalAlpha = 0.5 * this.editor_alpha;\n for (let i2 = 0; i2 < groups.length; ++i2) {\n const group = groups[i2];\n if (!overlapBounding(this.visible_area, group._bounding)) {\n continue;\n }\n group.draw(this, ctx);\n }\n ctx.restore();\n }\n adjustNodesSize() {\n const nodes = this.graph._nodes;\n for (let i2 = 0; i2 < nodes.length; ++i2) {\n nodes[i2].size = nodes[i2].computeSize();\n }\n this.setDirty(true, true);\n }\n /**\n * resizes the canvas to a given size, if no size is passed, then it tries to fill the parentNode\n **/\n resize(width2, height) {\n if (!width2 && !height) {\n const parent = this.canvas.parentNode;\n width2 = parent.offsetWidth;\n height = parent.offsetHeight;\n }\n if (this.canvas.width == width2 && this.canvas.height == height)\n return;\n this.canvas.width = width2;\n this.canvas.height = height;\n this.bgcanvas.width = this.canvas.width;\n this.bgcanvas.height = this.canvas.height;\n this.setDirty(true, true);\n }\n /**\n * switches to live mode (node shapes are not rendered, only the content)\n * this feature was designed when graphs where meant to create user interfaces\n **/\n switchLiveMode(transition) {\n if (!transition) {\n this.live_mode = !this.live_mode;\n this.dirty_canvas = true;\n this.dirty_bgcanvas = true;\n return;\n }\n const self = this;\n const delta2 = this.live_mode ? 1.1 : 0.9;\n if (this.live_mode) {\n this.live_mode = false;\n this.editor_alpha = 0.1;\n }\n const t = setInterval(function() {\n self.editor_alpha *= delta2;\n self.dirty_canvas = true;\n self.dirty_bgcanvas = true;\n if (delta2 < 1 && self.editor_alpha < 0.01) {\n clearInterval(t);\n if (delta2 < 1) self.live_mode = true;\n }\n if (delta2 > 1 && self.editor_alpha > 0.99) {\n clearInterval(t);\n self.editor_alpha = 1;\n }\n }, 1);\n }\n onNodeSelectionChange() {\n }\n /**\n * Determines the furthest nodes in each direction for the currently selected nodes\n * @return {{left: LGraphNode, top: LGraphNode, right: LGraphNode, bottom: LGraphNode}}\n */\n boundaryNodesForSelection() {\n return LGraphCanvas.getBoundaryNodes(Object.values(this.selected_nodes));\n }\n showLinkMenu(link, e) {\n const graph = this.graph;\n const node_left = graph.getNodeById(link.origin_id);\n const node_right = graph.getNodeById(link.target_id);\n const fromType = node_left?.outputs?.[link.origin_slot] ? node_left.outputs[link.origin_slot].type : false;\n const destType = node_right?.outputs?.[link.target_slot] ? node_right.inputs[link.target_slot].type : false;\n const options = [\"Add Node\", null, \"Delete\", null];\n const menu = new LiteGraph.ContextMenu(options, {\n event: e,\n title: link.data != null ? link.data.constructor.name : null,\n callback: inner_clicked\n });\n function inner_clicked(v2, options2, e2) {\n switch (v2) {\n case \"Add Node\":\n LGraphCanvas.onMenuAdd(null, null, e2, menu, function(node2) {\n if (!node2.inputs?.length || !node2.outputs?.length) return;\n if (node_left.connectByType(link.origin_slot, node2, fromType)) {\n node2.connectByType(link.target_slot, node_right, destType);\n node2.pos[0] -= node2.size[0] * 0.5;\n }\n });\n break;\n case \"Delete\":\n graph.removeLink(link.id);\n break;\n }\n }\n return false;\n }\n createDefaultNodeForSlot(optPass) {\n optPass = optPass || {};\n const opts = Object.assign(\n {\n nodeFrom: null,\n slotFrom: null,\n nodeTo: null,\n slotTo: null,\n position: [],\n nodeType: null,\n posAdd: [0, 0],\n posSizeFix: [0, 0]\n // alpha, adjust the position x,y based on the new node size w,h\n },\n optPass\n );\n const that2 = this;\n const isFrom = opts.nodeFrom && opts.slotFrom !== null;\n const isTo = !isFrom && opts.nodeTo && opts.slotTo !== null;\n if (!isFrom && !isTo) {\n console.warn(\"No data passed to createDefaultNodeForSlot \" + opts.nodeFrom + \" \" + opts.slotFrom + \" \" + opts.nodeTo + \" \" + opts.slotTo);\n return false;\n }\n if (!opts.nodeType) {\n console.warn(\"No type to createDefaultNodeForSlot\");\n return false;\n }\n const nodeX = isFrom ? opts.nodeFrom : opts.nodeTo;\n let slotX = isFrom ? opts.slotFrom : opts.slotTo;\n let iSlotConn = false;\n switch (typeof slotX) {\n case \"string\":\n iSlotConn = isFrom ? nodeX.findOutputSlot(slotX, false) : nodeX.findInputSlot(slotX, false);\n slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX];\n break;\n case \"object\":\n iSlotConn = isFrom ? nodeX.findOutputSlot(slotX.name) : nodeX.findInputSlot(slotX.name);\n break;\n case \"number\":\n iSlotConn = slotX;\n slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX];\n break;\n case \"undefined\":\n default:\n console.warn(\"Cant get slot information \" + slotX);\n return false;\n }\n if (slotX === false || iSlotConn === false) {\n console.warn(\"createDefaultNodeForSlot bad slotX \" + slotX + \" \" + iSlotConn);\n }\n const fromSlotType = slotX.type == LiteGraph.EVENT ? \"_event_\" : slotX.type;\n const slotTypesDefault = isFrom ? LiteGraph.slot_types_default_out : LiteGraph.slot_types_default_in;\n if (slotTypesDefault?.[fromSlotType]) {\n let nodeNewType = false;\n if (typeof slotTypesDefault[fromSlotType] == \"object\") {\n for (const typeX in slotTypesDefault[fromSlotType]) {\n if (opts.nodeType == slotTypesDefault[fromSlotType][typeX] || opts.nodeType == \"AUTO\") {\n nodeNewType = slotTypesDefault[fromSlotType][typeX];\n break;\n }\n }\n } else {\n if (opts.nodeType == slotTypesDefault[fromSlotType] || opts.nodeType == \"AUTO\") nodeNewType = slotTypesDefault[fromSlotType];\n }\n if (nodeNewType) {\n let nodeNewOpts = false;\n if (typeof nodeNewType == \"object\" && nodeNewType.node) {\n nodeNewOpts = nodeNewType;\n nodeNewType = nodeNewType.node;\n }\n const newNode = LiteGraph.createNode(nodeNewType);\n if (newNode) {\n if (nodeNewOpts) {\n if (nodeNewOpts.properties) {\n for (const i2 in nodeNewOpts.properties) {\n newNode.addProperty(i2, nodeNewOpts.properties[i2]);\n }\n }\n if (nodeNewOpts.inputs) {\n newNode.inputs = [];\n for (const i2 in nodeNewOpts.inputs) {\n newNode.addOutput(\n nodeNewOpts.inputs[i2][0],\n nodeNewOpts.inputs[i2][1]\n );\n }\n }\n if (nodeNewOpts.outputs) {\n newNode.outputs = [];\n for (const i2 in nodeNewOpts.outputs) {\n newNode.addOutput(\n nodeNewOpts.outputs[i2][0],\n nodeNewOpts.outputs[i2][1]\n );\n }\n }\n if (nodeNewOpts.title) {\n newNode.title = nodeNewOpts.title;\n }\n if (nodeNewOpts.json) {\n newNode.configure(nodeNewOpts.json);\n }\n }\n that2.graph.add(newNode);\n newNode.pos = [\n opts.position[0] + opts.posAdd[0] + (opts.posSizeFix[0] ? opts.posSizeFix[0] * newNode.size[0] : 0),\n opts.position[1] + opts.posAdd[1] + (opts.posSizeFix[1] ? opts.posSizeFix[1] * newNode.size[1] : 0)\n ];\n if (isFrom) {\n opts.nodeFrom.connectByType(iSlotConn, newNode, fromSlotType);\n } else {\n opts.nodeTo.connectByTypeOutput(iSlotConn, newNode, fromSlotType);\n }\n return true;\n }\n console.log(\"failed creating \" + nodeNewType);\n }\n }\n return false;\n }\n showConnectionMenu(optPass) {\n optPass ||= {};\n const opts = Object.assign(\n {\n nodeFrom: null,\n slotFrom: null,\n nodeTo: null,\n slotTo: null,\n e: null,\n allow_searchbox: this.allow_searchbox,\n showSearchBox: this.showSearchBox\n },\n optPass\n );\n const that2 = this;\n const isFrom = opts.nodeFrom && opts.slotFrom;\n const isTo = !isFrom && opts.nodeTo && opts.slotTo;\n if (!isFrom && !isTo) {\n console.warn(\"No data passed to showConnectionMenu\");\n return;\n }\n const nodeX = isFrom ? opts.nodeFrom : opts.nodeTo;\n let slotX = isFrom ? opts.slotFrom : opts.slotTo;\n let iSlotConn = false;\n switch (typeof slotX) {\n case \"string\":\n iSlotConn = isFrom ? nodeX.findOutputSlot(slotX, false) : nodeX.findInputSlot(slotX, false);\n slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX];\n break;\n case \"object\":\n iSlotConn = isFrom ? nodeX.findOutputSlot(slotX.name) : nodeX.findInputSlot(slotX.name);\n break;\n case \"number\":\n iSlotConn = slotX;\n slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX];\n break;\n default:\n console.warn(\"Cant get slot information \" + slotX);\n return;\n }\n const options = [\"Add Node\", null];\n if (opts.allow_searchbox) {\n options.push(\"Search\");\n options.push(null);\n }\n const fromSlotType = slotX.type == LiteGraph.EVENT ? \"_event_\" : slotX.type;\n const slotTypesDefault = isFrom ? LiteGraph.slot_types_default_out : LiteGraph.slot_types_default_in;\n if (slotTypesDefault?.[fromSlotType]) {\n if (typeof slotTypesDefault[fromSlotType] == \"object\") {\n for (const typeX in slotTypesDefault[fromSlotType]) {\n options.push(slotTypesDefault[fromSlotType][typeX]);\n }\n } else {\n options.push(slotTypesDefault[fromSlotType]);\n }\n }\n const menu = new LiteGraph.ContextMenu(options, {\n event: opts.e,\n title: (slotX && slotX.name != \"\" ? slotX.name + (fromSlotType ? \" | \" : \"\") : \"\") + (slotX && fromSlotType ? fromSlotType : \"\"),\n callback: inner_clicked\n });\n function inner_clicked(v2, options2, e) {\n switch (v2) {\n case \"Add Node\":\n LGraphCanvas.onMenuAdd(null, null, e, menu, function(node2) {\n if (isFrom) {\n opts.nodeFrom.connectByType(iSlotConn, node2, fromSlotType);\n } else {\n opts.nodeTo.connectByTypeOutput(iSlotConn, node2, fromSlotType);\n }\n });\n break;\n case \"Search\":\n if (isFrom) {\n opts.showSearchBox(e, { node_from: opts.nodeFrom, slot_from: slotX, type_filter_in: fromSlotType });\n } else {\n opts.showSearchBox(e, { node_to: opts.nodeTo, slot_from: slotX, type_filter_out: fromSlotType });\n }\n break;\n default: {\n that2.createDefaultNodeForSlot(Object.assign(opts, {\n position: [opts.e.canvasX, opts.e.canvasY],\n nodeType: v2\n }));\n break;\n }\n }\n }\n }\n // refactor: there are different dialogs, some uses createDialog some dont\n prompt(title, value, callback, event2, multiline) {\n const that2 = this;\n title = title || \"\";\n const dialog = document.createElement(\"div\");\n dialog.is_modified = false;\n dialog.className = \"graphdialog rounded\";\n dialog.innerHTML = multiline ? \" \" : \" \";\n dialog.close = function() {\n that2.prompt_box = null;\n if (dialog.parentNode) {\n dialog.parentNode.removeChild(dialog);\n }\n };\n const graphcanvas = LGraphCanvas.active_canvas;\n const canvas = graphcanvas.canvas;\n canvas.parentNode.appendChild(dialog);\n if (this.ds.scale > 1) dialog.style.transform = \"scale(\" + this.ds.scale + \")\";\n let dialogCloseTimer = null;\n let prevent_timeout = 0;\n LiteGraph.pointerListenerAdd(dialog, \"leave\", function() {\n if (prevent_timeout)\n return;\n if (LiteGraph.dialog_close_on_mouse_leave) {\n if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave)\n dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay);\n }\n });\n LiteGraph.pointerListenerAdd(dialog, \"enter\", function() {\n if (LiteGraph.dialog_close_on_mouse_leave && dialogCloseTimer)\n clearTimeout(dialogCloseTimer);\n });\n const selInDia = dialog.querySelectorAll(\"select\");\n if (selInDia) {\n for (const selIn of selInDia) {\n selIn.addEventListener(\"click\", function() {\n prevent_timeout++;\n });\n selIn.addEventListener(\"blur\", function() {\n prevent_timeout = 0;\n });\n selIn.addEventListener(\"change\", function() {\n prevent_timeout = -1;\n });\n }\n }\n this.prompt_box?.close();\n this.prompt_box = dialog;\n const name_element = dialog.querySelector(\".name\");\n name_element.innerText = title;\n const value_element = dialog.querySelector(\".value\");\n value_element.value = value;\n value_element.select();\n const input = value_element;\n input.addEventListener(\"keydown\", function(e) {\n dialog.is_modified = true;\n if (e.keyCode == 27) {\n dialog.close();\n } else if (e.keyCode == 13 && e.target.localName != \"textarea\") {\n if (callback) {\n callback(this.value);\n }\n dialog.close();\n } else {\n return;\n }\n e.preventDefault();\n e.stopPropagation();\n });\n const button = dialog.querySelector(\"button\");\n button.addEventListener(\"click\", function() {\n callback?.(input.value);\n that2.setDirty(true);\n dialog.close();\n });\n const rect = canvas.getBoundingClientRect();\n let offsetx = -20;\n let offsety = -20;\n if (rect) {\n offsetx -= rect.left;\n offsety -= rect.top;\n }\n if (event2) {\n dialog.style.left = event2.clientX + offsetx + \"px\";\n dialog.style.top = event2.clientY + offsety + \"px\";\n } else {\n dialog.style.left = canvas.width * 0.5 + offsetx + \"px\";\n dialog.style.top = canvas.height * 0.5 + offsety + \"px\";\n }\n setTimeout(function() {\n input.focus();\n const clickTime = Date.now();\n function handleOutsideClick(e) {\n if (e.target === canvas && Date.now() - clickTime > 256) {\n dialog.close();\n canvas.parentNode.removeEventListener(\"click\", handleOutsideClick);\n canvas.parentNode.removeEventListener(\"touchend\", handleOutsideClick);\n }\n }\n canvas.parentNode.addEventListener(\"click\", handleOutsideClick);\n canvas.parentNode.addEventListener(\"touchend\", handleOutsideClick);\n }, 10);\n return dialog;\n }\n showSearchBox(event2, options) {\n const def_options = {\n slot_from: null,\n node_from: null,\n node_to: null,\n do_type_filter: LiteGraph.search_filter_enabled,\n // @ts-expect-error\n type_filter_in: false,\n // @ts-expect-error\n type_filter_out: false,\n show_general_if_none_on_typefilter: true,\n show_general_after_typefiltered: true,\n hide_on_mouse_leave: LiteGraph.search_hide_on_mouse_leave,\n show_all_if_empty: true,\n show_all_on_open: LiteGraph.search_show_all_on_open\n };\n options = Object.assign(def_options, options || {});\n const that2 = this;\n const graphcanvas = LGraphCanvas.active_canvas;\n const canvas = graphcanvas.canvas;\n const root_document = canvas.ownerDocument || document;\n const dialog = document.createElement(\"div\");\n dialog.className = \"litegraph litesearchbox graphdialog rounded\";\n dialog.innerHTML = \"Search \";\n if (options.do_type_filter) {\n dialog.innerHTML += \"\";\n dialog.innerHTML += \"\";\n }\n dialog.innerHTML += \"
\";\n if (root_document.fullscreenElement)\n root_document.fullscreenElement.appendChild(dialog);\n else {\n root_document.body.appendChild(dialog);\n root_document.body.style.overflow = \"hidden\";\n }\n let selIn;\n let selOut;\n if (options.do_type_filter) {\n selIn = dialog.querySelector(\".slot_in_type_filter\");\n selOut = dialog.querySelector(\".slot_out_type_filter\");\n }\n dialog.close = function() {\n that2.search_box = null;\n this.blur();\n canvas.focus();\n root_document.body.style.overflow = \"\";\n setTimeout(function() {\n that2.canvas.focus();\n }, 20);\n dialog.parentNode?.removeChild(dialog);\n };\n if (this.ds.scale > 1) {\n dialog.style.transform = \"scale(\" + this.ds.scale + \")\";\n }\n if (options.hide_on_mouse_leave) {\n let prevent_timeout = false;\n let timeout_close = null;\n LiteGraph.pointerListenerAdd(dialog, \"enter\", function() {\n if (timeout_close) {\n clearTimeout(timeout_close);\n timeout_close = null;\n }\n });\n LiteGraph.pointerListenerAdd(dialog, \"leave\", function() {\n if (prevent_timeout)\n return;\n timeout_close = setTimeout(function() {\n dialog.close();\n }, typeof options.hide_on_mouse_leave === \"number\" ? options.hide_on_mouse_leave : 500);\n });\n if (options.do_type_filter) {\n selIn.addEventListener(\"click\", function() {\n prevent_timeout++;\n });\n selIn.addEventListener(\"blur\", function() {\n prevent_timeout = 0;\n });\n selIn.addEventListener(\"change\", function() {\n prevent_timeout = -1;\n });\n selOut.addEventListener(\"click\", function() {\n prevent_timeout++;\n });\n selOut.addEventListener(\"blur\", function() {\n prevent_timeout = 0;\n });\n selOut.addEventListener(\"change\", function() {\n prevent_timeout = -1;\n });\n }\n }\n that2.search_box?.close();\n that2.search_box = dialog;\n const helper = dialog.querySelector(\".helper\");\n let first = null;\n let timeout = null;\n let selected = null;\n const input = dialog.querySelector(\"input\");\n if (input) {\n input.addEventListener(\"blur\", function() {\n this.focus();\n });\n input.addEventListener(\"keydown\", function(e) {\n if (e.keyCode == 38) {\n changeSelection(false);\n } else if (e.keyCode == 40) {\n changeSelection(true);\n } else if (e.keyCode == 27) {\n dialog.close();\n } else if (e.keyCode == 13) {\n if (selected) {\n select(unescape(selected.dataset[\"type\"]));\n } else if (first) {\n select(first);\n } else {\n dialog.close();\n }\n } else {\n if (timeout) {\n clearInterval(timeout);\n }\n timeout = setTimeout(refreshHelper, 10);\n return;\n }\n e.preventDefault();\n e.stopPropagation();\n e.stopImmediatePropagation();\n return true;\n });\n }\n if (options.do_type_filter) {\n if (selIn) {\n const aSlots = LiteGraph.slot_types_in;\n const nSlots = aSlots.length;\n if (options.type_filter_in == LiteGraph.EVENT || options.type_filter_in == LiteGraph.ACTION)\n options.type_filter_in = \"_event_\";\n for (let iK = 0; iK < nSlots; iK++) {\n const opt = document.createElement(\"option\");\n opt.value = aSlots[iK];\n opt.innerHTML = aSlots[iK];\n selIn.appendChild(opt);\n if (options.type_filter_in !== false && (options.type_filter_in + \"\").toLowerCase() == (aSlots[iK] + \"\").toLowerCase()) {\n opt.selected = true;\n }\n }\n selIn.addEventListener(\"change\", function() {\n refreshHelper();\n });\n }\n if (selOut) {\n const aSlots = LiteGraph.slot_types_out;\n const nSlots = aSlots.length;\n if (options.type_filter_out == LiteGraph.EVENT || options.type_filter_out == LiteGraph.ACTION)\n options.type_filter_out = \"_event_\";\n for (let iK = 0; iK < nSlots; iK++) {\n const opt = document.createElement(\"option\");\n opt.value = aSlots[iK];\n opt.innerHTML = aSlots[iK];\n selOut.appendChild(opt);\n if (options.type_filter_out !== false && (options.type_filter_out + \"\").toLowerCase() == (aSlots[iK] + \"\").toLowerCase())\n opt.selected = true;\n }\n selOut.addEventListener(\"change\", function() {\n refreshHelper();\n });\n }\n }\n const rect = canvas.getBoundingClientRect();\n const left = (event2 ? event2.clientX : rect.left + rect.width * 0.5) - 80;\n const top = (event2 ? event2.clientY : rect.top + rect.height * 0.5) - 20;\n dialog.style.left = left + \"px\";\n dialog.style.top = top + \"px\";\n if (event2.layerY > rect.height - 200)\n helper.style.maxHeight = rect.height - event2.layerY - 20 + \"px\";\n requestAnimationFrame(function() {\n input.focus();\n });\n if (options.show_all_on_open) refreshHelper();\n function select(name) {\n if (name) {\n if (that2.onSearchBoxSelection) {\n that2.onSearchBoxSelection(name, event2, graphcanvas);\n } else {\n const extra = LiteGraph.searchbox_extras[name.toLowerCase()];\n if (extra)\n name = extra.type;\n graphcanvas.graph.beforeChange();\n const node2 = LiteGraph.createNode(name);\n if (node2) {\n node2.pos = graphcanvas.convertEventToCanvasOffset(\n event2\n );\n graphcanvas.graph.add(node2, false);\n }\n if (extra?.data) {\n if (extra.data.properties) {\n for (const i2 in extra.data.properties) {\n node2.addProperty(i2, extra.data.properties[i2]);\n }\n }\n if (extra.data.inputs) {\n node2.inputs = [];\n for (const i2 in extra.data.inputs) {\n node2.addOutput(\n extra.data.inputs[i2][0],\n extra.data.inputs[i2][1]\n );\n }\n }\n if (extra.data.outputs) {\n node2.outputs = [];\n for (const i2 in extra.data.outputs) {\n node2.addOutput(\n extra.data.outputs[i2][0],\n extra.data.outputs[i2][1]\n );\n }\n }\n if (extra.data.title) {\n node2.title = extra.data.title;\n }\n if (extra.data.json) {\n node2.configure(extra.data.json);\n }\n }\n if (options.node_from) {\n let iS = false;\n switch (typeof options.slot_from) {\n case \"string\":\n iS = options.node_from.findOutputSlot(options.slot_from);\n break;\n case \"object\":\n iS = options.slot_from.name ? options.node_from.findOutputSlot(options.slot_from.name) : -1;\n if (iS == -1 && typeof options.slot_from.slot_index !== \"undefined\") iS = options.slot_from.slot_index;\n break;\n case \"number\":\n iS = options.slot_from;\n break;\n default:\n iS = 0;\n }\n if (typeof options.node_from.outputs[iS] !== \"undefined\") {\n if (iS !== false && iS > -1) {\n options.node_from.connectByType(iS, node2, options.node_from.outputs[iS].type);\n }\n }\n }\n if (options.node_to) {\n let iS = false;\n switch (typeof options.slot_from) {\n case \"string\":\n iS = options.node_to.findInputSlot(options.slot_from);\n break;\n case \"object\":\n iS = options.slot_from.name ? options.node_to.findInputSlot(options.slot_from.name) : -1;\n if (iS == -1 && typeof options.slot_from.slot_index !== \"undefined\") iS = options.slot_from.slot_index;\n break;\n case \"number\":\n iS = options.slot_from;\n break;\n default:\n iS = 0;\n }\n if (typeof options.node_to.inputs[iS] !== \"undefined\") {\n if (iS !== false && iS > -1) {\n options.node_to.connectByTypeOutput(iS, node2, options.node_to.inputs[iS].type);\n }\n }\n }\n graphcanvas.graph.afterChange();\n }\n }\n dialog.close();\n }\n function changeSelection(forward) {\n const prev = selected;\n if (!selected) {\n selected = forward ? helper.childNodes[0] : helper.childNodes[helper.childNodes.length];\n } else {\n selected.classList.remove(\"selected\");\n selected = forward ? selected.nextSibling : selected.previousSibling;\n selected ||= prev;\n }\n if (!selected) return;\n selected.classList.add(\"selected\");\n selected.scrollIntoView({ block: \"end\", behavior: \"smooth\" });\n }\n function refreshHelper() {\n timeout = null;\n let str = input.value;\n first = null;\n helper.innerHTML = \"\";\n if (!str && !options.show_all_if_empty) return;\n if (that2.onSearchBox) {\n const list = that2.onSearchBox(helper, str, graphcanvas);\n if (list) {\n for (let i2 = 0; i2 < list.length; ++i2) {\n addResult(list[i2]);\n }\n }\n } else {\n let inner_test_filter = function(type, optsIn) {\n optsIn = optsIn || {};\n const optsDef = {\n skipFilter: false,\n inTypeOverride: false,\n outTypeOverride: false\n };\n const opts = Object.assign(optsDef, optsIn);\n const ctor = LiteGraph.registered_node_types[type];\n if (filter && ctor.filter != filter)\n return false;\n if ((!options.show_all_if_empty || str) && type.toLowerCase().indexOf(str) === -1 && (!ctor.title || ctor.title.toLowerCase().indexOf(str) === -1))\n return false;\n if (options.do_type_filter && !opts.skipFilter) {\n const sType = type;\n let sV = opts.inTypeOverride !== false ? opts.inTypeOverride : sIn.value;\n if (sIn && sV) {\n if (LiteGraph.registered_slot_in_types[sV]?.nodes) {\n const doesInc = LiteGraph.registered_slot_in_types[sV].nodes.includes(sType);\n if (doesInc !== false) ;\n else {\n return false;\n }\n }\n }\n sV = sOut.value;\n if (opts.outTypeOverride !== false) sV = opts.outTypeOverride;\n if (sOut && sV) {\n if (LiteGraph.registered_slot_out_types[sV]?.nodes) {\n const doesInc = LiteGraph.registered_slot_out_types[sV].nodes.includes(sType);\n if (doesInc !== false) ;\n else {\n return false;\n }\n }\n }\n }\n return true;\n };\n let c = 0;\n str = str.toLowerCase();\n const filter = graphcanvas.filter || graphcanvas.graph.filter;\n let sIn = false;\n let sOut = false;\n if (options.do_type_filter && that2.search_box) {\n sIn = that2.search_box.querySelector(\".slot_in_type_filter\");\n sOut = that2.search_box.querySelector(\".slot_out_type_filter\");\n }\n for (const i2 in LiteGraph.searchbox_extras) {\n const extra = LiteGraph.searchbox_extras[i2];\n if ((!options.show_all_if_empty || str) && extra.desc.toLowerCase().indexOf(str) === -1)\n continue;\n const ctor = LiteGraph.registered_node_types[extra.type];\n if (ctor && ctor.filter != filter)\n continue;\n if (!inner_test_filter(extra.type))\n continue;\n addResult(extra.desc, \"searchbox_extra\");\n if (LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit) {\n break;\n }\n }\n let filtered = null;\n if (Array.prototype.filter) {\n const keys = Object.keys(LiteGraph.registered_node_types);\n filtered = keys.filter(inner_test_filter);\n } else {\n filtered = [];\n for (const i2 in LiteGraph.registered_node_types) {\n if (inner_test_filter(i2))\n filtered.push(i2);\n }\n }\n for (let i2 = 0; i2 < filtered.length; i2++) {\n addResult(filtered[i2]);\n if (LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit)\n break;\n }\n if (options.show_general_after_typefiltered && (sIn.value || sOut.value)) {\n filtered_extra = [];\n for (const i2 in LiteGraph.registered_node_types) {\n if (inner_test_filter(i2, { inTypeOverride: sIn && sIn.value ? \"*\" : false, outTypeOverride: sOut && sOut.value ? \"*\" : false }))\n filtered_extra.push(i2);\n }\n for (let i2 = 0; i2 < filtered_extra.length; i2++) {\n addResult(filtered_extra[i2], \"generic_type\");\n if (LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit)\n break;\n }\n }\n if ((sIn.value || sOut.value) && (helper.childNodes.length == 0 && options.show_general_if_none_on_typefilter)) {\n filtered_extra = [];\n for (const i2 in LiteGraph.registered_node_types) {\n if (inner_test_filter(i2, { skipFilter: true }))\n filtered_extra.push(i2);\n }\n for (let i2 = 0; i2 < filtered_extra.length; i2++) {\n addResult(filtered_extra[i2], \"not_in_filter\");\n if (LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit)\n break;\n }\n }\n }\n function addResult(type, className) {\n const help = document.createElement(\"div\");\n first ||= type;\n const nodeType = LiteGraph.registered_node_types[type];\n if (nodeType?.title) {\n help.innerText = nodeType?.title;\n const typeEl = document.createElement(\"span\");\n typeEl.className = \"litegraph lite-search-item-type\";\n typeEl.textContent = type;\n help.append(typeEl);\n } else {\n help.innerText = type;\n }\n help.dataset[\"type\"] = escape(type);\n help.className = \"litegraph lite-search-item\";\n if (className) {\n help.className += \" \" + className;\n }\n help.addEventListener(\"click\", function() {\n select(unescape(this.dataset[\"type\"]));\n });\n helper.appendChild(help);\n }\n }\n return dialog;\n }\n showEditPropertyValue(node2, property, options) {\n if (!node2 || node2.properties[property] === void 0) return;\n options = options || {};\n const info = node2.getPropertyInfo(property);\n const type = info.type;\n let input_html = \"\";\n if (type == \"string\" || type == \"number\" || type == \"array\" || type == \"object\") {\n input_html = \"\";\n } else if ((type == \"enum\" || type == \"combo\") && info.values) {\n input_html = \"\";\n } else if (type == \"boolean\" || type == \"toggle\") {\n input_html = \"\";\n } else {\n console.warn(\"unknown type: \" + type);\n return;\n }\n const dialog = this.createDialog(\n \"\" + (info.label || property) + \"\" + input_html + \"\",\n options\n );\n let input;\n if ((type == \"enum\" || type == \"combo\") && info.values) {\n input = dialog.querySelector(\"select\");\n input.addEventListener(\"change\", function(e) {\n dialog.modified();\n setValue(e.target?.value);\n });\n } else if (type == \"boolean\" || type == \"toggle\") {\n input = dialog.querySelector(\"input\");\n input?.addEventListener(\"click\", function() {\n dialog.modified();\n setValue(!!input.checked);\n });\n } else {\n input = dialog.querySelector(\"input\");\n if (input) {\n input.addEventListener(\"blur\", function() {\n this.focus();\n });\n let v2 = node2.properties[property] !== void 0 ? node2.properties[property] : \"\";\n if (type !== \"string\") {\n v2 = JSON.stringify(v2);\n }\n input.value = v2;\n input.addEventListener(\"keydown\", function(e) {\n if (e.keyCode == 27) {\n dialog.close();\n } else if (e.keyCode == 13) {\n inner();\n } else if (e.keyCode != 13) {\n dialog.modified();\n return;\n }\n e.preventDefault();\n e.stopPropagation();\n });\n }\n }\n input?.focus();\n const button = dialog.querySelector(\"button\");\n button.addEventListener(\"click\", inner);\n function inner() {\n setValue(input.value);\n }\n function setValue(value) {\n if (info?.values && typeof info.values === \"object\" && info.values[value] != void 0)\n value = info.values[value];\n if (typeof node2.properties[property] == \"number\") {\n value = Number(value);\n }\n if (type == \"array\" || type == \"object\") {\n value = JSON.parse(value);\n }\n node2.properties[property] = value;\n if (node2.graph) {\n node2.graph._version++;\n }\n node2.onPropertyChanged?.(property, value);\n options.onclose?.();\n dialog.close();\n node2.setDirtyCanvas(true, true);\n }\n return dialog;\n }\n // TODO refactor, theer are different dialog, some uses createDialog, some dont\n createDialog(html, options) {\n const def_options = { checkForInput: false, closeOnLeave: true, closeOnLeave_checkModified: true };\n options = Object.assign(def_options, options || {});\n const dialog = document.createElement(\"div\");\n dialog.className = \"graphdialog\";\n dialog.innerHTML = html;\n dialog.is_modified = false;\n const rect = this.canvas.getBoundingClientRect();\n let offsetx = -20;\n let offsety = -20;\n if (rect) {\n offsetx -= rect.left;\n offsety -= rect.top;\n }\n if (options.position) {\n offsetx += options.position[0];\n offsety += options.position[1];\n } else if (options.event) {\n offsetx += options.event.clientX;\n offsety += options.event.clientY;\n } else {\n offsetx += this.canvas.width * 0.5;\n offsety += this.canvas.height * 0.5;\n }\n dialog.style.left = offsetx + \"px\";\n dialog.style.top = offsety + \"px\";\n this.canvas.parentNode.appendChild(dialog);\n if (options.checkForInput) {\n const aI = dialog.querySelectorAll(\"input\");\n aI?.forEach(function(iX) {\n iX.addEventListener(\"keydown\", function(e) {\n dialog.modified();\n if (e.keyCode == 27) {\n dialog.close();\n } else if (e.keyCode != 13) {\n return;\n }\n e.preventDefault();\n e.stopPropagation();\n });\n iX.focus();\n });\n }\n dialog.modified = function() {\n dialog.is_modified = true;\n };\n dialog.close = function() {\n dialog.parentNode?.removeChild(dialog);\n };\n let dialogCloseTimer = null;\n let prevent_timeout = 0;\n dialog.addEventListener(\"mouseleave\", function() {\n if (prevent_timeout)\n return;\n if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave)\n dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay);\n });\n dialog.addEventListener(\"mouseenter\", function() {\n if (options.closeOnLeave || LiteGraph.dialog_close_on_mouse_leave) {\n if (dialogCloseTimer) clearTimeout(dialogCloseTimer);\n }\n });\n const selInDia = dialog.querySelectorAll(\"select\");\n selInDia?.forEach(function(selIn) {\n selIn.addEventListener(\"click\", function() {\n prevent_timeout++;\n });\n selIn.addEventListener(\"blur\", function() {\n prevent_timeout = 0;\n });\n selIn.addEventListener(\"change\", function() {\n prevent_timeout = -1;\n });\n });\n return dialog;\n }\n createPanel(title, options) {\n options = options || {};\n const ref_window2 = options.window || window;\n const root = document.createElement(\"div\");\n root.className = \"litegraph dialog\";\n root.innerHTML = \"
\";\n root.header = root.querySelector(\".dialog-header\");\n if (options.width)\n root.style.width = options.width + (typeof options.width === \"number\" ? \"px\" : \"\");\n if (options.height)\n root.style.height = options.height + (typeof options.height === \"number\" ? \"px\" : \"\");\n if (options.closable) {\n const close = document.createElement(\"span\");\n close.innerHTML = \"✕\";\n close.classList.add(\"close\");\n close.addEventListener(\"click\", function() {\n root.close();\n });\n root.header.appendChild(close);\n }\n root.title_element = root.querySelector(\".dialog-title\");\n root.title_element.innerText = title;\n root.content = root.querySelector(\".dialog-content\");\n root.alt_content = root.querySelector(\".dialog-alt-content\");\n root.footer = root.querySelector(\".dialog-footer\");\n root.close = function() {\n if (typeof root.onClose == \"function\") root.onClose();\n root.parentNode?.removeChild(root);\n this.parentNode?.removeChild(this);\n };\n root.toggleAltContent = function(force) {\n let vTo;\n let vAlt;\n if (typeof force != \"undefined\") {\n vTo = force ? \"block\" : \"none\";\n vAlt = force ? \"none\" : \"block\";\n } else {\n vTo = root.alt_content.style.display != \"block\" ? \"block\" : \"none\";\n vAlt = root.alt_content.style.display != \"block\" ? \"none\" : \"block\";\n }\n root.alt_content.style.display = vTo;\n root.content.style.display = vAlt;\n };\n root.toggleFooterVisibility = function(force) {\n let vTo;\n if (typeof force != \"undefined\") {\n vTo = force ? \"block\" : \"none\";\n } else {\n vTo = root.footer.style.display != \"block\" ? \"block\" : \"none\";\n }\n root.footer.style.display = vTo;\n };\n root.clear = function() {\n this.content.innerHTML = \"\";\n };\n root.addHTML = function(code, classname, on_footer) {\n const elem = document.createElement(\"div\");\n if (classname)\n elem.className = classname;\n elem.innerHTML = code;\n if (on_footer)\n root.footer.appendChild(elem);\n else\n root.content.appendChild(elem);\n return elem;\n };\n root.addButton = function(name, callback, options2) {\n const elem = document.createElement(\"button\");\n elem.innerText = name;\n elem.options = options2;\n elem.classList.add(\"btn\");\n elem.addEventListener(\"click\", callback);\n root.footer.appendChild(elem);\n return elem;\n };\n root.addSeparator = function() {\n const elem = document.createElement(\"div\");\n elem.className = \"separator\";\n root.content.appendChild(elem);\n };\n root.addWidget = function(type, name, value, options2, callback) {\n options2 = options2 || {};\n let str_value = String(value);\n type = type.toLowerCase();\n if (type == \"number\")\n str_value = value.toFixed(3);\n const elem = document.createElement(\"div\");\n elem.className = \"property\";\n elem.innerHTML = \"\";\n elem.querySelector(\".property_name\").innerText = options2.label || name;\n const value_element = elem.querySelector(\".property_value\");\n value_element.innerText = str_value;\n elem.dataset[\"property\"] = name;\n elem.dataset[\"type\"] = options2.type || type;\n elem.options = options2;\n elem.value = value;\n if (type == \"code\")\n elem.addEventListener(\"click\", function() {\n root.inner_showCodePad(this.dataset[\"property\"]);\n });\n else if (type == \"boolean\") {\n elem.classList.add(\"boolean\");\n if (value)\n elem.classList.add(\"bool-on\");\n elem.addEventListener(\"click\", function() {\n const propname = this.dataset[\"property\"];\n this.value = !this.value;\n this.classList.toggle(\"bool-on\");\n this.querySelector(\".property_value\").innerText = this.value ? \"true\" : \"false\";\n innerChange(propname, this.value);\n });\n } else if (type == \"string\" || type == \"number\") {\n value_element.setAttribute(\"contenteditable\", true);\n value_element.addEventListener(\"keydown\", function(e) {\n if (e.code == \"Enter\" && (type != \"string\" || !e.shiftKey)) {\n e.preventDefault();\n this.blur();\n }\n });\n value_element.addEventListener(\"blur\", function() {\n let v2 = this.innerText;\n const propname = this.parentNode.dataset[\"property\"];\n const proptype = this.parentNode.dataset[\"type\"];\n if (proptype == \"number\")\n v2 = Number(v2);\n innerChange(propname, v2);\n });\n } else if (type == \"enum\" || type == \"combo\") {\n const str_value2 = LGraphCanvas.getPropertyPrintableValue(value, options2.values);\n value_element.innerText = str_value2;\n value_element.addEventListener(\"click\", function(event2) {\n const values2 = options2.values || [];\n const propname = this.parentNode.dataset[\"property\"];\n const elem_that = this;\n new LiteGraph.ContextMenu(\n values2,\n {\n event: event2,\n className: \"dark\",\n callback: inner_clicked\n },\n // @ts-expect-error\n ref_window2\n );\n function inner_clicked(v2) {\n elem_that.innerText = v2;\n innerChange(propname, v2);\n return false;\n }\n });\n }\n root.content.appendChild(elem);\n function innerChange(name2, value2) {\n options2.callback?.(name2, value2, options2);\n callback?.(name2, value2, options2);\n }\n return elem;\n };\n if (root.onOpen && typeof root.onOpen == \"function\") root.onOpen();\n return root;\n }\n closePanels() {\n document.querySelector(\"#node-panel\")?.close();\n document.querySelector(\"#option-panel\")?.close();\n }\n showShowNodePanel(node2) {\n this.SELECTED_NODE = node2;\n this.closePanels();\n const ref_window2 = this.getCanvasWindow();\n const graphcanvas = this;\n const panel = this.createPanel(node2.title || \"\", {\n closable: true,\n window: ref_window2,\n onOpen: function() {\n graphcanvas.NODEPANEL_IS_OPEN = true;\n },\n onClose: function() {\n graphcanvas.NODEPANEL_IS_OPEN = false;\n graphcanvas.node_panel = null;\n }\n });\n graphcanvas.node_panel = panel;\n panel.id = \"node-panel\";\n panel.node = node2;\n panel.classList.add(\"settings\");\n function inner_refresh() {\n panel.content.innerHTML = \"\";\n panel.addHTML(`${node2.type}${node2.constructor.desc || \"\"}`);\n panel.addHTML(\"

Properties

\");\n const fUpdate = function(name, value) {\n graphcanvas.graph.beforeChange(node2);\n switch (name) {\n case \"Title\":\n node2.title = value;\n break;\n case \"Mode\": {\n const kV = Object.values(LiteGraph.NODE_MODES).indexOf(value);\n if (kV >= 0 && LiteGraph.NODE_MODES[kV]) {\n node2.changeMode(kV);\n } else {\n console.warn(\"unexpected mode: \" + value);\n }\n break;\n }\n case \"Color\":\n if (LGraphCanvas.node_colors[value]) {\n node2.color = LGraphCanvas.node_colors[value].color;\n node2.bgcolor = LGraphCanvas.node_colors[value].bgcolor;\n } else {\n console.warn(\"unexpected color: \" + value);\n }\n break;\n default:\n node2.setProperty(name, value);\n break;\n }\n graphcanvas.graph.afterChange();\n graphcanvas.dirty_canvas = true;\n };\n panel.addWidget(\"string\", \"Title\", node2.title, {}, fUpdate);\n panel.addWidget(\"combo\", \"Mode\", LiteGraph.NODE_MODES[node2.mode], { values: LiteGraph.NODE_MODES }, fUpdate);\n const nodeCol = node2.color !== void 0 ? Object.keys(LGraphCanvas.node_colors).filter(function(nK) {\n return LGraphCanvas.node_colors[nK].color == node2.color;\n }) : \"\";\n panel.addWidget(\"combo\", \"Color\", nodeCol, { values: Object.keys(LGraphCanvas.node_colors) }, fUpdate);\n for (const pName in node2.properties) {\n const value = node2.properties[pName];\n const info = node2.getPropertyInfo(pName);\n if (node2.onAddPropertyToPanel?.(pName, panel))\n continue;\n panel.addWidget(info.widget || info.type, pName, value, info, fUpdate);\n }\n panel.addSeparator();\n node2.onShowCustomPanelInfo?.(panel);\n panel.footer.innerHTML = \"\";\n panel.addButton(\"Delete\", function() {\n if (node2.block_delete)\n return;\n node2.graph.remove(node2);\n panel.close();\n }).classList.add(\"delete\");\n }\n panel.inner_showCodePad = function(propname) {\n panel.classList.remove(\"settings\");\n panel.classList.add(\"centered\");\n panel.alt_content.innerHTML = \"\";\n const textarea = panel.alt_content.querySelector(\"textarea\");\n const fDoneWith = function() {\n panel.toggleAltContent(false);\n panel.toggleFooterVisibility(true);\n textarea.parentNode.removeChild(textarea);\n panel.classList.add(\"settings\");\n panel.classList.remove(\"centered\");\n inner_refresh();\n };\n textarea.value = node2.properties[propname];\n textarea.addEventListener(\"keydown\", function(e) {\n if (e.code == \"Enter\" && e.ctrlKey) {\n node2.setProperty(propname, textarea.value);\n fDoneWith();\n }\n });\n panel.toggleAltContent(true);\n panel.toggleFooterVisibility(false);\n textarea.style.height = \"calc(100% - 40px)\";\n const assign = panel.addButton(\"Assign\", function() {\n node2.setProperty(propname, textarea.value);\n fDoneWith();\n });\n panel.alt_content.appendChild(assign);\n const button = panel.addButton(\"Close\", fDoneWith);\n button.style.float = \"right\";\n panel.alt_content.appendChild(button);\n };\n inner_refresh();\n this.canvas.parentNode.appendChild(panel);\n }\n showSubgraphPropertiesDialog(node2) {\n console.log(\"showing subgraph properties dialog\");\n const old_panel = this.canvas.parentNode.querySelector(\".subgraph_dialog\");\n old_panel?.close();\n const panel = this.createPanel(\"Subgraph Inputs\", { closable: true, width: 500 });\n panel.node = node2;\n panel.classList.add(\"subgraph_dialog\");\n function inner_refresh() {\n panel.clear();\n if (node2.inputs)\n for (let i2 = 0; i2 < node2.inputs.length; ++i2) {\n const input = node2.inputs[i2];\n if (input.not_subgraph_input)\n continue;\n const html2 = \" \";\n const elem2 = panel.addHTML(html2, \"subgraph_property\");\n elem2.dataset[\"name\"] = input.name;\n elem2.dataset[\"slot\"] = i2;\n elem2.querySelector(\".name\").innerText = input.name;\n elem2.querySelector(\".type\").innerText = input.type;\n elem2.querySelector(\"button\").addEventListener(\"click\", function() {\n node2.removeInput(Number(this.parentNode.dataset[\"slot\"]));\n inner_refresh();\n });\n }\n }\n const html = \" + NameType\";\n const elem = panel.addHTML(html, \"subgraph_property extra\", true);\n elem.querySelector(\"button\").addEventListener(\"click\", function() {\n const elem2 = this.parentNode;\n const name = elem2.querySelector(\".name\").value;\n const type = elem2.querySelector(\".type\").value;\n if (!name || node2.findInputSlot(name) != -1)\n return;\n node2.addInput(name, type);\n elem2.querySelector(\".name\").value = \"\";\n elem2.querySelector(\".type\").value = \"\";\n inner_refresh();\n });\n inner_refresh();\n this.canvas.parentNode.appendChild(panel);\n return panel;\n }\n showSubgraphPropertiesDialogRight(node2) {\n const old_panel = this.canvas.parentNode.querySelector(\".subgraph_dialog\");\n old_panel?.close();\n const panel = this.createPanel(\"Subgraph Outputs\", { closable: true, width: 500 });\n panel.node = node2;\n panel.classList.add(\"subgraph_dialog\");\n function inner_refresh() {\n panel.clear();\n if (node2.outputs)\n for (let i2 = 0; i2 < node2.outputs.length; ++i2) {\n const input = node2.outputs[i2];\n if (input.not_subgraph_output)\n continue;\n const html2 = \" \";\n const elem2 = panel.addHTML(html2, \"subgraph_property\");\n elem2.dataset[\"name\"] = input.name;\n elem2.dataset[\"slot\"] = i2;\n elem2.querySelector(\".name\").innerText = input.name;\n elem2.querySelector(\".type\").innerText = input.type;\n elem2.querySelector(\"button\").addEventListener(\"click\", function() {\n node2.removeOutput(Number(this.parentNode.dataset[\"slot\"]));\n inner_refresh();\n });\n }\n }\n const html = \" + NameType\";\n const elem = panel.addHTML(html, \"subgraph_property extra\", true);\n elem.querySelector(\".name\").addEventListener(\"keydown\", function(e) {\n if (e.keyCode == 13) addOutput.apply(this);\n });\n elem.querySelector(\"button\").addEventListener(\"click\", function() {\n addOutput.apply(this);\n });\n function addOutput() {\n const elem2 = this.parentNode;\n const name = elem2.querySelector(\".name\").value;\n const type = elem2.querySelector(\".type\").value;\n if (!name || node2.findOutputSlot(name) != -1)\n return;\n node2.addOutput(name, type);\n elem2.querySelector(\".name\").value = \"\";\n elem2.querySelector(\".type\").value = \"\";\n inner_refresh();\n }\n inner_refresh();\n this.canvas.parentNode.appendChild(panel);\n return panel;\n }\n checkPanels() {\n if (!this.canvas) return;\n const panels = this.canvas.parentNode.querySelectorAll(\".litegraph.dialog\");\n for (let i2 = 0; i2 < panels.length; ++i2) {\n const panel = panels[i2];\n if (!panel.node) continue;\n if (!panel.node.graph || panel.graph != this.graph) panel.close();\n }\n }\n getCanvasMenuOptions() {\n let options = null;\n if (this.getMenuOptions) {\n options = this.getMenuOptions();\n } else {\n options = [\n {\n content: \"Add Node\",\n has_submenu: true,\n // @ts-expect-error Might be broken? Or just param overlap\n callback: LGraphCanvas.onMenuAdd\n },\n { content: \"Add Group\", callback: LGraphCanvas.onGroupAdd }\n //{ content: \"Arrange\", callback: that.graph.arrange },\n //{content:\"Collapse All\", callback: LGraphCanvas.onMenuCollapseAll }\n ];\n if (Object.keys(this.selected_nodes).length > 1) {\n options.push({\n content: \"Align\",\n has_submenu: true,\n callback: LGraphCanvas.onGroupAlign\n });\n }\n if (this._graph_stack && this._graph_stack.length > 0) {\n options.push(null, {\n content: \"Close subgraph\",\n callback: this.closeSubgraph.bind(this)\n });\n }\n }\n const extra = this.getExtraMenuOptions?.(this, options);\n return extra ? options.concat(extra) : options;\n }\n //called by processContextMenu to extract the menu list\n getNodeMenuOptions(node2) {\n let options = null;\n if (node2.getMenuOptions) {\n options = node2.getMenuOptions(this);\n } else {\n options = [\n {\n content: \"Inputs\",\n has_submenu: true,\n disabled: true,\n callback: LGraphCanvas.showMenuNodeOptionalInputs\n },\n {\n content: \"Outputs\",\n has_submenu: true,\n disabled: true,\n callback: LGraphCanvas.showMenuNodeOptionalOutputs\n },\n null,\n {\n content: \"Properties\",\n has_submenu: true,\n callback: LGraphCanvas.onShowMenuNodeProperties\n },\n {\n content: \"Properties Panel\",\n callback: function(item, options2, e, menu, node3) {\n LGraphCanvas.active_canvas.showShowNodePanel(node3);\n }\n },\n null,\n {\n content: \"Title\",\n callback: LGraphCanvas.onShowPropertyEditor\n },\n {\n content: \"Mode\",\n has_submenu: true,\n callback: LGraphCanvas.onMenuNodeMode\n }\n ];\n if (node2.resizable !== false) {\n options.push({\n content: \"Resize\",\n callback: LGraphCanvas.onMenuResizeNode\n });\n }\n if (node2.collapsible) {\n options.push({\n content: node2.collapsed ? \"Expand\" : \"Collapse\",\n callback: LGraphCanvas.onMenuNodeCollapse\n });\n }\n options.push(\n {\n content: node2.pinned ? \"Unpin\" : \"Pin\",\n callback: (...args) => {\n LGraphCanvas.onMenuNodePin(...args);\n for (const i2 in this.selected_nodes) {\n const node3 = this.selected_nodes[i2];\n node3.pin();\n }\n this.setDirty(true, true);\n }\n },\n {\n content: \"Colors\",\n has_submenu: true,\n callback: LGraphCanvas.onMenuNodeColors\n },\n {\n content: \"Shapes\",\n has_submenu: true,\n callback: LGraphCanvas.onMenuNodeShapes\n },\n null\n );\n }\n const inputs = node2.onGetInputs?.();\n if (inputs?.length)\n options[0].disabled = false;\n const outputs = node2.onGetOutputs?.();\n if (outputs?.length)\n options[1].disabled = false;\n const extra = node2.getExtraMenuOptions?.(this, options);\n if (extra) {\n extra.push(null);\n options = extra.concat(options);\n }\n if (node2.clonable !== false) {\n options.push({\n content: \"Clone\",\n callback: LGraphCanvas.onMenuNodeClone\n });\n }\n if (Object.keys(this.selected_nodes).length > 1) {\n options.push({\n content: \"Align Selected To\",\n has_submenu: true,\n callback: LGraphCanvas.onNodeAlign\n });\n options.push({\n content: \"Distribute Nodes\",\n has_submenu: true,\n callback: LGraphCanvas.createDistributeMenu\n });\n }\n options.push(null, {\n content: \"Remove\",\n disabled: !(node2.removable !== false && !node2.block_delete),\n callback: LGraphCanvas.onMenuNodeRemove\n });\n node2.graph?.onGetNodeMenuOptions?.(options, node2);\n return options;\n }\n getGroupMenuOptions(group) {\n console.warn(\"LGraphCanvas.getGroupMenuOptions is deprecated, use LGraphGroup.getMenuOptions instead\");\n return group.getMenuOptions();\n }\n processContextMenu(node2, event2) {\n const that2 = this;\n const canvas = LGraphCanvas.active_canvas;\n const ref_window2 = canvas.getCanvasWindow();\n let menu_info = null;\n const options = {\n event: event2,\n callback: inner_option_clicked,\n extra: node2\n };\n if (node2) options.title = node2.type;\n let slot = null;\n if (node2) {\n slot = node2.getSlotInPosition(event2.canvasX, event2.canvasY);\n LGraphCanvas.active_node = node2;\n }\n if (slot) {\n menu_info = [];\n if (node2.getSlotMenuOptions) {\n menu_info = node2.getSlotMenuOptions(slot);\n } else {\n if (slot?.output?.links?.length)\n menu_info.push({ content: \"Disconnect Links\", slot });\n const _slot = slot.input || slot.output;\n if (_slot.removable) {\n menu_info.push(\n _slot.locked ? \"Cannot remove\" : { content: \"Remove Slot\", slot }\n );\n }\n if (!_slot.nameLocked)\n menu_info.push({ content: \"Rename Slot\", slot });\n }\n options.title = (slot.input ? slot.input.type : slot.output.type) || \"*\";\n if (slot.input && slot.input.type == LiteGraph.ACTION)\n options.title = \"Action\";\n if (slot.output && slot.output.type == LiteGraph.EVENT)\n options.title = \"Event\";\n } else if (node2) {\n menu_info = this.getNodeMenuOptions(node2);\n } else {\n menu_info = this.getCanvasMenuOptions();\n const group = this.graph.getGroupOnPos(\n event2.canvasX,\n event2.canvasY\n );\n if (group) {\n menu_info.push(null, {\n content: \"Edit Group\",\n has_submenu: true,\n submenu: {\n title: \"Group\",\n extra: group,\n options: group.getMenuOptions()\n }\n });\n }\n }\n if (!menu_info)\n return;\n new LiteGraph.ContextMenu(menu_info, options, ref_window2);\n function inner_option_clicked(v2, options2) {\n if (!v2) return;\n if (v2.content == \"Remove Slot\") {\n const info = v2.slot;\n node2.graph.beforeChange();\n if (info.input) {\n node2.removeInput(info.slot);\n } else if (info.output) {\n node2.removeOutput(info.slot);\n }\n node2.graph.afterChange();\n return;\n } else if (v2.content == \"Disconnect Links\") {\n const info = v2.slot;\n node2.graph.beforeChange();\n if (info.output) {\n node2.disconnectOutput(info.slot);\n } else if (info.input) {\n node2.disconnectInput(info.slot);\n }\n node2.graph.afterChange();\n return;\n } else if (v2.content == \"Rename Slot\") {\n const info = v2.slot;\n const slot_info = info.input ? node2.getInputInfo(info.slot) : node2.getOutputInfo(info.slot);\n const dialog = that2.createDialog(\n \"Name\",\n options2\n );\n const input = dialog.querySelector(\"input\");\n if (input && slot_info) {\n input.value = slot_info.label || \"\";\n }\n const inner = function() {\n node2.graph.beforeChange();\n if (input.value) {\n if (slot_info) {\n slot_info.label = input.value;\n }\n that2.setDirty(true);\n }\n dialog.close();\n node2.graph.afterChange();\n };\n dialog.querySelector(\"button\").addEventListener(\"click\", inner);\n input.addEventListener(\"keydown\", function(e) {\n dialog.is_modified = true;\n if (e.keyCode == 27) {\n dialog.close();\n } else if (e.keyCode == 13) {\n inner();\n } else if (e.keyCode != 13 && e.target.localName != \"textarea\") {\n return;\n }\n e.preventDefault();\n e.stopPropagation();\n });\n input.focus();\n }\n }\n }\n}\nclass LGraph {\n //default supported types\n static supported_types = [\"number\", \"string\", \"boolean\"];\n static STATUS_STOPPED = 1;\n static STATUS_RUNNING = 2;\n _version;\n links;\n list_of_graphcanvas;\n status;\n last_node_id;\n last_link_id;\n /** The largest ID created by this graph */\n last_reroute_id;\n _nodes;\n _nodes_by_id;\n _nodes_in_order;\n _nodes_executable;\n _groups;\n iteration;\n globaltime;\n runningtime;\n fixedtime;\n fixedtime_lapse;\n elapsed_time;\n last_update_time;\n starttime;\n catch_errors;\n execution_timer_id;\n errors_in_execution;\n execution_time;\n _last_trigger_time;\n filter;\n _subgraph_node;\n config;\n vars;\n nodes_executing;\n nodes_actioning;\n nodes_executedAction;\n extra;\n inputs;\n outputs;\n _input_nodes;\n constructor(o) {\n if (LiteGraph.debug) console.log(\"Graph created\");\n this.list_of_graphcanvas = null;\n this.clear();\n if (o) this.configure(o);\n }\n // TODO: Remove\n //used to know which types of connections support this graph (some graphs do not allow certain types)\n getSupportedTypes() {\n return this.supported_types || LGraph.supported_types;\n }\n /**\n * Removes all nodes from this graph\n */\n clear() {\n this.stop();\n this.status = LGraph.STATUS_STOPPED;\n this.last_node_id = 0;\n this.last_link_id = 0;\n this._version = -1;\n if (this._nodes) {\n for (let i2 = 0; i2 < this._nodes.length; ++i2) {\n this._nodes[i2].onRemoved?.();\n }\n }\n this._nodes = [];\n this._nodes_by_id = {};\n this._nodes_in_order = [];\n this._nodes_executable = null;\n this._groups = [];\n this.links = {};\n this.iteration = 0;\n this.config = {};\n this.vars = {};\n this.extra = {};\n this.globaltime = 0;\n this.runningtime = 0;\n this.fixedtime = 0;\n this.fixedtime_lapse = 0.01;\n this.elapsed_time = 0.01;\n this.last_update_time = 0;\n this.starttime = 0;\n this.catch_errors = true;\n this.nodes_executing = [];\n this.nodes_actioning = [];\n this.nodes_executedAction = [];\n this.inputs = {};\n this.outputs = {};\n this.change();\n this.sendActionToCanvas(\"clear\");\n }\n get nodes() {\n return this._nodes;\n }\n get groups() {\n return this._groups;\n }\n /**\n * Attach Canvas to this graph\n * @param {GraphCanvas} graph_canvas\n */\n attachCanvas(graphcanvas) {\n if (graphcanvas.constructor != LGraphCanvas)\n throw \"attachCanvas expects a LGraphCanvas instance\";\n if (graphcanvas.graph != this)\n graphcanvas.graph?.detachCanvas(graphcanvas);\n graphcanvas.graph = this;\n this.list_of_graphcanvas ||= [];\n this.list_of_graphcanvas.push(graphcanvas);\n }\n /**\n * Detach Canvas from this graph\n * @param {GraphCanvas} graph_canvas\n */\n detachCanvas(graphcanvas) {\n if (!this.list_of_graphcanvas) return;\n const pos2 = this.list_of_graphcanvas.indexOf(graphcanvas);\n if (pos2 == -1) return;\n graphcanvas.graph = null;\n this.list_of_graphcanvas.splice(pos2, 1);\n }\n /**\n * Starts running this graph every interval milliseconds.\n * @param {number} interval amount of milliseconds between executions, if 0 then it renders to the monitor refresh rate\n */\n start(interval) {\n if (this.status == LGraph.STATUS_RUNNING) return;\n this.status = LGraph.STATUS_RUNNING;\n this.onPlayEvent?.();\n this.sendEventToAllNodes(\"onStart\");\n this.starttime = LiteGraph.getTime();\n this.last_update_time = this.starttime;\n interval ||= 0;\n const that2 = this;\n if (interval == 0 && typeof window != \"undefined\" && window.requestAnimationFrame) {\n let on_frame = function() {\n if (that2.execution_timer_id != -1) return;\n window.requestAnimationFrame(on_frame);\n that2.onBeforeStep?.();\n that2.runStep(1, !that2.catch_errors);\n that2.onAfterStep?.();\n };\n this.execution_timer_id = -1;\n on_frame();\n } else {\n this.execution_timer_id = setInterval(function() {\n that2.onBeforeStep?.();\n that2.runStep(1, !that2.catch_errors);\n that2.onAfterStep?.();\n }, interval);\n }\n }\n /**\n * Stops the execution loop of the graph\n */\n stop() {\n if (this.status == LGraph.STATUS_STOPPED) return;\n this.status = LGraph.STATUS_STOPPED;\n this.onStopEvent?.();\n if (this.execution_timer_id != null) {\n if (this.execution_timer_id != -1) {\n clearInterval(this.execution_timer_id);\n }\n this.execution_timer_id = null;\n }\n this.sendEventToAllNodes(\"onStop\");\n }\n /**\n * Run N steps (cycles) of the graph\n * @param {number} num number of steps to run, default is 1\n * @param {Boolean} do_not_catch_errors [optional] if you want to try/catch errors\n * @param {number} limit max number of nodes to execute (used to execute from start to a node)\n */\n runStep(num, do_not_catch_errors, limit) {\n num = num || 1;\n const start = LiteGraph.getTime();\n this.globaltime = 1e-3 * (start - this.starttime);\n const nodes = this._nodes_executable ? this._nodes_executable : this._nodes;\n if (!nodes) return;\n limit = limit || nodes.length;\n if (do_not_catch_errors) {\n for (let i2 = 0; i2 < num; i2++) {\n for (let j = 0; j < limit; ++j) {\n const node2 = nodes[j];\n if (node2.mode == LiteGraph.ALWAYS && node2.onExecute) {\n node2.doExecute?.();\n }\n }\n this.fixedtime += this.fixedtime_lapse;\n this.onExecuteStep?.();\n }\n this.onAfterExecute?.();\n } else {\n try {\n for (let i2 = 0; i2 < num; i2++) {\n for (let j = 0; j < limit; ++j) {\n const node2 = nodes[j];\n if (node2.mode == LiteGraph.ALWAYS) {\n node2.onExecute?.();\n }\n }\n this.fixedtime += this.fixedtime_lapse;\n this.onExecuteStep?.();\n }\n this.onAfterExecute?.();\n this.errors_in_execution = false;\n } catch (err) {\n this.errors_in_execution = true;\n if (LiteGraph.throw_errors) throw err;\n if (LiteGraph.debug) console.log(\"Error during execution: \" + err);\n this.stop();\n }\n }\n const now = LiteGraph.getTime();\n let elapsed = now - start;\n if (elapsed == 0) elapsed = 1;\n this.execution_time = 1e-3 * elapsed;\n this.globaltime += 1e-3 * elapsed;\n this.iteration += 1;\n this.elapsed_time = (now - this.last_update_time) * 1e-3;\n this.last_update_time = now;\n this.nodes_executing = [];\n this.nodes_actioning = [];\n this.nodes_executedAction = [];\n }\n /**\n * Updates the graph execution order according to relevance of the nodes (nodes with only outputs have more relevance than\n * nodes with only inputs.\n */\n updateExecutionOrder() {\n this._nodes_in_order = this.computeExecutionOrder(false);\n this._nodes_executable = [];\n for (let i2 = 0; i2 < this._nodes_in_order.length; ++i2) {\n if (this._nodes_in_order[i2].onExecute) {\n this._nodes_executable.push(this._nodes_in_order[i2]);\n }\n }\n }\n //This is more internal, it computes the executable nodes in order and returns it\n computeExecutionOrder(only_onExecute, set_level) {\n let L = [];\n const S = [];\n const M = {};\n const visited_links = {};\n const remaining_links = {};\n for (let i2 = 0, l2 = this._nodes.length; i2 < l2; ++i2) {\n const node2 = this._nodes[i2];\n if (only_onExecute && !node2.onExecute) {\n continue;\n }\n M[node2.id] = node2;\n let num = 0;\n if (node2.inputs) {\n for (let j = 0, l22 = node2.inputs.length; j < l22; j++) {\n if (node2.inputs[j]?.link != null) {\n num += 1;\n }\n }\n }\n if (num == 0) {\n S.push(node2);\n if (set_level) node2._level = 1;\n } else {\n if (set_level) node2._level = 0;\n remaining_links[node2.id] = num;\n }\n }\n while (true) {\n if (S.length == 0) break;\n const node2 = S.shift();\n L.push(node2);\n delete M[node2.id];\n if (!node2.outputs) continue;\n for (let i2 = 0; i2 < node2.outputs.length; i2++) {\n const output = node2.outputs[i2];\n if (output?.links == null || output.links.length == 0)\n continue;\n for (let j = 0; j < output.links.length; j++) {\n const link_id = output.links[j];\n const link = this.links[link_id];\n if (!link) continue;\n if (visited_links[link.id]) continue;\n const target_node = this.getNodeById(link.target_id);\n if (target_node == null) {\n visited_links[link.id] = true;\n continue;\n }\n if (set_level && (!target_node._level || target_node._level <= node2._level)) {\n target_node._level = node2._level + 1;\n }\n visited_links[link.id] = true;\n remaining_links[target_node.id] -= 1;\n if (remaining_links[target_node.id] == 0) S.push(target_node);\n }\n }\n }\n for (const i2 in M) {\n L.push(M[i2]);\n }\n if (L.length != this._nodes.length && LiteGraph.debug)\n console.warn(\"something went wrong, nodes missing\");\n const l = L.length;\n for (let i2 = 0; i2 < l; ++i2) {\n L[i2].order = i2;\n }\n L = L.sort(function(A, B) {\n const Ap = A.constructor.priority || A.priority || 0;\n const Bp = B.constructor.priority || B.priority || 0;\n return Ap == Bp ? A.order - B.order : Ap - Bp;\n });\n for (let i2 = 0; i2 < l; ++i2) {\n L[i2].order = i2;\n }\n return L;\n }\n /**\n * Returns all the nodes that could affect this one (ancestors) by crawling all the inputs recursively.\n * It doesn't include the node itself\n * @return {Array} an array with all the LGraphNodes that affect this node, in order of execution\n */\n getAncestors(node2) {\n const ancestors = [];\n const pending = [node2];\n const visited = {};\n while (pending.length) {\n const current = pending.shift();\n if (!current.inputs) continue;\n if (!visited[current.id] && current != node2) {\n visited[current.id] = true;\n ancestors.push(current);\n }\n for (let i2 = 0; i2 < current.inputs.length; ++i2) {\n const input = current.getInputNode(i2);\n if (input && ancestors.indexOf(input) == -1) {\n pending.push(input);\n }\n }\n }\n ancestors.sort(function(a, b) {\n return a.order - b.order;\n });\n return ancestors;\n }\n /**\n * Positions every node in a more readable manner\n */\n arrange(margin, layout) {\n margin = margin || 100;\n const nodes = this.computeExecutionOrder(false, true);\n const columns = [];\n for (let i2 = 0; i2 < nodes.length; ++i2) {\n const node2 = nodes[i2];\n const col = node2._level || 1;\n columns[col] ||= [];\n columns[col].push(node2);\n }\n let x2 = margin;\n for (let i2 = 0; i2 < columns.length; ++i2) {\n const column = columns[i2];\n if (!column) continue;\n let max_size = 100;\n let y2 = margin + LiteGraph.NODE_TITLE_HEIGHT;\n for (let j = 0; j < column.length; ++j) {\n const node2 = column[j];\n node2.pos[0] = layout == LiteGraph.VERTICAL_LAYOUT ? y2 : x2;\n node2.pos[1] = layout == LiteGraph.VERTICAL_LAYOUT ? x2 : y2;\n const max_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 1 : 0;\n if (node2.size[max_size_index] > max_size) {\n max_size = node2.size[max_size_index];\n }\n const node_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 0 : 1;\n y2 += node2.size[node_size_index] + margin + LiteGraph.NODE_TITLE_HEIGHT;\n }\n x2 += max_size + margin;\n }\n this.setDirtyCanvas(true, true);\n }\n /**\n * Returns the amount of time the graph has been running in milliseconds\n * @return {number} number of milliseconds the graph has been running\n */\n getTime() {\n return this.globaltime;\n }\n /**\n * Returns the amount of time accumulated using the fixedtime_lapse var. This is used in context where the time increments should be constant\n * @return {number} number of milliseconds the graph has been running\n */\n getFixedTime() {\n return this.fixedtime;\n }\n /**\n * Returns the amount of time it took to compute the latest iteration. Take into account that this number could be not correct\n * if the nodes are using graphical actions\n * @return {number} number of milliseconds it took the last cycle\n */\n getElapsedTime() {\n return this.elapsed_time;\n }\n /**\n * Sends an event to all the nodes, useful to trigger stuff\n * @param {String} eventname the name of the event (function to be called)\n * @param {Array} params parameters in array format\n */\n sendEventToAllNodes(eventname, params, mode) {\n mode = mode || LiteGraph.ALWAYS;\n const nodes = this._nodes_in_order ? this._nodes_in_order : this._nodes;\n if (!nodes) return;\n for (let j = 0, l = nodes.length; j < l; ++j) {\n const node2 = nodes[j];\n if (node2.constructor === LiteGraph.Subgraph && eventname != \"onExecute\") {\n if (node2.mode == mode) {\n node2.sendEventToAllNodes(eventname, params, mode);\n }\n continue;\n }\n if (!node2[eventname] || node2.mode != mode) continue;\n if (params === void 0) {\n node2[eventname]();\n } else if (params && params.constructor === Array) {\n node2[eventname].apply(node2, params);\n } else {\n node2[eventname](params);\n }\n }\n }\n sendActionToCanvas(action, params) {\n if (!this.list_of_graphcanvas) return;\n for (let i2 = 0; i2 < this.list_of_graphcanvas.length; ++i2) {\n const c = this.list_of_graphcanvas[i2];\n c[action]?.apply(c, params);\n }\n }\n /**\n * Adds a new node instance to this graph\n * @param {LGraphNode} node the instance of the node\n */\n add(node2, skip_compute_order) {\n if (!node2) return;\n if (node2 instanceof LGraphGroup) {\n this._groups.push(node2);\n this.setDirtyCanvas(true);\n this.change();\n node2.graph = this;\n this._version++;\n return;\n }\n if (node2.id != -1 && this._nodes_by_id[node2.id] != null) {\n console.warn(\n \"LiteGraph: there is already a node with this ID, changing it\"\n );\n node2.id = LiteGraph.use_uuids ? LiteGraph.uuidv4() : ++this.last_node_id;\n }\n if (this._nodes.length >= LiteGraph.MAX_NUMBER_OF_NODES) {\n throw \"LiteGraph: max number of nodes in a graph reached\";\n }\n if (LiteGraph.use_uuids) {\n if (node2.id == null || node2.id == -1)\n node2.id = LiteGraph.uuidv4();\n } else {\n if (node2.id == null || node2.id == -1) {\n node2.id = ++this.last_node_id;\n } else if (typeof node2.id === \"number\" && this.last_node_id < node2.id) {\n this.last_node_id = node2.id;\n }\n }\n node2.graph = this;\n this._version++;\n this._nodes.push(node2);\n this._nodes_by_id[node2.id] = node2;\n node2.onAdded?.(this);\n if (this.config.align_to_grid) node2.alignToGrid();\n if (!skip_compute_order) this.updateExecutionOrder();\n this.onNodeAdded?.(node2);\n this.setDirtyCanvas(true);\n this.change();\n return node2;\n }\n /**\n * Removes a node from the graph\n * @param {LGraphNode} node the instance of the node\n */\n remove(node2) {\n if (node2 instanceof LGraphGroup) {\n const index = this._groups.indexOf(node2);\n if (index != -1) {\n this._groups.splice(index, 1);\n }\n node2.graph = null;\n this._version++;\n this.setDirtyCanvas(true, true);\n this.change();\n return;\n }\n if (this._nodes_by_id[node2.id] == null) return;\n if (node2.ignore_remove) return;\n this.beforeChange();\n if (node2.inputs) {\n for (let i2 = 0; i2 < node2.inputs.length; i2++) {\n const slot = node2.inputs[i2];\n if (slot.link != null)\n node2.disconnectInput(i2);\n }\n }\n if (node2.outputs) {\n for (let i2 = 0; i2 < node2.outputs.length; i2++) {\n const slot = node2.outputs[i2];\n if (slot.links?.length)\n node2.disconnectOutput(i2);\n }\n }\n node2.onRemoved?.();\n node2.graph = null;\n this._version++;\n if (this.list_of_graphcanvas) {\n for (let i2 = 0; i2 < this.list_of_graphcanvas.length; ++i2) {\n const canvas = this.list_of_graphcanvas[i2];\n if (canvas.selected_nodes[node2.id])\n delete canvas.selected_nodes[node2.id];\n if (canvas.node_dragged == node2)\n canvas.node_dragged = null;\n }\n }\n const pos2 = this._nodes.indexOf(node2);\n if (pos2 != -1) this._nodes.splice(pos2, 1);\n delete this._nodes_by_id[node2.id];\n this.onNodeRemoved?.(node2);\n this.sendActionToCanvas(\"checkPanels\");\n this.setDirtyCanvas(true, true);\n this.afterChange();\n this.change();\n this.updateExecutionOrder();\n }\n /**\n * Returns a node by its id.\n * @param {Number} id\n */\n getNodeById(id) {\n return id != null ? this._nodes_by_id[id] : null;\n }\n /**\n * Returns a list of nodes that matches a class\n * @param {Class} classObject the class itself (not an string)\n * @return {Array} a list with all the nodes of this type\n */\n // eslint-disable-next-line @typescript-eslint/no-unsafe-function-type\n findNodesByClass(classObject, result) {\n result = result || [];\n result.length = 0;\n for (let i2 = 0, l = this._nodes.length; i2 < l; ++i2) {\n if (this._nodes[i2].constructor === classObject)\n result.push(this._nodes[i2]);\n }\n return result;\n }\n /**\n * Returns a list of nodes that matches a type\n * @param {String} type the name of the node type\n * @return {Array} a list with all the nodes of this type\n */\n findNodesByType(type, result) {\n const matchType = type.toLowerCase();\n result = result || [];\n result.length = 0;\n for (let i2 = 0, l = this._nodes.length; i2 < l; ++i2) {\n if (this._nodes[i2].type.toLowerCase() == matchType)\n result.push(this._nodes[i2]);\n }\n return result;\n }\n /**\n * Returns the first node that matches a name in its title\n * @param {String} name the name of the node to search\n * @return {Node} the node or null\n */\n findNodeByTitle(title) {\n for (let i2 = 0, l = this._nodes.length; i2 < l; ++i2) {\n if (this._nodes[i2].title == title)\n return this._nodes[i2];\n }\n return null;\n }\n /**\n * Returns a list of nodes that matches a name\n * @param {String} name the name of the node to search\n * @return {Array} a list with all the nodes with this name\n */\n findNodesByTitle(title) {\n const result = [];\n for (let i2 = 0, l = this._nodes.length; i2 < l; ++i2) {\n if (this._nodes[i2].title == title)\n result.push(this._nodes[i2]);\n }\n return result;\n }\n /**\n * Returns the top-most node in this position of the canvas\n * @param {number} x the x coordinate in canvas space\n * @param {number} y the y coordinate in canvas space\n * @param {Array} nodes_list a list with all the nodes to search from, by default is all the nodes in the graph\n * @return {LGraphNode} the node at this position or null\n */\n getNodeOnPos(x2, y2, nodes_list, margin) {\n nodes_list = nodes_list || this._nodes;\n const nRet = null;\n for (let i2 = nodes_list.length - 1; i2 >= 0; i2--) {\n const n = nodes_list[i2];\n const skip_title = n.constructor.title_mode == LiteGraph.NO_TITLE;\n if (n.isPointInside(x2, y2, margin, skip_title)) {\n return n;\n }\n }\n return nRet;\n }\n /**\n * Returns the top-most group in that position\n * @param x The x coordinate in canvas space\n * @param y The y coordinate in canvas space\n * @return The group or null\n */\n getGroupOnPos(x2, y2, { margin = 2 } = {}) {\n return this._groups.reverse().find((g) => g.isPointInside(\n x2,\n y2,\n margin,\n /* skip_title */\n true\n ));\n }\n /**\n * Checks that the node type matches the node type registered, used when replacing a nodetype by a newer version during execution\n * this replaces the ones using the old version with the new version\n */\n checkNodeTypes() {\n for (let i2 = 0; i2 < this._nodes.length; i2++) {\n const node2 = this._nodes[i2];\n const ctor = LiteGraph.registered_node_types[node2.type];\n if (node2.constructor == ctor) continue;\n console.log(\"node being replaced by newer version: \" + node2.type);\n const newnode = LiteGraph.createNode(node2.type);\n this._nodes[i2] = newnode;\n newnode.configure(node2.serialize());\n newnode.graph = this;\n this._nodes_by_id[newnode.id] = newnode;\n if (node2.inputs) newnode.inputs = node2.inputs.concat();\n if (node2.outputs) newnode.outputs = node2.outputs.concat();\n }\n this.updateExecutionOrder();\n }\n // ********** GLOBALS *****************\n onAction(action, param, options) {\n this._input_nodes = this.findNodesByClass(\n // @ts-expect-error Never impl.\n LiteGraph.GraphInput,\n this._input_nodes\n );\n for (let i2 = 0; i2 < this._input_nodes.length; ++i2) {\n const node2 = this._input_nodes[i2];\n if (node2.properties.name != action) continue;\n node2.actionDo(action, param, options);\n break;\n }\n }\n trigger(action, param) {\n this.onTrigger?.(action, param);\n }\n /**\n * Tell this graph it has a global graph input of this type\n * @param {String} name\n * @param {String} type\n * @param {*} value [optional]\n */\n addInput(name, type, value) {\n const input = this.inputs[name];\n if (input) return;\n this.beforeChange();\n this.inputs[name] = { name, type, value };\n this._version++;\n this.afterChange();\n this.onInputAdded?.(name, type);\n this.onInputsOutputsChange?.();\n }\n /**\n * Assign a data to the global graph input\n * @param {String} name\n * @param {*} data\n */\n setInputData(name, data) {\n const input = this.inputs[name];\n if (!input) return;\n input.value = data;\n }\n /**\n * Returns the current value of a global graph input\n * @param {String} name\n * @return {*} the data\n */\n getInputData(name) {\n const input = this.inputs[name];\n return input ? input.value : null;\n }\n /**\n * Changes the name of a global graph input\n * @param {String} old_name\n * @param {String} new_name\n */\n renameInput(old_name, name) {\n if (name == old_name) return;\n if (!this.inputs[old_name]) return false;\n if (this.inputs[name]) {\n console.error(\"there is already one input with that name\");\n return false;\n }\n this.inputs[name] = this.inputs[old_name];\n delete this.inputs[old_name];\n this._version++;\n this.onInputRenamed?.(old_name, name);\n this.onInputsOutputsChange?.();\n }\n /**\n * Changes the type of a global graph input\n * @param {String} name\n * @param {String} type\n */\n changeInputType(name, type) {\n if (!this.inputs[name]) return false;\n if (this.inputs[name].type && String(this.inputs[name].type).toLowerCase() == String(type).toLowerCase()) {\n return;\n }\n this.inputs[name].type = type;\n this._version++;\n this.onInputTypeChanged?.(name, type);\n }\n /**\n * Removes a global graph input\n * @param {String} name\n * @param {String} type\n */\n removeInput(name) {\n if (!this.inputs[name]) return false;\n delete this.inputs[name];\n this._version++;\n this.onInputRemoved?.(name);\n this.onInputsOutputsChange?.();\n return true;\n }\n /**\n * Creates a global graph output\n * @param {String} name\n * @param {String} type\n * @param {*} value\n */\n addOutput(name, type, value) {\n this.outputs[name] = { name, type, value };\n this._version++;\n this.onOutputAdded?.(name, type);\n this.onInputsOutputsChange?.();\n }\n /**\n * Assign a data to the global output\n * @param {String} name\n * @param {String} value\n */\n setOutputData(name, value) {\n const output = this.outputs[name];\n if (!output) return;\n output.value = value;\n }\n /**\n * Returns the current value of a global graph output\n * @param {String} name\n * @return {*} the data\n */\n getOutputData(name) {\n const output = this.outputs[name];\n if (!output) return null;\n return output.value;\n }\n /**\n * Renames a global graph output\n * @param {String} old_name\n * @param {String} new_name\n */\n renameOutput(old_name, name) {\n if (!this.outputs[old_name]) return false;\n if (this.outputs[name]) {\n console.error(\"there is already one output with that name\");\n return false;\n }\n this.outputs[name] = this.outputs[old_name];\n delete this.outputs[old_name];\n this._version++;\n this.onOutputRenamed?.(old_name, name);\n this.onInputsOutputsChange?.();\n }\n /**\n * Changes the type of a global graph output\n * @param {String} name\n * @param {String} type\n */\n changeOutputType(name, type) {\n if (!this.outputs[name]) return false;\n if (this.outputs[name].type && String(this.outputs[name].type).toLowerCase() == String(type).toLowerCase()) {\n return;\n }\n this.outputs[name].type = type;\n this._version++;\n this.onOutputTypeChanged?.(name, type);\n }\n /**\n * Removes a global graph output\n * @param {String} name\n */\n removeOutput(name) {\n if (!this.outputs[name]) return false;\n delete this.outputs[name];\n this._version++;\n this.onOutputRemoved?.(name);\n this.onInputsOutputsChange?.();\n return true;\n }\n // TODO: Clean up - never implemented.\n triggerInput(name, value) {\n const nodes = this.findNodesByTitle(name);\n for (let i2 = 0; i2 < nodes.length; ++i2) {\n nodes[i2].onTrigger(value);\n }\n }\n // TODO: Clean up - never implemented.\n setCallback(name, func) {\n const nodes = this.findNodesByTitle(name);\n for (let i2 = 0; i2 < nodes.length; ++i2) {\n nodes[i2].setTrigger(func);\n }\n }\n //used for undo, called before any change is made to the graph\n beforeChange(info) {\n this.onBeforeChange?.(this, info);\n this.sendActionToCanvas(\"onBeforeChange\", this);\n }\n //used to resend actions, called after any change is made to the graph\n afterChange(info) {\n this.onAfterChange?.(this, info);\n this.sendActionToCanvas(\"onAfterChange\", this);\n }\n connectionChange(node2) {\n this.updateExecutionOrder();\n this.onConnectionChange?.(node2);\n this._version++;\n this.sendActionToCanvas(\"onConnectionChange\");\n }\n /**\n * returns if the graph is in live mode\n */\n isLive() {\n if (!this.list_of_graphcanvas) return false;\n for (let i2 = 0; i2 < this.list_of_graphcanvas.length; ++i2) {\n const c = this.list_of_graphcanvas[i2];\n if (c.live_mode) return true;\n }\n return false;\n }\n /**\n * clears the triggered slot animation in all links (stop visual animation)\n */\n clearTriggeredSlots() {\n for (const i2 in this.links) {\n const link_info = this.links[i2];\n if (!link_info) continue;\n if (link_info._last_time)\n link_info._last_time = 0;\n }\n }\n /* Called when something visually changed (not the graph!) */\n change() {\n if (LiteGraph.debug) {\n console.log(\"Graph changed\");\n }\n this.sendActionToCanvas(\"setDirty\", [true, true]);\n this.on_change?.(this);\n }\n setDirtyCanvas(fg, bg) {\n this.sendActionToCanvas(\"setDirty\", [fg, bg]);\n }\n /**\n * Destroys a link\n * @param {Number} link_id\n */\n removeLink(link_id) {\n const link = this.links[link_id];\n if (!link) return;\n const node2 = this.getNodeById(link.target_id);\n node2?.disconnectInput(link.target_slot);\n }\n //save and recover app state ***************************************\n /**\n * Creates a Object containing all the info about this graph, it can be serialized\n * @return {Object} value of the node\n */\n serialize(option) {\n const nodes = !LiteGraph.use_uuids && option?.sortNodes ? [...this._nodes].sort((a, b) => a.id - b.id) : this._nodes;\n const nodes_info = nodes.map((node2) => node2.serialize());\n const links = [];\n for (const linkId in this.links) {\n let link = this.links[linkId];\n if (!link.serialize) {\n console.warn(\n \"weird LLink bug, link info is not a LLink but a regular object\"\n );\n const link2 = new LLink();\n for (const j in link) {\n link2[j] = link[j];\n }\n this.links[linkId] = link2;\n link = link2;\n }\n links.push(link.serialize());\n }\n const groups_info = [];\n for (let i2 = 0; i2 < this._groups.length; ++i2) {\n groups_info.push(this._groups[i2].serialize());\n }\n const data = {\n last_node_id: this.last_node_id,\n last_link_id: this.last_link_id,\n nodes: nodes_info,\n links,\n groups: groups_info,\n config: this.config,\n extra: this.extra,\n version: LiteGraph.VERSION\n };\n this.onSerialize?.(data);\n return data;\n }\n /**\n * Configure a graph from a JSON string\n * @param {String} str configure a graph from a JSON string\n * @param {Boolean} returns if there was any error parsing\n */\n configure(data, keep_old) {\n if (!data) return;\n if (!keep_old) this.clear();\n const nodesData = data.nodes;\n if (Array.isArray(data.links)) {\n const links = [];\n for (const link_data of data.links) {\n if (!link_data) {\n console.warn(\"serialized graph link data contains errors, skipping.\");\n continue;\n }\n const link = new LLink();\n link.configure(link_data);\n links[link.id] = link;\n }\n data.links = links;\n }\n for (const i2 in data) {\n if (i2 == \"nodes\" || i2 == \"groups\")\n continue;\n this[i2] = data[i2];\n }\n let error = false;\n this._nodes = [];\n if (nodesData) {\n for (let i2 = 0, l = nodesData.length; i2 < l; ++i2) {\n const n_info = nodesData[i2];\n let node2 = LiteGraph.createNode(n_info.type, n_info.title);\n if (!node2) {\n if (LiteGraph.debug) console.log(\"Node not found or has errors: \" + n_info.type);\n node2 = new LGraphNode(void 0);\n node2.last_serialization = n_info;\n node2.has_errors = true;\n error = true;\n }\n node2.id = n_info.id;\n this.add(node2, true);\n }\n for (let i2 = 0, l = nodesData.length; i2 < l; ++i2) {\n const n_info = nodesData[i2];\n const node2 = this.getNodeById(n_info.id);\n node2?.configure(n_info);\n }\n }\n this._groups.length = 0;\n if (data.groups) {\n for (let i2 = 0; i2 < data.groups.length; ++i2) {\n const group = new LiteGraph.LGraphGroup();\n group.configure(data.groups[i2]);\n this.add(group);\n }\n }\n this.updateExecutionOrder();\n this.extra = data.extra || {};\n this.onConfigure?.(data);\n this._version++;\n this.setDirtyCanvas(true, true);\n return error;\n }\n load(url, callback) {\n const that2 = this;\n if (url instanceof Blob || url instanceof File) {\n const reader = new FileReader();\n reader.addEventListener(\"load\", function(event2) {\n const data = JSON.parse(event2.target.result.toString());\n that2.configure(data);\n callback?.();\n });\n reader.readAsText(url);\n return;\n }\n const req = new XMLHttpRequest();\n req.open(\"GET\", url, true);\n req.send(null);\n req.onload = function() {\n if (req.status !== 200) {\n console.error(\"Error loading graph:\", req.status, req.response);\n return;\n }\n const data = JSON.parse(req.response);\n that2.configure(data);\n callback?.();\n };\n req.onerror = function(err) {\n console.error(\"Error loading graph:\", err);\n };\n }\n // eslint-disable-next-line @typescript-eslint/no-unused-vars\n onNodeTrace(node2, msg) {\n }\n}\nclass ContextMenu {\n options;\n parentMenu;\n root;\n current_submenu;\n lock;\n // TODO: Interface for values requires functionality change - currently accepts an array of strings, functions, objects, nulls, or undefined.\n constructor(values2, options) {\n options ||= {};\n this.options = options;\n const parent = options.parentMenu;\n if (parent) {\n if (!(parent instanceof ContextMenu)) {\n console.error(\"parentMenu must be of class ContextMenu, ignoring it\");\n options.parentMenu = null;\n } else {\n this.parentMenu = parent;\n this.parentMenu.lock = true;\n this.parentMenu.current_submenu = this;\n }\n if (parent.options?.className === \"dark\") {\n options.className = \"dark\";\n }\n }\n const eventClass = options.event ? options.event.constructor.name : null;\n if (eventClass !== \"MouseEvent\" && eventClass !== \"CustomEvent\" && eventClass !== \"PointerEvent\") {\n console.error(`Event passed to ContextMenu is not of type MouseEvent or CustomEvent. Ignoring it. (${eventClass})`);\n options.event = null;\n }\n const root = document.createElement(\"div\");\n let classes = \"litegraph litecontextmenu litemenubar-panel\";\n if (options.className) classes += \" \" + options.className;\n root.className = classes;\n root.style.minWidth = \"100\";\n root.style.minHeight = \"100\";\n root.style.pointerEvents = \"none\";\n setTimeout(function() {\n root.style.pointerEvents = \"auto\";\n }, 100);\n LiteGraph.pointerListenerAdd(\n root,\n \"up\",\n function(e) {\n e.preventDefault();\n return true;\n },\n true\n );\n root.addEventListener(\n \"contextmenu\",\n function(e) {\n if (e.button != 2) return false;\n e.preventDefault();\n return false;\n },\n true\n );\n LiteGraph.pointerListenerAdd(\n root,\n \"down\",\n (e) => {\n if (e.button == 2) {\n this.close();\n e.preventDefault();\n return true;\n }\n },\n true\n );\n function on_mouse_wheel(e) {\n const pos2 = parseInt(root.style.top);\n root.style.top = (pos2 + e.deltaY * options.scroll_speed).toFixed() + \"px\";\n e.preventDefault();\n return true;\n }\n if (!options.scroll_speed) {\n options.scroll_speed = 0.1;\n }\n root.addEventListener(\"wheel\", on_mouse_wheel, true);\n this.root = root;\n if (options.title) {\n const element = document.createElement(\"div\");\n element.className = \"litemenu-title\";\n element.innerHTML = options.title;\n root.appendChild(element);\n }\n for (let i2 = 0; i2 < values2.length; i2++) {\n const value = values2[i2];\n let name = Array.isArray(values2) ? value : String(i2);\n if (typeof name !== \"string\") {\n name = name != null ? name.content === void 0 ? String(name) : name.content : name;\n }\n this.addItem(name, value, options);\n }\n LiteGraph.pointerListenerAdd(root, \"enter\", function() {\n if (root.closing_timer) {\n clearTimeout(root.closing_timer);\n }\n });\n const ownerDocument = (options.event?.target).ownerDocument;\n const root_document = ownerDocument || document;\n if (root_document.fullscreenElement)\n root_document.fullscreenElement.appendChild(root);\n else\n root_document.body.appendChild(root);\n let left = options.left || 0;\n let top = options.top || 0;\n if (options.event) {\n left = options.event.clientX - 10;\n top = options.event.clientY - 10;\n if (options.title) top -= 20;\n if (parent) {\n const rect = parent.root.getBoundingClientRect();\n left = rect.left + rect.width;\n }\n const body_rect = document.body.getBoundingClientRect();\n const root_rect = root.getBoundingClientRect();\n if (body_rect.height == 0)\n console.error(\"document.body height is 0. That is dangerous, set html,body { height: 100%; }\");\n if (body_rect.width && left > body_rect.width - root_rect.width - 10)\n left = body_rect.width - root_rect.width - 10;\n if (body_rect.height && top > body_rect.height - root_rect.height - 10)\n top = body_rect.height - root_rect.height - 10;\n }\n root.style.left = left + \"px\";\n root.style.top = top + \"px\";\n if (options.scale)\n root.style.transform = `scale(${options.scale})`;\n }\n addItem(name, value, options) {\n options ||= {};\n const element = document.createElement(\"div\");\n element.className = \"litemenu-entry submenu\";\n let disabled = false;\n if (value === null) {\n element.classList.add(\"separator\");\n } else {\n if (typeof value === \"string\") {\n element.innerHTML = name;\n } else {\n element.innerHTML = value?.title ?? name;\n if (value.disabled) {\n disabled = true;\n element.classList.add(\"disabled\");\n element.setAttribute(\"aria-disabled\", \"true\");\n }\n if (value.submenu || value.has_submenu) {\n element.classList.add(\"has_submenu\");\n element.setAttribute(\"aria-haspopup\", \"true\");\n element.setAttribute(\"aria-expanded\", \"false\");\n }\n if (value.className)\n element.className += \" \" + value.className;\n }\n element.value = value;\n element.setAttribute(\"role\", \"menuitem\");\n if (typeof value === \"function\") {\n element.dataset[\"value\"] = name;\n element.onclick_callback = value;\n } else {\n element.dataset[\"value\"] = String(value);\n }\n }\n this.root.appendChild(element);\n if (!disabled) element.addEventListener(\"click\", inner_onclick);\n if (!disabled && options.autoopen)\n LiteGraph.pointerListenerAdd(element, \"enter\", inner_over);\n const setAriaExpanded = () => {\n const entries = this.root.querySelectorAll(\"div.litemenu-entry.has_submenu\");\n if (entries) {\n for (let i2 = 0; i2 < entries.length; i2++) {\n entries[i2].setAttribute(\"aria-expanded\", \"false\");\n }\n }\n element.setAttribute(\"aria-expanded\", \"true\");\n };\n function inner_over(e) {\n const value2 = this.value;\n if (!value2 || !value2.has_submenu) return;\n inner_onclick.call(this, e);\n setAriaExpanded();\n }\n const that2 = this;\n function inner_onclick(e) {\n const value2 = this.value;\n let close_parent = true;\n that2.current_submenu?.close(e);\n if (value2?.has_submenu || value2?.submenu) setAriaExpanded();\n if (options.callback) {\n const r = options.callback.call(\n this,\n value2,\n options,\n e,\n that2,\n options.node\n );\n if (r === true) close_parent = false;\n }\n if (typeof value2 === \"object\") {\n if (value2.callback && !options.ignore_item_callbacks && value2.disabled !== true) {\n const r = value2.callback.call(\n this,\n value2,\n options,\n e,\n that2,\n options.extra\n );\n if (r === true) close_parent = false;\n }\n if (value2.submenu) {\n if (!value2.submenu.options)\n throw \"ContextMenu submenu needs options\";\n new that2.constructor(value2.submenu.options, {\n callback: value2.submenu.callback,\n event: e,\n parentMenu: that2,\n ignore_item_callbacks: value2.submenu.ignore_item_callbacks,\n title: value2.submenu.title,\n extra: value2.submenu.extra,\n autoopen: options.autoopen\n });\n close_parent = false;\n }\n }\n if (close_parent && !that2.lock)\n that2.close();\n }\n return element;\n }\n close(e, ignore_parent_menu) {\n this.root.parentNode?.removeChild(this.root);\n if (this.parentMenu && !ignore_parent_menu) {\n this.parentMenu.lock = false;\n this.parentMenu.current_submenu = null;\n if (e === void 0) {\n this.parentMenu.close();\n } else if (e && !ContextMenu.isCursorOverElement(e, this.parentMenu.root)) {\n ContextMenu.trigger(this.parentMenu.root, LiteGraph.pointerevents_method + \"leave\", e);\n }\n }\n this.current_submenu?.close(e, true);\n if (this.root.closing_timer)\n clearTimeout(this.root.closing_timer);\n }\n //this code is used to trigger events easily (used in the context menu mouseleave\n static trigger(element, event_name, params, origin) {\n const evt = document.createEvent(\"CustomEvent\");\n evt.initCustomEvent(event_name, true, true, params);\n evt.srcElement = origin;\n if (element.dispatchEvent) element.dispatchEvent(evt);\n else if (element.__events) element.__events.dispatchEvent(evt);\n return evt;\n }\n //returns the top most menu\n getTopMenu() {\n return this.options.parentMenu ? this.options.parentMenu.getTopMenu() : this;\n }\n getFirstEvent() {\n return this.options.parentMenu ? this.options.parentMenu.getFirstEvent() : this.options.event;\n }\n static isCursorOverElement(event2, element) {\n const left = event2.clientX;\n const top = event2.clientY;\n const rect = element.getBoundingClientRect();\n if (!rect) return false;\n if (top > rect.top && top < rect.top + rect.height && left > rect.left && left < rect.left + rect.width) {\n return true;\n }\n return false;\n }\n}\nclass CurveEditor {\n points;\n selected;\n nearest;\n size;\n must_update;\n margin;\n _nearest;\n constructor(points) {\n this.points = points;\n this.selected = -1;\n this.nearest = -1;\n this.size = null;\n this.must_update = true;\n this.margin = 5;\n }\n static sampleCurve(f, points) {\n if (!points)\n return;\n for (let i2 = 0; i2 < points.length - 1; ++i2) {\n const p = points[i2];\n const pn = points[i2 + 1];\n if (pn[0] < f)\n continue;\n const r = pn[0] - p[0];\n if (Math.abs(r) < 1e-5)\n return p[1];\n const local_f = (f - p[0]) / r;\n return p[1] * (1 - local_f) + pn[1] * local_f;\n }\n return 0;\n }\n draw(ctx, size, graphcanvas, background_color, line_color, inactive = false) {\n const points = this.points;\n if (!points)\n return;\n this.size = size;\n const w2 = size[0] - this.margin * 2;\n const h = size[1] - this.margin * 2;\n line_color = line_color || \"#666\";\n ctx.save();\n ctx.translate(this.margin, this.margin);\n if (background_color) {\n ctx.fillStyle = \"#111\";\n ctx.fillRect(0, 0, w2, h);\n ctx.fillStyle = \"#222\";\n ctx.fillRect(w2 * 0.5, 0, 1, h);\n ctx.strokeStyle = \"#333\";\n ctx.strokeRect(0, 0, w2, h);\n }\n ctx.strokeStyle = line_color;\n if (inactive)\n ctx.globalAlpha = 0.5;\n ctx.beginPath();\n for (let i2 = 0; i2 < points.length; ++i2) {\n const p = points[i2];\n ctx.lineTo(p[0] * w2, (1 - p[1]) * h);\n }\n ctx.stroke();\n ctx.globalAlpha = 1;\n if (!inactive)\n for (let i2 = 0; i2 < points.length; ++i2) {\n const p = points[i2];\n ctx.fillStyle = this.selected == i2 ? \"#FFF\" : this.nearest == i2 ? \"#DDD\" : \"#AAA\";\n ctx.beginPath();\n ctx.arc(p[0] * w2, (1 - p[1]) * h, 2, 0, Math.PI * 2);\n ctx.fill();\n }\n ctx.restore();\n }\n //localpos is mouse in curve editor space\n onMouseDown(localpos, graphcanvas) {\n const points = this.points;\n if (!points)\n return;\n if (localpos[1] < 0)\n return;\n const w2 = this.size[0] - this.margin * 2;\n const h = this.size[1] - this.margin * 2;\n const x2 = localpos[0] - this.margin;\n const y2 = localpos[1] - this.margin;\n const pos2 = [x2, y2];\n const max_dist = 30 / graphcanvas.ds.scale;\n this.selected = this.getCloserPoint(pos2, max_dist);\n if (this.selected == -1) {\n const point = [x2 / w2, 1 - y2 / h];\n points.push(point);\n points.sort(function(a, b) {\n return a[0] - b[0];\n });\n this.selected = points.indexOf(point);\n this.must_update = true;\n }\n if (this.selected != -1)\n return true;\n }\n onMouseMove(localpos, graphcanvas) {\n const points = this.points;\n if (!points)\n return;\n const s = this.selected;\n if (s < 0)\n return;\n const x2 = (localpos[0] - this.margin) / (this.size[0] - this.margin * 2);\n const y2 = (localpos[1] - this.margin) / (this.size[1] - this.margin * 2);\n const curvepos = [localpos[0] - this.margin, localpos[1] - this.margin];\n const max_dist = 30 / graphcanvas.ds.scale;\n this._nearest = this.getCloserPoint(curvepos, max_dist);\n const point = points[s];\n if (point) {\n const is_edge_point = s == 0 || s == points.length - 1;\n if (!is_edge_point && (localpos[0] < -10 || localpos[0] > this.size[0] + 10 || localpos[1] < -10 || localpos[1] > this.size[1] + 10)) {\n points.splice(s, 1);\n this.selected = -1;\n return;\n }\n if (!is_edge_point)\n point[0] = clamp(x2, 0, 1);\n else\n point[0] = s == 0 ? 0 : 1;\n point[1] = 1 - clamp(y2, 0, 1);\n points.sort(function(a, b) {\n return a[0] - b[0];\n });\n this.selected = points.indexOf(point);\n this.must_update = true;\n }\n }\n // Former params: localpos, graphcanvas\n onMouseUp() {\n this.selected = -1;\n return false;\n }\n getCloserPoint(pos2, max_dist) {\n const points = this.points;\n if (!points)\n return -1;\n max_dist = max_dist || 30;\n const w2 = this.size[0] - this.margin * 2;\n const h = this.size[1] - this.margin * 2;\n const num = points.length;\n const p2 = [0, 0];\n let min_dist = 1e6;\n let closest = -1;\n for (let i2 = 0; i2 < num; ++i2) {\n const p = points[i2];\n p2[0] = p[0] * w2;\n p2[1] = (1 - p[1]) * h;\n const dist = distance(pos2, p2);\n if (dist > min_dist || dist > max_dist)\n continue;\n closest = i2;\n min_dist = dist;\n }\n return closest;\n }\n}\nclass LiteGraphGlobal {\n // Enums\n SlotShape = SlotShape;\n SlotDirection = SlotDirection;\n SlotType = SlotType;\n LabelPosition = LabelPosition;\n VERSION = 0.4;\n CANVAS_GRID_SIZE = 10;\n NODE_TITLE_HEIGHT = 30;\n NODE_TITLE_TEXT_Y = 20;\n NODE_SLOT_HEIGHT = 20;\n NODE_WIDGET_HEIGHT = 20;\n NODE_WIDTH = 140;\n NODE_MIN_WIDTH = 50;\n NODE_COLLAPSED_RADIUS = 10;\n NODE_COLLAPSED_WIDTH = 80;\n NODE_TITLE_COLOR = \"#999\";\n NODE_SELECTED_TITLE_COLOR = \"#FFF\";\n NODE_TEXT_SIZE = 14;\n NODE_TEXT_COLOR = \"#AAA\";\n NODE_TEXT_HIGHLIGHT_COLOR = \"#EEE\";\n NODE_SUBTEXT_SIZE = 12;\n NODE_DEFAULT_COLOR = \"#333\";\n NODE_DEFAULT_BGCOLOR = \"#353535\";\n NODE_DEFAULT_BOXCOLOR = \"#666\";\n NODE_DEFAULT_SHAPE = \"box\";\n NODE_BOX_OUTLINE_COLOR = \"#FFF\";\n DEFAULT_SHADOW_COLOR = \"rgba(0,0,0,0.5)\";\n DEFAULT_GROUP_FONT = 24;\n DEFAULT_GROUP_FONT_SIZE;\n WIDGET_BGCOLOR = \"#222\";\n WIDGET_OUTLINE_COLOR = \"#666\";\n WIDGET_TEXT_COLOR = \"#DDD\";\n WIDGET_SECONDARY_TEXT_COLOR = \"#999\";\n LINK_COLOR = \"#9A9\";\n // TODO: This is a workaround until LGraphCanvas.link_type_colors is no longer static.\n static DEFAULT_EVENT_LINK_COLOR = \"#A86\";\n EVENT_LINK_COLOR = \"#A86\";\n CONNECTING_LINK_COLOR = \"#AFA\";\n MAX_NUMBER_OF_NODES = 1e4;\n //avoid infinite loops\n DEFAULT_POSITION = [100, 100];\n //default node position\n VALID_SHAPES = [\"default\", \"box\", \"round\", \"card\"];\n //,\"circle\"\n //shapes are used for nodes but also for slots\n BOX_SHAPE = RenderShape.BOX;\n ROUND_SHAPE = RenderShape.ROUND;\n CIRCLE_SHAPE = RenderShape.CIRCLE;\n CARD_SHAPE = RenderShape.CARD;\n ARROW_SHAPE = RenderShape.ARROW;\n GRID_SHAPE = RenderShape.GRID;\n // intended for slot arrays\n //enums\n INPUT = NodeSlotType.INPUT;\n OUTPUT = NodeSlotType.OUTPUT;\n // TODO: -1 can lead to ambiguity in JS; these should be updated to a more explicit constant or Symbol.\n EVENT = -1;\n //for outputs\n ACTION = -1;\n //for inputs\n NODE_MODES = [\"Always\", \"On Event\", \"Never\", \"On Trigger\"];\n // helper, will add \"On Request\" and more in the future\n NODE_MODES_COLORS = [\"#666\", \"#422\", \"#333\", \"#224\", \"#626\"];\n // use with node_box_coloured_by_mode\n ALWAYS = LGraphEventMode.ALWAYS;\n ON_EVENT = LGraphEventMode.ON_EVENT;\n NEVER = LGraphEventMode.NEVER;\n ON_TRIGGER = LGraphEventMode.ON_TRIGGER;\n UP = LinkDirection.UP;\n DOWN = LinkDirection.DOWN;\n LEFT = LinkDirection.LEFT;\n RIGHT = LinkDirection.RIGHT;\n CENTER = LinkDirection.CENTER;\n LINK_RENDER_MODES = [\"Straight\", \"Linear\", \"Spline\"];\n // helper\n HIDDEN_LINK = LinkRenderType.HIDDEN_LINK;\n STRAIGHT_LINK = LinkRenderType.STRAIGHT_LINK;\n LINEAR_LINK = LinkRenderType.LINEAR_LINK;\n SPLINE_LINK = LinkRenderType.SPLINE_LINK;\n NORMAL_TITLE = TitleMode.NORMAL_TITLE;\n NO_TITLE = TitleMode.NO_TITLE;\n TRANSPARENT_TITLE = TitleMode.TRANSPARENT_TITLE;\n AUTOHIDE_TITLE = TitleMode.AUTOHIDE_TITLE;\n VERTICAL_LAYOUT = \"vertical\";\n // arrange nodes vertically\n proxy = null;\n //used to redirect calls\n node_images_path = \"\";\n debug = false;\n catch_exceptions = true;\n throw_errors = true;\n allow_scripts = false;\n //if set to true some nodes like Formula would be allowed to evaluate code that comes from unsafe sources (like node configuration), which could lead to exploits\n registered_node_types = {};\n //nodetypes by string\n node_types_by_file_extension = {};\n //used for dropping files in the canvas\n Nodes = {};\n //node types by classname\n Globals = {};\n //used to store vars between graphs\n searchbox_extras = {};\n //used to add extra features to the search box\n auto_sort_node_types = false;\n // [true!] If set to true, will automatically sort node types / categories in the context menus\n node_box_coloured_when_on = false;\n // [true!] this make the nodes box (top left circle) coloured when triggered (execute/action), visual feedback\n node_box_coloured_by_mode = false;\n // [true!] nodebox based on node mode, visual feedback\n dialog_close_on_mouse_leave = false;\n // [false on mobile] better true if not touch device, TODO add an helper/listener to close if false\n dialog_close_on_mouse_leave_delay = 500;\n shift_click_do_break_link_from = false;\n // [false!] prefer false if results too easy to break links - implement with ALT or TODO custom keys\n click_do_break_link_to = false;\n // [false!]prefer false, way too easy to break links\n ctrl_alt_click_do_break_link = true;\n // [true!] who accidentally ctrl-alt-clicks on an in/output? nobody! that's who!\n search_hide_on_mouse_leave = true;\n // [false on mobile] better true if not touch device, TODO add an helper/listener to close if false\n search_filter_enabled = false;\n // [true!] enable filtering slots type in the search widget, !requires auto_load_slot_types or manual set registered_slot_[in/out]_types and slot_types_[in/out]\n search_show_all_on_open = true;\n // [true!] opens the results list when opening the search widget\n auto_load_slot_types = false;\n // [if want false, use true, run, get vars values to be statically set, than disable] nodes types and nodeclass association with node types need to be calculated, if dont want this, calculate once and set registered_slot_[in/out]_types and slot_types_[in/out]\n // set these values if not using auto_load_slot_types\n registered_slot_in_types = {};\n // slot types for nodeclass\n registered_slot_out_types = {};\n // slot types for nodeclass\n slot_types_in = [];\n // slot types IN\n slot_types_out = [];\n // slot types OUT\n slot_types_default_in = {};\n // specify for each IN slot type a(/many) default node(s), use single string, array, or object (with node, title, parameters, ..) like for search\n slot_types_default_out = {};\n // specify for each OUT slot type a(/many) default node(s), use single string, array, or object (with node, title, parameters, ..) like for search\n alt_drag_do_clone_nodes = false;\n // [true!] very handy, ALT click to clone and drag the new node\n do_add_triggers_slots = false;\n // [true!] will create and connect event slots when using action/events connections, !WILL CHANGE node mode when using onTrigger (enable mode colors), onExecuted does not need this\n allow_multi_output_for_events = true;\n // [false!] being events, it is strongly reccomended to use them sequentially, one by one\n middle_click_slot_add_default_node = false;\n //[true!] allows to create and connect a ndoe clicking with the third button (wheel)\n release_link_on_empty_shows_menu = false;\n //[true!] dragging a link to empty space will open a menu, add from list, search or defaults\n pointerevents_method = \"pointer\";\n // \"mouse\"|\"pointer\" use mouse for retrocompatibility issues? (none found @ now)\n // TODO implement pointercancel, gotpointercapture, lostpointercapture, (pointerover, pointerout if necessary)\n ctrl_shift_v_paste_connect_unselected_outputs = true;\n //[true!] allows ctrl + shift + v to paste nodes with the outputs of the unselected nodes connected with the inputs of the newly pasted nodes\n // if true, all newly created nodes/links will use string UUIDs for their id fields instead of integers.\n // use this if you must have node IDs that are unique across all graphs and subgraphs.\n use_uuids = false;\n // Whether to highlight the bounding box of selected groups\n highlight_selected_group = true;\n // TODO: Remove legacy accessors\n LGraph = LGraph;\n LLink = LLink;\n LGraphNode = LGraphNode;\n LGraphGroup = LGraphGroup;\n DragAndScale = DragAndScale;\n LGraphCanvas = LGraphCanvas;\n ContextMenu = ContextMenu;\n CurveEditor = CurveEditor;\n static {\n LGraphCanvas.link_type_colors = {\n \"-1\": LiteGraphGlobal.DEFAULT_EVENT_LINK_COLOR,\n number: \"#AAA\",\n node: \"#DCA\"\n };\n }\n constructor() {\n if (typeof performance != \"undefined\") {\n this.getTime = performance.now.bind(performance);\n } else if (typeof Date != \"undefined\" && Date.now) {\n this.getTime = Date.now.bind(Date);\n } else if (typeof process != \"undefined\") {\n this.getTime = function() {\n const t = process.hrtime();\n return t[0] * 1e-3 + t[1] * 1e-6;\n };\n } else {\n this.getTime = function() {\n return (/* @__PURE__ */ new Date()).getTime();\n };\n }\n }\n /**\n * Register a node class so it can be listed when the user wants to create a new one\n * @param {String} type name of the node and path\n * @param {Class} base_class class containing the structure of a node\n */\n registerNodeType(type, base_class) {\n if (!base_class.prototype)\n throw \"Cannot register a simple object, it must be a class with a prototype\";\n base_class.type = type;\n if (this.debug) console.log(\"Node registered: \" + type);\n const classname = base_class.name;\n const pos2 = type.lastIndexOf(\"/\");\n base_class.category = type.substring(0, pos2);\n base_class.title ||= classname;\n for (const i2 in LGraphNode.prototype) {\n base_class.prototype[i2] ||= LGraphNode.prototype[i2];\n }\n const prev = this.registered_node_types[type];\n if (prev) {\n console.log(\"replacing node type: \" + type);\n }\n if (!Object.prototype.hasOwnProperty.call(base_class.prototype, \"shape\")) {\n Object.defineProperty(base_class.prototype, \"shape\", {\n set(v2) {\n switch (v2) {\n case \"default\":\n delete this._shape;\n break;\n case \"box\":\n this._shape = RenderShape.BOX;\n break;\n case \"round\":\n this._shape = RenderShape.ROUND;\n break;\n case \"circle\":\n this._shape = RenderShape.CIRCLE;\n break;\n case \"card\":\n this._shape = RenderShape.CARD;\n break;\n default:\n this._shape = v2;\n }\n },\n get() {\n return this._shape;\n },\n enumerable: true,\n configurable: true\n });\n if (base_class.supported_extensions) {\n for (const i2 in base_class.supported_extensions) {\n const ext = base_class.supported_extensions[i2];\n if (ext && typeof ext === \"string\") {\n this.node_types_by_file_extension[ext.toLowerCase()] = base_class;\n }\n }\n }\n }\n this.registered_node_types[type] = base_class;\n if (base_class.constructor.name) this.Nodes[classname] = base_class;\n this.onNodeTypeRegistered?.(type, base_class);\n if (prev) this.onNodeTypeReplaced?.(type, base_class, prev);\n if (base_class.prototype.onPropertyChange)\n console.warn(`LiteGraph node class ${type} has onPropertyChange method, it must be called onPropertyChanged with d at the end`);\n if (this.auto_load_slot_types) new base_class(base_class.title || \"tmpnode\");\n }\n /**\n * removes a node type from the system\n * @param {String|Object} type name of the node or the node constructor itself\n */\n unregisterNodeType(type) {\n const base_class = typeof type === \"string\" ? this.registered_node_types[type] : type;\n if (!base_class) throw \"node type not found: \" + type;\n delete this.registered_node_types[base_class.type];\n const name = base_class.constructor.name;\n if (name) delete this.Nodes[name];\n }\n /**\n * Save a slot type and his node\n * @param {String|Object} type name of the node or the node constructor itself\n * @param {String} slot_type name of the slot type (variable type), eg. string, number, array, boolean, ..\n */\n registerNodeAndSlotType(type, slot_type, out) {\n out ||= false;\n const base_class = typeof type === \"string\" && this.registered_node_types[type] !== \"anonymous\" ? this.registered_node_types[type] : type;\n const class_type = base_class.constructor.type;\n let allTypes = [];\n if (typeof slot_type === \"string\") {\n allTypes = slot_type.split(\",\");\n } else if (slot_type == this.EVENT || slot_type == this.ACTION) {\n allTypes = [\"_event_\"];\n } else {\n allTypes = [\"*\"];\n }\n for (let i2 = 0; i2 < allTypes.length; ++i2) {\n let slotType = allTypes[i2];\n if (slotType === \"\") slotType = \"*\";\n const registerTo = out ? \"registered_slot_out_types\" : \"registered_slot_in_types\";\n if (this[registerTo][slotType] === void 0)\n this[registerTo][slotType] = { nodes: [] };\n if (!this[registerTo][slotType].nodes.includes(class_type))\n this[registerTo][slotType].nodes.push(class_type);\n const types = out ? this.slot_types_out : this.slot_types_in;\n if (!types.includes(slotType.toLowerCase())) {\n types.push(slotType.toLowerCase());\n types.sort();\n }\n }\n }\n /**\n * Create a new nodetype by passing a function, it wraps it with a proper class and generates inputs according to the parameters of the function.\n * Useful to wrap simple methods that do not require properties, and that only process some input to generate an output.\n * @param {String} name node name with namespace (p.e.: 'math/sum')\n * @param {Function} func\n * @param {Array} param_types [optional] an array containing the type of every parameter, otherwise parameters will accept any type\n * @param {String} return_type [optional] string with the return type, otherwise it will be generic\n * @param {Object} properties [optional] properties to be configurable\n */\n wrapFunctionAsNode(name, func, param_types, return_type, properties) {\n const params = Array(func.length);\n let code = \"\";\n const names = this.getParameterNames(func);\n for (let i2 = 0; i2 < names.length; ++i2) {\n code += `this.addInput('${names[i2]}',${param_types && param_types[i2] ? `'${param_types[i2]}'` : \"0\"});\n`;\n }\n code += `this.addOutput('out',${return_type ? `'${return_type}'` : 0});\n`;\n if (properties) code += `this.properties = ${JSON.stringify(properties)};\n`;\n const classobj = Function(code);\n classobj.title = name.split(\"/\").pop();\n classobj.desc = \"Generated from \" + func.name;\n classobj.prototype.onExecute = function onExecute() {\n for (let i2 = 0; i2 < params.length; ++i2) {\n params[i2] = this.getInputData(i2);\n }\n const r = func.apply(this, params);\n this.setOutputData(0, r);\n };\n this.registerNodeType(name, classobj);\n }\n /**\n * Removes all previously registered node's types\n */\n clearRegisteredTypes() {\n this.registered_node_types = {};\n this.node_types_by_file_extension = {};\n this.Nodes = {};\n this.searchbox_extras = {};\n }\n /**\n * Adds this method to all nodetypes, existing and to be created\n * (You can add it to LGraphNode.prototype but then existing node types wont have it)\n * @param {Function} func\n */\n // eslint-disable-next-line @typescript-eslint/no-unsafe-function-type\n addNodeMethod(name, func) {\n LGraphNode.prototype[name] = func;\n for (const i2 in this.registered_node_types) {\n const type = this.registered_node_types[i2];\n if (type.prototype[name]) type.prototype[\"_\" + name] = type.prototype[name];\n type.prototype[name] = func;\n }\n }\n /**\n * Create a node of a given type with a name. The node is not attached to any graph yet.\n * @param {String} type full name of the node class. p.e. \"math/sin\"\n * @param {String} name a name to distinguish from other nodes\n * @param {Object} options to set options\n */\n createNode(type, title, options) {\n const base_class = this.registered_node_types[type];\n if (!base_class) {\n if (this.debug) console.log(`GraphNode type \"${type}\" not registered.`);\n return null;\n }\n title = title || base_class.title || type;\n let node2 = null;\n if (this.catch_exceptions) {\n try {\n node2 = new base_class(title);\n } catch (err) {\n console.error(err);\n return null;\n }\n } else {\n node2 = new base_class(title);\n }\n node2.type = type;\n if (!node2.title && title) node2.title = title;\n node2.properties ||= {};\n node2.properties_info ||= [];\n node2.flags ||= {};\n node2.size ||= node2.computeSize();\n node2.pos ||= this.DEFAULT_POSITION.concat();\n node2.mode ||= this.ALWAYS;\n if (options) {\n for (const i2 in options) {\n node2[i2] = options[i2];\n }\n }\n node2.onNodeCreated?.();\n return node2;\n }\n /**\n * Returns a registered node type with a given name\n * @param {String} type full name of the node class. p.e. \"math/sin\"\n * @return {Class} the node class\n */\n getNodeType(type) {\n return this.registered_node_types[type];\n }\n /**\n * Returns a list of node types matching one category\n * @param {String} category category name\n * @return {Array} array with all the node classes\n */\n getNodeTypesInCategory(category, filter) {\n const r = [];\n for (const i2 in this.registered_node_types) {\n const type = this.registered_node_types[i2];\n if (type.filter != filter) continue;\n if (category == \"\") {\n if (type.category == null) r.push(type);\n } else if (type.category == category) {\n r.push(type);\n }\n }\n if (this.auto_sort_node_types) {\n r.sort(function(a, b) {\n return a.title.localeCompare(b.title);\n });\n }\n return r;\n }\n /**\n * Returns a list with all the node type categories\n * @param {String} filter only nodes with ctor.filter equal can be shown\n * @return {Array} array with all the names of the categories\n */\n getNodeTypesCategories(filter) {\n const categories = { \"\": 1 };\n for (const i2 in this.registered_node_types) {\n const type = this.registered_node_types[i2];\n if (type.category && !type.skip_list) {\n if (type.filter != filter)\n continue;\n categories[type.category] = 1;\n }\n }\n const result = [];\n for (const i2 in categories) {\n result.push(i2);\n }\n return this.auto_sort_node_types ? result.sort() : result;\n }\n //debug purposes: reloads all the js scripts that matches a wildcard\n reloadNodes(folder_wildcard) {\n const tmp = document.getElementsByTagName(\"script\");\n const script_files = [];\n for (let i2 = 0; i2 < tmp.length; i2++) {\n script_files.push(tmp[i2]);\n }\n const docHeadObj = document.getElementsByTagName(\"head\")[0];\n folder_wildcard = document.location.href + folder_wildcard;\n for (let i2 = 0; i2 < script_files.length; i2++) {\n const src = script_files[i2].src;\n if (!src || src.substr(0, folder_wildcard.length) != folder_wildcard)\n continue;\n try {\n if (this.debug) console.log(\"Reloading: \" + src);\n const dynamicScript = document.createElement(\"script\");\n dynamicScript.type = \"text/javascript\";\n dynamicScript.src = src;\n docHeadObj.appendChild(dynamicScript);\n docHeadObj.removeChild(script_files[i2]);\n } catch (err) {\n if (this.throw_errors) throw err;\n if (this.debug) console.log(\"Error while reloading \" + src);\n }\n }\n if (this.debug) console.log(\"Nodes reloaded\");\n }\n //separated just to improve if it doesn't work\n cloneObject(obj, target) {\n if (obj == null) return null;\n const r = JSON.parse(JSON.stringify(obj));\n if (!target) return r;\n for (const i2 in r) {\n target[i2] = r[i2];\n }\n return target;\n }\n /*\n * https://gist.github.com/jed/982883?permalink_comment_id=852670#gistcomment-852670\n */\n uuidv4() {\n return (\"10000000-1000-4000-8000\" + -1e11).replace(/[018]/g, (a) => (a ^ Math.random() * 16 >> a / 4).toString(16));\n }\n /**\n * Returns if the types of two slots are compatible (taking into account wildcards, etc)\n * @param {String} type_a output\n * @param {String} type_b input\n * @return {Boolean} true if they can be connected\n */\n isValidConnection(type_a, type_b) {\n if (type_a == \"\" || type_a === \"*\") type_a = 0;\n if (type_b == \"\" || type_b === \"*\") type_b = 0;\n if (!type_a || !type_b || type_a == type_b || type_a == this.EVENT && type_b == this.ACTION)\n return true;\n type_a = String(type_a);\n type_b = String(type_b);\n type_a = type_a.toLowerCase();\n type_b = type_b.toLowerCase();\n if (type_a.indexOf(\",\") == -1 && type_b.indexOf(\",\") == -1)\n return type_a == type_b;\n const supported_types_a = type_a.split(\",\");\n const supported_types_b = type_b.split(\",\");\n for (let i2 = 0; i2 < supported_types_a.length; ++i2) {\n for (let j = 0; j < supported_types_b.length; ++j) {\n if (this.isValidConnection(supported_types_a[i2], supported_types_b[j]))\n return true;\n }\n }\n return false;\n }\n /**\n * Register a string in the search box so when the user types it it will recommend this node\n * @param {String} node_type the node recommended\n * @param {String} description text to show next to it\n * @param {Object} data it could contain info of how the node should be configured\n * @return {Boolean} true if they can be connected\n */\n registerSearchboxExtra(node_type, description, data) {\n this.searchbox_extras[description.toLowerCase()] = {\n type: node_type,\n desc: description,\n data\n };\n }\n /**\n * Wrapper to load files (from url using fetch or from file using FileReader)\n * @param {String|File|Blob} url the url of the file (or the file itself)\n * @param {String} type an string to know how to fetch it: \"text\",\"arraybuffer\",\"json\",\"blob\"\n * @param {Function} on_complete callback(data)\n * @param {Function} on_error in case of an error\n * @return {FileReader|Promise} returns the object used to\n */\n fetchFile(url, type, on_complete, on_error) {\n if (!url) return null;\n type = type || \"text\";\n if (typeof url === \"string\") {\n if (url.substr(0, 4) == \"http\" && this.proxy)\n url = this.proxy + url.substr(url.indexOf(\":\") + 3);\n return fetch(url).then(function(response) {\n if (!response.ok)\n throw new Error(\"File not found\");\n if (type == \"arraybuffer\")\n return response.arrayBuffer();\n else if (type == \"text\" || type == \"string\")\n return response.text();\n else if (type == \"json\")\n return response.json();\n else if (type == \"blob\")\n return response.blob();\n }).then(function(data) {\n on_complete?.(data);\n }).catch(function(error) {\n console.error(\"error fetching file:\", url);\n on_error?.(error);\n });\n } else if (url instanceof File || url instanceof Blob) {\n const reader = new FileReader();\n reader.onload = function(e) {\n let v2 = e.target.result;\n if (type == \"json\")\n v2 = JSON.parse(v2);\n on_complete?.(v2);\n };\n if (type == \"arraybuffer\")\n return reader.readAsArrayBuffer(url);\n else if (type == \"text\" || type == \"json\")\n return reader.readAsText(url);\n else if (type == \"blob\")\n return reader.readAsBinaryString(url);\n }\n return null;\n }\n //used to create nodes from wrapping functions\n getParameterNames(func) {\n return (func + \"\").replace(/[/][/].*$/gm, \"\").replace(/\\s+/g, \"\").replace(/[/][*][^/*]*[*][/]/g, \"\").split(\"){\", 1)[0].replace(/^[^(]*[(]/, \"\").replace(/=[^,]+/g, \"\").split(\",\").filter(Boolean);\n }\n /* helper for interaction: pointer, touch, mouse Listeners\n used by LGraphCanvas DragAndScale ContextMenu*/\n pointerListenerAdd(oDOM, sEvIn, fCall, capture = false) {\n if (!oDOM || !oDOM.addEventListener || !sEvIn || typeof fCall !== \"function\") return;\n let sMethod = LiteGraph.pointerevents_method;\n let sEvent = sEvIn;\n if (sMethod == \"pointer\" && !window.PointerEvent) {\n console.warn(\"sMethod=='pointer' && !window.PointerEvent\");\n console.log(\"Converting pointer[\" + sEvent + \"] : down move up cancel enter TO touchstart touchmove touchend, etc ..\");\n switch (sEvent) {\n case \"down\": {\n sMethod = \"touch\";\n sEvent = \"start\";\n break;\n }\n case \"move\": {\n sMethod = \"touch\";\n break;\n }\n case \"up\": {\n sMethod = \"touch\";\n sEvent = \"end\";\n break;\n }\n case \"cancel\": {\n sMethod = \"touch\";\n break;\n }\n case \"enter\": {\n console.log(\"debug: Should I send a move event?\");\n break;\n }\n default: {\n console.warn(\"PointerEvent not available in this browser ? The event \" + sEvent + \" would not be called\");\n }\n }\n }\n switch (sEvent) {\n case \"down\":\n case \"up\":\n case \"move\":\n case \"over\":\n case \"out\":\n case \"enter\": {\n oDOM.addEventListener(sMethod + sEvent, fCall, capture);\n }\n case \"leave\":\n case \"cancel\":\n case \"gotpointercapture\":\n case \"lostpointercapture\": {\n if (sMethod != \"mouse\") {\n return oDOM.addEventListener(sMethod + sEvent, fCall, capture);\n }\n }\n default:\n return oDOM.addEventListener(sEvent, fCall, capture);\n }\n }\n pointerListenerRemove(oDOM, sEvent, fCall, capture = false) {\n if (!oDOM || !oDOM.removeEventListener || !sEvent || typeof fCall !== \"function\") return;\n switch (sEvent) {\n case \"down\":\n case \"up\":\n case \"move\":\n case \"over\":\n case \"out\":\n case \"enter\": {\n if (LiteGraph.pointerevents_method == \"pointer\" || LiteGraph.pointerevents_method == \"mouse\") {\n oDOM.removeEventListener(LiteGraph.pointerevents_method + sEvent, fCall, capture);\n }\n }\n case \"leave\":\n case \"cancel\":\n case \"gotpointercapture\":\n case \"lostpointercapture\": {\n if (LiteGraph.pointerevents_method == \"pointer\") {\n return oDOM.removeEventListener(LiteGraph.pointerevents_method + sEvent, fCall, capture);\n }\n }\n default:\n return oDOM.removeEventListener(sEvent, fCall, capture);\n }\n }\n getTime;\n compareObjects(a, b) {\n for (const i2 in a) {\n if (a[i2] != b[i2]) return false;\n }\n return true;\n }\n distance = distance;\n colorToString(c) {\n return \"rgba(\" + Math.round(c[0] * 255).toFixed() + \",\" + Math.round(c[1] * 255).toFixed() + \",\" + Math.round(c[2] * 255).toFixed() + \",\" + (c.length == 4 ? c[3].toFixed(2) : \"1.0\") + \")\";\n }\n isInsideRectangle = isInsideRectangle;\n //[minx,miny,maxx,maxy]\n growBounding(bounding, x2, y2) {\n if (x2 < bounding[0]) {\n bounding[0] = x2;\n } else if (x2 > bounding[2]) {\n bounding[2] = x2;\n }\n if (y2 < bounding[1]) {\n bounding[1] = y2;\n } else if (y2 > bounding[3]) {\n bounding[3] = y2;\n }\n }\n overlapBounding = overlapBounding;\n //point inside bounding box\n isInsideBounding(p, bb) {\n if (p[0] < bb[0][0] || p[1] < bb[0][1] || p[0] > bb[1][0] || p[1] > bb[1][1]) {\n return false;\n }\n return true;\n }\n //Convert a hex value to its decimal value - the inputted hex must be in the\n //\tformat of a hex triplet - the kind we use for HTML colours. The function\n //\twill return an array with three values.\n hex2num(hex) {\n if (hex.charAt(0) == \"#\") {\n hex = hex.slice(1);\n }\n hex = hex.toUpperCase();\n const hex_alphabets = \"0123456789ABCDEF\";\n const value = new Array(3);\n let k = 0;\n let int1, int2;\n for (let i2 = 0; i2 < 6; i2 += 2) {\n int1 = hex_alphabets.indexOf(hex.charAt(i2));\n int2 = hex_alphabets.indexOf(hex.charAt(i2 + 1));\n value[k] = int1 * 16 + int2;\n k++;\n }\n return value;\n }\n //Give a array with three values as the argument and the function will return\n //\tthe corresponding hex triplet.\n num2hex(triplet) {\n const hex_alphabets = \"0123456789ABCDEF\";\n let hex = \"#\";\n let int1, int2;\n for (let i2 = 0; i2 < 3; i2++) {\n int1 = triplet[i2] / 16;\n int2 = triplet[i2] % 16;\n hex += hex_alphabets.charAt(int1) + hex_alphabets.charAt(int2);\n }\n return hex;\n }\n closeAllContextMenus(ref_window2) {\n ref_window2 = ref_window2 || window;\n const elements = ref_window2.document.querySelectorAll(\".litecontextmenu\");\n if (!elements.length) return;\n const result = [];\n for (let i2 = 0; i2 < elements.length; i2++) {\n result.push(elements[i2]);\n }\n for (let i2 = 0; i2 < result.length; i2++) {\n if (result[i2].close) {\n result[i2].close();\n } else if (result[i2].parentNode) {\n result[i2].parentNode.removeChild(result[i2]);\n }\n }\n }\n extendClass(target, origin) {\n for (const i2 in origin) {\n if (target.hasOwnProperty(i2)) continue;\n target[i2] = origin[i2];\n }\n if (origin.prototype) {\n for (const i2 in origin.prototype) {\n if (!origin.prototype.hasOwnProperty(i2)) continue;\n if (target.prototype.hasOwnProperty(i2)) continue;\n if (origin.prototype.__lookupGetter__(i2)) {\n target.prototype.__defineGetter__(\n i2,\n origin.prototype.__lookupGetter__(i2)\n );\n } else {\n target.prototype[i2] = origin.prototype[i2];\n }\n if (origin.prototype.__lookupSetter__(i2)) {\n target.prototype.__defineSetter__(\n i2,\n origin.prototype.__lookupSetter__(i2)\n );\n }\n }\n }\n }\n}\nfunction loadPolyfills() {\n if (typeof window != \"undefined\" && window.CanvasRenderingContext2D && !window.CanvasRenderingContext2D.prototype.roundRect) {\n window.CanvasRenderingContext2D.prototype.roundRect = function(x2, y2, w2, h, radius, radius_low) {\n var top_left_radius = 0;\n var top_right_radius = 0;\n var bottom_left_radius = 0;\n var bottom_right_radius = 0;\n if (radius === 0) {\n this.rect(x2, y2, w2, h);\n return;\n }\n if (radius_low === void 0)\n radius_low = radius;\n if (radius != null && radius.constructor === Array) {\n if (radius.length == 1)\n top_left_radius = top_right_radius = bottom_left_radius = bottom_right_radius = radius[0];\n else if (radius.length == 2) {\n top_left_radius = bottom_right_radius = radius[0];\n top_right_radius = bottom_left_radius = radius[1];\n } else if (radius.length == 4) {\n top_left_radius = radius[0];\n top_right_radius = radius[1];\n bottom_left_radius = radius[2];\n bottom_right_radius = radius[3];\n } else\n return;\n } else {\n top_left_radius = radius || 0;\n top_right_radius = radius || 0;\n bottom_left_radius = radius_low || 0;\n bottom_right_radius = radius_low || 0;\n }\n this.moveTo(x2 + top_left_radius, y2);\n this.lineTo(x2 + w2 - top_right_radius, y2);\n this.quadraticCurveTo(x2 + w2, y2, x2 + w2, y2 + top_right_radius);\n this.lineTo(x2 + w2, y2 + h - bottom_right_radius);\n this.quadraticCurveTo(\n x2 + w2,\n y2 + h,\n x2 + w2 - bottom_right_radius,\n y2 + h\n );\n this.lineTo(x2 + bottom_right_radius, y2 + h);\n this.quadraticCurveTo(x2, y2 + h, x2, y2 + h - bottom_left_radius);\n this.lineTo(x2, y2 + bottom_left_radius);\n this.quadraticCurveTo(x2, y2, x2 + top_left_radius, y2);\n };\n }\n if (typeof window != \"undefined\" && !window[\"requestAnimationFrame\"]) {\n window.requestAnimationFrame = // @ts-expect-error Legacy code\n window.webkitRequestAnimationFrame || window.mozRequestAnimationFrame || function(callback) {\n window.setTimeout(callback, 1e3 / 60);\n };\n }\n}\nconst LiteGraph = new LiteGraphGlobal();\nfunction clamp(v2, a, b) {\n return a > v2 ? a : b < v2 ? b : v2;\n}\nloadPolyfills();\nexport {\n BadgePosition,\n ContextMenu,\n CurveEditor,\n DragAndScale,\n LGraph,\n LGraphBadge,\n LGraphCanvas,\n LGraphGroup,\n LGraphNode,\n LLink,\n LiteGraph,\n clamp\n};\n//# sourceMappingURL=litegraph.es.js.map\n","// @ts-strict-ignore\nimport { app, ANIM_PREVIEW_WIDGET } from './app'\nimport { LGraphCanvas, LGraphNode, LiteGraph } from '@comfyorg/litegraph'\nimport type { Vector4 } from '@comfyorg/litegraph'\n\nconst SIZE = Symbol()\n\ninterface Rect {\n height: number\n width: number\n x: number\n y: number\n}\n\nexport interface DOMWidget {\n type: string\n name: string\n computedHeight?: number\n element?: T\n options: any\n value?: any\n y?: number\n callback?: (value: any) => void\n draw?: (\n ctx: CanvasRenderingContext2D,\n node: LGraphNode,\n widgetWidth: number,\n y: number,\n widgetHeight: number\n ) => void\n onRemove?: () => void\n}\n\nfunction intersect(a: Rect, b: Rect): Vector4 | null {\n const x = Math.max(a.x, b.x)\n const num1 = Math.min(a.x + a.width, b.x + b.width)\n const y = Math.max(a.y, b.y)\n const num2 = Math.min(a.y + a.height, b.y + b.height)\n if (num1 >= x && num2 >= y) return [x, y, num1 - x, num2 - y]\n else return null\n}\n\nfunction getClipPath(\n node: LGraphNode,\n element: HTMLElement,\n canvasRect: DOMRect\n): string {\n const selectedNode: LGraphNode = Object.values(\n app.canvas.selected_nodes\n )[0] as LGraphNode\n if (selectedNode && selectedNode !== node) {\n const elRect = element.getBoundingClientRect()\n const MARGIN = 7\n const scale = app.canvas.ds.scale\n\n const bounding = selectedNode.getBounding()\n const intersection = intersect(\n {\n x: elRect.x / scale - canvasRect.left,\n y: elRect.y / scale - canvasRect.top,\n width: elRect.width / scale,\n height: elRect.height / scale\n },\n {\n x: selectedNode.pos[0] + app.canvas.ds.offset[0] - MARGIN,\n y:\n selectedNode.pos[1] +\n app.canvas.ds.offset[1] -\n LiteGraph.NODE_TITLE_HEIGHT -\n MARGIN,\n width: bounding[2] + MARGIN + MARGIN,\n height: bounding[3] + MARGIN + MARGIN\n }\n )\n\n if (!intersection) {\n return ''\n }\n\n const clipX = canvasRect.left + intersection[0] - elRect.x / scale + 'px'\n const clipY = canvasRect.top + intersection[1] - elRect.y / scale + 'px'\n const clipWidth = intersection[2] + 'px'\n const clipHeight = intersection[3] + 'px'\n const path = `polygon(0% 0%, 0% 100%, ${clipX} 100%, ${clipX} ${clipY}, calc(${clipX} + ${clipWidth}) ${clipY}, calc(${clipX} + ${clipWidth}) calc(${clipY} + ${clipHeight}), ${clipX} calc(${clipY} + ${clipHeight}), ${clipX} 100%, 100% 100%, 100% 0%)`\n return path\n }\n return ''\n}\n\nfunction computeSize(size: [number, number]): void {\n if (this.widgets?.[0]?.last_y == null) return\n\n let y = this.widgets[0].last_y\n let freeSpace = size[1] - y\n\n let widgetHeight = 0\n let dom = []\n for (const w of this.widgets) {\n if (w.type === 'converted-widget') {\n // Ignore\n delete w.computedHeight\n } else if (w.computeSize) {\n widgetHeight += w.computeSize()[1] + 4\n } else if (w.element) {\n // Extract DOM widget size info\n const styles = getComputedStyle(w.element)\n let minHeight =\n w.options.getMinHeight?.() ??\n parseInt(styles.getPropertyValue('--comfy-widget-min-height'))\n let maxHeight =\n w.options.getMaxHeight?.() ??\n parseInt(styles.getPropertyValue('--comfy-widget-max-height'))\n\n let prefHeight =\n w.options.getHeight?.() ??\n styles.getPropertyValue('--comfy-widget-height')\n if (prefHeight.endsWith?.('%')) {\n prefHeight =\n size[1] *\n (parseFloat(prefHeight.substring(0, prefHeight.length - 1)) / 100)\n } else {\n prefHeight = parseInt(prefHeight)\n if (isNaN(minHeight)) {\n minHeight = prefHeight\n }\n }\n if (isNaN(minHeight)) {\n minHeight = 50\n }\n if (!isNaN(maxHeight)) {\n if (!isNaN(prefHeight)) {\n prefHeight = Math.min(prefHeight, maxHeight)\n } else {\n prefHeight = maxHeight\n }\n }\n dom.push({\n minHeight,\n prefHeight,\n w\n })\n } else {\n widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 4\n }\n }\n\n freeSpace -= widgetHeight\n\n // Calculate sizes with all widgets at their min height\n const prefGrow = [] // Nodes that want to grow to their prefd size\n const canGrow = [] // Nodes that can grow to auto size\n let growBy = 0\n for (const d of dom) {\n freeSpace -= d.minHeight\n if (isNaN(d.prefHeight)) {\n canGrow.push(d)\n d.w.computedHeight = d.minHeight\n } else {\n const diff = d.prefHeight - d.minHeight\n if (diff > 0) {\n prefGrow.push(d)\n growBy += diff\n d.diff = diff\n } else {\n d.w.computedHeight = d.minHeight\n }\n }\n }\n\n if (this.imgs && !this.widgets.find((w) => w.name === ANIM_PREVIEW_WIDGET)) {\n // Allocate space for image\n freeSpace -= 220\n }\n\n this.freeWidgetSpace = freeSpace\n\n if (freeSpace < 0) {\n // Not enough space for all widgets so we need to grow\n size[1] -= freeSpace\n this.graph.setDirtyCanvas(true)\n } else {\n // Share the space between each\n const growDiff = freeSpace - growBy\n if (growDiff > 0) {\n // All pref sizes can be fulfilled\n freeSpace = growDiff\n for (const d of prefGrow) {\n d.w.computedHeight = d.prefHeight\n }\n } else {\n // We need to grow evenly\n const shared = -growDiff / prefGrow.length\n for (const d of prefGrow) {\n d.w.computedHeight = d.prefHeight - shared\n }\n freeSpace = 0\n }\n\n if (freeSpace > 0 && canGrow.length) {\n // Grow any that are auto height\n const shared = freeSpace / canGrow.length\n for (const d of canGrow) {\n d.w.computedHeight += shared\n }\n }\n }\n\n // Position each of the widgets\n for (const w of this.widgets) {\n w.y = y\n if (w.computedHeight) {\n y += w.computedHeight\n } else if (w.computeSize) {\n y += w.computeSize()[1] + 4\n } else {\n y += LiteGraph.NODE_WIDGET_HEIGHT + 4\n }\n }\n}\n\n// Override the compute visible nodes function to allow us to hide/show DOM elements when the node goes offscreen\nconst elementWidgets = new Set()\n//@ts-ignore\nconst computeVisibleNodes = LGraphCanvas.prototype.computeVisibleNodes\n//@ts-ignore\nLGraphCanvas.prototype.computeVisibleNodes = function (): LGraphNode[] {\n const visibleNodes = computeVisibleNodes.apply(this, arguments)\n\n for (const node of app.graph.nodes) {\n if (elementWidgets.has(node)) {\n const hidden = visibleNodes.indexOf(node) === -1\n for (const w of node.widgets) {\n if (w.element) {\n w.element.dataset.isInVisibleNodes = hidden ? 'false' : 'true'\n const shouldOtherwiseHide = w.element.dataset.shouldHide === 'true'\n const isCollapsed = w.element.dataset.collapsed === 'true'\n const wasHidden = w.element.hidden\n const actualHidden = hidden || shouldOtherwiseHide || isCollapsed\n w.element.hidden = actualHidden\n w.element.style.display = actualHidden ? 'none' : null\n if (actualHidden && !wasHidden) {\n w.options.onHide?.(w)\n }\n }\n }\n }\n }\n\n return visibleNodes\n}\n\nlet enableDomClipping = true\n\nexport function addDomClippingSetting(): void {\n app.ui.settings.addSetting({\n id: 'Comfy.DOMClippingEnabled',\n category: ['Comfy', 'Node', 'DOMClippingEnabled'],\n name: 'Enable DOM element clipping (enabling may reduce performance)',\n type: 'boolean',\n defaultValue: enableDomClipping,\n onChange(value) {\n enableDomClipping = !!value\n }\n })\n}\n\nLGraphNode.prototype.addDOMWidget = function (\n name: string,\n type: string,\n element: HTMLElement,\n options: Record\n): DOMWidget {\n options = { hideOnZoom: true, selectOn: ['focus', 'click'], ...options }\n\n if (!element.parentElement) {\n app.canvasContainer.append(element)\n }\n element.hidden = true\n element.style.display = 'none'\n\n let mouseDownHandler\n if (element.blur) {\n mouseDownHandler = (event) => {\n if (!element.contains(event.target)) {\n element.blur()\n }\n }\n document.addEventListener('mousedown', mouseDownHandler)\n }\n\n const { nodeData } = this.constructor\n const tooltip = (nodeData?.input.required?.[name] ??\n nodeData?.input.optional?.[name])?.[1]?.tooltip\n if (tooltip && !element.title) {\n element.title = tooltip\n }\n\n const widget: DOMWidget = {\n type,\n name,\n get value() {\n return options.getValue?.() ?? undefined\n },\n set value(v) {\n options.setValue?.(v)\n widget.callback?.(widget.value)\n },\n draw: function (\n ctx: CanvasRenderingContext2D,\n node: LGraphNode,\n widgetWidth: number,\n y: number,\n widgetHeight: number\n ) {\n if (widget.computedHeight == null) {\n computeSize.call(node, node.size)\n }\n\n const hidden =\n (!!options.hideOnZoom && app.canvas.ds.scale < 0.5) ||\n widget.computedHeight <= 0 ||\n widget.type === 'converted-widget' ||\n widget.type === 'hidden'\n element.dataset.shouldHide = hidden ? 'true' : 'false'\n const isInVisibleNodes = element.dataset.isInVisibleNodes === 'true'\n const isCollapsed = element.dataset.collapsed === 'true'\n const actualHidden = hidden || !isInVisibleNodes || isCollapsed\n const wasHidden = element.hidden\n element.hidden = actualHidden\n element.style.display = actualHidden ? 'none' : null\n if (actualHidden && !wasHidden) {\n widget.options.onHide?.(widget)\n }\n if (actualHidden) {\n return\n }\n\n const margin = 10\n const elRect = ctx.canvas.getBoundingClientRect()\n const transform = new DOMMatrix()\n .scaleSelf(\n elRect.width / ctx.canvas.width,\n elRect.height / ctx.canvas.height\n )\n .multiplySelf(ctx.getTransform())\n .translateSelf(margin, margin + y)\n\n const scale = new DOMMatrix().scaleSelf(transform.a, transform.d)\n\n Object.assign(element.style, {\n transformOrigin: '0 0',\n transform: scale,\n left: `${transform.a + transform.e}px`,\n top: `${transform.d + transform.f}px`,\n width: `${widgetWidth - margin * 2}px`,\n height: `${(widget.computedHeight ?? 50) - margin * 2}px`,\n position: 'absolute',\n zIndex: app.graph.nodes.indexOf(node),\n pointerEvents: app.canvas.read_only ? 'none' : 'auto'\n })\n\n if (enableDomClipping) {\n element.style.clipPath = getClipPath(node, element, elRect)\n element.style.willChange = 'clip-path'\n }\n\n this.options.onDraw?.(widget)\n },\n element,\n options,\n onRemove() {\n if (mouseDownHandler) {\n document.removeEventListener('mousedown', mouseDownHandler)\n }\n element.remove()\n }\n }\n\n for (const evt of options.selectOn) {\n element.addEventListener(evt, () => {\n app.canvas.selectNode(this)\n app.canvas.bringToFront(this)\n })\n }\n\n this.addCustomWidget(widget)\n elementWidgets.add(this)\n\n const collapse = this.collapse\n this.collapse = function () {\n collapse.apply(this, arguments)\n if (this.flags?.collapsed) {\n element.hidden = true\n element.style.display = 'none'\n }\n element.dataset.collapsed = this.flags?.collapsed ? 'true' : 'false'\n }\n\n const onRemoved = this.onRemoved\n this.onRemoved = function () {\n element.remove()\n elementWidgets.delete(this)\n onRemoved?.apply(this, arguments)\n }\n\n if (!this[SIZE]) {\n this[SIZE] = true\n const onResize = this.onResize\n this.onResize = function (size) {\n options.beforeResize?.call(widget, this)\n computeSize.call(this, size)\n onResize?.apply(this, arguments)\n options.afterResize?.call(widget, this)\n }\n }\n\n return widget\n}\n","// @ts-strict-ignore\nimport { api } from './api'\nimport './domWidget'\nimport type { ComfyApp } from './app'\nimport type { IWidget, LGraphNode } from '@comfyorg/litegraph'\nimport { InputSpec } from '@/types/apiTypes'\nimport { useSettingStore } from '@/stores/settingStore'\nimport { useToastStore } from '@/stores/toastStore'\n\nexport type ComfyWidgetConstructor = (\n node: LGraphNode,\n inputName: string,\n inputData: InputSpec,\n app?: ComfyApp,\n widgetName?: string\n) => { widget: IWidget; minWidth?: number; minHeight?: number }\n\nlet controlValueRunBefore = false\nexport function updateControlWidgetLabel(widget) {\n let replacement = 'after'\n let find = 'before'\n if (controlValueRunBefore) {\n ;[find, replacement] = [replacement, find]\n }\n widget.label = (widget.label ?? widget.name).replace(find, replacement)\n}\n\nconst IS_CONTROL_WIDGET = Symbol()\nconst HAS_EXECUTED = Symbol()\n\nfunction getNumberDefaults(\n inputData: InputSpec,\n defaultStep,\n precision,\n enable_rounding\n) {\n let defaultVal = inputData[1]['default']\n let { min, max, step, round } = inputData[1]\n\n if (defaultVal == undefined) defaultVal = 0\n if (min == undefined) min = 0\n if (max == undefined) max = 2048\n if (step == undefined) step = defaultStep\n // precision is the number of decimal places to show.\n // by default, display the the smallest number of decimal places such that changes of size step are visible.\n if (precision == undefined) {\n precision = Math.max(-Math.floor(Math.log10(step)), 0)\n }\n\n if (enable_rounding && (round == undefined || round === true)) {\n // by default, round the value to those decimal places shown.\n round = Math.round(1000000 * Math.pow(0.1, precision)) / 1000000\n }\n\n return {\n val: defaultVal,\n config: { min, max, step: 10.0 * step, round, precision }\n }\n}\n\nexport function addValueControlWidget(\n node,\n targetWidget,\n defaultValue = 'randomize',\n values,\n widgetName,\n inputData: InputSpec\n) {\n let name = inputData[1]?.control_after_generate\n if (typeof name !== 'string') {\n name = widgetName\n }\n const widgets = addValueControlWidgets(\n node,\n targetWidget,\n defaultValue,\n {\n addFilterList: false,\n controlAfterGenerateName: name\n },\n inputData\n )\n return widgets[0]\n}\n\nexport function addValueControlWidgets(\n node,\n targetWidget,\n defaultValue = 'randomize',\n options,\n inputData: InputSpec\n) {\n if (!defaultValue) defaultValue = 'randomize'\n if (!options) options = {}\n\n const getName = (defaultName, optionName) => {\n let name = defaultName\n if (options[optionName]) {\n name = options[optionName]\n } else if (typeof inputData?.[1]?.[defaultName] === 'string') {\n name = inputData?.[1]?.[defaultName]\n } else if (inputData?.[1]?.control_prefix) {\n name = inputData?.[1]?.control_prefix + ' ' + name\n }\n return name\n }\n\n const widgets = []\n const valueControl = node.addWidget(\n 'combo',\n getName('control_after_generate', 'controlAfterGenerateName'),\n defaultValue,\n function () {},\n {\n values: ['fixed', 'increment', 'decrement', 'randomize'],\n serialize: false // Don't include this in prompt.\n }\n )\n valueControl.tooltip =\n 'Allows the linked widget to be changed automatically, for example randomizing the noise seed.'\n valueControl[IS_CONTROL_WIDGET] = true\n updateControlWidgetLabel(valueControl)\n widgets.push(valueControl)\n\n const isCombo = targetWidget.type === 'combo'\n let comboFilter\n if (isCombo) {\n valueControl.options.values.push('increment-wrap')\n }\n if (isCombo && options.addFilterList !== false) {\n comboFilter = node.addWidget(\n 'string',\n getName('control_filter_list', 'controlFilterListName'),\n '',\n function () {},\n {\n serialize: false // Don't include this in prompt.\n }\n )\n updateControlWidgetLabel(comboFilter)\n comboFilter.tooltip =\n \"Allows for filtering the list of values when changing the value via the control generate mode. Allows for RegEx matches in the format /abc/ to only filter to values containing 'abc'.\"\n\n widgets.push(comboFilter)\n }\n\n const applyWidgetControl = () => {\n var v = valueControl.value\n\n if (isCombo && v !== 'fixed') {\n let values = targetWidget.options.values\n const filter = comboFilter?.value\n if (filter) {\n let check\n if (filter.startsWith('/') && filter.endsWith('/')) {\n try {\n const regex = new RegExp(filter.substring(1, filter.length - 1))\n check = (item) => regex.test(item)\n } catch (error) {\n console.error(\n 'Error constructing RegExp filter for node ' + node.id,\n filter,\n error\n )\n }\n }\n if (!check) {\n const lower = filter.toLocaleLowerCase()\n check = (item) => item.toLocaleLowerCase().includes(lower)\n }\n values = values.filter((item) => check(item))\n if (!values.length && targetWidget.options.values.length) {\n console.warn(\n 'Filter for node ' + node.id + ' has filtered out all items',\n filter\n )\n }\n }\n let current_index = values.indexOf(targetWidget.value)\n let current_length = values.length\n\n switch (v) {\n case 'increment':\n current_index += 1\n break\n case 'increment-wrap':\n current_index += 1\n if (current_index >= current_length) {\n current_index = 0\n }\n break\n case 'decrement':\n current_index -= 1\n break\n case 'randomize':\n current_index = Math.floor(Math.random() * current_length)\n break\n default:\n break\n }\n current_index = Math.max(0, current_index)\n current_index = Math.min(current_length - 1, current_index)\n if (current_index >= 0) {\n let value = values[current_index]\n targetWidget.value = value\n targetWidget.callback(value)\n }\n } else {\n //number\n let min = targetWidget.options.min\n let max = targetWidget.options.max\n // limit to something that javascript can handle\n max = Math.min(1125899906842624, max)\n min = Math.max(-1125899906842624, min)\n let range = (max - min) / (targetWidget.options.step / 10)\n\n //adjust values based on valueControl Behaviour\n switch (v) {\n case 'fixed':\n break\n case 'increment':\n targetWidget.value += targetWidget.options.step / 10\n break\n case 'decrement':\n targetWidget.value -= targetWidget.options.step / 10\n break\n case 'randomize':\n targetWidget.value =\n Math.floor(Math.random() * range) *\n (targetWidget.options.step / 10) +\n min\n break\n default:\n break\n }\n /*check if values are over or under their respective\n * ranges and set them to min or max.*/\n if (targetWidget.value < min) targetWidget.value = min\n\n if (targetWidget.value > max) targetWidget.value = max\n targetWidget.callback(targetWidget.value)\n }\n }\n\n valueControl.beforeQueued = () => {\n if (controlValueRunBefore) {\n // Don't run on first execution\n if (valueControl[HAS_EXECUTED]) {\n applyWidgetControl()\n }\n }\n valueControl[HAS_EXECUTED] = true\n }\n\n valueControl.afterQueued = () => {\n if (!controlValueRunBefore) {\n applyWidgetControl()\n }\n }\n\n return widgets\n}\n\nfunction seedWidget(node, inputName, inputData: InputSpec, app, widgetName) {\n const seed = createIntWidget(node, inputName, inputData, app, true)\n const seedControl = addValueControlWidget(\n node,\n seed.widget,\n 'randomize',\n undefined,\n widgetName,\n inputData\n )\n\n seed.widget.linkedWidgets = [seedControl]\n return seed\n}\n\nfunction createIntWidget(\n node,\n inputName,\n inputData: InputSpec,\n app,\n isSeedInput: boolean = false\n) {\n const control = inputData[1]?.control_after_generate\n if (!isSeedInput && control) {\n return seedWidget(\n node,\n inputName,\n inputData,\n app,\n typeof control === 'string' ? control : undefined\n )\n }\n\n let widgetType = isSlider(inputData[1]['display'], app)\n const { val, config } = getNumberDefaults(inputData, 1, 0, true)\n Object.assign(config, { precision: 0 })\n return {\n widget: node.addWidget(\n widgetType,\n inputName,\n val,\n function (v) {\n const s = this.options.step / 10\n let sh = this.options.min % s\n if (isNaN(sh)) {\n sh = 0\n }\n this.value = Math.round((v - sh) / s) * s + sh\n },\n config\n )\n }\n}\n\nfunction addMultilineWidget(node, name: string, opts, app: ComfyApp) {\n const inputEl = document.createElement('textarea')\n inputEl.className = 'comfy-multiline-input'\n inputEl.value = opts.defaultVal\n inputEl.placeholder = opts.placeholder || name\n if (app.vueAppReady) {\n inputEl.spellcheck = useSettingStore().get(\n 'Comfy.TextareaWidget.Spellcheck'\n )\n }\n\n const widget = node.addDOMWidget(name, 'customtext', inputEl, {\n getValue() {\n return inputEl.value\n },\n setValue(v) {\n inputEl.value = v\n }\n })\n widget.inputEl = inputEl\n\n inputEl.addEventListener('input', () => {\n widget.callback?.(widget.value)\n })\n\n return { minWidth: 400, minHeight: 200, widget }\n}\n\nfunction isSlider(display, app) {\n if (app.ui.settings.getSettingValue('Comfy.DisableSliders')) {\n return 'number'\n }\n\n return display === 'slider' ? 'slider' : 'number'\n}\n\nexport function initWidgets(app) {\n app.ui.settings.addSetting({\n id: 'Comfy.WidgetControlMode',\n category: ['Comfy', 'Node Widget', 'WidgetControlMode'],\n name: 'Widget control mode',\n tooltip:\n 'Controls when widget values are updated (randomize/increment/decrement), either before the prompt is queued or after.',\n type: 'combo',\n defaultValue: 'after',\n options: ['before', 'after'],\n onChange(value) {\n controlValueRunBefore = value === 'before'\n for (const n of app.graph.nodes) {\n if (!n.widgets) continue\n for (const w of n.widgets) {\n if (w[IS_CONTROL_WIDGET]) {\n updateControlWidgetLabel(w)\n if (w.linkedWidgets) {\n for (const l of w.linkedWidgets) {\n updateControlWidgetLabel(l)\n }\n }\n }\n }\n }\n app.graph.setDirtyCanvas(true)\n }\n })\n}\n\nexport const ComfyWidgets: Record = {\n 'INT:seed': seedWidget,\n 'INT:noise_seed': seedWidget,\n FLOAT(node, inputName, inputData: InputSpec, app) {\n let widgetType: 'number' | 'slider' = isSlider(inputData[1]['display'], app)\n let precision = app.ui.settings.getSettingValue(\n 'Comfy.FloatRoundingPrecision'\n )\n let disable_rounding = app.ui.settings.getSettingValue(\n 'Comfy.DisableFloatRounding'\n )\n if (precision == 0) precision = undefined\n const { val, config } = getNumberDefaults(\n inputData,\n 0.5,\n precision,\n !disable_rounding\n )\n return {\n widget: node.addWidget(\n widgetType,\n inputName,\n val,\n function (v) {\n if (config.round) {\n this.value =\n Math.round((v + Number.EPSILON) / config.round) * config.round\n if (this.value > config.max) this.value = config.max\n if (this.value < config.min) this.value = config.min\n } else {\n this.value = v\n }\n },\n config\n )\n }\n },\n INT(node, inputName, inputData: InputSpec, app) {\n return createIntWidget(node, inputName, inputData, app)\n },\n BOOLEAN(node, inputName, inputData) {\n let defaultVal = false\n let options = {}\n if (inputData[1]) {\n if (inputData[1].default) defaultVal = inputData[1].default\n if (inputData[1].label_on) options['on'] = inputData[1].label_on\n if (inputData[1].label_off) options['off'] = inputData[1].label_off\n }\n return {\n widget: node.addWidget('toggle', inputName, defaultVal, () => {}, options)\n }\n },\n STRING(node, inputName, inputData: InputSpec, app) {\n const defaultVal = inputData[1].default || ''\n const multiline = !!inputData[1].multiline\n\n let res\n if (multiline) {\n res = addMultilineWidget(\n node,\n inputName,\n { defaultVal, ...inputData[1] },\n app\n )\n } else {\n res = {\n widget: node.addWidget('text', inputName, defaultVal, () => {}, {})\n }\n }\n\n if (inputData[1].dynamicPrompts != undefined)\n res.widget.dynamicPrompts = inputData[1].dynamicPrompts\n\n return res\n },\n COMBO(node, inputName, inputData: InputSpec) {\n const type = inputData[0]\n let defaultValue = type[0]\n if (inputData[1] && inputData[1].default) {\n defaultValue = inputData[1].default\n }\n const res = {\n widget: node.addWidget('combo', inputName, defaultValue, () => {}, {\n values: type\n })\n }\n if (inputData[1]?.control_after_generate) {\n // TODO make combo handle a widget node type?\n res.widget.linkedWidgets = addValueControlWidgets(\n node,\n res.widget,\n undefined,\n undefined,\n inputData\n )\n }\n return res\n },\n IMAGEUPLOAD(node: LGraphNode, inputName: string, inputData: InputSpec, app) {\n // TODO make image upload handle a custom node type?\n const imageWidget = node.widgets.find(\n (w) => w.name === (inputData[1]?.widget ?? 'image')\n )\n let uploadWidget\n\n function showImage(name) {\n const img = new Image()\n img.onload = () => {\n // @ts-expect-error\n node.imgs = [img]\n app.graph.setDirtyCanvas(true)\n }\n let folder_separator = name.lastIndexOf('/')\n let subfolder = ''\n if (folder_separator > -1) {\n subfolder = name.substring(0, folder_separator)\n name = name.substring(folder_separator + 1)\n }\n img.src = api.apiURL(\n `/view?filename=${encodeURIComponent(name)}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`\n )\n // @ts-expect-error\n node.setSizeForImage?.()\n }\n\n var default_value = imageWidget.value\n Object.defineProperty(imageWidget, 'value', {\n set: function (value) {\n this._real_value = value\n },\n\n get: function () {\n if (!this._real_value) {\n return default_value\n }\n\n let value = this._real_value\n if (value.filename) {\n let real_value = value\n value = ''\n if (real_value.subfolder) {\n value = real_value.subfolder + '/'\n }\n\n value += real_value.filename\n\n if (real_value.type && real_value.type !== 'input')\n value += ` [${real_value.type}]`\n }\n return value\n }\n })\n\n // Add our own callback to the combo widget to render an image when it changes\n // TODO: Explain this?\n // @ts-expect-error\n const cb = node.callback\n imageWidget.callback = function () {\n showImage(imageWidget.value)\n if (cb) {\n return cb.apply(this, arguments)\n }\n }\n\n // On load if we have a value then render the image\n // The value isnt set immediately so we need to wait a moment\n // No change callbacks seem to be fired on initial setting of the value\n requestAnimationFrame(() => {\n if (imageWidget.value) {\n showImage(imageWidget.value)\n }\n })\n\n async function uploadFile(file, updateNode, pasted = false) {\n try {\n // Wrap file in formdata so it includes filename\n const body = new FormData()\n body.append('image', file)\n if (pasted) body.append('subfolder', 'pasted')\n const resp = await api.fetchApi('/upload/image', {\n method: 'POST',\n body\n })\n\n if (resp.status === 200) {\n const data = await resp.json()\n // Add the file to the dropdown list and update the widget value\n let path = data.name\n if (data.subfolder) path = data.subfolder + '/' + path\n\n if (!imageWidget.options.values.includes(path)) {\n imageWidget.options.values.push(path)\n }\n\n if (updateNode) {\n showImage(path)\n imageWidget.value = path\n }\n } else {\n useToastStore().addAlert(resp.status + ' - ' + resp.statusText)\n }\n } catch (error) {\n useToastStore().addAlert(error)\n }\n }\n\n const fileInput = document.createElement('input')\n Object.assign(fileInput, {\n type: 'file',\n accept: 'image/jpeg,image/png,image/webp',\n style: 'display: none',\n onchange: async () => {\n if (fileInput.files.length) {\n await uploadFile(fileInput.files[0], true)\n }\n }\n })\n document.body.append(fileInput)\n\n // Create the button widget for selecting the files\n uploadWidget = node.addWidget('button', inputName, 'image', () => {\n fileInput.click()\n })\n uploadWidget.label = 'choose file to upload'\n uploadWidget.serialize = false\n\n // Add handler to check if an image is being dragged over our node\n // @ts-expect-error\n node.onDragOver = function (e) {\n if (e.dataTransfer && e.dataTransfer.items) {\n const image = [...e.dataTransfer.items].find((f) => f.kind === 'file')\n return !!image\n }\n\n return false\n }\n\n // On drop upload files\n // @ts-expect-error\n node.onDragDrop = function (e) {\n console.log('onDragDrop called')\n let handled = false\n for (const file of e.dataTransfer.files) {\n if (file.type.startsWith('image/')) {\n uploadFile(file, !handled) // Dont await these, any order is fine, only update on first one\n handled = true\n }\n }\n\n return handled\n }\n\n // @ts-expect-error\n node.pasteFile = function (file) {\n if (file.type.startsWith('image/')) {\n const is_pasted =\n file.name === 'image.png' && file.lastModified - Date.now() < 2000\n uploadFile(file, true, is_pasted)\n return true\n }\n return false\n }\n\n return { widget: uploadWidget }\n }\n}\n","import type { ComfyWorkflowJSON } from '@/types/comfyWorkflow'\n\nexport const defaultGraph: ComfyWorkflowJSON = {\n last_node_id: 9,\n last_link_id: 9,\n nodes: [\n {\n id: 7,\n type: 'CLIPTextEncode',\n pos: [413, 389],\n size: [425.27801513671875, 180.6060791015625],\n flags: {},\n order: 3,\n mode: 0,\n inputs: [{ name: 'clip', type: 'CLIP', link: 5 }],\n outputs: [\n {\n name: 'CONDITIONING',\n type: 'CONDITIONING',\n links: [6],\n slot_index: 0\n }\n ],\n properties: {},\n widgets_values: ['text, watermark']\n },\n {\n id: 6,\n type: 'CLIPTextEncode',\n pos: [415, 186],\n size: [422.84503173828125, 164.31304931640625],\n flags: {},\n order: 2,\n mode: 0,\n inputs: [{ name: 'clip', type: 'CLIP', link: 3 }],\n outputs: [\n {\n name: 'CONDITIONING',\n type: 'CONDITIONING',\n links: [4],\n slot_index: 0\n }\n ],\n properties: {},\n widgets_values: [\n 'beautiful scenery nature glass bottle landscape, , purple galaxy bottle,'\n ]\n },\n {\n id: 5,\n type: 'EmptyLatentImage',\n pos: [473, 609],\n size: [315, 106],\n flags: {},\n order: 1,\n mode: 0,\n outputs: [{ name: 'LATENT', type: 'LATENT', links: [2], slot_index: 0 }],\n properties: {},\n widgets_values: [512, 512, 1]\n },\n {\n id: 3,\n type: 'KSampler',\n pos: [863, 186],\n size: [315, 262],\n flags: {},\n order: 4,\n mode: 0,\n inputs: [\n { name: 'model', type: 'MODEL', link: 1 },\n { name: 'positive', type: 'CONDITIONING', link: 4 },\n { name: 'negative', type: 'CONDITIONING', link: 6 },\n { name: 'latent_image', type: 'LATENT', link: 2 }\n ],\n outputs: [{ name: 'LATENT', type: 'LATENT', links: [7], slot_index: 0 }],\n properties: {},\n widgets_values: [156680208700286, true, 20, 8, 'euler', 'normal', 1]\n },\n {\n id: 8,\n type: 'VAEDecode',\n pos: [1209, 188],\n size: [210, 46],\n flags: {},\n order: 5,\n mode: 0,\n inputs: [\n { name: 'samples', type: 'LATENT', link: 7 },\n { name: 'vae', type: 'VAE', link: 8 }\n ],\n outputs: [{ name: 'IMAGE', type: 'IMAGE', links: [9], slot_index: 0 }],\n properties: {}\n },\n {\n id: 9,\n type: 'SaveImage',\n pos: [1451, 189],\n size: [210, 26],\n flags: {},\n order: 6,\n mode: 0,\n inputs: [{ name: 'images', type: 'IMAGE', link: 9 }],\n properties: {}\n },\n {\n id: 4,\n type: 'CheckpointLoaderSimple',\n pos: [26, 474],\n size: [315, 98],\n flags: {},\n order: 0,\n mode: 0,\n outputs: [\n { name: 'MODEL', type: 'MODEL', links: [1], slot_index: 0 },\n { name: 'CLIP', type: 'CLIP', links: [3, 5], slot_index: 1 },\n { name: 'VAE', type: 'VAE', links: [8], slot_index: 2 }\n ],\n properties: {},\n widgets_values: ['v1-5-pruned-emaonly.ckpt']\n }\n ],\n links: [\n [1, 4, 0, 3, 0, 'MODEL'],\n [2, 5, 0, 3, 3, 'LATENT'],\n [3, 4, 1, 6, 0, 'CLIP'],\n [4, 6, 0, 3, 1, 'CONDITIONING'],\n [5, 4, 1, 7, 0, 'CLIP'],\n [6, 7, 0, 3, 2, 'CONDITIONING'],\n [7, 3, 0, 8, 0, 'LATENT'],\n [8, 4, 2, 8, 1, 'VAE'],\n [9, 8, 0, 9, 0, 'IMAGE']\n ],\n groups: [],\n config: {},\n extra: {},\n version: 0.4\n}\n","// @ts-strict-ignore\nexport function getFromPngBuffer(buffer: ArrayBuffer) {\n // Get the PNG data as a Uint8Array\n const pngData = new Uint8Array(buffer)\n const dataView = new DataView(pngData.buffer)\n\n // Check that the PNG signature is present\n if (dataView.getUint32(0) !== 0x89504e47) {\n console.error('Not a valid PNG file')\n return\n }\n\n // Start searching for chunks after the PNG signature\n let offset = 8\n let txt_chunks: Record = {}\n // Loop through the chunks in the PNG file\n while (offset < pngData.length) {\n // Get the length of the chunk\n const length = dataView.getUint32(offset)\n // Get the chunk type\n const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8))\n if (type === 'tEXt' || type == 'comf' || type === 'iTXt') {\n // Get the keyword\n let keyword_end = offset + 8\n while (pngData[keyword_end] !== 0) {\n keyword_end++\n }\n const keyword = String.fromCharCode(\n ...pngData.slice(offset + 8, keyword_end)\n )\n // Get the text\n const contentArraySegment = pngData.slice(\n keyword_end + 1,\n offset + 8 + length\n )\n const contentJson = new TextDecoder('utf-8').decode(contentArraySegment)\n txt_chunks[keyword] = contentJson\n }\n\n offset += 12 + length\n }\n return txt_chunks\n}\n\nexport function getFromPngFile(file: File) {\n return new Promise>((r) => {\n const reader = new FileReader()\n reader.onload = (event) => {\n r(getFromPngBuffer(event.target.result as ArrayBuffer))\n }\n\n reader.readAsArrayBuffer(file)\n })\n}\n","// @ts-strict-ignore\nexport function getFromFlacBuffer(buffer: ArrayBuffer): Record {\n const dataView = new DataView(buffer)\n\n // Verify the FLAC signature\n const signature = String.fromCharCode(...new Uint8Array(buffer, 0, 4))\n if (signature !== 'fLaC') {\n console.error('Not a valid FLAC file')\n return\n }\n\n // Parse metadata blocks\n let offset = 4\n let vorbisComment = null\n while (offset < dataView.byteLength) {\n const isLastBlock = dataView.getUint8(offset) & 0x80\n const blockType = dataView.getUint8(offset) & 0x7f\n const blockSize = dataView.getUint32(offset, false) & 0xffffff\n offset += 4\n\n if (blockType === 4) {\n // Vorbis Comment block type\n vorbisComment = parseVorbisComment(\n new DataView(buffer, offset, blockSize)\n )\n }\n\n offset += blockSize\n if (isLastBlock) break\n }\n\n return vorbisComment\n}\n\nexport function getFromFlacFile(file: File): Promise> {\n return new Promise((r) => {\n const reader = new FileReader()\n reader.onload = function (event) {\n const arrayBuffer = event.target.result as ArrayBuffer\n r(getFromFlacBuffer(arrayBuffer))\n }\n reader.readAsArrayBuffer(file)\n })\n}\n\n// Function to parse the Vorbis Comment block\nfunction parseVorbisComment(dataView: DataView): Record {\n let offset = 0\n const vendorLength = dataView.getUint32(offset, true)\n offset += 4\n const vendorString = getString(dataView, offset, vendorLength)\n offset += vendorLength\n\n const userCommentListLength = dataView.getUint32(offset, true)\n offset += 4\n const comments = {}\n for (let i = 0; i < userCommentListLength; i++) {\n const commentLength = dataView.getUint32(offset, true)\n offset += 4\n const comment = getString(dataView, offset, commentLength)\n offset += commentLength\n\n const ind = comment.indexOf('=')\n const key = comment.substring(0, ind)\n\n comments[key] = comment.substring(ind + 1)\n }\n\n return comments\n}\n\nfunction getString(dataView: DataView, offset: number, length: number): string {\n let string = ''\n for (let i = 0; i < length; i++) {\n string += String.fromCharCode(dataView.getUint8(offset + i))\n }\n return string\n}\n","// @ts-strict-ignore\nimport { LiteGraph } from '@comfyorg/litegraph'\nimport { api } from './api'\nimport { getFromPngFile } from './metadata/png'\nimport { getFromFlacFile } from './metadata/flac'\n\n// Original functions left in for backwards compatibility\nexport function getPngMetadata(file: File): Promise> {\n return getFromPngFile(file)\n}\n\nexport function getFlacMetadata(file: File): Promise> {\n return getFromFlacFile(file)\n}\n\nfunction parseExifData(exifData) {\n // Check for the correct TIFF header (0x4949 for little-endian or 0x4D4D for big-endian)\n const isLittleEndian = String.fromCharCode(...exifData.slice(0, 2)) === 'II'\n\n // Function to read 16-bit and 32-bit integers from binary data\n function readInt(offset, isLittleEndian, length) {\n let arr = exifData.slice(offset, offset + length)\n if (length === 2) {\n return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint16(\n 0,\n isLittleEndian\n )\n } else if (length === 4) {\n return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint32(\n 0,\n isLittleEndian\n )\n }\n }\n\n // Read the offset to the first IFD (Image File Directory)\n const ifdOffset = readInt(4, isLittleEndian, 4)\n\n function parseIFD(offset) {\n const numEntries = readInt(offset, isLittleEndian, 2)\n const result = {}\n\n for (let i = 0; i < numEntries; i++) {\n const entryOffset = offset + 2 + i * 12\n const tag = readInt(entryOffset, isLittleEndian, 2)\n const type = readInt(entryOffset + 2, isLittleEndian, 2)\n const numValues = readInt(entryOffset + 4, isLittleEndian, 4)\n const valueOffset = readInt(entryOffset + 8, isLittleEndian, 4)\n\n // Read the value(s) based on the data type\n let value\n if (type === 2) {\n // ASCII string\n value = new TextDecoder('utf-8').decode(\n exifData.subarray(valueOffset, valueOffset + numValues - 1)\n )\n }\n\n result[tag] = value\n }\n\n return result\n }\n\n // Parse the first IFD\n const ifdData = parseIFD(ifdOffset)\n return ifdData\n}\n\nfunction splitValues(input) {\n var output = {}\n for (var key in input) {\n var value = input[key]\n var splitValues = value.split(':', 2)\n output[splitValues[0]] = splitValues[1]\n }\n return output\n}\n\nexport function getWebpMetadata(file) {\n return new Promise>((r) => {\n const reader = new FileReader()\n reader.onload = (event) => {\n const webp = new Uint8Array(event.target.result as ArrayBuffer)\n const dataView = new DataView(webp.buffer)\n\n // Check that the WEBP signature is present\n if (\n dataView.getUint32(0) !== 0x52494646 ||\n dataView.getUint32(8) !== 0x57454250\n ) {\n console.error('Not a valid WEBP file')\n r({})\n return\n }\n\n // Start searching for chunks after the WEBP signature\n let offset = 12\n let txt_chunks = {}\n // Loop through the chunks in the WEBP file\n while (offset < webp.length) {\n const chunk_length = dataView.getUint32(offset + 4, true)\n const chunk_type = String.fromCharCode(\n ...webp.slice(offset, offset + 4)\n )\n if (chunk_type === 'EXIF') {\n if (\n String.fromCharCode(...webp.slice(offset + 8, offset + 8 + 6)) ==\n 'Exif\\0\\0'\n ) {\n offset += 6\n }\n let data = parseExifData(\n webp.slice(offset + 8, offset + 8 + chunk_length)\n )\n for (var key in data) {\n const value = data[key] as string\n if (typeof value === 'string') {\n const index = value.indexOf(':')\n txt_chunks[value.slice(0, index)] = value.slice(index + 1)\n }\n }\n break\n }\n\n offset += 8 + chunk_length\n }\n\n r(txt_chunks)\n }\n\n reader.readAsArrayBuffer(file)\n })\n}\n\nexport function getLatentMetadata(file) {\n return new Promise((r) => {\n const reader = new FileReader()\n reader.onload = (event) => {\n const safetensorsData = new Uint8Array(event.target.result as ArrayBuffer)\n const dataView = new DataView(safetensorsData.buffer)\n let header_size = dataView.getUint32(0, true)\n let offset = 8\n let header = JSON.parse(\n new TextDecoder().decode(\n safetensorsData.slice(offset, offset + header_size)\n )\n )\n r(header.__metadata__)\n }\n\n var slice = file.slice(0, 1024 * 1024 * 4)\n reader.readAsArrayBuffer(slice)\n })\n}\n\nexport async function importA1111(graph, parameters) {\n const p = parameters.lastIndexOf('\\nSteps:')\n if (p > -1) {\n const embeddings = await api.getEmbeddings()\n const opts = parameters\n .substr(p)\n .split('\\n')[1]\n .match(\n new RegExp('\\\\s*([^:]+:\\\\s*([^\"\\\\{].*?|\".*?\"|\\\\{.*?\\\\}))\\\\s*(,|$)', 'g')\n )\n .reduce((p, n) => {\n const s = n.split(':')\n if (s[1].endsWith(',')) {\n s[1] = s[1].substr(0, s[1].length - 1)\n }\n p[s[0].trim().toLowerCase()] = s[1].trim()\n return p\n }, {})\n const p2 = parameters.lastIndexOf('\\nNegative prompt:', p)\n if (p2 > -1) {\n let positive = parameters.substr(0, p2).trim()\n let negative = parameters.substring(p2 + 18, p).trim()\n\n const ckptNode = LiteGraph.createNode('CheckpointLoaderSimple')\n const clipSkipNode = LiteGraph.createNode('CLIPSetLastLayer')\n const positiveNode = LiteGraph.createNode('CLIPTextEncode')\n const negativeNode = LiteGraph.createNode('CLIPTextEncode')\n const samplerNode = LiteGraph.createNode('KSampler')\n const imageNode = LiteGraph.createNode('EmptyLatentImage')\n const vaeNode = LiteGraph.createNode('VAEDecode')\n const vaeLoaderNode = LiteGraph.createNode('VAELoader')\n const saveNode = LiteGraph.createNode('SaveImage')\n let hrSamplerNode = null\n let hrSteps = null\n\n const ceil64 = (v) => Math.ceil(v / 64) * 64\n\n const getWidget = (node, name) => {\n return node.widgets.find((w) => w.name === name)\n }\n\n const setWidgetValue = (node, name, value, isOptionPrefix?) => {\n const w = getWidget(node, name)\n if (isOptionPrefix) {\n const o = w.options.values.find((w) => w.startsWith(value))\n if (o) {\n w.value = o\n } else {\n console.warn(`Unknown value '${value}' for widget '${name}'`, node)\n w.value = value\n }\n } else {\n w.value = value\n }\n }\n\n const createLoraNodes = (clipNode, text, prevClip, prevModel) => {\n const loras = []\n text = text.replace(/]+)>/g, function (m, c) {\n const s = c.split(':')\n const weight = parseFloat(s[1])\n if (isNaN(weight)) {\n console.warn('Invalid LORA', m)\n } else {\n loras.push({ name: s[0], weight })\n }\n return ''\n })\n\n for (const l of loras) {\n const loraNode = LiteGraph.createNode('LoraLoader')\n graph.add(loraNode)\n setWidgetValue(loraNode, 'lora_name', l.name, true)\n setWidgetValue(loraNode, 'strength_model', l.weight)\n setWidgetValue(loraNode, 'strength_clip', l.weight)\n prevModel.node.connect(prevModel.index, loraNode, 0)\n prevClip.node.connect(prevClip.index, loraNode, 1)\n prevModel = { node: loraNode, index: 0 }\n prevClip = { node: loraNode, index: 1 }\n }\n\n prevClip.node.connect(1, clipNode, 0)\n prevModel.node.connect(0, samplerNode, 0)\n if (hrSamplerNode) {\n prevModel.node.connect(0, hrSamplerNode, 0)\n }\n\n return { text, prevModel, prevClip }\n }\n\n const replaceEmbeddings = (text) => {\n if (!embeddings.length) return text\n return text.replaceAll(\n new RegExp(\n '\\\\b(' +\n embeddings\n .map((e) => e.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\$&'))\n .join('\\\\b|\\\\b') +\n ')\\\\b',\n 'ig'\n ),\n 'embedding:$1'\n )\n }\n\n const popOpt = (name) => {\n const v = opts[name]\n delete opts[name]\n return v\n }\n\n graph.clear()\n graph.add(ckptNode)\n graph.add(clipSkipNode)\n graph.add(positiveNode)\n graph.add(negativeNode)\n graph.add(samplerNode)\n graph.add(imageNode)\n graph.add(vaeNode)\n graph.add(vaeLoaderNode)\n graph.add(saveNode)\n\n ckptNode.connect(1, clipSkipNode, 0)\n clipSkipNode.connect(0, positiveNode, 0)\n clipSkipNode.connect(0, negativeNode, 0)\n ckptNode.connect(0, samplerNode, 0)\n positiveNode.connect(0, samplerNode, 1)\n negativeNode.connect(0, samplerNode, 2)\n imageNode.connect(0, samplerNode, 3)\n vaeNode.connect(0, saveNode, 0)\n samplerNode.connect(0, vaeNode, 0)\n vaeLoaderNode.connect(0, vaeNode, 1)\n\n const handlers = {\n model(v) {\n setWidgetValue(ckptNode, 'ckpt_name', v, true)\n },\n vae(v) {\n setWidgetValue(vaeLoaderNode, 'vae_name', v, true)\n },\n 'cfg scale'(v) {\n setWidgetValue(samplerNode, 'cfg', +v)\n },\n 'clip skip'(v) {\n setWidgetValue(clipSkipNode, 'stop_at_clip_layer', -v)\n },\n sampler(v) {\n let name = v.toLowerCase().replace('++', 'pp').replaceAll(' ', '_')\n if (name.includes('karras')) {\n name = name.replace('karras', '').replace(/_+$/, '')\n setWidgetValue(samplerNode, 'scheduler', 'karras')\n } else {\n setWidgetValue(samplerNode, 'scheduler', 'normal')\n }\n const w = getWidget(samplerNode, 'sampler_name')\n const o = w.options.values.find(\n (w) => w === name || w === 'sample_' + name\n )\n if (o) {\n setWidgetValue(samplerNode, 'sampler_name', o)\n }\n },\n size(v) {\n const wxh = v.split('x')\n const w = ceil64(+wxh[0])\n const h = ceil64(+wxh[1])\n const hrUp = popOpt('hires upscale')\n const hrSz = popOpt('hires resize')\n hrSteps = popOpt('hires steps')\n let hrMethod = popOpt('hires upscaler')\n\n setWidgetValue(imageNode, 'width', w)\n setWidgetValue(imageNode, 'height', h)\n\n if (hrUp || hrSz) {\n let uw, uh\n if (hrUp) {\n uw = w * hrUp\n uh = h * hrUp\n } else {\n const s = hrSz.split('x')\n uw = +s[0]\n uh = +s[1]\n }\n\n let upscaleNode\n let latentNode\n\n if (hrMethod.startsWith('Latent')) {\n latentNode = upscaleNode = LiteGraph.createNode('LatentUpscale')\n graph.add(upscaleNode)\n samplerNode.connect(0, upscaleNode, 0)\n\n switch (hrMethod) {\n case 'Latent (nearest-exact)':\n hrMethod = 'nearest-exact'\n break\n }\n setWidgetValue(upscaleNode, 'upscale_method', hrMethod, true)\n } else {\n const decode = LiteGraph.createNode('VAEDecodeTiled')\n graph.add(decode)\n samplerNode.connect(0, decode, 0)\n vaeLoaderNode.connect(0, decode, 1)\n\n const upscaleLoaderNode =\n LiteGraph.createNode('UpscaleModelLoader')\n graph.add(upscaleLoaderNode)\n setWidgetValue(upscaleLoaderNode, 'model_name', hrMethod, true)\n\n const modelUpscaleNode = LiteGraph.createNode(\n 'ImageUpscaleWithModel'\n )\n graph.add(modelUpscaleNode)\n decode.connect(0, modelUpscaleNode, 1)\n upscaleLoaderNode.connect(0, modelUpscaleNode, 0)\n\n upscaleNode = LiteGraph.createNode('ImageScale')\n graph.add(upscaleNode)\n modelUpscaleNode.connect(0, upscaleNode, 0)\n\n const vaeEncodeNode = (latentNode =\n LiteGraph.createNode('VAEEncodeTiled'))\n graph.add(vaeEncodeNode)\n upscaleNode.connect(0, vaeEncodeNode, 0)\n vaeLoaderNode.connect(0, vaeEncodeNode, 1)\n }\n\n setWidgetValue(upscaleNode, 'width', ceil64(uw))\n setWidgetValue(upscaleNode, 'height', ceil64(uh))\n\n hrSamplerNode = LiteGraph.createNode('KSampler')\n graph.add(hrSamplerNode)\n ckptNode.connect(0, hrSamplerNode, 0)\n positiveNode.connect(0, hrSamplerNode, 1)\n negativeNode.connect(0, hrSamplerNode, 2)\n latentNode.connect(0, hrSamplerNode, 3)\n hrSamplerNode.connect(0, vaeNode, 0)\n }\n },\n steps(v) {\n setWidgetValue(samplerNode, 'steps', +v)\n },\n seed(v) {\n setWidgetValue(samplerNode, 'seed', +v)\n }\n }\n\n for (const opt in opts) {\n if (opt in handlers) {\n handlers[opt](popOpt(opt))\n }\n }\n\n if (hrSamplerNode) {\n setWidgetValue(\n hrSamplerNode,\n 'steps',\n hrSteps ? +hrSteps : getWidget(samplerNode, 'steps').value\n )\n setWidgetValue(\n hrSamplerNode,\n 'cfg',\n getWidget(samplerNode, 'cfg').value\n )\n setWidgetValue(\n hrSamplerNode,\n 'scheduler',\n getWidget(samplerNode, 'scheduler').value\n )\n setWidgetValue(\n hrSamplerNode,\n 'sampler_name',\n getWidget(samplerNode, 'sampler_name').value\n )\n setWidgetValue(\n hrSamplerNode,\n 'denoise',\n +(popOpt('denoising strength') || '1')\n )\n }\n\n let n = createLoraNodes(\n positiveNode,\n positive,\n { node: clipSkipNode, index: 0 },\n { node: ckptNode, index: 0 }\n )\n positive = n.text\n n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel)\n negative = n.text\n\n setWidgetValue(positiveNode, 'text', replaceEmbeddings(positive))\n setWidgetValue(negativeNode, 'text', replaceEmbeddings(negative))\n\n graph.arrange()\n\n for (const opt of [\n 'model hash',\n 'ensd',\n 'version',\n 'vae hash',\n 'ti hashes',\n 'lora hashes',\n 'hashes'\n ]) {\n delete opts[opt]\n }\n\n console.warn('Unhandled parameters:', opts)\n }\n }\n}\n","// @ts-strict-ignore\nimport { app } from '../app'\nimport { $el } from '../ui'\n\nexport function calculateImageGrid(imgs, dw, dh) {\n let best = 0\n let w = imgs[0].naturalWidth\n let h = imgs[0].naturalHeight\n const numImages = imgs.length\n\n let cellWidth, cellHeight, cols, rows, shiftX\n // compact style\n for (let c = 1; c <= numImages; c++) {\n const r = Math.ceil(numImages / c)\n const cW = dw / c\n const cH = dh / r\n const scaleX = cW / w\n const scaleY = cH / h\n\n const scale = Math.min(scaleX, scaleY, 1)\n const imageW = w * scale\n const imageH = h * scale\n const area = imageW * imageH * numImages\n\n if (area > best) {\n best = area\n cellWidth = imageW\n cellHeight = imageH\n cols = c\n rows = r\n shiftX = c * ((cW - imageW) / 2)\n }\n }\n\n return { cellWidth, cellHeight, cols, rows, shiftX }\n}\n\nexport function createImageHost(node) {\n const el = $el('div.comfy-img-preview')\n let currentImgs\n let first = true\n\n function updateSize() {\n let w = null\n let h = null\n\n if (currentImgs) {\n let elH = el.clientHeight\n if (first) {\n first = false\n // On first run, if we are small then grow a bit\n if (elH < 190) {\n elH = 190\n }\n el.style.setProperty('--comfy-widget-min-height', elH.toString())\n } else {\n el.style.setProperty('--comfy-widget-min-height', null)\n }\n\n const nw = node.size[0]\n ;({ cellWidth: w, cellHeight: h } = calculateImageGrid(\n currentImgs,\n nw - 20,\n elH\n ))\n w += 'px'\n h += 'px'\n\n el.style.setProperty('--comfy-img-preview-width', w)\n el.style.setProperty('--comfy-img-preview-height', h)\n }\n }\n return {\n el,\n updateImages(imgs) {\n if (imgs !== currentImgs) {\n if (currentImgs == null) {\n requestAnimationFrame(() => {\n updateSize()\n })\n }\n el.replaceChildren(...imgs)\n currentImgs = imgs\n node.onResize(node.size)\n node.graph.setDirtyCanvas(true, true)\n }\n },\n getHeight() {\n updateSize()\n },\n onDraw() {\n // Element from point uses a hittest find elements so we need to toggle pointer events\n el.style.pointerEvents = 'all'\n const over = document.elementFromPoint(\n app.canvas.mouse[0],\n app.canvas.mouse[1]\n )\n el.style.pointerEvents = 'none'\n\n if (!over) return\n // Set the overIndex so Open Image etc work\n const idx = currentImgs.indexOf(over)\n node.overIndex = idx\n }\n }\n}\n","// @ts-strict-ignore\n/*\n Original implementation:\n https://github.com/TahaSh/drag-to-reorder\n MIT License\n\n Copyright (c) 2023 Taha Shashtari\n\n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n\n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.\n*/\n\nimport { $el } from '../ui'\n\n$el('style', {\n parent: document.head,\n textContent: `\n .draggable-item {\n position: relative;\n will-change: transform;\n user-select: none;\n }\n .draggable-item.is-idle {\n transition: 0.25s ease transform;\n }\n .draggable-item.is-draggable {\n z-index: 10;\n }\n `\n})\n\nexport class DraggableList extends EventTarget {\n listContainer\n draggableItem\n pointerStartX\n pointerStartY\n scrollYMax\n itemsGap = 0\n items = []\n itemSelector\n handleClass = 'drag-handle'\n off = []\n offDrag = []\n\n constructor(element, itemSelector) {\n super()\n this.listContainer = element\n this.itemSelector = itemSelector\n\n if (!this.listContainer) return\n\n this.off.push(this.on(this.listContainer, 'mousedown', this.dragStart))\n this.off.push(this.on(this.listContainer, 'touchstart', this.dragStart))\n this.off.push(this.on(document, 'mouseup', this.dragEnd))\n this.off.push(this.on(document, 'touchend', this.dragEnd))\n }\n\n getAllItems() {\n if (!this.items?.length) {\n this.items = Array.from(\n this.listContainer.querySelectorAll(this.itemSelector)\n )\n this.items.forEach((element) => {\n element.classList.add('is-idle')\n })\n }\n return this.items\n }\n\n getIdleItems() {\n return this.getAllItems().filter((item) =>\n item.classList.contains('is-idle')\n )\n }\n\n isItemAbove(item) {\n return item.hasAttribute('data-is-above')\n }\n\n isItemToggled(item) {\n return item.hasAttribute('data-is-toggled')\n }\n\n on(source, event, listener, options?) {\n listener = listener.bind(this)\n source.addEventListener(event, listener, options)\n return () => source.removeEventListener(event, listener)\n }\n\n dragStart(e) {\n if (e.target.classList.contains(this.handleClass)) {\n this.draggableItem = e.target.closest(this.itemSelector)\n }\n\n if (!this.draggableItem) return\n\n this.pointerStartX = e.clientX || e.touches[0].clientX\n this.pointerStartY = e.clientY || e.touches[0].clientY\n this.scrollYMax =\n this.listContainer.scrollHeight - this.listContainer.clientHeight\n\n this.setItemsGap()\n this.initDraggableItem()\n this.initItemsState()\n\n this.offDrag.push(this.on(document, 'mousemove', this.drag))\n this.offDrag.push(\n this.on(document, 'touchmove', this.drag, { passive: false })\n )\n\n this.dispatchEvent(\n new CustomEvent('dragstart', {\n detail: {\n element: this.draggableItem,\n position: this.getAllItems().indexOf(this.draggableItem)\n }\n })\n )\n }\n\n setItemsGap() {\n if (this.getIdleItems().length <= 1) {\n this.itemsGap = 0\n return\n }\n\n const item1 = this.getIdleItems()[0]\n const item2 = this.getIdleItems()[1]\n\n const item1Rect = item1.getBoundingClientRect()\n const item2Rect = item2.getBoundingClientRect()\n\n this.itemsGap = Math.abs(item1Rect.bottom - item2Rect.top)\n }\n\n initItemsState() {\n this.getIdleItems().forEach((item, i) => {\n if (this.getAllItems().indexOf(this.draggableItem) > i) {\n item.dataset.isAbove = ''\n }\n })\n }\n\n initDraggableItem() {\n this.draggableItem.classList.remove('is-idle')\n this.draggableItem.classList.add('is-draggable')\n }\n\n drag(e) {\n if (!this.draggableItem) return\n\n e.preventDefault()\n\n const clientX = e.clientX || e.touches[0].clientX\n const clientY = e.clientY || e.touches[0].clientY\n\n const listRect = this.listContainer.getBoundingClientRect()\n\n if (clientY > listRect.bottom) {\n if (this.listContainer.scrollTop < this.scrollYMax) {\n this.listContainer.scrollBy(0, 10)\n this.pointerStartY -= 10\n }\n } else if (clientY < listRect.top && this.listContainer.scrollTop > 0) {\n this.pointerStartY += 10\n this.listContainer.scrollBy(0, -10)\n }\n\n const pointerOffsetX = clientX - this.pointerStartX\n const pointerOffsetY = clientY - this.pointerStartY\n\n this.updateIdleItemsStateAndPosition()\n this.draggableItem.style.transform = `translate(${pointerOffsetX}px, ${pointerOffsetY}px)`\n }\n\n updateIdleItemsStateAndPosition() {\n const draggableItemRect = this.draggableItem.getBoundingClientRect()\n const draggableItemY = draggableItemRect.top + draggableItemRect.height / 2\n\n // Update state\n this.getIdleItems().forEach((item) => {\n const itemRect = item.getBoundingClientRect()\n const itemY = itemRect.top + itemRect.height / 2\n if (this.isItemAbove(item)) {\n if (draggableItemY <= itemY) {\n item.dataset.isToggled = ''\n } else {\n delete item.dataset.isToggled\n }\n } else {\n if (draggableItemY >= itemY) {\n item.dataset.isToggled = ''\n } else {\n delete item.dataset.isToggled\n }\n }\n })\n\n // Update position\n this.getIdleItems().forEach((item) => {\n if (this.isItemToggled(item)) {\n const direction = this.isItemAbove(item) ? 1 : -1\n item.style.transform = `translateY(${direction * (draggableItemRect.height + this.itemsGap)}px)`\n } else {\n item.style.transform = ''\n }\n })\n }\n\n dragEnd() {\n if (!this.draggableItem) return\n\n this.applyNewItemsOrder()\n this.cleanup()\n }\n\n applyNewItemsOrder() {\n const reorderedItems = []\n\n let oldPosition = -1\n this.getAllItems().forEach((item, index) => {\n if (item === this.draggableItem) {\n oldPosition = index\n return\n }\n if (!this.isItemToggled(item)) {\n reorderedItems[index] = item\n return\n }\n const newIndex = this.isItemAbove(item) ? index + 1 : index - 1\n reorderedItems[newIndex] = item\n })\n\n for (let index = 0; index < this.getAllItems().length; index++) {\n const item = reorderedItems[index]\n if (typeof item === 'undefined') {\n reorderedItems[index] = this.draggableItem\n }\n }\n\n reorderedItems.forEach((item) => {\n this.listContainer.appendChild(item)\n })\n\n this.items = reorderedItems\n\n this.dispatchEvent(\n new CustomEvent('dragend', {\n detail: {\n element: this.draggableItem,\n oldPosition,\n newPosition: reorderedItems.indexOf(this.draggableItem)\n }\n })\n )\n }\n\n cleanup() {\n this.itemsGap = 0\n this.items = []\n this.unsetDraggableItem()\n this.unsetItemState()\n\n this.offDrag.forEach((f) => f())\n this.offDrag = []\n }\n\n unsetDraggableItem() {\n this.draggableItem.style = null\n this.draggableItem.classList.remove('is-draggable')\n this.draggableItem.classList.add('is-idle')\n this.draggableItem = null\n }\n\n unsetItemState() {\n this.getIdleItems().forEach((item, i) => {\n delete item.dataset.isAbove\n delete item.dataset.isToggled\n item.style.transform = ''\n })\n }\n\n dispose() {\n this.off.forEach((f) => f())\n }\n}\n","// @ts-strict-ignore\nimport { api } from './api'\nimport type { ComfyApp } from './app'\nimport { $el } from './ui'\n\n// Simple date formatter\nconst parts = {\n d: (d) => d.getDate(),\n M: (d) => d.getMonth() + 1,\n h: (d) => d.getHours(),\n m: (d) => d.getMinutes(),\n s: (d) => d.getSeconds()\n}\nconst format =\n Object.keys(parts)\n .map((k) => k + k + '?')\n .join('|') + '|yyy?y?'\n\nfunction formatDate(text: string, date: Date) {\n return text.replace(new RegExp(format, 'g'), (text: string): string => {\n if (text === 'yy') return (date.getFullYear() + '').substring(2)\n if (text === 'yyyy') return date.getFullYear().toString()\n if (text[0] in parts) {\n const p = parts[text[0]](date)\n return (p + '').padStart(text.length, '0')\n }\n return text\n })\n}\n\nexport function clone(obj) {\n try {\n if (typeof structuredClone !== 'undefined') {\n return structuredClone(obj)\n }\n } catch (error) {\n // structuredClone is stricter than using JSON.parse/stringify so fallback to that\n }\n\n return JSON.parse(JSON.stringify(obj))\n}\n\nexport function applyTextReplacements(app: ComfyApp, value: string): string {\n return value.replace(/%([^%]+)%/g, function (match, text) {\n const split = text.split('.')\n if (split.length !== 2) {\n // Special handling for dates\n if (split[0].startsWith('date:')) {\n return formatDate(split[0].substring(5), new Date())\n }\n\n if (text !== 'width' && text !== 'height') {\n // Dont warn on standard replacements\n console.warn('Invalid replacement pattern', text)\n }\n return match\n }\n\n // Find node with matching S&R property name\n let nodes = app.graph.nodes.filter(\n (n) => n.properties?.['Node name for S&R'] === split[0]\n )\n // If we cant, see if there is a node with that title\n if (!nodes.length) {\n nodes = app.graph.nodes.filter((n) => n.title === split[0])\n }\n if (!nodes.length) {\n console.warn('Unable to find node', split[0])\n return match\n }\n\n if (nodes.length > 1) {\n console.warn('Multiple nodes matched', split[0], 'using first match')\n }\n\n const node = nodes[0]\n\n const widget = node.widgets?.find((w) => w.name === split[1])\n if (!widget) {\n console.warn('Unable to find widget', split[1], 'on node', split[0], node)\n return match\n }\n\n return ((widget.value ?? '') + '').replaceAll(/\\/|\\\\/g, '_')\n })\n}\n\nexport async function addStylesheet(\n urlOrFile: string,\n relativeTo?: string\n): Promise {\n return new Promise((res, rej) => {\n let url\n if (urlOrFile.endsWith('.js')) {\n url = urlOrFile.substr(0, urlOrFile.length - 2) + 'css'\n } else {\n url = new URL(\n urlOrFile,\n relativeTo ?? `${window.location.protocol}//${window.location.host}`\n ).toString()\n }\n $el('link', {\n parent: document.head,\n rel: 'stylesheet',\n type: 'text/css',\n href: url,\n onload: res,\n onerror: rej\n })\n })\n}\n\n/**\n * @param { string } filename\n * @param { Blob } blob\n */\nexport function downloadBlob(filename, blob) {\n const url = URL.createObjectURL(blob)\n const a = $el('a', {\n href: url,\n download: filename,\n style: { display: 'none' },\n parent: document.body\n })\n a.click()\n setTimeout(function () {\n a.remove()\n window.URL.revokeObjectURL(url)\n }, 0)\n}\n\nexport function prop(\n target: object,\n name: string,\n defaultValue: T,\n onChanged?: (\n currentValue: T,\n previousValue: T,\n target: object,\n name: string\n ) => void\n): T {\n let currentValue\n Object.defineProperty(target, name, {\n get() {\n return currentValue\n },\n set(newValue) {\n const prevValue = currentValue\n currentValue = newValue\n onChanged?.(currentValue, prevValue, target, name)\n }\n })\n return defaultValue\n}\n\nexport function getStorageValue(id: string) {\n const clientId = api.clientId ?? api.initialClientId\n return (\n (clientId && sessionStorage.getItem(`${id}:${clientId}`)) ??\n localStorage.getItem(id)\n )\n}\n\nexport function setStorageValue(id: string, value: string) {\n const clientId = api.clientId ?? api.initialClientId\n if (clientId) {\n sessionStorage.setItem(`${id}:${clientId}`, value)\n }\n localStorage.setItem(id, value)\n}\n","import { memoize } from 'lodash'\n\ntype RGB = { r: number; g: number; b: number }\ntype HSL = { h: number; s: number; l: number }\ntype HSLA = { h: number; s: number; l: number; a: number }\ntype ColorFormat = 'hex' | 'rgb' | 'rgba' | 'hsl' | 'hsla'\n\nexport interface ColorAdjustOptions {\n lightness?: number\n opacity?: number\n}\n\nfunction rgbToHsl({ r, g, b }: RGB): HSL {\n r /= 255\n g /= 255\n b /= 255\n const max = Math.max(r, g, b),\n min = Math.min(r, g, b)\n let h = 0,\n s = 0\n const l: number = (max + min) / 2\n\n if (max !== min) {\n const d = max - min\n s = l > 0.5 ? d / (2 - max - min) : d / (max + min)\n switch (max) {\n case r:\n h = (g - b) / d + (g < b ? 6 : 0)\n break\n case g:\n h = (b - r) / d + 2\n break\n case b:\n h = (r - g) / d + 4\n break\n }\n h /= 6\n }\n\n return { h, s, l }\n}\n\nfunction hexToRgb(hex: string): RGB {\n let r = 0,\n g = 0,\n b = 0\n // 3 digits\n if (hex.length == 4) {\n r = parseInt(hex[1] + hex[1], 16)\n g = parseInt(hex[2] + hex[2], 16)\n b = parseInt(hex[3] + hex[3], 16)\n }\n // 6 digits\n else if (hex.length == 7) {\n r = parseInt(hex.slice(1, 3), 16)\n g = parseInt(hex.slice(3, 5), 16)\n b = parseInt(hex.slice(5, 7), 16)\n }\n return { r, g, b }\n}\n\nconst identifyColorFormat = (color: string): ColorFormat | null => {\n if (!color) return null\n if (color.startsWith('#') && (color.length === 4 || color.length === 7))\n return 'hex'\n if (/rgba?\\(\\s*\\d+\\s*,\\s*\\d+\\s*,\\s*\\d+\\s*/.test(color))\n return color.includes('rgba') ? 'rgba' : 'rgb'\n if (/hsla?\\(\\s*\\d+(\\.\\d+)?\\s*,\\s*\\d+(\\.\\d+)?%\\s*,\\s*\\d+(\\.\\d+)?%/.test(color))\n return color.includes('hsla') ? 'hsla' : 'hsl'\n return null\n}\n\nconst isHSLA = (color: unknown): color is HSLA => {\n if (typeof color !== 'object' || color === null) return false\n\n return ['h', 's', 'l', 'a'].every(\n (key) =>\n typeof (color as Record)[key] === 'number' &&\n !isNaN((color as Record)[key])\n )\n}\n\nfunction parseToHSLA(color: string, format: ColorFormat): HSLA | null {\n let match: RegExpMatchArray | null\n\n switch (format) {\n case 'hex': {\n const hsl = rgbToHsl(hexToRgb(color))\n return {\n h: Math.round(hsl.h * 360),\n s: +(hsl.s * 100).toFixed(1),\n l: +(hsl.l * 100).toFixed(1),\n a: 1\n }\n }\n\n case 'rgb':\n case 'rgba': {\n match = color.match(/\\d+(\\.\\d+)?/g)\n if (!match || match.length < 3) return null\n const [r, g, b] = match.map(Number)\n const hsl = rgbToHsl({ r, g, b })\n\n const a = format === 'rgba' && match[3] ? parseFloat(match[3]) : 1\n\n return {\n h: Math.round(hsl.h * 360),\n s: +(hsl.s * 100).toFixed(1),\n l: +(hsl.l * 100).toFixed(1),\n a\n }\n }\n\n case 'hsl':\n case 'hsla': {\n match = color.match(/\\d+(\\.\\d+)?/g)\n if (!match || match.length < 3) return null\n const [h, s, l] = match.map(Number)\n const a = format === 'hsla' && match[3] ? parseFloat(match[3]) : 1\n return { h, s, l, a }\n }\n default:\n return null\n }\n}\n\nconst applyColorAdjustments = (\n color: string,\n options: ColorAdjustOptions\n): string => {\n if (!Object.keys(options).length) return color\n\n const format = identifyColorFormat(color)\n if (!format) {\n console.warn(`Unsupported color format in color palette: ${color}`)\n return color\n }\n\n const hsla = parseToHSLA(color, format)\n if (!isHSLA(hsla)) {\n console.warn(`Invalid color values in color palette: ${color}`)\n return color\n }\n\n if (options.lightness) {\n hsla.l = Math.max(0, Math.min(100, hsla.l + options.lightness * 100.0))\n }\n\n if (options.opacity) {\n hsla.a = Math.max(0, Math.min(1, options.opacity))\n }\n\n return `hsla(${hsla.h}, ${hsla.s}%, ${hsla.l}%, ${hsla.a})`\n}\n\nexport const adjustColor: (\n color: string,\n options: ColorAdjustOptions\n) => string = memoize(\n applyColorAdjustments,\n (color: string, options: ColorAdjustOptions): string =>\n `${color}-${JSON.stringify(options)}`\n)\n","// @ts-strict-ignore\nimport { $el } from '../../ui'\nimport { ComfyButton } from './button'\nimport { prop } from '../../utils'\n\nexport class ComfyButtonGroup {\n element = $el('div.comfyui-button-group')\n buttons: (HTMLElement | ComfyButton)[]\n\n constructor(...buttons: (HTMLElement | ComfyButton)[]) {\n this.buttons = prop(this, 'buttons', buttons, () => this.update())\n }\n\n insert(button: ComfyButton, index: number) {\n this.buttons.splice(index, 0, button)\n this.update()\n }\n\n append(button: ComfyButton) {\n this.buttons.push(button)\n this.update()\n }\n\n remove(indexOrButton: ComfyButton | number) {\n if (typeof indexOrButton !== 'number') {\n indexOrButton = this.buttons.indexOf(indexOrButton)\n }\n if (indexOrButton > -1) {\n const r = this.buttons.splice(indexOrButton, 1)\n this.update()\n return r\n }\n }\n\n update() {\n this.element.replaceChildren(...this.buttons.map((b) => b['element'] ?? b))\n }\n}\n","// @ts-strict-ignore\nexport type ClassList = string | string[] | Record\n\nexport function applyClasses(\n element: HTMLElement,\n classList: ClassList,\n ...requiredClasses: string[]\n) {\n classList ??= ''\n\n let str: string\n if (typeof classList === 'string') {\n str = classList\n } else if (classList instanceof Array) {\n str = classList.join(' ')\n } else {\n str = Object.entries(classList).reduce((p, c) => {\n if (c[1]) {\n p += (p.length ? ' ' : '') + c[0]\n }\n return p\n }, '')\n }\n element.className = str\n if (requiredClasses) {\n element.classList.add(...requiredClasses)\n }\n}\n\nexport function toggleElement(\n element: HTMLElement,\n {\n onHide,\n onShow\n }: {\n onHide?: (el: HTMLElement) => void\n onShow?: (el: HTMLElement, value) => void\n } = {}\n) {\n let placeholder: HTMLElement | Comment\n let hidden: boolean\n return (value) => {\n if (value) {\n if (hidden) {\n hidden = false\n placeholder.replaceWith(element)\n }\n onShow?.(element, value)\n } else {\n if (!placeholder) {\n placeholder = document.createComment('')\n }\n hidden = true\n element.replaceWith(placeholder)\n onHide?.(element)\n }\n }\n}\n","// @ts-strict-ignore\nimport { $el } from '../../ui'\nimport { applyClasses, ClassList, toggleElement } from '../utils'\nimport { prop } from '../../utils'\nimport type { ComfyPopup } from './popup'\nimport type { ComfyComponent } from '.'\nimport type { ComfyApp } from '@/scripts/app'\nimport { Settings } from '@/types/apiTypes'\n\ntype ComfyButtonProps = {\n icon?: string\n overIcon?: string\n iconSize?: number\n content?: string | HTMLElement\n tooltip?: string\n enabled?: boolean\n action?: (e: Event, btn: ComfyButton) => void\n classList?: ClassList\n visibilitySetting?: { id: keyof Settings; showValue: boolean }\n app?: ComfyApp\n}\n\nexport class ComfyButton implements ComfyComponent {\n #over = 0\n #popupOpen = false\n isOver = false\n iconElement = $el('i.mdi')\n contentElement = $el('span')\n popup: ComfyPopup\n element: HTMLElement\n overIcon: string\n iconSize: number\n content: string | HTMLElement\n icon: string\n tooltip: string\n classList: ClassList\n hidden: boolean\n enabled: boolean\n action: (e: Event, btn: ComfyButton) => void\n\n constructor({\n icon,\n overIcon,\n iconSize,\n content,\n tooltip,\n action,\n classList = 'comfyui-button',\n visibilitySetting,\n app,\n enabled = true\n }: ComfyButtonProps) {\n this.element = $el(\n 'button',\n {\n onmouseenter: () => {\n this.isOver = true\n if (this.overIcon) {\n this.updateIcon()\n }\n },\n onmouseleave: () => {\n this.isOver = false\n if (this.overIcon) {\n this.updateIcon()\n }\n }\n },\n [this.iconElement, this.contentElement]\n )\n\n this.icon = prop(\n this,\n 'icon',\n icon,\n toggleElement(this.iconElement, { onShow: this.updateIcon })\n )\n this.overIcon = prop(this, 'overIcon', overIcon, () => {\n if (this.isOver) {\n this.updateIcon()\n }\n })\n this.iconSize = prop(this, 'iconSize', iconSize, this.updateIcon)\n this.content = prop(\n this,\n 'content',\n content,\n toggleElement(this.contentElement, {\n onShow: (el, v) => {\n if (typeof v === 'string') {\n el.textContent = v\n } else {\n el.replaceChildren(v)\n }\n }\n })\n )\n\n this.tooltip = prop(this, 'tooltip', tooltip, (v) => {\n if (v) {\n this.element.title = v\n } else {\n this.element.removeAttribute('title')\n }\n })\n if (tooltip !== undefined) {\n this.element.setAttribute('aria-label', tooltip)\n }\n this.classList = prop(this, 'classList', classList, this.updateClasses)\n this.hidden = prop(this, 'hidden', false, this.updateClasses)\n this.enabled = prop(this, 'enabled', enabled, () => {\n this.updateClasses()\n ;(this.element as HTMLButtonElement).disabled = !this.enabled\n })\n this.action = prop(this, 'action', action)\n this.element.addEventListener('click', (e) => {\n if (this.popup) {\n // we are either a touch device or triggered by click not hover\n if (!this.#over) {\n this.popup.toggle()\n }\n }\n this.action?.(e, this)\n })\n\n if (visibilitySetting?.id) {\n const settingUpdated = () => {\n this.hidden =\n app.ui.settings.getSettingValue(visibilitySetting.id) !==\n visibilitySetting.showValue\n }\n app.ui.settings.addEventListener(\n visibilitySetting.id + '.change',\n settingUpdated\n )\n settingUpdated()\n }\n }\n\n updateIcon = () =>\n (this.iconElement.className = `mdi mdi-${(this.isOver && this.overIcon) || this.icon}${this.iconSize ? ' mdi-' + this.iconSize + 'px' : ''}`)\n updateClasses = () => {\n const internalClasses = []\n if (this.hidden) {\n internalClasses.push('hidden')\n }\n if (!this.enabled) {\n internalClasses.push('disabled')\n }\n if (this.popup) {\n if (this.#popupOpen) {\n internalClasses.push('popup-open')\n } else {\n internalClasses.push('popup-closed')\n }\n }\n applyClasses(this.element, this.classList, ...internalClasses)\n }\n\n withPopup(popup: ComfyPopup, mode: 'click' | 'hover' = 'click') {\n this.popup = popup\n\n if (mode === 'hover') {\n for (const el of [this.element, this.popup.element]) {\n el.addEventListener('mouseenter', () => {\n this.popup.open = !!++this.#over\n })\n el.addEventListener('mouseleave', () => {\n this.popup.open = !!--this.#over\n })\n }\n }\n\n popup.addEventListener('change', () => {\n this.#popupOpen = popup.open\n this.updateClasses()\n })\n\n return this\n }\n}\n","// @ts-strict-ignore\nimport { prop } from '../../utils'\nimport { $el } from '../../ui'\nimport { applyClasses, ClassList } from '../utils'\n\nexport class ComfyPopup extends EventTarget {\n element = $el('div.comfyui-popup')\n open: boolean\n children: HTMLElement[]\n target: HTMLElement\n ignoreTarget: boolean\n container: HTMLElement\n position: string\n closeOnEscape: boolean\n horizontal: string\n classList: ClassList\n\n constructor(\n {\n target,\n container = document.body,\n classList = '',\n ignoreTarget = true,\n closeOnEscape = true,\n position = 'absolute',\n horizontal = 'left'\n }: {\n target: HTMLElement\n container?: HTMLElement\n classList?: ClassList\n ignoreTarget?: boolean\n closeOnEscape?: boolean\n position?: 'absolute' | 'relative'\n horizontal?: 'left' | 'right'\n },\n ...children: HTMLElement[]\n ) {\n super()\n this.target = target\n this.ignoreTarget = ignoreTarget\n this.container = container\n this.position = position\n this.closeOnEscape = closeOnEscape\n this.horizontal = horizontal\n\n container.append(this.element)\n\n this.children = prop(this, 'children', children, () => {\n this.element.replaceChildren(...this.children)\n this.update()\n })\n this.classList = prop(this, 'classList', classList, () =>\n applyClasses(this.element, this.classList, 'comfyui-popup', horizontal)\n )\n this.open = prop(this, 'open', false, (v, o) => {\n if (v === o) return\n if (v) {\n this.#show()\n } else {\n this.#hide()\n }\n })\n }\n\n toggle() {\n this.open = !this.open\n }\n\n #hide() {\n this.element.classList.remove('open')\n window.removeEventListener('resize', this.update)\n window.removeEventListener('click', this.#clickHandler, { capture: true })\n window.removeEventListener('keydown', this.#escHandler, { capture: true })\n\n this.dispatchEvent(new CustomEvent('close'))\n this.dispatchEvent(new CustomEvent('change'))\n }\n\n #show() {\n this.element.classList.add('open')\n this.update()\n\n window.addEventListener('resize', this.update)\n window.addEventListener('click', this.#clickHandler, { capture: true })\n if (this.closeOnEscape) {\n window.addEventListener('keydown', this.#escHandler, { capture: true })\n }\n\n this.dispatchEvent(new CustomEvent('open'))\n this.dispatchEvent(new CustomEvent('change'))\n }\n\n #escHandler = (e) => {\n if (e.key === 'Escape') {\n this.open = false\n e.preventDefault()\n e.stopImmediatePropagation()\n }\n }\n\n #clickHandler = (e) => {\n /** @type {any} */\n const target = e.target\n if (\n !this.element.contains(target) &&\n this.ignoreTarget &&\n !this.target.contains(target)\n ) {\n this.open = false\n }\n }\n\n update = () => {\n const rect = this.target.getBoundingClientRect()\n this.element.style.setProperty('--bottom', 'unset')\n if (this.position === 'absolute') {\n if (this.horizontal === 'left') {\n this.element.style.setProperty('--left', rect.left + 'px')\n } else {\n this.element.style.setProperty(\n '--left',\n rect.right - this.element.clientWidth + 'px'\n )\n }\n this.element.style.setProperty('--top', rect.bottom + 'px')\n this.element.style.setProperty('--limit', rect.bottom + 'px')\n } else {\n this.element.style.setProperty('--left', 0 + 'px')\n this.element.style.setProperty('--top', rect.height + 'px')\n this.element.style.setProperty('--limit', rect.height + 'px')\n }\n\n const thisRect = this.element.getBoundingClientRect()\n if (thisRect.height < 30) {\n // Move up instead\n this.element.style.setProperty('--top', 'unset')\n this.element.style.setProperty('--bottom', rect.height + 5 + 'px')\n this.element.style.setProperty('--limit', rect.height + 5 + 'px')\n }\n }\n}\n","import { $el } from '../../ui'\nimport { ComfyButton } from './button'\nimport { prop } from '../../utils'\nimport { ComfyPopup } from './popup'\n\nexport class ComfySplitButton {\n arrow: ComfyButton\n element: HTMLElement\n popup: ComfyPopup\n items: Array\n\n constructor(\n {\n primary,\n mode,\n horizontal = 'left',\n position = 'relative'\n }: {\n primary: ComfyButton\n mode?: 'hover' | 'click'\n horizontal?: 'left' | 'right'\n position?: 'relative' | 'absolute'\n },\n ...items: Array\n ) {\n this.arrow = new ComfyButton({\n icon: 'chevron-down'\n })\n this.element = $el(\n 'div.comfyui-split-button' + (mode === 'hover' ? '.hover' : ''),\n [\n $el(\n 'div.comfyui-split-primary',\n {\n ariaLabel: 'Queue current workflow'\n },\n primary.element\n ),\n $el(\n 'div.comfyui-split-arrow',\n {\n ariaLabel: 'Open extra opens',\n ariaHasPopup: 'true'\n },\n this.arrow.element\n )\n ]\n )\n this.popup = new ComfyPopup({\n target: this.element,\n container: position === 'relative' ? this.element : document.body,\n classList:\n 'comfyui-split-button-popup' + (mode === 'hover' ? ' hover' : ''),\n closeOnEscape: mode === 'click',\n position,\n horizontal\n })\n\n this.arrow.withPopup(this.popup, mode)\n\n this.items = prop(this, 'items', items, () => this.update())\n }\n\n update() {\n this.popup.element.replaceChildren(\n ...this.items.map((b) => ('element' in b ? b.element : b))\n )\n }\n}\n","import type { ComfyApp } from '@/scripts/app'\nimport { $el } from '../../ui'\nimport { downloadBlob } from '../../utils'\nimport { ComfyButtonGroup } from '../components/buttonGroup'\nimport { showPromptDialog } from '@/services/dialogService'\nimport { useSettingStore } from '@/stores/settingStore'\nimport './menu.css'\n\n// Export to make sure following components are shimmed and exported by vite\nexport { ComfyButton } from '../components/button'\nexport { ComfySplitButton } from '../components/splitButton'\nexport { ComfyPopup } from '../components/popup'\n\nexport class ComfyAppMenu {\n app: ComfyApp\n actionsGroup: ComfyButtonGroup\n settingsGroup: ComfyButtonGroup\n viewGroup: ComfyButtonGroup\n element: HTMLElement\n\n constructor(app: ComfyApp) {\n this.app = app\n\n // Keep the group as there are custom scripts attaching extra\n // elements to it.\n this.actionsGroup = new ComfyButtonGroup()\n this.settingsGroup = new ComfyButtonGroup()\n this.viewGroup = new ComfyButtonGroup()\n\n this.element = $el('div.flex.gap-2.mx-2', [\n this.actionsGroup.element,\n this.settingsGroup.element,\n this.viewGroup.element\n ])\n }\n\n async getFilename(defaultName: string): Promise {\n if (useSettingStore().get('Comfy.PromptFilename')) {\n let filename = await showPromptDialog({\n title: 'Export Workflow',\n message: 'Enter the filename:',\n defaultValue: defaultName\n })\n if (!filename) return null\n if (!filename.toLowerCase().endsWith('.json')) {\n filename += '.json'\n }\n return filename\n }\n return defaultName\n }\n\n async exportWorkflow(\n filename: string,\n promptProperty: 'workflow' | 'output'\n ): Promise {\n if (this.app.workflowManager.activeWorkflow?.path) {\n filename = this.app.workflowManager.activeWorkflow.name\n }\n const p = await this.app.graphToPrompt()\n const json = JSON.stringify(p[promptProperty], null, 2)\n const blob = new Blob([json], { type: 'application/json' })\n const file = await this.getFilename(filename)\n if (!file) return\n downloadBlob(file, blob)\n }\n}\n","import { ref, computed } from 'vue'\nimport { defineStore } from 'pinia'\nimport { api } from '@/scripts/api'\nimport { ComfyWorkflow } from '@/scripts/workflows'\nimport type { ComfyNode, ComfyWorkflowJSON } from '@/types/comfyWorkflow'\nimport type {\n ExecutedWsMessage,\n ExecutingWsMessage,\n ExecutionCachedWsMessage,\n ExecutionStartWsMessage,\n ProgressWsMessage\n} from '@/types/apiTypes'\n\nexport interface QueuedPrompt {\n nodes: Record\n workflow?: ComfyWorkflow\n}\n\nexport const useExecutionStore = defineStore('execution', () => {\n const activePromptId = ref(null)\n const queuedPrompts = ref>({})\n const executingNodeId = ref(null)\n const executingNode = computed(() => {\n if (!executingNodeId.value) return null\n\n const workflow: ComfyWorkflow | undefined = activePrompt.value?.workflow\n if (!workflow) return null\n\n const canvasState: ComfyWorkflowJSON | null =\n workflow.changeTracker?.activeState ?? null\n if (!canvasState) return null\n\n return (\n canvasState.nodes.find(\n (n: ComfyNode) => String(n.id) === executingNodeId.value\n ) ?? null\n )\n })\n\n // This is the progress of the currently executing node, if any\n const _executingNodeProgress = ref(null)\n const executingNodeProgress = computed(() =>\n _executingNodeProgress.value\n ? Math.round(\n (_executingNodeProgress.value.value /\n _executingNodeProgress.value.max) *\n 100\n )\n : null\n )\n\n const activePrompt = computed(\n () => queuedPrompts.value[activePromptId.value ?? '']\n )\n\n const totalNodesToExecute = computed(() => {\n if (!activePrompt.value) return 0\n return Object.values(activePrompt.value.nodes).length\n })\n\n const isIdle = computed(() => !activePromptId.value)\n\n const nodesExecuted = computed(() => {\n if (!activePrompt.value) return 0\n return Object.values(activePrompt.value.nodes).filter(Boolean).length\n })\n\n const executionProgress = computed(() => {\n if (!activePrompt.value) return 0\n const total = totalNodesToExecute.value\n const done = nodesExecuted.value\n return Math.round((done / total) * 100)\n })\n\n function bindExecutionEvents() {\n api.addEventListener(\n 'execution_start',\n handleExecutionStart as EventListener\n )\n api.addEventListener(\n 'execution_cached',\n handleExecutionCached as EventListener\n )\n api.addEventListener('executed', handleExecuted as EventListener)\n api.addEventListener('executing', handleExecuting as EventListener)\n api.addEventListener('progress', handleProgress as EventListener)\n }\n\n function unbindExecutionEvents() {\n api.removeEventListener(\n 'execution_start',\n handleExecutionStart as EventListener\n )\n api.removeEventListener(\n 'execution_cached',\n handleExecutionCached as EventListener\n )\n api.removeEventListener('executed', handleExecuted as EventListener)\n api.removeEventListener('executing', handleExecuting as EventListener)\n api.removeEventListener('progress', handleProgress as EventListener)\n }\n\n function handleExecutionStart(e: CustomEvent) {\n activePromptId.value = e.detail.prompt_id\n queuedPrompts.value[activePromptId.value] ??= { nodes: {} }\n }\n\n function handleExecutionCached(e: CustomEvent) {\n if (!activePrompt.value) return\n for (const n of e.detail.nodes) {\n activePrompt.value.nodes[n] = true\n }\n }\n\n function handleExecuted(e: CustomEvent) {\n if (!activePrompt.value) return\n activePrompt.value.nodes[e.detail.node] = true\n }\n\n function handleExecuting(e: CustomEvent) {\n // Clear the current node progress when a new node starts executing\n _executingNodeProgress.value = null\n\n if (!activePrompt.value) return\n\n if (executingNodeId.value && activePrompt.value) {\n // Seems sometimes nodes that are cached fire executing but not executed\n activePrompt.value.nodes[executingNodeId.value] = true\n }\n executingNodeId.value = e.detail ? String(e.detail) : null\n if (!executingNodeId.value) {\n if (activePromptId.value) {\n delete queuedPrompts.value[activePromptId.value]\n }\n activePromptId.value = null\n }\n }\n\n function handleProgress(e: CustomEvent) {\n _executingNodeProgress.value = e.detail\n }\n\n function storePrompt({\n nodes,\n id,\n workflow\n }: {\n nodes: string[]\n id: string\n workflow: ComfyWorkflow\n }) {\n queuedPrompts.value[id] ??= { nodes: {} }\n const queuedPrompt = queuedPrompts.value[id]\n queuedPrompt.nodes = {\n ...nodes.reduce((p: Record, n) => {\n p[n] = false\n return p\n }, {}),\n ...queuedPrompt.nodes\n }\n queuedPrompt.workflow = workflow\n\n console.debug(\n `queued task ${id} with ${Object.values(queuedPrompt.nodes).length} nodes`\n )\n }\n\n return {\n isIdle,\n activePromptId,\n queuedPrompts,\n executingNodeId,\n activePrompt,\n totalNodesToExecute,\n nodesExecuted,\n executionProgress,\n executingNode,\n executingNodeProgress,\n bindExecutionEvents,\n unbindExecutionEvents,\n storePrompt\n }\n})\n","import type { ComfyApp } from './app'\nimport { api } from './api'\nimport { clone } from './utils'\nimport { LGraphCanvas, LiteGraph } from '@comfyorg/litegraph'\nimport { ComfyWorkflow } from './workflows'\nimport type { ComfyWorkflowJSON } from '@/types/comfyWorkflow'\nimport { LGraphNode } from '@comfyorg/litegraph'\nimport { ExecutedWsMessage } from '@/types/apiTypes'\nimport { useExecutionStore } from '@/stores/executionStore'\n\nexport class ChangeTracker {\n static MAX_HISTORY = 50\n #app?: ComfyApp\n undoQueue: ComfyWorkflowJSON[] = []\n redoQueue: ComfyWorkflowJSON[] = []\n activeState: ComfyWorkflowJSON | null = null\n isOurLoad: boolean = false\n changeCount: number = 0\n\n ds?: { scale: number; offset: [number, number] }\n nodeOutputs?: Record\n\n get app(): ComfyApp {\n // Global tracker has #app set, while other trackers have workflow bounded\n return this.#app ?? this.workflow.manager.app\n }\n\n constructor(public workflow: ComfyWorkflow) {}\n\n #setApp(app: ComfyApp) {\n this.#app = app\n }\n\n store() {\n this.ds = {\n scale: this.app.canvas.ds.scale,\n offset: [this.app.canvas.ds.offset[0], this.app.canvas.ds.offset[1]]\n }\n }\n\n restore() {\n if (this.ds) {\n this.app.canvas.ds.scale = this.ds.scale\n this.app.canvas.ds.offset = this.ds.offset\n }\n if (this.nodeOutputs) {\n this.app.nodeOutputs = this.nodeOutputs\n }\n }\n\n checkState() {\n if (!this.app.graph || this.changeCount) return\n\n const currentState = this.app.graph.serialize()\n if (!this.activeState) {\n this.activeState = clone(currentState)\n return\n }\n if (!ChangeTracker.graphEqual(this.activeState, currentState)) {\n this.undoQueue.push(this.activeState)\n if (this.undoQueue.length > ChangeTracker.MAX_HISTORY) {\n this.undoQueue.shift()\n }\n this.activeState = clone(currentState)\n this.redoQueue.length = 0\n this.workflow.unsaved = true\n api.dispatchEvent(\n new CustomEvent('graphChanged', { detail: this.activeState })\n )\n }\n }\n\n async updateState(source: ComfyWorkflowJSON[], target: ComfyWorkflowJSON[]) {\n const prevState = source.pop()\n if (prevState) {\n target.push(this.activeState!)\n this.isOurLoad = true\n await this.app.loadGraphData(prevState, false, false, this.workflow, {\n showMissingModelsDialog: false,\n showMissingNodesDialog: false\n })\n this.activeState = prevState\n }\n }\n\n async undo() {\n await this.updateState(this.undoQueue, this.redoQueue)\n }\n\n async redo() {\n await this.updateState(this.redoQueue, this.undoQueue)\n }\n\n async undoRedo(e: KeyboardEvent) {\n if (e.ctrlKey || e.metaKey) {\n if (e.key === 'y' || e.key == 'Z') {\n await this.redo()\n return true\n } else if (e.key === 'z') {\n await this.undo()\n return true\n }\n }\n }\n\n beforeChange() {\n this.changeCount++\n }\n\n afterChange() {\n if (!--this.changeCount) {\n this.checkState()\n }\n }\n\n static init(app: ComfyApp) {\n const changeTracker = () =>\n app.workflowManager.activeWorkflow?.changeTracker ?? globalTracker\n globalTracker.#setApp(app)\n\n const loadGraphData = app.loadGraphData\n app.loadGraphData = async function (...args) {\n const v = await loadGraphData.apply(this, args)\n const ct = changeTracker()\n if (ct.isOurLoad) {\n ct.isOurLoad = false\n } else {\n ct.checkState()\n }\n return v\n }\n\n let keyIgnored = false\n window.addEventListener(\n 'keydown',\n (e: KeyboardEvent) => {\n // Do not trigger on repeat events (Holding down a key)\n // This can happen when user is holding down \"Space\" to pan the canvas.\n if (e.repeat) return\n\n const activeEl = document.activeElement\n requestAnimationFrame(async () => {\n let bindInputEl: Element | null = null\n // If we are auto queue in change mode then we do want to trigger on inputs\n if (!app.ui.autoQueueEnabled || app.ui.autoQueueMode === 'instant') {\n if (\n activeEl?.tagName === 'INPUT' ||\n (activeEl && 'type' in activeEl && activeEl.type === 'textarea')\n ) {\n // Ignore events on inputs, they have their native history\n return\n }\n bindInputEl = activeEl\n }\n\n keyIgnored =\n e.key === 'Control' ||\n e.key === 'Shift' ||\n e.key === 'Alt' ||\n e.key === 'Meta'\n if (keyIgnored) return\n\n // Check if this is a ctrl+z ctrl+y\n if (await changeTracker().undoRedo(e)) return\n\n // If our active element is some type of input then handle changes after they're done\n if (ChangeTracker.bindInput(app, bindInputEl)) return\n changeTracker().checkState()\n })\n },\n true\n )\n\n window.addEventListener('keyup', (e) => {\n if (keyIgnored) {\n keyIgnored = false\n changeTracker().checkState()\n }\n })\n\n // Handle clicking DOM elements (e.g. widgets)\n window.addEventListener('mouseup', () => {\n changeTracker().checkState()\n })\n\n // Handle prompt queue event for dynamic widget changes\n api.addEventListener('promptQueued', () => {\n changeTracker().checkState()\n })\n\n api.addEventListener('graphCleared', () => {\n changeTracker().checkState()\n })\n\n // Handle litegraph clicks\n const processMouseUp = LGraphCanvas.prototype.processMouseUp\n LGraphCanvas.prototype.processMouseUp = function (e) {\n const v = processMouseUp.apply(this, [e])\n changeTracker().checkState()\n return v\n }\n const processMouseDown = LGraphCanvas.prototype.processMouseDown\n LGraphCanvas.prototype.processMouseDown = function (e) {\n const v = processMouseDown.apply(this, [e])\n changeTracker().checkState()\n return v\n }\n\n // Handle litegraph dialog popup for number/string widgets\n const prompt = LGraphCanvas.prototype.prompt\n LGraphCanvas.prototype.prompt = function (\n title: string,\n value: any,\n callback: (v: any) => void,\n event: any\n ) {\n const extendedCallback = (v: any) => {\n callback(v)\n changeTracker().checkState()\n }\n return prompt.apply(this, [title, value, extendedCallback, event])\n }\n\n // Handle litegraph context menu for COMBO widgets\n const close = LiteGraph.ContextMenu.prototype.close\n LiteGraph.ContextMenu.prototype.close = function (e: MouseEvent) {\n const v = close.apply(this, [e])\n changeTracker().checkState()\n return v\n }\n\n // Detects nodes being added via the node search dialog\n const onNodeAdded = LiteGraph.LGraph.prototype.onNodeAdded\n LiteGraph.LGraph.prototype.onNodeAdded = function (node: LGraphNode) {\n const v = onNodeAdded?.apply(this, [node])\n if (!app?.configuringGraph) {\n const ct = changeTracker()\n if (!ct.isOurLoad) {\n ct.checkState()\n }\n }\n return v\n }\n\n // Handle multiple commands as a single transaction\n document.addEventListener('litegraph:canvas', (e: Event) => {\n const detail = (e as CustomEvent).detail\n if (detail.subType === 'before-change') {\n changeTracker().beforeChange()\n } else if (detail.subType === 'after-change') {\n changeTracker().afterChange()\n }\n })\n\n // Store node outputs\n api.addEventListener('executed', (e: CustomEvent) => {\n const detail = e.detail\n const workflow =\n useExecutionStore().queuedPrompts[detail.prompt_id]?.workflow\n const changeTracker = workflow?.changeTracker\n if (!changeTracker) return\n changeTracker.nodeOutputs ??= {}\n const nodeOutputs = changeTracker.nodeOutputs\n const output = nodeOutputs[detail.node]\n if (detail.merge && output) {\n for (const k in detail.output ?? {}) {\n const v = output[k]\n if (v instanceof Array) {\n output[k] = v.concat(detail.output[k])\n } else {\n output[k] = detail.output[k]\n }\n }\n } else {\n nodeOutputs[detail.node] = detail.output\n }\n })\n }\n\n static bindInput(app: ComfyApp, activeEl: Element | null): boolean {\n if (\n !activeEl ||\n activeEl.tagName === 'CANVAS' ||\n activeEl.tagName === 'BODY'\n ) {\n return false\n }\n\n for (const evt of ['change', 'input', 'blur']) {\n const htmlElement = activeEl as HTMLElement\n if (`on${evt}` in htmlElement) {\n const listener = () => {\n app.workflowManager.activeWorkflow?.changeTracker?.checkState()\n htmlElement.removeEventListener(evt, listener)\n }\n htmlElement.addEventListener(evt, listener)\n return true\n }\n }\n return false\n }\n\n static graphEqual(a: any, b: any, path = '') {\n if (a === b) return true\n\n if (typeof a == 'object' && a && typeof b == 'object' && b) {\n const keys = Object.getOwnPropertyNames(a)\n\n if (keys.length != Object.getOwnPropertyNames(b).length) {\n return false\n }\n\n for (const key of keys) {\n let av = a[key]\n let bv = b[key]\n if (!path && key === 'nodes') {\n // Nodes need to be sorted as the order changes when selecting nodes\n av = [...av].sort((a, b) => a.id - b.id)\n bv = [...bv].sort((a, b) => a.id - b.id)\n } else if (path === 'extra.ds') {\n // Ignore view changes\n continue\n }\n if (!ChangeTracker.graphEqual(av, bv, path + (path ? '.' : '') + key)) {\n return false\n }\n }\n\n return true\n }\n\n return false\n }\n}\n\nexport const globalTracker = new ChangeTracker({} as ComfyWorkflow)\n","// @ts-strict-ignore\nimport { ComfyDialog } from '../dialog'\nimport { $el } from '../../ui'\n\nexport class ComfyAsyncDialog extends ComfyDialog {\n #resolve: (value: any) => void\n\n constructor(actions?: Array) {\n super(\n 'dialog.comfy-dialog.comfyui-dialog',\n actions?.map((opt) => {\n if (typeof opt === 'string') {\n opt = { text: opt }\n }\n return $el('button.comfyui-button', {\n type: 'button',\n textContent: opt.text,\n onclick: () => this.close(opt.value ?? opt.text)\n })\n })\n )\n }\n\n show(html: string | HTMLElement | HTMLElement[]) {\n this.element.addEventListener('close', () => {\n this.close()\n })\n\n super.show(html)\n\n return new Promise((resolve) => {\n this.#resolve = resolve\n })\n }\n\n showModal(html: string | HTMLElement | HTMLElement[]) {\n this.element.addEventListener('close', () => {\n this.close()\n })\n\n super.show(html)\n this.element.showModal()\n\n return new Promise((resolve) => {\n this.#resolve = resolve\n })\n }\n\n close(result = null) {\n this.#resolve(result)\n this.element.close()\n super.close()\n }\n\n static async prompt({ title = null, message, actions }) {\n const dialog = new ComfyAsyncDialog(actions)\n const content = [$el('span', message)]\n if (title) {\n content.unshift($el('h3', title))\n }\n const res = await dialog.showModal(content)\n dialog.element.remove()\n return res\n }\n}\n","// @ts-strict-ignore\nimport type { ComfyApp } from './app'\nimport { api } from './api'\nimport { ChangeTracker } from './changeTracker'\nimport { ComfyAsyncDialog } from './ui/components/asyncDialog'\nimport { setStorageValue } from './utils'\nimport { LGraphCanvas, LGraph } from '@comfyorg/litegraph'\nimport { appendJsonExt, trimJsonExt } from '@/utils/formatUtil'\nimport {\n useWorkflowStore,\n useWorkflowBookmarkStore\n} from '@/stores/workflowStore'\nimport { useExecutionStore } from '@/stores/executionStore'\nimport { markRaw, toRaw } from 'vue'\nimport { UserDataFullInfo } from '@/types/apiTypes'\nimport { useToastStore } from '@/stores/toastStore'\nimport { showPromptDialog } from '@/services/dialogService'\n\nexport class ComfyWorkflowManager extends EventTarget {\n executionStore: ReturnType | null\n workflowStore: ReturnType | null\n workflowBookmarkStore: ReturnType | null\n\n app: ComfyApp\n #unsavedCount = 0\n\n get workflowLookup(): Record {\n return this.workflowStore?.workflowLookup ?? {}\n }\n\n get workflows(): ComfyWorkflow[] {\n return this.workflowStore?.workflows ?? []\n }\n\n get openWorkflows(): ComfyWorkflow[] {\n return (this.workflowStore?.openWorkflows ?? []) as ComfyWorkflow[]\n }\n\n get _activeWorkflow(): ComfyWorkflow | null {\n if (!this.app.vueAppReady) return null\n return this.workflowStore!.activeWorkflow as ComfyWorkflow | null\n }\n\n set _activeWorkflow(workflow: ComfyWorkflow | null) {\n if (!this.app.vueAppReady) return\n this.workflowStore!.activeWorkflow = workflow ? workflow : null\n }\n\n get activeWorkflow(): ComfyWorkflow | null {\n return this._activeWorkflow ?? this.openWorkflows[0]\n }\n\n get activePromptId() {\n return this.executionStore?.activePromptId\n }\n\n get activePrompt() {\n return this.executionStore?.activePrompt\n }\n\n constructor(app: ComfyApp) {\n super()\n this.app = app\n ChangeTracker.init(app)\n }\n\n async loadWorkflows() {\n try {\n const [files, _] = await Promise.all([\n api.listUserDataFullInfo('workflows'),\n this.workflowBookmarkStore?.loadBookmarks()\n ])\n\n files.forEach((file: UserDataFullInfo) => {\n let workflow = this.workflowLookup[file.path]\n if (!workflow) {\n workflow = new ComfyWorkflow(this, file.path, file.path.split('/'))\n this.workflowLookup[workflow.path] = workflow\n }\n })\n } catch (error) {\n useToastStore().addAlert(\n 'Error loading workflows: ' + (error.message ?? error)\n )\n }\n }\n\n /**\n * @param {string | ComfyWorkflow | null} workflow\n */\n setWorkflow(workflow) {\n if (workflow && typeof workflow === 'string') {\n const found = this.workflows.find((w) => w.path === workflow)\n if (found) {\n workflow = found\n workflow.unsaved = !workflow\n }\n }\n\n if (!(toRaw(workflow) instanceof ComfyWorkflow)) {\n // Still not found, either reloading a deleted workflow or blank\n workflow = new ComfyWorkflow(\n this,\n workflow ||\n 'Unsaved Workflow' +\n (this.#unsavedCount++ ? ` (${this.#unsavedCount})` : '')\n )\n this.workflowLookup[workflow.key] = workflow\n }\n\n if (!workflow.isOpen) {\n // Opening a new workflow\n workflow.track()\n }\n\n this._activeWorkflow = workflow\n\n setStorageValue('Comfy.PreviousWorkflow', this.activeWorkflow.path ?? '')\n this.dispatchEvent(new CustomEvent('changeWorkflow'))\n }\n\n storePrompt({ nodes, id }) {\n this.executionStore?.storePrompt({\n nodes,\n id,\n workflow: this.activeWorkflow\n })\n }\n\n async closeWorkflow(workflow: ComfyWorkflow, warnIfUnsaved: boolean = true) {\n if (!workflow.isOpen) {\n return true\n }\n if (workflow.unsaved && warnIfUnsaved) {\n const res = await ComfyAsyncDialog.prompt({\n title: 'Save Changes?',\n message: `Do you want to save changes to \"${workflow.path ?? workflow.name}\" before closing?`,\n actions: ['Yes', 'No', 'Cancel']\n })\n if (res === 'Yes') {\n const active = this.activeWorkflow\n if (active !== workflow) {\n // We need to switch to the workflow to save it\n await workflow.load()\n }\n\n if (!(await workflow.save())) {\n // Save was canceled, restore the previous workflow\n if (active !== workflow) {\n await active.load()\n }\n return\n }\n } else if (res === 'Cancel') {\n return\n }\n }\n workflow.changeTracker = null\n workflow.isOpen = false\n if (this.openWorkflows.length > 0) {\n this._activeWorkflow = this.openWorkflows[0]\n await this._activeWorkflow.load()\n } else {\n // Load default\n await this.app.loadGraphData()\n }\n }\n}\n\nexport class ComfyWorkflow {\n name: string\n path: string | null\n pathParts: string[] | null\n unsaved = false\n // Raw\n manager: ComfyWorkflowManager\n changeTracker: ChangeTracker | null = null\n isOpen: boolean = false\n\n get isTemporary() {\n return !this.path\n }\n\n get isPersisted() {\n return !this.isTemporary\n }\n\n get key() {\n return this.pathParts?.join('/') ?? this.name + '.json'\n }\n\n get isBookmarked() {\n return this.manager.workflowBookmarkStore?.isBookmarked(this.path) ?? false\n }\n\n /**\n * @deprecated Use isBookmarked instead\n */\n get isFavorite() {\n return this.isBookmarked\n }\n\n constructor(\n manager: ComfyWorkflowManager,\n path: string,\n pathParts?: string[]\n ) {\n this.manager = markRaw(manager)\n if (pathParts) {\n this.updatePath(path, pathParts)\n } else {\n this.name = path\n this.unsaved = true\n }\n }\n\n private updatePath(path: string, pathParts: string[]) {\n this.path = path\n\n if (!pathParts) {\n if (!path.includes('\\\\')) {\n pathParts = path.split('/')\n } else {\n pathParts = path.split('\\\\')\n }\n }\n\n this.pathParts = pathParts\n this.name = trimJsonExt(pathParts[pathParts.length - 1])\n }\n\n async getWorkflowData() {\n const resp = await api.getUserData('workflows/' + this.path)\n if (resp.status !== 200) {\n useToastStore().addAlert(\n `Error loading workflow file '${this.path}': ${resp.status} ${resp.statusText}`\n )\n return\n }\n return await resp.json()\n }\n\n async load() {\n if (this.isOpen) {\n await this.manager.app.loadGraphData(\n this.changeTracker.activeState,\n true,\n true,\n this,\n {\n showMissingModelsDialog: false,\n showMissingNodesDialog: false\n }\n )\n } else {\n const data = await this.getWorkflowData()\n if (!data) return\n await this.manager.app.loadGraphData(data, true, true, this)\n }\n }\n\n async save(saveAs = false) {\n const createNewFile = !this.path || saveAs\n return !!(await this._save(\n createNewFile ? null : this.path,\n /* overwrite */ !createNewFile\n ))\n }\n\n async favorite(value: boolean) {\n try {\n if (this.isBookmarked === value) return\n this.manager.workflowBookmarkStore?.setBookmarked(this.path, value)\n this.manager.dispatchEvent(new CustomEvent('favorite', { detail: this }))\n } catch (error) {\n useToastStore().addAlert(\n 'Error favoriting workflow ' +\n this.path +\n '\\n' +\n (error.message ?? error)\n )\n }\n }\n\n async rename(path: string) {\n path = appendJsonExt(path)\n let resp = await api.moveUserData(\n 'workflows/' + this.path,\n 'workflows/' + path\n )\n\n if (resp.status === 409) {\n if (\n !confirm(\n `Workflow '${path}' already exists, do you want to overwrite it?`\n )\n )\n return resp\n resp = await api.moveUserData(\n 'workflows/' + this.path,\n 'workflows/' + path,\n { overwrite: true }\n )\n }\n\n if (resp.status !== 200) {\n useToastStore().addAlert(\n `Error renaming workflow file '${this.path}': ${resp.status} ${resp.statusText}`\n )\n return\n }\n\n const isFav = this.isFavorite\n if (isFav) {\n await this.favorite(false)\n }\n path = (await resp.json()).substring('workflows/'.length)\n this.updatePath(path, null)\n if (isFav) {\n await this.favorite(true)\n }\n this.manager.dispatchEvent(new CustomEvent('rename', { detail: this }))\n setStorageValue('Comfy.PreviousWorkflow', this.path ?? '')\n }\n\n async insert() {\n const data = await this.getWorkflowData()\n if (!data) return\n\n const old = localStorage.getItem('litegrapheditor_clipboard')\n const graph = new LGraph(data)\n const canvas = new LGraphCanvas(null, graph, {\n skip_events: true,\n skip_render: true\n })\n canvas.selectNodes()\n canvas.copyToClipboard()\n this.manager.app.canvas.pasteFromClipboard()\n localStorage.setItem('litegrapheditor_clipboard', old)\n }\n\n async delete() {\n // TODO: fix delete of current workflow - should mark workflow as unsaved and when saving use old name by default\n\n if (this.isFavorite) {\n await this.favorite(false)\n }\n const resp = await api.deleteUserData('workflows/' + this.path)\n if (resp.status !== 204) {\n useToastStore().addAlert(\n `Error removing user data file '${this.path}': ${resp.status} ${resp.statusText}`\n )\n }\n\n this.unsaved = true\n this.path = null\n this.pathParts = null\n this.manager.workflows.splice(this.manager.workflows.indexOf(this), 1)\n this.manager.dispatchEvent(new CustomEvent('delete', { detail: this }))\n }\n\n track() {\n if (this.changeTracker) {\n this.changeTracker.restore()\n } else {\n this.changeTracker = markRaw(new ChangeTracker(this))\n }\n this.isOpen = true\n }\n\n private async _save(path: string | null, overwrite: boolean) {\n if (!path) {\n path = await showPromptDialog({\n title: 'Save workflow',\n message: 'Enter the filename:',\n defaultValue: trimJsonExt(this.path) ?? this.name ?? 'workflow'\n })\n if (!path) return\n }\n\n path = appendJsonExt(path)\n\n const workflow = this.manager.app.serializeGraph()\n const json = JSON.stringify(workflow, null, 2)\n let resp = await api.storeUserData('workflows/' + path, json, {\n stringify: false,\n throwOnError: false,\n overwrite\n })\n if (resp.status === 409) {\n if (\n !confirm(\n `Workflow '${path}' already exists, do you want to overwrite it?`\n )\n )\n return\n resp = await api.storeUserData('workflows/' + path, json, {\n stringify: false\n })\n }\n\n if (resp.status !== 200) {\n useToastStore().addAlert(\n `Error saving workflow '${this.path}': ${resp.status} ${resp.statusText}`\n )\n return\n }\n\n path = (await resp.json()).substring('workflows/'.length)\n\n if (!this.path) {\n // Saved new workflow, patch this instance\n const oldKey = this.key\n this.updatePath(path, null)\n\n // Update workflowLookup: change the key from the old unsaved path to the new saved path\n delete this.manager.workflowStore.workflowLookup[oldKey]\n this.manager.workflowStore.workflowLookup[this.key] = this\n\n await this.manager.loadWorkflows()\n this.unsaved = false\n this.manager.dispatchEvent(new CustomEvent('rename', { detail: this }))\n setStorageValue('Comfy.PreviousWorkflow', this.path ?? '')\n } else if (path !== this.path) {\n // Saved as, open the new copy\n await this.manager.loadWorkflows()\n const workflow = this.manager.workflowLookup[path]\n await workflow.load()\n } else {\n // Normal save\n this.unsaved = false\n this.manager.dispatchEvent(new CustomEvent('save', { detail: this }))\n }\n\n return true\n }\n}\n","/**\n * Fuse.js v7.0.0 - Lightweight fuzzy-search (http://fusejs.io)\n *\n * Copyright (c) 2023 Kiro Risk (http://kiro.me)\n * All Rights Reserved. Apache Software License 2.0\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n */\n\nfunction isArray(value) {\n return !Array.isArray\n ? getTag(value) === '[object Array]'\n : Array.isArray(value)\n}\n\n// Adapted from: https://github.com/lodash/lodash/blob/master/.internal/baseToString.js\nconst INFINITY = 1 / 0;\nfunction baseToString(value) {\n // Exit early for strings to avoid a performance hit in some environments.\n if (typeof value == 'string') {\n return value\n }\n let result = value + '';\n return result == '0' && 1 / value == -INFINITY ? '-0' : result\n}\n\nfunction toString(value) {\n return value == null ? '' : baseToString(value)\n}\n\nfunction isString(value) {\n return typeof value === 'string'\n}\n\nfunction isNumber(value) {\n return typeof value === 'number'\n}\n\n// Adapted from: https://github.com/lodash/lodash/blob/master/isBoolean.js\nfunction isBoolean(value) {\n return (\n value === true ||\n value === false ||\n (isObjectLike(value) && getTag(value) == '[object Boolean]')\n )\n}\n\nfunction isObject(value) {\n return typeof value === 'object'\n}\n\n// Checks if `value` is object-like.\nfunction isObjectLike(value) {\n return isObject(value) && value !== null\n}\n\nfunction isDefined(value) {\n return value !== undefined && value !== null\n}\n\nfunction isBlank(value) {\n return !value.trim().length\n}\n\n// Gets the `toStringTag` of `value`.\n// Adapted from: https://github.com/lodash/lodash/blob/master/.internal/getTag.js\nfunction getTag(value) {\n return value == null\n ? value === undefined\n ? '[object Undefined]'\n : '[object Null]'\n : Object.prototype.toString.call(value)\n}\n\nconst EXTENDED_SEARCH_UNAVAILABLE = 'Extended search is not available';\n\nconst INCORRECT_INDEX_TYPE = \"Incorrect 'index' type\";\n\nconst LOGICAL_SEARCH_INVALID_QUERY_FOR_KEY = (key) =>\n `Invalid value for key ${key}`;\n\nconst PATTERN_LENGTH_TOO_LARGE = (max) =>\n `Pattern length exceeds max of ${max}.`;\n\nconst MISSING_KEY_PROPERTY = (name) => `Missing ${name} property in key`;\n\nconst INVALID_KEY_WEIGHT_VALUE = (key) =>\n `Property 'weight' in key '${key}' must be a positive integer`;\n\nconst hasOwn = Object.prototype.hasOwnProperty;\n\nclass KeyStore {\n constructor(keys) {\n this._keys = [];\n this._keyMap = {};\n\n let totalWeight = 0;\n\n keys.forEach((key) => {\n let obj = createKey(key);\n\n this._keys.push(obj);\n this._keyMap[obj.id] = obj;\n\n totalWeight += obj.weight;\n });\n\n // Normalize weights so that their sum is equal to 1\n this._keys.forEach((key) => {\n key.weight /= totalWeight;\n });\n }\n get(keyId) {\n return this._keyMap[keyId]\n }\n keys() {\n return this._keys\n }\n toJSON() {\n return JSON.stringify(this._keys)\n }\n}\n\nfunction createKey(key) {\n let path = null;\n let id = null;\n let src = null;\n let weight = 1;\n let getFn = null;\n\n if (isString(key) || isArray(key)) {\n src = key;\n path = createKeyPath(key);\n id = createKeyId(key);\n } else {\n if (!hasOwn.call(key, 'name')) {\n throw new Error(MISSING_KEY_PROPERTY('name'))\n }\n\n const name = key.name;\n src = name;\n\n if (hasOwn.call(key, 'weight')) {\n weight = key.weight;\n\n if (weight <= 0) {\n throw new Error(INVALID_KEY_WEIGHT_VALUE(name))\n }\n }\n\n path = createKeyPath(name);\n id = createKeyId(name);\n getFn = key.getFn;\n }\n\n return { path, id, weight, src, getFn }\n}\n\nfunction createKeyPath(key) {\n return isArray(key) ? key : key.split('.')\n}\n\nfunction createKeyId(key) {\n return isArray(key) ? key.join('.') : key\n}\n\nfunction get(obj, path) {\n let list = [];\n let arr = false;\n\n const deepGet = (obj, path, index) => {\n if (!isDefined(obj)) {\n return\n }\n if (!path[index]) {\n // If there's no path left, we've arrived at the object we care about.\n list.push(obj);\n } else {\n let key = path[index];\n\n const value = obj[key];\n\n if (!isDefined(value)) {\n return\n }\n\n // If we're at the last value in the path, and if it's a string/number/bool,\n // add it to the list\n if (\n index === path.length - 1 &&\n (isString(value) || isNumber(value) || isBoolean(value))\n ) {\n list.push(toString(value));\n } else if (isArray(value)) {\n arr = true;\n // Search each item in the array.\n for (let i = 0, len = value.length; i < len; i += 1) {\n deepGet(value[i], path, index + 1);\n }\n } else if (path.length) {\n // An object. Recurse further.\n deepGet(value, path, index + 1);\n }\n }\n };\n\n // Backwards compatibility (since path used to be a string)\n deepGet(obj, isString(path) ? path.split('.') : path, 0);\n\n return arr ? list : list[0]\n}\n\nconst MatchOptions = {\n // Whether the matches should be included in the result set. When `true`, each record in the result\n // set will include the indices of the matched characters.\n // These can consequently be used for highlighting purposes.\n includeMatches: false,\n // When `true`, the matching function will continue to the end of a search pattern even if\n // a perfect match has already been located in the string.\n findAllMatches: false,\n // Minimum number of characters that must be matched before a result is considered a match\n minMatchCharLength: 1\n};\n\nconst BasicOptions = {\n // When `true`, the algorithm continues searching to the end of the input even if a perfect\n // match is found before the end of the same input.\n isCaseSensitive: false,\n // When true, the matching function will continue to the end of a search pattern even if\n includeScore: false,\n // List of properties that will be searched. This also supports nested properties.\n keys: [],\n // Whether to sort the result list, by score\n shouldSort: true,\n // Default sort function: sort by ascending score, ascending index\n sortFn: (a, b) =>\n a.score === b.score ? (a.idx < b.idx ? -1 : 1) : a.score < b.score ? -1 : 1\n};\n\nconst FuzzyOptions = {\n // Approximately where in the text is the pattern expected to be found?\n location: 0,\n // At what point does the match algorithm give up. A threshold of '0.0' requires a perfect match\n // (of both letters and location), a threshold of '1.0' would match anything.\n threshold: 0.6,\n // Determines how close the match must be to the fuzzy location (specified above).\n // An exact letter match which is 'distance' characters away from the fuzzy location\n // would score as a complete mismatch. A distance of '0' requires the match be at\n // the exact location specified, a threshold of '1000' would require a perfect match\n // to be within 800 characters of the fuzzy location to be found using a 0.8 threshold.\n distance: 100\n};\n\nconst AdvancedOptions = {\n // When `true`, it enables the use of unix-like search commands\n useExtendedSearch: false,\n // The get function to use when fetching an object's properties.\n // The default will search nested paths *ie foo.bar.baz*\n getFn: get,\n // When `true`, search will ignore `location` and `distance`, so it won't matter\n // where in the string the pattern appears.\n // More info: https://fusejs.io/concepts/scoring-theory.html#fuzziness-score\n ignoreLocation: false,\n // When `true`, the calculation for the relevance score (used for sorting) will\n // ignore the field-length norm.\n // More info: https://fusejs.io/concepts/scoring-theory.html#field-length-norm\n ignoreFieldNorm: false,\n // The weight to determine how much field length norm effects scoring.\n fieldNormWeight: 1\n};\n\nvar Config = {\n ...BasicOptions,\n ...MatchOptions,\n ...FuzzyOptions,\n ...AdvancedOptions\n};\n\nconst SPACE = /[^ ]+/g;\n\n// Field-length norm: the shorter the field, the higher the weight.\n// Set to 3 decimals to reduce index size.\nfunction norm(weight = 1, mantissa = 3) {\n const cache = new Map();\n const m = Math.pow(10, mantissa);\n\n return {\n get(value) {\n const numTokens = value.match(SPACE).length;\n\n if (cache.has(numTokens)) {\n return cache.get(numTokens)\n }\n\n // Default function is 1/sqrt(x), weight makes that variable\n const norm = 1 / Math.pow(numTokens, 0.5 * weight);\n\n // In place of `toFixed(mantissa)`, for faster computation\n const n = parseFloat(Math.round(norm * m) / m);\n\n cache.set(numTokens, n);\n\n return n\n },\n clear() {\n cache.clear();\n }\n }\n}\n\nclass FuseIndex {\n constructor({\n getFn = Config.getFn,\n fieldNormWeight = Config.fieldNormWeight\n } = {}) {\n this.norm = norm(fieldNormWeight, 3);\n this.getFn = getFn;\n this.isCreated = false;\n\n this.setIndexRecords();\n }\n setSources(docs = []) {\n this.docs = docs;\n }\n setIndexRecords(records = []) {\n this.records = records;\n }\n setKeys(keys = []) {\n this.keys = keys;\n this._keysMap = {};\n keys.forEach((key, idx) => {\n this._keysMap[key.id] = idx;\n });\n }\n create() {\n if (this.isCreated || !this.docs.length) {\n return\n }\n\n this.isCreated = true;\n\n // List is Array\n if (isString(this.docs[0])) {\n this.docs.forEach((doc, docIndex) => {\n this._addString(doc, docIndex);\n });\n } else {\n // List is Array\n this.docs.forEach((doc, docIndex) => {\n this._addObject(doc, docIndex);\n });\n }\n\n this.norm.clear();\n }\n // Adds a doc to the end of the index\n add(doc) {\n const idx = this.size();\n\n if (isString(doc)) {\n this._addString(doc, idx);\n } else {\n this._addObject(doc, idx);\n }\n }\n // Removes the doc at the specified index of the index\n removeAt(idx) {\n this.records.splice(idx, 1);\n\n // Change ref index of every subsquent doc\n for (let i = idx, len = this.size(); i < len; i += 1) {\n this.records[i].i -= 1;\n }\n }\n getValueForItemAtKeyId(item, keyId) {\n return item[this._keysMap[keyId]]\n }\n size() {\n return this.records.length\n }\n _addString(doc, docIndex) {\n if (!isDefined(doc) || isBlank(doc)) {\n return\n }\n\n let record = {\n v: doc,\n i: docIndex,\n n: this.norm.get(doc)\n };\n\n this.records.push(record);\n }\n _addObject(doc, docIndex) {\n let record = { i: docIndex, $: {} };\n\n // Iterate over every key (i.e, path), and fetch the value at that key\n this.keys.forEach((key, keyIndex) => {\n let value = key.getFn ? key.getFn(doc) : this.getFn(doc, key.path);\n\n if (!isDefined(value)) {\n return\n }\n\n if (isArray(value)) {\n let subRecords = [];\n const stack = [{ nestedArrIndex: -1, value }];\n\n while (stack.length) {\n const { nestedArrIndex, value } = stack.pop();\n\n if (!isDefined(value)) {\n continue\n }\n\n if (isString(value) && !isBlank(value)) {\n let subRecord = {\n v: value,\n i: nestedArrIndex,\n n: this.norm.get(value)\n };\n\n subRecords.push(subRecord);\n } else if (isArray(value)) {\n value.forEach((item, k) => {\n stack.push({\n nestedArrIndex: k,\n value: item\n });\n });\n } else ;\n }\n record.$[keyIndex] = subRecords;\n } else if (isString(value) && !isBlank(value)) {\n let subRecord = {\n v: value,\n n: this.norm.get(value)\n };\n\n record.$[keyIndex] = subRecord;\n }\n });\n\n this.records.push(record);\n }\n toJSON() {\n return {\n keys: this.keys,\n records: this.records\n }\n }\n}\n\nfunction createIndex(\n keys,\n docs,\n { getFn = Config.getFn, fieldNormWeight = Config.fieldNormWeight } = {}\n) {\n const myIndex = new FuseIndex({ getFn, fieldNormWeight });\n myIndex.setKeys(keys.map(createKey));\n myIndex.setSources(docs);\n myIndex.create();\n return myIndex\n}\n\nfunction parseIndex(\n data,\n { getFn = Config.getFn, fieldNormWeight = Config.fieldNormWeight } = {}\n) {\n const { keys, records } = data;\n const myIndex = new FuseIndex({ getFn, fieldNormWeight });\n myIndex.setKeys(keys);\n myIndex.setIndexRecords(records);\n return myIndex\n}\n\nfunction computeScore$1(\n pattern,\n {\n errors = 0,\n currentLocation = 0,\n expectedLocation = 0,\n distance = Config.distance,\n ignoreLocation = Config.ignoreLocation\n } = {}\n) {\n const accuracy = errors / pattern.length;\n\n if (ignoreLocation) {\n return accuracy\n }\n\n const proximity = Math.abs(expectedLocation - currentLocation);\n\n if (!distance) {\n // Dodge divide by zero error.\n return proximity ? 1.0 : accuracy\n }\n\n return accuracy + proximity / distance\n}\n\nfunction convertMaskToIndices(\n matchmask = [],\n minMatchCharLength = Config.minMatchCharLength\n) {\n let indices = [];\n let start = -1;\n let end = -1;\n let i = 0;\n\n for (let len = matchmask.length; i < len; i += 1) {\n let match = matchmask[i];\n if (match && start === -1) {\n start = i;\n } else if (!match && start !== -1) {\n end = i - 1;\n if (end - start + 1 >= minMatchCharLength) {\n indices.push([start, end]);\n }\n start = -1;\n }\n }\n\n // (i-1 - start) + 1 => i - start\n if (matchmask[i - 1] && i - start >= minMatchCharLength) {\n indices.push([start, i - 1]);\n }\n\n return indices\n}\n\n// Machine word size\nconst MAX_BITS = 32;\n\nfunction search(\n text,\n pattern,\n patternAlphabet,\n {\n location = Config.location,\n distance = Config.distance,\n threshold = Config.threshold,\n findAllMatches = Config.findAllMatches,\n minMatchCharLength = Config.minMatchCharLength,\n includeMatches = Config.includeMatches,\n ignoreLocation = Config.ignoreLocation\n } = {}\n) {\n if (pattern.length > MAX_BITS) {\n throw new Error(PATTERN_LENGTH_TOO_LARGE(MAX_BITS))\n }\n\n const patternLen = pattern.length;\n // Set starting location at beginning text and initialize the alphabet.\n const textLen = text.length;\n // Handle the case when location > text.length\n const expectedLocation = Math.max(0, Math.min(location, textLen));\n // Highest score beyond which we give up.\n let currentThreshold = threshold;\n // Is there a nearby exact match? (speedup)\n let bestLocation = expectedLocation;\n\n // Performance: only computer matches when the minMatchCharLength > 1\n // OR if `includeMatches` is true.\n const computeMatches = minMatchCharLength > 1 || includeMatches;\n // A mask of the matches, used for building the indices\n const matchMask = computeMatches ? Array(textLen) : [];\n\n let index;\n\n // Get all exact matches, here for speed up\n while ((index = text.indexOf(pattern, bestLocation)) > -1) {\n let score = computeScore$1(pattern, {\n currentLocation: index,\n expectedLocation,\n distance,\n ignoreLocation\n });\n\n currentThreshold = Math.min(score, currentThreshold);\n bestLocation = index + patternLen;\n\n if (computeMatches) {\n let i = 0;\n while (i < patternLen) {\n matchMask[index + i] = 1;\n i += 1;\n }\n }\n }\n\n // Reset the best location\n bestLocation = -1;\n\n let lastBitArr = [];\n let finalScore = 1;\n let binMax = patternLen + textLen;\n\n const mask = 1 << (patternLen - 1);\n\n for (let i = 0; i < patternLen; i += 1) {\n // Scan for the best match; each iteration allows for one more error.\n // Run a binary search to determine how far from the match location we can stray\n // at this error level.\n let binMin = 0;\n let binMid = binMax;\n\n while (binMin < binMid) {\n const score = computeScore$1(pattern, {\n errors: i,\n currentLocation: expectedLocation + binMid,\n expectedLocation,\n distance,\n ignoreLocation\n });\n\n if (score <= currentThreshold) {\n binMin = binMid;\n } else {\n binMax = binMid;\n }\n\n binMid = Math.floor((binMax - binMin) / 2 + binMin);\n }\n\n // Use the result from this iteration as the maximum for the next.\n binMax = binMid;\n\n let start = Math.max(1, expectedLocation - binMid + 1);\n let finish = findAllMatches\n ? textLen\n : Math.min(expectedLocation + binMid, textLen) + patternLen;\n\n // Initialize the bit array\n let bitArr = Array(finish + 2);\n\n bitArr[finish + 1] = (1 << i) - 1;\n\n for (let j = finish; j >= start; j -= 1) {\n let currentLocation = j - 1;\n let charMatch = patternAlphabet[text.charAt(currentLocation)];\n\n if (computeMatches) {\n // Speed up: quick bool to int conversion (i.e, `charMatch ? 1 : 0`)\n matchMask[currentLocation] = +!!charMatch;\n }\n\n // First pass: exact match\n bitArr[j] = ((bitArr[j + 1] << 1) | 1) & charMatch;\n\n // Subsequent passes: fuzzy match\n if (i) {\n bitArr[j] |=\n ((lastBitArr[j + 1] | lastBitArr[j]) << 1) | 1 | lastBitArr[j + 1];\n }\n\n if (bitArr[j] & mask) {\n finalScore = computeScore$1(pattern, {\n errors: i,\n currentLocation,\n expectedLocation,\n distance,\n ignoreLocation\n });\n\n // This match will almost certainly be better than any existing match.\n // But check anyway.\n if (finalScore <= currentThreshold) {\n // Indeed it is\n currentThreshold = finalScore;\n bestLocation = currentLocation;\n\n // Already passed `loc`, downhill from here on in.\n if (bestLocation <= expectedLocation) {\n break\n }\n\n // When passing `bestLocation`, don't exceed our current distance from `expectedLocation`.\n start = Math.max(1, 2 * expectedLocation - bestLocation);\n }\n }\n }\n\n // No hope for a (better) match at greater error levels.\n const score = computeScore$1(pattern, {\n errors: i + 1,\n currentLocation: expectedLocation,\n expectedLocation,\n distance,\n ignoreLocation\n });\n\n if (score > currentThreshold) {\n break\n }\n\n lastBitArr = bitArr;\n }\n\n const result = {\n isMatch: bestLocation >= 0,\n // Count exact matches (those with a score of 0) to be \"almost\" exact\n score: Math.max(0.001, finalScore)\n };\n\n if (computeMatches) {\n const indices = convertMaskToIndices(matchMask, minMatchCharLength);\n if (!indices.length) {\n result.isMatch = false;\n } else if (includeMatches) {\n result.indices = indices;\n }\n }\n\n return result\n}\n\nfunction createPatternAlphabet(pattern) {\n let mask = {};\n\n for (let i = 0, len = pattern.length; i < len; i += 1) {\n const char = pattern.charAt(i);\n mask[char] = (mask[char] || 0) | (1 << (len - i - 1));\n }\n\n return mask\n}\n\nclass BitapSearch {\n constructor(\n pattern,\n {\n location = Config.location,\n threshold = Config.threshold,\n distance = Config.distance,\n includeMatches = Config.includeMatches,\n findAllMatches = Config.findAllMatches,\n minMatchCharLength = Config.minMatchCharLength,\n isCaseSensitive = Config.isCaseSensitive,\n ignoreLocation = Config.ignoreLocation\n } = {}\n ) {\n this.options = {\n location,\n threshold,\n distance,\n includeMatches,\n findAllMatches,\n minMatchCharLength,\n isCaseSensitive,\n ignoreLocation\n };\n\n this.pattern = isCaseSensitive ? pattern : pattern.toLowerCase();\n\n this.chunks = [];\n\n if (!this.pattern.length) {\n return\n }\n\n const addChunk = (pattern, startIndex) => {\n this.chunks.push({\n pattern,\n alphabet: createPatternAlphabet(pattern),\n startIndex\n });\n };\n\n const len = this.pattern.length;\n\n if (len > MAX_BITS) {\n let i = 0;\n const remainder = len % MAX_BITS;\n const end = len - remainder;\n\n while (i < end) {\n addChunk(this.pattern.substr(i, MAX_BITS), i);\n i += MAX_BITS;\n }\n\n if (remainder) {\n const startIndex = len - MAX_BITS;\n addChunk(this.pattern.substr(startIndex), startIndex);\n }\n } else {\n addChunk(this.pattern, 0);\n }\n }\n\n searchIn(text) {\n const { isCaseSensitive, includeMatches } = this.options;\n\n if (!isCaseSensitive) {\n text = text.toLowerCase();\n }\n\n // Exact match\n if (this.pattern === text) {\n let result = {\n isMatch: true,\n score: 0\n };\n\n if (includeMatches) {\n result.indices = [[0, text.length - 1]];\n }\n\n return result\n }\n\n // Otherwise, use Bitap algorithm\n const {\n location,\n distance,\n threshold,\n findAllMatches,\n minMatchCharLength,\n ignoreLocation\n } = this.options;\n\n let allIndices = [];\n let totalScore = 0;\n let hasMatches = false;\n\n this.chunks.forEach(({ pattern, alphabet, startIndex }) => {\n const { isMatch, score, indices } = search(text, pattern, alphabet, {\n location: location + startIndex,\n distance,\n threshold,\n findAllMatches,\n minMatchCharLength,\n includeMatches,\n ignoreLocation\n });\n\n if (isMatch) {\n hasMatches = true;\n }\n\n totalScore += score;\n\n if (isMatch && indices) {\n allIndices = [...allIndices, ...indices];\n }\n });\n\n let result = {\n isMatch: hasMatches,\n score: hasMatches ? totalScore / this.chunks.length : 1\n };\n\n if (hasMatches && includeMatches) {\n result.indices = allIndices;\n }\n\n return result\n }\n}\n\nclass BaseMatch {\n constructor(pattern) {\n this.pattern = pattern;\n }\n static isMultiMatch(pattern) {\n return getMatch(pattern, this.multiRegex)\n }\n static isSingleMatch(pattern) {\n return getMatch(pattern, this.singleRegex)\n }\n search(/*text*/) {}\n}\n\nfunction getMatch(pattern, exp) {\n const matches = pattern.match(exp);\n return matches ? matches[1] : null\n}\n\n// Token: 'file\n\nclass ExactMatch extends BaseMatch {\n constructor(pattern) {\n super(pattern);\n }\n static get type() {\n return 'exact'\n }\n static get multiRegex() {\n return /^=\"(.*)\"$/\n }\n static get singleRegex() {\n return /^=(.*)$/\n }\n search(text) {\n const isMatch = text === this.pattern;\n\n return {\n isMatch,\n score: isMatch ? 0 : 1,\n indices: [0, this.pattern.length - 1]\n }\n }\n}\n\n// Token: !fire\n\nclass InverseExactMatch extends BaseMatch {\n constructor(pattern) {\n super(pattern);\n }\n static get type() {\n return 'inverse-exact'\n }\n static get multiRegex() {\n return /^!\"(.*)\"$/\n }\n static get singleRegex() {\n return /^!(.*)$/\n }\n search(text) {\n const index = text.indexOf(this.pattern);\n const isMatch = index === -1;\n\n return {\n isMatch,\n score: isMatch ? 0 : 1,\n indices: [0, text.length - 1]\n }\n }\n}\n\n// Token: ^file\n\nclass PrefixExactMatch extends BaseMatch {\n constructor(pattern) {\n super(pattern);\n }\n static get type() {\n return 'prefix-exact'\n }\n static get multiRegex() {\n return /^\\^\"(.*)\"$/\n }\n static get singleRegex() {\n return /^\\^(.*)$/\n }\n search(text) {\n const isMatch = text.startsWith(this.pattern);\n\n return {\n isMatch,\n score: isMatch ? 0 : 1,\n indices: [0, this.pattern.length - 1]\n }\n }\n}\n\n// Token: !^fire\n\nclass InversePrefixExactMatch extends BaseMatch {\n constructor(pattern) {\n super(pattern);\n }\n static get type() {\n return 'inverse-prefix-exact'\n }\n static get multiRegex() {\n return /^!\\^\"(.*)\"$/\n }\n static get singleRegex() {\n return /^!\\^(.*)$/\n }\n search(text) {\n const isMatch = !text.startsWith(this.pattern);\n\n return {\n isMatch,\n score: isMatch ? 0 : 1,\n indices: [0, text.length - 1]\n }\n }\n}\n\n// Token: .file$\n\nclass SuffixExactMatch extends BaseMatch {\n constructor(pattern) {\n super(pattern);\n }\n static get type() {\n return 'suffix-exact'\n }\n static get multiRegex() {\n return /^\"(.*)\"\\$$/\n }\n static get singleRegex() {\n return /^(.*)\\$$/\n }\n search(text) {\n const isMatch = text.endsWith(this.pattern);\n\n return {\n isMatch,\n score: isMatch ? 0 : 1,\n indices: [text.length - this.pattern.length, text.length - 1]\n }\n }\n}\n\n// Token: !.file$\n\nclass InverseSuffixExactMatch extends BaseMatch {\n constructor(pattern) {\n super(pattern);\n }\n static get type() {\n return 'inverse-suffix-exact'\n }\n static get multiRegex() {\n return /^!\"(.*)\"\\$$/\n }\n static get singleRegex() {\n return /^!(.*)\\$$/\n }\n search(text) {\n const isMatch = !text.endsWith(this.pattern);\n return {\n isMatch,\n score: isMatch ? 0 : 1,\n indices: [0, text.length - 1]\n }\n }\n}\n\nclass FuzzyMatch extends BaseMatch {\n constructor(\n pattern,\n {\n location = Config.location,\n threshold = Config.threshold,\n distance = Config.distance,\n includeMatches = Config.includeMatches,\n findAllMatches = Config.findAllMatches,\n minMatchCharLength = Config.minMatchCharLength,\n isCaseSensitive = Config.isCaseSensitive,\n ignoreLocation = Config.ignoreLocation\n } = {}\n ) {\n super(pattern);\n this._bitapSearch = new BitapSearch(pattern, {\n location,\n threshold,\n distance,\n includeMatches,\n findAllMatches,\n minMatchCharLength,\n isCaseSensitive,\n ignoreLocation\n });\n }\n static get type() {\n return 'fuzzy'\n }\n static get multiRegex() {\n return /^\"(.*)\"$/\n }\n static get singleRegex() {\n return /^(.*)$/\n }\n search(text) {\n return this._bitapSearch.searchIn(text)\n }\n}\n\n// Token: 'file\n\nclass IncludeMatch extends BaseMatch {\n constructor(pattern) {\n super(pattern);\n }\n static get type() {\n return 'include'\n }\n static get multiRegex() {\n return /^'\"(.*)\"$/\n }\n static get singleRegex() {\n return /^'(.*)$/\n }\n search(text) {\n let location = 0;\n let index;\n\n const indices = [];\n const patternLen = this.pattern.length;\n\n // Get all exact matches\n while ((index = text.indexOf(this.pattern, location)) > -1) {\n location = index + patternLen;\n indices.push([index, location - 1]);\n }\n\n const isMatch = !!indices.length;\n\n return {\n isMatch,\n score: isMatch ? 0 : 1,\n indices\n }\n }\n}\n\n// ❗Order is important. DO NOT CHANGE.\nconst searchers = [\n ExactMatch,\n IncludeMatch,\n PrefixExactMatch,\n InversePrefixExactMatch,\n InverseSuffixExactMatch,\n SuffixExactMatch,\n InverseExactMatch,\n FuzzyMatch\n];\n\nconst searchersLen = searchers.length;\n\n// Regex to split by spaces, but keep anything in quotes together\nconst SPACE_RE = / +(?=(?:[^\\\"]*\\\"[^\\\"]*\\\")*[^\\\"]*$)/;\nconst OR_TOKEN = '|';\n\n// Return a 2D array representation of the query, for simpler parsing.\n// Example:\n// \"^core go$ | rb$ | py$ xy$\" => [[\"^core\", \"go$\"], [\"rb$\"], [\"py$\", \"xy$\"]]\nfunction parseQuery(pattern, options = {}) {\n return pattern.split(OR_TOKEN).map((item) => {\n let query = item\n .trim()\n .split(SPACE_RE)\n .filter((item) => item && !!item.trim());\n\n let results = [];\n for (let i = 0, len = query.length; i < len; i += 1) {\n const queryItem = query[i];\n\n // 1. Handle multiple query match (i.e, once that are quoted, like `\"hello world\"`)\n let found = false;\n let idx = -1;\n while (!found && ++idx < searchersLen) {\n const searcher = searchers[idx];\n let token = searcher.isMultiMatch(queryItem);\n if (token) {\n results.push(new searcher(token, options));\n found = true;\n }\n }\n\n if (found) {\n continue\n }\n\n // 2. Handle single query matches (i.e, once that are *not* quoted)\n idx = -1;\n while (++idx < searchersLen) {\n const searcher = searchers[idx];\n let token = searcher.isSingleMatch(queryItem);\n if (token) {\n results.push(new searcher(token, options));\n break\n }\n }\n }\n\n return results\n })\n}\n\n// These extended matchers can return an array of matches, as opposed\n// to a singl match\nconst MultiMatchSet = new Set([FuzzyMatch.type, IncludeMatch.type]);\n\n/**\n * Command-like searching\n * ======================\n *\n * Given multiple search terms delimited by spaces.e.g. `^jscript .python$ ruby !java`,\n * search in a given text.\n *\n * Search syntax:\n *\n * | Token | Match type | Description |\n * | ----------- | -------------------------- | -------------------------------------- |\n * | `jscript` | fuzzy-match | Items that fuzzy match `jscript` |\n * | `=scheme` | exact-match | Items that are `scheme` |\n * | `'python` | include-match | Items that include `python` |\n * | `!ruby` | inverse-exact-match | Items that do not include `ruby` |\n * | `^java` | prefix-exact-match | Items that start with `java` |\n * | `!^earlang` | inverse-prefix-exact-match | Items that do not start with `earlang` |\n * | `.js$` | suffix-exact-match | Items that end with `.js` |\n * | `!.go$` | inverse-suffix-exact-match | Items that do not end with `.go` |\n *\n * A single pipe character acts as an OR operator. For example, the following\n * query matches entries that start with `core` and end with either`go`, `rb`,\n * or`py`.\n *\n * ```\n * ^core go$ | rb$ | py$\n * ```\n */\nclass ExtendedSearch {\n constructor(\n pattern,\n {\n isCaseSensitive = Config.isCaseSensitive,\n includeMatches = Config.includeMatches,\n minMatchCharLength = Config.minMatchCharLength,\n ignoreLocation = Config.ignoreLocation,\n findAllMatches = Config.findAllMatches,\n location = Config.location,\n threshold = Config.threshold,\n distance = Config.distance\n } = {}\n ) {\n this.query = null;\n this.options = {\n isCaseSensitive,\n includeMatches,\n minMatchCharLength,\n findAllMatches,\n ignoreLocation,\n location,\n threshold,\n distance\n };\n\n this.pattern = isCaseSensitive ? pattern : pattern.toLowerCase();\n this.query = parseQuery(this.pattern, this.options);\n }\n\n static condition(_, options) {\n return options.useExtendedSearch\n }\n\n searchIn(text) {\n const query = this.query;\n\n if (!query) {\n return {\n isMatch: false,\n score: 1\n }\n }\n\n const { includeMatches, isCaseSensitive } = this.options;\n\n text = isCaseSensitive ? text : text.toLowerCase();\n\n let numMatches = 0;\n let allIndices = [];\n let totalScore = 0;\n\n // ORs\n for (let i = 0, qLen = query.length; i < qLen; i += 1) {\n const searchers = query[i];\n\n // Reset indices\n allIndices.length = 0;\n numMatches = 0;\n\n // ANDs\n for (let j = 0, pLen = searchers.length; j < pLen; j += 1) {\n const searcher = searchers[j];\n const { isMatch, indices, score } = searcher.search(text);\n\n if (isMatch) {\n numMatches += 1;\n totalScore += score;\n if (includeMatches) {\n const type = searcher.constructor.type;\n if (MultiMatchSet.has(type)) {\n allIndices = [...allIndices, ...indices];\n } else {\n allIndices.push(indices);\n }\n }\n } else {\n totalScore = 0;\n numMatches = 0;\n allIndices.length = 0;\n break\n }\n }\n\n // OR condition, so if TRUE, return\n if (numMatches) {\n let result = {\n isMatch: true,\n score: totalScore / numMatches\n };\n\n if (includeMatches) {\n result.indices = allIndices;\n }\n\n return result\n }\n }\n\n // Nothing was matched\n return {\n isMatch: false,\n score: 1\n }\n }\n}\n\nconst registeredSearchers = [];\n\nfunction register(...args) {\n registeredSearchers.push(...args);\n}\n\nfunction createSearcher(pattern, options) {\n for (let i = 0, len = registeredSearchers.length; i < len; i += 1) {\n let searcherClass = registeredSearchers[i];\n if (searcherClass.condition(pattern, options)) {\n return new searcherClass(pattern, options)\n }\n }\n\n return new BitapSearch(pattern, options)\n}\n\nconst LogicalOperator = {\n AND: '$and',\n OR: '$or'\n};\n\nconst KeyType = {\n PATH: '$path',\n PATTERN: '$val'\n};\n\nconst isExpression = (query) =>\n !!(query[LogicalOperator.AND] || query[LogicalOperator.OR]);\n\nconst isPath = (query) => !!query[KeyType.PATH];\n\nconst isLeaf = (query) =>\n !isArray(query) && isObject(query) && !isExpression(query);\n\nconst convertToExplicit = (query) => ({\n [LogicalOperator.AND]: Object.keys(query).map((key) => ({\n [key]: query[key]\n }))\n});\n\n// When `auto` is `true`, the parse function will infer and initialize and add\n// the appropriate `Searcher` instance\nfunction parse(query, options, { auto = true } = {}) {\n const next = (query) => {\n let keys = Object.keys(query);\n\n const isQueryPath = isPath(query);\n\n if (!isQueryPath && keys.length > 1 && !isExpression(query)) {\n return next(convertToExplicit(query))\n }\n\n if (isLeaf(query)) {\n const key = isQueryPath ? query[KeyType.PATH] : keys[0];\n\n const pattern = isQueryPath ? query[KeyType.PATTERN] : query[key];\n\n if (!isString(pattern)) {\n throw new Error(LOGICAL_SEARCH_INVALID_QUERY_FOR_KEY(key))\n }\n\n const obj = {\n keyId: createKeyId(key),\n pattern\n };\n\n if (auto) {\n obj.searcher = createSearcher(pattern, options);\n }\n\n return obj\n }\n\n let node = {\n children: [],\n operator: keys[0]\n };\n\n keys.forEach((key) => {\n const value = query[key];\n\n if (isArray(value)) {\n value.forEach((item) => {\n node.children.push(next(item));\n });\n }\n });\n\n return node\n };\n\n if (!isExpression(query)) {\n query = convertToExplicit(query);\n }\n\n return next(query)\n}\n\n// Practical scoring function\nfunction computeScore(\n results,\n { ignoreFieldNorm = Config.ignoreFieldNorm }\n) {\n results.forEach((result) => {\n let totalScore = 1;\n\n result.matches.forEach(({ key, norm, score }) => {\n const weight = key ? key.weight : null;\n\n totalScore *= Math.pow(\n score === 0 && weight ? Number.EPSILON : score,\n (weight || 1) * (ignoreFieldNorm ? 1 : norm)\n );\n });\n\n result.score = totalScore;\n });\n}\n\nfunction transformMatches(result, data) {\n const matches = result.matches;\n data.matches = [];\n\n if (!isDefined(matches)) {\n return\n }\n\n matches.forEach((match) => {\n if (!isDefined(match.indices) || !match.indices.length) {\n return\n }\n\n const { indices, value } = match;\n\n let obj = {\n indices,\n value\n };\n\n if (match.key) {\n obj.key = match.key.src;\n }\n\n if (match.idx > -1) {\n obj.refIndex = match.idx;\n }\n\n data.matches.push(obj);\n });\n}\n\nfunction transformScore(result, data) {\n data.score = result.score;\n}\n\nfunction format(\n results,\n docs,\n {\n includeMatches = Config.includeMatches,\n includeScore = Config.includeScore\n } = {}\n) {\n const transformers = [];\n\n if (includeMatches) transformers.push(transformMatches);\n if (includeScore) transformers.push(transformScore);\n\n return results.map((result) => {\n const { idx } = result;\n\n const data = {\n item: docs[idx],\n refIndex: idx\n };\n\n if (transformers.length) {\n transformers.forEach((transformer) => {\n transformer(result, data);\n });\n }\n\n return data\n })\n}\n\nclass Fuse {\n constructor(docs, options = {}, index) {\n this.options = { ...Config, ...options };\n\n if (\n this.options.useExtendedSearch &&\n !true\n ) {\n throw new Error(EXTENDED_SEARCH_UNAVAILABLE)\n }\n\n this._keyStore = new KeyStore(this.options.keys);\n\n this.setCollection(docs, index);\n }\n\n setCollection(docs, index) {\n this._docs = docs;\n\n if (index && !(index instanceof FuseIndex)) {\n throw new Error(INCORRECT_INDEX_TYPE)\n }\n\n this._myIndex =\n index ||\n createIndex(this.options.keys, this._docs, {\n getFn: this.options.getFn,\n fieldNormWeight: this.options.fieldNormWeight\n });\n }\n\n add(doc) {\n if (!isDefined(doc)) {\n return\n }\n\n this._docs.push(doc);\n this._myIndex.add(doc);\n }\n\n remove(predicate = (/* doc, idx */) => false) {\n const results = [];\n\n for (let i = 0, len = this._docs.length; i < len; i += 1) {\n const doc = this._docs[i];\n if (predicate(doc, i)) {\n this.removeAt(i);\n i -= 1;\n len -= 1;\n\n results.push(doc);\n }\n }\n\n return results\n }\n\n removeAt(idx) {\n this._docs.splice(idx, 1);\n this._myIndex.removeAt(idx);\n }\n\n getIndex() {\n return this._myIndex\n }\n\n search(query, { limit = -1 } = {}) {\n const {\n includeMatches,\n includeScore,\n shouldSort,\n sortFn,\n ignoreFieldNorm\n } = this.options;\n\n let results = isString(query)\n ? isString(this._docs[0])\n ? this._searchStringList(query)\n : this._searchObjectList(query)\n : this._searchLogical(query);\n\n computeScore(results, { ignoreFieldNorm });\n\n if (shouldSort) {\n results.sort(sortFn);\n }\n\n if (isNumber(limit) && limit > -1) {\n results = results.slice(0, limit);\n }\n\n return format(results, this._docs, {\n includeMatches,\n includeScore\n })\n }\n\n _searchStringList(query) {\n const searcher = createSearcher(query, this.options);\n const { records } = this._myIndex;\n const results = [];\n\n // Iterate over every string in the index\n records.forEach(({ v: text, i: idx, n: norm }) => {\n if (!isDefined(text)) {\n return\n }\n\n const { isMatch, score, indices } = searcher.searchIn(text);\n\n if (isMatch) {\n results.push({\n item: text,\n idx,\n matches: [{ score, value: text, norm, indices }]\n });\n }\n });\n\n return results\n }\n\n _searchLogical(query) {\n\n const expression = parse(query, this.options);\n\n const evaluate = (node, item, idx) => {\n if (!node.children) {\n const { keyId, searcher } = node;\n\n const matches = this._findMatches({\n key: this._keyStore.get(keyId),\n value: this._myIndex.getValueForItemAtKeyId(item, keyId),\n searcher\n });\n\n if (matches && matches.length) {\n return [\n {\n idx,\n item,\n matches\n }\n ]\n }\n\n return []\n }\n\n const res = [];\n for (let i = 0, len = node.children.length; i < len; i += 1) {\n const child = node.children[i];\n const result = evaluate(child, item, idx);\n if (result.length) {\n res.push(...result);\n } else if (node.operator === LogicalOperator.AND) {\n return []\n }\n }\n return res\n };\n\n const records = this._myIndex.records;\n const resultMap = {};\n const results = [];\n\n records.forEach(({ $: item, i: idx }) => {\n if (isDefined(item)) {\n let expResults = evaluate(expression, item, idx);\n\n if (expResults.length) {\n // Dedupe when adding\n if (!resultMap[idx]) {\n resultMap[idx] = { idx, item, matches: [] };\n results.push(resultMap[idx]);\n }\n expResults.forEach(({ matches }) => {\n resultMap[idx].matches.push(...matches);\n });\n }\n }\n });\n\n return results\n }\n\n _searchObjectList(query) {\n const searcher = createSearcher(query, this.options);\n const { keys, records } = this._myIndex;\n const results = [];\n\n // List is Array\n records.forEach(({ $: item, i: idx }) => {\n if (!isDefined(item)) {\n return\n }\n\n let matches = [];\n\n // Iterate over every key (i.e, path), and fetch the value at that key\n keys.forEach((key, keyIndex) => {\n matches.push(\n ...this._findMatches({\n key,\n value: item[keyIndex],\n searcher\n })\n );\n });\n\n if (matches.length) {\n results.push({\n idx,\n item,\n matches\n });\n }\n });\n\n return results\n }\n _findMatches({ key, value, searcher }) {\n if (!isDefined(value)) {\n return []\n }\n\n let matches = [];\n\n if (isArray(value)) {\n value.forEach(({ v: text, i: idx, n: norm }) => {\n if (!isDefined(text)) {\n return\n }\n\n const { isMatch, score, indices } = searcher.searchIn(text);\n\n if (isMatch) {\n matches.push({\n score,\n key,\n value: text,\n idx,\n norm,\n indices\n });\n }\n });\n } else {\n const { v: text, n: norm } = value;\n\n const { isMatch, score, indices } = searcher.searchIn(text);\n\n if (isMatch) {\n matches.push({ score, key, value: text, norm, indices });\n }\n }\n\n return matches\n }\n}\n\nFuse.version = '7.0.0';\nFuse.createIndex = createIndex;\nFuse.parseIndex = parseIndex;\nFuse.config = Config;\n\n{\n Fuse.parseQuery = parse;\n}\n\n{\n register(ExtendedSearch);\n}\n\nexport { Fuse as default };\n","// @ts-strict-ignore\nimport { ComfyNodeDefImpl } from '@/stores/nodeDefStore'\nimport Fuse, { IFuseOptions, FuseSearchOptions } from 'fuse.js'\nimport _ from 'lodash'\n\nexport type SearchAuxScore = number[]\n\ninterface ExtraSearchOptions {\n matchWildcards?: boolean\n}\n\nexport class FuseSearch {\n private fuse: Fuse\n private readonly keys: string[]\n public readonly data: T[]\n public readonly advancedScoring: boolean\n\n constructor(\n data: T[],\n options?: IFuseOptions,\n createIndex: boolean = true,\n advancedScoring: boolean = false\n ) {\n this.data = data\n this.keys = (options.keys ?? []) as string[]\n this.advancedScoring = advancedScoring\n const index =\n createIndex && options?.keys\n ? Fuse.createIndex(options.keys, data)\n : undefined\n this.fuse = new Fuse(data, options, index)\n }\n\n public search(query: string, options?: FuseSearchOptions): T[] {\n const fuseResult = !query\n ? this.data.map((x) => ({ item: x, score: 0 }))\n : this.fuse.search(query, options)\n\n if (!this.advancedScoring) {\n return fuseResult.map((x) => x.item)\n }\n\n const aux = fuseResult\n .map((x) => ({\n item: x.item,\n scores: this.calcAuxScores(query.toLocaleLowerCase(), x.item, x.score)\n }))\n .sort((a, b) => this.compareAux(a.scores, b.scores))\n\n return aux.map((x) => x.item)\n }\n\n public calcAuxScores(query: string, entry: T, score: number): SearchAuxScore {\n let values: string[] = []\n if (!this.keys.length) values = [entry as string]\n else values = this.keys.map((x) => entry[x])\n const scores = values.map((x) => this.calcAuxSingle(query, x, score))\n let result = scores.sort(this.compareAux)[0]\n\n const deprecated = values.some((x) =>\n x.toLocaleLowerCase().includes('deprecated')\n )\n result[0] += deprecated && result[0] != 0 ? 5 : 0\n if (entry['postProcessSearchScores']) {\n result = entry['postProcessSearchScores'](result) as SearchAuxScore\n }\n return result\n }\n\n public calcAuxSingle(\n query: string,\n item: string,\n score: number\n ): SearchAuxScore {\n const itemWords = item\n .split(/ |\\b|(?<=[a-z])(?=[A-Z])|(?=[A-Z][a-z])/)\n .map((x) => x.toLocaleLowerCase())\n const queryParts = query.split(' ')\n item = item.toLocaleLowerCase()\n\n let main = 9\n let aux1 = 0\n let aux2 = 0\n\n if (item == query) {\n main = 0\n } else if (item.startsWith(query)) {\n main = 1\n aux2 = item.length\n } else if (itemWords.includes(query)) {\n main = 2\n aux1 = item.indexOf(query) + item.length * 0.5\n aux2 = item.length\n } else if (item.includes(query)) {\n main = 3\n aux1 = item.indexOf(query) + item.length * 0.5\n aux2 = item.length\n } else if (queryParts.every((x) => itemWords.includes(x))) {\n const indexes = queryParts.map((x) => itemWords.indexOf(x))\n const min = Math.min(...indexes)\n const max = Math.max(...indexes)\n main = 4\n aux1 = max - min + max * 0.5 + item.length * 0.5\n aux2 = item.length\n } else if (queryParts.every((x) => item.includes(x))) {\n const min = Math.min(...queryParts.map((x) => item.indexOf(x)))\n const max = Math.max(...queryParts.map((x) => item.indexOf(x) + x.length))\n main = 5\n aux1 = max - min + max * 0.5 + item.length * 0.5\n aux2 = item.length\n }\n\n const lengthPenalty =\n 0.2 *\n (1 -\n Math.min(item.length, query.length) /\n Math.max(item.length, query.length))\n return [main, aux1, aux2, score + lengthPenalty]\n }\n\n public compareAux(a: SearchAuxScore, b: SearchAuxScore) {\n for (let i = 0; i < Math.min(a.length, b.length); i++) {\n if (a[i] !== b[i]) {\n return a[i] - b[i]\n }\n }\n return a.length - b.length\n }\n}\n\nexport type FilterAndValue = [NodeFilter, T]\n\nexport abstract class NodeFilter {\n public abstract readonly id: string\n public abstract readonly name: string\n public abstract readonly invokeSequence: string\n public abstract readonly longInvokeSequence: string\n public readonly fuseSearch: FuseSearch\n\n constructor(\n nodeDefs: ComfyNodeDefImpl[],\n options?: IFuseOptions\n ) {\n this.fuseSearch = new FuseSearch(this.getAllNodeOptions(nodeDefs), options)\n }\n\n private getAllNodeOptions(nodeDefs: ComfyNodeDefImpl[]): FilterOptionT[] {\n return [\n ...new Set(\n nodeDefs.reduce((acc, nodeDef) => {\n return [...acc, ...this.getNodeOptions(nodeDef)]\n }, [])\n )\n ]\n }\n\n public abstract getNodeOptions(node: ComfyNodeDefImpl): FilterOptionT[]\n\n public matches(\n node: ComfyNodeDefImpl,\n value: FilterOptionT,\n extraOptions?: ExtraSearchOptions\n ): boolean {\n const matchWildcards = extraOptions?.matchWildcards !== false\n if (matchWildcards && value === '*') {\n return true\n }\n const options = this.getNodeOptions(node)\n return (\n options.includes(value) ||\n (matchWildcards && _.some(options, (option) => option === '*'))\n )\n }\n}\n\nexport class InputTypeFilter extends NodeFilter {\n public readonly id: string = 'input'\n public readonly name = 'Input Type'\n public readonly invokeSequence = 'i'\n public readonly longInvokeSequence = 'input'\n\n public override getNodeOptions(node: ComfyNodeDefImpl): string[] {\n return node.input.all.map((input) => input.type)\n }\n}\n\nexport class OutputTypeFilter extends NodeFilter {\n public readonly id: string = 'output'\n public readonly name = 'Output Type'\n public readonly invokeSequence = 'o'\n public readonly longInvokeSequence = 'output'\n\n public override getNodeOptions(node: ComfyNodeDefImpl): string[] {\n return node.output.all.map((output) => output.type)\n }\n}\n\nexport class NodeSourceFilter extends NodeFilter {\n public readonly id: string = 'source'\n public readonly name = 'Source'\n public readonly invokeSequence = 's'\n public readonly longInvokeSequence = 'source'\n\n public override getNodeOptions(node: ComfyNodeDefImpl): string[] {\n return [node.nodeSource.displayText]\n }\n}\n\nexport class NodeCategoryFilter extends NodeFilter {\n public readonly id: string = 'category'\n public readonly name = 'Category'\n public readonly invokeSequence = 'c'\n public readonly longInvokeSequence = 'category'\n\n public override getNodeOptions(node: ComfyNodeDefImpl): string[] {\n return [node.category]\n }\n}\n\nexport class NodeSearchService {\n public readonly nodeFuseSearch: FuseSearch\n public readonly nodeFilters: NodeFilter[]\n\n constructor(data: ComfyNodeDefImpl[]) {\n this.nodeFuseSearch = new FuseSearch(\n data,\n {\n keys: ['name', 'display_name'],\n includeScore: true,\n threshold: 0.3,\n shouldSort: false,\n useExtendedSearch: true\n },\n true,\n true\n )\n\n const filterSearchOptions = {\n includeScore: true,\n threshold: 0.3,\n shouldSort: true\n }\n\n this.nodeFilters = [\n new InputTypeFilter(data, filterSearchOptions),\n new OutputTypeFilter(data, filterSearchOptions),\n new NodeCategoryFilter(data, filterSearchOptions)\n ]\n\n if (data[0].python_module !== undefined) {\n this.nodeFilters.push(new NodeSourceFilter(data, filterSearchOptions))\n }\n }\n\n public endsWithFilterStartSequence(query: string): boolean {\n return query.endsWith(':')\n }\n\n public searchNode(\n query: string,\n filters: FilterAndValue[] = [],\n options?: FuseSearchOptions,\n extraOptions?: ExtraSearchOptions\n ): ComfyNodeDefImpl[] {\n const matchedNodes = this.nodeFuseSearch.search(query)\n\n const results = matchedNodes.filter((node) => {\n return _.every(filters, (filterAndValue) => {\n const [filter, value] = filterAndValue\n return filter.matches(node, value, extraOptions)\n })\n })\n\n return options?.limit ? results.slice(0, options.limit) : results\n }\n\n public getFilterById(id: string): NodeFilter | undefined {\n return this.nodeFilters.find((filter) => filter.id === id)\n }\n}\n","import {\n NodeSearchService,\n type SearchAuxScore\n} from '@/services/nodeSearchService'\nimport {\n type ComfyNodeDef,\n type ComfyInputsSpec as ComfyInputsSpecSchema,\n type InputSpec\n} from '@/types/apiTypes'\nimport { defineStore } from 'pinia'\nimport { ComfyWidgetConstructor } from '@/scripts/widgets'\nimport { TreeNode } from 'primevue/treenode'\nimport { buildTree } from '@/utils/treeUtil'\nimport { computed, ref } from 'vue'\nimport axios from 'axios'\nimport {\n type NodeSource,\n NodeSourceType,\n getNodeSource\n} from '@/types/nodeSource'\nimport type { LGraphNode } from '@comfyorg/litegraph'\n\nexport interface BaseInputSpec {\n name: string\n type: string\n tooltip?: string\n default?: T\n\n forceInput?: boolean\n}\n\nexport interface NumericInputSpec extends BaseInputSpec {\n min?: number\n max?: number\n step?: number\n}\n\nexport interface IntInputSpec extends NumericInputSpec {\n type: 'INT'\n}\n\nexport interface FloatInputSpec extends NumericInputSpec {\n type: 'FLOAT'\n round?: number\n}\n\nexport interface BooleanInputSpec extends BaseInputSpec {\n type: 'BOOLEAN'\n labelOn?: string\n labelOff?: string\n}\n\nexport interface StringInputSpec extends BaseInputSpec {\n type: 'STRING'\n multiline?: boolean\n dynamicPrompts?: boolean\n}\n\nexport interface ComboInputSpec extends BaseInputSpec {\n type: 'COMBO'\n comboOptions: any[]\n controlAfterGenerate?: boolean\n imageUpload?: boolean\n}\n\nexport class ComfyInputsSpec {\n required: Record\n optional: Record\n hidden?: Record\n\n constructor(obj: ComfyInputsSpecSchema) {\n this.required = ComfyInputsSpec.transformInputSpecRecord(obj.required ?? {})\n this.optional = ComfyInputsSpec.transformInputSpecRecord(obj.optional ?? {})\n this.hidden = obj.hidden\n }\n\n private static transformInputSpecRecord(\n record: Record\n ): Record {\n const result: Record = {}\n for (const [key, value] of Object.entries(record)) {\n result[key] = ComfyInputsSpec.transformSingleInputSpec(key, value)\n }\n return result\n }\n\n private static isInputSpec(obj: any): boolean {\n return (\n Array.isArray(obj) &&\n obj.length >= 1 &&\n (typeof obj[0] === 'string' || Array.isArray(obj[0]))\n )\n }\n\n private static transformSingleInputSpec(\n name: string,\n value: any\n ): BaseInputSpec {\n if (!ComfyInputsSpec.isInputSpec(value)) return value\n\n const [typeRaw, _spec] = value\n const spec = _spec ?? {}\n const type = Array.isArray(typeRaw) ? 'COMBO' : value[0]\n\n switch (type) {\n case 'COMBO':\n return {\n name,\n type,\n ...spec,\n comboOptions: typeRaw,\n default: spec.default ?? typeRaw[0]\n } as ComboInputSpec\n case 'INT':\n case 'FLOAT':\n case 'BOOLEAN':\n case 'STRING':\n default:\n return { name, type, ...spec } as BaseInputSpec\n }\n }\n\n get all() {\n return [...Object.values(this.required), ...Object.values(this.optional)]\n }\n\n getInput(name: string): BaseInputSpec | undefined {\n return this.required[name] ?? this.optional[name]\n }\n}\n\nexport class ComfyOutputSpec {\n constructor(\n public index: number,\n // Name is not unique for output params\n public name: string,\n public type: string,\n public is_list: boolean,\n public comboOptions?: any[],\n public tooltip?: string\n ) {}\n}\n\nexport class ComfyOutputsSpec {\n constructor(public outputs: ComfyOutputSpec[]) {}\n\n get all() {\n return this.outputs\n }\n}\n\n/**\n * Note: This class does not implement the ComfyNodeDef interface, as we are\n * using a custom output spec for output definitions.\n */\nexport class ComfyNodeDefImpl {\n name: string\n display_name: string\n category: string\n python_module: string\n description: string\n deprecated: boolean\n experimental: boolean\n input: ComfyInputsSpec\n output: ComfyOutputsSpec\n nodeSource: NodeSource\n\n constructor(obj: ComfyNodeDef) {\n this.name = obj.name\n this.display_name = obj.display_name\n this.category = obj.category\n this.python_module = obj.python_module\n this.description = obj.description\n this.deprecated = obj.deprecated ?? obj.category === ''\n this.experimental =\n obj.experimental ?? obj.category.startsWith('_for_testing')\n this.input = new ComfyInputsSpec(obj.input ?? {})\n this.output = ComfyNodeDefImpl.transformOutputSpec(obj)\n this.nodeSource = getNodeSource(obj.python_module)\n }\n\n private static transformOutputSpec(obj: any): ComfyOutputsSpec {\n const { output, output_is_list, output_name, output_tooltips } = obj\n const result = (output ?? []).map((type: string | any[], index: number) => {\n const typeString = Array.isArray(type) ? 'COMBO' : type\n\n return new ComfyOutputSpec(\n index,\n output_name?.[index],\n typeString,\n output_is_list?.[index],\n Array.isArray(type) ? type : undefined,\n output_tooltips?.[index]\n )\n })\n return new ComfyOutputsSpec(result)\n }\n\n get nodePath(): string {\n return (this.category ? this.category + '/' : '') + this.name\n }\n\n get isDummyFolder(): boolean {\n return this.name === ''\n }\n\n postProcessSearchScores(scores: SearchAuxScore): SearchAuxScore {\n const nodeFrequencyStore = useNodeFrequencyStore()\n const nodeFrequency = nodeFrequencyStore.getNodeFrequencyByName(this.name)\n return [scores[0], -nodeFrequency, ...scores.slice(1)]\n }\n\n get isCoreNode(): boolean {\n return this.nodeSource.type === NodeSourceType.Core\n }\n\n get nodeLifeCycleBadgeText(): string {\n if (this.deprecated) return '[DEPR]'\n if (this.experimental) return '[BETA]'\n return ''\n }\n}\n\nexport const SYSTEM_NODE_DEFS: Record = {\n PrimitiveNode: {\n name: 'PrimitiveNode',\n display_name: 'Primitive',\n category: 'utils',\n input: { required: {}, optional: {} },\n output: ['*'],\n output_name: ['connect to widget input'],\n output_is_list: [false],\n output_node: false,\n python_module: 'nodes',\n description: 'Primitive values like numbers, strings, and booleans.'\n },\n Reroute: {\n name: 'Reroute',\n display_name: 'Reroute',\n category: 'utils',\n input: { required: { '': ['*'] }, optional: {} },\n output: ['*'],\n output_name: [''],\n output_is_list: [false],\n output_node: false,\n python_module: 'nodes',\n description: 'Reroute the connection to another node.'\n },\n Note: {\n name: 'Note',\n display_name: 'Note',\n category: 'utils',\n input: { required: {}, optional: {} },\n output: [],\n output_name: [],\n output_is_list: [],\n output_node: false,\n python_module: 'nodes',\n description: 'Node that add notes to your project'\n }\n}\n\nexport function buildNodeDefTree(nodeDefs: ComfyNodeDefImpl[]): TreeNode {\n return buildTree(nodeDefs, (nodeDef: ComfyNodeDefImpl) =>\n nodeDef.nodePath.split('/')\n )\n}\n\nexport function createDummyFolderNodeDef(folderPath: string): ComfyNodeDefImpl {\n return new ComfyNodeDefImpl({\n name: '',\n display_name: '',\n category: folderPath.endsWith('/') ? folderPath.slice(0, -1) : folderPath,\n python_module: 'nodes',\n description: 'Dummy Folder Node (User should never see this string)',\n input: {},\n output: [],\n output_name: [],\n output_is_list: [],\n output_node: false\n } as ComfyNodeDef)\n}\n\nexport const useNodeDefStore = defineStore('nodeDef', () => {\n const nodeDefsByName = ref>({})\n const nodeDefsByDisplayName = ref>({})\n const widgets = ref>({})\n const showDeprecated = ref(false)\n const showExperimental = ref(false)\n\n const nodeDefs = computed(() => Object.values(nodeDefsByName.value))\n const visibleNodeDefs = computed(() =>\n nodeDefs.value.filter(\n (nodeDef: ComfyNodeDefImpl) =>\n (showDeprecated.value || !nodeDef.deprecated) &&\n (showExperimental.value || !nodeDef.experimental)\n )\n )\n const nodeSearchService = computed(\n () => new NodeSearchService(visibleNodeDefs.value)\n )\n const nodeTree = computed(() => buildNodeDefTree(visibleNodeDefs.value))\n\n function updateNodeDefs(nodeDefs: ComfyNodeDef[]) {\n const newNodeDefsByName: Record = {}\n const newNodeDefsByDisplayName: Record = {}\n for (const nodeDef of nodeDefs) {\n try {\n const nodeDefImpl = new ComfyNodeDefImpl(nodeDef)\n newNodeDefsByName[nodeDef.name] = nodeDefImpl\n newNodeDefsByDisplayName[nodeDef.display_name] = nodeDefImpl\n } catch (e) {\n // Avoid breaking the app for invalid nodeDefs\n // NodeDef validation is now optional for performance reasons\n console.error('Error adding nodeDef:', e)\n }\n }\n nodeDefsByName.value = newNodeDefsByName\n nodeDefsByDisplayName.value = newNodeDefsByDisplayName\n }\n function addNodeDef(nodeDef: ComfyNodeDef) {\n const nodeDefImpl = new ComfyNodeDefImpl(nodeDef)\n nodeDefsByName.value[nodeDef.name] = nodeDefImpl\n nodeDefsByDisplayName.value[nodeDef.display_name] = nodeDefImpl\n }\n function getWidgetType(type: string, inputName: string) {\n if (type === 'COMBO') {\n return 'COMBO'\n } else if (`${type}:${inputName}` in widgets.value) {\n return `${type}:${inputName}`\n } else if (type in widgets.value) {\n return type\n } else {\n return null\n }\n }\n function inputIsWidget(spec: BaseInputSpec) {\n return getWidgetType(spec.type, spec.name) !== null\n }\n function fromLGraphNode(node: LGraphNode): ComfyNodeDefImpl | null {\n // Frontend-only nodes don't have nodeDef\n return nodeDefsByName.value[node.constructor?.nodeData?.name] ?? null\n }\n\n return {\n nodeDefsByName,\n nodeDefsByDisplayName,\n widgets,\n showDeprecated,\n showExperimental,\n\n nodeDefs,\n visibleNodeDefs,\n nodeSearchService,\n nodeTree,\n\n updateNodeDefs,\n addNodeDef,\n getWidgetType,\n inputIsWidget,\n fromLGraphNode\n }\n})\n\nexport const useNodeFrequencyStore = defineStore('nodeFrequency', () => {\n const topNodeDefLimit = ref(64)\n const nodeFrequencyLookup = ref>({})\n const nodeNamesByFrequency = computed(() =>\n Object.keys(nodeFrequencyLookup.value)\n )\n const isLoaded = ref(false)\n\n const loadNodeFrequencies = async () => {\n if (!isLoaded.value) {\n try {\n const response = await axios.get('assets/sorted-custom-node-map.json')\n nodeFrequencyLookup.value = response.data\n isLoaded.value = true\n } catch (error) {\n console.error('Error loading node frequencies:', error)\n }\n }\n }\n\n const getNodeFrequency = (nodeDef: ComfyNodeDefImpl) => {\n return getNodeFrequencyByName(nodeDef.name)\n }\n\n const getNodeFrequencyByName = (nodeName: string) => {\n return nodeFrequencyLookup.value[nodeName] ?? 0\n }\n\n const nodeDefStore = useNodeDefStore()\n const topNodeDefs = computed(() => {\n return nodeNamesByFrequency.value\n .map((nodeName: string) => nodeDefStore.nodeDefsByName[nodeName])\n .filter((nodeDef: ComfyNodeDefImpl) => nodeDef !== undefined)\n .slice(0, topNodeDefLimit.value)\n })\n\n return {\n nodeNamesByFrequency,\n topNodeDefs,\n isLoaded,\n loadNodeFrequencies,\n getNodeFrequency,\n getNodeFrequencyByName\n }\n})\n","import { ref } from 'vue'\nimport { defineStore } from 'pinia'\nimport { api } from '@/scripts/api'\n\n/** (Internal helper) finds a value in a metadata object from any of a list of keys. */\nfunction _findInMetadata(metadata: any, ...keys: string[]): string | null {\n for (const key of keys) {\n if (key in metadata) {\n return metadata[key]\n }\n for (const k in metadata) {\n if (k.endsWith(key)) {\n return metadata[k]\n }\n }\n }\n return null\n}\n\n/** Defines and holds metadata for a model */\nexport class ComfyModelDef {\n /** Proper filename of the model */\n readonly file_name: string\n /** Normalized filename of the model, with all backslashes replaced with forward slashes */\n readonly normalized_file_name: string\n /** Directory containing the model, eg 'checkpoints' */\n readonly directory: string\n /** Simplified copy of name, used as a default title. Excludes the directory and the '.safetensors' file extension */\n readonly simplified_file_name: string\n /** Key for the model, used to uniquely identify the model. */\n readonly key: string\n /** Title / display name of the model, sometimes same as the name but not always */\n title: string\n /** Metadata: architecture ID for the model, such as 'stable-diffusion-xl-v1-base' */\n architecture_id: string = ''\n /** Metadata: author of the model */\n author: string = ''\n /** Metadata: resolution of the model, eg '1024x1024' */\n resolution: string = ''\n /** Metadata: description of the model */\n description: string = ''\n /** Metadata: usage hint for the model */\n usage_hint: string = ''\n /** Metadata: trigger phrase for the model */\n trigger_phrase: string = ''\n /** Metadata: tags list for the model */\n tags: string[] = []\n /** Metadata: image for the model */\n image: string = ''\n /** Whether the model metadata has been loaded from the server, used for `load()` */\n has_loaded_metadata: boolean = false\n /** If true, a metadata load request has been triggered, but may or may not yet have finished loading */\n is_load_requested: boolean = false\n /** If true, this is a fake model object used as a placeholder for something (eg a loading icon) */\n is_fake_object: boolean = false\n /** A string full of auto-computed lowercase-only searchable text for this model */\n searchable: string = ''\n\n constructor(name: string, directory: string) {\n this.file_name = name\n this.normalized_file_name = name.replaceAll('\\\\', '/')\n this.simplified_file_name = this.normalized_file_name.split('/').pop() ?? ''\n if (this.simplified_file_name.endsWith('.safetensors')) {\n this.simplified_file_name = this.simplified_file_name.slice(\n 0,\n -'.safetensors'.length\n )\n }\n this.title = this.simplified_file_name\n this.directory = directory\n this.key = `${directory}/${this.normalized_file_name}`\n this.updateSearchable()\n }\n\n updateSearchable() {\n this.searchable = [\n this.file_name,\n this.title,\n this.author,\n this.description,\n this.trigger_phrase,\n this.tags.join(', ')\n ]\n .join('\\n')\n .toLowerCase()\n }\n\n /** Loads the model metadata from the server, filling in this object if data is available */\n async load(): Promise {\n if (this.has_loaded_metadata || this.is_load_requested) {\n return\n }\n this.is_load_requested = true\n try {\n const metadata = await api.viewMetadata(this.directory, this.file_name)\n if (!metadata) {\n return\n }\n this.title =\n _findInMetadata(\n metadata,\n 'modelspec.title',\n 'title',\n 'display_name',\n 'name'\n ) || this.title\n this.architecture_id =\n _findInMetadata(metadata, 'modelspec.architecture', 'architecture') ||\n ''\n this.author =\n _findInMetadata(metadata, 'modelspec.author', 'author') || ''\n this.description =\n _findInMetadata(metadata, 'modelspec.description', 'description') || ''\n this.resolution =\n _findInMetadata(metadata, 'modelspec.resolution', 'resolution') || ''\n this.usage_hint =\n _findInMetadata(metadata, 'modelspec.usage_hint', 'usage_hint') || ''\n this.trigger_phrase =\n _findInMetadata(\n metadata,\n 'modelspec.trigger_phrase',\n 'trigger_phrase'\n ) || ''\n this.image =\n _findInMetadata(\n metadata,\n 'modelspec.thumbnail',\n 'thumbnail',\n 'image',\n 'icon'\n ) || ''\n const tagsCommaSeparated =\n _findInMetadata(metadata, 'modelspec.tags', 'tags') || ''\n this.tags = tagsCommaSeparated.split(',').map((tag) => tag.trim())\n this.has_loaded_metadata = true\n this.updateSearchable()\n } catch (error) {\n console.error('Error loading model metadata', this.file_name, this, error)\n }\n }\n}\n\nexport class ModelFolder {\n models: Record = {}\n\n constructor(directory: string, models: string[]) {\n for (const model of models) {\n this.models[model] = new ComfyModelDef(model, directory)\n }\n }\n\n async loadModelMetadata(modelName: string) {\n if (this.models[modelName]) {\n await this.models[modelName].load()\n }\n }\n}\n\nconst folderBlacklist = ['configs', 'custom_nodes']\n\n/** Model store handler, wraps individual per-folder model stores */\nexport const useModelStore = defineStore('modelStore', () => {\n const modelStoreMap = ref>({})\n const isLoading = ref | null>>({})\n const modelFolders = ref([])\n\n async function getModelsInFolderCached(\n folder: string\n ): Promise {\n if (folder in modelStoreMap.value) {\n return modelStoreMap.value[folder]\n }\n if (isLoading.value[folder]) {\n return isLoading.value[folder]\n }\n const promise = api.getModels(folder).then((models) => {\n if (!models) {\n return null\n }\n const store = new ModelFolder(folder, models)\n modelStoreMap.value[folder] = store\n isLoading.value[folder] = null\n return store\n })\n isLoading.value[folder] = promise\n return promise\n }\n\n function clearCache() {\n Object.keys(modelStoreMap.value).forEach((key) => {\n delete modelStoreMap.value[key]\n })\n }\n\n async function getModelFolders() {\n modelFolders.value = (await api.getModelFolders()).filter(\n (folder) => !folderBlacklist.includes(folder)\n )\n }\n\n return {\n modelStoreMap,\n isLoading,\n modelFolders,\n getModelsInFolderCached,\n clearCache,\n getModelFolders\n }\n})\n","import type { Keybinding } from '@/types/keyBindingTypes'\n\nexport const CORE_KEYBINDINGS: Keybinding[] = [\n {\n combo: {\n ctrl: true,\n key: 'Enter'\n },\n commandId: 'Comfy.QueuePrompt'\n },\n {\n combo: {\n ctrl: true,\n shift: true,\n key: 'Enter'\n },\n commandId: 'Comfy.QueuePromptFront'\n },\n {\n combo: {\n ctrl: true,\n alt: true,\n key: 'Enter'\n },\n commandId: 'Comfy.Interrupt'\n },\n {\n combo: {\n key: 'r'\n },\n commandId: 'Comfy.RefreshNodeDefinitions'\n },\n {\n combo: {\n key: 'q'\n },\n commandId: 'Workspace.ToggleSidebarTab.queue'\n },\n {\n combo: {\n key: 'w'\n },\n commandId: 'Workspace.ToggleSidebarTab.workflows'\n },\n {\n combo: {\n key: 'n'\n },\n commandId: 'Workspace.ToggleSidebarTab.node-library'\n },\n {\n combo: {\n key: 'm'\n },\n commandId: 'Workspace.ToggleSidebarTab.model-library'\n },\n {\n combo: {\n key: 's',\n ctrl: true\n },\n commandId: 'Comfy.ExportWorkflow'\n },\n {\n combo: {\n key: 'o',\n ctrl: true\n },\n commandId: 'Comfy.OpenWorkflow'\n },\n {\n combo: {\n key: 'Backspace'\n },\n commandId: 'Comfy.ClearWorkflow'\n },\n {\n combo: {\n key: 'd',\n ctrl: true\n },\n commandId: 'Comfy.LoadDefaultWorkflow'\n },\n {\n combo: {\n key: 'g',\n ctrl: true\n },\n commandId: 'Comfy.Graph.GroupSelectedNodes'\n },\n {\n combo: {\n key: ',',\n ctrl: true\n },\n commandId: 'Comfy.ShowSettingsDialog'\n },\n // For '=' both holding shift and not holding shift\n {\n combo: {\n key: '=',\n alt: true\n },\n commandId: 'Comfy.Canvas.ZoomIn',\n targetSelector: '#graph-canvas'\n },\n {\n combo: {\n key: '+',\n alt: true,\n shift: true\n },\n commandId: 'Comfy.Canvas.ZoomIn',\n targetSelector: '#graph-canvas'\n },\n // For number pad '+'\n {\n combo: {\n key: '+',\n alt: true\n },\n commandId: 'Comfy.Canvas.ZoomIn',\n targetSelector: '#graph-canvas'\n },\n {\n combo: {\n key: '-',\n alt: true\n },\n commandId: 'Comfy.Canvas.ZoomOut',\n targetSelector: '#graph-canvas'\n },\n {\n combo: {\n key: 'p'\n },\n commandId: 'Comfy.Canvas.ToggleSelectedNodes.Pin',\n targetSelector: '#graph-canvas'\n },\n {\n combo: {\n key: 'c',\n alt: true\n },\n commandId: 'Comfy.Canvas.ToggleSelectedNodes.Collapse',\n targetSelector: '#graph-canvas'\n },\n {\n combo: {\n key: 'b',\n ctrl: true\n },\n commandId: 'Comfy.Canvas.ToggleSelectedNodes.Bypass',\n targetSelector: '#graph-canvas'\n },\n {\n combo: {\n key: 'm',\n ctrl: true\n },\n commandId: 'Comfy.Canvas.ToggleSelectedNodes.Mute',\n targetSelector: '#graph-canvas'\n },\n {\n combo: {\n key: '`',\n ctrl: true\n },\n commandId: 'Workspace.ToggleBottomPanelTab.integrated-terminal'\n }\n]\n","import { defineStore } from 'pinia'\nimport { computed, Ref, ref, toRaw } from 'vue'\nimport { Keybinding, KeyCombo } from '@/types/keyBindingTypes'\nimport { useSettingStore } from './settingStore'\nimport { CORE_KEYBINDINGS } from './coreKeybindings'\nimport type { ComfyExtension } from '@/types/comfy'\n\nexport class KeybindingImpl implements Keybinding {\n commandId: string\n combo: KeyComboImpl\n targetSelector?: string\n\n constructor(obj: Keybinding) {\n this.commandId = obj.commandId\n this.combo = new KeyComboImpl(obj.combo)\n this.targetSelector = obj.targetSelector\n }\n\n equals(other: any): boolean {\n if (toRaw(other) instanceof KeybindingImpl) {\n return (\n this.commandId === other.commandId &&\n this.combo.equals(other.combo) &&\n this.targetSelector === other.targetSelector\n )\n }\n return false\n }\n}\n\nexport class KeyComboImpl implements KeyCombo {\n key: string\n // ctrl or meta(cmd on mac)\n ctrl: boolean\n alt: boolean\n shift: boolean\n\n constructor(obj: KeyCombo) {\n this.key = obj.key\n this.ctrl = obj.ctrl ?? false\n this.alt = obj.alt ?? false\n this.shift = obj.shift ?? false\n }\n\n static fromEvent(event: KeyboardEvent) {\n return new KeyComboImpl({\n key: event.key,\n ctrl: event.ctrlKey || event.metaKey,\n alt: event.altKey,\n shift: event.shiftKey\n })\n }\n\n equals(other: any): boolean {\n if (toRaw(other) instanceof KeyComboImpl) {\n return (\n this.key === other.key &&\n this.ctrl === other.ctrl &&\n this.alt === other.alt &&\n this.shift === other.shift\n )\n }\n return false\n }\n\n serialize(): string {\n return `${this.key}:${this.ctrl}:${this.alt}:${this.shift}`\n }\n\n deserialize(serialized: string): KeyComboImpl {\n const [key, ctrl, alt, shift] = serialized.split(':')\n return new KeyComboImpl({\n key,\n ctrl: ctrl === 'true',\n alt: alt === 'true',\n shift: shift === 'true'\n })\n }\n\n toString(): string {\n return this.getKeySequences().join(' + ')\n }\n\n get hasModifier(): boolean {\n return this.ctrl || this.alt || this.shift\n }\n\n get isModifier(): boolean {\n return ['Control', 'Meta', 'Alt', 'Shift'].includes(this.key)\n }\n\n getKeySequences(): string[] {\n const sequences: string[] = []\n if (this.ctrl) {\n sequences.push('Ctrl')\n }\n if (this.alt) {\n sequences.push('Alt')\n }\n if (this.shift) {\n sequences.push('Shift')\n }\n sequences.push(this.key)\n return sequences\n }\n}\n\nexport const useKeybindingStore = defineStore('keybinding', () => {\n /**\n * Default keybindings provided by core and extensions.\n */\n const defaultKeybindings = ref>({})\n /**\n * User-defined keybindings.\n */\n const userKeybindings = ref>({})\n /**\n * User-defined keybindings that unset default keybindings.\n */\n const userUnsetKeybindings = ref>({})\n\n const keybindingByKeyCombo = computed>(() => {\n const result: Record = {\n ...defaultKeybindings.value,\n ...userKeybindings.value\n }\n\n for (const keybinding of Object.values(userUnsetKeybindings.value)) {\n const serializedCombo = keybinding.combo.serialize()\n if (result[serializedCombo]?.equals(keybinding)) {\n delete result[serializedCombo]\n }\n }\n return result\n })\n\n const keybindings = computed(() =>\n Object.values(keybindingByKeyCombo.value)\n )\n\n function getKeybinding(combo: KeyComboImpl) {\n return keybindingByKeyCombo.value[combo.serialize()]\n }\n\n function createKeybindingsByCommandId(keybindings: KeybindingImpl[]) {\n const result: Record = {}\n for (const keybinding of keybindings) {\n if (!(keybinding.commandId in result)) {\n result[keybinding.commandId] = []\n }\n result[keybinding.commandId].push(keybinding)\n }\n return result\n }\n\n const keybindingsByCommandId = computed>(\n () => {\n return createKeybindingsByCommandId(keybindings.value)\n }\n )\n\n function getKeybindingsByCommandId(commandId: string) {\n return keybindingsByCommandId.value[commandId] ?? []\n }\n\n const defaultKeybindingsByCommandId = computed<\n Record\n >(() => {\n return createKeybindingsByCommandId(Object.values(defaultKeybindings.value))\n })\n\n function getKeybindingByCommandId(commandId: string) {\n return getKeybindingsByCommandId(commandId)[0]\n }\n\n function addKeybinding(\n target: Ref>,\n keybinding: KeybindingImpl,\n { existOk = false }: { existOk: boolean }\n ) {\n if (!existOk && keybinding.combo.serialize() in target.value) {\n throw new Error(\n `Keybinding on ${keybinding.combo} already exists on ${\n target.value[keybinding.combo.serialize()].commandId\n }`\n )\n }\n target.value[keybinding.combo.serialize()] = keybinding\n }\n\n function addDefaultKeybinding(keybinding: KeybindingImpl) {\n addKeybinding(defaultKeybindings, keybinding, { existOk: false })\n }\n\n function addUserKeybinding(keybinding: KeybindingImpl) {\n const defaultKeybinding =\n defaultKeybindings.value[keybinding.combo.serialize()]\n const userUnsetKeybinding =\n userUnsetKeybindings.value[keybinding.combo.serialize()]\n\n // User is adding back a keybinding that was an unsetted default keybinding.\n if (\n keybinding.equals(defaultKeybinding) &&\n keybinding.equals(userUnsetKeybinding)\n ) {\n delete userUnsetKeybindings.value[keybinding.combo.serialize()]\n return\n }\n\n // Unset keybinding on default keybinding if it exists and is not the same as userUnsetKeybinding\n if (defaultKeybinding && !defaultKeybinding.equals(userUnsetKeybinding)) {\n unsetKeybinding(defaultKeybinding)\n }\n\n addKeybinding(userKeybindings, keybinding, { existOk: true })\n }\n\n function unsetKeybinding(keybinding: KeybindingImpl) {\n const serializedCombo = keybinding.combo.serialize()\n if (!(serializedCombo in keybindingByKeyCombo.value)) {\n throw new Error(`Keybinding on ${keybinding.combo} does not exist`)\n }\n\n if (userKeybindings.value[serializedCombo]?.equals(keybinding)) {\n delete userKeybindings.value[serializedCombo]\n return\n }\n\n if (defaultKeybindings.value[serializedCombo]?.equals(keybinding)) {\n addKeybinding(userUnsetKeybindings, keybinding, { existOk: false })\n return\n }\n\n throw new Error(`NOT_REACHED`)\n }\n\n /**\n * Update the keybinding on given command if it is different from the current keybinding.\n *\n * @returns true if the keybinding is updated, false otherwise.\n */\n function updateKeybindingOnCommand(keybinding: KeybindingImpl): boolean {\n const currentKeybinding = getKeybindingByCommandId(keybinding.commandId)\n if (currentKeybinding?.equals(keybinding)) {\n return false\n }\n if (currentKeybinding) {\n unsetKeybinding(currentKeybinding)\n }\n addUserKeybinding(keybinding)\n return true\n }\n\n function loadUserKeybindings() {\n const settingStore = useSettingStore()\n // Unset bindings first as new bindings might conflict with default bindings.\n const unsetBindings = settingStore.get('Comfy.Keybinding.UnsetBindings')\n for (const keybinding of unsetBindings) {\n unsetKeybinding(new KeybindingImpl(keybinding))\n }\n const newBindings = settingStore.get('Comfy.Keybinding.NewBindings')\n for (const keybinding of newBindings) {\n addUserKeybinding(new KeybindingImpl(keybinding))\n }\n }\n\n function loadCoreKeybindings() {\n for (const keybinding of CORE_KEYBINDINGS) {\n addDefaultKeybinding(new KeybindingImpl(keybinding))\n }\n }\n\n function loadExtensionKeybindings(extension: ComfyExtension) {\n if (extension.keybindings) {\n for (const keybinding of extension.keybindings) {\n try {\n addDefaultKeybinding(new KeybindingImpl(keybinding))\n } catch (error) {\n console.warn(\n `Failed to load keybinding for extension ${extension.name}`,\n error\n )\n }\n }\n }\n }\n\n async function persistUserKeybindings() {\n const settingStore = useSettingStore()\n // TODO(https://github.com/Comfy-Org/ComfyUI_frontend/issues/1079):\n // Allow setting multiple values at once in settingStore\n await settingStore.set(\n 'Comfy.Keybinding.NewBindings',\n Object.values(userKeybindings.value)\n )\n await settingStore.set(\n 'Comfy.Keybinding.UnsetBindings',\n Object.values(userUnsetKeybindings.value)\n )\n }\n\n function resetKeybindings() {\n userKeybindings.value = {}\n userUnsetKeybindings.value = {}\n }\n\n function isCommandKeybindingModified(commandId: string): boolean {\n const currentKeybinding: KeybindingImpl | undefined =\n getKeybindingByCommandId(commandId)\n const defaultKeybinding: KeybindingImpl | undefined =\n defaultKeybindingsByCommandId.value[commandId]?.[0]\n\n return !(\n (currentKeybinding === undefined && defaultKeybinding === undefined) ||\n currentKeybinding?.equals(defaultKeybinding)\n )\n }\n\n return {\n keybindings,\n getKeybinding,\n getKeybindingsByCommandId,\n getKeybindingByCommandId,\n addDefaultKeybinding,\n addUserKeybinding,\n unsetKeybinding,\n updateKeybindingOnCommand,\n loadUserKeybindings,\n loadCoreKeybindings,\n loadExtensionKeybindings,\n persistUserKeybindings,\n resetKeybindings,\n isCommandKeybindingModified\n }\n})\n","import { defineStore } from 'pinia'\nimport type { MenuItem } from 'primevue/menuitem'\nimport { ref } from 'vue'\nimport { useCommandStore } from './commandStore'\nimport { ComfyExtension } from '@/types/comfy'\n\nexport const useMenuItemStore = defineStore('menuItem', () => {\n const commandStore = useCommandStore()\n const menuItems = ref([])\n\n const registerMenuGroup = (path: string[], items: MenuItem[]) => {\n let currentLevel = menuItems.value\n\n // Traverse the path, creating nodes if necessary\n for (let i = 0; i < path.length; i++) {\n const segment = path[i]\n let found = currentLevel.find((item) => item.label === segment)\n\n if (!found) {\n // Create a new node if it doesn't exist\n found = {\n label: segment,\n items: []\n }\n currentLevel.push(found)\n }\n\n // Ensure the found item has an 'items' array\n if (!found.items) {\n found.items = []\n }\n\n // Move to the next level\n currentLevel = found.items\n }\n\n if (currentLevel.length > 0) {\n currentLevel.push({\n separator: true\n })\n }\n // Add the new items to the last level\n currentLevel.push(...items)\n }\n\n const registerCommands = (path: string[], commandIds: string[]) => {\n const items = commandIds\n .map((commandId) => commandStore.getCommand(commandId))\n .map(\n (command) =>\n ({\n command: command.function,\n label: command.menubarLabel,\n icon: command.icon,\n tooltip: command.tooltip,\n comfyCommand: command\n }) as MenuItem\n )\n registerMenuGroup(path, items)\n }\n\n const loadExtensionMenuCommands = (extension: ComfyExtension) => {\n if (!extension.menuCommands) {\n return\n }\n\n const extensionCommandIds = new Set(\n extension.commands?.map((command) => command.id) ?? []\n )\n extension.menuCommands.forEach((menuCommand) => {\n const commands = menuCommand.commands.filter((command) =>\n extensionCommandIds.has(command)\n )\n if (commands.length) {\n registerCommands(menuCommand.path, commands)\n }\n })\n }\n\n // Core menu commands\n registerCommands(['Workflow'], ['Comfy.NewBlankWorkflow'])\n\n registerCommands(\n ['Workflow'],\n ['Comfy.OpenWorkflow', 'Comfy.BrowseTemplates']\n )\n registerCommands(\n ['Workflow'],\n [\n 'Comfy.SaveWorkflow',\n 'Comfy.SaveWorkflowAs',\n 'Comfy.ExportWorkflow',\n 'Comfy.ExportWorkflowAPI'\n ]\n )\n\n registerCommands(['Edit'], ['Comfy.Undo', 'Comfy.Redo'])\n registerCommands(['Edit'], ['Comfy.ClearWorkflow'])\n registerCommands(['Edit'], ['Comfy.OpenClipspace'])\n\n registerMenuGroup(\n ['Help'],\n [\n {\n icon: 'pi pi-github',\n label: 'ComfyUI Issues',\n url: 'https://github.com/comfyanonymous/ComfyUI/issues'\n },\n {\n icon: 'pi pi-info-circle',\n label: 'ComfyUI Docs',\n url: 'https://docs.comfy.org/'\n },\n {\n icon: 'pi pi-discord',\n label: 'Comfy-Org',\n url: 'https://www.comfy.org/discord'\n }\n ]\n )\n\n return {\n menuItems,\n registerMenuGroup,\n registerCommands,\n loadExtensionMenuCommands\n }\n})\n","\n\n\n","import { useI18n } from 'vue-i18n'\nimport { markRaw } from 'vue'\nimport IntegratedTerminal from '@/components/bottomPanel/tabs/IntegratedTerminal.vue'\nimport { BottomPanelExtension } from '@/types/extensionTypes'\n\nexport const useIntegratedTerminalTab = (): BottomPanelExtension => {\n const { t } = useI18n()\n return {\n id: 'integrated-terminal',\n title: t('terminal'),\n component: markRaw(IntegratedTerminal),\n type: 'vue'\n }\n}\n","import type { BottomPanelExtension } from '@/types/extensionTypes'\nimport { defineStore } from 'pinia'\nimport { computed, ref } from 'vue'\nimport { useCommandStore } from '@/stores/commandStore'\nimport { useIntegratedTerminalTab } from '@/hooks/bottomPanelTabs/integratedTerminalTab'\nimport { ComfyExtension } from '@/types/comfy'\n\nexport const useBottomPanelStore = defineStore('bottomPanel', () => {\n const bottomPanelVisible = ref(false)\n const toggleBottomPanel = () => {\n // If there are no tabs, don't show the bottom panel\n if (bottomPanelTabs.value.length === 0) {\n return\n }\n bottomPanelVisible.value = !bottomPanelVisible.value\n }\n\n const bottomPanelTabs = ref([])\n const activeBottomPanelTabId = ref(null)\n const activeBottomPanelTab = computed(() => {\n return (\n bottomPanelTabs.value.find(\n (tab) => tab.id === activeBottomPanelTabId.value\n ) ?? null\n )\n })\n const setActiveTab = (tabId: string) => {\n activeBottomPanelTabId.value = tabId\n }\n const toggleBottomPanelTab = (tabId: string) => {\n if (activeBottomPanelTabId.value === tabId && bottomPanelVisible.value) {\n bottomPanelVisible.value = false\n } else {\n activeBottomPanelTabId.value = tabId\n bottomPanelVisible.value = true\n }\n }\n const registerBottomPanelTab = (tab: BottomPanelExtension) => {\n bottomPanelTabs.value = [...bottomPanelTabs.value, tab]\n if (bottomPanelTabs.value.length === 1) {\n activeBottomPanelTabId.value = tab.id\n }\n useCommandStore().registerCommand({\n id: `Workspace.ToggleBottomPanelTab.${tab.id}`,\n icon: 'pi pi-list',\n label: tab.title,\n function: () => toggleBottomPanelTab(tab.id)\n })\n }\n\n const registerCoreBottomPanelTabs = () => {\n registerBottomPanelTab(useIntegratedTerminalTab())\n }\n\n const registerExtensionBottomPanelTabs = (extension: ComfyExtension) => {\n if (extension.bottomPanelTabs) {\n extension.bottomPanelTabs.forEach(registerBottomPanelTab)\n }\n }\n\n return {\n bottomPanelVisible,\n toggleBottomPanel,\n bottomPanelTabs,\n activeBottomPanelTab,\n activeBottomPanelTabId,\n setActiveTab,\n toggleBottomPanelTab,\n registerBottomPanelTab,\n registerCoreBottomPanelTabs,\n registerExtensionBottomPanelTabs\n }\n})\n","import { ref, computed, markRaw } from 'vue'\nimport { defineStore } from 'pinia'\nimport type { ComfyExtension } from '@/types/comfy'\nimport { useKeybindingStore } from './keybindingStore'\nimport { useCommandStore } from './commandStore'\nimport { useSettingStore } from './settingStore'\nimport { app } from '@/scripts/app'\nimport { useMenuItemStore } from './menuItemStore'\nimport { useBottomPanelStore } from './workspace/bottomPanelStore'\n\nexport const useExtensionStore = defineStore('extension', () => {\n // For legacy reasons, the name uniquely identifies an extension\n const extensionByName = ref>({})\n const extensions = computed(() => Object.values(extensionByName.value))\n // Not using computed because disable extension requires reloading of the page.\n // Dynamically update this list won't affect extensions that are already loaded.\n const disabledExtensionNames = ref>(new Set())\n\n // Disabled extension names that are currently not in the extension list.\n // If a node pack is disabled in the backend, we shouldn't remove the configuration\n // of the frontend extension disable list, in case the node pack is re-enabled.\n const inactiveDisabledExtensionNames = computed(() => {\n return Array.from(disabledExtensionNames.value).filter(\n (name) => !(name in extensionByName.value)\n )\n })\n\n const isExtensionEnabled = (name: string) =>\n !disabledExtensionNames.value.has(name)\n const enabledExtensions = computed(() => {\n return extensions.value.filter((ext) => isExtensionEnabled(ext.name))\n })\n\n function registerExtension(extension: ComfyExtension) {\n if (!extension.name) {\n throw new Error(\"Extensions must have a 'name' property.\")\n }\n\n if (extensionByName.value[extension.name]) {\n throw new Error(`Extension named '${extension.name}' already registered.`)\n }\n\n if (disabledExtensionNames.value.has(extension.name)) {\n console.log(`Extension ${extension.name} is disabled.`)\n }\n\n extensionByName.value[extension.name] = markRaw(extension)\n useKeybindingStore().loadExtensionKeybindings(extension)\n useCommandStore().loadExtensionCommands(extension)\n useMenuItemStore().loadExtensionMenuCommands(extension)\n useSettingStore().loadExtensionSettings(extension)\n useBottomPanelStore().registerExtensionBottomPanelTabs(extension)\n /*\n * Extensions are currently stored in both extensionStore and app.extensions.\n * Legacy jest tests still depend on app.extensions being populated.\n */\n app.extensions.push(extension)\n }\n\n function loadDisabledExtensionNames() {\n disabledExtensionNames.value = new Set(\n useSettingStore().get('Comfy.Extension.Disabled')\n )\n // pysssss.Locking is replaced by pin/unpin in ComfyUI core.\n // https://github.com/Comfy-Org/litegraph.js/pull/117\n disabledExtensionNames.value.add('pysssss.Locking')\n // pysssss.SnapToGrid is replaced by Comfy.Graph.AlwaysSnapToGrid in ComfyUI core.\n // pysssss.SnapToGrid tries to write global app.shiftDown state, which is no longer\n // allowed since v1.3.12.\n // https://github.com/Comfy-Org/ComfyUI_frontend/issues/1176\n disabledExtensionNames.value.add('pysssss.SnapToGrid')\n }\n\n // Some core extensions are registered before the store is initialized, e.g.\n // colorPalette.\n // Register them manually here so the state of app.extensions and\n // extensionByName are in sync.\n for (const ext of app.extensions) {\n extensionByName.value[ext.name] = markRaw(ext)\n }\n\n return {\n extensions,\n enabledExtensions,\n inactiveDisabledExtensionNames,\n isExtensionEnabled,\n registerExtension,\n loadDisabledExtensionNames\n }\n})\n","// @ts-strict-ignore\nimport { ComfyLogging } from './logging'\nimport { ComfyWidgetConstructor, ComfyWidgets, initWidgets } from './widgets'\nimport { ComfyUI, $el } from './ui'\nimport { api } from './api'\nimport { defaultGraph } from './defaultGraph'\nimport {\n getPngMetadata,\n getWebpMetadata,\n getFlacMetadata,\n importA1111,\n getLatentMetadata\n} from './pnginfo'\nimport { addDomClippingSetting } from './domWidget'\nimport { createImageHost, calculateImageGrid } from './ui/imagePreview'\nimport { DraggableList } from './ui/draggableList'\nimport { applyTextReplacements, addStylesheet } from './utils'\nimport type { ComfyExtension, MissingNodeType } from '@/types/comfy'\nimport {\n type ComfyWorkflowJSON,\n type NodeId,\n validateComfyWorkflow\n} from '../types/comfyWorkflow'\nimport { ComfyNodeDef, StatusWsMessageStatus } from '@/types/apiTypes'\nimport { adjustColor, ColorAdjustOptions } from '@/utils/colorUtil'\nimport { ComfyAppMenu } from './ui/menu/index'\nimport { getStorageValue } from './utils'\nimport { ComfyWorkflowManager, ComfyWorkflow } from './workflows'\nimport {\n LGraphCanvas,\n LGraph,\n LGraphNode,\n LiteGraph\n} from '@comfyorg/litegraph'\nimport { StorageLocation } from '@/types/settingTypes'\nimport { ExtensionManager } from '@/types/extensionTypes'\nimport {\n ComfyNodeDefImpl,\n SYSTEM_NODE_DEFS,\n useNodeDefStore\n} from '@/stores/nodeDefStore'\nimport { INodeInputSlot, Vector2 } from '@comfyorg/litegraph'\nimport _ from 'lodash'\nimport {\n showExecutionErrorDialog,\n showLoadWorkflowWarning,\n showMissingModelsWarning\n} from '@/services/dialogService'\nimport { useSettingStore } from '@/stores/settingStore'\nimport { useToastStore } from '@/stores/toastStore'\nimport { useModelStore } from '@/stores/modelStore'\nimport type { ToastMessageOptions } from 'primevue/toast'\nimport { useWorkspaceStore } from '@/stores/workspaceStateStore'\nimport { useExecutionStore } from '@/stores/executionStore'\nimport { IWidget } from '@comfyorg/litegraph'\nimport { useExtensionStore } from '@/stores/extensionStore'\nimport { KeyComboImpl, useKeybindingStore } from '@/stores/keybindingStore'\nimport { useCommandStore } from '@/stores/commandStore'\nimport { shallowReactive } from 'vue'\n\nexport const ANIM_PREVIEW_WIDGET = '$$comfy_animation_preview'\n\nfunction sanitizeNodeName(string) {\n let entityMap = {\n '&': '',\n '<': '',\n '>': '',\n '\"': '',\n \"'\": '',\n '`': '',\n '=': ''\n }\n return String(string).replace(/[&<>\"'`=]/g, function fromEntityMap(s) {\n return entityMap[s]\n })\n}\n\ntype Clipspace = {\n widgets?: { type?: string; name?: string; value?: any }[] | null\n imgs?: HTMLImageElement[] | null\n original_imgs?: HTMLImageElement[] | null\n images?: any[] | null\n selectedIndex: number\n img_paste_mode: string\n}\n\n/**\n * @typedef {import(\"types/comfy\").ComfyExtension} ComfyExtension\n */\n\nexport class ComfyApp {\n /**\n * List of entries to queue\n * @type {{number: number, batchCount: number}[]}\n */\n #queueItems = []\n /**\n * If the queue is currently being processed\n * @type {boolean}\n */\n #processingQueue = false\n\n /**\n * Content Clipboard\n * @type {serialized node object}\n */\n static clipspace: Clipspace | null = null\n static clipspace_invalidate_handler: (() => void) | null = null\n static open_maskeditor = null\n static clipspace_return_node = null\n\n // Force vite to import utils.ts as part of index.\n // Force import of DraggableList.\n static utils = {\n applyTextReplacements,\n addStylesheet,\n DraggableList\n }\n\n vueAppReady: boolean\n ui: ComfyUI\n logging: ComfyLogging\n extensions: ComfyExtension[]\n extensionManager: ExtensionManager\n _nodeOutputs: Record\n nodePreviewImages: Record\n graph: LGraph\n enableWorkflowViewRestore: any\n canvas: LGraphCanvas\n dragOverNode: LGraphNode | null\n canvasEl: HTMLCanvasElement\n // x, y, scale\n zoom_drag_start: [number, number, number] | null\n lastNodeErrors: any[] | null\n lastExecutionError: { node_id: number } | null\n progress: { value: number; max: number } | null\n configuringGraph: boolean\n isNewUserSession: boolean\n storageLocation: StorageLocation\n multiUserServer: boolean\n ctx: CanvasRenderingContext2D\n widgets: Record\n workflowManager: ComfyWorkflowManager\n bodyTop: HTMLElement\n bodyLeft: HTMLElement\n bodyRight: HTMLElement\n bodyBottom: HTMLElement\n canvasContainer: HTMLElement\n menu: ComfyAppMenu\n bypassBgColor: string\n // Set by Comfy.Clipspace extension\n openClipspace: () => void = () => {}\n\n /**\n * @deprecated Use useExecutionStore().executingNodeId instead\n */\n get runningNodeId(): string | null {\n return useExecutionStore().executingNodeId\n }\n\n /**\n * @deprecated Use useWorkspaceStore().shiftDown instead\n */\n get shiftDown(): boolean {\n return useWorkspaceStore().shiftDown\n }\n\n constructor() {\n this.vueAppReady = false\n this.ui = new ComfyUI(this)\n this.logging = new ComfyLogging(this)\n this.workflowManager = new ComfyWorkflowManager(this)\n this.bodyTop = $el('div.comfyui-body-top', { parent: document.body })\n this.bodyLeft = $el('div.comfyui-body-left', { parent: document.body })\n this.bodyRight = $el('div.comfyui-body-right', { parent: document.body })\n this.bodyBottom = $el('div.comfyui-body-bottom', { parent: document.body })\n this.canvasContainer = $el('div.graph-canvas-container', {\n parent: document.body\n })\n this.menu = new ComfyAppMenu(this)\n this.bypassBgColor = '#FF00FF'\n\n /**\n * List of extensions that are registered with the app\n * @type {ComfyExtension[]}\n */\n this.extensions = []\n\n /**\n * Stores the execution output data for each node\n * @type {Record}\n */\n this.nodeOutputs = {}\n\n /**\n * Stores the preview image data for each node\n * @type {Record}\n */\n this.nodePreviewImages = {}\n }\n\n get nodeOutputs() {\n return this._nodeOutputs\n }\n\n set nodeOutputs(value) {\n this._nodeOutputs = value\n this.#invokeExtensions('onNodeOutputsUpdated', value)\n }\n\n getPreviewFormatParam() {\n let preview_format = this.ui.settings.getSettingValue('Comfy.PreviewFormat')\n if (preview_format) return `&preview=${preview_format}`\n else return ''\n }\n\n getRandParam() {\n return '&rand=' + Math.random()\n }\n\n static isImageNode(node) {\n return (\n node.imgs ||\n (node &&\n node.widgets &&\n node.widgets.findIndex((obj) => obj.name === 'image') >= 0)\n )\n }\n\n static onClipspaceEditorSave() {\n if (ComfyApp.clipspace_return_node) {\n ComfyApp.pasteFromClipspace(ComfyApp.clipspace_return_node)\n }\n }\n\n static onClipspaceEditorClosed() {\n ComfyApp.clipspace_return_node = null\n }\n\n static copyToClipspace(node) {\n var widgets = null\n if (node.widgets) {\n widgets = node.widgets.map(({ type, name, value }) => ({\n type,\n name,\n value\n }))\n }\n\n var imgs = undefined\n var orig_imgs = undefined\n if (node.imgs != undefined) {\n imgs = []\n orig_imgs = []\n\n for (let i = 0; i < node.imgs.length; i++) {\n imgs[i] = new Image()\n imgs[i].src = node.imgs[i].src\n orig_imgs[i] = imgs[i]\n }\n }\n\n var selectedIndex = 0\n if (node.imageIndex) {\n selectedIndex = node.imageIndex\n }\n\n ComfyApp.clipspace = {\n widgets: widgets,\n imgs: imgs,\n original_imgs: orig_imgs,\n images: node.images,\n selectedIndex: selectedIndex,\n img_paste_mode: 'selected' // reset to default im_paste_mode state on copy action\n }\n\n ComfyApp.clipspace_return_node = null\n\n if (ComfyApp.clipspace_invalidate_handler) {\n ComfyApp.clipspace_invalidate_handler()\n }\n }\n\n static pasteFromClipspace(node) {\n if (ComfyApp.clipspace) {\n // image paste\n if (ComfyApp.clipspace.imgs && node.imgs) {\n if (node.images && ComfyApp.clipspace.images) {\n if (ComfyApp.clipspace['img_paste_mode'] == 'selected') {\n node.images = [\n ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']]\n ]\n } else {\n node.images = ComfyApp.clipspace.images\n }\n\n if (app.nodeOutputs[node.id + ''])\n app.nodeOutputs[node.id + ''].images = node.images\n }\n\n if (ComfyApp.clipspace.imgs) {\n // deep-copy to cut link with clipspace\n if (ComfyApp.clipspace['img_paste_mode'] == 'selected') {\n const img = new Image()\n img.src =\n ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src\n node.imgs = [img]\n node.imageIndex = 0\n } else {\n const imgs = []\n for (let i = 0; i < ComfyApp.clipspace.imgs.length; i++) {\n imgs[i] = new Image()\n imgs[i].src = ComfyApp.clipspace.imgs[i].src\n node.imgs = imgs\n }\n }\n }\n }\n\n if (node.widgets) {\n if (ComfyApp.clipspace.images) {\n const clip_image =\n ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']]\n const index = node.widgets.findIndex((obj) => obj.name === 'image')\n if (index >= 0) {\n if (\n node.widgets[index].type != 'image' &&\n typeof node.widgets[index].value == 'string' &&\n clip_image.filename\n ) {\n node.widgets[index].value =\n (clip_image.subfolder ? clip_image.subfolder + '/' : '') +\n clip_image.filename +\n (clip_image.type ? ` [${clip_image.type}]` : '')\n } else {\n node.widgets[index].value = clip_image\n }\n }\n }\n if (ComfyApp.clipspace.widgets) {\n ComfyApp.clipspace.widgets.forEach(({ type, name, value }) => {\n const prop = Object.values(node.widgets).find(\n // @ts-expect-errorg\n (obj) => obj.type === type && obj.name === name\n )\n // @ts-expect-error\n if (prop && prop.type != 'button') {\n if (\n // @ts-expect-error\n prop.type != 'image' &&\n // @ts-expect-error\n typeof prop.value == 'string' &&\n value.filename\n ) {\n // @ts-expect-error\n prop.value =\n (value.subfolder ? value.subfolder + '/' : '') +\n value.filename +\n (value.type ? ` [${value.type}]` : '')\n } else {\n // @ts-expect-error\n prop.value = value\n // @ts-expect-error\n prop.callback(value)\n }\n }\n })\n }\n }\n\n app.graph.setDirtyCanvas(true)\n }\n }\n\n get enabledExtensions() {\n if (!this.vueAppReady) {\n return this.extensions\n }\n return useExtensionStore().enabledExtensions\n }\n\n /**\n * Invoke an extension callback\n * @param {keyof ComfyExtension} method The extension callback to execute\n * @param {any[]} args Any arguments to pass to the callback\n * @returns\n */\n #invokeExtensions(method, ...args) {\n let results = []\n for (const ext of this.enabledExtensions) {\n if (method in ext) {\n try {\n results.push(ext[method](...args, this))\n } catch (error) {\n console.error(\n `Error calling extension '${ext.name}' method '${method}'`,\n { error },\n { extension: ext },\n { args }\n )\n }\n }\n }\n return results\n }\n\n /**\n * Invoke an async extension callback\n * Each callback will be invoked concurrently\n * @param {string} method The extension callback to execute\n * @param {...any} args Any arguments to pass to the callback\n * @returns\n */\n async #invokeExtensionsAsync(method, ...args) {\n return await Promise.all(\n this.enabledExtensions.map(async (ext) => {\n if (method in ext) {\n try {\n return await ext[method](...args, this)\n } catch (error) {\n console.error(\n `Error calling extension '${ext.name}' method '${method}'`,\n { error },\n { extension: ext },\n { args }\n )\n }\n }\n })\n )\n }\n\n #addRestoreWorkflowView() {\n const serialize = LGraph.prototype.serialize\n const self = this\n LGraph.prototype.serialize = function () {\n const workflow = serialize.apply(this, arguments)\n\n // Store the drag & scale info in the serialized workflow if the setting is enabled\n if (self.enableWorkflowViewRestore.value) {\n if (!workflow.extra) {\n workflow.extra = {}\n }\n workflow.extra.ds = {\n scale: self.canvas.ds.scale,\n offset: self.canvas.ds.offset\n }\n } else if (workflow.extra?.ds) {\n // Clear any old view data\n delete workflow.extra.ds\n }\n\n return workflow\n }\n this.enableWorkflowViewRestore = this.ui.settings.addSetting({\n id: 'Comfy.EnableWorkflowViewRestore',\n category: ['Comfy', 'Workflow', 'EnableWorkflowViewRestore'],\n name: 'Save and restore canvas position and zoom level in workflows',\n type: 'boolean',\n defaultValue: true\n })\n }\n\n /**\n * Adds special context menu handling for nodes\n * e.g. this adds Open Image functionality for nodes that show images\n * @param {*} node The node to add the menu handler\n */\n #addNodeContextMenuHandler(node) {\n function getCopyImageOption(img) {\n if (typeof window.ClipboardItem === 'undefined') return []\n return [\n {\n content: 'Copy Image',\n callback: async () => {\n const url = new URL(img.src)\n url.searchParams.delete('preview')\n\n const writeImage = async (blob) => {\n await navigator.clipboard.write([\n new ClipboardItem({\n [blob.type]: blob\n })\n ])\n }\n\n try {\n const data = await fetch(url)\n const blob = await data.blob()\n try {\n await writeImage(blob)\n } catch (error) {\n // Chrome seems to only support PNG on write, convert and try again\n if (blob.type !== 'image/png') {\n const canvas = $el('canvas', {\n width: img.naturalWidth,\n height: img.naturalHeight\n }) as HTMLCanvasElement\n const ctx = canvas.getContext('2d')\n let image\n if (typeof window.createImageBitmap === 'undefined') {\n image = new Image()\n const p = new Promise((resolve, reject) => {\n image.onload = resolve\n image.onerror = reject\n }).finally(() => {\n URL.revokeObjectURL(image.src)\n })\n image.src = URL.createObjectURL(blob)\n await p\n } else {\n image = await createImageBitmap(blob)\n }\n try {\n ctx.drawImage(image, 0, 0)\n canvas.toBlob(writeImage, 'image/png')\n } finally {\n if (typeof image.close === 'function') {\n image.close()\n }\n }\n\n return\n }\n throw error\n }\n } catch (error) {\n useToastStore().addAlert(\n 'Error copying image: ' + (error.message ?? error)\n )\n }\n }\n }\n ]\n }\n\n node.prototype.getExtraMenuOptions = function (_, options) {\n if (this.imgs) {\n // If this node has images then we add an open in new tab item\n let img\n if (this.imageIndex != null) {\n // An image is selected so select that\n img = this.imgs[this.imageIndex]\n } else if (this.overIndex != null) {\n // No image is selected but one is hovered\n img = this.imgs[this.overIndex]\n }\n if (img) {\n options.unshift(\n {\n content: 'Open Image',\n callback: () => {\n let url = new URL(img.src)\n url.searchParams.delete('preview')\n window.open(url, '_blank')\n }\n },\n ...getCopyImageOption(img),\n {\n content: 'Save Image',\n callback: () => {\n const a = document.createElement('a')\n let url = new URL(img.src)\n url.searchParams.delete('preview')\n a.href = url.toString()\n a.setAttribute(\n 'download',\n new URLSearchParams(url.search).get('filename')\n )\n document.body.append(a)\n a.click()\n requestAnimationFrame(() => a.remove())\n }\n }\n )\n }\n }\n\n options.push({\n content: 'Bypass',\n callback: (obj) => {\n if (this.mode === 4) this.mode = 0\n else this.mode = 4\n this.graph.change()\n }\n })\n\n // prevent conflict of clipspace content\n if (!ComfyApp.clipspace_return_node) {\n options.push({\n content: 'Copy (Clipspace)',\n callback: (obj) => {\n ComfyApp.copyToClipspace(this)\n }\n })\n\n if (ComfyApp.clipspace != null) {\n options.push({\n content: 'Paste (Clipspace)',\n callback: () => {\n ComfyApp.pasteFromClipspace(this)\n }\n })\n }\n\n if (ComfyApp.isImageNode(this)) {\n options.push({\n content: 'Open in MaskEditor',\n callback: (obj) => {\n ComfyApp.copyToClipspace(this)\n ComfyApp.clipspace_return_node = this\n ComfyApp.open_maskeditor()\n }\n })\n }\n }\n }\n }\n\n #addNodeKeyHandler(node) {\n const app = this\n const origNodeOnKeyDown = node.prototype.onKeyDown\n\n node.prototype.onKeyDown = function (e) {\n if (origNodeOnKeyDown && origNodeOnKeyDown.apply(this, e) === false) {\n return false\n }\n\n if (this.flags.collapsed || !this.imgs || this.imageIndex === null) {\n return\n }\n\n let handled = false\n\n if (e.key === 'ArrowLeft' || e.key === 'ArrowRight') {\n if (e.key === 'ArrowLeft') {\n this.imageIndex -= 1\n } else if (e.key === 'ArrowRight') {\n this.imageIndex += 1\n }\n this.imageIndex %= this.imgs.length\n\n if (this.imageIndex < 0) {\n this.imageIndex = this.imgs.length + this.imageIndex\n }\n handled = true\n } else if (e.key === 'Escape') {\n this.imageIndex = null\n handled = true\n }\n\n if (handled === true) {\n e.preventDefault()\n e.stopImmediatePropagation()\n return false\n }\n }\n }\n\n /**\n * Adds Custom drawing logic for nodes\n * e.g. Draws images and handles thumbnail navigation on nodes that output images\n * @param {*} node The node to add the draw handler\n */\n #addDrawBackgroundHandler(node) {\n const app = this\n\n function getImageTop(node) {\n let shiftY\n if (node.imageOffset != null) {\n shiftY = node.imageOffset\n } else {\n if (node.widgets?.length) {\n const w = node.widgets[node.widgets.length - 1]\n shiftY = w.last_y\n if (w.computeSize) {\n shiftY += w.computeSize()[1] + 4\n } else if (w.computedHeight) {\n shiftY += w.computedHeight\n } else {\n shiftY += LiteGraph.NODE_WIDGET_HEIGHT + 4\n }\n } else {\n shiftY = node.computeSize()[1]\n }\n }\n return shiftY\n }\n\n node.prototype.setSizeForImage = function (force) {\n if (!force && this.animatedImages) return\n\n if (this.inputHeight || this.freeWidgetSpace > 210) {\n this.setSize(this.size)\n return\n }\n const minHeight = getImageTop(this) + 220\n if (this.size[1] < minHeight) {\n this.setSize([this.size[0], minHeight])\n }\n }\n\n function unsafeDrawBackground(ctx) {\n if (!this.flags.collapsed) {\n let imgURLs = []\n let imagesChanged = false\n\n const output = app.nodeOutputs[this.id + '']\n if (output?.images) {\n this.animatedImages = output?.animated?.find(Boolean)\n if (this.images !== output.images) {\n this.images = output.images\n imagesChanged = true\n imgURLs = imgURLs.concat(\n output.images.map((params) => {\n return api.apiURL(\n '/view?' +\n new URLSearchParams(params).toString() +\n (this.animatedImages ? '' : app.getPreviewFormatParam()) +\n app.getRandParam()\n )\n })\n )\n }\n }\n\n const preview = app.nodePreviewImages[this.id + '']\n if (this.preview !== preview) {\n this.preview = preview\n imagesChanged = true\n if (preview != null) {\n imgURLs.push(preview)\n }\n }\n\n if (imagesChanged) {\n this.imageIndex = null\n if (imgURLs.length > 0) {\n Promise.all(\n imgURLs.map((src) => {\n return new Promise((r) => {\n const img = new Image()\n img.onload = () => r(img)\n img.onerror = () => r(null)\n img.src = src\n })\n })\n ).then((imgs) => {\n if (\n (!output || this.images === output.images) &&\n (!preview || this.preview === preview)\n ) {\n this.imgs = imgs.filter(Boolean)\n this.setSizeForImage?.()\n app.graph.setDirtyCanvas(true)\n }\n })\n } else {\n this.imgs = null\n }\n }\n\n const is_all_same_aspect_ratio = (imgs) => {\n // assume: imgs.length >= 2\n let ratio = imgs[0].naturalWidth / imgs[0].naturalHeight\n\n for (let i = 1; i < imgs.length; i++) {\n let this_ratio = imgs[i].naturalWidth / imgs[i].naturalHeight\n if (ratio != this_ratio) return false\n }\n\n return true\n }\n\n if (this.imgs?.length) {\n const widgetIdx = this.widgets?.findIndex(\n (w) => w.name === ANIM_PREVIEW_WIDGET\n )\n\n if (this.animatedImages) {\n // Instead of using the canvas we'll use a IMG\n if (widgetIdx > -1) {\n // Replace content\n const widget = this.widgets[widgetIdx]\n widget.options.host.updateImages(this.imgs)\n } else {\n const host = createImageHost(this)\n this.setSizeForImage(true)\n const widget = this.addDOMWidget(\n ANIM_PREVIEW_WIDGET,\n 'img',\n host.el,\n {\n host,\n getHeight: host.getHeight,\n onDraw: host.onDraw,\n hideOnZoom: false\n }\n )\n widget.serializeValue = () => undefined\n widget.options.host.updateImages(this.imgs)\n }\n return\n }\n\n if (widgetIdx > -1) {\n this.widgets[widgetIdx].onRemove?.()\n this.widgets.splice(widgetIdx, 1)\n }\n\n const canvas = app.graph.list_of_graphcanvas[0]\n const mouse = canvas.graph_mouse\n if (!canvas.pointer_is_down && this.pointerDown) {\n if (\n mouse[0] === this.pointerDown.pos[0] &&\n mouse[1] === this.pointerDown.pos[1]\n ) {\n this.imageIndex = this.pointerDown.index\n }\n this.pointerDown = null\n }\n\n let imageIndex = this.imageIndex\n const numImages = this.imgs.length\n if (numImages === 1 && !imageIndex) {\n this.imageIndex = imageIndex = 0\n }\n\n const top = getImageTop(this)\n var shiftY = top\n\n let dw = this.size[0]\n let dh = this.size[1]\n dh -= shiftY\n\n if (imageIndex == null) {\n var cellWidth, cellHeight, shiftX, cell_padding, cols\n\n const compact_mode = is_all_same_aspect_ratio(this.imgs)\n if (!compact_mode) {\n // use rectangle cell style and border line\n cell_padding = 2\n // Prevent infinite canvas2d scale-up\n const largestDimension = this.imgs.reduce(\n (acc, current) =>\n Math.max(acc, current.naturalWidth, current.naturalHeight),\n 0\n )\n const fakeImgs = []\n fakeImgs.length = this.imgs.length\n fakeImgs[0] = {\n naturalWidth: largestDimension,\n naturalHeight: largestDimension\n }\n ;({ cellWidth, cellHeight, cols, shiftX } = calculateImageGrid(\n fakeImgs,\n dw,\n dh\n ))\n } else {\n cell_padding = 0\n ;({ cellWidth, cellHeight, cols, shiftX } = calculateImageGrid(\n this.imgs,\n dw,\n dh\n ))\n }\n\n let anyHovered = false\n this.imageRects = []\n for (let i = 0; i < numImages; i++) {\n const img = this.imgs[i]\n const row = Math.floor(i / cols)\n const col = i % cols\n const x = col * cellWidth + shiftX\n const y = row * cellHeight + shiftY\n if (!anyHovered) {\n anyHovered = LiteGraph.isInsideRectangle(\n mouse[0],\n mouse[1],\n x + this.pos[0],\n y + this.pos[1],\n cellWidth,\n cellHeight\n )\n if (anyHovered) {\n this.overIndex = i\n let value = 110\n if (canvas.pointer_is_down) {\n if (!this.pointerDown || this.pointerDown.index !== i) {\n this.pointerDown = { index: i, pos: [...mouse] }\n }\n value = 125\n }\n ctx.filter = `contrast(${value}%) brightness(${value}%)`\n canvas.canvas.style.cursor = 'pointer'\n }\n }\n this.imageRects.push([x, y, cellWidth, cellHeight])\n\n let wratio = cellWidth / img.width\n let hratio = cellHeight / img.height\n var ratio = Math.min(wratio, hratio)\n\n let imgHeight = ratio * img.height\n let imgY =\n row * cellHeight + shiftY + (cellHeight - imgHeight) / 2\n let imgWidth = ratio * img.width\n let imgX = col * cellWidth + shiftX + (cellWidth - imgWidth) / 2\n\n ctx.drawImage(\n img,\n imgX + cell_padding,\n imgY + cell_padding,\n imgWidth - cell_padding * 2,\n imgHeight - cell_padding * 2\n )\n if (!compact_mode) {\n // rectangle cell and border line style\n ctx.strokeStyle = '#8F8F8F'\n ctx.lineWidth = 1\n ctx.strokeRect(\n x + cell_padding,\n y + cell_padding,\n cellWidth - cell_padding * 2,\n cellHeight - cell_padding * 2\n )\n }\n\n ctx.filter = 'none'\n }\n\n if (!anyHovered) {\n this.pointerDown = null\n this.overIndex = null\n }\n } else {\n // Draw individual\n let w = this.imgs[imageIndex].naturalWidth\n let h = this.imgs[imageIndex].naturalHeight\n\n const scaleX = dw / w\n const scaleY = dh / h\n const scale = Math.min(scaleX, scaleY, 1)\n\n w *= scale\n h *= scale\n\n let x = (dw - w) / 2\n let y = (dh - h) / 2 + shiftY\n ctx.drawImage(this.imgs[imageIndex], x, y, w, h)\n\n const drawButton = (x, y, sz, text) => {\n const hovered = LiteGraph.isInsideRectangle(\n mouse[0],\n mouse[1],\n x + this.pos[0],\n y + this.pos[1],\n sz,\n sz\n )\n let fill = '#333'\n let textFill = '#fff'\n let isClicking = false\n if (hovered) {\n canvas.canvas.style.cursor = 'pointer'\n if (canvas.pointer_is_down) {\n fill = '#1e90ff'\n isClicking = true\n } else {\n fill = '#eee'\n textFill = '#000'\n }\n } else {\n this.pointerWasDown = null\n }\n\n ctx.fillStyle = fill\n ctx.beginPath()\n ctx.roundRect(x, y, sz, sz, [4])\n ctx.fill()\n ctx.fillStyle = textFill\n ctx.font = '12px Arial'\n ctx.textAlign = 'center'\n ctx.fillText(text, x + 15, y + 20)\n\n return isClicking\n }\n\n if (numImages > 1) {\n if (\n drawButton(\n dw - 40,\n dh + top - 40,\n 30,\n `${this.imageIndex + 1}/${numImages}`\n )\n ) {\n let i =\n this.imageIndex + 1 >= numImages ? 0 : this.imageIndex + 1\n if (!this.pointerDown || !this.pointerDown.index === i) {\n this.pointerDown = { index: i, pos: [...mouse] }\n }\n }\n\n if (drawButton(dw - 40, top + 10, 30, `x`)) {\n if (!this.pointerDown || !this.pointerDown.index === null) {\n this.pointerDown = { index: null, pos: [...mouse] }\n }\n }\n }\n }\n }\n }\n }\n\n node.prototype.onDrawBackground = function (ctx) {\n try {\n unsafeDrawBackground.call(this, ctx)\n } catch (error) {\n console.error('Error drawing node background', error)\n }\n }\n }\n\n /**\n * Adds a handler allowing drag+drop of files onto the window to load workflows\n */\n #addDropHandler() {\n // Get prompt from dropped PNG or json\n document.addEventListener('drop', async (event) => {\n event.preventDefault()\n event.stopPropagation()\n\n const n = this.dragOverNode\n this.dragOverNode = null\n // Node handles file drop, we dont use the built in onDropFile handler as its buggy\n // If you drag multiple files it will call it multiple times with the same file\n // @ts-expect-error This is not a standard event. TODO fix it.\n if (n && n.onDragDrop && (await n.onDragDrop(event))) {\n return\n }\n // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that\n if (\n event.dataTransfer.files.length &&\n event.dataTransfer.files[0].type !== 'image/bmp'\n ) {\n await this.handleFile(event.dataTransfer.files[0])\n } else {\n // Try loading the first URI in the transfer list\n const validTypes = ['text/uri-list', 'text/x-moz-url']\n const match = [...event.dataTransfer.types].find((t) =>\n validTypes.find((v) => t === v)\n )\n if (match) {\n const uri = event.dataTransfer.getData(match)?.split('\\n')?.[0]\n if (uri) {\n await this.handleFile(await (await fetch(uri)).blob())\n }\n }\n }\n })\n\n // Always clear over node on drag leave\n this.canvasEl.addEventListener('dragleave', async () => {\n if (this.dragOverNode) {\n this.dragOverNode = null\n this.graph.setDirtyCanvas(false, true)\n }\n })\n\n // Add handler for dropping onto a specific node\n this.canvasEl.addEventListener(\n 'dragover',\n (e) => {\n this.canvas.adjustMouseEvent(e)\n // @ts-expect-error: canvasX and canvasY are added by adjustMouseEvent in litegraph\n const node = this.graph.getNodeOnPos(e.canvasX, e.canvasY)\n if (node) {\n // @ts-expect-error This is not a standard event. TODO fix it.\n if (node.onDragOver && node.onDragOver(e)) {\n this.dragOverNode = node\n\n // dragover event is fired very frequently, run this on an animation frame\n requestAnimationFrame(() => {\n this.graph.setDirtyCanvas(false, true)\n })\n return\n }\n }\n this.dragOverNode = null\n },\n false\n )\n }\n\n /**\n * Adds a handler on paste that extracts and loads images or workflows from pasted JSON data\n */\n #addPasteHandler() {\n document.addEventListener('paste', async (e: ClipboardEvent) => {\n // ctrl+shift+v is used to paste nodes with connections\n // this is handled by litegraph\n if (this.shiftDown) return\n\n // @ts-expect-error: Property 'clipboardData' does not exist on type 'Window & typeof globalThis'.\n // Did you mean 'Clipboard'?ts(2551)\n // TODO: Not sure what the code wants to do.\n let data = e.clipboardData || window.clipboardData\n const items = data.items\n\n // Look for image paste data\n for (const item of items) {\n if (item.type.startsWith('image/')) {\n var imageNode = null\n\n // If an image node is selected, paste into it\n if (\n this.canvas.current_node &&\n this.canvas.current_node.is_selected &&\n ComfyApp.isImageNode(this.canvas.current_node)\n ) {\n imageNode = this.canvas.current_node\n }\n\n // No image node selected: add a new one\n if (!imageNode) {\n const newNode = LiteGraph.createNode('LoadImage')\n // @ts-expect-error array to Float32Array\n newNode.pos = [...this.canvas.graph_mouse]\n imageNode = this.graph.add(newNode)\n this.graph.change()\n }\n const blob = item.getAsFile()\n imageNode.pasteFile(blob)\n return\n }\n }\n\n // No image found. Look for node data\n data = data.getData('text/plain')\n let workflow: ComfyWorkflowJSON | null = null\n try {\n data = data.slice(data.indexOf('{'))\n workflow = JSON.parse(data)\n } catch (err) {\n try {\n data = data.slice(data.indexOf('workflow\\n'))\n data = data.slice(data.indexOf('{'))\n workflow = JSON.parse(data)\n } catch (error) {\n workflow = null\n }\n }\n\n if (workflow && workflow.version && workflow.nodes && workflow.extra) {\n await this.loadGraphData(workflow)\n } else {\n if (\n (e.target instanceof HTMLTextAreaElement &&\n e.target.type === 'textarea') ||\n (e.target instanceof HTMLInputElement && e.target.type === 'text')\n ) {\n return\n }\n\n // Litegraph default paste\n this.canvas.pasteFromClipboard()\n }\n })\n }\n\n /**\n * Adds a handler on copy that serializes selected nodes to JSON\n */\n #addCopyHandler() {\n document.addEventListener('copy', (e) => {\n if (!(e.target instanceof Element)) {\n return\n }\n if (\n (e.target instanceof HTMLTextAreaElement &&\n e.target.type === 'textarea') ||\n (e.target instanceof HTMLInputElement && e.target.type === 'text')\n ) {\n // Default system copy\n return\n }\n const isTargetInGraph =\n e.target.classList.contains('litegraph') ||\n e.target.classList.contains('graph-canvas-container')\n\n // copy nodes and clear clipboard\n if (isTargetInGraph && this.canvas.selected_nodes) {\n this.canvas.copyToClipboard()\n e.clipboardData.setData('text', ' ') //clearData doesn't remove images from clipboard\n e.preventDefault()\n e.stopImmediatePropagation()\n return false\n }\n })\n }\n\n /**\n * Handle mouse\n *\n * Move group by header\n */\n #addProcessMouseHandler() {\n const self = this\n\n const origProcessMouseDown = LGraphCanvas.prototype.processMouseDown\n LGraphCanvas.prototype.processMouseDown = function (e) {\n // prepare for ctrl+shift drag: zoom start\n if (e.ctrlKey && e.shiftKey && e.buttons) {\n self.zoom_drag_start = [e.x, e.y, this.ds.scale]\n return\n }\n\n const res = origProcessMouseDown.apply(this, arguments)\n\n this.selected_group_moving = false\n\n if (this.selected_group && !this.selected_group_resizing) {\n var font_size =\n this.selected_group.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE\n var height = font_size * 1.4\n\n // Move group by header\n if (\n LiteGraph.isInsideRectangle(\n e.canvasX,\n e.canvasY,\n this.selected_group.pos[0],\n this.selected_group.pos[1],\n this.selected_group.size[0],\n height\n )\n ) {\n this.selected_group_moving = true\n }\n }\n\n return res\n }\n const origProcessMouseMove = LGraphCanvas.prototype.processMouseMove\n LGraphCanvas.prototype.processMouseMove = function (e) {\n // handle ctrl+shift drag\n if (e.ctrlKey && e.shiftKey && self.zoom_drag_start) {\n // stop canvas zoom action\n if (!e.buttons) {\n self.zoom_drag_start = null\n return\n }\n\n // calculate delta\n let deltaY = e.y - self.zoom_drag_start[1]\n let startScale = self.zoom_drag_start[2]\n\n let scale = startScale - deltaY / 100\n\n this.ds.changeScale(scale, [\n self.zoom_drag_start[0],\n self.zoom_drag_start[1]\n ])\n this.graph.change()\n\n return\n }\n\n const orig_selected_group = this.selected_group\n\n if (\n this.selected_group &&\n !this.selected_group_resizing &&\n !this.selected_group_moving\n ) {\n this.selected_group = null\n }\n\n const res = origProcessMouseMove.apply(this, arguments)\n\n if (\n orig_selected_group &&\n !this.selected_group_resizing &&\n !this.selected_group_moving\n ) {\n this.selected_group = orig_selected_group\n }\n\n return res\n }\n }\n\n /**\n * Handle keypress\n */\n #addProcessKeyHandler() {\n const origProcessKey = LGraphCanvas.prototype.processKey\n LGraphCanvas.prototype.processKey = function (e: KeyboardEvent) {\n if (!this.graph) {\n return\n }\n\n var block_default = false\n\n if (e.target instanceof Element && e.target.localName == 'input') {\n return\n }\n\n if (e.type == 'keydown' && !e.repeat) {\n const keyCombo = KeyComboImpl.fromEvent(e)\n const keybindingStore = useKeybindingStore()\n const keybinding = keybindingStore.getKeybinding(keyCombo)\n if (keybinding && keybinding.targetSelector === '#graph-canvas') {\n useCommandStore().execute(keybinding.commandId)\n block_default = true\n }\n\n // Ctrl+C Copy\n if (e.key === 'c' && (e.metaKey || e.ctrlKey)) {\n // Trigger onCopy\n return true\n }\n\n // Ctrl+V Paste\n if (\n (e.key === 'v' || e.key == 'V') &&\n (e.metaKey || e.ctrlKey) &&\n !e.shiftKey\n ) {\n // Trigger onPaste\n return true\n }\n }\n\n this.graph.change()\n\n if (block_default) {\n e.preventDefault()\n e.stopImmediatePropagation()\n return false\n }\n\n // Fall through to Litegraph defaults\n return origProcessKey.apply(this, arguments)\n }\n }\n\n /**\n * Draws group header bar\n */\n #addDrawGroupsHandler() {\n const self = this\n const origDrawGroups = LGraphCanvas.prototype.drawGroups\n LGraphCanvas.prototype.drawGroups = function (canvas, ctx) {\n if (!this.graph) {\n return\n }\n\n var groups = this.graph.groups\n\n ctx.save()\n ctx.globalAlpha = 0.7 * this.editor_alpha\n\n for (var i = 0; i < groups.length; ++i) {\n var group = groups[i]\n\n if (!LiteGraph.overlapBounding(this.visible_area, group._bounding)) {\n continue\n } //out of the visible area\n\n ctx.fillStyle = group.color || '#335'\n ctx.strokeStyle = group.color || '#335'\n var pos = group._pos\n var size = group._size\n ctx.globalAlpha = 0.25 * this.editor_alpha\n ctx.beginPath()\n var font_size = group.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE\n ctx.rect(pos[0] + 0.5, pos[1] + 0.5, size[0], font_size * 1.4)\n ctx.fill()\n ctx.globalAlpha = this.editor_alpha\n }\n\n ctx.restore()\n\n const res = origDrawGroups.apply(this, arguments)\n return res\n }\n }\n\n /**\n * Draws node highlights (executing, drag drop) and progress bar\n */\n #addDrawNodeHandler() {\n const origDrawNodeShape = LGraphCanvas.prototype.drawNodeShape\n const self = this\n LGraphCanvas.prototype.drawNodeShape = function (\n node,\n ctx,\n size,\n fgcolor,\n bgcolor,\n selected\n ) {\n const res = origDrawNodeShape.apply(this, arguments)\n\n const nodeErrors = self.lastNodeErrors?.[node.id]\n\n let color = null\n let lineWidth = 1\n if (node.id === +self.runningNodeId) {\n color = '#0f0'\n } else if (self.dragOverNode && node.id === self.dragOverNode.id) {\n color = 'dodgerblue'\n } else if (nodeErrors?.errors) {\n color = 'red'\n lineWidth = 2\n } else if (\n self.lastExecutionError &&\n +self.lastExecutionError.node_id === node.id\n ) {\n color = '#f0f'\n lineWidth = 2\n }\n\n if (color) {\n const shape =\n node._shape || node.constructor.shape || LiteGraph.ROUND_SHAPE\n ctx.lineWidth = lineWidth\n ctx.globalAlpha = 0.8\n ctx.beginPath()\n if (shape == LiteGraph.BOX_SHAPE)\n ctx.rect(\n -6,\n -6 - LiteGraph.NODE_TITLE_HEIGHT,\n 12 + size[0] + 1,\n 12 + size[1] + LiteGraph.NODE_TITLE_HEIGHT\n )\n else if (\n shape == LiteGraph.ROUND_SHAPE ||\n (shape == LiteGraph.CARD_SHAPE && node.flags.collapsed)\n )\n ctx.roundRect(\n -6,\n -6 - LiteGraph.NODE_TITLE_HEIGHT,\n 12 + size[0] + 1,\n 12 + size[1] + LiteGraph.NODE_TITLE_HEIGHT,\n this.round_radius * 2\n )\n else if (shape == LiteGraph.CARD_SHAPE)\n ctx.roundRect(\n -6,\n -6 - LiteGraph.NODE_TITLE_HEIGHT,\n 12 + size[0] + 1,\n 12 + size[1] + LiteGraph.NODE_TITLE_HEIGHT,\n [this.round_radius * 2, this.round_radius * 2, 2, 2]\n )\n else if (shape == LiteGraph.CIRCLE_SHAPE)\n ctx.arc(\n size[0] * 0.5,\n size[1] * 0.5,\n size[0] * 0.5 + 6,\n 0,\n Math.PI * 2\n )\n ctx.strokeStyle = color\n ctx.stroke()\n ctx.strokeStyle = fgcolor\n ctx.globalAlpha = 1\n }\n\n if (self.progress && node.id === +self.runningNodeId) {\n ctx.fillStyle = 'green'\n ctx.fillRect(\n 0,\n 0,\n size[0] * (self.progress.value / self.progress.max),\n 6\n )\n ctx.fillStyle = bgcolor\n }\n\n // Highlight inputs that failed validation\n if (nodeErrors) {\n ctx.lineWidth = 2\n ctx.strokeStyle = 'red'\n for (const error of nodeErrors.errors) {\n if (error.extra_info && error.extra_info.input_name) {\n const inputIndex = node.findInputSlot(error.extra_info.input_name)\n if (inputIndex !== -1) {\n let pos = node.getConnectionPos(true, inputIndex)\n ctx.beginPath()\n ctx.arc(\n pos[0] - node.pos[0],\n pos[1] - node.pos[1],\n 12,\n 0,\n 2 * Math.PI,\n false\n )\n ctx.stroke()\n }\n }\n }\n }\n\n return res\n }\n\n const origDrawNode = LGraphCanvas.prototype.drawNode\n LGraphCanvas.prototype.drawNode = function (node, ctx) {\n const editor_alpha = this.editor_alpha\n const old_color = node.color\n const old_bgcolor = node.bgcolor\n\n if (node.mode === 2) {\n // never\n this.editor_alpha = 0.4\n }\n\n // ComfyUI's custom node mode enum value 4 => bypass/never.\n let bgColor: string\n if (node.mode === 4) {\n // never\n bgColor = app.bypassBgColor\n this.editor_alpha = 0.2\n } else {\n bgColor = old_bgcolor || LiteGraph.NODE_DEFAULT_BGCOLOR\n }\n\n const adjustments: ColorAdjustOptions = {}\n\n const opacity = useSettingStore().get('Comfy.Node.Opacity')\n if (opacity) adjustments.opacity = opacity\n\n if (useSettingStore().get('Comfy.ColorPalette') === 'light') {\n adjustments.lightness = 0.5\n\n // Lighten title bar of colored nodes on light theme\n if (old_color) {\n node.color = adjustColor(old_color, { lightness: 0.5 })\n }\n }\n\n node.bgcolor = adjustColor(bgColor, adjustments)\n\n const res = origDrawNode.apply(this, arguments)\n\n this.editor_alpha = editor_alpha\n node.color = old_color\n node.bgcolor = old_bgcolor\n\n return res\n }\n }\n\n /**\n * Handles updates from the API socket\n */\n #addApiUpdateHandlers() {\n api.addEventListener(\n 'status',\n ({ detail }: CustomEvent) => {\n this.ui.setStatus(detail)\n }\n )\n\n api.addEventListener('progress', ({ detail }) => {\n this.progress = detail\n this.graph.setDirtyCanvas(true, false)\n })\n\n api.addEventListener('executing', ({ detail }) => {\n this.progress = null\n this.graph.setDirtyCanvas(true, false)\n delete this.nodePreviewImages[this.runningNodeId]\n })\n\n api.addEventListener('executed', ({ detail }) => {\n const output = this.nodeOutputs[detail.display_node || detail.node]\n if (detail.merge && output) {\n for (const k in detail.output ?? {}) {\n const v = output[k]\n if (v instanceof Array) {\n output[k] = v.concat(detail.output[k])\n } else {\n output[k] = detail.output[k]\n }\n }\n } else {\n this.nodeOutputs[detail.display_node || detail.node] = detail.output\n }\n const node = this.graph.getNodeById(detail.display_node || detail.node)\n if (node) {\n // @ts-expect-error\n if (node.onExecuted)\n // @ts-expect-error\n node.onExecuted(detail.output)\n }\n })\n\n api.addEventListener('execution_start', ({ detail }) => {\n this.lastExecutionError = null\n this.graph.nodes.forEach((node) => {\n // @ts-expect-error\n if (node.onExecutionStart)\n // @ts-expect-error\n node.onExecutionStart()\n })\n })\n\n api.addEventListener('execution_error', ({ detail }) => {\n this.lastExecutionError = detail\n showExecutionErrorDialog(detail)\n this.canvas.draw(true, true)\n })\n\n api.addEventListener('b_preview', ({ detail }) => {\n const id = this.runningNodeId\n if (id == null) return\n\n const blob = detail\n const blobUrl = URL.createObjectURL(blob)\n // @ts-expect-error\n this.nodePreviewImages[id] = [blobUrl]\n })\n\n api.init()\n }\n\n #addConfigureHandler() {\n const app = this\n const configure = LGraph.prototype.configure\n // Flag that the graph is configuring to prevent nodes from running checks while its still loading\n LGraph.prototype.configure = function () {\n app.configuringGraph = true\n try {\n return configure.apply(this, arguments)\n } finally {\n app.configuringGraph = false\n }\n }\n }\n\n #addWidgetLinkHandling() {\n app.canvas.getWidgetLinkType = function (widget, node) {\n const nodeDefStore = useNodeDefStore()\n const nodeDef = nodeDefStore.nodeDefsByName[node.type]\n const input = nodeDef.input.getInput(widget.name)\n return input?.type\n }\n\n type ConnectingWidgetLink = {\n subType: 'connectingWidgetLink'\n widget: IWidget\n node: LGraphNode\n link: { node: LGraphNode; slot: number }\n }\n\n document.addEventListener(\n 'litegraph:canvas',\n async (e: CustomEvent) => {\n if (e.detail.subType === 'connectingWidgetLink') {\n const { convertToInput } = await import(\n '@/extensions/core/widgetInputs'\n )\n\n const { node, link, widget } = e.detail\n if (!node || !link || !widget) return\n\n const nodeData = node.constructor.nodeData\n if (!nodeData) return\n const all = {\n ...nodeData?.input?.required,\n ...nodeData?.input?.optional\n }\n const inputSpec = all[widget.name]\n if (!inputSpec) return\n\n const input = convertToInput(node, widget, inputSpec)\n if (!input) return\n\n const originNode = link.node\n\n originNode.connect(link.slot, node, node.inputs.lastIndexOf(input))\n }\n }\n )\n }\n\n #addAfterConfigureHandler() {\n const app = this\n const onConfigure = app.graph.onConfigure\n app.graph.onConfigure = function () {\n // Fire callbacks before the onConfigure, this is used by widget inputs to setup the config\n for (const node of app.graph.nodes) {\n // @ts-expect-error\n node.onGraphConfigured?.()\n }\n\n const r = onConfigure?.apply(this, arguments)\n\n // Fire after onConfigure, used by primitives to generate widget using input nodes config\n for (const node of app.graph.nodes) {\n node.onAfterGraphConfigured?.()\n }\n\n return r\n }\n }\n\n /**\n * Loads all extensions from the API into the window in parallel\n */\n async #loadExtensions() {\n useExtensionStore().loadDisabledExtensionNames()\n\n const extensions = await api.getExtensions()\n this.logging.addEntry('Comfy.App', 'debug', { Extensions: extensions })\n\n // Need to load core extensions first as some custom extensions\n // may depend on them.\n await import('../extensions/core/index')\n await Promise.all(\n extensions\n .filter((extension) => !extension.includes('extensions/core'))\n .map(async (ext) => {\n try {\n await import(/* @vite-ignore */ api.fileURL(ext))\n } catch (error) {\n console.error('Error loading extension', ext, error)\n }\n })\n )\n }\n\n async #migrateSettings() {\n this.isNewUserSession = true\n // Store all current settings\n const settings = Object.keys(this.ui.settings).reduce((p, n) => {\n const v = localStorage[`Comfy.Settings.${n}`]\n if (v) {\n try {\n p[n] = JSON.parse(v)\n } catch (error) {}\n }\n return p\n }, {})\n\n await api.storeSettings(settings)\n }\n\n async #setUser() {\n const userConfig = await api.getUserConfig()\n this.storageLocation = userConfig.storage\n if (typeof userConfig.migrated == 'boolean') {\n // Single user mode migrated true/false for if the default user is created\n if (!userConfig.migrated && this.storageLocation === 'server') {\n // Default user not created yet\n await this.#migrateSettings()\n }\n return\n }\n\n this.multiUserServer = true\n let user = localStorage['Comfy.userId']\n const users = userConfig.users ?? {}\n if (!user || !users[user]) {\n // Lift spinner / BlockUI for user selection.\n if (this.vueAppReady) useWorkspaceStore().spinner = false\n\n // This will rarely be hit so move the loading to on demand\n const { UserSelectionScreen } = await import('./ui/userSelection')\n\n this.ui.menuContainer.style.display = 'none'\n const { userId, username, created } =\n await new UserSelectionScreen().show(users, user)\n this.ui.menuContainer.style.display = ''\n\n user = userId\n localStorage['Comfy.userName'] = username\n localStorage['Comfy.userId'] = user\n\n if (created) {\n api.user = user\n await this.#migrateSettings()\n }\n }\n\n api.user = user\n\n this.ui.settings.addSetting({\n id: 'Comfy.SwitchUser',\n name: 'Switch User',\n type: (name) => {\n let currentUser = localStorage['Comfy.userName']\n if (currentUser) {\n currentUser = ` (${currentUser})`\n }\n return $el('tr', [\n $el('td', [\n $el('label', {\n textContent: name\n })\n ]),\n $el('td', [\n $el('button', {\n textContent: name + (currentUser ?? ''),\n onclick: () => {\n delete localStorage['Comfy.userId']\n delete localStorage['Comfy.userName']\n window.location.reload()\n }\n })\n ])\n ])\n },\n // TODO: Is that the correct default value?\n defaultValue: undefined\n })\n }\n\n /**\n * Set up the app on the page\n */\n async setup(canvasEl: HTMLCanvasElement) {\n this.canvasEl = canvasEl\n await this.#setUser()\n\n this.resizeCanvas()\n\n await Promise.all([\n this.workflowManager.loadWorkflows(),\n this.ui.settings.load()\n ])\n await this.#loadExtensions()\n\n addDomClippingSetting()\n this.#addProcessMouseHandler()\n this.#addProcessKeyHandler()\n this.#addConfigureHandler()\n this.#addApiUpdateHandlers()\n this.#addRestoreWorkflowView()\n\n this.graph = new LGraph()\n\n this.#addAfterConfigureHandler()\n\n // Make LGraphCanvas shallow reactive so that any change on the root object\n // triggers reactivity.\n this.canvas = shallowReactive(\n new LGraphCanvas(canvasEl, this.graph, {\n skip_events: true,\n skip_render: true\n })\n )\n // Bind event/ start rendering later, so that event handlers get reactive canvas reference.\n this.canvas.options.skip_events = false\n this.canvas.options.skip_render = false\n this.canvas.bindEvents()\n this.canvas.startRendering()\n\n this.ctx = canvasEl.getContext('2d')\n\n LiteGraph.alt_drag_do_clone_nodes = true\n\n this.graph.start()\n\n // Ensure the canvas fills the window\n this.resizeCanvas()\n window.addEventListener('resize', () => this.resizeCanvas())\n const ro = new ResizeObserver(() => this.resizeCanvas())\n ro.observe(this.bodyTop)\n ro.observe(this.bodyLeft)\n ro.observe(this.bodyRight)\n ro.observe(this.bodyBottom)\n\n await this.#invokeExtensionsAsync('init')\n await this.registerNodes()\n initWidgets(this)\n\n // Load previous workflow\n let restored = false\n try {\n const loadWorkflow = async (json) => {\n if (json) {\n const workflow = JSON.parse(json)\n const workflowName = getStorageValue('Comfy.PreviousWorkflow')\n await this.loadGraphData(workflow, true, true, workflowName)\n return true\n }\n }\n const clientId = api.initialClientId ?? api.clientId\n restored =\n (clientId &&\n (await loadWorkflow(\n sessionStorage.getItem(`workflow:${clientId}`)\n ))) ||\n (await loadWorkflow(localStorage.getItem('workflow')))\n } catch (err) {\n console.error('Error loading previous workflow', err)\n }\n\n // We failed to restore a workflow so load the default\n if (!restored) {\n await this.loadGraphData()\n }\n\n // Save current workflow automatically\n setInterval(() => {\n const workflow = JSON.stringify(this.serializeGraph())\n localStorage.setItem('workflow', workflow)\n if (api.clientId) {\n sessionStorage.setItem(`workflow:${api.clientId}`, workflow)\n }\n }, 1000)\n\n this.#addDrawNodeHandler()\n this.#addDrawGroupsHandler()\n this.#addDropHandler()\n this.#addCopyHandler()\n this.#addPasteHandler()\n this.#addWidgetLinkHandling()\n\n await this.#invokeExtensionsAsync('setup')\n }\n\n resizeCanvas() {\n // Limit minimal scale to 1, see https://github.com/comfyanonymous/ComfyUI/pull/845\n const scale = Math.max(window.devicePixelRatio, 1)\n\n // Clear fixed width and height while calculating rect so it uses 100% instead\n this.canvasEl.height = this.canvasEl.width = NaN\n const { width, height } = this.canvasEl.getBoundingClientRect()\n this.canvasEl.width = Math.round(width * scale)\n this.canvasEl.height = Math.round(height * scale)\n this.canvasEl.getContext('2d').scale(scale, scale)\n this.canvas?.draw(true, true)\n }\n\n private updateVueAppNodeDefs(defs: Record) {\n // Frontend only nodes registered by custom nodes.\n // Example: https://github.com/rgthree/rgthree-comfy/blob/dd534e5384be8cf0c0fa35865afe2126ba75ac55/src_web/comfyui/fast_groups_bypasser.ts#L10\n const rawDefs = Object.fromEntries(\n Object.entries(LiteGraph.registered_node_types).map(([name, node]) => [\n name,\n {\n name,\n display_name: name,\n category: node.category || '__frontend_only__',\n input: { required: {}, optional: {} },\n output: [],\n output_name: [],\n output_is_list: [],\n python_module: 'custom_nodes.frontend_only',\n description: `Frontend only node for ${name}`\n }\n ])\n )\n\n const allNodeDefs = {\n ...rawDefs,\n ...defs,\n ...SYSTEM_NODE_DEFS\n }\n\n const nodeDefStore = useNodeDefStore()\n const nodeDefArray: ComfyNodeDef[] = Object.values(allNodeDefs)\n this.#invokeExtensions('beforeRegisterVueAppNodeDefs', nodeDefArray, this)\n nodeDefStore.updateNodeDefs(nodeDefArray)\n nodeDefStore.widgets = this.widgets\n }\n\n /**\n * Registers nodes with the graph\n */\n async registerNodes() {\n // Load node definitions from the backend\n const defs = await api.getNodeDefs({\n validate: useSettingStore().get('Comfy.Validation.NodeDefs')\n })\n await this.registerNodesFromDefs(defs)\n await this.#invokeExtensionsAsync('registerCustomNodes')\n if (this.vueAppReady) {\n this.updateVueAppNodeDefs(defs)\n }\n }\n\n getWidgetType(inputData, inputName) {\n const type = inputData[0]\n\n if (Array.isArray(type)) {\n return 'COMBO'\n } else if (`${type}:${inputName}` in this.widgets) {\n return `${type}:${inputName}`\n } else if (type in this.widgets) {\n return type\n } else {\n return null\n }\n }\n\n async registerNodeDef(nodeId: string, nodeData: ComfyNodeDef) {\n const self = this\n const node = class ComfyNode extends LGraphNode {\n static comfyClass? = nodeData.name\n // TODO: change to \"title?\" once litegraph.d.ts has been updated\n static title = nodeData.display_name || nodeData.name\n static nodeData? = nodeData\n static category?: string\n\n constructor(title?: string) {\n super(title)\n const requiredInputs = nodeData.input.required\n\n var inputs = nodeData['input']['required']\n if (nodeData['input']['optional'] != undefined) {\n inputs = Object.assign(\n {},\n nodeData['input']['required'],\n nodeData['input']['optional']\n )\n }\n const config = { minWidth: 1, minHeight: 1 }\n for (const inputName in inputs) {\n const inputData = inputs[inputName]\n const type = inputData[0]\n const inputIsRequired = requiredInputs && inputName in requiredInputs\n\n let widgetCreated = true\n const widgetType = self.getWidgetType(inputData, inputName)\n if (widgetType) {\n if (widgetType === 'COMBO') {\n Object.assign(\n config,\n self.widgets.COMBO(this, inputName, inputData, app) || {}\n )\n } else {\n Object.assign(\n config,\n self.widgets[widgetType](this, inputName, inputData, app) || {}\n )\n }\n } else {\n // Node connection inputs\n const inputOptions = inputIsRequired\n ? {}\n : { shape: LiteGraph.SlotShape.HollowCircle }\n this.addInput(inputName, type, inputOptions)\n widgetCreated = false\n }\n\n // @ts-expect-error\n if (widgetCreated && !inputIsRequired && config?.widget) {\n // @ts-expect-error\n if (!config.widget.options) config.widget.options = {}\n // @ts-expect-error\n config.widget.options.inputIsOptional = true\n }\n\n // @ts-expect-error\n if (widgetCreated && inputData[1]?.forceInput && config?.widget) {\n // @ts-expect-error\n if (!config.widget.options) config.widget.options = {}\n // @ts-expect-error\n config.widget.options.forceInput = inputData[1].forceInput\n }\n // @ts-expect-error\n if (widgetCreated && inputData[1]?.defaultInput && config?.widget) {\n // @ts-expect-error\n if (!config.widget.options) config.widget.options = {}\n // @ts-expect-error\n config.widget.options.defaultInput = inputData[1].defaultInput\n }\n }\n\n for (const o in nodeData['output']) {\n let output = nodeData['output'][o]\n if (output instanceof Array) output = 'COMBO'\n const outputName = nodeData['output_name'][o] || output\n const outputIsList = nodeData['output_is_list'][o]\n const outputOptions = outputIsList\n ? { shape: LiteGraph.GRID_SHAPE }\n : {}\n this.addOutput(outputName, output, outputOptions)\n }\n\n const s = this.computeSize()\n s[0] = Math.max(config.minWidth, s[0] * 1.5)\n s[1] = Math.max(config.minHeight, s[1])\n this.size = s\n this.serialize_widgets = true\n\n app.#invokeExtensionsAsync('nodeCreated', this)\n }\n\n configure(data: any) {\n // Keep 'name', 'type', and 'shape' information from the original node definition.\n const merge = (\n current: Record,\n incoming: Record\n ) => {\n const result = { ...incoming }\n if (current.widget === undefined && incoming.widget !== undefined) {\n // Field must be input as only inputs can be converted\n this.inputs.push(current as INodeInputSlot)\n return incoming\n }\n for (const key of ['name', 'type', 'shape']) {\n if (current[key] !== undefined) {\n result[key] = current[key]\n }\n }\n return result\n }\n for (const field of ['inputs', 'outputs']) {\n const slots = data[field] ?? []\n data[field] = slots.map((slot, i) =>\n merge(this[field][i] ?? {}, slot)\n )\n }\n super.configure(data)\n }\n }\n node.prototype.comfyClass = nodeData.name\n\n this.#addNodeContextMenuHandler(node)\n this.#addDrawBackgroundHandler(node)\n this.#addNodeKeyHandler(node)\n\n await this.#invokeExtensionsAsync('beforeRegisterNodeDef', node, nodeData)\n LiteGraph.registerNodeType(nodeId, node)\n // Note: Do not move this to the class definition, it will be overwritten\n node.category = nodeData.category\n }\n\n async registerNodesFromDefs(defs: Record) {\n await this.#invokeExtensionsAsync('addCustomNodeDefs', defs)\n\n // Generate list of known widgets\n this.widgets = Object.assign(\n {},\n ComfyWidgets,\n ...(await this.#invokeExtensionsAsync('getCustomWidgets')).filter(Boolean)\n )\n\n // Register a node for each definition\n for (const nodeId in defs) {\n this.registerNodeDef(nodeId, defs[nodeId])\n }\n }\n\n loadTemplateData(templateData) {\n if (!templateData?.templates) {\n return\n }\n\n const old = localStorage.getItem('litegrapheditor_clipboard')\n\n var maxY, nodeBottom, node\n\n for (const template of templateData.templates) {\n if (!template?.data) {\n continue\n }\n\n localStorage.setItem('litegrapheditor_clipboard', template.data)\n app.canvas.pasteFromClipboard()\n\n // Move mouse position down to paste the next template below\n\n maxY = false\n\n for (const i in app.canvas.selected_nodes) {\n node = app.canvas.selected_nodes[i]\n\n nodeBottom = node.pos[1] + node.size[1]\n\n if (maxY === false || nodeBottom > maxY) {\n maxY = nodeBottom\n }\n }\n\n app.canvas.graph_mouse[1] = maxY + 50\n }\n\n localStorage.setItem('litegrapheditor_clipboard', old)\n }\n\n #showMissingNodesError(missingNodeTypes: MissingNodeType[]) {\n if (useSettingStore().get('Comfy.Workflow.ShowMissingNodesWarning')) {\n showLoadWorkflowWarning({ missingNodeTypes })\n }\n\n this.logging.addEntry('Comfy.App', 'warn', {\n MissingNodes: missingNodeTypes\n })\n }\n\n #showMissingModelsError(missingModels, paths) {\n if (useSettingStore().get('Comfy.Workflow.ShowMissingModelsWarning')) {\n showMissingModelsWarning({\n missingModels,\n paths\n })\n }\n\n this.logging.addEntry('Comfy.App', 'warn', {\n MissingModels: missingModels\n })\n }\n\n async changeWorkflow(callback, workflow = null) {\n try {\n this.workflowManager.activeWorkflow?.changeTracker?.store()\n } catch (error) {\n console.error(error)\n }\n await callback()\n try {\n this.workflowManager.setWorkflow(workflow)\n this.workflowManager.activeWorkflow?.track()\n } catch (error) {\n console.error(error)\n }\n }\n\n async loadGraphData(\n graphData?: ComfyWorkflowJSON,\n clean: boolean = true,\n restore_view: boolean = true,\n workflow: string | null | ComfyWorkflow = null,\n { showMissingNodesDialog = true, showMissingModelsDialog = true } = {}\n ) {\n if (clean !== false) {\n this.clean()\n }\n\n let reset_invalid_values = false\n if (!graphData) {\n graphData = defaultGraph\n reset_invalid_values = true\n }\n\n if (typeof structuredClone === 'undefined') {\n graphData = JSON.parse(JSON.stringify(graphData))\n } else {\n graphData = structuredClone(graphData)\n }\n\n try {\n this.workflowManager.setWorkflow(workflow)\n } catch (error) {\n console.error(error)\n }\n\n if (useSettingStore().get('Comfy.Validation.Workflows')) {\n // TODO: Show validation error in a dialog.\n const validatedGraphData = await validateComfyWorkflow(\n graphData,\n /* onError=*/ (err) => {\n useToastStore().addAlert(err)\n }\n )\n // If the validation failed, use the original graph data.\n // Ideally we should not block users from loading the workflow.\n graphData = validatedGraphData ?? graphData\n }\n\n const missingNodeTypes: MissingNodeType[] = []\n const missingModels = []\n await this.#invokeExtensionsAsync(\n 'beforeConfigureGraph',\n graphData,\n missingNodeTypes\n // TODO: missingModels\n )\n for (let n of graphData.nodes) {\n // Patch T2IAdapterLoader to ControlNetLoader since they are the same node now\n if (n.type == 'T2IAdapterLoader') n.type = 'ControlNetLoader'\n if (n.type == 'ConditioningAverage ') n.type = 'ConditioningAverage' //typo fix\n if (n.type == 'SDV_img2vid_Conditioning')\n n.type = 'SVD_img2vid_Conditioning' //typo fix\n\n // Find missing node types\n if (!(n.type in LiteGraph.registered_node_types)) {\n missingNodeTypes.push(n.type)\n n.type = sanitizeNodeName(n.type)\n }\n }\n if (\n graphData.models &&\n useSettingStore().get('Comfy.Workflow.ShowMissingModelsWarning')\n ) {\n for (let m of graphData.models) {\n const models_available = await useModelStore().getModelsInFolderCached(\n m.directory\n )\n if (models_available === null) {\n // @ts-expect-error\n m.directory_invalid = true\n missingModels.push(m)\n } else if (!(m.name in models_available.models)) {\n missingModels.push(m)\n }\n }\n }\n\n try {\n this.graph.configure(graphData)\n if (\n restore_view &&\n this.enableWorkflowViewRestore.value &&\n graphData.extra?.ds\n ) {\n // @ts-expect-error\n // Need to set strict: true for zod to match the type [number, number]\n // https://github.com/colinhacks/zod/issues/3056\n this.canvas.ds.offset = graphData.extra.ds.offset\n this.canvas.ds.scale = graphData.extra.ds.scale\n }\n\n try {\n this.workflowManager.activeWorkflow?.track()\n } catch (error) {\n // TODO: Do we want silently fail here?\n }\n } catch (error) {\n let errorHint = []\n // Try extracting filename to see if it was caused by an extension script\n const filename =\n error.fileName ||\n (error.stack || '').match(/(\\/extensions\\/.*\\.js)/)?.[1]\n const pos = (filename || '').indexOf('/extensions/')\n if (pos > -1) {\n errorHint.push(\n $el('span', {\n textContent: 'This may be due to the following script:'\n }),\n $el('br'),\n $el('span', {\n style: {\n fontWeight: 'bold'\n },\n textContent: filename.substring(pos)\n })\n )\n }\n\n // Show dialog to let the user know something went wrong loading the data\n this.ui.dialog.show(\n $el('div', [\n $el('p', {\n textContent: 'Loading aborted due to error reloading workflow data'\n }),\n $el('pre', {\n style: { padding: '5px', backgroundColor: 'rgba(255,0,0,0.2)' },\n textContent: error.toString()\n }),\n $el('pre', {\n style: {\n padding: '5px',\n color: '#ccc',\n fontSize: '10px',\n maxHeight: '50vh',\n overflow: 'auto',\n backgroundColor: 'rgba(0,0,0,0.2)'\n },\n textContent: error.stack || 'No stacktrace available'\n }),\n ...errorHint\n ]).outerHTML\n )\n\n return\n }\n for (const node of this.graph.nodes) {\n const size = node.computeSize()\n size[0] = Math.max(node.size[0], size[0])\n size[1] = Math.max(node.size[1], size[1])\n node.size = size\n if (node.widgets) {\n // If you break something in the backend and want to patch workflows in the frontend\n // This is the place to do this\n for (let widget of node.widgets) {\n if (node.type == 'KSampler' || node.type == 'KSamplerAdvanced') {\n if (widget.name == 'sampler_name') {\n if (\n typeof widget.value === 'string' &&\n widget.value.startsWith('sample_')\n ) {\n widget.value = widget.value.slice(7)\n }\n }\n }\n if (\n node.type == 'KSampler' ||\n node.type == 'KSamplerAdvanced' ||\n node.type == 'PrimitiveNode'\n ) {\n if (widget.name == 'control_after_generate') {\n if (widget.value === true) {\n // @ts-expect-error change widget type from boolean to string\n widget.value = 'randomize'\n } else if (widget.value === false) {\n // @ts-expect-error change widget type from boolean to string\n widget.value = 'fixed'\n }\n }\n }\n if (reset_invalid_values) {\n if (widget.type == 'combo') {\n if (\n !widget.options.values.includes(widget.value as string) &&\n widget.options.values.length > 0\n ) {\n widget.value = widget.options.values[0]\n }\n }\n }\n }\n }\n\n this.#invokeExtensions('loadedGraphNode', node)\n }\n\n // TODO: Properly handle if both nodes and models are missing (sequential dialogs?)\n if (missingNodeTypes.length && showMissingNodesDialog) {\n this.#showMissingNodesError(missingNodeTypes)\n }\n if (missingModels.length && showMissingModelsDialog) {\n const paths = await api.getFolderPaths()\n this.#showMissingModelsError(missingModels, paths)\n }\n await this.#invokeExtensionsAsync('afterConfigureGraph', missingNodeTypes)\n requestAnimationFrame(() => {\n this.graph.setDirtyCanvas(true, true)\n })\n }\n\n /**\n * Serializes a graph using preferred user settings.\n * @param graph The litegraph to serialize.\n * @returns A serialized graph (aka workflow) with preferred user settings.\n */\n serializeGraph(graph: LGraph = this.graph) {\n const sortNodes = useSettingStore().get('Comfy.Workflow.SortNodeIdOnSave')\n return graph.serialize({ sortNodes })\n }\n\n /**\n * Converts the current graph workflow for sending to the API.\n * Note: Node widgets are updated before serialization to prepare queueing.\n * @returns The workflow and node links\n */\n async graphToPrompt(graph = this.graph, clean = true) {\n for (const outerNode of this.graph.computeExecutionOrder(false)) {\n if (outerNode.widgets) {\n for (const widget of outerNode.widgets) {\n // Allow widgets to run callbacks before a prompt has been queued\n // e.g. random seed before every gen\n widget.beforeQueued?.()\n }\n }\n\n const innerNodes = outerNode['getInnerNodes']\n ? outerNode['getInnerNodes']()\n : [outerNode]\n for (const node of innerNodes) {\n if (node.isVirtualNode) {\n // Don't serialize frontend only nodes but let them make changes\n if (node.applyToGraph) {\n node.applyToGraph()\n }\n }\n }\n }\n\n const workflow = this.serializeGraph(graph)\n const output = {}\n // Process nodes in order of execution\n for (const outerNode of graph.computeExecutionOrder(false)) {\n const skipNode = outerNode.mode === 2 || outerNode.mode === 4\n const innerNodes =\n !skipNode && outerNode['getInnerNodes']\n ? outerNode['getInnerNodes']()\n : [outerNode]\n for (const node of innerNodes) {\n if (node.isVirtualNode) {\n continue\n }\n\n if (node.mode === 2 || node.mode === 4) {\n // Don't serialize muted nodes\n continue\n }\n\n const inputs = {}\n const widgets = node.widgets\n\n // Store all widget values\n if (widgets) {\n for (const i in widgets) {\n const widget = widgets[i]\n if (!widget.options || widget.options.serialize !== false) {\n inputs[widget.name] = widget.serializeValue\n ? await widget.serializeValue(node, i)\n : widget.value\n }\n }\n }\n\n // Store all node links\n for (let i in node.inputs) {\n let parent = node.getInputNode(i)\n if (parent) {\n let link = node.getInputLink(i)\n while (parent.mode === 4 || parent.isVirtualNode) {\n let found = false\n if (parent.isVirtualNode) {\n link = parent.getInputLink(link.origin_slot)\n if (link) {\n parent = parent.getInputNode(link.target_slot)\n if (parent) {\n found = true\n }\n }\n } else if (link && parent.mode === 4) {\n let all_inputs = [link.origin_slot]\n if (parent.inputs) {\n all_inputs = all_inputs.concat(Object.keys(parent.inputs))\n for (let parent_input in all_inputs) {\n parent_input = all_inputs[parent_input]\n if (\n parent.inputs[parent_input]?.type === node.inputs[i].type\n ) {\n link = parent.getInputLink(parent_input)\n if (link) {\n parent = parent.getInputNode(parent_input)\n }\n found = true\n break\n }\n }\n }\n }\n\n if (!found) {\n break\n }\n }\n\n if (link) {\n if (parent?.updateLink) {\n link = parent.updateLink(link)\n }\n if (link) {\n inputs[node.inputs[i].name] = [\n String(link.origin_id),\n parseInt(link.origin_slot)\n ]\n }\n }\n }\n }\n\n let node_data = {\n inputs,\n class_type: node.comfyClass\n }\n\n if (this.ui.settings.getSettingValue('Comfy.DevMode')) {\n // Ignored by the backend.\n node_data['_meta'] = {\n title: node.title\n }\n }\n\n output[String(node.id)] = node_data\n }\n }\n\n // Remove inputs connected to removed nodes\n if (clean) {\n for (const o in output) {\n for (const i in output[o].inputs) {\n if (\n Array.isArray(output[o].inputs[i]) &&\n output[o].inputs[i].length === 2 &&\n !output[output[o].inputs[i][0]]\n ) {\n delete output[o].inputs[i]\n }\n }\n }\n }\n\n return { workflow, output }\n }\n\n #formatPromptError(error) {\n if (error == null) {\n return '(unknown error)'\n } else if (typeof error === 'string') {\n return error\n } else if (error.stack && error.message) {\n return error.toString()\n } else if (error.response) {\n let message = error.response.error.message\n if (error.response.error.details)\n message += ': ' + error.response.error.details\n for (const [nodeID, nodeError] of Object.entries(\n error.response.node_errors\n )) {\n // @ts-expect-error\n message += '\\n' + nodeError.class_type + ':'\n // @ts-expect-error\n for (const errorReason of nodeError.errors) {\n message +=\n '\\n - ' + errorReason.message + ': ' + errorReason.details\n }\n }\n return message\n }\n return '(unknown error)'\n }\n\n async queuePrompt(number, batchCount = 1) {\n this.#queueItems.push({ number, batchCount })\n\n // Only have one action process the items so each one gets a unique seed correctly\n if (this.#processingQueue) {\n return\n }\n\n this.#processingQueue = true\n this.lastNodeErrors = null\n\n try {\n while (this.#queueItems.length) {\n ;({ number, batchCount } = this.#queueItems.pop())\n\n for (let i = 0; i < batchCount; i++) {\n const p = await this.graphToPrompt()\n\n try {\n const res = await api.queuePrompt(number, p)\n this.lastNodeErrors = res.node_errors\n if (this.lastNodeErrors.length > 0) {\n this.canvas.draw(true, true)\n } else {\n try {\n this.workflowManager.storePrompt({\n id: res.prompt_id,\n nodes: Object.keys(p.output)\n })\n } catch (error) {}\n }\n } catch (error) {\n const formattedError = this.#formatPromptError(error)\n this.ui.dialog.show(formattedError)\n if (error.response) {\n this.lastNodeErrors = error.response.node_errors\n this.canvas.draw(true, true)\n }\n break\n }\n\n for (const n of p.workflow.nodes) {\n const node = this.graph.getNodeById(n.id)\n if (node.widgets) {\n for (const widget of node.widgets) {\n // Allow widgets to run callbacks after a prompt has been queued\n // e.g. random seed after every gen\n // @ts-expect-error\n if (widget.afterQueued) {\n // @ts-expect-error\n widget.afterQueued()\n }\n }\n }\n }\n\n this.canvas.draw(true, true)\n await this.ui.queue.update()\n }\n }\n } finally {\n this.#processingQueue = false\n }\n api.dispatchEvent(\n new CustomEvent('promptQueued', { detail: { number, batchCount } })\n )\n return !this.lastNodeErrors\n }\n\n showErrorOnFileLoad(file) {\n this.ui.dialog.show(\n $el('div', [\n $el('p', { textContent: `Unable to find workflow in ${file.name}` })\n ]).outerHTML\n )\n }\n\n /**\n * Loads workflow data from the specified file\n * @param {File} file\n */\n async handleFile(file) {\n const removeExt = (f) => {\n if (!f) return f\n const p = f.lastIndexOf('.')\n if (p === -1) return f\n return f.substring(0, p)\n }\n const fileName = removeExt(file.name)\n if (file.type === 'image/png') {\n const pngInfo = await getPngMetadata(file)\n if (pngInfo?.workflow) {\n await this.loadGraphData(\n JSON.parse(pngInfo.workflow),\n true,\n true,\n fileName\n )\n } else if (pngInfo?.prompt) {\n this.loadApiJson(JSON.parse(pngInfo.prompt), fileName)\n } else if (pngInfo?.parameters) {\n this.changeWorkflow(() => {\n importA1111(this.graph, pngInfo.parameters)\n }, fileName)\n } else {\n this.showErrorOnFileLoad(file)\n }\n } else if (file.type === 'image/webp') {\n const pngInfo = await getWebpMetadata(file)\n // Support loading workflows from that webp custom node.\n const workflow = pngInfo?.workflow || pngInfo?.Workflow\n const prompt = pngInfo?.prompt || pngInfo?.Prompt\n\n if (workflow) {\n this.loadGraphData(JSON.parse(workflow), true, true, fileName)\n } else if (prompt) {\n this.loadApiJson(JSON.parse(prompt), fileName)\n } else {\n this.showErrorOnFileLoad(file)\n }\n } else if (file.type === 'audio/flac' || file.type === 'audio/x-flac') {\n const pngInfo = await getFlacMetadata(file)\n const workflow = pngInfo?.workflow || pngInfo?.Workflow\n const prompt = pngInfo?.prompt || pngInfo?.Prompt\n\n if (workflow) {\n this.loadGraphData(JSON.parse(workflow), true, true, fileName)\n } else if (prompt) {\n this.loadApiJson(JSON.parse(prompt), fileName)\n } else {\n this.showErrorOnFileLoad(file)\n }\n } else if (\n file.type === 'application/json' ||\n file.name?.endsWith('.json')\n ) {\n const reader = new FileReader()\n reader.onload = async () => {\n const readerResult = reader.result as string\n const jsonContent = JSON.parse(readerResult)\n if (jsonContent?.templates) {\n this.loadTemplateData(jsonContent)\n } else if (this.isApiJson(jsonContent)) {\n this.loadApiJson(jsonContent, fileName)\n } else {\n await this.loadGraphData(\n JSON.parse(readerResult),\n true,\n false,\n fileName\n )\n }\n }\n reader.readAsText(file)\n } else if (\n file.name?.endsWith('.latent') ||\n file.name?.endsWith('.safetensors')\n ) {\n const info = await getLatentMetadata(file)\n // TODO define schema to LatentMetadata\n // @ts-expect-error\n if (info.workflow) {\n await this.loadGraphData(\n // @ts-expect-error\n JSON.parse(info.workflow),\n true,\n true,\n fileName\n )\n // @ts-expect-error\n } else if (info.prompt) {\n // @ts-expect-error\n this.loadApiJson(JSON.parse(info.prompt))\n } else {\n this.showErrorOnFileLoad(file)\n }\n } else {\n this.showErrorOnFileLoad(file)\n }\n }\n\n isApiJson(data) {\n // @ts-expect-error\n return Object.values(data).every((v) => v.class_type)\n }\n\n loadApiJson(apiData, fileName: string) {\n const missingNodeTypes = Object.values(apiData).filter(\n // @ts-expect-error\n (n) => !LiteGraph.registered_node_types[n.class_type]\n )\n if (missingNodeTypes.length) {\n this.#showMissingNodesError(\n // @ts-expect-error\n missingNodeTypes.map((t) => t.class_type)\n )\n return\n }\n\n const ids = Object.keys(apiData)\n app.graph.clear()\n for (const id of ids) {\n const data = apiData[id]\n const node = LiteGraph.createNode(data.class_type)\n node.id = isNaN(+id) ? id : +id\n node.title = data._meta?.title ?? node.title\n app.graph.add(node)\n }\n\n this.changeWorkflow(() => {\n for (const id of ids) {\n const data = apiData[id]\n const node = app.graph.getNodeById(id)\n for (const input in data.inputs ?? {}) {\n const value = data.inputs[input]\n if (value instanceof Array) {\n const [fromId, fromSlot] = value\n const fromNode = app.graph.getNodeById(fromId)\n let toSlot = node.inputs?.findIndex((inp) => inp.name === input)\n if (toSlot == null || toSlot === -1) {\n try {\n // Target has no matching input, most likely a converted widget\n const widget = node.widgets?.find((w) => w.name === input)\n // @ts-expect-error\n if (widget && node.convertWidgetToInput?.(widget)) {\n toSlot = node.inputs?.length - 1\n }\n } catch (error) {}\n }\n if (toSlot != null || toSlot !== -1) {\n fromNode.connect(fromSlot, node, toSlot)\n }\n } else {\n const widget = node.widgets?.find((w) => w.name === input)\n if (widget) {\n widget.value = value\n widget.callback?.(value)\n }\n }\n }\n }\n app.graph.arrange()\n }, fileName)\n\n for (const id of ids) {\n const data = apiData[id]\n const node = app.graph.getNodeById(id)\n for (const input in data.inputs ?? {}) {\n const value = data.inputs[input]\n if (value instanceof Array) {\n const [fromId, fromSlot] = value\n const fromNode = app.graph.getNodeById(fromId)\n let toSlot = node.inputs?.findIndex((inp) => inp.name === input)\n if (toSlot == null || toSlot === -1) {\n try {\n // Target has no matching input, most likely a converted widget\n const widget = node.widgets?.find((w) => w.name === input)\n // @ts-expect-error\n if (widget && node.convertWidgetToInput?.(widget)) {\n toSlot = node.inputs?.length - 1\n }\n } catch (error) {}\n }\n if (toSlot != null || toSlot !== -1) {\n fromNode.connect(fromSlot, node, toSlot)\n }\n } else {\n const widget = node.widgets?.find((w) => w.name === input)\n if (widget) {\n widget.value = value\n widget.callback?.(value)\n }\n }\n }\n }\n\n app.graph.arrange()\n }\n\n /**\n * Registers a Comfy web extension with the app\n * @param {ComfyExtension} extension\n */\n registerExtension(extension: ComfyExtension) {\n if (this.vueAppReady) {\n useExtensionStore().registerExtension(extension)\n } else {\n // For jest testing.\n this.extensions.push(extension)\n }\n }\n\n /**\n * Refresh combo list on whole nodes\n */\n async refreshComboInNodes() {\n const requestToastMessage: ToastMessageOptions = {\n severity: 'info',\n summary: 'Update',\n detail: 'Update requested'\n }\n if (this.vueAppReady) {\n useToastStore().add(requestToastMessage)\n useModelStore().clearCache()\n }\n\n const defs = await api.getNodeDefs({\n validate: useSettingStore().get('Comfy.Validation.NodeDefs')\n })\n\n for (const nodeId in defs) {\n this.registerNodeDef(nodeId, defs[nodeId])\n }\n for (let nodeNum in this.graph.nodes) {\n const node = this.graph.nodes[nodeNum]\n const def = defs[node.type]\n // @ts-expect-error\n // Allow primitive nodes to handle refresh\n node.refreshComboInNode?.(defs)\n\n if (!def) continue\n\n for (const widgetNum in node.widgets) {\n const widget = node.widgets[widgetNum]\n if (\n widget.type == 'combo' &&\n def['input']['required'][widget.name] !== undefined\n ) {\n widget.options.values = def['input']['required'][widget.name][0]\n }\n }\n }\n\n await this.#invokeExtensionsAsync('refreshComboInNodes', defs)\n\n if (this.vueAppReady) {\n this.updateVueAppNodeDefs(defs)\n useToastStore().remove(requestToastMessage)\n useToastStore().add({\n severity: 'success',\n summary: 'Updated',\n detail: 'Node definitions updated',\n life: 1000\n })\n }\n }\n\n resetView() {\n app.canvas.ds.scale = 1\n app.canvas.ds.offset = [0, 0]\n app.graph.setDirtyCanvas(true, true)\n }\n\n /**\n * Clean current state\n */\n clean() {\n this.nodeOutputs = {}\n this.nodePreviewImages = {}\n this.lastNodeErrors = null\n this.lastExecutionError = null\n }\n\n addNodeOnGraph(\n nodeDef: ComfyNodeDef | ComfyNodeDefImpl,\n options: Record = {}\n ): LGraphNode {\n const node = LiteGraph.createNode(\n nodeDef.name,\n nodeDef.display_name,\n options\n )\n this.graph.add(node)\n return node\n }\n\n clientPosToCanvasPos(pos: Vector2): Vector2 {\n const rect = this.canvasContainer.getBoundingClientRect()\n const containerOffsets = [rect.left, rect.top]\n return _.zip(pos, this.canvas.ds.offset, containerOffsets).map(\n ([p, o1, o2]) => (p - o2) / this.canvas.ds.scale - o1\n ) as Vector2\n }\n\n canvasPosToClientPos(pos: Vector2): Vector2 {\n const rect = this.canvasContainer.getBoundingClientRect()\n const containerOffsets = [rect.left, rect.top]\n return _.zip(pos, this.canvas.ds.offset, containerOffsets).map(\n ([p, o1, o2]) => (p + o1) * this.canvas.ds.scale + o2\n ) as Vector2\n }\n\n getCanvasCenter(): Vector2 {\n const dpi = Math.max(window.devicePixelRatio ?? 1, 1)\n const [x, y, w, h] = app.canvas.ds.visible_area\n return [x + w / dpi / 2, y + h / dpi / 2]\n }\n\n public goToNode(nodeId: NodeId) {\n const graphNode = this.graph.getNodeById(nodeId)\n if (!graphNode) return\n this.canvas.centerOnNode(graphNode)\n }\n}\n\nexport const app = new ComfyApp()\n","import { LGraphNode, LGraphGroup, LGraphCanvas } from '@comfyorg/litegraph'\nimport { defineStore } from 'pinia'\nimport { shallowRef } from 'vue'\n\nexport const useTitleEditorStore = defineStore('titleEditor', () => {\n const titleEditorTarget = shallowRef(null)\n\n return {\n titleEditorTarget\n }\n})\n\nexport const useCanvasStore = defineStore('canvas', () => {\n /**\n * The LGraphCanvas instance.\n *\n * The root LGraphCanvas object is shallow reactive.\n */\n const canvas = shallowRef(null)\n\n return {\n canvas\n }\n})\n","import { useToastStore } from '@/stores/toastStore'\nimport { useI18n } from 'vue-i18n'\n\nexport function useErrorHandling() {\n const toast = useToastStore()\n const { t } = useI18n()\n\n const toastErrorHandler = (error: any) => {\n toast.add({\n severity: 'error',\n summary: t('error'),\n detail: error.message,\n life: 3000\n })\n }\n\n const wrapWithErrorHandling =\n (action: (...args: any[]) => any, errorHandler?: (error: any) => void) =>\n (...args: any[]) => {\n try {\n return action(...args)\n } catch (e) {\n ;(errorHandler ?? toastErrorHandler)(e)\n }\n }\n\n const wrapWithErrorHandlingAsync =\n (\n action: ((...args: any[]) => Promise) | ((...args: any[]) => any),\n errorHandler?: (error: any) => void\n ) =>\n async (...args: any[]) => {\n try {\n return await action(...args)\n } catch (e) {\n ;(errorHandler ?? toastErrorHandler)(e)\n }\n }\n\n return { wrapWithErrorHandling, wrapWithErrorHandlingAsync }\n}\n","import { defineStore } from 'pinia'\nimport { computed, ref } from 'vue'\nimport { ComfyWorkflow } from '@/scripts/workflows'\nimport { buildTree } from '@/utils/treeUtil'\nimport { api } from '@/scripts/api'\n\nexport const useWorkflowStore = defineStore('workflow', () => {\n const activeWorkflow = ref(null)\n const workflowLookup = ref>({})\n const workflows = computed(() => Object.values(workflowLookup.value))\n const persistedWorkflows = computed(() =>\n workflows.value.filter((workflow) => workflow.isPersisted)\n )\n const openWorkflows = computed(() =>\n workflows.value.filter((workflow) => workflow.isOpen)\n )\n const bookmarkedWorkflows = computed(() =>\n workflows.value.filter((workflow) => workflow.isBookmarked)\n )\n const modifiedWorkflows = computed(() =>\n workflows.value.filter((workflow) => workflow.unsaved)\n )\n\n const buildWorkflowTree = (workflows: ComfyWorkflow[]) => {\n return buildTree(workflows, (workflow: ComfyWorkflow) =>\n workflow.key.split('/')\n )\n }\n const workflowsTree = computed(() =>\n buildWorkflowTree(persistedWorkflows.value)\n )\n // Bookmarked workflows tree is flat.\n const bookmarkedWorkflowsTree = computed(() =>\n buildTree(bookmarkedWorkflows.value, (workflow: ComfyWorkflow) => [\n workflow.path ?? 'temporary_workflow'\n ])\n )\n // Open workflows tree is flat.\n const openWorkflowsTree = computed(() =>\n buildTree(openWorkflows.value, (workflow: ComfyWorkflow) => [workflow.key])\n )\n\n const loadOpenedWorkflowIndexShift = async (shift: number) => {\n const index = openWorkflows.value.indexOf(\n activeWorkflow.value as ComfyWorkflow\n )\n if (index !== -1) {\n const length = openWorkflows.value.length\n const nextIndex = (index + shift + length) % length\n const nextWorkflow = openWorkflows.value[nextIndex]\n if (nextWorkflow) {\n await nextWorkflow.load()\n }\n }\n }\n\n const loadNextOpenedWorkflow = async () => {\n await loadOpenedWorkflowIndexShift(1)\n }\n\n const loadPreviousOpenedWorkflow = async () => {\n await loadOpenedWorkflowIndexShift(-1)\n }\n\n return {\n activeWorkflow,\n workflows,\n openWorkflows,\n bookmarkedWorkflows,\n modifiedWorkflows,\n workflowLookup,\n workflowsTree,\n bookmarkedWorkflowsTree,\n openWorkflowsTree,\n buildWorkflowTree,\n loadNextOpenedWorkflow,\n loadPreviousOpenedWorkflow\n }\n})\n\nexport const useWorkflowBookmarkStore = defineStore('workflowBookmark', () => {\n const bookmarks = ref>(new Set())\n\n const isBookmarked = (path: string) => bookmarks.value.has(path)\n\n const loadBookmarks = async () => {\n const resp = await api.getUserData('workflows/.index.json')\n if (resp.status === 200) {\n const info = await resp.json()\n bookmarks.value = new Set(info?.favorites ?? [])\n }\n }\n\n const saveBookmarks = async () => {\n await api.storeUserData('workflows/.index.json', {\n favorites: Array.from(bookmarks.value)\n })\n }\n\n const setBookmarked = (path: string, value: boolean) => {\n if (value) {\n bookmarks.value.add(path)\n } else {\n bookmarks.value.delete(path)\n }\n saveBookmarks()\n }\n\n const toggleBookmarked = (path: string) => {\n setBookmarked(path, !bookmarks.value.has(path))\n }\n\n return {\n isBookmarked,\n loadBookmarks,\n saveBookmarks,\n setBookmarked,\n toggleBookmarked\n }\n})\n","import { app } from '@/scripts/app'\nimport { api } from '@/scripts/api'\nimport { defineStore } from 'pinia'\nimport { computed, ref } from 'vue'\nimport { globalTracker } from '@/scripts/changeTracker'\nimport { useSettingStore } from '@/stores/settingStore'\nimport { useToastStore } from '@/stores/toastStore'\nimport {\n showSettingsDialog,\n showTemplateWorkflowsDialog\n} from '@/services/dialogService'\nimport { useQueueSettingsStore, useQueueStore } from './queueStore'\nimport { LiteGraph } from '@comfyorg/litegraph'\nimport { ComfyExtension } from '@/types/comfy'\nimport { LGraphGroup } from '@comfyorg/litegraph'\nimport { useTitleEditorStore } from './graphStore'\nimport { useErrorHandling } from '@/hooks/errorHooks'\nimport { useWorkflowStore } from './workflowStore'\nimport { type KeybindingImpl, useKeybindingStore } from './keybindingStore'\nimport { useBottomPanelStore } from './workspace/bottomPanelStore'\nimport { LGraphNode } from '@comfyorg/litegraph'\n\nexport interface ComfyCommand {\n id: string\n function: () => void | Promise\n\n label?: string | (() => string)\n icon?: string | (() => string)\n tooltip?: string | (() => string)\n /** Menubar item label, if different from command label */\n menubarLabel?: string | (() => string)\n versionAdded?: string\n}\n\nexport class ComfyCommandImpl implements ComfyCommand {\n id: string\n function: () => void | Promise\n _label?: string | (() => string)\n _icon?: string | (() => string)\n _tooltip?: string | (() => string)\n _menubarLabel?: string | (() => string)\n versionAdded?: string\n\n constructor(command: ComfyCommand) {\n this.id = command.id\n this.function = command.function\n this._label = command.label\n this._icon = command.icon\n this._tooltip = command.tooltip\n this._menubarLabel = command.menubarLabel ?? command.label\n this.versionAdded = command.versionAdded\n }\n\n get label() {\n return typeof this._label === 'function' ? this._label() : this._label\n }\n\n get icon() {\n return typeof this._icon === 'function' ? this._icon() : this._icon\n }\n\n get tooltip() {\n return typeof this._tooltip === 'function' ? this._tooltip() : this._tooltip\n }\n\n get menubarLabel() {\n return typeof this._menubarLabel === 'function'\n ? this._menubarLabel()\n : this._menubarLabel\n }\n\n get keybinding(): KeybindingImpl | null {\n return useKeybindingStore().getKeybindingByCommandId(this.id)\n }\n}\n\nconst getTracker = () =>\n app.workflowManager.activeWorkflow?.changeTracker ?? globalTracker\n\nconst getSelectedNodes = (): LGraphNode[] => {\n const selectedNodes = app.canvas.selected_nodes\n const result: LGraphNode[] = []\n if (selectedNodes) {\n for (const i in selectedNodes) {\n const node = selectedNodes[i]\n result.push(node)\n }\n }\n return result\n}\n\nconst toggleSelectedNodesMode = (mode: number) => {\n getSelectedNodes().forEach((node) => {\n if (node.mode === mode) {\n node.mode = 0 // always\n } else {\n node.mode = mode\n }\n })\n}\n\nexport const useCommandStore = defineStore('command', () => {\n const settingStore = useSettingStore()\n\n const commandsById = ref>({})\n const commands = computed(() => Object.values(commandsById.value))\n\n const registerCommand = (command: ComfyCommand) => {\n if (commandsById.value[command.id]) {\n console.warn(`Command ${command.id} already registered`)\n }\n commandsById.value[command.id] = new ComfyCommandImpl(command)\n }\n\n const commandDefinitions: ComfyCommand[] = [\n {\n id: 'Comfy.NewBlankWorkflow',\n icon: 'pi pi-plus',\n label: 'New Blank Workflow',\n menubarLabel: 'New',\n function: () => {\n app.workflowManager.setWorkflow(null)\n app.clean()\n app.graph.clear()\n app.workflowManager.activeWorkflow?.track()\n }\n },\n {\n id: 'Comfy.OpenWorkflow',\n icon: 'pi pi-folder-open',\n label: 'Open Workflow',\n menubarLabel: 'Open',\n function: () => {\n app.ui.loadFile()\n }\n },\n {\n id: 'Comfy.LoadDefaultWorkflow',\n icon: 'pi pi-code',\n label: 'Load Default Workflow',\n function: async () => {\n await app.loadGraphData()\n }\n },\n {\n id: 'Comfy.SaveWorkflow',\n icon: 'pi pi-save',\n label: 'Save Workflow',\n menubarLabel: 'Save',\n function: () => {\n app.workflowManager.activeWorkflow?.save()\n }\n },\n {\n id: 'Comfy.SaveWorkflowAs',\n icon: 'pi pi-save',\n label: 'Save Workflow As',\n menubarLabel: 'Save As',\n function: () => {\n app.workflowManager.activeWorkflow?.save(true)\n }\n },\n {\n id: 'Comfy.ExportWorkflow',\n icon: 'pi pi-download',\n label: 'Export Workflow',\n menubarLabel: 'Export',\n function: () => {\n app.menu.exportWorkflow('workflow', 'workflow')\n }\n },\n {\n id: 'Comfy.ExportWorkflowAPI',\n icon: 'pi pi-download',\n label: 'Export Workflow (API Format)',\n menubarLabel: 'Export (API)',\n function: () => {\n app.menu.exportWorkflow('workflow_api', 'output')\n }\n },\n {\n id: 'Comfy.Undo',\n icon: 'pi pi-undo',\n label: 'Undo',\n function: async () => {\n await getTracker().undo()\n }\n },\n {\n id: 'Comfy.Redo',\n icon: 'pi pi-refresh',\n label: 'Redo',\n function: async () => {\n await getTracker().redo()\n }\n },\n {\n id: 'Comfy.ClearWorkflow',\n icon: 'pi pi-trash',\n label: 'Clear Workflow',\n function: () => {\n if (\n !settingStore.get('Comfy.ComfirmClear') ||\n confirm('Clear workflow?')\n ) {\n app.clean()\n app.graph.clear()\n api.dispatchEvent(new CustomEvent('graphCleared'))\n }\n }\n },\n {\n id: 'Comfy.Canvas.ResetView',\n icon: 'pi pi-expand',\n label: 'Reset View',\n function: () => {\n app.resetView()\n }\n },\n {\n id: 'Comfy.OpenClipspace',\n icon: 'pi pi-clipboard',\n label: 'Clipspace',\n function: () => {\n app.openClipspace()\n }\n },\n {\n id: 'Comfy.RefreshNodeDefinitions',\n icon: 'pi pi-refresh',\n label: 'Refresh Node Definitions',\n function: async () => {\n await app.refreshComboInNodes()\n }\n },\n {\n id: 'Comfy.Interrupt',\n icon: 'pi pi-stop',\n label: 'Interrupt',\n function: async () => {\n await api.interrupt()\n useToastStore().add({\n severity: 'info',\n summary: 'Interrupted',\n detail: 'Execution has been interrupted',\n life: 1000\n })\n }\n },\n {\n id: 'Comfy.ClearPendingTasks',\n icon: 'pi pi-stop',\n label: 'Clear Pending Tasks',\n function: async () => {\n await useQueueStore().clear(['queue'])\n useToastStore().add({\n severity: 'info',\n summary: 'Confirmed',\n detail: 'Pending tasks deleted',\n life: 3000\n })\n }\n },\n {\n id: 'Comfy.BrowseTemplates',\n icon: 'pi pi-folder-open',\n label: 'Browse Templates',\n function: showTemplateWorkflowsDialog\n },\n {\n id: 'Comfy.Canvas.ZoomIn',\n icon: 'pi pi-plus',\n label: 'Zoom In',\n function: () => {\n const ds = app.canvas.ds\n ds.changeScale(\n ds.scale * 1.1,\n ds.element ? [ds.element.width / 2, ds.element.height / 2] : undefined\n )\n app.canvas.setDirty(true, true)\n }\n },\n {\n id: 'Comfy.Canvas.ZoomOut',\n icon: 'pi pi-minus',\n label: 'Zoom Out',\n function: () => {\n const ds = app.canvas.ds\n ds.changeScale(\n ds.scale / 1.1,\n ds.element ? [ds.element.width / 2, ds.element.height / 2] : undefined\n )\n app.canvas.setDirty(true, true)\n }\n },\n {\n id: 'Comfy.Canvas.ToggleLock',\n icon: 'pi pi-lock',\n label: 'Toggle Lock',\n function: () => {\n app.canvas['read_only'] = !app.canvas['read_only']\n }\n },\n {\n id: 'Comfy.Canvas.ToggleLinkVisibility',\n icon: 'pi pi-eye',\n label: 'Toggle Link Visibility',\n versionAdded: '1.3.6',\n\n function: (() => {\n let lastLinksRenderMode = LiteGraph.SPLINE_LINK\n\n return () => {\n const currentMode = settingStore.get('Comfy.LinkRenderMode')\n\n if (currentMode === LiteGraph.HIDDEN_LINK) {\n // If links are hidden, restore the last positive value or default to spline mode\n settingStore.set('Comfy.LinkRenderMode', lastLinksRenderMode)\n } else {\n // If links are visible, store the current mode and hide links\n lastLinksRenderMode = currentMode\n settingStore.set('Comfy.LinkRenderMode', LiteGraph.HIDDEN_LINK)\n }\n }\n })()\n },\n {\n id: 'Comfy.QueuePrompt',\n icon: 'pi pi-play',\n label: 'Queue Prompt',\n versionAdded: '1.3.7',\n function: () => {\n const batchCount = useQueueSettingsStore().batchCount\n app.queuePrompt(0, batchCount)\n }\n },\n {\n id: 'Comfy.QueuePromptFront',\n icon: 'pi pi-play',\n label: 'Queue Prompt (Front)',\n versionAdded: '1.3.7',\n function: () => {\n const batchCount = useQueueSettingsStore().batchCount\n app.queuePrompt(-1, batchCount)\n }\n },\n {\n id: 'Comfy.ShowSettingsDialog',\n icon: 'pi pi-cog',\n label: 'Settings',\n versionAdded: '1.3.7',\n function: () => {\n showSettingsDialog()\n }\n },\n {\n id: 'Comfy.Graph.GroupSelectedNodes',\n icon: 'pi pi-sitemap',\n label: 'Group Selected Nodes',\n versionAdded: '1.3.7',\n function: () => {\n if (\n !app.canvas.selected_nodes ||\n Object.keys(app.canvas.selected_nodes).length === 0\n ) {\n useToastStore().add({\n severity: 'error',\n summary: 'No nodes selected',\n detail: 'Please select nodes to group',\n life: 3000\n })\n return\n }\n const group = new LGraphGroup()\n const padding = useSettingStore().get(\n 'Comfy.GroupSelectedNodes.Padding'\n )\n group.addNodes(Object.values(app.canvas.selected_nodes), padding)\n app.canvas.graph.add(group)\n useTitleEditorStore().titleEditorTarget = group\n }\n },\n {\n id: 'Workspace.NextOpenedWorkflow',\n icon: 'pi pi-step-forward',\n label: 'Next Opened Workflow',\n versionAdded: '1.3.9',\n function: () => {\n useWorkflowStore().loadNextOpenedWorkflow()\n }\n },\n {\n id: 'Workspace.PreviousOpenedWorkflow',\n icon: 'pi pi-step-backward',\n label: 'Previous Opened Workflow',\n versionAdded: '1.3.9',\n function: () => {\n useWorkflowStore().loadPreviousOpenedWorkflow()\n }\n },\n {\n id: 'Comfy.Canvas.ToggleSelectedNodes.Mute',\n icon: 'pi pi-volume-off',\n label: 'Mute/Unmute Selected Nodes',\n versionAdded: '1.3.11',\n function: () => {\n toggleSelectedNodesMode(2) // muted\n }\n },\n {\n id: 'Comfy.Canvas.ToggleSelectedNodes.Bypass',\n icon: 'pi pi-shield',\n label: 'Bypass/Unbypass Selected Nodes',\n versionAdded: '1.3.11',\n function: () => {\n toggleSelectedNodesMode(4) // bypassed\n }\n },\n {\n id: 'Comfy.Canvas.ToggleSelectedNodes.Pin',\n icon: 'pi pi-pin',\n label: 'Pin/Unpin Selected Nodes',\n versionAdded: '1.3.11',\n function: () => {\n getSelectedNodes().forEach((node) => {\n node.pin(!node.pinned)\n })\n }\n },\n {\n id: 'Comfy.Canvas.ToggleSelectedNodes.Collapse',\n icon: 'pi pi-minus',\n label: 'Collapse/Expand Selected Nodes',\n versionAdded: '1.3.11',\n function: () => {\n getSelectedNodes().forEach((node) => {\n node.collapse()\n })\n }\n },\n {\n id: 'Comfy.ToggleTheme',\n icon: 'pi pi-moon',\n label: 'Toggle Theme',\n versionAdded: '1.3.12',\n function: (() => {\n let previousDarkTheme: string = 'dark'\n\n // Official light theme is the only light theme supported now.\n const isDarkMode = (themeId: string) => themeId !== 'light'\n return () => {\n const currentTheme = settingStore.get('Comfy.ColorPalette')\n if (isDarkMode(currentTheme)) {\n previousDarkTheme = currentTheme\n settingStore.set('Comfy.ColorPalette', 'light')\n } else {\n settingStore.set('Comfy.ColorPalette', previousDarkTheme)\n }\n }\n })()\n },\n {\n id: 'Workspace.ToggleBottomPanel',\n icon: 'pi pi-list',\n label: 'Toggle Bottom Panel',\n versionAdded: '1.3.22',\n function: () => {\n useBottomPanelStore().toggleBottomPanel()\n }\n }\n ]\n\n commandDefinitions.forEach(registerCommand)\n const getCommand = (command: string) => {\n return commandsById.value[command]\n }\n\n const { wrapWithErrorHandlingAsync } = useErrorHandling()\n const execute = async (\n commandId: string,\n errorHandler?: (error: any) => void\n ) => {\n const command = getCommand(commandId)\n if (command) {\n await wrapWithErrorHandlingAsync(command.function, errorHandler)()\n } else {\n throw new Error(`Command ${commandId} not found`)\n }\n }\n\n const isRegistered = (command: string) => {\n return !!commandsById.value[command]\n }\n\n const loadExtensionCommands = (extension: ComfyExtension) => {\n if (extension.commands) {\n for (const command of extension.commands) {\n registerCommand(command)\n }\n }\n }\n\n return {\n commands,\n execute,\n getCommand,\n registerCommand,\n isRegistered,\n loadExtensionCommands\n }\n})\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-tree {\\n background: \".concat(dt('tree.background'), \";\\n color: \").concat(dt('tree.color'), \";\\n padding: \").concat(dt('tree.padding'), \";\\n}\\n\\n.p-tree-root-children,\\n.p-tree-node-children {\\n display: flex;\\n list-style-type: none;\\n flex-direction: column;\\n margin: 0;\\n gap: \").concat(dt('tree.gap'), \";\\n}\\n\\n.p-tree-root-children {\\n padding: \").concat(dt('tree.gap'), \" 0 0 0;\\n}\\n\\n.p-tree-node-children {\\n padding: \").concat(dt('tree.gap'), \" 0 0 \").concat(dt('tree.indent'), \";\\n}\\n\\n.p-tree-node {\\n padding: 0;\\n outline: 0 none;\\n}\\n\\n.p-tree-node-content {\\n border-radius: \").concat(dt('tree.node.border.radius'), \";\\n padding: \").concat(dt('tree.node.padding'), \";\\n display: flex;\\n align-items: center;\\n outline-color: transparent;\\n color: \").concat(dt('tree.node.color'), \";\\n gap: \").concat(dt('tree.node.gap'), \";\\n transition: background \").concat(dt('tree.transition.duration'), \", color \").concat(dt('tree.transition.duration'), \", outline-color \").concat(dt('tree.transition.duration'), \", box-shadow \").concat(dt('tree.transition.duration'), \";\\n}\\n\\n.p-tree-node:focus-visible > .p-tree-node-content {\\n box-shadow: \").concat(dt('tree.node.focus.ring.shadow'), \";\\n outline: \").concat(dt('tree.node.focus.ring.width'), \" \").concat(dt('tree.node.focus.ring.style'), \" \").concat(dt('tree.node.focus.ring.color'), \";\\n outline-offset: \").concat(dt('tree.node.focus.ring.offset'), \";\\n}\\n\\n.p-tree-node-content.p-tree-node-selectable:not(.p-tree-node-selected):hover {\\n background: \").concat(dt('tree.node.hover.background'), \";\\n color: \").concat(dt('tree.node.hover.color'), \";\\n}\\n\\n.p-tree-node-content.p-tree-node-selectable:not(.p-tree-node-selected):hover .p-tree-node-icon {\\n color: \").concat(dt('tree.node.icon.hover.color'), \";\\n}\\n\\n.p-tree-node-content.p-tree-node-selected {\\n background: \").concat(dt('tree.node.selected.background'), \";\\n color: \").concat(dt('tree.node.selected.color'), \";\\n}\\n\\n.p-tree-node-content.p-tree-node-selected .p-tree-node-toggle-button {\\n color: inherit;\\n}\\n\\n.p-tree-node-toggle-button {\\n cursor: pointer;\\n user-select: none;\\n display: inline-flex;\\n align-items: center;\\n justify-content: center;\\n overflow: hidden;\\n position: relative;\\n flex-shrink: 0;\\n width: \").concat(dt('tree.node.toggle.button.size'), \";\\n height: \").concat(dt('tree.node.toggle.button.size'), \";\\n color: \").concat(dt('tree.node.toggle.button.color'), \";\\n border: 0 none;\\n background: transparent;\\n border-radius: \").concat(dt('tree.node.toggle.button.border.radius'), \";\\n transition: background \").concat(dt('tree.transition.duration'), \", color \").concat(dt('tree.transition.duration'), \", border-color \").concat(dt('tree.transition.duration'), \", outline-color \").concat(dt('tree.transition.duration'), \", box-shadow \").concat(dt('tree.transition.duration'), \";\\n outline-color: transparent;\\n padding: 0;\\n}\\n\\n.p-tree-node-toggle-button:enabled:hover {\\n background: \").concat(dt('tree.node.toggle.button.hover.background'), \";\\n color: \").concat(dt('tree.node.toggle.button.hover.color'), \";\\n}\\n\\n.p-tree-node-content.p-tree-node-selected .p-tree-node-toggle-button:hover {\\n background: \").concat(dt('tree.node.toggle.button.selected.hover.background'), \";\\n color: \").concat(dt('tree.node.toggle.button.selected.hover.color'), \";\\n}\\n\\n.p-tree-root {\\n overflow: auto;\\n}\\n\\n.p-tree-node-selectable {\\n cursor: pointer;\\n user-select: none;\\n}\\n\\n.p-tree-node-leaf > .p-tree-node-content .p-tree-node-toggle-button {\\n visibility: hidden;\\n}\\n\\n.p-tree-node-icon {\\n color: \").concat(dt('tree.node.icon.color'), \";\\n transition: color \").concat(dt('tree.transition.duration'), \";\\n}\\n\\n.p-tree-node-content.p-tree-node-selected .p-tree-node-icon {\\n color: \").concat(dt('tree.node.icon.selected.color'), \";\\n}\\n\\n.p-tree-filter-input {\\n width: 100%;\\n}\\n\\n.p-tree-loading {\\n position: relative;\\n height: 100%;\\n}\\n\\n.p-tree-loading-icon {\\n font-size: \").concat(dt('tree.loading.icon.size'), \";\\n width: \").concat(dt('tree.loading.icon.size'), \";\\n height: \").concat(dt('tree.loading.icon.size'), \";\\n}\\n\\n.p-tree .p-tree-mask {\\n position: absolute;\\n z-index: 1;\\n display: flex;\\n align-items: center;\\n justify-content: center;\\n}\\n\\n.p-tree-flex-scrollable {\\n display: flex;\\n flex: 1;\\n height: 100%;\\n flex-direction: column;\\n}\\n\\n.p-tree-flex-scrollable .p-tree-root {\\n flex: 1;\\n}\\n\");\n};\nvar classes = {\n root: function root(_ref2) {\n var props = _ref2.props;\n return ['p-tree p-component', {\n 'p-tree-selectable': props.selectionMode != null,\n 'p-tree-loading': props.loading,\n 'p-tree-flex-scrollable': props.scrollHeight === 'flex'\n }];\n },\n mask: 'p-tree-mask p-overlay-mask',\n loadingIcon: 'p-tree-loading-icon',\n pcFilterInput: 'p-tree-filter-input',\n wrapper: 'p-tree-root',\n //TODO: discuss\n rootChildren: 'p-tree-root-children',\n node: function node(_ref3) {\n var instance = _ref3.instance;\n return ['p-tree-node', {\n 'p-tree-node-leaf': instance.leaf\n }];\n },\n nodeContent: function nodeContent(_ref4) {\n var instance = _ref4.instance;\n return ['p-tree-node-content', instance.node.styleClass, {\n 'p-tree-node-selectable': instance.selectable,\n 'p-tree-node-selected': instance.checkboxMode && instance.$parentInstance.highlightOnSelect ? instance.checked : instance.selected\n }];\n },\n nodeToggleButton: 'p-tree-node-toggle-button',\n nodeToggleIcon: 'p-tree-node-toggle-icon',\n nodeCheckbox: 'p-tree-node-checkbox',\n nodeIcon: 'p-tree-node-icon',\n nodeLabel: 'p-tree-node-label',\n nodeChildren: 'p-tree-node-children'\n};\nvar TreeStyle = BaseStyle.extend({\n name: 'tree',\n theme: theme,\n classes: classes\n});\n\nexport { TreeStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import { resolveFieldData } from '@primeuix/utils/object';\nimport SearchIcon from '@primevue/icons/search';\nimport SpinnerIcon from '@primevue/icons/spinner';\nimport IconField from 'primevue/iconfield';\nimport InputIcon from 'primevue/inputicon';\nimport InputText from 'primevue/inputtext';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport TreeStyle from 'primevue/tree/style';\nimport { getAttribute, findSingle, find } from '@primeuix/utils/dom';\nimport CheckIcon from '@primevue/icons/check';\nimport ChevronDownIcon from '@primevue/icons/chevrondown';\nimport ChevronRightIcon from '@primevue/icons/chevronright';\nimport MinusIcon from '@primevue/icons/minus';\nimport Checkbox from 'primevue/checkbox';\nimport Ripple from 'primevue/ripple';\nimport { resolveComponent, resolveDirective, openBlock, createElementBlock, mergeProps, createElementVNode, withDirectives, Fragment, createBlock, resolveDynamicComponent, normalizeClass, withCtx, createCommentVNode, withModifiers, createTextVNode, toDisplayString, renderList, renderSlot, createVNode } from 'vue';\n\nvar script$2 = {\n name: 'BaseTree',\n \"extends\": BaseComponent,\n props: {\n value: {\n type: null,\n \"default\": null\n },\n expandedKeys: {\n type: null,\n \"default\": null\n },\n selectionKeys: {\n type: null,\n \"default\": null\n },\n selectionMode: {\n type: String,\n \"default\": null\n },\n metaKeySelection: {\n type: Boolean,\n \"default\": false\n },\n loading: {\n type: Boolean,\n \"default\": false\n },\n loadingIcon: {\n type: String,\n \"default\": undefined\n },\n loadingMode: {\n type: String,\n \"default\": 'mask'\n },\n filter: {\n type: Boolean,\n \"default\": false\n },\n filterBy: {\n type: String,\n \"default\": 'label'\n },\n filterMode: {\n type: String,\n \"default\": 'lenient'\n },\n filterPlaceholder: {\n type: String,\n \"default\": null\n },\n filterLocale: {\n type: String,\n \"default\": undefined\n },\n highlightOnSelect: {\n type: Boolean,\n \"default\": false\n },\n scrollHeight: {\n type: String,\n \"default\": null\n },\n level: {\n type: Number,\n \"default\": 0\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n ariaLabel: {\n type: String,\n \"default\": null\n }\n },\n style: TreeStyle,\n provide: function provide() {\n return {\n $pcTree: this,\n $parentInstance: this\n };\n }\n};\n\nfunction _typeof$1(o) { \"@babel/helpers - typeof\"; return _typeof$1 = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof$1(o); }\nfunction _createForOfIteratorHelper$1(r, e) { var t = \"undefined\" != typeof Symbol && r[Symbol.iterator] || r[\"@@iterator\"]; if (!t) { if (Array.isArray(r) || (t = _unsupportedIterableToArray$1(r)) || e) { t && (r = t); var _n = 0, F = function F() {}; return { s: F, n: function n() { return _n >= r.length ? { done: !0 } : { done: !1, value: r[_n++] }; }, e: function e(r) { throw r; }, f: F }; } throw new TypeError(\"Invalid attempt to iterate non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); } var o, a = !0, u = !1; return { s: function s() { t = t.call(r); }, n: function n() { var r = t.next(); return a = r.done, r; }, e: function e(r) { u = !0, o = r; }, f: function f() { try { a || null == t[\"return\"] || t[\"return\"](); } finally { if (u) throw o; } } }; }\nfunction ownKeys$1(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread$1(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys$1(Object(t), !0).forEach(function (r) { _defineProperty$1(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys$1(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty$1(e, r, t) { return (r = _toPropertyKey$1(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey$1(t) { var i = _toPrimitive$1(t, \"string\"); return \"symbol\" == _typeof$1(i) ? i : i + \"\"; }\nfunction _toPrimitive$1(t, r) { if (\"object\" != _typeof$1(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof$1(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nfunction _toConsumableArray$1(r) { return _arrayWithoutHoles$1(r) || _iterableToArray$1(r) || _unsupportedIterableToArray$1(r) || _nonIterableSpread$1(); }\nfunction _nonIterableSpread$1() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray$1(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray$1(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray$1(r, a) : void 0; } }\nfunction _iterableToArray$1(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles$1(r) { if (Array.isArray(r)) return _arrayLikeToArray$1(r); }\nfunction _arrayLikeToArray$1(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nvar script$1 = {\n name: 'TreeNode',\n hostName: 'Tree',\n \"extends\": BaseComponent,\n emits: ['node-toggle', 'node-click', 'checkbox-change'],\n props: {\n node: {\n type: null,\n \"default\": null\n },\n expandedKeys: {\n type: null,\n \"default\": null\n },\n loadingMode: {\n type: String,\n \"default\": 'mask'\n },\n selectionKeys: {\n type: null,\n \"default\": null\n },\n selectionMode: {\n type: String,\n \"default\": null\n },\n templates: {\n type: null,\n \"default\": null\n },\n level: {\n type: Number,\n \"default\": null\n },\n index: null\n },\n nodeTouched: false,\n toggleClicked: false,\n mounted: function mounted() {\n this.setAllNodesTabIndexes();\n },\n methods: {\n toggle: function toggle() {\n this.$emit('node-toggle', this.node);\n this.toggleClicked = true;\n },\n label: function label(node) {\n return typeof node.label === 'function' ? node.label() : node.label;\n },\n onChildNodeToggle: function onChildNodeToggle(node) {\n this.$emit('node-toggle', node);\n },\n getPTOptions: function getPTOptions(key) {\n return this.ptm(key, {\n context: {\n index: this.index,\n expanded: this.expanded,\n selected: this.selected,\n checked: this.checked,\n leaf: this.leaf\n }\n });\n },\n onClick: function onClick(event) {\n if (this.toggleClicked || getAttribute(event.target, '[data-pc-section=\"nodetogglebutton\"]') || getAttribute(event.target.parentElement, '[data-pc-section=\"nodetogglebutton\"]')) {\n this.toggleClicked = false;\n return;\n }\n if (this.isCheckboxSelectionMode()) {\n if (this.node.selectable != false) {\n this.toggleCheckbox();\n }\n } else {\n this.$emit('node-click', {\n originalEvent: event,\n nodeTouched: this.nodeTouched,\n node: this.node\n });\n }\n this.nodeTouched = false;\n },\n onChildNodeClick: function onChildNodeClick(event) {\n this.$emit('node-click', event);\n },\n onTouchEnd: function onTouchEnd() {\n this.nodeTouched = true;\n },\n onKeyDown: function onKeyDown(event) {\n if (!this.isSameNode(event)) return;\n switch (event.code) {\n case 'Tab':\n this.onTabKey(event);\n break;\n case 'ArrowDown':\n this.onArrowDown(event);\n break;\n case 'ArrowUp':\n this.onArrowUp(event);\n break;\n case 'ArrowRight':\n this.onArrowRight(event);\n break;\n case 'ArrowLeft':\n this.onArrowLeft(event);\n break;\n case 'Enter':\n case 'NumpadEnter':\n case 'Space':\n this.onEnterKey(event);\n break;\n }\n },\n onArrowDown: function onArrowDown(event) {\n var nodeElement = event.target.getAttribute('data-pc-section') === 'nodetogglebutton' ? event.target.closest('[role=\"treeitem\"]') : event.target;\n var listElement = nodeElement.children[1];\n if (listElement) {\n this.focusRowChange(nodeElement, listElement.children[0]);\n } else {\n if (nodeElement.nextElementSibling) {\n this.focusRowChange(nodeElement, nodeElement.nextElementSibling);\n } else {\n var nextSiblingAncestor = this.findNextSiblingOfAncestor(nodeElement);\n if (nextSiblingAncestor) {\n this.focusRowChange(nodeElement, nextSiblingAncestor);\n }\n }\n }\n event.preventDefault();\n },\n onArrowUp: function onArrowUp(event) {\n var nodeElement = event.target;\n if (nodeElement.previousElementSibling) {\n this.focusRowChange(nodeElement, nodeElement.previousElementSibling, this.findLastVisibleDescendant(nodeElement.previousElementSibling));\n } else {\n var parentNodeElement = this.getParentNodeElement(nodeElement);\n if (parentNodeElement) {\n this.focusRowChange(nodeElement, parentNodeElement);\n }\n }\n event.preventDefault();\n },\n onArrowRight: function onArrowRight(event) {\n var _this = this;\n if (this.leaf || this.expanded) return;\n event.currentTarget.tabIndex = -1;\n this.$emit('node-toggle', this.node);\n this.$nextTick(function () {\n _this.onArrowDown(event);\n });\n },\n onArrowLeft: function onArrowLeft(event) {\n var togglerElement = findSingle(event.currentTarget, '[data-pc-section=\"nodetogglebutton\"]');\n if (this.level === 0 && !this.expanded) {\n return false;\n }\n if (this.expanded && !this.leaf) {\n togglerElement.click();\n return false;\n }\n var target = this.findBeforeClickableNode(event.currentTarget);\n if (target) {\n this.focusRowChange(event.currentTarget, target);\n }\n },\n onEnterKey: function onEnterKey(event) {\n this.setTabIndexForSelectionMode(event, this.nodeTouched);\n this.onClick(event);\n event.preventDefault();\n },\n onTabKey: function onTabKey() {\n this.setAllNodesTabIndexes();\n },\n setAllNodesTabIndexes: function setAllNodesTabIndexes() {\n var nodes = find(this.$refs.currentNode.closest('[data-pc-section=\"rootchildren\"]'), '[role=\"treeitem\"]');\n var hasSelectedNode = _toConsumableArray$1(nodes).some(function (node) {\n return node.getAttribute('aria-selected') === 'true' || node.getAttribute('aria-checked') === 'true';\n });\n _toConsumableArray$1(nodes).forEach(function (node) {\n node.tabIndex = -1;\n });\n if (hasSelectedNode) {\n var selectedNodes = _toConsumableArray$1(nodes).filter(function (node) {\n return node.getAttribute('aria-selected') === 'true' || node.getAttribute('aria-checked') === 'true';\n });\n selectedNodes[0].tabIndex = 0;\n return;\n }\n _toConsumableArray$1(nodes)[0].tabIndex = 0;\n },\n setTabIndexForSelectionMode: function setTabIndexForSelectionMode(event, nodeTouched) {\n if (this.selectionMode !== null) {\n var elements = _toConsumableArray$1(find(this.$refs.currentNode.parentElement, '[role=\"treeitem\"]'));\n event.currentTarget.tabIndex = nodeTouched === false ? -1 : 0;\n if (elements.every(function (element) {\n return element.tabIndex === -1;\n })) {\n elements[0].tabIndex = 0;\n }\n }\n },\n focusRowChange: function focusRowChange(firstFocusableRow, currentFocusedRow, lastVisibleDescendant) {\n firstFocusableRow.tabIndex = '-1';\n currentFocusedRow.tabIndex = '0';\n this.focusNode(lastVisibleDescendant || currentFocusedRow);\n },\n findBeforeClickableNode: function findBeforeClickableNode(node) {\n var parentListElement = node.closest('ul').closest('li');\n if (parentListElement) {\n var prevNodeButton = findSingle(parentListElement, 'button');\n if (prevNodeButton && prevNodeButton.style.visibility !== 'hidden') {\n return parentListElement;\n }\n return this.findBeforeClickableNode(node.previousElementSibling);\n }\n return null;\n },\n toggleCheckbox: function toggleCheckbox() {\n var _selectionKeys = this.selectionKeys ? _objectSpread$1({}, this.selectionKeys) : {};\n var _check = !this.checked;\n this.propagateDown(this.node, _check, _selectionKeys);\n this.$emit('checkbox-change', {\n node: this.node,\n check: _check,\n selectionKeys: _selectionKeys\n });\n },\n propagateDown: function propagateDown(node, check, selectionKeys) {\n if (check && node.selectable != false) selectionKeys[node.key] = {\n checked: true,\n partialChecked: false\n };else delete selectionKeys[node.key];\n if (node.children && node.children.length) {\n var _iterator = _createForOfIteratorHelper$1(node.children),\n _step;\n try {\n for (_iterator.s(); !(_step = _iterator.n()).done;) {\n var child = _step.value;\n this.propagateDown(child, check, selectionKeys);\n }\n } catch (err) {\n _iterator.e(err);\n } finally {\n _iterator.f();\n }\n }\n },\n propagateUp: function propagateUp(event) {\n var check = event.check;\n var _selectionKeys = _objectSpread$1({}, event.selectionKeys);\n var checkedChildCount = 0;\n var childPartialSelected = false;\n var _iterator2 = _createForOfIteratorHelper$1(this.node.children),\n _step2;\n try {\n for (_iterator2.s(); !(_step2 = _iterator2.n()).done;) {\n var child = _step2.value;\n if (_selectionKeys[child.key] && _selectionKeys[child.key].checked) checkedChildCount++;else if (_selectionKeys[child.key] && _selectionKeys[child.key].partialChecked) childPartialSelected = true;\n }\n } catch (err) {\n _iterator2.e(err);\n } finally {\n _iterator2.f();\n }\n if (check && checkedChildCount === this.node.children.length) {\n _selectionKeys[this.node.key] = {\n checked: true,\n partialChecked: false\n };\n } else {\n if (!check) {\n delete _selectionKeys[this.node.key];\n }\n if (childPartialSelected || checkedChildCount > 0 && checkedChildCount !== this.node.children.length) _selectionKeys[this.node.key] = {\n checked: false,\n partialChecked: true\n };else delete _selectionKeys[this.node.key];\n }\n this.$emit('checkbox-change', {\n node: event.node,\n check: event.check,\n selectionKeys: _selectionKeys\n });\n },\n onChildCheckboxChange: function onChildCheckboxChange(event) {\n this.$emit('checkbox-change', event);\n },\n findNextSiblingOfAncestor: function findNextSiblingOfAncestor(nodeElement) {\n var parentNodeElement = this.getParentNodeElement(nodeElement);\n if (parentNodeElement) {\n if (parentNodeElement.nextElementSibling) return parentNodeElement.nextElementSibling;else return this.findNextSiblingOfAncestor(parentNodeElement);\n } else {\n return null;\n }\n },\n findLastVisibleDescendant: function findLastVisibleDescendant(nodeElement) {\n var childrenListElement = nodeElement.children[1];\n if (childrenListElement) {\n var lastChildElement = childrenListElement.children[childrenListElement.children.length - 1];\n return this.findLastVisibleDescendant(lastChildElement);\n } else {\n return nodeElement;\n }\n },\n getParentNodeElement: function getParentNodeElement(nodeElement) {\n var parentNodeElement = nodeElement.parentElement.parentElement;\n return getAttribute(parentNodeElement, 'role') === 'treeitem' ? parentNodeElement : null;\n },\n focusNode: function focusNode(element) {\n element.focus();\n },\n isCheckboxSelectionMode: function isCheckboxSelectionMode() {\n return this.selectionMode === 'checkbox';\n },\n isSameNode: function isSameNode(event) {\n return event.currentTarget && (event.currentTarget.isSameNode(event.target) || event.currentTarget.isSameNode(event.target.closest('[role=\"treeitem\"]')));\n }\n },\n computed: {\n hasChildren: function hasChildren() {\n return this.node.children && this.node.children.length > 0;\n },\n expanded: function expanded() {\n return this.expandedKeys && this.expandedKeys[this.node.key] === true;\n },\n leaf: function leaf() {\n return this.node.leaf === false ? false : !(this.node.children && this.node.children.length);\n },\n selectable: function selectable() {\n return this.node.selectable === false ? false : this.selectionMode != null;\n },\n selected: function selected() {\n return this.selectionMode && this.selectionKeys ? this.selectionKeys[this.node.key] === true : false;\n },\n checkboxMode: function checkboxMode() {\n return this.selectionMode === 'checkbox' && this.node.selectable !== false;\n },\n checked: function checked() {\n return this.selectionKeys ? this.selectionKeys[this.node.key] && this.selectionKeys[this.node.key].checked : false;\n },\n partialChecked: function partialChecked() {\n return this.selectionKeys ? this.selectionKeys[this.node.key] && this.selectionKeys[this.node.key].partialChecked : false;\n },\n ariaChecked: function ariaChecked() {\n return this.selectionMode === 'single' || this.selectionMode === 'multiple' ? this.selected : undefined;\n },\n ariaSelected: function ariaSelected() {\n return this.checkboxMode ? this.checked : undefined;\n }\n },\n components: {\n Checkbox: Checkbox,\n ChevronDownIcon: ChevronDownIcon,\n ChevronRightIcon: ChevronRightIcon,\n CheckIcon: CheckIcon,\n MinusIcon: MinusIcon,\n SpinnerIcon: SpinnerIcon\n },\n directives: {\n ripple: Ripple\n }\n};\n\nvar _hoisted_1$1 = [\"aria-label\", \"aria-selected\", \"aria-expanded\", \"aria-setsize\", \"aria-posinset\", \"aria-level\", \"aria-checked\", \"tabindex\"];\nvar _hoisted_2 = [\"data-p-selected\", \"data-p-selectable\"];\nfunction render$1(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_SpinnerIcon = resolveComponent(\"SpinnerIcon\");\n var _component_Checkbox = resolveComponent(\"Checkbox\");\n var _component_TreeNode = resolveComponent(\"TreeNode\", true);\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createElementBlock(\"li\", mergeProps({\n ref: \"currentNode\",\n \"class\": _ctx.cx('node'),\n role: \"treeitem\",\n \"aria-label\": $options.label($props.node),\n \"aria-selected\": $options.ariaSelected,\n \"aria-expanded\": $options.expanded,\n \"aria-setsize\": $props.node.children ? $props.node.children.length : 0,\n \"aria-posinset\": $props.index + 1,\n \"aria-level\": $props.level,\n \"aria-checked\": $options.ariaChecked,\n tabindex: $props.index === 0 ? 0 : -1,\n onKeydown: _cache[4] || (_cache[4] = function () {\n return $options.onKeyDown && $options.onKeyDown.apply($options, arguments);\n })\n }, $props.level === 1 ? $options.getPTOptions('node') : _ctx.ptm('nodeChildren')), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('nodeContent'),\n onClick: _cache[2] || (_cache[2] = function () {\n return $options.onClick && $options.onClick.apply($options, arguments);\n }),\n onTouchend: _cache[3] || (_cache[3] = function () {\n return $options.onTouchEnd && $options.onTouchEnd.apply($options, arguments);\n }),\n style: $props.node.style\n }, $options.getPTOptions('nodeContent'), {\n \"data-p-selected\": $options.checkboxMode ? $options.checked : $options.selected,\n \"data-p-selectable\": $options.selectable\n }), [withDirectives((openBlock(), createElementBlock(\"button\", mergeProps({\n type: \"button\",\n \"class\": _ctx.cx('nodeToggleButton'),\n onClick: _cache[0] || (_cache[0] = function () {\n return $options.toggle && $options.toggle.apply($options, arguments);\n }),\n tabindex: \"-1\"\n }, $options.getPTOptions('nodeToggleButton')), [$props.node.loading && $props.loadingMode === 'icon' ? (openBlock(), createElementBlock(Fragment, {\n key: 0\n }, [$props.templates['nodetoggleicon'] || $props.templates['nodetogglericon'] ? (openBlock(), createBlock(resolveDynamicComponent($props.templates['nodetoggleicon'] || $props.templates['nodetogglericon']), {\n key: 0,\n \"class\": normalizeClass(_ctx.cx('nodeToggleIcon'))\n }, null, 8, [\"class\"])) : (openBlock(), createBlock(_component_SpinnerIcon, mergeProps({\n key: 1,\n spin: \"\",\n \"class\": _ctx.cx('nodetogglericon')\n }, _ctx.ptm('nodeToggleIcon')), null, 16, [\"class\"]))], 64)) : (openBlock(), createElementBlock(Fragment, {\n key: 1\n }, [$props.templates['nodetoggleicon'] || $props.templates['togglericon'] ? (openBlock(), createBlock(resolveDynamicComponent($props.templates['nodetoggleicon'] || $props.templates['togglericon']), {\n key: 0,\n node: $props.node,\n expanded: $options.expanded,\n \"class\": normalizeClass(_ctx.cx('nodeToggleIcon'))\n }, null, 8, [\"node\", \"expanded\", \"class\"])) : $options.expanded ? (openBlock(), createBlock(resolveDynamicComponent($props.node.expandedIcon ? 'span' : 'ChevronDownIcon'), mergeProps({\n key: 1,\n \"class\": _ctx.cx('nodeToggleIcon')\n }, $options.getPTOptions('nodeToggleIcon')), null, 16, [\"class\"])) : (openBlock(), createBlock(resolveDynamicComponent($props.node.collapsedIcon ? 'span' : 'ChevronRightIcon'), mergeProps({\n key: 2,\n \"class\": _ctx.cx('nodeToggleIcon')\n }, $options.getPTOptions('nodeToggleIcon')), null, 16, [\"class\"]))], 64))], 16)), [[_directive_ripple]]), $options.checkboxMode ? (openBlock(), createBlock(_component_Checkbox, {\n key: 0,\n modelValue: $options.checked,\n binary: true,\n indeterminate: $options.partialChecked,\n \"class\": normalizeClass(_ctx.cx('nodeCheckbox')),\n tabindex: -1,\n unstyled: _ctx.unstyled,\n pt: $options.getPTOptions('nodeCheckbox'),\n \"data-p-partialchecked\": $options.partialChecked\n }, {\n icon: withCtx(function (slotProps) {\n return [$props.templates['checkboxicon'] ? (openBlock(), createBlock(resolveDynamicComponent($props.templates['checkboxicon']), {\n key: 0,\n checked: slotProps.checked,\n partialChecked: $options.partialChecked,\n \"class\": normalizeClass(slotProps[\"class\"])\n }, null, 8, [\"checked\", \"partialChecked\", \"class\"])) : createCommentVNode(\"\", true)];\n }),\n _: 1\n }, 8, [\"modelValue\", \"indeterminate\", \"class\", \"unstyled\", \"pt\", \"data-p-partialchecked\"])) : createCommentVNode(\"\", true), $props.templates['nodeicon'] ? (openBlock(), createBlock(resolveDynamicComponent($props.templates['nodeicon']), mergeProps({\n key: 1,\n node: $props.node,\n \"class\": [_ctx.cx('nodeIcon')]\n }, $options.getPTOptions('nodeIcon')), null, 16, [\"node\", \"class\"])) : (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 2,\n \"class\": [_ctx.cx('nodeIcon'), $props.node.icon]\n }, $options.getPTOptions('nodeIcon')), null, 16)), createElementVNode(\"span\", mergeProps({\n \"class\": _ctx.cx('nodeLabel')\n }, $options.getPTOptions('nodeLabel'), {\n onKeydown: _cache[1] || (_cache[1] = withModifiers(function () {}, [\"stop\"]))\n }), [$props.templates[$props.node.type] || $props.templates['default'] ? (openBlock(), createBlock(resolveDynamicComponent($props.templates[$props.node.type] || $props.templates['default']), {\n key: 0,\n node: $props.node,\n selected: $options.checkboxMode ? $options.checked : $options.selected\n }, null, 8, [\"node\", \"selected\"])) : (openBlock(), createElementBlock(Fragment, {\n key: 1\n }, [createTextVNode(toDisplayString($options.label($props.node)), 1)], 64))], 16)], 16, _hoisted_2), $options.hasChildren && $options.expanded ? (openBlock(), createElementBlock(\"ul\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('nodeChildren'),\n role: \"group\"\n }, _ctx.ptm('nodeChildren')), [(openBlock(true), createElementBlock(Fragment, null, renderList($props.node.children, function (childNode) {\n return openBlock(), createBlock(_component_TreeNode, {\n key: childNode.key,\n node: childNode,\n templates: $props.templates,\n level: $props.level + 1,\n loadingMode: $props.loadingMode,\n expandedKeys: $props.expandedKeys,\n onNodeToggle: $options.onChildNodeToggle,\n onNodeClick: $options.onChildNodeClick,\n selectionMode: $props.selectionMode,\n selectionKeys: $props.selectionKeys,\n onCheckboxChange: $options.propagateUp,\n unstyled: _ctx.unstyled,\n pt: _ctx.pt\n }, null, 8, [\"node\", \"templates\", \"level\", \"loadingMode\", \"expandedKeys\", \"onNodeToggle\", \"onNodeClick\", \"selectionMode\", \"selectionKeys\", \"onCheckboxChange\", \"unstyled\", \"pt\"]);\n }), 128))], 16)) : createCommentVNode(\"\", true)], 16, _hoisted_1$1);\n}\n\nscript$1.render = render$1;\n\nfunction _typeof(o) { \"@babel/helpers - typeof\"; return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) { return typeof o; } : function (o) { return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o; }, _typeof(o); }\nfunction _createForOfIteratorHelper(r, e) { var t = \"undefined\" != typeof Symbol && r[Symbol.iterator] || r[\"@@iterator\"]; if (!t) { if (Array.isArray(r) || (t = _unsupportedIterableToArray(r)) || e) { t && (r = t); var _n = 0, F = function F() {}; return { s: F, n: function n() { return _n >= r.length ? { done: !0 } : { done: !1, value: r[_n++] }; }, e: function e(r) { throw r; }, f: F }; } throw new TypeError(\"Invalid attempt to iterate non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); } var o, a = !0, u = !1; return { s: function s() { t = t.call(r); }, n: function n() { var r = t.next(); return a = r.done, r; }, e: function e(r) { u = !0, o = r; }, f: function f() { try { a || null == t[\"return\"] || t[\"return\"](); } finally { if (u) throw o; } } }; }\nfunction _toConsumableArray(r) { return _arrayWithoutHoles(r) || _iterableToArray(r) || _unsupportedIterableToArray(r) || _nonIterableSpread(); }\nfunction _nonIterableSpread() { throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _iterableToArray(r) { if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r); }\nfunction _arrayWithoutHoles(r) { if (Array.isArray(r)) return _arrayLikeToArray(r); }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _defineProperty(e, r, t) { return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: !0, configurable: !0, writable: !0 }) : e[r] = t, e; }\nfunction _toPropertyKey(t) { var i = _toPrimitive(t, \"string\"); return \"symbol\" == _typeof(i) ? i : i + \"\"; }\nfunction _toPrimitive(t, r) { if (\"object\" != _typeof(t) || !t) return t; var e = t[Symbol.toPrimitive]; if (void 0 !== e) { var i = e.call(t, r || \"default\"); if (\"object\" != _typeof(i)) return i; throw new TypeError(\"@@toPrimitive must return a primitive value.\"); } return (\"string\" === r ? String : Number)(t); }\nvar script = {\n name: 'Tree',\n \"extends\": script$2,\n inheritAttrs: false,\n emits: ['node-expand', 'node-collapse', 'update:expandedKeys', 'update:selectionKeys', 'node-select', 'node-unselect', 'filter'],\n data: function data() {\n return {\n d_expandedKeys: this.expandedKeys || {},\n filterValue: null\n };\n },\n watch: {\n expandedKeys: function expandedKeys(newValue) {\n this.d_expandedKeys = newValue;\n }\n },\n methods: {\n onNodeToggle: function onNodeToggle(node) {\n var key = node.key;\n if (this.d_expandedKeys[key]) {\n delete this.d_expandedKeys[key];\n this.$emit('node-collapse', node);\n } else {\n this.d_expandedKeys[key] = true;\n this.$emit('node-expand', node);\n }\n this.d_expandedKeys = _objectSpread({}, this.d_expandedKeys);\n this.$emit('update:expandedKeys', this.d_expandedKeys);\n },\n onNodeClick: function onNodeClick(event) {\n if (this.selectionMode != null && event.node.selectable !== false) {\n var metaSelection = event.nodeTouched ? false : this.metaKeySelection;\n var _selectionKeys = metaSelection ? this.handleSelectionWithMetaKey(event) : this.handleSelectionWithoutMetaKey(event);\n this.$emit('update:selectionKeys', _selectionKeys);\n }\n },\n onCheckboxChange: function onCheckboxChange(event) {\n this.$emit('update:selectionKeys', event.selectionKeys);\n if (event.check) this.$emit('node-select', event.node);else this.$emit('node-unselect', event.node);\n },\n handleSelectionWithMetaKey: function handleSelectionWithMetaKey(event) {\n var originalEvent = event.originalEvent;\n var node = event.node;\n var metaKey = originalEvent.metaKey || originalEvent.ctrlKey;\n var selected = this.isNodeSelected(node);\n var _selectionKeys;\n if (selected && metaKey) {\n if (this.isSingleSelectionMode()) {\n _selectionKeys = {};\n } else {\n _selectionKeys = _objectSpread({}, this.selectionKeys);\n delete _selectionKeys[node.key];\n }\n this.$emit('node-unselect', node);\n } else {\n if (this.isSingleSelectionMode()) {\n _selectionKeys = {};\n } else if (this.isMultipleSelectionMode()) {\n _selectionKeys = !metaKey ? {} : this.selectionKeys ? _objectSpread({}, this.selectionKeys) : {};\n }\n _selectionKeys[node.key] = true;\n this.$emit('node-select', node);\n }\n return _selectionKeys;\n },\n handleSelectionWithoutMetaKey: function handleSelectionWithoutMetaKey(event) {\n var node = event.node;\n var selected = this.isNodeSelected(node);\n var _selectionKeys;\n if (this.isSingleSelectionMode()) {\n if (selected) {\n _selectionKeys = {};\n this.$emit('node-unselect', node);\n } else {\n _selectionKeys = {};\n _selectionKeys[node.key] = true;\n this.$emit('node-select', node);\n }\n } else {\n if (selected) {\n _selectionKeys = _objectSpread({}, this.selectionKeys);\n delete _selectionKeys[node.key];\n this.$emit('node-unselect', node);\n } else {\n _selectionKeys = this.selectionKeys ? _objectSpread({}, this.selectionKeys) : {};\n _selectionKeys[node.key] = true;\n this.$emit('node-select', node);\n }\n }\n return _selectionKeys;\n },\n isSingleSelectionMode: function isSingleSelectionMode() {\n return this.selectionMode === 'single';\n },\n isMultipleSelectionMode: function isMultipleSelectionMode() {\n return this.selectionMode === 'multiple';\n },\n isNodeSelected: function isNodeSelected(node) {\n return this.selectionMode && this.selectionKeys ? this.selectionKeys[node.key] === true : false;\n },\n isChecked: function isChecked(node) {\n return this.selectionKeys ? this.selectionKeys[node.key] && this.selectionKeys[node.key].checked : false;\n },\n isNodeLeaf: function isNodeLeaf(node) {\n return node.leaf === false ? false : !(node.children && node.children.length);\n },\n onFilterKeydown: function onFilterKeydown(event) {\n if (event.code === 'Enter' || event.code === 'NumpadEnter') {\n event.preventDefault();\n }\n this.$emit('filter', {\n originalEvent: event,\n value: event.target.value\n });\n },\n findFilteredNodes: function findFilteredNodes(node, paramsWithoutNode) {\n if (node) {\n var matched = false;\n if (node.children) {\n var childNodes = _toConsumableArray(node.children);\n node.children = [];\n var _iterator = _createForOfIteratorHelper(childNodes),\n _step;\n try {\n for (_iterator.s(); !(_step = _iterator.n()).done;) {\n var childNode = _step.value;\n var copyChildNode = _objectSpread({}, childNode);\n if (this.isFilterMatched(copyChildNode, paramsWithoutNode)) {\n matched = true;\n node.children.push(copyChildNode);\n }\n }\n } catch (err) {\n _iterator.e(err);\n } finally {\n _iterator.f();\n }\n }\n if (matched) {\n return true;\n }\n }\n },\n isFilterMatched: function isFilterMatched(node, _ref) {\n var searchFields = _ref.searchFields,\n filterText = _ref.filterText,\n strict = _ref.strict;\n var matched = false;\n var _iterator2 = _createForOfIteratorHelper(searchFields),\n _step2;\n try {\n for (_iterator2.s(); !(_step2 = _iterator2.n()).done;) {\n var field = _step2.value;\n var fieldValue = String(resolveFieldData(node, field)).toLocaleLowerCase(this.filterLocale);\n if (fieldValue.indexOf(filterText) > -1) {\n matched = true;\n }\n }\n } catch (err) {\n _iterator2.e(err);\n } finally {\n _iterator2.f();\n }\n if (!matched || strict && !this.isNodeLeaf(node)) {\n matched = this.findFilteredNodes(node, {\n searchFields: searchFields,\n filterText: filterText,\n strict: strict\n }) || matched;\n }\n return matched;\n }\n },\n computed: {\n filteredValue: function filteredValue() {\n var filteredNodes = [];\n var searchFields = this.filterBy.split(',');\n var filterText = this.filterValue.trim().toLocaleLowerCase(this.filterLocale);\n var strict = this.filterMode === 'strict';\n var _iterator3 = _createForOfIteratorHelper(this.value),\n _step3;\n try {\n for (_iterator3.s(); !(_step3 = _iterator3.n()).done;) {\n var node = _step3.value;\n var _node = _objectSpread({}, node);\n var paramsWithoutNode = {\n searchFields: searchFields,\n filterText: filterText,\n strict: strict\n };\n if (strict && (this.findFilteredNodes(_node, paramsWithoutNode) || this.isFilterMatched(_node, paramsWithoutNode)) || !strict && (this.isFilterMatched(_node, paramsWithoutNode) || this.findFilteredNodes(_node, paramsWithoutNode))) {\n filteredNodes.push(_node);\n }\n }\n } catch (err) {\n _iterator3.e(err);\n } finally {\n _iterator3.f();\n }\n return filteredNodes;\n },\n valueToRender: function valueToRender() {\n if (this.filterValue && this.filterValue.trim().length > 0) return this.filteredValue;else return this.value;\n }\n },\n components: {\n TreeNode: script$1,\n InputText: InputText,\n InputIcon: InputIcon,\n IconField: IconField,\n SearchIcon: SearchIcon,\n SpinnerIcon: SpinnerIcon\n }\n};\n\nvar _hoisted_1 = [\"aria-labelledby\", \"aria-label\"];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_SpinnerIcon = resolveComponent(\"SpinnerIcon\");\n var _component_InputText = resolveComponent(\"InputText\");\n var _component_SearchIcon = resolveComponent(\"SearchIcon\");\n var _component_InputIcon = resolveComponent(\"InputIcon\");\n var _component_IconField = resolveComponent(\"IconField\");\n var _component_TreeNode = resolveComponent(\"TreeNode\");\n return openBlock(), createElementBlock(\"div\", mergeProps({\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [_ctx.loading && _ctx.loadingMode === 'mask' ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n \"class\": _ctx.cx('mask')\n }, _ctx.ptm('mask')), [renderSlot(_ctx.$slots, \"loadingicon\", {\n \"class\": normalizeClass(_ctx.cx('loadingIcon'))\n }, function () {\n return [_ctx.loadingIcon ? (openBlock(), createElementBlock(\"i\", mergeProps({\n key: 0,\n \"class\": [_ctx.cx('loadingIcon'), 'pi-spin', _ctx.loadingIcon]\n }, _ctx.ptm('loadingIcon')), null, 16)) : (openBlock(), createBlock(_component_SpinnerIcon, mergeProps({\n key: 1,\n spin: \"\",\n \"class\": _ctx.cx('loadingIcon')\n }, _ctx.ptm('loadingIcon')), null, 16, [\"class\"]))];\n })], 16)) : createCommentVNode(\"\", true), _ctx.filter ? (openBlock(), createBlock(_component_IconField, {\n key: 1,\n unstyled: _ctx.unstyled,\n pt: _ctx.ptm('pcFilterContainer')\n }, {\n \"default\": withCtx(function () {\n return [createVNode(_component_InputText, {\n modelValue: $data.filterValue,\n \"onUpdate:modelValue\": _cache[0] || (_cache[0] = function ($event) {\n return $data.filterValue = $event;\n }),\n autocomplete: \"off\",\n \"class\": normalizeClass(_ctx.cx('pcFilter')),\n placeholder: _ctx.filterPlaceholder,\n unstyled: _ctx.unstyled,\n onKeydown: $options.onFilterKeydown,\n pt: _ctx.ptm('pcFilter')\n }, null, 8, [\"modelValue\", \"class\", \"placeholder\", \"unstyled\", \"onKeydown\", \"pt\"]), createVNode(_component_InputIcon, {\n unstyled: _ctx.unstyled,\n pt: _ctx.ptm('pcFilterIconContainer')\n }, {\n \"default\": withCtx(function () {\n return [renderSlot(_ctx.$slots, _ctx.$slots.filtericon ? 'filtericon' : 'searchicon', {\n \"class\": normalizeClass(_ctx.cx('filterIcon'))\n }, function () {\n return [createVNode(_component_SearchIcon, mergeProps({\n \"class\": _ctx.cx('filterIcon')\n }, _ctx.ptm('filterIcon')), null, 16, [\"class\"])];\n })];\n }),\n _: 3\n }, 8, [\"unstyled\", \"pt\"])];\n }),\n _: 3\n }, 8, [\"unstyled\", \"pt\"])) : createCommentVNode(\"\", true), createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('wrapper'),\n style: {\n maxHeight: _ctx.scrollHeight\n }\n }, _ctx.ptm('wrapper')), [createElementVNode(\"ul\", mergeProps({\n \"class\": _ctx.cx('rootChildren'),\n role: \"tree\",\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel\n }, _ctx.ptm('rootChildren')), [(openBlock(true), createElementBlock(Fragment, null, renderList($options.valueToRender, function (node, index) {\n return openBlock(), createBlock(_component_TreeNode, {\n key: node.key,\n node: node,\n templates: _ctx.$slots,\n level: _ctx.level + 1,\n index: index,\n expandedKeys: $data.d_expandedKeys,\n onNodeToggle: $options.onNodeToggle,\n onNodeClick: $options.onNodeClick,\n selectionMode: _ctx.selectionMode,\n selectionKeys: _ctx.selectionKeys,\n onCheckboxChange: $options.onCheckboxChange,\n loadingMode: _ctx.loadingMode,\n unstyled: _ctx.unstyled,\n pt: _ctx.pt\n }, null, 8, [\"node\", \"templates\", \"level\", \"index\", \"expandedKeys\", \"onNodeToggle\", \"onNodeClick\", \"selectionMode\", \"selectionKeys\", \"onCheckboxChange\", \"loadingMode\", \"unstyled\", \"pt\"]);\n }), 128))], 16, _hoisted_1)], 16)], 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseStyle from '@primevue/core/base/style';\n\nvar theme = function theme(_ref) {\n var dt = _ref.dt;\n return \"\\n.p-contextmenu {\\n background: \".concat(dt('contextmenu.background'), \";\\n color: \").concat(dt('contextmenu.color'), \";\\n border: 1px solid \").concat(dt('contextmenu.border.color'), \";\\n border-radius: \").concat(dt('contextmenu.border.radius'), \";\\n box-shadow: \").concat(dt('contextmenu.shadow'), \";\\n min-width: 12.5rem;\\n}\\n\\n.p-contextmenu-root-list,\\n.p-contextmenu-submenu {\\n margin: 0;\\n padding: \").concat(dt('contextmenu.list.padding'), \";\\n list-style: none;\\n outline: 0 none;\\n display: flex;\\n flex-direction: column;\\n gap: \").concat(dt('contextmenu.list.gap'), \";\\n}\\n\\n.p-contextmenu-submenu {\\n position: absolute;\\n display: flex;\\n flex-direction: column;\\n min-width: 100%;\\n z-index: 1;\\n background: \").concat(dt('contextmenu.background'), \";\\n color: \").concat(dt('contextmenu.color'), \";\\n border: 1px solid \").concat(dt('contextmenu.border.color'), \";\\n border-radius: \").concat(dt('contextmenu.border.radius'), \";\\n box-shadow: \").concat(dt('contextmenu.shadow'), \";\\n}\\n\\n.p-contextmenu-item {\\n position: relative;\\n}\\n\\n.p-contextmenu-item-content {\\n transition: background \").concat(dt('contextmenu.transition.duration'), \", color \").concat(dt('contextmenu.transition.duration'), \";\\n border-radius: \").concat(dt('contextmenu.item.border.radius'), \";\\n color: \").concat(dt('contextmenu.item.color'), \";\\n}\\n\\n.p-contextmenu-item-link {\\n cursor: pointer;\\n display: flex;\\n align-items: center;\\n text-decoration: none;\\n overflow: hidden;\\n position: relative;\\n color: inherit;\\n padding: \").concat(dt('contextmenu.item.padding'), \";\\n gap: \").concat(dt('contextmenu.item.gap'), \";\\n user-select: none;\\n}\\n\\n.p-contextmenu-item-label {\\n line-height: 1;\\n}\\n\\n.p-contextmenu-item-icon {\\n color: \").concat(dt('contextmenu.item.icon.color'), \";\\n}\\n\\n.p-contextmenu-submenu-icon {\\n color: \").concat(dt('contextmenu.submenu.icon.color'), \";\\n margin-left: auto;\\n font-size: \").concat(dt('contextmenu.submenu.icon.size'), \";\\n width: \").concat(dt('contextmenu.submenu.icon.size'), \";\\n height: \").concat(dt('contextmenu.submenu.icon.size'), \";\\n}\\n\\n.p-contextmenu-item.p-focus > .p-contextmenu-item-content {\\n color: \").concat(dt('contextmenu.item.focus.color'), \";\\n background: \").concat(dt('contextmenu.item.focus.background'), \";\\n}\\n\\n.p-contextmenu-item.p-focus > .p-contextmenu-item-content .p-contextmenu-item-icon {\\n color: \").concat(dt('contextmenu.item.icon.focus.color'), \";\\n}\\n\\n.p-contextmenu-item.p-focus > .p-contextmenu-item-content .p-contextmenu-submenu-icon {\\n color: \").concat(dt('contextmenu.submenu.icon.focus.color'), \";\\n}\\n\\n.p-contextmenu-item:not(.p-disabled) > .p-contextmenu-item-content:hover {\\n color: \").concat(dt('contextmenu.item.focus.color'), \";\\n background: \").concat(dt('contextmenu.item.focus.background'), \";\\n}\\n\\n.p-contextmenu-item:not(.p-disabled) > .p-contextmenu-item-content:hover .p-contextmenu-item-icon {\\n color: \").concat(dt('contextmenu.item.icon.focus.color'), \";\\n}\\n\\n.p-contextmenu-item:not(.p-disabled) > .p-contextmenu-item-content:hover .p-contextmenu-submenu-icon {\\n color: \").concat(dt('contextmenu.submenu.icon.focus.color'), \";\\n}\\n\\n.p-contextmenu-item-active > .p-contextmenu-item-content {\\n color: \").concat(dt('contextmenu.item.active.color'), \";\\n background: \").concat(dt('contextmenu.item.active.background'), \";\\n}\\n\\n.p-contextmenu-item-active > .p-contextmenu-item-content .p-contextmenu-item-icon {\\n color: \").concat(dt('contextmenu.item.icon.active.color'), \";\\n}\\n\\n.p-contextmenu-item-active > .p-contextmenu-item-content .p-contextmenu-submenu-icon {\\n color: \").concat(dt('contextmenu.submenu.icon.active.color'), \";\\n}\\n\\n.p-contextmenu-separator {\\n border-top: 1px solid \").concat(dt('contextmenu.separator.border.color'), \";\\n}\\n\\n.p-contextmenu-enter-from,\\n.p-contextmenu-leave-active {\\n opacity: 0;\\n}\\n\\n.p-contextmenu-enter-active {\\n transition: opacity 250ms;\\n}\\n\");\n};\nvar classes = {\n root: 'p-contextmenu p-component',\n rootList: 'p-contextmenu-root-list',\n item: function item(_ref2) {\n var instance = _ref2.instance,\n processedItem = _ref2.processedItem;\n return ['p-contextmenu-item', {\n 'p-contextmenu-item-active': instance.isItemActive(processedItem),\n 'p-focus': instance.isItemFocused(processedItem),\n 'p-disabled': instance.isItemDisabled(processedItem)\n }];\n },\n itemContent: 'p-contextmenu-item-content',\n itemLink: 'p-contextmenu-item-link',\n itemIcon: 'p-contextmenu-item-icon',\n itemLabel: 'p-contextmenu-item-label',\n submenuIcon: 'p-contextmenu-submenu-icon',\n submenu: 'p-contextmenu-submenu',\n separator: 'p-contextmenu-separator'\n};\nvar ContextMenuStyle = BaseStyle.extend({\n name: 'contextmenu',\n theme: theme,\n classes: classes\n});\n\nexport { ContextMenuStyle as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n name: 'AngleRightIcon',\n \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n d: \"M5.25 11.1728C5.14929 11.1694 5.05033 11.1455 4.9592 11.1025C4.86806 11.0595 4.78666 10.9984 4.72 10.9228C4.57955 10.7822 4.50066 10.5916 4.50066 10.3928C4.50066 10.1941 4.57955 10.0035 4.72 9.86283L7.72 6.86283L4.72 3.86283C4.66067 3.71882 4.64765 3.55991 4.68275 3.40816C4.71785 3.25642 4.79932 3.11936 4.91585 3.01602C5.03238 2.91268 5.17819 2.84819 5.33305 2.83149C5.4879 2.81479 5.64411 2.84671 5.78 2.92283L9.28 6.42283C9.42045 6.56346 9.49934 6.75408 9.49934 6.95283C9.49934 7.15158 9.42045 7.34221 9.28 7.48283L5.78 10.9228C5.71333 10.9984 5.63193 11.0595 5.5408 11.1025C5.44966 11.1455 5.35071 11.1694 5.25 11.1728Z\",\n fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n return openBlock(), createElementBlock(\"svg\", mergeProps({\n width: \"14\",\n height: \"14\",\n viewBox: \"0 0 14 14\",\n fill: \"none\",\n xmlns: \"http://www.w3.org/2000/svg\"\n }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import { UniqueComponentId } from '@primevue/core/utils';\nimport { nestedPosition, focus, findSingle, addStyle, getHiddenElementOuterWidth, getHiddenElementOuterHeight, getViewport, isTouchDevice } from '@primeuix/utils/dom';\nimport { resolve, isNotEmpty, isPrintableCharacter, isEmpty, findLastIndex } from '@primeuix/utils/object';\nimport { ZIndex } from '@primeuix/utils/zindex';\nimport Portal from 'primevue/portal';\nimport BaseComponent from '@primevue/core/basecomponent';\nimport ContextMenuStyle from 'primevue/contextmenu/style';\nimport AngleRightIcon from '@primevue/icons/angleright';\nimport Ripple from 'primevue/ripple';\nimport { mergeProps, resolveComponent, resolveDirective, openBlock, createBlock, Transition, withCtx, createElementBlock, Fragment, renderList, createElementVNode, withDirectives, resolveDynamicComponent, normalizeClass, createCommentVNode, toDisplayString, createVNode } from 'vue';\n\nvar script$2 = {\n name: 'BaseContextMenu',\n \"extends\": BaseComponent,\n props: {\n model: {\n type: Array,\n \"default\": null\n },\n appendTo: {\n type: [String, Object],\n \"default\": 'body'\n },\n autoZIndex: {\n type: Boolean,\n \"default\": true\n },\n baseZIndex: {\n type: Number,\n \"default\": 0\n },\n global: {\n type: Boolean,\n \"default\": false\n },\n tabindex: {\n type: Number,\n \"default\": 0\n },\n ariaLabelledby: {\n type: String,\n \"default\": null\n },\n ariaLabel: {\n type: String,\n \"default\": null\n }\n },\n style: ContextMenuStyle,\n provide: function provide() {\n return {\n $pcContextMenu: this,\n $parentInstance: this\n };\n }\n};\n\nvar script$1 = {\n name: 'ContextMenuSub',\n hostName: 'ContextMenu',\n \"extends\": BaseComponent,\n emits: ['item-click', 'item-mouseenter', 'item-mousemove'],\n props: {\n items: {\n type: Array,\n \"default\": null\n },\n menuId: {\n type: String,\n \"default\": null\n },\n focusedItemId: {\n type: String,\n \"default\": null\n },\n root: {\n type: Boolean,\n \"default\": false\n },\n visible: {\n type: Boolean,\n \"default\": false\n },\n level: {\n type: Number,\n \"default\": 0\n },\n templates: {\n type: Object,\n \"default\": null\n },\n activeItemPath: {\n type: Object,\n \"default\": null\n },\n tabindex: {\n type: Number,\n \"default\": 0\n }\n },\n methods: {\n getItemId: function getItemId(processedItem) {\n return \"\".concat(this.menuId, \"_\").concat(processedItem.key);\n },\n getItemKey: function getItemKey(processedItem) {\n return this.getItemId(processedItem);\n },\n getItemProp: function getItemProp(processedItem, name, params) {\n return processedItem && processedItem.item ? resolve(processedItem.item[name], params) : undefined;\n },\n getItemLabel: function getItemLabel(processedItem) {\n return this.getItemProp(processedItem, 'label');\n },\n getItemLabelId: function getItemLabelId(processedItem) {\n return \"\".concat(this.menuId, \"_\").concat(processedItem.key, \"_label\");\n },\n getPTOptions: function getPTOptions(key, processedItem, index) {\n return this.ptm(key, {\n context: {\n item: processedItem.item,\n active: this.isItemActive(processedItem),\n focused: this.isItemFocused(processedItem),\n disabled: this.isItemDisabled(processedItem),\n index: index\n }\n });\n },\n isItemActive: function isItemActive(processedItem) {\n return this.activeItemPath.some(function (path) {\n return path.key === processedItem.key;\n });\n },\n isItemVisible: function isItemVisible(processedItem) {\n return this.getItemProp(processedItem, 'visible') !== false;\n },\n isItemDisabled: function isItemDisabled(processedItem) {\n return this.getItemProp(processedItem, 'disabled');\n },\n isItemFocused: function isItemFocused(processedItem) {\n return this.focusedItemId === this.getItemId(processedItem);\n },\n isItemGroup: function isItemGroup(processedItem) {\n return isNotEmpty(processedItem.items);\n },\n onItemClick: function onItemClick(event, processedItem) {\n this.getItemProp(processedItem, 'command', {\n originalEvent: event,\n item: processedItem.item\n });\n this.$emit('item-click', {\n originalEvent: event,\n processedItem: processedItem,\n isFocus: true\n });\n },\n onItemMouseEnter: function onItemMouseEnter(event, processedItem) {\n this.$emit('item-mouseenter', {\n originalEvent: event,\n processedItem: processedItem\n });\n },\n onItemMouseMove: function onItemMouseMove(event, processedItem) {\n this.$emit('item-mousemove', {\n originalEvent: event,\n processedItem: processedItem,\n isFocus: true\n });\n },\n getAriaSetSize: function getAriaSetSize() {\n var _this = this;\n return this.items.filter(function (processedItem) {\n return _this.isItemVisible(processedItem) && !_this.getItemProp(processedItem, 'separator');\n }).length;\n },\n getAriaPosInset: function getAriaPosInset(index) {\n var _this2 = this;\n return index - this.items.slice(0, index).filter(function (processedItem) {\n return _this2.isItemVisible(processedItem) && _this2.getItemProp(processedItem, 'separator');\n }).length + 1;\n },\n onEnter: function onEnter() {\n nestedPosition(this.$refs.container, this.level);\n },\n getMenuItemProps: function getMenuItemProps(processedItem, index) {\n return {\n action: mergeProps({\n \"class\": this.cx('itemLink'),\n tabindex: -1,\n 'aria-hidden': true\n }, this.getPTOptions('itemLink', processedItem, index)),\n icon: mergeProps({\n \"class\": [this.cx('itemIcon'), this.getItemProp(processedItem, 'icon')]\n }, this.getPTOptions('itemIcon', processedItem, index)),\n label: mergeProps({\n \"class\": this.cx('itemLabel')\n }, this.getPTOptions('itemLabel', processedItem, index)),\n submenuicon: mergeProps({\n \"class\": this.cx('submenuIcon')\n }, this.getPTOptions('submenuicon', processedItem, index))\n };\n }\n },\n components: {\n AngleRightIcon: AngleRightIcon\n },\n directives: {\n ripple: Ripple\n }\n};\n\nvar _hoisted_1 = [\"tabindex\"];\nvar _hoisted_2 = [\"id\", \"aria-label\", \"aria-disabled\", \"aria-expanded\", \"aria-haspopup\", \"aria-level\", \"aria-setsize\", \"aria-posinset\", \"data-p-active\", \"data-p-focused\", \"data-p-disabled\"];\nvar _hoisted_3 = [\"onClick\", \"onMouseenter\", \"onMousemove\"];\nvar _hoisted_4 = [\"href\", \"target\"];\nvar _hoisted_5 = [\"id\"];\nvar _hoisted_6 = [\"id\"];\nfunction render$1(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_AngleRightIcon = resolveComponent(\"AngleRightIcon\");\n var _component_ContextMenuSub = resolveComponent(\"ContextMenuSub\", true);\n var _directive_ripple = resolveDirective(\"ripple\");\n return openBlock(), createBlock(Transition, mergeProps({\n name: \"p-contextmenusub\",\n onEnter: $options.onEnter\n }, _ctx.ptm('menu.transition')), {\n \"default\": withCtx(function () {\n return [($props.root ? true : $props.visible) ? (openBlock(), createElementBlock(\"ul\", mergeProps({\n key: 0,\n ref: \"container\",\n tabindex: $props.tabindex\n }, _ctx.ptm('rootList')), [(openBlock(true), createElementBlock(Fragment, null, renderList($props.items, function (processedItem, index) {\n return openBlock(), createElementBlock(Fragment, {\n key: $options.getItemKey(processedItem)\n }, [$options.isItemVisible(processedItem) && !$options.getItemProp(processedItem, 'separator') ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 0,\n id: $options.getItemId(processedItem),\n style: $options.getItemProp(processedItem, 'style'),\n \"class\": [_ctx.cx('item', {\n processedItem: processedItem\n }), $options.getItemProp(processedItem, 'class')],\n role: \"menuitem\",\n \"aria-label\": $options.getItemLabel(processedItem),\n \"aria-disabled\": $options.isItemDisabled(processedItem) || undefined,\n \"aria-expanded\": $options.isItemGroup(processedItem) ? $options.isItemActive(processedItem) : undefined,\n \"aria-haspopup\": $options.isItemGroup(processedItem) && !$options.getItemProp(processedItem, 'to') ? 'menu' : undefined,\n \"aria-level\": $props.level + 1,\n \"aria-setsize\": $options.getAriaSetSize(),\n \"aria-posinset\": $options.getAriaPosInset(index),\n ref_for: true\n }, $options.getPTOptions('item', processedItem, index), {\n \"data-p-active\": $options.isItemActive(processedItem),\n \"data-p-focused\": $options.isItemFocused(processedItem),\n \"data-p-disabled\": $options.isItemDisabled(processedItem)\n }), [createElementVNode(\"div\", mergeProps({\n \"class\": _ctx.cx('itemContent'),\n onClick: function onClick($event) {\n return $options.onItemClick($event, processedItem);\n },\n onMouseenter: function onMouseenter($event) {\n return $options.onItemMouseEnter($event, processedItem);\n },\n onMousemove: function onMousemove($event) {\n return $options.onItemMouseMove($event, processedItem);\n },\n ref_for: true\n }, $options.getPTOptions('itemContent', processedItem, index)), [!$props.templates.item ? withDirectives((openBlock(), createElementBlock(\"a\", mergeProps({\n key: 0,\n href: $options.getItemProp(processedItem, 'url'),\n \"class\": _ctx.cx('itemLink'),\n target: $options.getItemProp(processedItem, 'target'),\n tabindex: \"-1\",\n ref_for: true\n }, $options.getPTOptions('itemLink', processedItem, index)), [$props.templates.itemicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.itemicon), {\n key: 0,\n item: processedItem.item,\n \"class\": normalizeClass(_ctx.cx('itemIcon'))\n }, null, 8, [\"item\", \"class\"])) : $options.getItemProp(processedItem, 'icon') ? (openBlock(), createElementBlock(\"span\", mergeProps({\n key: 1,\n \"class\": [_ctx.cx('itemIcon'), $options.getItemProp(processedItem, 'icon')],\n ref_for: true\n }, $options.getPTOptions('itemIcon', processedItem, index)), null, 16)) : createCommentVNode(\"\", true), createElementVNode(\"span\", mergeProps({\n id: $options.getItemLabelId(processedItem),\n \"class\": _ctx.cx('itemLabel'),\n ref_for: true\n }, $options.getPTOptions('itemLabel', processedItem, index)), toDisplayString($options.getItemLabel(processedItem)), 17, _hoisted_5), $options.getItemProp(processedItem, 'items') ? (openBlock(), createElementBlock(Fragment, {\n key: 2\n }, [$props.templates.submenuicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.submenuicon), {\n key: 0,\n active: $options.isItemActive(processedItem),\n \"class\": normalizeClass(_ctx.cx('submenuIcon'))\n }, null, 8, [\"active\", \"class\"])) : (openBlock(), createBlock(_component_AngleRightIcon, mergeProps({\n key: 1,\n \"class\": _ctx.cx('submenuIcon'),\n ref_for: true\n }, $options.getPTOptions('submenuicon', processedItem, index)), null, 16, [\"class\"]))], 64)) : createCommentVNode(\"\", true)], 16, _hoisted_4)), [[_directive_ripple]]) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.item), {\n key: 1,\n item: processedItem.item,\n hasSubmenu: $options.getItemProp(processedItem, 'items'),\n label: $options.getItemLabel(processedItem),\n props: $options.getMenuItemProps(processedItem, index)\n }, null, 8, [\"item\", \"hasSubmenu\", \"label\", \"props\"]))], 16, _hoisted_3), $options.isItemVisible(processedItem) && $options.isItemGroup(processedItem) ? (openBlock(), createBlock(_component_ContextMenuSub, mergeProps({\n key: 0,\n id: $options.getItemId(processedItem) + '_list',\n role: \"menu\",\n \"class\": _ctx.cx('submenu'),\n menuId: $props.menuId,\n focusedItemId: $props.focusedItemId,\n items: processedItem.items,\n templates: $props.templates,\n activeItemPath: $props.activeItemPath,\n level: $props.level + 1,\n visible: $options.isItemActive(processedItem) && $options.isItemGroup(processedItem),\n pt: _ctx.pt,\n unstyled: _ctx.unstyled,\n onItemClick: _cache[0] || (_cache[0] = function ($event) {\n return _ctx.$emit('item-click', $event);\n }),\n onItemMouseenter: _cache[1] || (_cache[1] = function ($event) {\n return _ctx.$emit('item-mouseenter', $event);\n }),\n onItemMousemove: _cache[2] || (_cache[2] = function ($event) {\n return _ctx.$emit('item-mousemove', $event);\n }),\n \"aria-labelledby\": $options.getItemLabelId(processedItem),\n ref_for: true\n }, _ctx.ptm('submenu')), null, 16, [\"id\", \"class\", \"menuId\", \"focusedItemId\", \"items\", \"templates\", \"activeItemPath\", \"level\", \"visible\", \"pt\", \"unstyled\", \"aria-labelledby\"])) : createCommentVNode(\"\", true)], 16, _hoisted_2)) : createCommentVNode(\"\", true), $options.isItemVisible(processedItem) && $options.getItemProp(processedItem, 'separator') ? (openBlock(), createElementBlock(\"li\", mergeProps({\n key: 1,\n id: $options.getItemId(processedItem),\n style: $options.getItemProp(processedItem, 'style'),\n \"class\": [_ctx.cx('separator'), $options.getItemProp(processedItem, 'class')],\n role: \"separator\",\n ref_for: true\n }, _ctx.ptm('separator')), null, 16, _hoisted_6)) : createCommentVNode(\"\", true)], 64);\n }), 128))], 16, _hoisted_1)) : createCommentVNode(\"\", true)];\n }),\n _: 1\n }, 16, [\"onEnter\"]);\n}\n\nscript$1.render = render$1;\n\nvar script = {\n name: 'ContextMenu',\n \"extends\": script$2,\n inheritAttrs: false,\n emits: ['focus', 'blur', 'show', 'hide', 'before-show', 'before-hide'],\n target: null,\n outsideClickListener: null,\n resizeListener: null,\n documentContextMenuListener: null,\n pageX: null,\n pageY: null,\n container: null,\n list: null,\n data: function data() {\n return {\n id: this.$attrs.id,\n focused: false,\n focusedItemInfo: {\n index: -1,\n level: 0,\n parentKey: ''\n },\n activeItemPath: [],\n visible: false,\n submenuVisible: false\n };\n },\n watch: {\n '$attrs.id': function $attrsId(newValue) {\n this.id = newValue || UniqueComponentId();\n },\n activeItemPath: function activeItemPath(newPath) {\n if (isNotEmpty(newPath)) {\n this.bindOutsideClickListener();\n this.bindResizeListener();\n } else if (!this.visible) {\n this.unbindOutsideClickListener();\n this.unbindResizeListener();\n }\n }\n },\n mounted: function mounted() {\n this.id = this.id || UniqueComponentId();\n if (this.global) {\n this.bindDocumentContextMenuListener();\n }\n },\n beforeUnmount: function beforeUnmount() {\n this.unbindResizeListener();\n this.unbindOutsideClickListener();\n this.unbindDocumentContextMenuListener();\n if (this.container && this.autoZIndex) {\n ZIndex.clear(this.container);\n }\n this.target = null;\n this.container = null;\n },\n methods: {\n getItemProp: function getItemProp(item, name) {\n return item ? resolve(item[name]) : undefined;\n },\n getItemLabel: function getItemLabel(item) {\n return this.getItemProp(item, 'label');\n },\n isItemDisabled: function isItemDisabled(item) {\n return this.getItemProp(item, 'disabled');\n },\n isItemVisible: function isItemVisible(item) {\n return this.getItemProp(item, 'visible') !== false;\n },\n isItemGroup: function isItemGroup(item) {\n return isNotEmpty(this.getItemProp(item, 'items'));\n },\n isItemSeparator: function isItemSeparator(item) {\n return this.getItemProp(item, 'separator');\n },\n getProccessedItemLabel: function getProccessedItemLabel(processedItem) {\n return processedItem ? this.getItemLabel(processedItem.item) : undefined;\n },\n isProccessedItemGroup: function isProccessedItemGroup(processedItem) {\n return processedItem && isNotEmpty(processedItem.items);\n },\n toggle: function toggle(event) {\n this.visible ? this.hide() : this.show(event);\n },\n show: function show(event) {\n this.$emit('before-show');\n this.activeItemPath = [];\n this.focusedItemInfo = {\n index: -1,\n level: 0,\n parentKey: ''\n };\n focus(this.list);\n this.pageX = event.pageX;\n this.pageY = event.pageY;\n this.visible ? this.position() : this.visible = true;\n event.stopPropagation();\n event.preventDefault();\n },\n hide: function hide() {\n this.$emit('before-hide');\n this.visible = false;\n this.activeItemPath = [];\n this.focusedItemInfo = {\n index: -1,\n level: 0,\n parentKey: ''\n };\n },\n onFocus: function onFocus(event) {\n this.focused = true;\n this.focusedItemInfo = this.focusedItemInfo.index !== -1 ? this.focusedItemInfo : {\n index: -1,\n level: 0,\n parentKey: ''\n };\n this.$emit('focus', event);\n },\n onBlur: function onBlur(event) {\n this.focused = false;\n this.focusedItemInfo = {\n index: -1,\n level: 0,\n parentKey: ''\n };\n this.searchValue = '';\n this.$emit('blur', event);\n },\n onKeyDown: function onKeyDown(event) {\n var metaKey = event.metaKey || event.ctrlKey;\n switch (event.code) {\n case 'ArrowDown':\n this.onArrowDownKey(event);\n break;\n case 'ArrowUp':\n this.onArrowUpKey(event);\n break;\n case 'ArrowLeft':\n this.onArrowLeftKey(event);\n break;\n case 'ArrowRight':\n this.onArrowRightKey(event);\n break;\n case 'Home':\n this.onHomeKey(event);\n break;\n case 'End':\n this.onEndKey(event);\n break;\n case 'Space':\n this.onSpaceKey(event);\n break;\n case 'Enter':\n case 'NumpadEnter':\n this.onEnterKey(event);\n break;\n case 'Escape':\n this.onEscapeKey(event);\n break;\n case 'Tab':\n this.onTabKey(event);\n break;\n case 'PageDown':\n case 'PageUp':\n case 'Backspace':\n case 'ShiftLeft':\n case 'ShiftRight':\n //NOOP\n break;\n default:\n if (!metaKey && isPrintableCharacter(event.key)) {\n this.searchItems(event, event.key);\n }\n break;\n }\n },\n onItemChange: function onItemChange(event) {\n var processedItem = event.processedItem,\n isFocus = event.isFocus;\n if (isEmpty(processedItem)) return;\n var index = processedItem.index,\n key = processedItem.key,\n level = processedItem.level,\n parentKey = processedItem.parentKey,\n items = processedItem.items;\n var grouped = isNotEmpty(items);\n var activeItemPath = this.activeItemPath.filter(function (p) {\n return p.parentKey !== parentKey && p.parentKey !== key;\n });\n if (grouped) {\n activeItemPath.push(processedItem);\n this.submenuVisible = true;\n }\n this.focusedItemInfo = {\n index: index,\n level: level,\n parentKey: parentKey\n };\n this.activeItemPath = activeItemPath;\n isFocus && focus(this.list);\n },\n onItemClick: function onItemClick(event) {\n var processedItem = event.processedItem;\n var grouped = this.isProccessedItemGroup(processedItem);\n var selected = this.isSelected(processedItem);\n if (selected) {\n var index = processedItem.index,\n key = processedItem.key,\n level = processedItem.level,\n parentKey = processedItem.parentKey;\n this.activeItemPath = this.activeItemPath.filter(function (p) {\n return key !== p.key && key.startsWith(p.key);\n });\n this.focusedItemInfo = {\n index: index,\n level: level,\n parentKey: parentKey\n };\n focus(this.list);\n } else {\n grouped ? this.onItemChange(event) : this.hide();\n }\n },\n onItemMouseEnter: function onItemMouseEnter(event) {\n this.onItemChange(event);\n },\n onItemMouseMove: function onItemMouseMove(event) {\n if (this.focused) {\n this.changeFocusedItemIndex(event, event.processedItem.index);\n }\n },\n onArrowDownKey: function onArrowDownKey(event) {\n var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex();\n this.changeFocusedItemIndex(event, itemIndex);\n event.preventDefault();\n },\n onArrowUpKey: function onArrowUpKey(event) {\n if (event.altKey) {\n if (this.focusedItemInfo.index !== -1) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n !grouped && this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n }\n this.popup && this.hide();\n event.preventDefault();\n } else {\n var itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex();\n this.changeFocusedItemIndex(event, itemIndex);\n event.preventDefault();\n }\n },\n onArrowLeftKey: function onArrowLeftKey(event) {\n var _this = this;\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var parentItem = this.activeItemPath.find(function (p) {\n return p.key === processedItem.parentKey;\n });\n var root = isEmpty(processedItem.parent);\n if (!root) {\n this.focusedItemInfo = {\n index: -1,\n parentKey: parentItem ? parentItem.parentKey : ''\n };\n this.searchValue = '';\n this.onArrowDownKey(event);\n }\n this.activeItemPath = this.activeItemPath.filter(function (p) {\n return p.parentKey !== _this.focusedItemInfo.parentKey;\n });\n event.preventDefault();\n },\n onArrowRightKey: function onArrowRightKey(event) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n if (grouped) {\n this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n this.focusedItemInfo = {\n index: -1,\n parentKey: processedItem.key\n };\n this.searchValue = '';\n this.onArrowDownKey(event);\n }\n event.preventDefault();\n },\n onHomeKey: function onHomeKey(event) {\n this.changeFocusedItemIndex(event, this.findFirstItemIndex());\n event.preventDefault();\n },\n onEndKey: function onEndKey(event) {\n this.changeFocusedItemIndex(event, this.findLastItemIndex());\n event.preventDefault();\n },\n onEnterKey: function onEnterKey(event) {\n if (this.focusedItemInfo.index !== -1) {\n var element = findSingle(this.list, \"li[id=\\\"\".concat(\"\".concat(this.focusedItemIdx), \"\\\"]\"));\n var anchorElement = element && findSingle(element, '[data-pc-section=\"itemlink\"]');\n anchorElement ? anchorElement.click() : element && element.click();\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n !grouped && (this.focusedItemInfo.index = this.findFirstFocusedItemIndex());\n }\n event.preventDefault();\n },\n onSpaceKey: function onSpaceKey(event) {\n this.onEnterKey(event);\n },\n onEscapeKey: function onEscapeKey(event) {\n this.hide();\n !this.popup && (this.focusedItemInfo.index = this.findFirstFocusedItemIndex());\n event.preventDefault();\n },\n onTabKey: function onTabKey(event) {\n if (this.focusedItemInfo.index !== -1) {\n var processedItem = this.visibleItems[this.focusedItemInfo.index];\n var grouped = this.isProccessedItemGroup(processedItem);\n !grouped && this.onItemChange({\n originalEvent: event,\n processedItem: processedItem\n });\n }\n this.hide();\n },\n onEnter: function onEnter(el) {\n addStyle(el, {\n position: 'absolute'\n });\n this.position();\n if (this.autoZIndex) {\n ZIndex.set('menu', el, this.baseZIndex + this.$primevue.config.zIndex.menu);\n }\n },\n onAfterEnter: function onAfterEnter() {\n this.bindOutsideClickListener();\n this.bindResizeListener();\n this.$emit('show');\n focus(this.list);\n },\n onLeave: function onLeave() {\n this.$emit('hide');\n this.container = null;\n },\n onAfterLeave: function onAfterLeave(el) {\n if (this.autoZIndex) {\n ZIndex.clear(el);\n }\n this.unbindOutsideClickListener();\n this.unbindResizeListener();\n },\n position: function position() {\n var left = this.pageX + 1;\n var top = this.pageY + 1;\n var width = this.container.offsetParent ? this.container.offsetWidth : getHiddenElementOuterWidth(this.container);\n var height = this.container.offsetParent ? this.container.offsetHeight : getHiddenElementOuterHeight(this.container);\n var viewport = getViewport();\n\n //flip\n if (left + width - document.body.scrollLeft > viewport.width) {\n left -= width;\n }\n\n //flip\n if (top + height - document.body.scrollTop > viewport.height) {\n top -= height;\n }\n\n //fit\n if (left < document.body.scrollLeft) {\n left = document.body.scrollLeft;\n }\n\n //fit\n if (top < document.body.scrollTop) {\n top = document.body.scrollTop;\n }\n this.container.style.left = left + 'px';\n this.container.style.top = top + 'px';\n },\n bindOutsideClickListener: function bindOutsideClickListener() {\n var _this2 = this;\n if (!this.outsideClickListener) {\n this.outsideClickListener = function (event) {\n var isOutsideContainer = _this2.container && !_this2.container.contains(event.target);\n var isOutsideTarget = _this2.visible ? !(_this2.target && (_this2.target === event.target || _this2.target.contains(event.target))) : true;\n if (isOutsideContainer && isOutsideTarget) {\n _this2.hide();\n }\n };\n document.addEventListener('click', this.outsideClickListener);\n }\n },\n unbindOutsideClickListener: function unbindOutsideClickListener() {\n if (this.outsideClickListener) {\n document.removeEventListener('click', this.outsideClickListener);\n this.outsideClickListener = null;\n }\n },\n bindResizeListener: function bindResizeListener() {\n var _this3 = this;\n if (!this.resizeListener) {\n this.resizeListener = function () {\n if (_this3.visible && !isTouchDevice()) {\n _this3.hide();\n }\n };\n window.addEventListener('resize', this.resizeListener);\n }\n },\n unbindResizeListener: function unbindResizeListener() {\n if (this.resizeListener) {\n window.removeEventListener('resize', this.resizeListener);\n this.resizeListener = null;\n }\n },\n bindDocumentContextMenuListener: function bindDocumentContextMenuListener() {\n var _this4 = this;\n if (!this.documentContextMenuListener) {\n this.documentContextMenuListener = function (event) {\n event.button === 2 && _this4.show(event);\n };\n document.addEventListener('contextmenu', this.documentContextMenuListener);\n }\n },\n unbindDocumentContextMenuListener: function unbindDocumentContextMenuListener() {\n if (this.documentContextMenuListener) {\n document.removeEventListener('contextmenu', this.documentContextMenuListener);\n this.documentContextMenuListener = null;\n }\n },\n isItemMatched: function isItemMatched(processedItem) {\n var _this$getProccessedIt;\n return this.isValidItem(processedItem) && ((_this$getProccessedIt = this.getProccessedItemLabel(processedItem)) === null || _this$getProccessedIt === void 0 ? void 0 : _this$getProccessedIt.toLocaleLowerCase().startsWith(this.searchValue.toLocaleLowerCase()));\n },\n isValidItem: function isValidItem(processedItem) {\n return !!processedItem && !this.isItemDisabled(processedItem.item) && !this.isItemSeparator(processedItem.item) && this.isItemVisible(processedItem.item);\n },\n isValidSelectedItem: function isValidSelectedItem(processedItem) {\n return this.isValidItem(processedItem) && this.isSelected(processedItem);\n },\n isSelected: function isSelected(processedItem) {\n return this.activeItemPath.some(function (p) {\n return p.key === processedItem.key;\n });\n },\n findFirstItemIndex: function findFirstItemIndex() {\n var _this5 = this;\n return this.visibleItems.findIndex(function (processedItem) {\n return _this5.isValidItem(processedItem);\n });\n },\n findLastItemIndex: function findLastItemIndex() {\n var _this6 = this;\n return findLastIndex(this.visibleItems, function (processedItem) {\n return _this6.isValidItem(processedItem);\n });\n },\n findNextItemIndex: function findNextItemIndex(index) {\n var _this7 = this;\n var matchedItemIndex = index < this.visibleItems.length - 1 ? this.visibleItems.slice(index + 1).findIndex(function (processedItem) {\n return _this7.isValidItem(processedItem);\n }) : -1;\n return matchedItemIndex > -1 ? matchedItemIndex + index + 1 : index;\n },\n findPrevItemIndex: function findPrevItemIndex(index) {\n var _this8 = this;\n var matchedItemIndex = index > 0 ? findLastIndex(this.visibleItems.slice(0, index), function (processedItem) {\n return _this8.isValidItem(processedItem);\n }) : -1;\n return matchedItemIndex > -1 ? matchedItemIndex : index;\n },\n findSelectedItemIndex: function findSelectedItemIndex() {\n var _this9 = this;\n return this.visibleItems.findIndex(function (processedItem) {\n return _this9.isValidSelectedItem(processedItem);\n });\n },\n findFirstFocusedItemIndex: function findFirstFocusedItemIndex() {\n var selectedIndex = this.findSelectedItemIndex();\n return selectedIndex < 0 ? this.findFirstItemIndex() : selectedIndex;\n },\n findLastFocusedItemIndex: function findLastFocusedItemIndex() {\n var selectedIndex = this.findSelectedItemIndex();\n return selectedIndex < 0 ? this.findLastItemIndex() : selectedIndex;\n },\n searchItems: function searchItems(event, _char) {\n var _this10 = this;\n this.searchValue = (this.searchValue || '') + _char;\n var itemIndex = -1;\n var matched = false;\n if (this.focusedItemInfo.index !== -1) {\n itemIndex = this.visibleItems.slice(this.focusedItemInfo.index).findIndex(function (processedItem) {\n return _this10.isItemMatched(processedItem);\n });\n itemIndex = itemIndex === -1 ? this.visibleItems.slice(0, this.focusedItemInfo.index).findIndex(function (processedItem) {\n return _this10.isItemMatched(processedItem);\n }) : itemIndex + this.focusedItemInfo.index;\n } else {\n itemIndex = this.visibleItems.findIndex(function (processedItem) {\n return _this10.isItemMatched(processedItem);\n });\n }\n if (itemIndex !== -1) {\n matched = true;\n }\n if (itemIndex === -1 && this.focusedItemInfo.index === -1) {\n itemIndex = this.findFirstFocusedItemIndex();\n }\n if (itemIndex !== -1) {\n this.changeFocusedItemIndex(event, itemIndex);\n }\n if (this.searchTimeout) {\n clearTimeout(this.searchTimeout);\n }\n this.searchTimeout = setTimeout(function () {\n _this10.searchValue = '';\n _this10.searchTimeout = null;\n }, 500);\n return matched;\n },\n changeFocusedItemIndex: function changeFocusedItemIndex(event, index) {\n if (this.focusedItemInfo.index !== index) {\n this.focusedItemInfo.index = index;\n this.scrollInView();\n }\n },\n scrollInView: function scrollInView() {\n var index = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : -1;\n var id = index !== -1 ? \"\".concat(this.id, \"_\").concat(index) : this.focusedItemIdx;\n var element = findSingle(this.list, \"li[id=\\\"\".concat(id, \"\\\"]\"));\n if (element) {\n element.scrollIntoView && element.scrollIntoView({\n block: 'nearest',\n inline: 'start'\n });\n }\n },\n createProcessedItems: function createProcessedItems(items) {\n var _this11 = this;\n var level = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0;\n var parent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};\n var parentKey = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : '';\n var processedItems = [];\n items && items.forEach(function (item, index) {\n var key = (parentKey !== '' ? parentKey + '_' : '') + index;\n var newItem = {\n item: item,\n index: index,\n level: level,\n key: key,\n parent: parent,\n parentKey: parentKey\n };\n newItem['items'] = _this11.createProcessedItems(item.items, level + 1, newItem, key);\n processedItems.push(newItem);\n });\n return processedItems;\n },\n containerRef: function containerRef(el) {\n this.container = el;\n },\n listRef: function listRef(el) {\n this.list = el ? el.$el : undefined;\n }\n },\n computed: {\n processedItems: function processedItems() {\n return this.createProcessedItems(this.model || []);\n },\n visibleItems: function visibleItems() {\n var _this12 = this;\n var processedItem = this.activeItemPath.find(function (p) {\n return p.key === _this12.focusedItemInfo.parentKey;\n });\n return processedItem ? processedItem.items : this.processedItems;\n },\n focusedItemIdx: function focusedItemIdx() {\n return this.focusedItemInfo.index !== -1 ? \"\".concat(this.id).concat(isNotEmpty(this.focusedItemInfo.parentKey) ? '_' + this.focusedItemInfo.parentKey : '', \"_\").concat(this.focusedItemInfo.index) : null;\n }\n },\n components: {\n ContextMenuSub: script$1,\n Portal: Portal\n }\n};\n\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n var _component_ContextMenuSub = resolveComponent(\"ContextMenuSub\");\n var _component_Portal = resolveComponent(\"Portal\");\n return openBlock(), createBlock(_component_Portal, {\n appendTo: _ctx.appendTo\n }, {\n \"default\": withCtx(function () {\n return [createVNode(Transition, mergeProps({\n name: \"p-contextmenu\",\n onEnter: $options.onEnter,\n onAfterEnter: $options.onAfterEnter,\n onLeave: $options.onLeave,\n onAfterLeave: $options.onAfterLeave\n }, _ctx.ptm('transition')), {\n \"default\": withCtx(function () {\n return [$data.visible ? (openBlock(), createElementBlock(\"div\", mergeProps({\n key: 0,\n ref: $options.containerRef,\n \"class\": _ctx.cx('root')\n }, _ctx.ptmi('root')), [createVNode(_component_ContextMenuSub, {\n ref: $options.listRef,\n id: $data.id + '_list',\n \"class\": normalizeClass(_ctx.cx('rootList')),\n role: \"menubar\",\n root: true,\n tabindex: _ctx.tabindex,\n \"aria-orientation\": \"vertical\",\n \"aria-activedescendant\": $data.focused ? $options.focusedItemIdx : undefined,\n menuId: $data.id,\n focusedItemId: $data.focused ? $options.focusedItemIdx : undefined,\n items: $options.processedItems,\n templates: _ctx.$slots,\n activeItemPath: $data.activeItemPath,\n \"aria-labelledby\": _ctx.ariaLabelledby,\n \"aria-label\": _ctx.ariaLabel,\n level: 0,\n visible: $data.submenuVisible,\n pt: _ctx.pt,\n unstyled: _ctx.unstyled,\n onFocus: $options.onFocus,\n onBlur: $options.onBlur,\n onKeydown: $options.onKeyDown,\n onItemClick: $options.onItemClick,\n onItemMouseenter: $options.onItemMouseEnter,\n onItemMousemove: $options.onItemMouseMove\n }, null, 8, [\"id\", \"class\", \"tabindex\", \"aria-activedescendant\", \"menuId\", \"focusedItemId\", \"items\", \"templates\", \"activeItemPath\", \"aria-labelledby\", \"aria-label\", \"visible\", \"pt\", \"unstyled\", \"onFocus\", \"onBlur\", \"onKeydown\", \"onItemClick\", \"onItemMouseenter\", \"onItemMousemove\"])], 16)) : createCommentVNode(\"\", true)];\n }),\n _: 1\n }, 16, [\"onEnter\", \"onAfterEnter\", \"onLeave\", \"onAfterLeave\"])];\n }),\n _: 1\n }, 8, [\"appendTo\"]);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","\n\n\n\n\n","function _arrayWithHoles(r) {\n if (Array.isArray(r)) return r;\n}\nexport { _arrayWithHoles as default };","function _iterableToArrayLimit(r, l) {\n var t = null == r ? null : \"undefined\" != typeof Symbol && r[Symbol.iterator] || r[\"@@iterator\"];\n if (null != t) {\n var e,\n n,\n i,\n u,\n a = [],\n f = !0,\n o = !1;\n try {\n if (i = (t = t.call(r)).next, 0 === l) {\n if (Object(t) !== t) return;\n f = !1;\n } else for (; !(f = (e = i.call(t)).done) && (a.push(e.value), a.length !== l); f = !0);\n } catch (r) {\n o = !0, n = r;\n } finally {\n try {\n if (!f && null != t[\"return\"] && (u = t[\"return\"](), Object(u) !== u)) return;\n } finally {\n if (o) throw n;\n }\n }\n return a;\n }\n}\nexport { _iterableToArrayLimit as default };","function _arrayLikeToArray(r, a) {\n (null == a || a > r.length) && (a = r.length);\n for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e];\n return n;\n}\nexport { _arrayLikeToArray as default };","import arrayLikeToArray from \"./arrayLikeToArray.js\";\nfunction _unsupportedIterableToArray(r, a) {\n if (r) {\n if (\"string\" == typeof r) return arrayLikeToArray(r, a);\n var t = {}.toString.call(r).slice(8, -1);\n return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? arrayLikeToArray(r, a) : void 0;\n }\n}\nexport { _unsupportedIterableToArray as default };","function _nonIterableRest() {\n throw new TypeError(\"Invalid attempt to destructure non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\");\n}\nexport { _nonIterableRest as default };","import arrayWithHoles from \"./arrayWithHoles.js\";\nimport iterableToArrayLimit from \"./iterableToArrayLimit.js\";\nimport unsupportedIterableToArray from \"./unsupportedIterableToArray.js\";\nimport nonIterableRest from \"./nonIterableRest.js\";\nfunction _slicedToArray(r, e) {\n return arrayWithHoles(r) || iterableToArrayLimit(r, e) || unsupportedIterableToArray(r, e) || nonIterableRest();\n}\nexport { _slicedToArray as default };","\"use strict\";\nObject.defineProperty(exports, \"__esModule\", { value: true });\nexports.bind = void 0;\nfunction bind(target, _a) {\n var type = _a.type, listener = _a.listener, options = _a.options;\n target.addEventListener(type, listener, options);\n return function unbind() {\n target.removeEventListener(type, listener, options);\n };\n}\nexports.bind = bind;\n","\"use strict\";\nvar __assign = (this && this.__assign) || function () {\n __assign = Object.assign || function(t) {\n for (var s, i = 1, n = arguments.length; i < n; i++) {\n s = arguments[i];\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))\n t[p] = s[p];\n }\n return t;\n };\n return __assign.apply(this, arguments);\n};\nObject.defineProperty(exports, \"__esModule\", { value: true });\nexports.bindAll = void 0;\nvar bind_1 = require(\"./bind\");\nfunction toOptions(value) {\n if (typeof value === 'undefined') {\n return undefined;\n }\n if (typeof value === 'boolean') {\n return {\n capture: value,\n };\n }\n return value;\n}\nfunction getBinding(original, sharedOptions) {\n if (sharedOptions == null) {\n return original;\n }\n var binding = __assign(__assign({}, original), { options: __assign(__assign({}, toOptions(sharedOptions)), toOptions(original.options)) });\n return binding;\n}\nfunction bindAll(target, bindings, sharedOptions) {\n var unbinds = bindings.map(function (original) {\n var binding = getBinding(original, sharedOptions);\n return (0, bind_1.bind)(target, binding);\n });\n return function unbindAll() {\n unbinds.forEach(function (unbind) { return unbind(); });\n };\n}\nexports.bindAll = bindAll;\n","\"use strict\";\nObject.defineProperty(exports, \"__esModule\", { value: true });\nexports.bindAll = exports.bind = void 0;\nvar bind_1 = require(\"./bind\");\nObject.defineProperty(exports, \"bind\", { enumerable: true, get: function () { return bind_1.bind; } });\nvar bind_all_1 = require(\"./bind-all\");\nObject.defineProperty(exports, \"bindAll\", { enumerable: true, get: function () { return bind_all_1.bindAll; } });\n","// pulling this into a separate file so adapter(s) that don't\n// need the honey pot can pay as little as possible for it.\nexport var honeyPotDataAttribute = 'data-pdnd-honey-pot';","import { honeyPotDataAttribute } from './honey-pot-data-attribute';\nexport function isHoneyPotElement(target) {\n return target instanceof Element && target.hasAttribute(honeyPotDataAttribute);\n}","import _slicedToArray from \"@babel/runtime/helpers/slicedToArray\";\nimport { isHoneyPotElement } from './is-honey-pot-element';\nexport function getElementFromPointWithoutHoneypot(client) {\n // eslint-disable-next-line no-restricted-syntax\n var _document$elementsFro = document.elementsFromPoint(client.x, client.y),\n _document$elementsFro2 = _slicedToArray(_document$elementsFro, 2),\n top = _document$elementsFro2[0],\n second = _document$elementsFro2[1];\n if (!top) {\n return null;\n }\n if (isHoneyPotElement(top)) {\n return second !== null && second !== void 0 ? second : null;\n }\n return top;\n}","function _typeof(o) {\n \"@babel/helpers - typeof\";\n\n return _typeof = \"function\" == typeof Symbol && \"symbol\" == typeof Symbol.iterator ? function (o) {\n return typeof o;\n } : function (o) {\n return o && \"function\" == typeof Symbol && o.constructor === Symbol && o !== Symbol.prototype ? \"symbol\" : typeof o;\n }, _typeof(o);\n}\nexport { _typeof as default };","import _typeof from \"./typeof.js\";\nfunction toPrimitive(t, r) {\n if (\"object\" != _typeof(t) || !t) return t;\n var e = t[Symbol.toPrimitive];\n if (void 0 !== e) {\n var i = e.call(t, r || \"default\");\n if (\"object\" != _typeof(i)) return i;\n throw new TypeError(\"@@toPrimitive must return a primitive value.\");\n }\n return (\"string\" === r ? String : Number)(t);\n}\nexport { toPrimitive as default };","import _typeof from \"./typeof.js\";\nimport toPrimitive from \"./toPrimitive.js\";\nfunction toPropertyKey(t) {\n var i = toPrimitive(t, \"string\");\n return \"symbol\" == _typeof(i) ? i : i + \"\";\n}\nexport { toPropertyKey as default };","import toPropertyKey from \"./toPropertyKey.js\";\nfunction _defineProperty(e, r, t) {\n return (r = toPropertyKey(r)) in e ? Object.defineProperty(e, r, {\n value: t,\n enumerable: !0,\n configurable: !0,\n writable: !0\n }) : e[r] = t, e;\n}\nexport { _defineProperty as default };","// Maximum possible z-index\n// https://stackoverflow.com/questions/491052/minimum-and-maximum-value-of-z-index\nexport var maxZIndex = 2147483647;","import _defineProperty from \"@babel/runtime/helpers/defineProperty\";\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nimport { bind, bindAll } from 'bind-event-listener';\nimport { maxZIndex } from '../util/max-z-index';\nimport { honeyPotDataAttribute } from './honey-pot-data-attribute';\nvar honeyPotSize = 2;\nvar halfHoneyPotSize = honeyPotSize / 2;\n\n/**\n * `clientX` and `clientY` can be in sub pixels (eg `2.332`)\n * However, browser hitbox testing is commonly do to the closest pixel.\n *\n * → https://issues.chromium.org/issues/40940531\n *\n * To be sure that the honey pot will be over the `client` position,\n * we `.floor()` `clientX` and`clientY` and then make it `2px` in size.\n **/\nfunction floorToClosestPixel(point) {\n return {\n x: Math.floor(point.x),\n y: Math.floor(point.y)\n };\n}\n\n/**\n * We want to make sure the honey pot sits around the users position.\n * This seemed to be the most resilient while testing.\n */\nfunction pullBackByHalfHoneyPotSize(point) {\n return {\n x: point.x - halfHoneyPotSize,\n y: point.y - halfHoneyPotSize\n };\n}\n\n/**\n * Prevent the honey pot from changing the window size.\n * This is super unlikely to occur, but just being safe.\n */\nfunction preventGoingBackwardsOffScreen(point) {\n return {\n x: Math.max(point.x, 0),\n y: Math.max(point.y, 0)\n };\n}\n\n/**\n * Prevent the honey pot from changing the window size.\n * This is super unlikely to occur, but just being safe.\n */\nfunction preventGoingForwardsOffScreen(point) {\n return {\n x: Math.min(point.x, window.innerWidth - honeyPotSize),\n y: Math.min(point.y, window.innerHeight - honeyPotSize)\n };\n}\n\n/**\n * Create a `2x2` `DOMRect` around the `client` position\n */\nfunction getHoneyPotRectFor(_ref) {\n var client = _ref.client;\n var point = preventGoingForwardsOffScreen(preventGoingBackwardsOffScreen(pullBackByHalfHoneyPotSize(floorToClosestPixel(client))));\n\n // When debugging, it is helpful to\n // make this element a bit bigger\n return DOMRect.fromRect({\n x: point.x,\n y: point.y,\n width: honeyPotSize,\n height: honeyPotSize\n });\n}\nfunction getRectStyles(_ref2) {\n var clientRect = _ref2.clientRect;\n return {\n left: \"\".concat(clientRect.left, \"px\"),\n top: \"\".concat(clientRect.top, \"px\"),\n width: \"\".concat(clientRect.width, \"px\"),\n height: \"\".concat(clientRect.height, \"px\")\n };\n}\nfunction isWithin(_ref3) {\n var client = _ref3.client,\n clientRect = _ref3.clientRect;\n return (\n // is within horizontal bounds\n client.x >= clientRect.x && client.x <= clientRect.x + clientRect.width &&\n // is within vertical bounds\n client.y >= clientRect.y && client.y <= clientRect.y + clientRect.height\n );\n}\n/**\n * The honey pot fix is designed to get around a painful bug in all browsers.\n *\n * [Overview](https://www.youtube.com/watch?v=udE9qbFTeQg)\n *\n * **Background**\n *\n * When a drag starts, browsers incorrectly think that the users pointer is\n * still depressed where the drag started. Any element that goes under this position\n * will be entered into, causing `\"mouseenter\"` events and `\":hover\"` styles to be applied.\n *\n * _This is a violation of the spec_\n *\n * > \"From the moment that the user agent is to initiate the drag-and-drop operation,\n * > until the end \tof the drag-and-drop operation, device input events\n * > (e.g. mouse and keyboard events) must be suppressed.\"\n * >\n * > - https://html.spec.whatwg.org/multipage/dnd.html#drag-and-drop-processing-model\n *\n * _Some impacts_\n *\n * - `\":hover\"` styles being applied where they shouldn't (looks messy)\n * - components such as tooltips responding to `\"mouseenter\"` can show during a drag,\n * and on an element the user isn't even over\n *\n * Bug: https://issues.chromium.org/issues/41129937\n *\n * **Honey pot fix**\n *\n * 1. Create an element where the browser thinks the depressed pointer is\n * to absorb the incorrect pointer events\n * 2. Remove that element when it is no longer needed\n */\nfunction mountHoneyPot(_ref4) {\n var initial = _ref4.initial;\n var element = document.createElement('div');\n element.setAttribute(honeyPotDataAttribute, 'true');\n\n // can shift during the drag thanks to Firefox\n var clientRect = getHoneyPotRectFor({\n client: initial\n });\n Object.assign(element.style, _objectSpread(_objectSpread({\n // Setting a background color explicitly to avoid any inherited styles.\n // Looks like this could be `opacity: 0`, but worried that _might_\n // cause the element to be ignored on some platforms.\n // When debugging, set backgroundColor to something like \"red\".\n backgroundColor: 'transparent',\n position: 'fixed',\n // Being explicit to avoid inheriting styles\n padding: 0,\n margin: 0,\n boxSizing: 'border-box'\n }, getRectStyles({\n clientRect: clientRect\n })), {}, {\n // We want this element to absorb pointer events,\n // it's kind of the whole point 😉\n pointerEvents: 'auto',\n // Want to make sure the honey pot is top of everything else.\n // Don't need to worry about native drag previews, as they will\n // have been rendered (and removed) before the honey pot is rendered\n zIndex: maxZIndex\n }));\n document.body.appendChild(element);\n\n /**\n * 🦊 In firefox we can get `\"pointermove\"` events after the drag\n * has started, which is a spec violation.\n * The final `\"pointermove\"` will reveal where the \"depressed\" position\n * is for our honey pot fix.\n */\n var unbindPointerMove = bind(window, {\n type: 'pointermove',\n listener: function listener(event) {\n var client = {\n x: event.clientX,\n y: event.clientY\n };\n clientRect = getHoneyPotRectFor({\n client: client\n });\n Object.assign(element.style, getRectStyles({\n clientRect: clientRect\n }));\n },\n // using capture so we are less likely to be impacted by event stopping\n options: {\n capture: true\n }\n });\n return function finish(_ref5) {\n var current = _ref5.current;\n // Don't need this any more\n unbindPointerMove();\n\n // If the user is hover the honey pot, we remove it\n // so that the user can continue to interact with the page normally.\n if (isWithin({\n client: current,\n clientRect: clientRect\n })) {\n element.remove();\n return;\n }\n function cleanup() {\n unbindPostDragEvents();\n element.remove();\n }\n var unbindPostDragEvents = bindAll(window, [{\n type: 'pointerdown',\n listener: cleanup\n }, {\n type: 'pointermove',\n listener: cleanup\n }, {\n type: 'focusin',\n listener: cleanup\n }, {\n type: 'focusout',\n listener: cleanup\n },\n // a 'pointerdown' should happen before 'dragstart', but just being super safe\n {\n type: 'dragstart',\n listener: cleanup\n },\n // if the user has dragged something out of the window\n // and then is dragging something back into the window\n // the first events we will see are \"dragenter\" (and then \"dragover\").\n // So if we see any of these we need to clear the post drag fix.\n {\n type: 'dragenter',\n listener: cleanup\n }, {\n type: 'dragover',\n listener: cleanup\n }\n\n // Not adding a \"wheel\" event listener, as \"wheel\" by itself does not\n // resolve the bug.\n ], {\n // Using `capture` so less likely to be impacted by other code stopping events\n capture: true\n });\n };\n}\nexport function makeHoneyPotFix() {\n var latestPointerMove = null;\n function bindEvents() {\n // For sanity, only collecting this value from when events are first bound.\n // This prevents the case where a super old \"pointermove\" could be used\n // from a prior interaction.\n latestPointerMove = null;\n return bind(window, {\n type: 'pointermove',\n listener: function listener(event) {\n latestPointerMove = {\n x: event.clientX,\n y: event.clientY\n };\n },\n // listening for pointer move in capture phase\n // so we are less likely to be impacted by events being stopped.\n options: {\n capture: true\n }\n });\n }\n function getOnPostDispatch() {\n var finish = null;\n return function onPostEvent(_ref6) {\n var eventName = _ref6.eventName,\n payload = _ref6.payload;\n // We are adding the honey pot `onDragStart` so we don't\n // impact the creation of the native drag preview.\n if (eventName === 'onDragStart') {\n var _latestPointerMove;\n var input = payload.location.initial.input;\n\n // Sometimes there will be no latest \"pointermove\" (eg iOS).\n // In which case, we use the start position of the drag.\n var initial = (_latestPointerMove = latestPointerMove) !== null && _latestPointerMove !== void 0 ? _latestPointerMove : {\n x: input.clientX,\n y: input.clientY\n };\n\n // Don't need to defensively call `finish()` as `onDrop` from\n // one interaction is guaranteed to be called before `onDragStart`\n // of the next.\n finish = mountHoneyPot({\n initial: initial\n });\n }\n if (eventName === 'onDrop') {\n var _finish;\n var _input = payload.location.current.input;\n (_finish = finish) === null || _finish === void 0 || _finish({\n current: {\n x: _input.clientX,\n y: _input.clientY\n }\n });\n finish = null;\n // this interaction is finished, we want to use\n // the latest \"pointermove\" for each interaction\n latestPointerMove = null;\n }\n };\n }\n return {\n bindEvents: bindEvents,\n getOnPostDispatch: getOnPostDispatch\n };\n}","import arrayLikeToArray from \"./arrayLikeToArray.js\";\nfunction _arrayWithoutHoles(r) {\n if (Array.isArray(r)) return arrayLikeToArray(r);\n}\nexport { _arrayWithoutHoles as default };","function _iterableToArray(r) {\n if (\"undefined\" != typeof Symbol && null != r[Symbol.iterator] || null != r[\"@@iterator\"]) return Array.from(r);\n}\nexport { _iterableToArray as default };","function _nonIterableSpread() {\n throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\");\n}\nexport { _nonIterableSpread as default };","import arrayWithoutHoles from \"./arrayWithoutHoles.js\";\nimport iterableToArray from \"./iterableToArray.js\";\nimport unsupportedIterableToArray from \"./unsupportedIterableToArray.js\";\nimport nonIterableSpread from \"./nonIterableSpread.js\";\nfunction _toConsumableArray(r) {\n return arrayWithoutHoles(r) || iterableToArray(r) || unsupportedIterableToArray(r) || nonIterableSpread();\n}\nexport { _toConsumableArray as default };","/** Provide a function that you only ever want to be called a single time */\nexport function once(fn) {\n var cache = null;\n return function wrapped() {\n if (!cache) {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n var result = fn.apply(this, args);\n cache = {\n result: result\n };\n }\n return cache.result;\n };\n}","import { once } from '../public-utils/once';\n\n// using `cache` as our `isFirefox()` result will not change in a browser\n\n/**\n * Returns `true` if a `Firefox` browser\n * */\nexport var isFirefox = once(function isFirefox() {\n if (process.env.NODE_ENV === 'test') {\n return false;\n }\n return navigator.userAgent.includes('Firefox');\n});","import { once } from '../public-utils/once';\n\n// using `cache` as our `isSafari()` result will not change in a browser\n\n/**\n * Returns `true` if a `Safari` browser.\n * Returns `true` if the browser is running on iOS (they are all Safari).\n * */\nexport var isSafari = once(function isSafari() {\n if (process.env.NODE_ENV === 'test') {\n return false;\n }\n var _navigator = navigator,\n userAgent = _navigator.userAgent;\n return userAgent.includes('AppleWebKit') && !userAgent.includes('Chrome');\n});","import { bindAll } from 'bind-event-listener';\nimport { isSafari } from '../is-safari';\n\n/* For \"dragenter\" events, the browser should set `relatedTarget` to the previous element.\n * For external drag operations, our first \"dragenter\" event should have a `event.relatedTarget` of `null`.\n *\n * Unfortunately in Safari `event.relatedTarget` is *always* set to `null`\n * Safari bug: https://bugs.webkit.org/show_bug.cgi?id=242627\n * To work around this we count \"dragenter\" and \"dragleave\" events */\n\n// Using symbols for event properties so we don't clash with\n// anything on the `event` object\nvar symbols = {\n isLeavingWindow: Symbol('leaving'),\n isEnteringWindow: Symbol('entering')\n};\nexport function isEnteringWindowInSafari(_ref) {\n var dragEnter = _ref.dragEnter;\n if (!isSafari()) {\n return false;\n }\n return dragEnter.hasOwnProperty(symbols.isEnteringWindow);\n}\nexport function isLeavingWindowInSafari(_ref2) {\n var dragLeave = _ref2.dragLeave;\n if (!isSafari()) {\n return false;\n }\n return dragLeave.hasOwnProperty(symbols.isLeavingWindow);\n}\n(function fixSafari() {\n // Don't do anything when server side rendering\n if (typeof window === 'undefined') {\n return;\n }\n\n // rather than checking the userAgent for \"jsdom\" we can do this check\n // so that the check will be removed completely in production code\n if (process.env.NODE_ENV === 'test') {\n return;\n }\n if (!isSafari()) {\n return;\n }\n function getInitialState() {\n return {\n enterCount: 0,\n isOverWindow: false\n };\n }\n var state = getInitialState();\n function resetState() {\n state = getInitialState();\n }\n\n // These event listeners are bound _forever_ and _never_ removed\n // We don't bother cleaning up these event listeners (for now)\n // as this workaround is only for Safari\n\n // This is how the event count works:\n //\n // lift (+1 enterCount)\n // - dragstart(draggable) [enterCount: 0]\n // - dragenter(draggable) [enterCount: 1]\n // leaving draggable (+0 enterCount)\n // - dragenter(document.body) [enterCount: 2]\n // - dragleave(draggable) [enterCount: 1]\n // leaving window (-1 enterCount)\n // - dragleave(document.body) [enterCount: 0] {leaving the window}\n\n // Things to note:\n // - dragenter and dragleave bubble\n // - the first dragenter when entering a window might not be on `window`\n // - it could be on an element that is pressed up against the window\n // - (so we cannot rely on `event.target` values)\n\n bindAll(window, [{\n type: 'dragstart',\n listener: function listener() {\n state.enterCount = 0;\n // drag start occurs in the source window\n state.isOverWindow = true;\n // When a drag first starts it will also trigger a \"dragenter\" on the draggable element\n }\n }, {\n type: 'drop',\n listener: resetState\n }, {\n type: 'dragend',\n listener: resetState\n }, {\n type: 'dragenter',\n listener: function listener(event) {\n if (!state.isOverWindow && state.enterCount === 0) {\n // Patching the `event` object\n // The `event` object is shared with all event listeners for the event\n // @ts-expect-error: adding property to the event object\n event[symbols.isEnteringWindow] = true;\n }\n state.isOverWindow = true;\n state.enterCount++;\n }\n }, {\n type: 'dragleave',\n listener: function listener(event) {\n state.enterCount--;\n if (state.isOverWindow && state.enterCount === 0) {\n // Patching the `event` object as it is shared with all event listeners\n // The `event` object is shared with all event listeners for the event\n // @ts-expect-error: adding property to the event object\n event[symbols.isLeavingWindow] = true;\n state.isOverWindow = false;\n }\n }\n }],\n // using `capture: true` so that adding event listeners\n // in bubble phase will have the correct symbols\n {\n capture: true\n });\n})();","/**\n * Does the `EventTarget` look like a `Node` based on \"duck typing\".\n *\n * Helpful when the `Node` might be outside of the current document\n * so we cannot to an `target instanceof Node` check.\n */\nfunction isNodeLike(target) {\n return 'nodeName' in target;\n}\n\n/**\n * Is an `EventTarget` a `Node` from another `window`?\n */\nexport function isFromAnotherWindow(eventTarget) {\n return isNodeLike(eventTarget) && eventTarget.ownerDocument !== document;\n}","import { isFirefox } from '../is-firefox';\nimport { isSafari } from '../is-safari';\nimport { isLeavingWindowInSafari } from './count-events-for-safari';\nimport { isFromAnotherWindow } from './is-from-another-window';\nexport function isLeavingWindow(_ref) {\n var dragLeave = _ref.dragLeave;\n var type = dragLeave.type,\n relatedTarget = dragLeave.relatedTarget;\n if (type !== 'dragleave') {\n return false;\n }\n if (isSafari()) {\n return isLeavingWindowInSafari({\n dragLeave: dragLeave\n });\n }\n\n // Standard check: if going to `null` we are leaving the `window`\n if (relatedTarget == null) {\n return true;\n }\n\n /**\n * 🦊 Exception: `iframe` in Firefox (`125.0`)\n *\n * Case 1: parent `window` → child `iframe`\n * `dragLeave.relatedTarget` is element _inside_ the child `iframe`\n * (foreign element)\n *\n * Case 2: child `iframe` → parent `window`\n * `dragLeave.relatedTarget` is the `iframe` in the parent `window`\n * (foreign element)\n */\n\n if (isFirefox()) {\n return isFromAnotherWindow(relatedTarget);\n }\n\n /**\n * 🌏 Exception: `iframe` in Chrome (`124.0`)\n *\n * Case 1: parent `window` → child `iframe`\n * `dragLeave.relatedTarget` is the `iframe` in the parent `window`\n *\n * Case 2: child `iframe` → parent `window`\n * `dragLeave.relatedTarget` is `null` *(standard check)*\n */\n\n // Case 2\n // Using `instanceof` check as the element will be in the same `window`\n return relatedTarget instanceof HTMLIFrameElement;\n}","export function getBindingsForBrokenDrags(_ref) {\n var onDragEnd = _ref.onDragEnd;\n return [\n // ## Detecting drag ending for removed draggables\n //\n // If a draggable element is removed during a drag and the user drops:\n // 1. if over a valid drop target: we get a \"drop\" event to know the drag is finished\n // 2. if not over a valid drop target (or cancelled): we get nothing\n // The \"dragend\" event will not fire on the source draggable if it has been\n // removed from the DOM.\n // So we need to figure out if a drag operation has finished by looking at other events\n // We can do this by looking at other events\n\n // ### First detection: \"pointermove\" events\n\n // 1. \"pointermove\" events cannot fire during a drag and drop operation\n // according to the spec. So if we get a \"pointermove\" it means that\n // the drag and drop operations has finished. So if we get a \"pointermove\"\n // we know that the drag is over\n // 2. 🦊😤 Drag and drop operations are _supposed_ to suppress\n // other pointer events. However, firefox will allow a few\n // pointer event to get through after a drag starts.\n // The most I've seen is 3\n {\n type: 'pointermove',\n listener: function () {\n var callCount = 0;\n return function listener() {\n // Using 20 as it is far bigger than the most observed (3)\n if (callCount < 20) {\n callCount++;\n return;\n }\n onDragEnd();\n };\n }()\n },\n // ### Second detection: \"pointerdown\" events\n\n // If we receive this event then we know that a drag operation has finished\n // and potentially another one is about to start.\n // Note: `pointerdown` fires on all browsers / platforms before \"dragstart\"\n {\n type: 'pointerdown',\n listener: onDragEnd\n }];\n}","export function getInput(event) {\n return {\n altKey: event.altKey,\n button: event.button,\n buttons: event.buttons,\n ctrlKey: event.ctrlKey,\n metaKey: event.metaKey,\n shiftKey: event.shiftKey,\n clientX: event.clientX,\n clientY: event.clientY,\n pageX: event.pageX,\n pageY: event.pageY\n };\n}","var rafSchd = function rafSchd(fn) {\n var lastArgs = [];\n var frameId = null;\n\n var wrapperFn = function wrapperFn() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n lastArgs = args;\n\n if (frameId) {\n return;\n }\n\n frameId = requestAnimationFrame(function () {\n frameId = null;\n fn.apply(void 0, lastArgs);\n });\n };\n\n wrapperFn.cancel = function () {\n if (!frameId) {\n return;\n }\n\n cancelAnimationFrame(frameId);\n frameId = null;\n };\n\n return wrapperFn;\n};\n\nexport default rafSchd;\n","import rafSchd from 'raf-schd';\nvar scheduleOnDrag = rafSchd(function (fn) {\n return fn();\n});\nvar dragStart = function () {\n var scheduled = null;\n function schedule(fn) {\n var frameId = requestAnimationFrame(function () {\n scheduled = null;\n fn();\n });\n scheduled = {\n frameId: frameId,\n fn: fn\n };\n }\n function flush() {\n if (scheduled) {\n cancelAnimationFrame(scheduled.frameId);\n scheduled.fn();\n scheduled = null;\n }\n }\n return {\n schedule: schedule,\n flush: flush\n };\n}();\nexport function makeDispatch(_ref) {\n var source = _ref.source,\n initial = _ref.initial,\n dispatchEvent = _ref.dispatchEvent;\n var previous = {\n dropTargets: []\n };\n function safeDispatch(args) {\n dispatchEvent(args);\n previous = {\n dropTargets: args.payload.location.current.dropTargets\n };\n }\n var dispatch = {\n start: function start(_ref2) {\n var nativeSetDragImage = _ref2.nativeSetDragImage;\n // Ensuring that both `onGenerateDragPreview` and `onDragStart` get the same location.\n // We do this so that `previous` is`[]` in `onDragStart` (which is logical)\n var location = {\n current: initial,\n previous: previous,\n initial: initial\n };\n // a `onGenerateDragPreview` does _not_ add another entry for `previous`\n // onDragPreview\n safeDispatch({\n eventName: 'onGenerateDragPreview',\n payload: {\n source: source,\n location: location,\n nativeSetDragImage: nativeSetDragImage\n }\n });\n dragStart.schedule(function () {\n safeDispatch({\n eventName: 'onDragStart',\n payload: {\n source: source,\n location: location\n }\n });\n });\n },\n dragUpdate: function dragUpdate(_ref3) {\n var current = _ref3.current;\n dragStart.flush();\n scheduleOnDrag.cancel();\n safeDispatch({\n eventName: 'onDropTargetChange',\n payload: {\n source: source,\n location: {\n initial: initial,\n previous: previous,\n current: current\n }\n }\n });\n },\n drag: function drag(_ref4) {\n var current = _ref4.current;\n scheduleOnDrag(function () {\n dragStart.flush();\n var location = {\n initial: initial,\n previous: previous,\n current: current\n };\n safeDispatch({\n eventName: 'onDrag',\n payload: {\n source: source,\n location: location\n }\n });\n });\n },\n drop: function drop(_ref5) {\n var current = _ref5.current,\n updatedSourcePayload = _ref5.updatedSourcePayload;\n dragStart.flush();\n scheduleOnDrag.cancel();\n safeDispatch({\n eventName: 'onDrop',\n payload: {\n source: updatedSourcePayload !== null && updatedSourcePayload !== void 0 ? updatedSourcePayload : source,\n location: {\n current: current,\n previous: previous,\n initial: initial\n }\n }\n });\n }\n };\n return dispatch;\n}","import _toConsumableArray from \"@babel/runtime/helpers/toConsumableArray\";\nimport { bindAll } from 'bind-event-listener';\nimport { getElementFromPointWithoutHoneypot } from '../honey-pot-fix/get-element-from-point-without-honey-pot';\nimport { isHoneyPotElement } from '../honey-pot-fix/is-honey-pot-element';\nimport { isLeavingWindow } from '../util/changing-window/is-leaving-window';\nimport { getBindingsForBrokenDrags } from '../util/detect-broken-drag';\nimport { getInput } from '../util/get-input';\nimport { makeDispatch } from './dispatch-consumer-event';\nvar globalState = {\n isActive: false\n};\nfunction canStart() {\n return !globalState.isActive;\n}\nfunction getNativeSetDragImage(event) {\n if (event.dataTransfer) {\n // need to use `.bind` as `setDragImage` is required\n // to be run with `event.dataTransfer` as the \"this\" context\n return event.dataTransfer.setDragImage.bind(event.dataTransfer);\n }\n return null;\n}\nfunction hasHierarchyChanged(_ref) {\n var current = _ref.current,\n next = _ref.next;\n if (current.length !== next.length) {\n return true;\n }\n // not checking stickiness, data or dropEffect,\n // just whether the hierarchy has changed\n for (var i = 0; i < current.length; i++) {\n if (current[i].element !== next[i].element) {\n return true;\n }\n }\n return false;\n}\nfunction start(_ref2) {\n var event = _ref2.event,\n dragType = _ref2.dragType,\n getDropTargetsOver = _ref2.getDropTargetsOver,\n dispatchEvent = _ref2.dispatchEvent;\n if (!canStart()) {\n return;\n }\n var initial = getStartLocation({\n event: event,\n dragType: dragType,\n getDropTargetsOver: getDropTargetsOver\n });\n globalState.isActive = true;\n var state = {\n current: initial\n };\n\n // Setting initial drop effect for the drag\n setDropEffectOnEvent({\n event: event,\n current: initial.dropTargets\n });\n var dispatch = makeDispatch({\n source: dragType.payload,\n dispatchEvent: dispatchEvent,\n initial: initial\n });\n function updateState(next) {\n // only looking at whether hierarchy has changed to determine whether something as 'changed'\n var hasChanged = hasHierarchyChanged({\n current: state.current.dropTargets,\n next: next.dropTargets\n });\n\n // Always updating the state to include latest data, dropEffect and stickiness\n // Only updating consumers if the hierarchy has changed in some way\n // Consumers can get the latest data by using `onDrag`\n state.current = next;\n if (hasChanged) {\n dispatch.dragUpdate({\n current: state.current\n });\n }\n }\n function onUpdateEvent(event) {\n var input = getInput(event);\n\n // If we are over the honey pot, we need to get the element\n // that the user would have been over if not for the honey pot\n var target = isHoneyPotElement(event.target) ? getElementFromPointWithoutHoneypot({\n x: input.clientX,\n y: input.clientY\n }) : event.target;\n var nextDropTargets = getDropTargetsOver({\n target: target,\n input: input,\n source: dragType.payload,\n current: state.current.dropTargets\n });\n if (nextDropTargets.length) {\n // 🩸 must call `event.preventDefault()` to allow a browser drop to occur\n event.preventDefault();\n setDropEffectOnEvent({\n event: event,\n current: nextDropTargets\n });\n }\n updateState({\n dropTargets: nextDropTargets,\n input: input\n });\n }\n function cancel() {\n // The spec behaviour is that when a drag is cancelled, or when dropping on no drop targets,\n // a \"dragleave\" event is fired on the active drop target before a \"dragend\" event.\n // We are replicating that behaviour in `cancel` if there are any active drop targets to\n // ensure consistent behaviour.\n //\n // Note: When cancelling, or dropping on no drop targets, a \"dragleave\" event\n // will have already cleared the dropTargets to `[]` (as that particular \"dragleave\" has a `relatedTarget` of `null`)\n\n if (state.current.dropTargets.length) {\n updateState({\n dropTargets: [],\n input: state.current.input\n });\n }\n dispatch.drop({\n current: state.current,\n updatedSourcePayload: null\n });\n finish();\n }\n function finish() {\n globalState.isActive = false;\n unbindEvents();\n }\n var unbindEvents = bindAll(window, [{\n // 👋 Note: we are repurposing the `dragover` event as our `drag` event\n // this is because firefox does not publish pointer coordinates during\n // a `drag` event, but does for every other type of drag event\n // `dragover` fires on all elements that are being dragged over\n // Because we are binding to `window` - our `dragover` is effectively the same as a `drag`\n // 🦊😤\n type: 'dragover',\n listener: function listener(event) {\n // We need to regularly calculate the drop targets in order to allow:\n // - dynamic `canDrop()` checks\n // - rapid updating `getData()` calls to attach data in response to user input (eg for edge detection)\n // Sadly we cannot schedule inspecting changes resulting from this event\n // we need to be able to conditionally cancel the event with `event.preventDefault()`\n // to enable the correct native drop experience.\n\n // 1. check to see if anything has changed\n onUpdateEvent(event);\n\n // 2. let consumers know a move has occurred\n // This will include the latest 'input' values\n dispatch.drag({\n current: state.current\n });\n }\n }, {\n type: 'dragenter',\n listener: onUpdateEvent\n }, {\n type: 'dragleave',\n listener: function listener(event) {\n if (!isLeavingWindow({\n dragLeave: event\n })) {\n return;\n }\n\n /**\n * At this point we don't know if a drag is being cancelled,\n * or if a drag is leaving the `window`.\n *\n * Both have:\n * 1. \"dragleave\" (with `relatedTarget: null`)\n * 2. \"dragend\" (a \"dragend\" can occur when outside the `window`)\n *\n * **Clearing drop targets**\n *\n * For either case we are clearing the the drop targets\n *\n * - cancelling: we clear drop targets in `\"dragend\"` anyway\n * - leaving the `window`: we clear the drop targets (to clear stickiness)\n *\n * **Leaving the window and finishing the drag**\n *\n * _internal drags_\n *\n * - The drag continues when the user is outside the `window`\n * and can resume if the user drags back over the `window`,\n * or end when the user drops in an external `window`.\n * - We will get a `\"dragend\"`, or we can listen for other\n * events to determine the drag is finished when the user re-enters the `window`).\n *\n * _external drags_\n *\n * - We conclude the drag operation.\n * - We have no idea if the user will drag back over the `window`,\n * or if the drag ends elsewhere.\n * - We will create a new drag if the user re-enters the `window`.\n *\n * **Not updating `input`**\n *\n * 🐛 Bug[Chrome] the final `\"dragleave\"` has default input values (eg `clientX == 0`)\n * Workaround: intentionally not updating `input` in \"dragleave\"\n * rather than the users current input values\n * - [Conversation](https://twitter.com/alexandereardon/status/1642697633864241152)\n * - [Bug](https://bugs.chromium.org/p/chromium/issues/detail?id=1429937)\n **/\n\n updateState({\n input: state.current.input,\n dropTargets: []\n });\n if (dragType.startedFrom === 'external') {\n cancel();\n }\n }\n }, {\n // A \"drop\" can only happen if the browser allowed the drop\n type: 'drop',\n listener: function listener(event) {\n // Capture the final input.\n // We are capturing the final `input` for the\n // most accurate honey pot experience\n state.current = {\n dropTargets: state.current.dropTargets,\n input: getInput(event)\n };\n\n /** If there are no drop targets, then we will get\n * a \"drop\" event if:\n * - `preventUnhandled()` is being used\n * - there is an unmanaged drop target (eg another library)\n * In these cases, it's up to the consumer\n * to handle the drop if it's not over one of our drop targets\n * - `preventUnhandled()` will cancel the \"drop\"\n * - unmanaged drop targets can handle the \"drop\" how they want to\n * We won't call `event.preventDefault()` in this call path */\n\n if (!state.current.dropTargets.length) {\n cancel();\n return;\n }\n event.preventDefault();\n\n // applying the latest drop effect to the event\n setDropEffectOnEvent({\n event: event,\n current: state.current.dropTargets\n });\n dispatch.drop({\n current: state.current,\n // When dropping something native, we need to extract the latest\n // `.items` from the \"drop\" event as it is now accessible\n updatedSourcePayload: dragType.type === 'external' ? dragType.getDropPayload(event) : null\n });\n finish();\n }\n }, {\n // \"dragend\" fires when on the drag source (eg a draggable element)\n // when the drag is finished.\n // \"dragend\" will fire after \"drop\" (if there was a successful drop)\n // \"dragend\" does not fire if the draggable source has been removed during the drag\n // or for external drag sources (eg files)\n\n // This \"dragend\" listener will not fire if there was a successful drop\n // as we will have already removed the event listener\n\n type: 'dragend',\n listener: function listener(event) {\n // In firefox, the position of the \"dragend\" event can\n // be a bit different to the last \"dragover\" event.\n // Updating the input so we can get the best possible\n // information for the honey pot.\n state.current = {\n dropTargets: state.current.dropTargets,\n input: getInput(event)\n };\n cancel();\n }\n }].concat(_toConsumableArray(getBindingsForBrokenDrags({\n onDragEnd: cancel\n }))),\n // Once we have started a managed drag operation it is important that we see / own all drag events\n // We got one adoption bug pop up where some code was stopping (`event.stopPropagation()`)\n // all \"drop\" events in the bubble phase on the `document.body`.\n // This meant that we never saw the \"drop\" event.\n {\n capture: true\n });\n dispatch.start({\n nativeSetDragImage: getNativeSetDragImage(event)\n });\n}\nfunction setDropEffectOnEvent(_ref3) {\n var _current$;\n var event = _ref3.event,\n current = _ref3.current;\n // setting the `dropEffect` to be the innerMost drop targets dropEffect\n var innerMost = (_current$ = current[0]) === null || _current$ === void 0 ? void 0 : _current$.dropEffect;\n if (innerMost != null && event.dataTransfer) {\n event.dataTransfer.dropEffect = innerMost;\n }\n}\nfunction getStartLocation(_ref4) {\n var event = _ref4.event,\n dragType = _ref4.dragType,\n getDropTargetsOver = _ref4.getDropTargetsOver;\n var input = getInput(event);\n\n // When dragging from outside of the browser,\n // the drag is not being sourced from any local drop targets\n if (dragType.startedFrom === 'external') {\n return {\n input: input,\n dropTargets: []\n };\n }\n var dropTargets = getDropTargetsOver({\n input: input,\n source: dragType.payload,\n target: event.target,\n current: []\n });\n return {\n input: input,\n dropTargets: dropTargets\n };\n}\nexport var lifecycle = {\n canStart: canStart,\n start: start\n};","// Extending `Map` to allow us to link Key and Values together\n\nvar ledger = new Map();\nfunction registerUsage(_ref) {\n var typeKey = _ref.typeKey,\n mount = _ref.mount;\n var entry = ledger.get(typeKey);\n if (entry) {\n entry.usageCount++;\n return entry;\n }\n var initial = {\n typeKey: typeKey,\n unmount: mount(),\n usageCount: 1\n };\n ledger.set(typeKey, initial);\n return initial;\n}\nexport function register(args) {\n var entry = registerUsage(args);\n return function unregister() {\n entry.usageCount--;\n if (entry.usageCount > 0) {\n return;\n }\n // Only a single usage left, remove it\n entry.unmount();\n ledger.delete(args.typeKey);\n };\n}","/** Create a new combined function that will call all the provided functions */\nexport function combine() {\n for (var _len = arguments.length, fns = new Array(_len), _key = 0; _key < _len; _key++) {\n fns[_key] = arguments[_key];\n }\n return function cleanup() {\n fns.forEach(function (fn) {\n return fn();\n });\n };\n}","export function addAttribute(element, _ref) {\n var attribute = _ref.attribute,\n value = _ref.value;\n element.setAttribute(attribute, value);\n return function () {\n return element.removeAttribute(attribute);\n };\n}","import _defineProperty from \"@babel/runtime/helpers/defineProperty\";\nimport _toConsumableArray from \"@babel/runtime/helpers/toConsumableArray\";\nfunction ownKeys(e, r) { var t = Object.keys(e); if (Object.getOwnPropertySymbols) { var o = Object.getOwnPropertySymbols(e); r && (o = o.filter(function (r) { return Object.getOwnPropertyDescriptor(e, r).enumerable; })), t.push.apply(t, o); } return t; }\nfunction _objectSpread(e) { for (var r = 1; r < arguments.length; r++) { var t = null != arguments[r] ? arguments[r] : {}; r % 2 ? ownKeys(Object(t), !0).forEach(function (r) { _defineProperty(e, r, t[r]); }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) { Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r)); }); } return e; }\nfunction _createForOfIteratorHelper(r, e) { var t = \"undefined\" != typeof Symbol && r[Symbol.iterator] || r[\"@@iterator\"]; if (!t) { if (Array.isArray(r) || (t = _unsupportedIterableToArray(r)) || e && r && \"number\" == typeof r.length) { t && (r = t); var _n = 0, F = function F() {}; return { s: F, n: function n() { return _n >= r.length ? { done: !0 } : { done: !1, value: r[_n++] }; }, e: function e(r) { throw r; }, f: F }; } throw new TypeError(\"Invalid attempt to iterate non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\"); } var o, a = !0, u = !1; return { s: function s() { t = t.call(r); }, n: function n() { var r = t.next(); return a = r.done, r; }, e: function e(r) { u = !0, o = r; }, f: function f() { try { a || null == t.return || t.return(); } finally { if (u) throw o; } } }; }\nfunction _unsupportedIterableToArray(r, a) { if (r) { if (\"string\" == typeof r) return _arrayLikeToArray(r, a); var t = {}.toString.call(r).slice(8, -1); return \"Object\" === t && r.constructor && (t = r.constructor.name), \"Map\" === t || \"Set\" === t ? Array.from(r) : \"Arguments\" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray(r, a) : void 0; } }\nfunction _arrayLikeToArray(r, a) { (null == a || a > r.length) && (a = r.length); for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; return n; }\nimport { combine } from '../public-utils/combine';\nimport { addAttribute } from '../util/add-attribute';\nfunction copyReverse(array) {\n return array.slice(0).reverse();\n}\nexport function makeDropTarget(_ref) {\n var typeKey = _ref.typeKey,\n defaultDropEffect = _ref.defaultDropEffect;\n var registry = new WeakMap();\n var dropTargetDataAtt = \"data-drop-target-for-\".concat(typeKey);\n var dropTargetSelector = \"[\".concat(dropTargetDataAtt, \"]\");\n function addToRegistry(args) {\n registry.set(args.element, args);\n return function () {\n return registry.delete(args.element);\n };\n }\n function dropTargetForConsumers(args) {\n // Guardrail: warn if the draggable element is already registered\n if (process.env.NODE_ENV !== 'production') {\n var existing = registry.get(args.element);\n if (existing) {\n // eslint-disable-next-line no-console\n console.warn(\"You have already registered a [\".concat(typeKey, \"] dropTarget on the same element\"), {\n existing: existing,\n proposed: args\n });\n }\n if (args.element instanceof HTMLIFrameElement) {\n // eslint-disable-next-line no-console\n console.warn(\"\\n We recommend not registering