Bump ComfyUI version to v0.3.18

Instructions shouldn't recommend to run comfyui with --listen

.github/workflows/stable-release.yml

@@ -12,7 +12,7 @@ on:
         description: 'CUDA version'
         required: true
         type: string
-        default: "124"
+        default: "126"
       python_minor:
         description: 'Python minor version'
         required: true

.github/workflows/test-build.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}

.github/workflows/test-unit.yml

@@ -18,7 +18,7 @@ jobs:
     - name: Set up Python      
       uses: actions/setup-python@v4
       with:
-        python-version: '3.10'
+        python-version: '3.12'
     - name: Install requirements
       run: |
         python -m pip install --upgrade pip

.github/workflows/windows_release_dependencies.yml

@@ -17,7 +17,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "124"
+        default: "126"
 
       python_minor:
         description: 'python minor version'

.github/workflows/windows_release_package.yml

@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "124"
+        default: "126"
 
       python_minor:
         description: 'python minor version'

CODEOWNERS

@@ -15,6 +15,7 @@
 # Python web server
 /api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 /app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
+/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 
 # Frontend assets
 /web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang

README.md

@@ -1,7 +1,7 @@
 <div align="center">
 
 # ComfyUI
-**The most powerful and modular diffusion model GUI and backend.**
+**The most powerful and modular visual AI engine and application.**
 
 
 [![Website][website-shield]][website-url]
@@ -31,10 +31,24 @@
 ![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
 </div>
 
-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/)
+ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
+
+## Get Started
+
+#### [Desktop Application](https://www.comfy.org/download)
+- The easiest way to get started. 
+- Available on Windows & macOS.
+
+#### [Windows Portable Package](#installing)
+- Get the latest commits and completely portable.
+- Available on Windows.
+
+#### [Manual Install](#manual-install-windows-linux)
+Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
+
+## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
+See what ComfyUI can do with the [example workflows](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.
@@ -47,11 +61,14 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
    - [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
    - [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
    - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
+   - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
 - Video Models
    - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
    - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
    - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
    - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
+   - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/)
+   - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
 - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
@@ -119,7 +136,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 
 # Installing
 
-## Windows
+## Windows Portable
 
 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).
 
@@ -129,6 +146,8 @@ Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
+If you have a 50 series Blackwell card like a 5090 or 5080 see [this discussion thread](https://github.com/comfyanonymous/ComfyUI/discussions/6643)
+
 #### 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.
@@ -137,9 +156,18 @@ See the [Config file](extra_model_paths.yaml.example) to set the search paths fo
 
 To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb)
 
+
+## [comfy-cli](https://docs.comfy.org/comfy-cli/getting-started)
+
+You can install and start ComfyUI using comfy-cli:
+```bash
+pip install comfy-cli
+comfy install
+```
+
 ## Manual Install (Windows, Linux)
 
-Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
+python 3.13 is supported but using 3.12 is recommended because some custom nodes and their dependencies might not support it yet.
 
 Git clone this repo.
 
@@ -151,11 +179,11 @@ 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/rocm6.2```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4```
 
-This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.3 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3```
 
 ### Intel GPUs (Windows and Linux)
 
@@ -185,7 +213,7 @@ Additional discussion and help can be found [here](https://github.com/comfyanony
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu126```
 
 This is the command to install pytorch nightly instead which might have performance improvements:
 
@@ -233,6 +261,13 @@ For models compatible with Ascend Extension for PyTorch (torch_npu). To get star
 3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page.
 4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
 
+#### Cambricon MLUs
+
+For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:
+
+1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html)
+2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html)
+3. Launch ComfyUI by running `python main.py`
 
 # Running
 
@@ -289,6 +324,8 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w
 
 ## Support and dev channel
 
+[Discord](https://comfy.org/discord): Try the #help or #feedback channels.
+
 [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/)
@@ -305,7 +342,7 @@ For any bugs, issues, or feature requests related to the frontend, please use th
 
 The new frontend is now the default for ComfyUI. However, please note:
 
-1. The frontend in the main ComfyUI repository is updated weekly.
+1. The frontend in the main ComfyUI repository is updated fortnightly.
 2. Daily releases are available in the separate frontend repository.
 
 To use the most up-to-date frontend version:
@@ -322,7 +359,7 @@ To use the most up-to-date frontend version:
    --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.
+This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.
 
 ### Accessing the Legacy Frontend
 

api_server/routes/internal/internal_routes.py

@@ -1,9 +1,9 @@
 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 folder_paths import folder_names_and_paths, get_directory_by_type
 from api_server.services.terminal_service import TerminalService
 import app.logger
+import os
 
 class InternalRoutes:
     '''
@@ -15,26 +15,10 @@ class InternalRoutes:
     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()]))
@@ -67,6 +51,20 @@ class InternalRoutes:
                 response[key] = folder_names_and_paths[key][0]
             return web.json_response(response)
 
+        @self.routes.get('/files/{directory_type}')
+        async def get_files(request: web.Request) -> web.Response:
+            directory_type = request.match_info['directory_type']
+            if directory_type not in ("output", "input", "temp"):
+                return web.json_response({"error": "Invalid directory type"}, status=400)
+
+            directory = get_directory_by_type(directory_type)
+            sorted_files = sorted(
+                (entry for entry in os.scandir(directory) if entry.is_file()),
+                key=lambda entry: -entry.stat().st_mtime
+            )
+            return web.json_response([entry.name for entry in sorted_files], status=200)
+
+
     def get_app(self):
         if self._app is None:
             self._app = web.Application()

api_server/services/file_service.py

@@ -1,13 +0,0 @@
-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)

app/custom_node_manager.py

@@ -4,12 +4,93 @@ import os
 import folder_paths
 import glob
 from aiohttp import web
+import json
+import logging
+from functools import lru_cache
+
+from utils.json_util import merge_json_recursive
+
+
+# Extra locale files to load into main.json
+EXTRA_LOCALE_FILES = [
+    "nodeDefs.json",
+    "commands.json",
+    "settings.json",
+]
+
+
+def safe_load_json_file(file_path: str) -> dict:
+    if not os.path.exists(file_path):
+        return {}
+
+    try:
+        with open(file_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+    except json.JSONDecodeError:
+        logging.error(f"Error loading {file_path}")
+        return {}
+
 
 class CustomNodeManager:
-    """
-    Placeholder to refactor the custom node management features from ComfyUI-Manager.
-    Currently it only contains the custom workflow templates feature.
-    """
+    @lru_cache(maxsize=1)
+    def build_translations(self):
+        """Load all custom nodes translations during initialization. Translations are
+        expected to be loaded from `locales/` folder.
+
+        The folder structure is expected to be the following:
+        - custom_nodes/
+            - custom_node_1/
+                - locales/
+                    - en/
+                        - main.json
+                        - commands.json
+                        - settings.json
+
+        returned translations are expected to be in the following format:
+        {
+            "en": {
+                "nodeDefs": {...},
+                "commands": {...},
+                "settings": {...},
+                ...{other main.json keys}
+            }
+        }
+        """
+
+        translations = {}
+
+        for folder in folder_paths.get_folder_paths("custom_nodes"):
+            # Sort glob results for deterministic ordering
+            for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))):
+                locales_dir = os.path.join(custom_node_dir, "locales")
+                if not os.path.exists(locales_dir):
+                    continue
+
+                for lang_dir in glob.glob(os.path.join(locales_dir, "*/")):
+                    lang_code = os.path.basename(os.path.dirname(lang_dir))
+
+                    if lang_code not in translations:
+                        translations[lang_code] = {}
+
+                    # Load main.json
+                    main_file = os.path.join(lang_dir, "main.json")
+                    node_translations = safe_load_json_file(main_file)
+
+                    # Load extra locale files
+                    for extra_file in EXTRA_LOCALE_FILES:
+                        extra_file_path = os.path.join(lang_dir, extra_file)
+                        key = extra_file.split(".")[0]
+                        json_data = safe_load_json_file(extra_file_path)
+                        if json_data:
+                            node_translations[key] = json_data
+
+                    if node_translations:
+                        translations[lang_code] = merge_json_recursive(
+                            translations[lang_code], node_translations
+                        )
+
+        return translations
+
     def add_routes(self, routes, webapp, loadedModules):
 
         @routes.get("/workflow_templates")
@@ -18,17 +99,36 @@ class CustomNodeManager:
             files = [
                 file
                 for folder in folder_paths.get_folder_paths("custom_nodes")
-                for file in glob.glob(os.path.join(folder, '*/example_workflows/*.json'))
+                for file in glob.glob(
+                    os.path.join(folder, "*/example_workflows/*.json")
+                )
             ]
-            workflow_templates_dict = {} # custom_nodes folder name -> example workflow names
+            workflow_templates_dict = (
+                {}
+            )  # custom_nodes folder name -> example workflow names
             for file in files:
-                custom_nodes_name = os.path.basename(os.path.dirname(os.path.dirname(file)))
+                custom_nodes_name = os.path.basename(
+                    os.path.dirname(os.path.dirname(file))
+                )
                 workflow_name = os.path.splitext(os.path.basename(file))[0]
-                workflow_templates_dict.setdefault(custom_nodes_name, []).append(workflow_name)
+                workflow_templates_dict.setdefault(custom_nodes_name, []).append(
+                    workflow_name
+                )
             return web.json_response(workflow_templates_dict)
 
         # Serve workflow templates from custom nodes.
         for module_name, module_dir in loadedModules:
-            workflows_dir = os.path.join(module_dir, 'example_workflows')
+            workflows_dir = os.path.join(module_dir, "example_workflows")
             if os.path.exists(workflows_dir):
-                webapp.add_routes([web.static('/api/workflow_templates/' + module_name, workflows_dir)])
+                webapp.add_routes(
+                    [
+                        web.static(
+                            "/api/workflow_templates/" + module_name, workflows_dir
+                        )
+                    ]
+                )
+
+        @routes.get("/i18n")
+        async def get_i18n(request):
+            """Returns translations from all custom nodes' locales folders."""
+            return web.json_response(self.build_translations())

comfy/cli_args.py

@@ -43,10 +43,11 @@ parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certific
 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("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.")
 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("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.")
+parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.")
+parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-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.")
@@ -176,7 +177,9 @@ parser.add_argument(
     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.")
+parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
+
+parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()
@@ -188,3 +191,6 @@ if args.windows_standalone_build:
 
 if args.disable_auto_launch:
     args.auto_launch = False
+
+if args.force_fp16:
+    args.fp16_unet = True

comfy/clip_model.py

@@ -102,9 +102,10 @@ class CLIPTextModel_(torch.nn.Module):
         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"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+
+        causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1)
 
-        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:

comfy/comfy_types/node_typing.py

@@ -66,13 +66,26 @@ class IO(StrEnum):
         b = frozenset(value.split(","))
         return not (b.issubset(a) or a.issubset(b))
 
+class RemoteInputOptions(TypedDict):
+    route: str
+    """The route to the remote source."""
+    refresh_button: bool
+    """Specifies whether to show a refresh button in the UI below the widget."""
+    control_after_refresh: Literal["first", "last"]
+    """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on."""
+    timeout: int
+    """The maximum amount of time to wait for a response from the remote source in milliseconds."""
+    max_retries: int
+    """The maximum number of retries before aborting the request."""
+    refresh: int
+    """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed."""
 
 class InputTypeOptions(TypedDict):
     """Provides type hinting for the return type of the INPUT_TYPES node function.
 
     Due to IDE limitations with unions, for now all options are available for all types (e.g. `label_on` is hinted even when the type is not `IO.BOOLEAN`).
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_datatypes
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/datatypes
     """
 
     default: bool | str | float | int | list | tuple
@@ -113,6 +126,14 @@ class InputTypeOptions(TypedDict):
     # defaultVal: str
     dynamicPrompts: bool
     """Causes the front-end to evaluate dynamic prompts (``STRING``)"""
+    # class InputTypeCombo(InputTypeOptions):
+    image_upload: bool
+    """Specifies whether the input should have an image upload button and image preview attached to it. Requires that the input's name is `image`."""
+    image_folder: Literal["input", "output", "temp"]
+    """Specifies which folder to get preview images from if the input has the ``image_upload`` flag.
+    """
+    remote: RemoteInputOptions
+    """Specifies the configuration for a remote input."""
 
 
 class HiddenInputTypeDict(TypedDict):
@@ -133,7 +154,7 @@ class HiddenInputTypeDict(TypedDict):
 class InputTypeDict(TypedDict):
     """Provides type hinting for node INPUT_TYPES.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_more_on_inputs
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs
     """
 
     required: dict[str, tuple[IO, InputTypeOptions]]
@@ -143,14 +164,14 @@ class InputTypeDict(TypedDict):
     hidden: HiddenInputTypeDict
     """Offers advanced functionality and server-client communication.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_more_on_inputs#hidden-inputs
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs
     """
 
 
 class ComfyNodeABC(ABC):
     """Abstract base class for Comfy nodes.  Includes the names and expected types of attributes.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview
     """
 
     DESCRIPTION: str
@@ -167,7 +188,7 @@ class ComfyNodeABC(ABC):
     CATEGORY: str
     """The category of the node, as per the "Add Node" menu.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#category
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#category
     """
     EXPERIMENTAL: bool
     """Flags a node as experimental, informing users that it may change or not work as expected."""
@@ -181,9 +202,9 @@ class ComfyNodeABC(ABC):
 
         * Must include the ``required`` key, which describes all inputs that must be connected for the node to execute.
         * The ``optional`` key can be added to describe inputs which do not need to be connected.
-        * The ``hidden`` key offers some advanced functionality.  More info at: https://docs.comfy.org/essentials/custom_node_more_on_inputs#hidden-inputs
+        * The ``hidden`` key offers some advanced functionality.  More info at: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs
 
-        Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#input-types
+        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#input-types
         """
         return {"required": {}}
 
@@ -198,7 +219,7 @@ class ComfyNodeABC(ABC):
 
     By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#output-node
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node
     """
     INPUT_IS_LIST: bool
     """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes.
@@ -209,7 +230,7 @@ class ComfyNodeABC(ABC):
 
     A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_lists#list-processing
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
     """
     OUTPUT_IS_LIST: tuple[bool]
     """A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items.
@@ -227,7 +248,7 @@ class ComfyNodeABC(ABC):
     the node should provide a class attribute `OUTPUT_IS_LIST`, which is a ``tuple[bool]``, of the same length as `RETURN_TYPES`,
     specifying which outputs which should be so treated.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_lists#list-processing
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
     """
 
     RETURN_TYPES: tuple[IO]
@@ -237,19 +258,19 @@ class ComfyNodeABC(ABC):
 
         RETURN_TYPES = (IO.INT, "INT", "CUSTOM_TYPE")
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#return-types
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types
     """
     RETURN_NAMES: tuple[str]
     """The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = ("count", "filter_string")``
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#return-names
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names
     """
     OUTPUT_TOOLTIPS: tuple[str]
     """A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`."""
     FUNCTION: str
     """The name of the function to execute as a literal string, e.g. `FUNCTION = "execute"`
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#function
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#function
     """
 
 
@@ -267,7 +288,7 @@ class CheckLazyMixin:
         Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name).
         Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params).
 
-        Comfy Docs: https://docs.comfy.org/essentials/custom_node_lazy_evaluation#defining-check-lazy-status
+        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status
         """
 
         need = [name for name in kwargs if kwargs[name] is None]

comfy/conds.py

@@ -3,9 +3,6 @@ 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
@@ -46,7 +43,7 @@ class CONDCrossAttn(CONDRegular):
             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])
+            mult_min = math.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
@@ -57,7 +54,7 @@ class CONDCrossAttn(CONDRegular):
         crossattn_max_len = self.cond.shape[1]
         for x in others:
             c = x.cond
-            crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
             conds.append(c)
 
         out = []

comfy/diffusers_convert.py

@@ -4,105 +4,6 @@ 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 #
 # ================#
@@ -213,6 +114,7 @@ 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
@@ -229,6 +131,7 @@ def cat_tensors(tensors):
 
     return out
 
+
 def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
     new_state_dict = {}
     capture_qkv_weight = {}
@@ -284,5 +187,3 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
 
 def convert_text_enc_state_dict(text_enc_dict):
     return text_enc_dict
-
-

comfy/extra_samplers/uni_pc.py

@@ -661,7 +661,7 @@ class UniPC:
 
             if x_t is None:
                 if use_predictor:
-                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+                    pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0]))  # 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
@@ -669,7 +669,7 @@ class UniPC:
             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)
+                    corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0]))  # torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
                 else:
                     corr_res = 0
                 D1_t = (model_t - model_prev_0)

comfy/k_diffusion/sampling.py

@@ -40,7 +40,7 @@ def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
 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)
+    sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t))
     return append_zero(sigmas)
 
 
@@ -1267,7 +1267,7 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
     return x
 
 @torch.no_grad()
-def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None, cfg_pp=False):
+def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False):
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
@@ -1289,50 +1289,80 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None
         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):
-        if s_churn > 0:
-            gamma = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
-            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)
+        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": sigma_hat, "denoised": denoised})
-        if sigmas[i + 1] == 0 or old_denoised is None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+        if sigma_down == 0 or old_denoised is None:
             # Euler method
             if cfg_pp:
-                d = to_d(x, sigma_hat, uncond_denoised)
-                x = denoised + d * sigmas[i + 1]
+                d = to_d(x, sigmas[i], uncond_denoised)
+                x = denoised + d * sigma_down
             else:
-                d = to_d(x, sigma_hat, denoised)
-                dt = sigmas[i + 1] - sigma_hat
+                d = to_d(x, sigmas[i], denoised)
+                dt = sigma_down - sigmas[i]
                 x = x + d * dt
         else:
             # Second order multistep method in https://arxiv.org/pdf/2308.02157
-            t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigmas[i + 1]), t_fn(sigmas[i - 1])
+            t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigma_down), t_fn(sigmas[i - 1])
             h = t_next - t
             c2 = (t_prev - t) / h
 
             phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
-            b1 = torch.nan_to_num(phi1_val - 1.0 / c2 * phi2_val, nan=0.0)
-            b2 = torch.nan_to_num(1.0 / c2 * phi2_val, nan=0.0)
+            b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
+            b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
 
             if cfg_pp:
                 x = x + (denoised - uncond_denoised)
+                x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)
+            else:
+                x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
 
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x + h * (b1 * denoised + b2 * old_denoised)
+        # Noise addition
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
 
-        old_denoised = denoised
+        if cfg_pp:
+            old_denoised = uncond_denoised
+        else:
+            old_denoised = denoised
     return x
 
 @torch.no_grad()
-def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
-    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=False)
+def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False)
 
 @torch.no_grad()
-def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
-    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=True)
+def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True)
+
+@torch.no_grad()
+def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False)
+
+@torch.no_grad()
+def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
+
+@torch.no_grad()
+def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
+    """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    old_d = None
+
+    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)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        dt = sigmas[i + 1] - sigmas[i]
+        if i == 0:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Gradient estimation
+            d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
+            x = x + d_bar * dt
+        old_d = d
+    return x

comfy/latent_formats.py

@@ -407,3 +407,52 @@ class Cosmos1CV8x8x8(LatentFormat):
     ]
 
     latent_rgb_factors_bias = [-0.1223, -0.1889, -0.1976]
+
+class Wan21(LatentFormat):
+    latent_channels = 16
+    latent_dimensions = 3
+
+    latent_rgb_factors = [
+            [-0.1299, -0.1692,  0.2932],
+            [ 0.0671,  0.0406,  0.0442],
+            [ 0.3568,  0.2548,  0.1747],
+            [ 0.0372,  0.2344,  0.1420],
+            [ 0.0313,  0.0189, -0.0328],
+            [ 0.0296, -0.0956, -0.0665],
+            [-0.3477, -0.4059, -0.2925],
+            [ 0.0166,  0.1902,  0.1975],
+            [-0.0412,  0.0267, -0.1364],
+            [-0.1293,  0.0740,  0.1636],
+            [ 0.0680,  0.3019,  0.1128],
+            [ 0.0032,  0.0581,  0.0639],
+            [-0.1251,  0.0927,  0.1699],
+            [ 0.0060, -0.0633,  0.0005],
+            [ 0.3477,  0.2275,  0.2950],
+            [ 0.1984,  0.0913,  0.1861]
+        ]
+
+    latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360]
+
+    def __init__(self):
+        self.scale_factor = 1.0
+        self.latents_mean = torch.tensor([
+            -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
+            0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921
+        ]).view(1, self.latent_channels, 1, 1, 1)
+        self.latents_std = torch.tensor([
+            2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
+            3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160
+        ]).view(1, self.latent_channels, 1, 1, 1)
+
+
+        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

comfy/ldm/flux/math.py

@@ -22,7 +22,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
 
 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():
+    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
         device = torch.device("cpu")
     else:
         device = pos.device

comfy/ldm/flux/model.py

@@ -109,9 +109,8 @@ class Flux(nn.Module):
         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))
+            if guidance is not None:
+                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)
@@ -186,7 +185,7 @@ class Flux(nn.Module):
         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, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
         patch_size = self.patch_size
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))

comfy/ldm/hunyuan_video/model.py

@@ -240,9 +240,8 @@ class HunyuanVideo(nn.Module):
         vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
 
         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))
+            if guidance is not None:
+                vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
         if txt_mask is not None and not torch.is_floating_point(txt_mask):
             txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
@@ -311,10 +310,10 @@ class HunyuanVideo(nn.Module):
             shape[i] = shape[i] // self.patch_size[i]
         img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size)
         img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
-        img = img.reshape(initial_shape)
+        img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
         return img
 
-    def forward(self, x, timestep, context, y, guidance, attention_mask=None, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, control=None, transformer_options={}, **kwargs):
         bs, c, t, h, w = x.shape
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])

comfy/ldm/lumina/model.py

@@ -0,0 +1,622 @@
+# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py
+from __future__ import annotations
+
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import comfy.ldm.common_dit
+
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, RMSNorm
+from comfy.ldm.modules.attention import optimized_attention_masked
+from comfy.ldm.flux.layers import EmbedND
+
+
+def modulate(x, scale):
+    return x * (1 + scale.unsqueeze(1))
+
+#############################################################################
+#                               Core NextDiT Model                              #
+#############################################################################
+
+
+class JointAttention(nn.Module):
+    """Multi-head attention module."""
+
+    def __init__(
+        self,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: Optional[int],
+        qk_norm: bool,
+        operation_settings={},
+    ):
+        """
+        Initialize the Attention module.
+
+        Args:
+            dim (int): Number of input dimensions.
+            n_heads (int): Number of heads.
+            n_kv_heads (Optional[int]): Number of kv heads, if using GQA.
+
+        """
+        super().__init__()
+        self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads
+        self.n_local_heads = n_heads
+        self.n_local_kv_heads = self.n_kv_heads
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = dim // n_heads
+
+        self.qkv = operation_settings.get("operations").Linear(
+            dim,
+            (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+        self.out = operation_settings.get("operations").Linear(
+            n_heads * self.head_dim,
+            dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+
+        if qk_norm:
+            self.q_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings)
+            self.k_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings)
+        else:
+            self.q_norm = self.k_norm = nn.Identity()
+
+    @staticmethod
+    def apply_rotary_emb(
+        x_in: torch.Tensor,
+        freqs_cis: 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:
+            x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings.
+            freqs_cis (torch.Tensor): Precomputed frequency tensor for complex
+                exponentials.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor
+                and key tensor with rotary embeddings.
+        """
+
+        t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2)
+        t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
+        return t_out.reshape(*x_in.shape)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+
+        Args:
+            x:
+            x_mask:
+            freqs_cis:
+
+        Returns:
+
+        """
+        bsz, seqlen, _ = x.shape
+
+        xq, xk, xv = torch.split(
+            self.qkv(x),
+            [
+                self.n_local_heads * self.head_dim,
+                self.n_local_kv_heads * self.head_dim,
+                self.n_local_kv_heads * self.head_dim,
+            ],
+            dim=-1,
+        )
+        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+
+        xq = self.q_norm(xq)
+        xk = self.k_norm(xk)
+
+        xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis)
+        xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis)
+
+        n_rep = self.n_local_heads // self.n_local_kv_heads
+        if n_rep >= 1:
+            xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
+            xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
+        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True)
+
+        return self.out(output)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+        operation_settings={},
+    ):
+        """
+        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.
+
+        """
+        super().__init__()
+        # 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 = operation_settings.get("operations").Linear(
+            dim,
+            hidden_dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+        self.w2 = operation_settings.get("operations").Linear(
+            hidden_dim,
+            dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+        self.w3 = operation_settings.get("operations").Linear(
+            dim,
+            hidden_dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+
+    # @torch.compile
+    def _forward_silu_gating(self, x1, x3):
+        return F.silu(x1) * x3
+
+    def forward(self, x):
+        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
+
+
+class JointTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        multiple_of: int,
+        ffn_dim_multiplier: float,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True,
+        operation_settings={},
+    ) -> None:
+        """
+        Initialize a TransformerBlock.
+
+        Args:
+            layer_id (int): Identifier for the layer.
+            dim (int): Embedding dimension of the input features.
+            n_heads (int): Number of attention heads.
+            n_kv_heads (Optional[int]): Number of attention heads in key and
+                value features (if using GQA), or set to None for the same as
+                query.
+            multiple_of (int):
+            ffn_dim_multiplier (float):
+            norm_eps (float):
+
+        """
+        super().__init__()
+        self.dim = dim
+        self.head_dim = dim // n_heads
+        self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings)
+        self.feed_forward = FeedForward(
+            dim=dim,
+            hidden_dim=4 * dim,
+            multiple_of=multiple_of,
+            ffn_dim_multiplier=ffn_dim_multiplier,
+            operation_settings=operation_settings,
+        )
+        self.layer_id = layer_id
+        self.attention_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.ffn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+
+        self.attention_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+
+        self.modulation = modulation
+        if modulation:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                operation_settings.get("operations").Linear(
+                    min(dim, 1024),
+                    4 * dim,
+                    bias=True,
+                    device=operation_settings.get("device"),
+                    dtype=operation_settings.get("dtype"),
+                ),
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        adaln_input: Optional[torch.Tensor]=None,
+    ):
+        """
+        Perform a forward pass through the TransformerBlock.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+            freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
+
+        Returns:
+            torch.Tensor: Output tensor after applying attention and
+                feedforward layers.
+
+        """
+        if self.modulation:
+            assert adaln_input is not None
+            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
+
+            x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
+                self.attention(
+                    modulate(self.attention_norm1(x), scale_msa),
+                    x_mask,
+                    freqs_cis,
+                )
+            )
+            x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
+                self.feed_forward(
+                    modulate(self.ffn_norm1(x), scale_mlp),
+                )
+            )
+        else:
+            assert adaln_input is None
+            x = x + self.attention_norm2(
+                self.attention(
+                    self.attention_norm1(x),
+                    x_mask,
+                    freqs_cis,
+                )
+            )
+            x = x + self.ffn_norm2(
+                self.feed_forward(
+                    self.ffn_norm1(x),
+                )
+            )
+        return x
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of NextDiT.
+    """
+
+    def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}):
+        super().__init__()
+        self.norm_final = operation_settings.get("operations").LayerNorm(
+            hidden_size,
+            elementwise_affine=False,
+            eps=1e-6,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+        self.linear = operation_settings.get("operations").Linear(
+            hidden_size,
+            patch_size * patch_size * out_channels,
+            bias=True,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operation_settings.get("operations").Linear(
+                min(hidden_size, 1024),
+                hidden_size,
+                bias=True,
+                device=operation_settings.get("device"),
+                dtype=operation_settings.get("dtype"),
+            ),
+        )
+
+    def forward(self, x, c):
+        scale = self.adaLN_modulation(c)
+        x = modulate(self.norm_final(x), scale)
+        x = self.linear(x)
+        return x
+
+
+class NextDiT(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 4,
+        dim: int = 4096,
+        n_layers: int = 32,
+        n_refiner_layers: int = 2,
+        n_heads: int = 32,
+        n_kv_heads: Optional[int] = None,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: Optional[float] = None,
+        norm_eps: float = 1e-5,
+        qk_norm: bool = False,
+        cap_feat_dim: int = 5120,
+        axes_dims: List[int] = (16, 56, 56),
+        axes_lens: List[int] = (1, 512, 512),
+        image_model=None,
+        device=None,
+        dtype=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.dtype = dtype
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.patch_size = patch_size
+
+        self.x_embedder = operation_settings.get("operations").Linear(
+            in_features=patch_size * patch_size * in_channels,
+            out_features=dim,
+            bias=True,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+
+        self.noise_refiner = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    modulation=True,
+                    operation_settings=operation_settings,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+        self.context_refiner = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    modulation=False,
+                    operation_settings=operation_settings,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+
+        self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings)
+        self.cap_embedder = nn.Sequential(
+            RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, **operation_settings),
+            operation_settings.get("operations").Linear(
+                cap_feat_dim,
+                dim,
+                bias=True,
+                device=operation_settings.get("device"),
+                dtype=operation_settings.get("dtype"),
+            ),
+        )
+
+        self.layers = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    operation_settings=operation_settings,
+                )
+                for layer_id in range(n_layers)
+            ]
+        )
+        self.norm_final = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings)
+
+        assert (dim // n_heads) == sum(axes_dims)
+        self.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+        self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims)
+        self.dim = dim
+        self.n_heads = n_heads
+
+    def unpatchify(
+        self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False
+    ) -> List[torch.Tensor]:
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        pH = pW = self.patch_size
+        imgs = []
+        for i in range(x.size(0)):
+            H, W = img_size[i]
+            begin = cap_size[i]
+            end = begin + (H // pH) * (W // pW)
+            imgs.append(
+                x[i][begin:end]
+                .view(H // pH, W // pW, pH, pW, self.out_channels)
+                .permute(4, 0, 2, 1, 3)
+                .flatten(3, 4)
+                .flatten(1, 2)
+            )
+
+        if return_tensor:
+            imgs = torch.stack(imgs, dim=0)
+        return imgs
+
+    def patchify_and_embed(
+        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
+        bsz = len(x)
+        pH = pW = self.patch_size
+        device = x[0].device
+        dtype = x[0].dtype
+
+        if cap_mask is not None:
+            l_effective_cap_len = cap_mask.sum(dim=1).tolist()
+        else:
+            l_effective_cap_len = [num_tokens] * bsz
+
+        if cap_mask is not None and not torch.is_floating_point(cap_mask):
+            cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max
+
+        img_sizes = [(img.size(1), img.size(2)) for img in x]
+        l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes]
+
+        max_seq_len = max(
+            (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len))
+        )
+        max_cap_len = max(l_effective_cap_len)
+        max_img_len = max(l_effective_img_len)
+
+        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device)
+
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+            H, W = img_sizes[i]
+            H_tokens, W_tokens = H // pH, W // pW
+            assert H_tokens * W_tokens == img_len
+
+            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
+            position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
+            row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
+            col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
+            position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
+            position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
+
+        freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype)
+
+        # build freqs_cis for cap and image individually
+        cap_freqs_cis_shape = list(freqs_cis.shape)
+        # cap_freqs_cis_shape[1] = max_cap_len
+        cap_freqs_cis_shape[1] = cap_feats.shape[1]
+        cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        img_freqs_cis_shape = list(freqs_cis.shape)
+        img_freqs_cis_shape[1] = max_img_len
+        img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+            cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len]
+            img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len]
+
+        # refine context
+        for layer in self.context_refiner:
+            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
+
+        # refine image
+        flat_x = []
+        for i in range(bsz):
+            img = x[i]
+            C, H, W = img.size()
+            img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1)
+            flat_x.append(img)
+        x = flat_x
+        padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype)
+        padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device)
+        for i in range(bsz):
+            padded_img_embed[i, :l_effective_img_len[i]] = x[i]
+            padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max
+
+        padded_img_embed = self.x_embedder(padded_img_embed)
+        padded_img_mask = padded_img_mask.unsqueeze(1)
+        for layer in self.noise_refiner:
+            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t)
+
+        if cap_mask is not None:
+            mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
+            mask[:, :max_cap_len] = cap_mask[:, :max_cap_len]
+        else:
+            mask = None
+
+        padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype)
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+
+            padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len]
+            padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len]
+
+        return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
+
+    # def forward(self, x, t, cap_feats, cap_mask):
+    def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+        t = 1.0 - timesteps
+        cap_feats = context
+        cap_mask = attention_mask
+        bs, c, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        """
+        Forward pass of NextDiT.
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of text tokens/features
+        """
+
+        t = self.t_embedder(t, dtype=x.dtype)  # (N, D)
+        adaln_input = t
+
+        cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
+
+        x_is_tensor = isinstance(x, torch.Tensor)
+        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens)
+        freqs_cis = freqs_cis.to(x.device)
+
+        for layer in self.layers:
+            x = layer(x, mask, freqs_cis, adaln_input)
+
+        x = self.final_layer(x, adaln_input)
+        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
+
+        return -x
+

comfy/ldm/modules/attention.py

@@ -1,4 +1,6 @@
 import math
+import sys
+
 import torch
 import torch.nn.functional as F
 from torch import nn, einsum
@@ -16,7 +18,11 @@ if model_management.xformers_enabled():
     import xformers.ops
 
 if model_management.sage_attention_enabled():
-    from sageattention import sageattn
+    try:
+        from sageattention import sageattn
+    except ModuleNotFoundError:
+        logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
+        exit(-1)
 
 from comfy.cli_args import args
 import comfy.ops
@@ -24,38 +30,24 @@ ops = comfy.ops.disable_weight_init
 
 FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype()
 
-def get_attn_precision(attn_precision):
+def get_attn_precision(attn_precision, current_dtype):
     if args.dont_upcast_attention:
         return None
-    if FORCE_UPCAST_ATTENTION_DTYPE is not None:
-        return FORCE_UPCAST_ATTENTION_DTYPE
+
+    if FORCE_UPCAST_ATTENTION_DTYPE is not None and current_dtype in FORCE_UPCAST_ATTENTION_DTYPE:
+        return FORCE_UPCAST_ATTENTION_DTYPE[current_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):
@@ -90,7 +82,7 @@ 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, skip_output_reshape=False):
-    attn_precision = get_attn_precision(attn_precision)
+    attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
         b, _, _, dim_head = q.shape
@@ -159,7 +151,7 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
 
 
 def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
-    attn_precision = get_attn_precision(attn_precision)
+    attn_precision = get_attn_precision(attn_precision, query.dtype)
 
     if skip_reshape:
         b, _, _, dim_head = query.shape
@@ -229,7 +221,7 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
     return hidden_states
 
 def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
-    attn_precision = get_attn_precision(attn_precision)
+    attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
         b, _, _, dim_head = q.shape

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -321,7 +321,7 @@ class SelfAttention(nn.Module):
 
 class RMSNorm(torch.nn.Module):
     def __init__(
-        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
+        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs
     ):
         """
         Initialize the RMSNorm normalization layer.

comfy/ldm/modules/diffusionmodules/model.py

@@ -297,7 +297,7 @@ def vae_attention():
     if model_management.xformers_enabled_vae():
         logging.info("Using xformers attention in VAE")
         return xformers_attention
-    elif model_management.pytorch_attention_enabled():
+    elif model_management.pytorch_attention_enabled_vae():
         logging.info("Using pytorch attention in VAE")
         return pytorch_attention
     else:
@@ -702,9 +702,6 @@ class Decoder(nn.Module):
                                         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
 

comfy/ldm/wan/model.py

@@ -0,0 +1,485 @@
+# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import math
+
+import torch
+import torch.nn as nn
+from einops import repeat
+
+from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.flux.layers import EmbedND
+from comfy.ldm.flux.math import apply_rope
+from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm
+import comfy.ldm.common_dit
+import comfy.model_management
+
+
+def sinusoidal_embedding_1d(dim, position):
+    # preprocess
+    assert dim % 2 == 0
+    half = dim // 2
+    position = position.type(torch.float32)
+
+    # calculation
+    sinusoid = torch.outer(
+        position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
+    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
+    return x
+
+
+class WanSelfAttention(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 eps=1e-6, operation_settings={}):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.window_size = window_size
+        self.qk_norm = qk_norm
+        self.eps = eps
+
+        # layers
+        self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.norm_q = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+        self.norm_k = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+
+    def forward(self, x, freqs):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, num_heads, C / num_heads]
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+        # query, key, value function
+        def qkv_fn(x):
+            q = self.norm_q(self.q(x)).view(b, s, n, d)
+            k = self.norm_k(self.k(x)).view(b, s, n, d)
+            v = self.v(x).view(b, s, n * d)
+            return q, k, v
+
+        q, k, v = qkv_fn(x)
+        q, k = apply_rope(q, k, freqs)
+
+        x = optimized_attention(
+            q.view(b, s, n * d),
+            k.view(b, s, n * d),
+            v,
+            heads=self.num_heads,
+        )
+
+        x = self.o(x)
+        return x
+
+
+class WanT2VCrossAttention(WanSelfAttention):
+
+    def forward(self, x, context):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+        """
+        # compute query, key, value
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(context))
+        v = self.v(context)
+
+        # compute attention
+        x = optimized_attention(q, k, v, heads=self.num_heads)
+
+        x = self.o(x)
+        return x
+
+
+class WanI2VCrossAttention(WanSelfAttention):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 eps=1e-6, operation_settings={}):
+        super().__init__(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings)
+
+        self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        # self.alpha = nn.Parameter(torch.zeros((1, )))
+        self.norm_k_img = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
+
+    def forward(self, x, context):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+        """
+        context_img = context[:, :257]
+        context = context[:, 257:]
+
+        # compute query, key, value
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(context))
+        v = self.v(context)
+        k_img = self.norm_k_img(self.k_img(context_img))
+        v_img = self.v_img(context_img)
+        img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads)
+        # compute attention
+        x = optimized_attention(q, k, v, heads=self.num_heads)
+
+        # output
+        x = x + img_x
+        x = self.o(x)
+        return x
+
+
+WAN_CROSSATTENTION_CLASSES = {
+    't2v_cross_attn': WanT2VCrossAttention,
+    'i2v_cross_attn': WanI2VCrossAttention,
+}
+
+
+class WanAttentionBlock(nn.Module):
+
+    def __init__(self,
+                 cross_attn_type,
+                 dim,
+                 ffn_dim,
+                 num_heads,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=False,
+                 eps=1e-6, operation_settings={}):
+        super().__init__()
+        self.dim = dim
+        self.ffn_dim = ffn_dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.qk_norm = qk_norm
+        self.cross_attn_norm = cross_attn_norm
+        self.eps = eps
+
+        # layers
+        self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,
+                                          eps, operation_settings=operation_settings)
+        self.norm3 = operation_settings.get("operations").LayerNorm(
+            dim, eps,
+            elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if cross_attn_norm else nn.Identity()
+        self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim,
+                                                                      num_heads,
+                                                                      (-1, -1),
+                                                                      qk_norm,
+                                                                      eps, operation_settings=operation_settings)
+        self.norm2 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.ffn = nn.Sequential(
+            operation_settings.get("operations").Linear(dim, ffn_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'),
+            operation_settings.get("operations").Linear(ffn_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+        # modulation
+        self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+    def forward(
+        self,
+        x,
+        e,
+        freqs,
+        context,
+    ):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, C]
+            e(Tensor): Shape [B, 6, C]
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        # assert e.dtype == torch.float32
+
+        e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
+        # assert e[0].dtype == torch.float32
+
+        # self-attention
+        y = self.self_attn(
+            self.norm1(x) * (1 + e[1]) + e[0],
+            freqs)
+
+        x = x + y * e[2]
+
+        # cross-attention & ffn function
+        def cross_attn_ffn(x, context, e):
+            x = x + self.cross_attn(self.norm3(x), context)
+            y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
+            x = x + y * e[5]
+            return x
+
+        x = cross_attn_ffn(x, context, e)
+        return x
+
+
+class Head(nn.Module):
+
+    def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}):
+        super().__init__()
+        self.dim = dim
+        self.out_dim = out_dim
+        self.patch_size = patch_size
+        self.eps = eps
+
+        # layers
+        out_dim = math.prod(patch_size) * out_dim
+        self.norm = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.head = operation_settings.get("operations").Linear(dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+        # modulation
+        self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+    def forward(self, x, e):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            e(Tensor): Shape [B, C]
+        """
+        # assert e.dtype == torch.float32
+        e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
+        x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
+        return x
+
+
+class MLPProj(torch.nn.Module):
+
+    def __init__(self, in_dim, out_dim, operation_settings={}):
+        super().__init__()
+
+        self.proj = torch.nn.Sequential(
+            operation_settings.get("operations").LayerNorm(in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear(in_dim, in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
+            torch.nn.GELU(), operation_settings.get("operations").Linear(in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
+            operation_settings.get("operations").LayerNorm(out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+
+class WanModel(torch.nn.Module):
+    r"""
+    Wan diffusion backbone supporting both text-to-video and image-to-video.
+    """
+
+    def __init__(self,
+                 model_type='t2v',
+                 patch_size=(1, 2, 2),
+                 text_len=512,
+                 in_dim=16,
+                 dim=2048,
+                 ffn_dim=8192,
+                 freq_dim=256,
+                 text_dim=4096,
+                 out_dim=16,
+                 num_heads=16,
+                 num_layers=32,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 image_model=None,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+        r"""
+        Initialize the diffusion model backbone.
+
+        Args:
+            model_type (`str`, *optional*, defaults to 't2v'):
+                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
+            patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
+                3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
+            text_len (`int`, *optional*, defaults to 512):
+                Fixed length for text embeddings
+            in_dim (`int`, *optional*, defaults to 16):
+                Input video channels (C_in)
+            dim (`int`, *optional*, defaults to 2048):
+                Hidden dimension of the transformer
+            ffn_dim (`int`, *optional*, defaults to 8192):
+                Intermediate dimension in feed-forward network
+            freq_dim (`int`, *optional*, defaults to 256):
+                Dimension for sinusoidal time embeddings
+            text_dim (`int`, *optional*, defaults to 4096):
+                Input dimension for text embeddings
+            out_dim (`int`, *optional*, defaults to 16):
+                Output video channels (C_out)
+            num_heads (`int`, *optional*, defaults to 16):
+                Number of attention heads
+            num_layers (`int`, *optional*, defaults to 32):
+                Number of transformer blocks
+            window_size (`tuple`, *optional*, defaults to (-1, -1)):
+                Window size for local attention (-1 indicates global attention)
+            qk_norm (`bool`, *optional*, defaults to True):
+                Enable query/key normalization
+            cross_attn_norm (`bool`, *optional*, defaults to False):
+                Enable cross-attention normalization
+            eps (`float`, *optional*, defaults to 1e-6):
+                Epsilon value for normalization layers
+        """
+
+        super().__init__()
+        self.dtype = dtype
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+        assert model_type in ['t2v', 'i2v']
+        self.model_type = model_type
+
+        self.patch_size = patch_size
+        self.text_len = text_len
+        self.in_dim = in_dim
+        self.dim = dim
+        self.ffn_dim = ffn_dim
+        self.freq_dim = freq_dim
+        self.text_dim = text_dim
+        self.out_dim = out_dim
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.window_size = window_size
+        self.qk_norm = qk_norm
+        self.cross_attn_norm = cross_attn_norm
+        self.eps = eps
+
+        # embeddings
+        self.patch_embedding = operations.Conv3d(
+            in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32)
+        self.text_embedding = nn.Sequential(
+            operations.Linear(text_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'),
+            operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+        self.time_embedding = nn.Sequential(
+            operations.Linear(freq_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.SiLU(), operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+        self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
+
+        # blocks
+        cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
+        self.blocks = nn.ModuleList([
+            WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
+                              window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
+            for _ in range(num_layers)
+        ])
+
+        # head
+        self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings)
+
+        d = dim // num_heads
+        self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)])
+
+        if model_type == 'i2v':
+            self.img_emb = MLPProj(1280, dim, operation_settings=operation_settings)
+        else:
+            self.img_emb = None
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        clip_fea=None,
+        freqs=None,
+    ):
+        r"""
+        Forward pass through the diffusion model
+
+        Args:
+            x (Tensor):
+                List of input video tensors with shape [B, C_in, F, H, W]
+            t (Tensor):
+                Diffusion timesteps tensor of shape [B]
+            context (List[Tensor]):
+                List of text embeddings each with shape [B, L, C]
+            seq_len (`int`):
+                Maximum sequence length for positional encoding
+            clip_fea (Tensor, *optional*):
+                CLIP image features for image-to-video mode
+            y (List[Tensor], *optional*):
+                Conditional video inputs for image-to-video mode, same shape as x
+
+        Returns:
+            List[Tensor]:
+                List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
+        """
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        grid_sizes = x.shape[2:]
+        x = x.flatten(2).transpose(1, 2)
+
+        # time embeddings
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
+        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
+
+        # context
+        context = self.text_embedding(context)
+
+        if clip_fea is not None and self.img_emb is not None:
+            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+            context = torch.concat([context_clip, context], dim=1)
+
+        # arguments
+        kwargs = dict(
+            e=e0,
+            freqs=freqs,
+            context=context)
+
+        for block in self.blocks:
+            x = block(x, **kwargs)
+
+        # head
+        x = self.head(x, e)
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+        return x
+        # return [u.float() for u in x]
+
+    def forward(self, x, timestep, context, clip_fea=None, **kwargs):
+        bs, c, t, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+        patch_size = self.patch_size
+        t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
+        h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
+        w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+        img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
+        img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
+        img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
+        img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
+
+        freqs = self.rope_embedder(img_ids).movedim(1, 2)
+        return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs)[:, :, :t, :h, :w]
+
+    def unpatchify(self, x, grid_sizes):
+        r"""
+        Reconstruct video tensors from patch embeddings.
+
+        Args:
+            x (List[Tensor]):
+                List of patchified features, each with shape [L, C_out * prod(patch_size)]
+            grid_sizes (Tensor):
+                Original spatial-temporal grid dimensions before patching,
+                    shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
+
+        Returns:
+            List[Tensor]:
+                Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8]
+        """
+
+        c = self.out_dim
+        u = x
+        b = u.shape[0]
+        u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c)
+        u = torch.einsum('bfhwpqrc->bcfphqwr', u)
+        u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)])
+        return u

comfy/ldm/wan/vae.py

@@ -0,0 +1,567 @@
+# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/vae.py
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from comfy.ldm.modules.diffusionmodules.model import vae_attention
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+CACHE_T = 2
+
+
+class CausalConv3d(ops.Conv3d):
+    """
+    Causal 3d convolusion.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._padding = (self.padding[2], self.padding[2], self.padding[1],
+                         self.padding[1], 2 * self.padding[0], 0)
+        self.padding = (0, 0, 0)
+
+    def forward(self, x, cache_x=None):
+        padding = list(self._padding)
+        if cache_x is not None and self._padding[4] > 0:
+            cache_x = cache_x.to(x.device)
+            x = torch.cat([cache_x, x], dim=2)
+            padding[4] -= cache_x.shape[2]
+        x = F.pad(x, padding)
+
+        return super().forward(x)
+
+
+class RMS_norm(nn.Module):
+
+    def __init__(self, dim, channel_first=True, images=True, bias=False):
+        super().__init__()
+        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
+        shape = (dim, *broadcastable_dims) if channel_first else (dim,)
+
+        self.channel_first = channel_first
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.ones(shape))
+        self.bias = nn.Parameter(torch.zeros(shape)) if bias else None
+
+    def forward(self, x):
+        return F.normalize(
+            x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0)
+
+
+class Upsample(nn.Upsample):
+
+    def forward(self, x):
+        """
+        Fix bfloat16 support for nearest neighbor interpolation.
+        """
+        return super().forward(x.float()).type_as(x)
+
+
+class Resample(nn.Module):
+
+    def __init__(self, dim, mode):
+        assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d',
+                        'downsample3d')
+        super().__init__()
+        self.dim = dim
+        self.mode = mode
+
+        # layers
+        if mode == 'upsample2d':
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                ops.Conv2d(dim, dim // 2, 3, padding=1))
+        elif mode == 'upsample3d':
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                ops.Conv2d(dim, dim // 2, 3, padding=1))
+            self.time_conv = CausalConv3d(
+                dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
+
+        elif mode == 'downsample2d':
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                ops.Conv2d(dim, dim, 3, stride=(2, 2)))
+        elif mode == 'downsample3d':
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                ops.Conv2d(dim, dim, 3, stride=(2, 2)))
+            self.time_conv = CausalConv3d(
+                dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
+
+        else:
+            self.resample = nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        b, c, t, h, w = x.size()
+        if self.mode == 'upsample3d':
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = 'Rep'
+                    feat_idx[0] += 1
+                else:
+
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] != 'Rep':
+                        # cache last frame of last two chunk
+                        cache_x = torch.cat([
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device), cache_x
+                        ],
+                                            dim=2)
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] == 'Rep':
+                        cache_x = torch.cat([
+                            torch.zeros_like(cache_x).to(cache_x.device),
+                            cache_x
+                        ],
+                                            dim=2)
+                    if feat_cache[idx] == 'Rep':
+                        x = self.time_conv(x)
+                    else:
+                        x = self.time_conv(x, feat_cache[idx])
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+
+                    x = x.reshape(b, 2, c, t, h, w)
+                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
+                                    3)
+                    x = x.reshape(b, c, t * 2, h, w)
+        t = x.shape[2]
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+        x = self.resample(x)
+        x = rearrange(x, '(b t) c h w -> b c t h w', t=t)
+
+        if self.mode == 'downsample3d':
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = x.clone()
+                    feat_idx[0] += 1
+                else:
+
+                    cache_x = x[:, :, -1:, :, :].clone()
+                    # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep':
+                    #     # cache last frame of last two chunk
+                    #     cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+
+                    x = self.time_conv(
+                        torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+        return x
+
+    def init_weight(self, conv):
+        conv_weight = conv.weight
+        nn.init.zeros_(conv_weight)
+        c1, c2, t, h, w = conv_weight.size()
+        one_matrix = torch.eye(c1, c2)
+        init_matrix = one_matrix
+        nn.init.zeros_(conv_weight)
+        #conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5
+        conv_weight.data[:, :, 1, 0, 0] = init_matrix  #* 0.5
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+    def init_weight2(self, conv):
+        conv_weight = conv.weight.data
+        nn.init.zeros_(conv_weight)
+        c1, c2, t, h, w = conv_weight.size()
+        init_matrix = torch.eye(c1 // 2, c2)
+        #init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2)
+        conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix
+        conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+
+class ResidualBlock(nn.Module):
+
+    def __init__(self, in_dim, out_dim, dropout=0.0):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        # layers
+        self.residual = nn.Sequential(
+            RMS_norm(in_dim, images=False), nn.SiLU(),
+            CausalConv3d(in_dim, out_dim, 3, padding=1),
+            RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout),
+            CausalConv3d(out_dim, out_dim, 3, padding=1))
+        self.shortcut = CausalConv3d(in_dim, out_dim, 1) \
+            if in_dim != out_dim else nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        h = self.shortcut(x)
+        for layer in self.residual:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    Causal self-attention with a single head.
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+        # layers
+        self.norm = RMS_norm(dim)
+        self.to_qkv = ops.Conv2d(dim, dim * 3, 1)
+        self.proj = ops.Conv2d(dim, dim, 1)
+        self.optimized_attention = vae_attention()
+
+    def forward(self, x):
+        identity = x
+        b, c, t, h, w = x.size()
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+        x = self.norm(x)
+        # compute query, key, value
+
+        q, k, v = self.to_qkv(x).chunk(3, dim=1)
+        x = self.optimized_attention(q, k, v)
+
+        # output
+        x = self.proj(x)
+        x = rearrange(x, '(b t) c h w-> b c t h w', t=t)
+        return x + identity
+
+
+class Encoder3d(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[True, True, False],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+
+        # dimensions
+        dims = [dim * u for u in [1] + dim_mult]
+        scale = 1.0
+
+        # init block
+        self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)
+
+        # downsample blocks
+        downsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            for _ in range(num_res_blocks):
+                downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    downsamples.append(AttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # downsample block
+            if i != len(dim_mult) - 1:
+                mode = 'downsample3d' if temperal_downsample[
+                    i] else 'downsample2d'
+                downsamples.append(Resample(out_dim, mode=mode))
+                scale /= 2.0
+        self.downsamples = nn.Sequential(*downsamples)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim),
+            ResidualBlock(out_dim, out_dim, dropout))
+
+        # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False), nn.SiLU(),
+            CausalConv3d(out_dim, z_dim, 3, padding=1))
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## downsamples
+        for layer in self.downsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+class Decoder3d(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_upsample=[False, True, True],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_upsample = temperal_upsample
+
+        # dimensions
+        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        scale = 1.0 / 2**(len(dim_mult) - 2)
+
+        # init block
+        self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]),
+            ResidualBlock(dims[0], dims[0], dropout))
+
+        # upsample blocks
+        upsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            if i == 1 or i == 2 or i == 3:
+                in_dim = in_dim // 2
+            for _ in range(num_res_blocks + 1):
+                upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    upsamples.append(AttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # upsample block
+            if i != len(dim_mult) - 1:
+                mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d'
+                upsamples.append(Resample(out_dim, mode=mode))
+                scale *= 2.0
+        self.upsamples = nn.Sequential(*upsamples)
+
+        # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False), nn.SiLU(),
+            CausalConv3d(out_dim, 3, 3, padding=1))
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        ## conv1
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## upsamples
+        for layer in self.upsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+def count_conv3d(model):
+    count = 0
+    for m in model.modules():
+        if isinstance(m, CausalConv3d):
+            count += 1
+    return count
+
+
+class WanVAE(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[True, True, False],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+        self.temperal_upsample = temperal_downsample[::-1]
+
+        # modules
+        self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks,
+                                 attn_scales, self.temperal_downsample, dropout)
+        self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
+        self.conv2 = CausalConv3d(z_dim, z_dim, 1)
+        self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks,
+                                 attn_scales, self.temperal_upsample, dropout)
+
+    def forward(self, x):
+        mu, log_var = self.encode(x)
+        z = self.reparameterize(mu, log_var)
+        x_recon = self.decode(z)
+        return x_recon, mu, log_var
+
+    def encode(self, x):
+        self.clear_cache()
+        ## cache
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+        ## 对encode输入的x，按时间拆分为1、4、4、4....
+        for i in range(iter_):
+            self._enc_conv_idx = [0]
+            if i == 0:
+                out = self.encoder(
+                    x[:, :, :1, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx)
+            else:
+                out_ = self.encoder(
+                    x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx)
+                out = torch.cat([out, out_], 2)
+        mu, log_var = self.conv1(out).chunk(2, dim=1)
+        self.clear_cache()
+        return mu
+
+    def decode(self, z):
+        self.clear_cache()
+        # z: [b,c,t,h,w]
+
+        iter_ = z.shape[2]
+        x = self.conv2(z)
+        for i in range(iter_):
+            self._conv_idx = [0]
+            if i == 0:
+                out = self.decoder(
+                    x[:, :, i:i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx)
+            else:
+                out_ = self.decoder(
+                    x[:, :, i:i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx)
+                out = torch.cat([out, out_], 2)
+        self.clear_cache()
+        return out
+
+    def reparameterize(self, mu, log_var):
+        std = torch.exp(0.5 * log_var)
+        eps = torch.randn_like(std)
+        return eps * std + mu
+
+    def sample(self, imgs, deterministic=False):
+        mu, log_var = self.encode(imgs)
+        if deterministic:
+            return mu
+        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
+        return mu + std * torch.randn_like(std)
+
+    def clear_cache(self):
+        self._conv_num = count_conv3d(self.decoder)
+        self._conv_idx = [0]
+        self._feat_map = [None] * self._conv_num
+        #cache encode
+        self._enc_conv_num = count_conv3d(self.encoder)
+        self._enc_conv_idx = [0]
+        self._enc_feat_map = [None] * self._enc_conv_num

comfy/lora.py

@@ -307,7 +307,6 @@ def model_lora_keys_unet(model, key_map={}):
             if 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
             else:
                 key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names
@@ -327,6 +326,13 @@ def model_lora_keys_unet(model, key_map={}):
                     diffusers_lora_key = diffusers_lora_key[:-2]
                 key_map[diffusers_lora_key] = unet_key
 
+    if isinstance(model, comfy.model_base.StableCascade_C):
+        for k in sdk:
+            if k.startswith("diffusion_model."):
+                if k.endswith(".weight"):
+                    key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
+                    key_map["lora_prior_unet_{}".format(key_lora)] = k
+
     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:

comfy/model_base.py

@@ -34,6 +34,8 @@ import comfy.ldm.flux.model
 import comfy.ldm.lightricks.model
 import comfy.ldm.hunyuan_video.model
 import comfy.ldm.cosmos.model
+import comfy.ldm.lumina.model
+import comfy.ldm.wan.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -148,7 +150,9 @@ class BaseModel(torch.nn.Module):
 
         xc = xc.to(dtype)
         t = self.model_sampling.timestep(t).float()
-        context = context.to(dtype)
+        if context is not None:
+            context = context.to(dtype)
+
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
@@ -163,9 +167,6 @@ class BaseModel(torch.nn.Module):
     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
 
@@ -549,6 +550,10 @@ class SD_X4Upscaler(BaseModel):
 
         out['c_concat'] = comfy.conds.CONDNoiseShape(image)
         out['y'] = comfy.conds.CONDRegular(noise_level)
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
         return out
 
 class IP2P:
@@ -806,7 +811,10 @@ class Flux(BaseModel):
             (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
             attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
             out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
-        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+
+        guidance = kwargs.get("guidance", 3.5)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
 class GenmoMochi(BaseModel):
@@ -863,7 +871,19 @@ class HunyuanVideo(BaseModel):
         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", 6.0)]))
+
+        image = kwargs.get("concat_latent_image", None)
+        noise = kwargs.get("noise", None)
+
+        if image is not None:
+            padding_shape = (noise.shape[0], 16, noise.shape[2] - 1, noise.shape[3], noise.shape[4])
+            latent_padding = torch.zeros(padding_shape, device=noise.device, dtype=noise.dtype)
+            image_latents = torch.cat([image.to(noise), latent_padding], dim=2)
+            out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_latents))
+
+        guidance = kwargs.get("guidance", 6.0)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
 class CosmosVideo(BaseModel):
@@ -892,3 +912,63 @@ class CosmosVideo(BaseModel):
             latent_image = latent_image + noise
         latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)
         return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5)
+
+class Lumina2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            if torch.numel(attention_mask) != attention_mask.sum():
+                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
+
+class WAN21(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
+        self.image_to_video = image_to_video
+
+    def concat_cond(self, **kwargs):
+        if not self.image_to_video:
+            return None
+
+        image = kwargs.get("concat_latent_image", None)
+        noise = kwargs.get("noise", None)
+        device = kwargs["device"]
+
+        if image is None:
+            image = torch.zeros_like(noise)
+
+        image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+        image = self.process_latent_in(image)
+        image = utils.resize_to_batch_size(image, noise.shape[0])
+
+        mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if mask is None:
+            mask = torch.zeros_like(noise)[:, :4]
+        else:
+            mask = 1.0 - torch.mean(mask, dim=1, keepdim=True)
+            mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            if mask.shape[-3] < noise.shape[-3]:
+                mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
+            mask = mask.repeat(1, 4, 1, 1, 1)
+            mask = utils.resize_to_batch_size(mask, noise.shape[0])
+
+        return torch.cat((mask, image), dim=1)
+
+    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)
+
+        clip_vision_output = kwargs.get("clip_vision_output", None)
+        if clip_vision_output is not None:
+            out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states)
+        return out

comfy/model_detection.py

@@ -136,7 +136,7 @@ def detect_unet_config(state_dict, key_prefix):
     if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video
         dit_config = {}
         dit_config["image_model"] = "hunyuan_video"
-        dit_config["in_channels"] = 16
+        dit_config["in_channels"] = state_dict['{}img_in.proj.weight'.format(key_prefix)].shape[1] #SkyReels img2video has 32 input channels
         dit_config["patch_size"] = [1, 2, 2]
         dit_config["out_channels"] = 16
         dit_config["vec_in_dim"] = 768
@@ -239,7 +239,7 @@ def detect_unet_config(state_dict, key_prefix):
             dit_config["micro_condition"] = False
         return dit_config
 
-    if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys:
+    if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys:  # Cosmos
         dit_config = {}
         dit_config["image_model"] = "cosmos"
         dit_config["max_img_h"] = 240
@@ -284,6 +284,42 @@ def detect_unet_config(state_dict, key_prefix):
             dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
         return dit_config
 
+    if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
+        dit_config = {}
+        dit_config["image_model"] = "lumina2"
+        dit_config["patch_size"] = 2
+        dit_config["in_channels"] = 16
+        dit_config["dim"] = 2304
+        dit_config["cap_feat_dim"] = 2304
+        dit_config["n_layers"] = 26
+        dit_config["n_heads"] = 24
+        dit_config["n_kv_heads"] = 8
+        dit_config["qk_norm"] = True
+        dit_config["axes_dims"] = [32, 32, 32]
+        dit_config["axes_lens"] = [300, 512, 512]
+        return dit_config
+
+    if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
+        dit_config = {}
+        dit_config["image_model"] = "wan2.1"
+        dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1]
+        dit_config["dim"] = dim
+        dit_config["num_heads"] = dim // 128
+        dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0]
+        dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+        dit_config["patch_size"] = (1, 2, 2)
+        dit_config["freq_dim"] = 256
+        dit_config["window_size"] = (-1, -1)
+        dit_config["qk_norm"] = True
+        dit_config["cross_attn_norm"] = True
+        dit_config["eps"] = 1e-6
+        dit_config["in_dim"] = state_dict['{}patch_embedding.weight'.format(key_prefix)].shape[1]
+        if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "i2v"
+        else:
+            dit_config["model_type"] = "t2v"
+        return dit_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 

comfy/model_management.py

@@ -50,7 +50,9 @@ 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()
+    temp = torch_version.split(".")
+    torch_version_numeric = (int(temp[0]), int(temp[1]))
+    xpu_available = (torch_version_numeric[0] < 2 or (torch_version_numeric[0] == 2 and torch_version_numeric[1] <= 4)) and torch.xpu.is_available()
 except:
     pass
 
@@ -93,6 +95,13 @@ try:
 except:
     npu_available = False
 
+try:
+    import torch_mlu  # noqa: F401
+    _ = torch.mlu.device_count()
+    mlu_available = torch.mlu.is_available()
+except:
+    mlu_available = False
+
 if args.cpu:
     cpu_state = CPUState.CPU
 
@@ -110,6 +119,12 @@ def is_ascend_npu():
         return True
     return False
 
+def is_mlu():
+    global mlu_available
+    if mlu_available:
+        return True
+    return False
+
 def get_torch_device():
     global directml_enabled
     global cpu_state
@@ -125,6 +140,8 @@ def get_torch_device():
             return torch.device("xpu", torch.xpu.current_device())
         elif is_ascend_npu():
             return torch.device("npu", torch.npu.current_device())
+        elif is_mlu():
+            return torch.device("mlu", torch.mlu.current_device())
         else:
             return torch.device(torch.cuda.current_device())
 
@@ -151,6 +168,12 @@ def get_total_memory(dev=None, torch_total_too=False):
             _, mem_total_npu = torch.npu.mem_get_info(dev)
             mem_total_torch = mem_reserved
             mem_total = mem_total_npu
+        elif is_mlu():
+            stats = torch.mlu.memory_stats(dev)
+            mem_reserved = stats['reserved_bytes.all.current']
+            _, mem_total_mlu = torch.mlu.mem_get_info(dev)
+            mem_total_torch = mem_reserved
+            mem_total = mem_total_mlu
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
@@ -218,7 +241,7 @@ def is_amd():
 
 MIN_WEIGHT_MEMORY_RATIO = 0.4
 if is_nvidia():
-    MIN_WEIGHT_MEMORY_RATIO = 0.2
+    MIN_WEIGHT_MEMORY_RATIO = 0.0
 
 ENABLE_PYTORCH_ATTENTION = False
 if args.use_pytorch_cross_attention:
@@ -227,22 +250,44 @@ if args.use_pytorch_cross_attention:
 
 try:
     if is_nvidia():
-        if int(torch_version[0]) >= 2:
+        if torch_version_numeric[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 is_intel_xpu() or is_ascend_npu():
+    if is_intel_xpu() or is_ascend_npu() or is_mlu():
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             ENABLE_PYTORCH_ATTENTION = True
 except:
     pass
 
+
+try:
+    if is_amd():
+        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
+        logging.info("AMD arch: {}".format(arch))
+        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
+            if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7:  # works on 2.6 but doesn't actually seem to improve much
+                if any((a in arch) for a in ["gfx1100", "gfx1101"]):  # TODO: more arches
+                    ENABLE_PYTORCH_ATTENTION = True
+except:
+    pass
+
+
 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)
 
+
+PRIORITIZE_FP16 = False  # TODO: remove and replace with something that shows exactly which dtype is faster than the other
 try:
-    if int(torch_version[0]) == 2 and int(torch_version[2]) >= 5:
+    if is_nvidia() and args.fast:
+        torch.backends.cuda.matmul.allow_fp16_accumulation = True
+        PRIORITIZE_FP16 = True  # TODO: limit to cards where it actually boosts performance
+except:
+    pass
+
+try:
+    if torch_version_numeric[0] == 2 and torch_version_numeric[1] >= 5:
         torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
 except:
     logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp")
@@ -256,15 +301,10 @@ 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
@@ -297,6 +337,8 @@ def get_torch_device_name(device):
         return "{} {}".format(device, torch.xpu.get_device_name(device))
     elif is_ascend_npu():
         return "{} {}".format(device, torch.npu.get_device_name(device))
+    elif is_mlu():
+        return "{} {}".format(device, torch.mlu.get_device_name(device))
     else:
         return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
 
@@ -535,14 +577,11 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             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)
             loaded_memory = loaded_model.model_loaded_memory()
             current_free_mem = get_free_memory(torch_dev) + loaded_memory
 
             lowvram_model_memory = max(64 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
             lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_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 = 0.1
@@ -668,6 +707,10 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
         if model_params * 2 > free_model_memory:
             return fp8_dtype
 
+    if PRIORITIZE_FP16:
+        if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params):
+            return torch.float16
+
     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:
@@ -885,6 +928,8 @@ def xformers_enabled():
         return False
     if is_ascend_npu():
         return False
+    if is_mlu():
+        return False
     if directml_enabled:
         return False
     return XFORMERS_IS_AVAILABLE
@@ -901,6 +946,11 @@ def pytorch_attention_enabled():
     global ENABLE_PYTORCH_ATTENTION
     return ENABLE_PYTORCH_ATTENTION
 
+def pytorch_attention_enabled_vae():
+    if is_amd():
+        return False  # enabling pytorch attention on AMD currently causes crash when doing high res
+    return pytorch_attention_enabled()
+
 def pytorch_attention_flash_attention():
     global ENABLE_PYTORCH_ATTENTION
     if ENABLE_PYTORCH_ATTENTION:
@@ -911,6 +961,10 @@ def pytorch_attention_flash_attention():
             return True
         if is_ascend_npu():
             return True
+        if is_mlu():
+            return True
+        if is_amd():
+            return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention
     return False
 
 def mac_version():
@@ -923,11 +977,11 @@ def force_upcast_attention_dtype():
     upcast = args.force_upcast_attention
 
     macos_version = mac_version()
-    if macos_version is not None and ((14, 5) <= macos_version <= (15, 2)):  # black image bug on recent versions of macOS
+    if macos_version is not None and ((14, 5) <= macos_version < (16,)):  # black image bug on recent versions of macOS
         upcast = True
 
     if upcast:
-        return torch.float32
+        return {torch.float16: torch.float32}
     else:
         return None
 
@@ -957,6 +1011,13 @@ def get_free_memory(dev=None, torch_free_too=False):
             mem_free_npu, _ = torch.npu.mem_get_info(dev)
             mem_free_torch = mem_reserved - mem_active
             mem_free_total = mem_free_npu + mem_free_torch
+        elif is_mlu():
+            stats = torch.mlu.memory_stats(dev)
+            mem_active = stats['active_bytes.all.current']
+            mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_mlu, _ = torch.mlu.mem_get_info(dev)
+            mem_free_torch = mem_reserved - mem_active
+            mem_free_total = mem_free_mlu + mem_free_torch
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
@@ -993,21 +1054,26 @@ def is_device_mps(device):
 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):
+def is_directml_enabled():
     global directml_enabled
+    if directml_enabled:
+        return True
 
+    return False
+
+def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
     if device is not None:
         if is_device_cpu(device):
             return False
 
-    if FORCE_FP16:
+    if args.force_fp16:
         return True
 
     if FORCE_FP32:
         return False
 
-    if directml_enabled:
-        return False
+    if is_directml_enabled():
+        return True
 
     if (device is not None and is_device_mps(device)) or mps_mode():
         return True
@@ -1021,6 +1087,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if is_ascend_npu():
         return True
 
+    if is_mlu():
+        return True
+
     if torch.version.hip:
         return True
 
@@ -1078,13 +1147,28 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     if is_intel_xpu():
         return True
 
+    if is_ascend_npu():
+        return True
+
+    if is_amd():
+        arch = torch.cuda.get_device_properties(device).gcnArchName
+        if any((a in arch) for a in ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]):  # RDNA2 and older don't support bf16
+            if manual_cast:
+                return True
+            return False
+
     props = torch.cuda.get_device_properties(device)
+
+    if is_mlu():
+        if props.major > 3:
+            return True
+
     if props.major >= 8:
         return True
 
     bf16_works = torch.cuda.is_bf16_supported()
 
-    if bf16_works or manual_cast:
+    if bf16_works and manual_cast:
         free_model_memory = maximum_vram_for_weights(device)
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True
@@ -1103,11 +1187,11 @@ def supports_fp8_compute(device=None):
     if props.minor < 9:
         return False
 
-    if int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) < 3):
+    if torch_version_numeric[0] < 2 or (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 3):
         return False
 
     if WINDOWS:
-        if (int(torch_version[0]) == 2 and int(torch_version[2]) < 4):
+        if (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 4):
             return False
 
     return True

comfy/model_patcher.py

@@ -96,8 +96,28 @@ 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
+
+    if hasattr(m, "weight_function"):
+        m.weight_function = []
+
+    if hasattr(m, "bias_function"):
+        m.bias_function = []
+
+def move_weight_functions(m, device):
+    if device is None:
+        return 0
+
+    memory = 0
+    if hasattr(m, "weight_function"):
+        for f in m.weight_function:
+            if hasattr(f, "move_to"):
+                memory += f.move_to(device=device)
+
+    if hasattr(m, "bias_function"):
+        for f in m.bias_function:
+            if hasattr(f, "move_to"):
+                memory += f.move_to(device=device)
+    return memory
 
 class LowVramPatch:
     def __init__(self, key, patches):
@@ -192,11 +212,13 @@ class ModelPatcher:
         self.backup = {}
         self.object_patches = {}
         self.object_patches_backup = {}
+        self.weight_wrapper_patches = {}
         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.force_cast_weights = False
         self.patches_uuid = uuid.uuid4()
         self.parent = None
 
@@ -250,11 +272,14 @@ class ModelPatcher:
         n.patches_uuid = self.patches_uuid
 
         n.object_patches = self.object_patches.copy()
+        n.weight_wrapper_patches = self.weight_wrapper_patches.copy()
         n.model_options = copy.deepcopy(self.model_options)
         n.backup = self.backup
         n.object_patches_backup = self.object_patches_backup
         n.parent = self
 
+        n.force_cast_weights = self.force_cast_weights
+
         # attachments
         n.attachments = {}
         for k in self.attachments:
@@ -402,6 +427,16 @@ class ModelPatcher:
     def add_object_patch(self, name, obj):
         self.object_patches[name] = obj
 
+    def set_model_compute_dtype(self, dtype):
+        self.add_object_patch("manual_cast_dtype", dtype)
+        if dtype is not None:
+            self.force_cast_weights = True
+        self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this
+
+    def add_weight_wrapper(self, name, function):
+        self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function]
+        self.patches_uuid = uuid.uuid4()
+
     def get_model_object(self, name: str) -> torch.nn.Module:
         """Retrieves a nested attribute from an object using dot notation considering
         object patches.
@@ -566,6 +601,9 @@ class ModelPatcher:
 
                 lowvram_weight = False
 
+                weight_key = "{}.weight".format(n)
+                bias_key = "{}.bias".format(n)
+
                 if not full_load and hasattr(m, "comfy_cast_weights"):
                     if mem_counter + module_mem >= lowvram_model_memory:
                         lowvram_weight = True
@@ -573,34 +611,46 @@ class ModelPatcher:
                         if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed
                             continue
 
-                weight_key = "{}.weight".format(n)
-                bias_key = "{}.bias".format(n)
-
+                cast_weight = self.force_cast_weights
                 if lowvram_weight:
+                    if hasattr(m, "comfy_cast_weights"):
+                        m.weight_function = []
+                        m.bias_function = []
+
                     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)
+                            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)
+                            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
+                    cast_weight = True
                 else:
                     if hasattr(m, "comfy_cast_weights"):
-                        if m.comfy_cast_weights:
-                            wipe_lowvram_weight(m)
+                        wipe_lowvram_weight(m)
 
                     if full_load or mem_counter + module_mem < lowvram_model_memory:
                         mem_counter += module_mem
                         load_completely.append((module_mem, n, m, params))
 
+                if cast_weight and hasattr(m, "comfy_cast_weights"):
+                    m.prev_comfy_cast_weights = m.comfy_cast_weights
+                    m.comfy_cast_weights = True
+
+                if weight_key in self.weight_wrapper_patches:
+                    m.weight_function.extend(self.weight_wrapper_patches[weight_key])
+
+                if bias_key in self.weight_wrapper_patches:
+                    m.bias_function.extend(self.weight_wrapper_patches[bias_key])
+
+                mem_counter += move_weight_functions(m, device_to)
+
             load_completely.sort(reverse=True)
             for x in load_completely:
                 n = x[1]
@@ -662,6 +712,7 @@ class ModelPatcher:
             self.unpatch_hooks()
             if self.model.model_lowvram:
                 for m in self.model.modules():
+                    move_weight_functions(m, device_to)
                     wipe_lowvram_weight(m)
 
                 self.model.model_lowvram = False
@@ -728,15 +779,19 @@ class ModelPatcher:
                     weight_key = "{}.weight".format(n)
                     bias_key = "{}.bias".format(n)
                     if move_weight:
+                        cast_weight = self.force_cast_weights
                         m.to(device_to)
+                        module_mem += move_weight_functions(m, device_to)
                         if lowvram_possible:
                             if weight_key in self.patches:
-                                m.weight_function = LowVramPatch(weight_key, self.patches)
+                                m.weight_function.append(LowVramPatch(weight_key, self.patches))
                                 patch_counter += 1
                             if bias_key in self.patches:
-                                m.bias_function = LowVramPatch(bias_key, self.patches)
+                                m.bias_function.append(LowVramPatch(bias_key, self.patches))
                                 patch_counter += 1
+                            cast_weight = True
 
+                        if cast_weight:
                             m.prev_comfy_cast_weights = m.comfy_cast_weights
                             m.comfy_cast_weights = True
                         m.comfy_patched_weights = False

comfy/model_sampling.py

@@ -31,6 +31,7 @@ class EPS:
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
         if max_denoise:
             noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
         else:
@@ -61,9 +62,11 @@ class CONST:
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
         return sigma * noise + (1.0 - sigma) * latent_image
 
     def inverse_noise_scaling(self, sigma, latent):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1))
         return latent / (1.0 - sigma)
 
 class ModelSamplingDiscrete(torch.nn.Module):

comfy/ops.py

@@ -38,21 +38,23 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
     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
+        has_function = len(s.bias_function) > 0
         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)
+            for f in s.bias_function:
+                bias = f(bias)
 
-    has_function = s.weight_function is not None
+    has_function = len(s.weight_function) > 0
     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)
+        for f in s.weight_function:
+            weight = f(weight)
     return weight, bias
 
 class CastWeightBiasOp:
     comfy_cast_weights = False
-    weight_function = None
-    bias_function = None
+    weight_function = []
+    bias_function = []
 
 class disable_weight_init:
     class Linear(torch.nn.Linear, CastWeightBiasOp):
@@ -64,7 +66,7 @@ class disable_weight_init:
             return torch.nn.functional.linear(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -78,7 +80,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -92,7 +94,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -106,7 +108,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -120,12 +122,11 @@ class disable_weight_init:
             return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 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
@@ -139,7 +140,7 @@ class disable_weight_init:
             return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -160,7 +161,7 @@ class disable_weight_init:
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -181,7 +182,7 @@ class disable_weight_init:
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -199,7 +200,7 @@ class disable_weight_init:
             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:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 if "out_dtype" in kwargs:

comfy/sampler_helpers.py

@@ -58,7 +58,6 @@ def convert_cond(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
         temp["uuid"] = uuid.uuid4()

comfy/samplers.py

@@ -12,7 +12,6 @@ import collections
 from comfy import model_management
 import math
 import logging
-import comfy.samplers
 import comfy.sampler_helpers
 import comfy.model_patcher
 import comfy.patcher_extension
@@ -178,7 +177,7 @@ def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.H
         cond = default_conds[i]
         for x in cond:
             # do get_area_and_mult to get all the expected values
-            p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
+            p = get_area_and_mult(x, x_in, timestep)
             if p is None:
                 continue
             # replace p's mult with calculated mult
@@ -215,7 +214,7 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
                     default_c.append(x)
                     has_default_conds = True
                     continue
-                p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
+                p = get_area_and_mult(x, x_in, timestep)
                 if p is None:
                     continue
                 if p.hooks is not None:
@@ -687,7 +686,8 @@ class Sampler:
 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", "res_multistep", "res_multistep_cfg_pp"]
+                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
+                  "gradient_estimation"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

comfy/sd.py

@@ -12,6 +12,7 @@ from .ldm.audio.autoencoder import AudioOobleckVAE
 import comfy.ldm.genmo.vae.model
 import comfy.ldm.lightricks.vae.causal_video_autoencoder
 import comfy.ldm.cosmos.vae
+import comfy.ldm.wan.vae
 import yaml
 import math
 
@@ -36,6 +37,8 @@ import comfy.text_encoders.genmo
 import comfy.text_encoders.lt
 import comfy.text_encoders.hunyuan_video
 import comfy.text_encoders.cosmos
+import comfy.text_encoders.lumina2
+import comfy.text_encoders.wan
 
 import comfy.model_patcher
 import comfy.lora
@@ -391,6 +394,18 @@ class VAE:
                 self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
+            elif "decoder.middle.0.residual.0.gamma" in sd:
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                self.upscale_index_formula = (4, 8, 8)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                self.downscale_index_formula = (4, 8, 8)
+                self.latent_dim = 3
+                self.latent_channels = 16
+                ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
+                self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
+                self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+                self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -657,6 +672,8 @@ class CLIPType(Enum):
     HUNYUAN_VIDEO = 9
     PIXART = 10
     COSMOS = 11
+    LUMINA2 = 12
+    WAN = 13
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
@@ -675,6 +692,7 @@ class TEModel(Enum):
     T5_BASE = 6
     LLAMA3_8 = 7
     T5_XXL_OLD = 8
+    GEMMA_2_2B = 9
 
 def detect_te_model(sd):
     if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -693,6 +711,8 @@ def detect_te_model(sd):
         return TEModel.T5_XXL_OLD
     if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
         return TEModel.T5_BASE
+    if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
+        return TEModel.GEMMA_2_2B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         return TEModel.LLAMA3_8
     return None
@@ -730,6 +750,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             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
 
+    tokenizer_data = {}
     clip_target = EmptyClass()
     clip_target.params = {}
     if len(clip_data) == 1:
@@ -757,6 +778,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             elif clip_type == CLIPType.PIXART:
                 clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer
+            elif clip_type == CLIPType.WAN:
+                clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer
+                tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
             else: #CLIPType.MOCHI
                 clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer
@@ -769,6 +794,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif te_model == TEModel.T5_BASE:
             clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
             clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
+        elif te_model == TEModel.GEMMA_2_2B:
+            clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         else:
             if clip_type == CLIPType.SD3:
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
@@ -798,7 +827,6 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         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)

comfy/sd1_clip.py

@@ -421,10 +421,10 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     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, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}):
+    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, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}, tokenizer_args={}):
         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.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
         self.max_length = max_length
         self.min_length = min_length
         self.end_token = None
@@ -585,9 +585,14 @@ class SDTokenizer:
         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)
+    def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None):
+        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)
+
         tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
         setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
 
@@ -600,7 +605,7 @@ class SD1Tokenizer:
         return getattr(self, self.clip).untokenize(token_weight_pair)
 
     def state_dict(self):
-        return {}
+        return getattr(self, self.clip).state_dict()
 
 class SD1CheckpointClipModel(SDClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):

comfy/supported_models.py

@@ -15,6 +15,8 @@ import comfy.text_encoders.genmo
 import comfy.text_encoders.lt
 import comfy.text_encoders.hunyuan_video
 import comfy.text_encoders.cosmos
+import comfy.text_encoders.lumina2
+import comfy.text_encoders.wan
 
 from . import supported_models_base
 from . import latent_formats
@@ -865,6 +867,78 @@ class CosmosI2V(CosmosT2V):
         out = model_base.CosmosVideo(self, image_to_video=True, device=device)
         return out
 
-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, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, CosmosT2V, CosmosI2V]
+class Lumina2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "lumina2",
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 6.0,
+    }
+
+    memory_usage_factor = 1.2
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux
+
+    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.Lumina2(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect))
+
+class WAN21_T2V(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "t2v",
+    }
+
+    sampling_settings = {
+        "shift": 8.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Wan21
+
+    memory_usage_factor = 1.0
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = self.unet_config.get("dim", 2000) / 2000
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21(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, "{}umt5xxl.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.wan.WanT5Tokenizer, comfy.text_encoders.wan.te(**t5_detect))
+
+class WAN21_I2V(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "i2v",
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21(self, image_to_video=True, device=device)
+        return out
+
+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, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V]
 
 models += [SVD_img2vid]

comfy/text_encoders/bert.py

@@ -118,7 +118,7 @@ class BertModel_(torch.nn.Module):
         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"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
 
         x, i = self.encoder(x, mask, intermediate_output)
         return x, i

comfy/text_encoders/llama.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from dataclasses import dataclass
 from typing import Optional, Any
 
@@ -21,15 +20,41 @@ class Llama2Config:
     max_position_embeddings: int = 8192
     rms_norm_eps: float = 1e-5
     rope_theta: float = 500000.0
+    transformer_type: str = "llama"
+    head_dim = 128
+    rms_norm_add = False
+    mlp_activation = "silu"
+
+@dataclass
+class Gemma2_2B_Config:
+    vocab_size: int = 256000
+    hidden_size: int = 2304
+    intermediate_size: int = 9216
+    num_hidden_layers: int = 26
+    num_attention_heads: int = 8
+    num_key_value_heads: int = 4
+    max_position_embeddings: int = 8192
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 10000.0
+    transformer_type: str = "gemma2"
+    head_dim = 256
+    rms_norm_add = True
+    mlp_activation = "gelu_pytorch_tanh"
 
 class RMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-5, device=None, dtype=None):
+    def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
         super().__init__()
         self.eps = eps
         self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+        self.add = add
 
     def forward(self, x: torch.Tensor):
-        return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
+        w = self.weight
+        if self.add:
+            w = w + 1.0
+
+        return comfy.ldm.common_dit.rms_norm(x, w, self.eps)
+
 
 
 def rotate_half(x):
@@ -68,13 +93,15 @@ class Attention(nn.Module):
         self.num_heads = config.num_attention_heads
         self.num_kv_heads = config.num_key_value_heads
         self.hidden_size = config.hidden_size
-        self.head_dim = self.hidden_size // self.num_heads
+
+        self.head_dim = config.head_dim
+        self.inner_size = self.num_heads * self.head_dim
 
         ops = ops or nn
-        self.q_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=False, device=device, dtype=dtype)
         self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
         self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
-        self.o_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
 
     def forward(
         self,
@@ -84,7 +111,6 @@ class Attention(nn.Module):
         optimized_attention=None,
     ):
         batch_size, seq_length, _ = hidden_states.shape
-
         xq = self.q_proj(hidden_states)
         xk = self.k_proj(hidden_states)
         xv = self.v_proj(hidden_states)
@@ -108,9 +134,13 @@ class MLP(nn.Module):
         self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)
         self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)
         self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        if config.mlp_activation == "silu":
+            self.activation = torch.nn.functional.silu
+        elif config.mlp_activation == "gelu_pytorch_tanh":
+            self.activation = lambda a: torch.nn.functional.gelu(a, approximate="tanh")
 
     def forward(self, x):
-        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+        return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x))
 
 class TransformerBlock(nn.Module):
     def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
@@ -146,6 +176,45 @@ class TransformerBlock(nn.Module):
 
         return x
 
+class TransformerBlockGemma2(nn.Module):
+    def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
+        super().__init__()
+        self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
+        self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        freqs_cis: Optional[torch.Tensor] = None,
+        optimized_attention=None,
+    ):
+        # Self Attention
+        residual = x
+        x = self.input_layernorm(x)
+        x = self.self_attn(
+            hidden_states=x,
+            attention_mask=attention_mask,
+            freqs_cis=freqs_cis,
+            optimized_attention=optimized_attention,
+        )
+
+        x = self.post_attention_layernorm(x)
+        x = residual + x
+
+        # MLP
+        residual = x
+        x = self.pre_feedforward_layernorm(x)
+        x = self.mlp(x)
+        x = self.post_feedforward_layernorm(x)
+        x = residual + x
+
+        return x
+
 class Llama2_(nn.Module):
     def __init__(self, config, device=None, dtype=None, ops=None):
         super().__init__()
@@ -158,17 +227,27 @@ class Llama2_(nn.Module):
             device=device,
             dtype=dtype
         )
+        if self.config.transformer_type == "gemma2":
+            transformer = TransformerBlockGemma2
+            self.normalize_in = True
+        else:
+            transformer = TransformerBlock
+            self.normalize_in = False
+
         self.layers = nn.ModuleList([
-            TransformerBlock(config, device=device, dtype=dtype, ops=ops)
+            transformer(config, device=device, dtype=dtype, ops=ops)
             for _ in range(config.num_hidden_layers)
         ])
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
         # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
     def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
         x = self.embed_tokens(x, out_dtype=dtype)
 
-        freqs_cis = precompute_freqs_cis(self.config.hidden_size // self.config.num_attention_heads,
+        if self.normalize_in:
+            x *= self.config.hidden_size ** 0.5
+
+        freqs_cis = precompute_freqs_cis(self.config.head_dim,
                                          x.shape[1],
                                          self.config.rope_theta,
                                          device=x.device)
@@ -206,16 +285,7 @@ class Llama2_(nn.Module):
 
         return x, intermediate
 
-
-class Llama2(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        config = Llama2Config(**config_dict)
-        self.num_layers = config.num_hidden_layers
-
-        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
-        self.dtype = dtype
-
+class BaseLlama:
     def get_input_embeddings(self):
         return self.model.embed_tokens
 
@@ -224,3 +294,23 @@ class Llama2(torch.nn.Module):
 
     def forward(self, input_ids, *args, **kwargs):
         return self.model(input_ids, *args, **kwargs)
+
+
+class Llama2(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Llama2Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
+
+class Gemma2_2B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Gemma2_2B_Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype

comfy/text_encoders/lumina2.py

@@ -0,0 +1,39 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.llama
+
+
+class Gemma2BTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False})
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+
+class LuminaTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer)
+
+
+class Gemma2_2BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class LuminaModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="gemma2_2b", clip_model=Gemma2_2BModel, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_scaled_fp8=None):
+    class LuminaTEModel_(LuminaModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["scaled_fp8"] = llama_scaled_fp8
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return LuminaTEModel_

comfy/text_encoders/spiece_tokenizer.py

@@ -1,21 +1,21 @@
 import torch
 
 class SPieceTokenizer:
-    add_eos = True
-
     @staticmethod
-    def from_pretrained(path):
-        return SPieceTokenizer(path)
+    def from_pretrained(path, **kwargs):
+        return SPieceTokenizer(path, **kwargs)
 
-    def __init__(self, tokenizer_path):
+    def __init__(self, tokenizer_path, add_bos=False, add_eos=True):
+        self.add_bos = add_bos
+        self.add_eos = add_eos
         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)
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
         else:
-            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos)
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
 
     def get_vocab(self):
         out = {}

comfy/text_encoders/t5.py

@@ -203,7 +203,7 @@ class T5Stack(torch.nn.Module):
         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"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
 
         intermediate = None
         optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)

comfy/text_encoders/umt5_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": "umt5",
+  "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": 256384
+}

comfy/text_encoders/wan.py

@@ -0,0 +1,37 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.t5
+import os
+
+class UMT5XXlModel(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__)), "umt5_config_xxl.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, zero_out_masked=True, model_options=model_options)
+
+class UMT5XXlTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+
+class WanT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="umt5xxl", tokenizer=UMT5XXlTokenizer)
+
+class WanT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs)
+
+def te(dtype_t5=None, t5xxl_scaled_fp8=None):
+    class WanTEModel(WanT5Model):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if t5xxl_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["scaled_fp8"] = t5xxl_scaled_fp8
+            if dtype_t5 is not None:
+                dtype = dtype_t5
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return WanTEModel

comfy/utils.py

@@ -43,13 +43,23 @@ if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in
     torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode])
     ALWAYS_SAFE_LOAD = True
     logging.info("Checkpoint files will always be loaded safely.")
-
+else:
+    logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.")
 
 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)
+        try:
+            sd = safetensors.torch.load_file(ckpt, device=device.type)
+        except Exception as e:
+            if len(e.args) > 0:
+                message = e.args[0]
+                if "HeaderTooLarge" in message:
+                    raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt))
+                if "MetadataIncompleteBuffer" in message:
+                    raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt))
+            raise e
     else:
         if safe_load or ALWAYS_SAFE_LOAD:
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True)

comfy_extras/nodes_flux.py

@@ -38,7 +38,26 @@ class FluxGuidance:
         return (c, )
 
 
+class FluxDisableGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "conditioning": ("CONDITIONING", ),
+            }}
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "advanced/conditioning/flux"
+    DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models"
+
+    def append(self, conditioning):
+        c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
+        return (c, )
+
+
 NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
     "FluxGuidance": FluxGuidance,
+    "FluxDisableGuidance": FluxDisableGuidance,
 }

comfy_extras/nodes_latent.py

@@ -2,10 +2,14 @@ import comfy.utils
 import comfy_extras.nodes_post_processing
 import torch
 
-def reshape_latent_to(target_shape, latent):
+
+def reshape_latent_to(target_shape, latent, repeat_batch=True):
     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])
+        latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center")
+    if repeat_batch:
+        return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+    else:
+        return latent
 
 
 class LatentAdd:
@@ -116,8 +120,7 @@ class LatentBatch:
         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")
+        s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False)
         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])])

comfy_extras/nodes_load_3d.py

@@ -19,14 +19,8 @@ class Load3D():
             "image": ("LOAD_3D", {}),
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
-            "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
-            "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
-            "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
         }}
 
     RETURN_TYPES = ("IMAGE", "MASK", "STRING")
@@ -38,22 +32,14 @@ class Load3D():
     CATEGORY = "3d"
 
     def process(self, model_file, image, **kwargs):
-        if isinstance(image, dict):
-            image_path = folder_paths.get_annotated_filepath(image['image'])
-            mask_path = folder_paths.get_annotated_filepath(image['mask'])
+        image_path = folder_paths.get_annotated_filepath(image['image'])
+        mask_path = folder_paths.get_annotated_filepath(image['mask'])
 
-            load_image_node = nodes.LoadImage()
-            output_image, ignore_mask = load_image_node.load_image(image=image_path)
-            ignore_image, output_mask = load_image_node.load_image(image=mask_path)
+        load_image_node = nodes.LoadImage()
+        output_image, ignore_mask = load_image_node.load_image(image=image_path)
+        ignore_image, output_mask = load_image_node.load_image(image=mask_path)
 
-            return output_image, output_mask, model_file,
-        else:
-            # to avoid the format is not dict which will happen the FE code is not compatibility to core,
-            # we need to this to double-check, it can be removed after merged FE into the core
-            image_path = folder_paths.get_annotated_filepath(image)
-            load_image_node = nodes.LoadImage()
-            output_image, output_mask = load_image_node.load_image(image=image_path)
-            return output_image, output_mask, model_file,
+        return output_image, output_mask, model_file,
 
 class Load3DAnimation():
     @classmethod
@@ -69,15 +55,8 @@ class Load3DAnimation():
             "image": ("LOAD_3D_ANIMATION", {}),
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
-            "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
-            "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
-            "animation_speed": (["0.1", "0.5", "1", "1.5", "2"], {"default": "1"}),
-            "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
         }}
 
     RETURN_TYPES = ("IMAGE", "MASK", "STRING")
@@ -89,34 +68,42 @@ class Load3DAnimation():
     CATEGORY = "3d"
 
     def process(self, model_file, image, **kwargs):
-        if isinstance(image, dict):
-            image_path = folder_paths.get_annotated_filepath(image['image'])
-            mask_path = folder_paths.get_annotated_filepath(image['mask'])
+        image_path = folder_paths.get_annotated_filepath(image['image'])
+        mask_path = folder_paths.get_annotated_filepath(image['mask'])
 
-            load_image_node = nodes.LoadImage()
-            output_image, ignore_mask = load_image_node.load_image(image=image_path)
-            ignore_image, output_mask = load_image_node.load_image(image=mask_path)
+        load_image_node = nodes.LoadImage()
+        output_image, ignore_mask = load_image_node.load_image(image=image_path)
+        ignore_image, output_mask = load_image_node.load_image(image=mask_path)
 
-            return output_image, output_mask, model_file,
-        else:
-            image_path = folder_paths.get_annotated_filepath(image)
-            load_image_node = nodes.LoadImage()
-            output_image, output_mask = load_image_node.load_image(image=image_path)
-            return output_image, output_mask, model_file,
+        return output_image, output_mask, model_file,
 
 class Preview3D():
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
             "model_file": ("STRING", {"default": "", "multiline": False}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
-            "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
-            "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
-            "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
+        }}
+
+    OUTPUT_NODE = True
+    RETURN_TYPES = ()
+
+    CATEGORY = "3d"
+
+    FUNCTION = "process"
+    EXPERIMENTAL = True
+
+    def process(self, model_file, **kwargs):
+        return {"ui": {"model_file": [model_file]}, "result": ()}
+
+class Preview3DAnimation():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model_file": ("STRING", {"default": "", "multiline": False}),
+            "material": (["original", "normal", "wireframe", "depth"],),
+            "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
         }}
 
     OUTPUT_NODE = True
@@ -133,11 +120,13 @@ class Preview3D():
 NODE_CLASS_MAPPINGS = {
     "Load3D": Load3D,
     "Load3DAnimation": Load3DAnimation,
-    "Preview3D": Preview3D
+    "Preview3D": Preview3D,
+    "Preview3DAnimation": Preview3DAnimation
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "Load3D": "Load 3D",
     "Load3DAnimation": "Load 3D - Animation",
-    "Preview3D": "Preview 3D"
+    "Preview3D": "Preview 3D",
+    "Preview3DAnimation": "Preview 3D - Animation"
 }

comfy_extras/nodes_lumina2.py

@@ -0,0 +1,104 @@
+from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
+import torch
+
+
+class RenormCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "cfg_trunc": ("FLOAT", {"default": 100, "min": 0.0, "max": 100.0, "step": 0.01}),
+                              "renorm_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, cfg_trunc, renorm_cfg):
+        def renorm_cfg_func(args):
+            cond_denoised = args["cond_denoised"]
+            uncond_denoised = args["uncond_denoised"]
+            cond_scale = args["cond_scale"]
+            timestep = args["timestep"]
+            x_orig = args["input"]
+            in_channels = model.model.diffusion_model.in_channels
+
+            if timestep[0] < cfg_trunc:
+                cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels]
+                cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:]
+                half_eps = uncond_eps + cond_scale * (cond_eps - uncond_eps)
+                half_rest = cond_rest
+
+                if float(renorm_cfg) > 0.0:
+                    ori_pos_norm = torch.linalg.vector_norm(cond_eps
+                            , dim=tuple(range(1, len(cond_eps.shape))), keepdim=True
+                    )
+                    max_new_norm = ori_pos_norm * float(renorm_cfg)
+                    new_pos_norm = torch.linalg.vector_norm(
+                            half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True
+                        )
+                    if new_pos_norm >= max_new_norm:
+                        half_eps = half_eps * (max_new_norm / new_pos_norm)
+            else:
+                cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels]
+                cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:]
+                half_eps = cond_eps
+                half_rest = cond_rest
+
+            cfg_result = torch.cat([half_eps, half_rest], dim=1)
+
+            # cfg_result = uncond_denoised + (cond_denoised - uncond_denoised) * cond_scale
+
+            return x_orig - cfg_result
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(renorm_cfg_func)
+        return (m, )
+
+
+class CLIPTextEncodeLumina2(ComfyNodeABC):
+    SYSTEM_PROMPT = {
+        "superior": "You are an assistant designed to generate superior images with the superior "\
+            "degree of image-text alignment based on textual prompts or user prompts.",
+        "alignment": "You are an assistant designed to generate high-quality images with the "\
+            "highest degree of image-text alignment based on textual prompts."
+    }
+    SYSTEM_PROMPT_TIP = "Lumina2 provide two types of system prompts:" \
+        "Superior: You are an assistant designed to generate superior images with the superior "\
+        "degree of image-text alignment based on textual prompts or user prompts. "\
+        "Alignment: You are an assistant designed to generate high-quality images with the highest "\
+        "degree of image-text alignment based on textual prompts."
+    @classmethod
+    def INPUT_TYPES(s) -> InputTypeDict:
+        return {
+            "required": {
+                "system_prompt": (list(CLIPTextEncodeLumina2.SYSTEM_PROMPT.keys()), {"tooltip": CLIPTextEncodeLumina2.SYSTEM_PROMPT_TIP}),
+                "user_prompt": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
+                "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."})
+            }
+        }
+    RETURN_TYPES = (IO.CONDITIONING,)
+    OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning"
+    DESCRIPTION = "Encodes a system prompt and a user 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, user_prompt, system_prompt):
+        if clip is None:
+            raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
+        system_prompt = CLIPTextEncodeLumina2.SYSTEM_PROMPT[system_prompt]
+        prompt = f'{system_prompt} <Prompt Start> {user_prompt}'
+        tokens = clip.tokenize(prompt)
+        return (clip.encode_from_tokens_scheduled(tokens), )
+
+
+NODE_CLASS_MAPPINGS = {
+    "CLIPTextEncodeLumina2": CLIPTextEncodeLumina2,
+    "RenormCFG": RenormCFG
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "CLIPTextEncodeLumina2": "CLIP Text Encode for Lumina2",
+}

comfy_extras/nodes_model_advanced.py

@@ -3,6 +3,8 @@ import comfy.model_sampling
 import comfy.latent_formats
 import nodes
 import torch
+import node_helpers
+
 
 class LCM(comfy.model_sampling.EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
@@ -294,6 +296,24 @@ class RescaleCFG:
         m.set_model_sampler_cfg_function(rescale_cfg)
         return (m, )
 
+class ModelComputeDtype:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "dtype": (["default", "fp32", "fp16", "bf16"],),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/debug/model"
+
+    def patch(self, model, dtype):
+        m = model.clone()
+        m.set_model_compute_dtype(node_helpers.string_to_torch_dtype(dtype))
+        return (m, )
+
+
 NODE_CLASS_MAPPINGS = {
     "ModelSamplingDiscrete": ModelSamplingDiscrete,
     "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
@@ -303,4 +323,5 @@ NODE_CLASS_MAPPINGS = {
     "ModelSamplingAuraFlow": ModelSamplingAuraFlow,
     "ModelSamplingFlux": ModelSamplingFlux,
     "RescaleCFG": RescaleCFG,
+    "ModelComputeDtype": ModelComputeDtype,
 }

comfy_extras/nodes_model_merging_model_specific.py

@@ -196,6 +196,54 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
+class ModelMergeCosmos7B(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_embedder."] = argument
+        arg_dict["extra_pos_embedder."] = argument
+        arg_dict["x_embedder."] = argument
+        arg_dict["t_embedder."] = argument
+        arg_dict["affline_norm."] = argument
+
+
+        for i in range(28):
+            arg_dict["blocks.block{}.".format(i)] = argument
+
+        arg_dict["final_layer."] = argument
+
+        return {"required": arg_dict}
+
+class ModelMergeCosmos14B(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_embedder."] = argument
+        arg_dict["extra_pos_embedder."] = argument
+        arg_dict["x_embedder."] = argument
+        arg_dict["t_embedder."] = argument
+        arg_dict["affline_norm."] = argument
+
+
+        for i in range(36):
+            arg_dict["blocks.block{}.".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
@@ -206,4 +254,6 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeSD35_Large": ModelMergeSD35_Large,
     "ModelMergeMochiPreview": ModelMergeMochiPreview,
     "ModelMergeLTXV": ModelMergeLTXV,
+    "ModelMergeCosmos7B": ModelMergeCosmos7B,
+    "ModelMergeCosmos14B": ModelMergeCosmos14B,
 }

comfy_extras/nodes_video.py

@@ -0,0 +1,76 @@
+import os
+import av
+import torch
+import folder_paths
+import json
+from fractions import Fraction
+
+
+class SaveWEBM:
+    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"}),
+                     "codec": (["vp9", "av1"],),
+                     "fps": ("FLOAT", {"default": 24.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
+                     "crf": ("FLOAT", {"default": 32.0, "min": 0, "max": 63.0, "step": 1, "tooltip": "Higher crf means lower quality with a smaller file size, lower crf means higher quality higher filesize."}),
+                     },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "image/video"
+
+    EXPERIMENTAL = True
+
+    def save_images(self, images, codec, fps, filename_prefix, crf, 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])
+
+        file = f"{filename}_{counter:05}_.webm"
+        container = av.open(os.path.join(full_output_folder, file), mode="w")
+
+        if prompt is not None:
+            container.metadata["prompt"] = json.dumps(prompt)
+
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                container.metadata[x] = json.dumps(extra_pnginfo[x])
+
+        codec_map = {"vp9": "libvpx-vp9", "av1": "libaom-av1"}
+        stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000))
+        stream.width = images.shape[-2]
+        stream.height = images.shape[-3]
+        stream.pix_fmt = "yuv420p"
+        stream.bit_rate = 0
+        stream.options = {'crf': str(crf)}
+
+        for frame in images:
+            frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24")
+            for packet in stream.encode(frame):
+                container.mux(packet)
+        container.mux(stream.encode())
+        container.close()
+
+        results = [{
+            "filename": file,
+            "subfolder": subfolder,
+            "type": self.type
+        }]
+
+        return {"ui": {"images": results, "animated": (True,)}}  # TODO: frontend side
+
+
+NODE_CLASS_MAPPINGS = {
+    "SaveWEBM": SaveWEBM,
+}

comfy_extras/nodes_wan.py

@@ -0,0 +1,54 @@
+import nodes
+import node_helpers
+import torch
+import comfy.model_management
+import comfy.utils
+
+
+class WanImageToVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE", ),
+                             "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 720, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                },
+                "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                             "start_image": ("IMAGE", ),
+                }}
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/video_models"
+
+    def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None):
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5
+            image[:start_image.shape[0]] = start_image
+
+            concat_latent_image = vae.encode(image[:, :, :, :3])
+            mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+            mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+            positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
+            negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return (positive, negative, out_latent)
+
+
+NODE_CLASS_MAPPINGS = {
+    "WanImageToVideo": WanImageToVideo,
+}

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.12"
+__version__ = "0.3.18"

folder_paths.py

@@ -7,11 +7,18 @@ import logging
 from typing import Literal
 from collections.abc import Collection
 
-supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
+from comfy.cli_args import args
+
+supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.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__))
+# --base-directory - Resets all default paths configured in folder_paths with a new base path
+if args.base_directory:
+    base_path = os.path.abspath(args.base_directory)
+else:
+    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"])
@@ -39,10 +46,10 @@ folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")]
 
 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")
+output_directory = os.path.join(base_path, "output")
+temp_directory = os.path.join(base_path, "temp")
+input_directory = os.path.join(base_path, "input")
+user_directory = os.path.join(base_path, "user")
 
 filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
 

latent_preview.py

@@ -12,7 +12,10 @@ MAX_PREVIEW_RESOLUTION = args.preview_size
 def preview_to_image(latent_image):
         latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
                             .mul(0xFF)  # to 0..255
-                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+                            )
+        if comfy.model_management.directml_enabled:
+                latents_ubyte = latents_ubyte.to(dtype=torch.uint8)
+        latents_ubyte = latents_ubyte.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
 
         return Image.fromarray(latents_ubyte.numpy())
 

main.py

@@ -138,6 +138,8 @@ import server
 from server import BinaryEventTypes
 import nodes
 import comfy.model_management
+import comfyui_version
+
 
 def cuda_malloc_warning():
     device = comfy.model_management.get_torch_device()
@@ -292,6 +294,7 @@ def start_comfyui(asyncio_loop=None):
 
 if __name__ == "__main__":
     # Running directly, just start ComfyUI.
+    logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
     event_loop, _, start_all_func = start_comfyui()
     try:
         event_loop.run_until_complete(start_all_func())

node_helpers.py

@@ -1,4 +1,5 @@
 import hashlib
+import torch
 
 from comfy.cli_args import args
 
@@ -35,3 +36,11 @@ def hasher():
         "sha512": hashlib.sha512
     }
     return hashfuncs[args.default_hashing_function]
+
+def string_to_torch_dtype(string):
+    if string == "fp32":
+        return torch.float32
+    if string == "fp16":
+        return torch.float16
+    if string == "bf16":
+        return torch.bfloat16

nodes.py

@@ -63,6 +63,8 @@ class CLIPTextEncode(ComfyNodeABC):
     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):
+        if clip is None:
+            raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
         tokens = clip.tokenize(text)
         return (clip.encode_from_tokens_scheduled(tokens), )
 
@@ -912,7 +914,7 @@ 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", "ltxv", "pixart", "cosmos"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -922,7 +924,7 @@ class CLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5\ncosmos: old t5 xxl"
+    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl"
 
     def load_clip(self, clip_name, type="stable_diffusion", device="default"):
         if type == "stable_cascade":
@@ -937,6 +939,12 @@ class CLIPLoader:
             clip_type = comfy.sd.CLIPType.LTXV
         elif type == "pixart":
             clip_type = comfy.sd.CLIPType.PIXART
+        elif type == "cosmos":
+            clip_type = comfy.sd.CLIPType.COSMOS
+        elif type == "lumina2":
+            clip_type = comfy.sd.CLIPType.LUMINA2
+        elif type == "wan":
+            clip_type = comfy.sd.CLIPType.WAN
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
@@ -1058,10 +1066,11 @@ class StyleModelApply:
         for t in conditioning:
             (txt, keys) = t
             keys = keys.copy()
-            if strength_type == "attn_bias" and strength != 1.0:
+            # even if the strength is 1.0 (i.e, no change), if there's already a mask, we have to add to it
+            if "attention_mask" in keys or (strength_type == "attn_bias" and strength != 1.0):
                 # math.log raises an error if the argument is zero
                 # torch.log returns -inf, which is what we want
-                attn_bias = torch.log(torch.Tensor([strength]))
+                attn_bias = torch.log(torch.Tensor([strength if strength_type == "attn_bias" else 1.0]))
                 # get the size of the mask image
                 mask_ref_size = keys.get("attention_mask_img_shape", (1, 1))
                 n_ref = mask_ref_size[0] * mask_ref_size[1]
@@ -1756,6 +1765,36 @@ class LoadImageMask:
 
         return True
 
+
+class LoadImageOutput(LoadImage):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("COMBO", {
+                    "image_upload": True,
+                    "image_folder": "output",
+                    "remote": {
+                        "route": "/internal/files/output",
+                        "refresh_button": True,
+                        "control_after_refresh": "first",
+                    },
+                }),
+            }
+        }
+
+    DESCRIPTION = "Load an image from the output folder. When the refresh button is clicked, the node will update the image list and automatically select the first image, allowing for easy iteration."
+    EXPERIMENTAL = True
+    FUNCTION = "load_image_output"
+
+    def load_image_output(self, image):
+        return self.load_image(f"{image} [output]")
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        return True
+
+
 class ImageScale:
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
     crop_methods = ["disabled", "center"]
@@ -1942,6 +1981,7 @@ NODE_CLASS_MAPPINGS = {
     "PreviewImage": PreviewImage,
     "LoadImage": LoadImage,
     "LoadImageMask": LoadImageMask,
+    "LoadImageOutput": LoadImageOutput,
     "ImageScale": ImageScale,
     "ImageScaleBy": ImageScaleBy,
     "ImageInvert": ImageInvert,
@@ -2042,6 +2082,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "PreviewImage": "Preview Image",
     "LoadImage": "Load Image",
     "LoadImageMask": "Load Image (as Mask)",
+    "LoadImageOutput": "Load Image (from Outputs)",
     "ImageScale": "Upscale Image",
     "ImageScaleBy": "Upscale Image By",
     "ImageUpscaleWithModel": "Upscale Image (using Model)",
@@ -2226,6 +2267,9 @@ def init_builtin_extra_nodes():
         "nodes_hooks.py",
         "nodes_load_3d.py",
         "nodes_cosmos.py",
+        "nodes_video.py",
+        "nodes_lumina2.py",
+        "nodes_wan.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.12"
+version = "0.3.18"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -8,7 +8,8 @@ transformers>=4.28.1
 tokenizers>=0.13.3
 sentencepiece
 safetensors>=0.4.2
-aiohttp
+aiohttp>=3.11.8
+yarl>=1.18.0
 pyyaml
 Pillow
 scipy
@@ -19,3 +20,4 @@ psutil
 kornia>=0.7.1
 spandrel
 soundfile
+av

server.py

@@ -52,6 +52,20 @@ async def cache_control(request: web.Request, handler):
         response.headers.setdefault('Cache-Control', 'no-cache')
     return response
 
+
+@web.middleware
+async def compress_body(request: web.Request, handler):
+    accept_encoding = request.headers.get("Accept-Encoding", "")
+    response: web.Response = await handler(request)
+    if not isinstance(response, web.Response):
+        return response
+    if response.content_type not in ["application/json", "text/plain"]:
+        return response
+    if response.body and "gzip" in accept_encoding:
+        response.enable_compression()
+    return response
+
+
 def create_cors_middleware(allowed_origin: str):
     @web.middleware
     async def cors_middleware(request: web.Request, handler):
@@ -136,7 +150,8 @@ class PromptServer():
         PromptServer.instance = self
 
         mimetypes.init()
-        mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8'
+        mimetypes.add_type('application/javascript; charset=utf-8', '.js')
+        mimetypes.add_type('image/webp', '.webp')
 
         self.user_manager = UserManager()
         self.model_file_manager = ModelFileManager()
@@ -150,6 +165,9 @@ class PromptServer():
         self.number = 0
 
         middlewares = [cache_control]
+        if args.enable_compress_response_body:
+            middlewares.append(compress_body)
+
         if args.enable_cors_header:
             middlewares.append(create_cors_middleware(args.enable_cors_header))
         else:
@@ -329,6 +347,9 @@ class PromptServer():
                 original_ref = json.loads(post.get("original_ref"))
                 filename, output_dir = folder_paths.annotated_filepath(original_ref['filename'])
 
+                if not filename:
+                    return web.Response(status=400)
+
                 # validation for security: prevent accessing arbitrary path
                 if filename[0] == '/' or '..' in filename:
                     return web.Response(status=400)
@@ -370,6 +391,9 @@ class PromptServer():
                 filename = request.rel_url.query["filename"]
                 filename,output_dir = folder_paths.annotated_filepath(filename)
 
+                if not filename:
+                    return web.Response(status=400)
+
                 # validation for security: prevent accessing arbitrary path
                 if filename[0] == '/' or '..' in filename:
                     return web.Response(status=400)

tests-unit/app_test/custom_node_manager_test.py

@@ -2,39 +2,146 @@ import pytest
 from aiohttp import web
 from unittest.mock import patch
 from app.custom_node_manager import CustomNodeManager
+import json
 
 pytestmark = (
     pytest.mark.asyncio
 )  # This applies the asyncio mark to all test functions in the module
 
+
 @pytest.fixture
 def custom_node_manager():
     return CustomNodeManager()
 
+
 @pytest.fixture
 def app(custom_node_manager):
     app = web.Application()
     routes = web.RouteTableDef()
-    custom_node_manager.add_routes(routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")])
+    custom_node_manager.add_routes(
+        routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")]
+    )
     app.add_routes(routes)
     return app
 
+
 async def test_get_workflow_templates(aiohttp_client, app, tmp_path):
     client = await aiohttp_client(app)
     # Setup temporary custom nodes file structure with 1 workflow file
     custom_nodes_dir = tmp_path / "custom_nodes"
-    example_workflows_dir = custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows"
+    example_workflows_dir = (
+        custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows"
+    )
     example_workflows_dir.mkdir(parents=True)
     template_file = example_workflows_dir / "workflow1.json"
-    template_file.write_text('')
+    template_file.write_text("")
 
-    with patch('folder_paths.folder_names_and_paths', {
-        'custom_nodes': ([str(custom_nodes_dir)], None)
-    }):
-        response = await client.get('/workflow_templates')
+    with patch(
+        "folder_paths.folder_names_and_paths",
+        {"custom_nodes": ([str(custom_nodes_dir)], None)},
+    ):
+        response = await client.get("/workflow_templates")
         assert response.status == 200
         workflows_dict = await response.json()
         assert isinstance(workflows_dict, dict)
         assert "ComfyUI-TestExtension1" in workflows_dict
         assert isinstance(workflows_dict["ComfyUI-TestExtension1"], list)
         assert workflows_dict["ComfyUI-TestExtension1"][0] == "workflow1"
+
+
+async def test_build_translations_empty_when_no_locales(custom_node_manager, tmp_path):
+    custom_nodes_dir = tmp_path / "custom_nodes"
+    custom_nodes_dir.mkdir(parents=True)
+
+    with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]):
+        translations = custom_node_manager.build_translations()
+        assert translations == {}
+
+
+async def test_build_translations_loads_all_files(custom_node_manager, tmp_path):
+    # Setup test directory structure
+    custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension"
+    locales_dir = custom_nodes_dir / "locales" / "en"
+    locales_dir.mkdir(parents=True)
+
+    # Create test translation files
+    main_content = {"title": "Test Extension"}
+    (locales_dir / "main.json").write_text(json.dumps(main_content))
+
+    node_defs = {"node1": "Node 1"}
+    (locales_dir / "nodeDefs.json").write_text(json.dumps(node_defs))
+
+    commands = {"cmd1": "Command 1"}
+    (locales_dir / "commands.json").write_text(json.dumps(commands))
+
+    settings = {"setting1": "Setting 1"}
+    (locales_dir / "settings.json").write_text(json.dumps(settings))
+
+    with patch(
+        "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"]
+    ):
+        translations = custom_node_manager.build_translations()
+
+        assert translations == {
+            "en": {
+                "title": "Test Extension",
+                "nodeDefs": {"node1": "Node 1"},
+                "commands": {"cmd1": "Command 1"},
+                "settings": {"setting1": "Setting 1"},
+            }
+        }
+
+
+async def test_build_translations_handles_invalid_json(custom_node_manager, tmp_path):
+    # Setup test directory structure
+    custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension"
+    locales_dir = custom_nodes_dir / "locales" / "en"
+    locales_dir.mkdir(parents=True)
+
+    # Create valid main.json
+    main_content = {"title": "Test Extension"}
+    (locales_dir / "main.json").write_text(json.dumps(main_content))
+
+    # Create invalid JSON file
+    (locales_dir / "nodeDefs.json").write_text("invalid json{")
+
+    with patch(
+        "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"]
+    ):
+        translations = custom_node_manager.build_translations()
+
+        assert translations == {
+            "en": {
+                "title": "Test Extension",
+            }
+        }
+
+
+async def test_build_translations_merges_multiple_extensions(
+    custom_node_manager, tmp_path
+):
+    # Setup test directory structure for two extensions
+    custom_nodes_dir = tmp_path / "custom_nodes"
+    ext1_dir = custom_nodes_dir / "extension1" / "locales" / "en"
+    ext2_dir = custom_nodes_dir / "extension2" / "locales" / "en"
+    ext1_dir.mkdir(parents=True)
+    ext2_dir.mkdir(parents=True)
+
+    # Create translation files for extension 1
+    ext1_main = {"title": "Extension 1", "shared": "Original"}
+    (ext1_dir / "main.json").write_text(json.dumps(ext1_main))
+
+    # Create translation files for extension 2
+    ext2_main = {"description": "Extension 2", "shared": "Override"}
+    (ext2_dir / "main.json").write_text(json.dumps(ext2_main))
+
+    with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]):
+        translations = custom_node_manager.build_translations()
+
+        assert translations == {
+            "en": {
+                "title": "Extension 1",
+                "description": "Extension 2",
+                "shared": "Override",  # Second extension should override first
+            }
+        }

tests-unit/comfy_test/folder_path_test.py

@@ -1,19 +1,23 @@
 ### 🗻 This file is created through the spirit of Mount Fuji at its peak
 # TODO(yoland): clean up this after I get back down
+import sys
 import pytest
 import os
 import tempfile
 from unittest.mock import patch
+from importlib import reload
 
 import folder_paths
+import comfy.cli_args
+from comfy.options import enable_args_parsing
+enable_args_parsing()
+
 
 @pytest.fixture()
 def clear_folder_paths():
-    # Clear the global dictionary before each test to ensure isolation
-    original = folder_paths.folder_names_and_paths.copy()
-    folder_paths.folder_names_and_paths.clear()
+    # Reload the module after each test to ensure isolation
     yield
-    folder_paths.folder_names_and_paths = original
+    reload(folder_paths)
 
 @pytest.fixture
 def temp_dir():
@@ -21,7 +25,21 @@ def temp_dir():
         yield tmpdirname
 
 
-def test_get_directory_by_type():
+@pytest.fixture
+def set_base_dir():
+    def _set_base_dir(base_dir):
+        # Mock CLI args
+        with patch.object(sys, 'argv', ["main.py", "--base-directory", base_dir]):
+            reload(comfy.cli_args)
+            reload(folder_paths)
+    yield _set_base_dir
+    # Reload the modules after each test to ensure isolation
+    with patch.object(sys, 'argv', ["main.py"]):
+        reload(comfy.cli_args)
+        reload(folder_paths)
+
+
+def test_get_directory_by_type(clear_folder_paths):
     test_dir = "/test/dir"
     folder_paths.set_output_directory(test_dir)
     assert folder_paths.get_directory_by_type("output") == test_dir
@@ -96,3 +114,49 @@ def test_get_save_image_path(temp_dir):
         assert counter == 1
         assert subfolder == ""
         assert filename_prefix == "test"
+
+
+def test_base_path_changes(set_base_dir):
+    test_dir = os.path.abspath("/test/dir")
+    set_base_dir(test_dir)
+
+    assert folder_paths.base_path == test_dir
+    assert folder_paths.models_dir == os.path.join(test_dir, "models")
+    assert folder_paths.input_directory == os.path.join(test_dir, "input")
+    assert folder_paths.output_directory == os.path.join(test_dir, "output")
+    assert folder_paths.temp_directory == os.path.join(test_dir, "temp")
+    assert folder_paths.user_directory == os.path.join(test_dir, "user")
+
+    assert os.path.join(test_dir, "custom_nodes") in folder_paths.get_folder_paths("custom_nodes")
+
+    for name in ["checkpoints", "loras", "vae", "configs", "embeddings", "controlnet", "classifiers"]:
+        assert folder_paths.get_folder_paths(name)[0] == os.path.join(test_dir, "models", name)
+
+
+def test_base_path_change_clears_old(set_base_dir):
+    test_dir = os.path.abspath("/test/dir")
+    set_base_dir(test_dir)
+
+    assert len(folder_paths.get_folder_paths("custom_nodes")) == 1
+
+    single_model_paths = [
+        "checkpoints",
+        "loras",
+        "vae",
+        "configs",
+        "clip_vision",
+        "style_models",
+        "diffusers",
+        "vae_approx",
+        "gligen",
+        "upscale_models",
+        "embeddings",
+        "hypernetworks",
+        "photomaker",
+        "classifiers",
+    ]
+    for name in single_model_paths:
+        assert len(folder_paths.get_folder_paths(name)) == 1
+
+    for name in ["controlnet", "diffusion_models", "text_encoders"]:
+        assert len(folder_paths.get_folder_paths(name)) == 2

tests-unit/server/routes/internal_routes_test.py

@@ -1,115 +0,0 @@
-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}")  # noqa: T201
-    except Exception as e:
-        print(f"Exception occurred during GET request: {e}")  # noqa: T201
-        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

tests-unit/server/services/file_service_test.py

@@ -1,54 +0,0 @@
-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}")

tests-unit/utils/extra_config_test.py

@@ -114,7 +114,7 @@ def test_load_extra_model_paths_expands_userpath(
     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')
+    mock_file.assert_called_once_with(dummy_yaml_file_name, 'r', encoding='utf-8')
 
 
 @patch('builtins.open', new_callable=mock_open)
@@ -145,7 +145,7 @@ def test_load_extra_model_paths_expands_appdata(
     else:
         expected_base_path = '/Users/TestUser/AppData/Roaming/ComfyUI'
     expected_calls = [
-        ('checkpoints', os.path.join(expected_base_path, 'models/checkpoints'), False),
+        ('checkpoints', os.path.normpath(os.path.join(expected_base_path, 'models/checkpoints')), False),
     ]
 
     assert mock_add_model_folder_path.call_count == len(expected_calls)
@@ -197,8 +197,8 @@ def test_load_extra_path_config_relative_base_path(
 
     load_extra_path_config(dummy_yaml_name)
 
-    expected_checkpoints = os.path.abspath(os.path.join(str(tmp_path), sub_folder, "checkpoints"))
-    expected_some_value = os.path.abspath(os.path.join(str(tmp_path), sub_folder, "some_value"))
+    expected_checkpoints = os.path.abspath(os.path.join(str(tmp_path), "my_rel_base", "checkpoints"))
+    expected_some_value = os.path.abspath(os.path.join(str(tmp_path), "my_rel_base", "some_value"))
 
     actual_paths = folder_paths.folder_names_and_paths["checkpoints"][0]
     assert len(actual_paths) == 1, "Should have one path added for 'checkpoints'."

tests-unit/utils/json_util_test.py

@@ -0,0 +1,71 @@
+from utils.json_util import merge_json_recursive
+
+
+def test_merge_simple_dicts():
+    base = {"a": 1, "b": 2}
+    update = {"b": 3, "c": 4}
+    expected = {"a": 1, "b": 3, "c": 4}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_nested_dicts():
+    base = {"a": {"x": 1, "y": 2}, "b": 3}
+    update = {"a": {"y": 4, "z": 5}}
+    expected = {"a": {"x": 1, "y": 4, "z": 5}, "b": 3}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_lists():
+    base = {"a": [1, 2], "b": 3}
+    update = {"a": [3, 4]}
+    expected = {"a": [1, 2, 3, 4], "b": 3}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_nested_lists():
+    base = {"a": {"x": [1, 2]}}
+    update = {"a": {"x": [3, 4]}}
+    expected = {"a": {"x": [1, 2, 3, 4]}}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_mixed_types():
+    base = {"a": [1, 2], "b": {"x": 1}}
+    update = {"a": [3], "b": {"y": 2}}
+    expected = {"a": [1, 2, 3], "b": {"x": 1, "y": 2}}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_overwrite_non_dict():
+    base = {"a": 1}
+    update = {"a": {"x": 2}}
+    expected = {"a": {"x": 2}}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_empty_dicts():
+    base = {}
+    update = {"a": 1}
+    expected = {"a": 1}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_none_values():
+    base = {"a": None}
+    update = {"a": {"x": 1}}
+    expected = {"a": {"x": 1}}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_different_types():
+    base = {"a": [1, 2]}
+    update = {"a": "string"}
+    expected = {"a": "string"}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_complex_nested():
+    base = {"a": [1, 2], "b": {"x": [3, 4], "y": {"p": 1}}}
+    update = {"a": [5], "b": {"x": [6], "y": {"q": 2}}}
+    expected = {"a": [1, 2, 5], "b": {"x": [3, 4, 6], "y": {"p": 1, "q": 2}}}
+    assert merge_json_recursive(base, update) == expected

utils/extra_config.py

@@ -4,7 +4,7 @@ import folder_paths
 import logging
 
 def load_extra_path_config(yaml_path):
-    with open(yaml_path, 'r') as stream:
+    with open(yaml_path, 'r', encoding='utf-8') as stream:
         config = yaml.safe_load(stream)
     yaml_dir = os.path.dirname(os.path.abspath(yaml_path))
     for c in config:
@@ -29,5 +29,6 @@ def load_extra_path_config(yaml_path):
                     full_path = os.path.join(base_path, full_path)
                 elif not os.path.isabs(full_path):
                     full_path = os.path.abspath(os.path.join(yaml_dir, y))
-                logging.info("Adding extra search path {} {}".format(x, full_path))
-                folder_paths.add_model_folder_path(x, full_path, is_default)
+                normalized_path = os.path.normpath(full_path)
+                logging.info("Adding extra search path {} {}".format(x, normalized_path))
+                folder_paths.add_model_folder_path(x, normalized_path, is_default)

utils/json_util.py

@@ -0,0 +1,26 @@
+def merge_json_recursive(base, update):
+    """Recursively merge two JSON-like objects.
+    - Dictionaries are merged recursively
+    - Lists are concatenated
+    - Other types are overwritten by the update value
+
+    Args:
+        base: Base JSON-like object
+        update: Update JSON-like object to merge into base
+
+    Returns:
+        Merged JSON-like object
+    """
+    if not isinstance(base, dict) or not isinstance(update, dict):
+        if isinstance(base, list) and isinstance(update, list):
+            return base + update
+        return update
+
+    merged = base.copy()
+    for key, value in update.items():
+        if key in merged:
+            merged[key] = merge_json_recursive(merged[key], value)
+        else:
+            merged[key] = value
+
+    return merged

web/assets/BaseViewTemplate-BNGF4K22.js

@@ -1,23 +0,0 @@
-import { d as defineComponent, o as openBlock, f as createElementBlock, J as renderSlot, T as normalizeClass } from "./index-DjNHn37O.js";
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "BaseViewTemplate",
-  props: {
-    dark: { type: Boolean, default: false }
-  },
-  setup(__props) {
-    const props = __props;
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", {
-        class: normalizeClass(["font-sans w-screen h-screen flex items-center justify-center pointer-events-auto overflow-auto", [
-          props.dark ? "text-neutral-300 bg-neutral-900 dark-theme" : "text-neutral-900 bg-neutral-300"
-        ]])
-      }, [
-        renderSlot(_ctx.$slots, "default")
-      ], 2);
-    };
-  }
-});
-export {
-  _sfc_main as _
-};
-//# sourceMappingURL=BaseViewTemplate-BNGF4K22.js.map

web/assets/BaseViewTemplate-BTbuZf5t.js

@@ -0,0 +1,51 @@
+import { d as defineComponent, T as ref, p as onMounted, b8 as isElectron, V as nextTick, b9 as electronAPI, o as openBlock, f as createElementBlock, i as withDirectives, v as vShow, j as unref, ba as isNativeWindow, m as createBaseVNode, A as renderSlot, aj as normalizeClass } from "./index-Bv0b06LE.js";
+const _hoisted_1 = { class: "flex-grow w-full flex items-center justify-center overflow-auto" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "BaseViewTemplate",
+  props: {
+    dark: { type: Boolean, default: false }
+  },
+  setup(__props) {
+    const props = __props;
+    const darkTheme = {
+      color: "rgba(0, 0, 0, 0)",
+      symbolColor: "#d4d4d4"
+    };
+    const lightTheme = {
+      color: "rgba(0, 0, 0, 0)",
+      symbolColor: "#171717"
+    };
+    const topMenuRef = ref(null);
+    onMounted(async () => {
+      if (isElectron()) {
+        await nextTick();
+        electronAPI().changeTheme({
+          ...props.dark ? darkTheme : lightTheme,
+          height: topMenuRef.value.getBoundingClientRect().height
+        });
+      }
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", {
+        class: normalizeClass(["font-sans w-screen h-screen flex flex-col", [
+          props.dark ? "text-neutral-300 bg-neutral-900 dark-theme" : "text-neutral-900 bg-neutral-300"
+        ]])
+      }, [
+        withDirectives(createBaseVNode("div", {
+          ref_key: "topMenuRef",
+          ref: topMenuRef,
+          class: "app-drag w-full h-[var(--comfy-topbar-height)]"
+        }, null, 512), [
+          [vShow, unref(isNativeWindow)()]
+        ]),
+        createBaseVNode("div", _hoisted_1, [
+          renderSlot(_ctx.$slots, "default")
+        ])
+      ], 2);
+    };
+  }
+});
+export {
+  _sfc_main as _
+};
+//# sourceMappingURL=BaseViewTemplate-BTbuZf5t.js.map

web/assets/DesktopStartView-D9r53Bue.js

@@ -0,0 +1,19 @@
+import { d as defineComponent, o as openBlock, y as createBlock, z as withCtx, k as createVNode, j as unref, bE as script } from "./index-Bv0b06LE.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "DesktopStartView",
+  setup(__props) {
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
+        default: withCtx(() => [
+          createVNode(unref(script), { class: "m-8 w-48 h-48" })
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=DesktopStartView-D9r53Bue.js.map

web/assets/DesktopUpdateView-C-R0415K.js

@@ -0,0 +1,58 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, T as ref, d8 as onUnmounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, j as unref, bg as t, k as createVNode, bE as script, l as script$1, b9 as electronAPI, _ as _export_sfc } from "./index-Bv0b06LE.js";
+import { s as script$2 } from "./index-A_bXPJCN.js";
+import { _ as _sfc_main$1 } from "./TerminalOutputDrawer-CKr7Br7O.js";
+import { _ as _sfc_main$2 } from "./BaseViewTemplate-BTbuZf5t.js";
+const _hoisted_1 = { class: "h-screen w-screen grid items-center justify-around overflow-y-auto" };
+const _hoisted_2 = { class: "relative m-8 text-center" };
+const _hoisted_3 = { class: "download-bg pi-download text-4xl font-bold" };
+const _hoisted_4 = { class: "m-8" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "DesktopUpdateView",
+  setup(__props) {
+    const electron = electronAPI();
+    const terminalVisible = ref(false);
+    const toggleConsoleDrawer = /* @__PURE__ */ __name(() => {
+      terminalVisible.value = !terminalVisible.value;
+    }, "toggleConsoleDrawer");
+    onUnmounted(() => electron.Validation.dispose());
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$2, { dark: "" }, {
+        default: withCtx(() => [
+          createBaseVNode("div", _hoisted_1, [
+            createBaseVNode("div", _hoisted_2, [
+              createBaseVNode("h1", _hoisted_3, toDisplayString(unref(t)("desktopUpdate.title")), 1),
+              createBaseVNode("div", _hoisted_4, [
+                createBaseVNode("span", null, toDisplayString(unref(t)("desktopUpdate.description")), 1)
+              ]),
+              createVNode(unref(script), { class: "m-8 w-48 h-48" }),
+              createVNode(unref(script$1), {
+                style: { "transform": "translateX(-50%)" },
+                class: "fixed bottom-0 left-1/2 my-8",
+                label: unref(t)("maintenance.consoleLogs"),
+                icon: "pi pi-desktop",
+                "icon-pos": "left",
+                severity: "secondary",
+                onClick: toggleConsoleDrawer
+              }, null, 8, ["label"]),
+              createVNode(_sfc_main$1, {
+                modelValue: terminalVisible.value,
+                "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => terminalVisible.value = $event),
+                header: unref(t)("g.terminal"),
+                "default-message": unref(t)("desktopUpdate.terminalDefaultMessage")
+              }, null, 8, ["modelValue", "header", "default-message"])
+            ])
+          ]),
+          createVNode(unref(script$2))
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+const DesktopUpdateView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-8d77828d"]]);
+export {
+  DesktopUpdateView as default
+};
+//# sourceMappingURL=DesktopUpdateView-C-R0415K.js.map

web/assets/DesktopUpdateView-CxchaIvw.css

@@ -0,0 +1,20 @@
+
+.download-bg[data-v-8d77828d]::before {
+  position: absolute;
+  margin: 0px;
+  color: var(--p-text-muted-color);
+  font-family: 'primeicons';
+  top: -2rem;
+  right: 2rem;
+  speak: none;
+  font-style: normal;
+  font-weight: normal;
+  font-variant: normal;
+  text-transform: none;
+  line-height: 1;
+  display: inline-block;
+  -webkit-font-smoothing: antialiased;
+  opacity: 0.02;
+  font-size: min(14rem, 90vw);
+  z-index: 0
+}

web/assets/DownloadGitView-PWqK5ke4.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, bW as useRouter } from "./index-DjNHn37O.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+import { d as defineComponent, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, l as script, bi as useRouter } from "./index-Bv0b06LE.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
 const _hoisted_1 = { class: "max-w-screen-sm flex flex-col gap-8 p-8 bg-[url('/assets/images/Git-Logo-White.svg')] bg-no-repeat bg-right-top bg-origin-padding" };
 const _hoisted_2 = { class: "mt-24 text-4xl font-bold text-red-500" };
 const _hoisted_3 = { class: "space-y-4" };
@@ -55,4 +55,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=DownloadGitView-DeC7MBzG.js.map
+//# sourceMappingURL=DownloadGitView-PWqK5ke4.js.map

web/assets/ExtensionPanel-Ba57xrmg.js

@@ -1,9 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, ab as ref, cn as FilterMatchMode, cs as useExtensionStore, a as useSettingStore, m as onMounted, c as computed, o as openBlock, k as createBlock, M as withCtx, N as createVNode, co as SearchBox, j as unref, bZ as script, H as createBaseVNode, f as createElementBlock, E as renderList, X as toDisplayString, aE as createTextVNode, F as Fragment, l as script$1, I as createCommentVNode, aI as script$3, bO as script$4, c4 as script$5, cp as _sfc_main$1 } from "./index-DjNHn37O.js";
-import { s as script$2, a as script$6 } from "./index-B5F0uxTQ.js";
-import "./index-B-aVupP5.js";
-import "./index-5HFeZax4.js";
+import { d as defineComponent, T as ref, dx as FilterMatchMode, dC as useExtensionStore, a as useSettingStore, p as onMounted, c as computed, o as openBlock, y as createBlock, z as withCtx, k as createVNode, dy as SearchBox, j as unref, bn as script, m as createBaseVNode, f as createElementBlock, D as renderList, E as toDisplayString, a8 as createTextVNode, F as Fragment, l as script$1, B as createCommentVNode, a5 as script$3, ay as script$4, br as script$5, dz as _sfc_main$1 } from "./index-Bv0b06LE.js";
+import { g as script$2, h as script$6 } from "./index-CgMyWf7n.js";
+import "./index-Dzu9WL4p.js";
 const _hoisted_1 = { class: "flex justify-end" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "ExtensionPanel",
@@ -180,4 +179,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ExtensionPanel-D4Phn0Zr.js.map
+//# sourceMappingURL=ExtensionPanel-Ba57xrmg.js.map

web/assets/GraphView-Bo28XDd0.css

@@ -0,0 +1,383 @@
+
+.comfy-menu-hamburger[data-v-82120b51] {
+    position: fixed;
+    z-index: 9999;
+    display: flex;
+    flex-direction: row
+}
+
+[data-v-e50caa15] .p-splitter-gutter {
+  pointer-events: auto;
+}
+[data-v-e50caa15] .p-splitter-gutter:hover,[data-v-e50caa15] .p-splitter-gutter[data-p-gutter-resizing='true'] {
+  transition: background-color 0.2s ease 300ms;
+  background-color: var(--p-primary-color);
+}
+.side-bar-panel[data-v-e50caa15] {
+  background-color: var(--bg-color);
+  pointer-events: auto;
+}
+.bottom-panel[data-v-e50caa15] {
+  background-color: var(--bg-color);
+  pointer-events: auto;
+}
+.splitter-overlay[data-v-e50caa15] {
+  pointer-events: none;
+  border-style: none;
+  background-color: transparent;
+}
+.splitter-overlay-root[data-v-e50caa15] {
+  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;
+}
+
+.p-buttongroup-vertical[data-v-27a9500c] {
+  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-27a9500c] {
+  margin: 0;
+  border-radius: 0;
+}
+
+.node-tooltip[data-v-f03142eb] {
+  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;
+}
+
+.group-title-editor.node-title-editor[data-v-12d3fd12] {
+  z-index: 9999;
+  padding: 0.25rem;
+}
+[data-v-12d3fd12] .editable-text {
+  width: 100%;
+  height: 100%;
+}
+[data-v-12d3fd12] .editable-text input {
+  width: 100%;
+  height: 100%;
+  /* Override the default font size */
+  font-size: inherit;
+}
+
+[data-v-fd0a74bd] .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;
+}
+
+.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;
+}
+
+.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-6ab4daa6] {
+  width: var(--sidebar-width);
+  height: var(--sidebar-width);
+  border-radius: 0;
+}
+.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-6ab4daa6],
+.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-6ab4daa6]:hover {
+  border-left: 4px solid var(--p-button-text-primary-color);
+}
+.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-6ab4daa6],
+.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-6ab4daa6]:hover {
+  border-right: 4px solid var(--p-button-text-primary-color);
+}
+
+.side-tool-bar-container[data-v-04875455] {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+
+  width: var(--sidebar-width);
+  height: 100%;
+
+  background-color: var(--comfy-menu-secondary-bg);
+  color: var(--fg-color);
+  box-shadow: var(--bar-shadow);
+
+  --sidebar-width: 4rem;
+  --sidebar-icon-size: 1.5rem;
+}
+.side-tool-bar-container.small-sidebar[data-v-04875455] {
+  --sidebar-width: 2.5rem;
+  --sidebar-icon-size: 1rem;
+}
+.side-tool-bar-end[data-v-04875455] {
+  align-self: flex-end;
+  margin-top: auto;
+}
+
+.status-indicator[data-v-fd6ae3af] {
+  position: absolute;
+  font-weight: 700;
+  font-size: 1.5rem;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%)
+}
+
+[data-v-54fadc45] .p-togglebutton {
+  position: relative;
+  flex-shrink: 0;
+  border-radius: 0px;
+  border-width: 0px;
+  border-right-width: 1px;
+  border-style: solid;
+  background-color: transparent;
+  padding: 0px;
+  border-right-color: var(--border-color)
+}
+[data-v-54fadc45] .p-togglebutton::before {
+  display: none
+}
+[data-v-54fadc45] .p-togglebutton:first-child {
+  border-left-width: 1px;
+  border-style: solid;
+  border-left-color: var(--border-color)
+}
+[data-v-54fadc45] .p-togglebutton:not(:first-child) {
+  border-left-width: 0px
+}
+[data-v-54fadc45] .p-togglebutton.p-togglebutton-checked {
+  height: 100%;
+  border-bottom-width: 1px;
+  border-style: solid;
+  border-bottom-color: var(--p-button-text-primary-color)
+}
+[data-v-54fadc45] .p-togglebutton:not(.p-togglebutton-checked) {
+  opacity: 0.75
+}
+[data-v-54fadc45] .p-togglebutton-checked .close-button,[data-v-54fadc45] .p-togglebutton:hover .close-button {
+  visibility: visible
+}
+[data-v-54fadc45] .p-togglebutton:hover .status-indicator {
+  display: none
+}
+[data-v-54fadc45] .p-togglebutton .close-button {
+  visibility: hidden
+}
+[data-v-54fadc45] .p-scrollpanel-content {
+  height: 100%
+}
+
+/* Scrollbar half opacity to avoid blocking the active tab bottom border */
+[data-v-54fadc45] .p-scrollpanel:hover .p-scrollpanel-bar,[data-v-54fadc45] .p-scrollpanel:active .p-scrollpanel-bar {
+  opacity: 0.5
+}
+[data-v-54fadc45] .p-selectbutton {
+  height: 100%;
+  border-radius: 0px
+}
+
+[data-v-6ab68035] .workflow-tabs {
+  background-color: var(--comfy-menu-bg);
+}
+
+[data-v-26957f1f] .p-inputtext {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.comfyui-queue-button[data-v-91a628af] .p-splitbutton-dropdown {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+.actionbar[data-v-ebd56d51] {
+  pointer-events: all;
+  position: fixed;
+  z-index: 1000;
+}
+.actionbar.is-docked[data-v-ebd56d51] {
+  position: static;
+  border-style: none;
+  background-color: transparent;
+  padding: 0px;
+}
+.actionbar.is-dragging[data-v-ebd56d51] {
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
+}
+[data-v-ebd56d51] .p-panel-content {
+  padding: 0.25rem;
+}
+.is-docked[data-v-ebd56d51] .p-panel-content {
+  padding: 0px;
+}
+[data-v-ebd56d51] .p-panel-header {
+  display: none;
+}
+.drag-handle[data-v-ebd56d51] {
+  height: -moz-max-content;
+  height: max-content;
+  width: 0.75rem;
+}
+
+.top-menubar[data-v-56df69d2] .p-menubar-item-link svg {
+  display: none;
+}
+[data-v-56df69d2] .p-menubar-submenu.dropdown-direction-up {
+  top: auto;
+  bottom: 100%;
+  flex-direction: column-reverse;
+}
+.keybinding-tag[data-v-56df69d2] {
+  background: var(--p-content-hover-background);
+  border-color: var(--p-content-border-color);
+  border-style: solid;
+}
+
+.comfyui-menu[data-v-68d3b5b9] {
+  width: 100vw;
+  height: var(--comfy-topbar-height);
+  background: var(--comfy-menu-bg);
+  color: var(--fg-color);
+  box-shadow: var(--bar-shadow);
+  font-family: Arial, Helvetica, sans-serif;
+  font-size: 0.8em;
+  box-sizing: border-box;
+  z-index: 1000;
+  order: 0;
+  grid-column: 1/-1;
+}
+.comfyui-menu.dropzone[data-v-68d3b5b9] {
+  background: var(--p-highlight-background);
+}
+.comfyui-menu.dropzone-active[data-v-68d3b5b9] {
+  background: var(--p-highlight-background-focus);
+}
+[data-v-68d3b5b9] .p-menubar-item-label {
+  line-height: revert;
+}
+.comfyui-logo[data-v-68d3b5b9] {
+  font-size: 1.2em;
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
+  cursor: default;
+}
+
+.comfyui-body[data-v-e89d9273] {
+  grid-template-columns: auto 1fr auto;
+  grid-template-rows: auto 1fr auto;
+}
+
+/**
+  +------------------+------------------+------------------+
+  |                                                        |
+  |  .comfyui-body-                                        |
+  |       top                                              |
+  | (spans all cols)                                       |
+  |                                                        |
+  +------------------+------------------+------------------+
+  |                  |                  |                  |
+  | .comfyui-body-   |   #graph-canvas  | .comfyui-body-   |
+  |      left        |                  |      right       |
+  |                  |                  |                  |
+  |                  |                  |                  |
+  +------------------+------------------+------------------+
+  |                                                        |
+  |  .comfyui-body-                                        |
+  |      bottom                                            |
+  | (spans all cols)                                       |
+  |                                                        |
+  +------------------+------------------+------------------+
+*/
+.comfyui-body-top[data-v-e89d9273] {
+  order: -5;
+  /* Span across all columns */
+  grid-column: 1/-1;
+  /* Position at the first row */
+  grid-row: 1;
+  /* Top menu bar dropdown needs to be above of graph canvas splitter overlay which is z-index: 999 */
+  /* Top menu bar z-index needs to be higher than bottom menu bar z-index as by default
+  pysssss's image feed is located at body-bottom, and it can overlap with the queue button, which
+  is located in body-top. */
+  z-index: 1001;
+  display: flex;
+  flex-direction: column;
+}
+.comfyui-body-left[data-v-e89d9273] {
+  order: -4;
+  /* Position in the first column */
+  grid-column: 1;
+  /* Position below the top element */
+  grid-row: 2;
+  z-index: 10;
+  display: flex;
+}
+.graph-canvas-container[data-v-e89d9273] {
+  width: 100%;
+  height: 100%;
+  order: -3;
+  grid-column: 2;
+  grid-row: 2;
+  position: relative;
+  overflow: hidden;
+}
+.comfyui-body-right[data-v-e89d9273] {
+  order: -2;
+  z-index: 10;
+  grid-column: 3;
+  grid-row: 2;
+}
+.comfyui-body-bottom[data-v-e89d9273] {
+  order: 4;
+  /* Span across all columns */
+  grid-column: 1/-1;
+  grid-row: 3;
+  /* Bottom menu bar dropdown needs to be above of graph canvas splitter overlay which is z-index: 999 */
+  z-index: 1000;
+  display: flex;
+  flex-direction: column;
+}

web/assets/GraphView-CIRWBKTm.css

@@ -1,273 +0,0 @@
-
-.comfy-menu-hamburger[data-v-5661bed0] {
-  pointer-events: auto;
-  position: fixed;
-  z-index: 9999;
-}
-
-[data-v-e50caa15] .p-splitter-gutter {
-  pointer-events: auto;
-}
-[data-v-e50caa15] .p-splitter-gutter:hover,[data-v-e50caa15] .p-splitter-gutter[data-p-gutter-resizing='true'] {
-  transition: background-color 0.2s ease 300ms;
-  background-color: var(--p-primary-color);
-}
-.side-bar-panel[data-v-e50caa15] {
-  background-color: var(--bg-color);
-  pointer-events: auto;
-}
-.bottom-panel[data-v-e50caa15] {
-  background-color: var(--bg-color);
-  pointer-events: auto;
-}
-.splitter-overlay[data-v-e50caa15] {
-  pointer-events: none;
-  border-style: none;
-  background-color: transparent;
-}
-.splitter-overlay-root[data-v-e50caa15] {
-  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;
-}
-
-.p-buttongroup-vertical[data-v-cf40dd39] {
-  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-cf40dd39] {
-  margin: 0;
-  border-radius: 0;
-}
-
-.node-tooltip[data-v-46859edf] {
-  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;
-}
-
-.group-title-editor.node-title-editor[data-v-12d3fd12] {
-  z-index: 9999;
-  padding: 0.25rem;
-}
-[data-v-12d3fd12] .editable-text {
-  width: 100%;
-  height: 100%;
-}
-[data-v-12d3fd12] .editable-text input {
-  width: 100%;
-  height: 100%;
-  /* Override the default font size */
-  font-size: inherit;
-}
-
-[data-v-5741c9ae] .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;
-}
-
-.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;
-}
-
-.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-6ab4daa6] {
-  width: var(--sidebar-width);
-  height: var(--sidebar-width);
-  border-radius: 0;
-}
-.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-6ab4daa6],
-.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-6ab4daa6]:hover {
-  border-left: 4px solid var(--p-button-text-primary-color);
-}
-.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-6ab4daa6],
-.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-6ab4daa6]: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-37d8d7b4] {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-
-  pointer-events: auto;
-
-  width: var(--sidebar-width);
-  height: 100%;
-
-  background-color: var(--comfy-menu-secondary-bg);
-  color: var(--fg-color);
-  box-shadow: var(--bar-shadow);
-}
-.side-tool-bar-end[data-v-37d8d7b4] {
-  align-self: flex-end;
-  margin-top: auto;
-}
-
-[data-v-b9328350] .p-inputtext {
-  border-top-left-radius: 0;
-  border-bottom-left-radius: 0;
-}
-
-.comfyui-queue-button[data-v-7f4f551b] .p-splitbutton-dropdown {
-  border-top-right-radius: 0;
-  border-bottom-right-radius: 0;
-}
-
-.actionbar[data-v-915e5456] {
-  pointer-events: all;
-  position: fixed;
-  z-index: 1000;
-}
-.actionbar.is-docked[data-v-915e5456] {
-  position: static;
-  border-style: none;
-  background-color: transparent;
-  padding: 0px;
-}
-.actionbar.is-dragging[data-v-915e5456] {
-  -webkit-user-select: none;
-     -moz-user-select: none;
-          user-select: none;
-}
-[data-v-915e5456] .p-panel-content {
-  padding: 0.25rem;
-}
-.is-docked[data-v-915e5456] .p-panel-content {
-  padding: 0px;
-}
-[data-v-915e5456] .p-panel-header {
-  display: none;
-}
-
-.top-menubar[data-v-6fecd137] .p-menubar-item-link svg {
-  display: none;
-}
-[data-v-6fecd137] .p-menubar-submenu.dropdown-direction-up {
-  top: auto;
-  bottom: 100%;
-  flex-direction: column-reverse;
-}
-.keybinding-tag[data-v-6fecd137] {
-  background: var(--p-content-hover-background);
-  border-color: var(--p-content-border-color);
-  border-style: solid;
-}
-
-.status-indicator[data-v-8d011a31] {
-  position: absolute;
-  font-weight: 700;
-  font-size: 1.5rem;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%)
-}
-
-[data-v-d485c044] .p-togglebutton::before {
-  display: none
-}
-[data-v-d485c044] .p-togglebutton {
-  position: relative;
-  flex-shrink: 0;
-  border-radius: 0px;
-  background-color: transparent;
-  padding: 0px
-}
-[data-v-d485c044] .p-togglebutton.p-togglebutton-checked {
-  border-bottom-width: 2px;
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web/assets/InstallView-DW9xwU_F.js

@@ -0,0 +1,971 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, T as ref, bq as useModel, o as openBlock, f as createElementBlock, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, br as script, bl as script$1, as as withModifiers, z as withCtx, ac as script$2, I as useI18n, c as computed, aj as normalizeClass, B as createCommentVNode, a5 as script$3, a8 as createTextVNode, b9 as electronAPI, _ as _export_sfc, p as onMounted, r as resolveDirective, bk as script$4, i as withDirectives, bs as script$5, bt as script$6, l as script$7, y as createBlock, bn as script$8, bu as MigrationItems, w as watchEffect, F as Fragment, D as renderList, bv as script$9, bw as mergeModels, bx as ValidationState, X as normalizeI18nKey, N as watch, by as checkMirrorReachable, bz as _sfc_main$7, bA as isInChina, bB as mergeValidationStates, bg as t, b3 as script$a, bC as CUDA_TORCH_URL, bD as NIGHTLY_CPU_TORCH_URL, bi as useRouter, ah as toRaw } from "./index-Bv0b06LE.js";
+import { s as script$b, a as script$c, b as script$d, c as script$e, d as script$f } from "./index-SeIZOWJp.js";
+import { P as PYTHON_MIRROR, a as PYPI_MIRROR } from "./uvMirrors-B-HKMf6X.js";
+import { _ as _sfc_main$8 } from "./BaseViewTemplate-BTbuZf5t.js";
+const _hoisted_1$5 = { class: "flex flex-col gap-6 w-[600px]" };
+const _hoisted_2$5 = { class: "flex flex-col gap-4" };
+const _hoisted_3$5 = { class: "text-2xl font-semibold text-neutral-100" };
+const _hoisted_4$5 = { class: "text-neutral-400 my-0" };
+const _hoisted_5$3 = { class: "flex flex-col bg-neutral-800 p-4 rounded-lg" };
+const _hoisted_6$3 = { class: "flex items-center gap-4" };
+const _hoisted_7$3 = { class: "flex-1" };
+const _hoisted_8$3 = { class: "text-lg font-medium text-neutral-100" };
+const _hoisted_9$3 = { class: "text-sm text-neutral-400 mt-1" };
+const _hoisted_10$3 = { class: "flex items-center gap-4" };
+const _hoisted_11$3 = { class: "flex-1" };
+const _hoisted_12$3 = { class: "text-lg font-medium text-neutral-100" };
+const _hoisted_13$1 = { class: "text-sm text-neutral-400 mt-1" };
+const _hoisted_14$1 = { class: "text-neutral-300" };
+const _hoisted_15 = { class: "font-medium mb-2" };
+const _hoisted_16 = { class: "list-disc pl-6 space-y-1" };
+const _hoisted_17 = { class: "font-medium mt-4 mb-2" };
+const _hoisted_18 = { class: "list-disc pl-6 space-y-1" };
+const _hoisted_19 = { class: "mt-4" };
+const _hoisted_20 = {
+  href: "https://comfy.org/privacy",
+  target: "_blank",
+  class: "text-blue-400 hover:text-blue-300 underline"
+};
+const _sfc_main$6 = /* @__PURE__ */ defineComponent({
+  __name: "DesktopSettingsConfiguration",
+  props: {
+    "autoUpdate": { type: Boolean, ...{ required: true } },
+    "autoUpdateModifiers": {},
+    "allowMetrics": { type: Boolean, ...{ required: true } },
+    "allowMetricsModifiers": {}
+  },
+  emits: ["update:autoUpdate", "update:allowMetrics"],
+  setup(__props) {
+    const showDialog = ref(false);
+    const autoUpdate = useModel(__props, "autoUpdate");
+    const allowMetrics = useModel(__props, "allowMetrics");
+    const showMetricsInfo = /* @__PURE__ */ __name(() => {
+      showDialog.value = true;
+    }, "showMetricsInfo");
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1$5, [
+        createBaseVNode("div", _hoisted_2$5, [
+          createBaseVNode("h2", _hoisted_3$5, toDisplayString(_ctx.$t("install.desktopAppSettings")), 1),
+          createBaseVNode("p", _hoisted_4$5, toDisplayString(_ctx.$t("install.desktopAppSettingsDescription")), 1)
+        ]),
+        createBaseVNode("div", _hoisted_5$3, [
+          createBaseVNode("div", _hoisted_6$3, [
+            createBaseVNode("div", _hoisted_7$3, [
+              createBaseVNode("h3", _hoisted_8$3, toDisplayString(_ctx.$t("install.settings.autoUpdate")), 1),
+              createBaseVNode("p", _hoisted_9$3, toDisplayString(_ctx.$t("install.settings.autoUpdateDescription")), 1)
+            ]),
+            createVNode(unref(script), {
+              modelValue: autoUpdate.value,
+              "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => autoUpdate.value = $event)
+            }, null, 8, ["modelValue"])
+          ]),
+          createVNode(unref(script$1)),
+          createBaseVNode("div", _hoisted_10$3, [
+            createBaseVNode("div", _hoisted_11$3, [
+              createBaseVNode("h3", _hoisted_12$3, toDisplayString(_ctx.$t("install.settings.allowMetrics")), 1),
+              createBaseVNode("p", _hoisted_13$1, toDisplayString(_ctx.$t("install.settings.allowMetricsDescription")), 1),
+              createBaseVNode("a", {
+                href: "#",
+                class: "text-sm text-blue-400 hover:text-blue-300 mt-1 inline-block",
+                onClick: withModifiers(showMetricsInfo, ["prevent"])
+              }, toDisplayString(_ctx.$t("install.settings.learnMoreAboutData")), 1)
+            ]),
+            createVNode(unref(script), {
+              modelValue: allowMetrics.value,
+              "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => allowMetrics.value = $event)
+            }, null, 8, ["modelValue"])
+          ])
+        ]),
+        createVNode(unref(script$2), {
+          visible: showDialog.value,
+          "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => showDialog.value = $event),
+          modal: "",
+          header: _ctx.$t("install.settings.dataCollectionDialog.title")
+        }, {
+          default: withCtx(() => [
+            createBaseVNode("div", _hoisted_14$1, [
+              createBaseVNode("h4", _hoisted_15, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.whatWeCollect")), 1),
+              createBaseVNode("ul", _hoisted_16, [
+                createBaseVNode("li", null, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.collect.errorReports")), 1),
+                createBaseVNode("li", null, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.collect.systemInfo")), 1),
+                createBaseVNode("li", null, toDisplayString(_ctx.$t(
+                  "install.settings.dataCollectionDialog.collect.userJourneyEvents"
+                )), 1)
+              ]),
+              createBaseVNode("h4", _hoisted_17, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.whatWeDoNotCollect")), 1),
+              createBaseVNode("ul", _hoisted_18, [
+                createBaseVNode("li", null, toDisplayString(_ctx.$t(
+                  "install.settings.dataCollectionDialog.doNotCollect.personalInformation"
+                )), 1),
+                createBaseVNode("li", null, toDisplayString(_ctx.$t(
+                  "install.settings.dataCollectionDialog.doNotCollect.workflowContents"
+                )), 1),
+                createBaseVNode("li", null, toDisplayString(_ctx.$t(
+                  "install.settings.dataCollectionDialog.doNotCollect.fileSystemInformation"
+                )), 1),
+                createBaseVNode("li", null, toDisplayString(_ctx.$t(
+                  "install.settings.dataCollectionDialog.doNotCollect.customNodeConfigurations"
+                )), 1)
+              ]),
+              createBaseVNode("div", _hoisted_19, [
+                createBaseVNode("a", _hoisted_20, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.viewFullPolicy")), 1)
+              ])
+            ])
+          ]),
+          _: 1
+        }, 8, ["visible", "header"])
+      ]);
+    };
+  }
+});
+const _imports_0 = "" + new URL("images/nvidia-logo.svg", import.meta.url).href;
+const _imports_1 = "" + new URL("images/apple-mps-logo.png", import.meta.url).href;
+const _imports_2 = "" + new URL("images/manual-configuration.svg", import.meta.url).href;
+const _hoisted_1$4 = { class: "flex flex-col gap-6 w-[600px] h-[30rem] select-none" };
+const _hoisted_2$4 = { class: "grow flex flex-col gap-4 text-neutral-300" };
+const _hoisted_3$4 = { class: "text-2xl font-semibold text-neutral-100" };
+const _hoisted_4$4 = { class: "m-1 text-neutral-400" };
+const _hoisted_5$2 = {
+  key: 0,
+  class: "m-1"
+};
+const _hoisted_6$2 = {
+  key: 1,
+  class: "m-1"
+};
+const _hoisted_7$2 = {
+  key: 2,
+  class: "text-neutral-300"
+};
+const _hoisted_8$2 = { class: "m-1" };
+const _hoisted_9$2 = { key: 3 };
+const _hoisted_10$2 = { class: "m-1" };
+const _hoisted_11$2 = { class: "m-1" };
+const _hoisted_12$2 = {
+  for: "cpu-mode",
+  class: "select-none"
+};
+const _sfc_main$5 = /* @__PURE__ */ defineComponent({
+  __name: "GpuPicker",
+  props: {
+    "device": {
+      required: true
+    },
+    "deviceModifiers": {}
+  },
+  emits: ["update:device"],
+  setup(__props) {
+    const { t: t2 } = useI18n();
+    const cpuMode = computed({
+      get: /* @__PURE__ */ __name(() => selected.value === "cpu", "get"),
+      set: /* @__PURE__ */ __name((value) => {
+        selected.value = value ? "cpu" : null;
+      }, "set")
+    });
+    const selected = useModel(__props, "device");
+    const electron = electronAPI();
+    const platform = electron.getPlatform();
+    const pickGpu = /* @__PURE__ */ __name((value) => {
+      const newValue = selected.value === value ? null : value;
+      selected.value = newValue;
+    }, "pickGpu");
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1$4, [
+        createBaseVNode("div", _hoisted_2$4, [
+          createBaseVNode("h2", _hoisted_3$4, toDisplayString(_ctx.$t("install.gpuSelection.selectGpu")), 1),
+          createBaseVNode("p", _hoisted_4$4, toDisplayString(_ctx.$t("install.gpuSelection.selectGpuDescription")) + ": ", 1),
+          createBaseVNode("div", {
+            class: normalizeClass(["flex gap-2 text-center transition-opacity", { selected: selected.value }])
+          }, [
+            unref(platform) !== "darwin" ? (openBlock(), createElementBlock("div", {
+              key: 0,
+              class: normalizeClass(["gpu-button", { selected: selected.value === "nvidia" }]),
+              role: "button",
+              onClick: _cache[0] || (_cache[0] = ($event) => pickGpu("nvidia"))
+            }, _cache[4] || (_cache[4] = [
+              createBaseVNode("img", {
+                class: "m-12",
+                alt: "NVIDIA logo",
+                width: "196",
+                height: "32",
+                src: _imports_0
+              }, null, -1)
+            ]), 2)) : createCommentVNode("", true),
+            unref(platform) === "darwin" ? (openBlock(), createElementBlock("div", {
+              key: 1,
+              class: normalizeClass(["gpu-button", { selected: selected.value === "mps" }]),
+              role: "button",
+              onClick: _cache[1] || (_cache[1] = ($event) => pickGpu("mps"))
+            }, _cache[5] || (_cache[5] = [
+              createBaseVNode("img", {
+                class: "rounded-lg hover-brighten",
+                alt: "Apple Metal Performance Shaders Logo",
+                width: "292",
+                ratio: "",
+                src: _imports_1
+              }, null, -1)
+            ]), 2)) : createCommentVNode("", true),
+            createBaseVNode("div", {
+              class: normalizeClass(["gpu-button", { selected: selected.value === "unsupported" }]),
+              role: "button",
+              onClick: _cache[2] || (_cache[2] = ($event) => pickGpu("unsupported"))
+            }, _cache[6] || (_cache[6] = [
+              createBaseVNode("img", {
+                class: "m-12",
+                alt: "Manual configuration",
+                width: "196",
+                src: _imports_2
+              }, null, -1)
+            ]), 2)
+          ], 2),
+          selected.value === "nvidia" ? (openBlock(), createElementBlock("p", _hoisted_5$2, [
+            createVNode(unref(script$3), {
+              icon: "pi pi-check",
+              severity: "success",
+              value: "CUDA"
+            }),
+            createTextVNode(" " + toDisplayString(_ctx.$t("install.gpuSelection.nvidiaDescription")), 1)
+          ])) : createCommentVNode("", true),
+          selected.value === "mps" ? (openBlock(), createElementBlock("p", _hoisted_6$2, [
+            createVNode(unref(script$3), {
+              icon: "pi pi-check",
+              severity: "success",
+              value: "MPS"
+            }),
+            createTextVNode(" " + toDisplayString(_ctx.$t("install.gpuSelection.mpsDescription")), 1)
+          ])) : createCommentVNode("", true),
+          selected.value === "unsupported" ? (openBlock(), createElementBlock("div", _hoisted_7$2, [
+            createBaseVNode("p", _hoisted_8$2, [
+              createVNode(unref(script$3), {
+                icon: "pi pi-exclamation-triangle",
+                severity: "warn",
+                value: unref(t2)("icon.exclamation-triangle")
+              }, null, 8, ["value"]),
+              createTextVNode(" " + toDisplayString(_ctx.$t("install.gpuSelection.customSkipsPython")), 1)
+            ]),
+            createBaseVNode("ul", null, [
+              createBaseVNode("li", null, [
+                createBaseVNode("strong", null, toDisplayString(_ctx.$t("install.gpuSelection.customComfyNeedsPython")), 1)
+              ]),
+              createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customManualVenv")), 1),
+              createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customInstallRequirements")), 1),
+              createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customMayNotWork")), 1)
+            ])
+          ])) : createCommentVNode("", true),
+          selected.value === "cpu" ? (openBlock(), createElementBlock("div", _hoisted_9$2, [
+            createBaseVNode("p", _hoisted_10$2, [
+              createVNode(unref(script$3), {
+                icon: "pi pi-exclamation-triangle",
+                severity: "warn",
+                value: unref(t2)("icon.exclamation-triangle")
+              }, null, 8, ["value"]),
+              createTextVNode(" " + toDisplayString(_ctx.$t("install.gpuSelection.cpuModeDescription")), 1)
+            ]),
+            createBaseVNode("p", _hoisted_11$2, toDisplayString(_ctx.$t("install.gpuSelection.cpuModeDescription2")), 1)
+          ])) : createCommentVNode("", true)
+        ]),
+        createBaseVNode("div", {
+          class: normalizeClass(["transition-opacity flex gap-3 h-0", {
+            "opacity-40": selected.value && selected.value !== "cpu"
+          }])
+        }, [
+          createVNode(unref(script), {
+            modelValue: cpuMode.value,
+            "onUpdate:modelValue": _cache[3] || (_cache[3] = ($event) => cpuMode.value = $event),
+            inputId: "cpu-mode",
+            class: "-translate-y-40"
+          }, null, 8, ["modelValue"]),
+          createBaseVNode("label", _hoisted_12$2, toDisplayString(_ctx.$t("install.gpuSelection.enableCpuMode")), 1)
+        ], 2)
+      ]);
+    };
+  }
+});
+const GpuPicker = /* @__PURE__ */ _export_sfc(_sfc_main$5, [["__scopeId", "data-v-79125ff6"]]);
+const _hoisted_1$3 = { class: "flex flex-col gap-6 w-[600px]" };
+const _hoisted_2$3 = { class: "flex flex-col gap-4" };
+const _hoisted_3$3 = { class: "text-2xl font-semibold text-neutral-100" };
+const _hoisted_4$3 = { class: "text-neutral-400 my-0" };
+const _hoisted_5$1 = { class: "flex gap-2" };
+const _hoisted_6$1 = { class: "bg-neutral-800 p-4 rounded-lg" };
+const _hoisted_7$1 = { class: "text-lg font-medium mt-0 mb-3 text-neutral-100" };
+const _hoisted_8$1 = { class: "flex flex-col gap-2" };
+const _hoisted_9$1 = { class: "flex items-center gap-2" };
+const _hoisted_10$1 = { class: "text-neutral-200" };
+const _hoisted_11$1 = { class: "pi pi-info-circle" };
+const _hoisted_12$1 = { class: "flex items-center gap-2" };
+const _hoisted_13 = { class: "text-neutral-200" };
+const _hoisted_14 = { class: "pi pi-info-circle" };
+const _sfc_main$4 = /* @__PURE__ */ defineComponent({
+  __name: "InstallLocationPicker",
+  props: {
+    "installPath": { required: true },
+    "installPathModifiers": {},
+    "pathError": { required: true },
+    "pathErrorModifiers": {}
+  },
+  emits: ["update:installPath", "update:pathError"],
+  setup(__props) {
+    const { t: t2 } = useI18n();
+    const installPath = useModel(__props, "installPath");
+    const pathError = useModel(__props, "pathError");
+    const pathExists = ref(false);
+    const appData = ref("");
+    const appPath = ref("");
+    const inputTouched = ref(false);
+    const electron = electronAPI();
+    onMounted(async () => {
+      const paths = await electron.getSystemPaths();
+      appData.value = paths.appData;
+      appPath.value = paths.appPath;
+      installPath.value = paths.defaultInstallPath;
+      await validatePath(paths.defaultInstallPath);
+    });
+    const validatePath = /* @__PURE__ */ __name(async (path) => {
+      try {
+        pathError.value = "";
+        pathExists.value = false;
+        const validation = await electron.validateInstallPath(path);
+        if (!validation.isValid) {
+          const errors = [];
+          if (validation.cannotWrite) errors.push(t2("install.cannotWrite"));
+          if (validation.freeSpace < validation.requiredSpace) {
+            const requiredGB = validation.requiredSpace / 1024 / 1024 / 1024;
+            errors.push(`${t2("install.insufficientFreeSpace")}: ${requiredGB} GB`);
+          }
+          if (validation.parentMissing) errors.push(t2("install.parentMissing"));
+          if (validation.error)
+            errors.push(`${t2("install.unhandledError")}: ${validation.error}`);
+          pathError.value = errors.join("\n");
+        }
+        if (validation.exists) pathExists.value = true;
+      } catch (error) {
+        pathError.value = t2("install.pathValidationFailed");
+      }
+    }, "validatePath");
+    const browsePath = /* @__PURE__ */ __name(async () => {
+      try {
+        const result = await electron.showDirectoryPicker();
+        if (result) {
+          installPath.value = result;
+          await validatePath(result);
+        }
+      } catch (error) {
+        pathError.value = t2("install.failedToSelectDirectory");
+      }
+    }, "browsePath");
+    const onFocus = /* @__PURE__ */ __name(() => {
+      if (!inputTouched.value) {
+        inputTouched.value = true;
+        return;
+      }
+      validatePath(installPath.value);
+    }, "onFocus");
+    return (_ctx, _cache) => {
+      const _directive_tooltip = resolveDirective("tooltip");
+      return openBlock(), createElementBlock("div", _hoisted_1$3, [
+        createBaseVNode("div", _hoisted_2$3, [
+          createBaseVNode("h2", _hoisted_3$3, toDisplayString(_ctx.$t("install.chooseInstallationLocation")), 1),
+          createBaseVNode("p", _hoisted_4$3, toDisplayString(_ctx.$t("install.installLocationDescription")), 1),
+          createBaseVNode("div", _hoisted_5$1, [
+            createVNode(unref(script$6), { class: "flex-1" }, {
+              default: withCtx(() => [
+                createVNode(unref(script$4), {
+                  modelValue: installPath.value,
+                  "onUpdate:modelValue": [
+                    _cache[0] || (_cache[0] = ($event) => installPath.value = $event),
+                    validatePath
+                  ],
+                  class: normalizeClass(["w-full", { "p-invalid": pathError.value }]),
+                  onFocus
+                }, null, 8, ["modelValue", "class"]),
+                withDirectives(createVNode(unref(script$5), { class: "pi pi-info-circle" }, null, 512), [
+                  [
+                    _directive_tooltip,
+                    _ctx.$t("install.installLocationTooltip"),
+                    void 0,
+                    { top: true }
+                  ]
+                ])
+              ]),
+              _: 1
+            }),
+            createVNode(unref(script$7), {
+              icon: "pi pi-folder",
+              onClick: browsePath,
+              class: "w-12"
+            })
+          ]),
+          pathError.value ? (openBlock(), createBlock(unref(script$8), {
+            key: 0,
+            severity: "error",
+            class: "whitespace-pre-line"
+          }, {
+            default: withCtx(() => [
+              createTextVNode(toDisplayString(pathError.value), 1)
+            ]),
+            _: 1
+          })) : createCommentVNode("", true),
+          pathExists.value ? (openBlock(), createBlock(unref(script$8), {
+            key: 1,
+            severity: "warn"
+          }, {
+            default: withCtx(() => [
+              createTextVNode(toDisplayString(_ctx.$t("install.pathExists")), 1)
+            ]),
+            _: 1
+          })) : createCommentVNode("", true)
+        ]),
+        createBaseVNode("div", _hoisted_6$1, [
+          createBaseVNode("h3", _hoisted_7$1, toDisplayString(_ctx.$t("install.systemLocations")), 1),
+          createBaseVNode("div", _hoisted_8$1, [
+            createBaseVNode("div", _hoisted_9$1, [
+              _cache[1] || (_cache[1] = createBaseVNode("i", { class: "pi pi-folder text-neutral-400" }, null, -1)),
+              _cache[2] || (_cache[2] = createBaseVNode("span", { class: "text-neutral-400" }, "App Data:", -1)),
+              createBaseVNode("span", _hoisted_10$1, toDisplayString(appData.value), 1),
+              withDirectives(createBaseVNode("span", _hoisted_11$1, null, 512), [
+                [_directive_tooltip, _ctx.$t("install.appDataLocationTooltip")]
+              ])
+            ]),
+            createBaseVNode("div", _hoisted_12$1, [
+              _cache[3] || (_cache[3] = createBaseVNode("i", { class: "pi pi-desktop text-neutral-400" }, null, -1)),
+              _cache[4] || (_cache[4] = createBaseVNode("span", { class: "text-neutral-400" }, "App Path:", -1)),
+              createBaseVNode("span", _hoisted_13, toDisplayString(appPath.value), 1),
+              withDirectives(createBaseVNode("span", _hoisted_14, null, 512), [
+                [_directive_tooltip, _ctx.$t("install.appPathLocationTooltip")]
+              ])
+            ])
+          ])
+        ])
+      ]);
+    };
+  }
+});
+const _hoisted_1$2 = { class: "flex flex-col gap-6 w-[600px]" };
+const _hoisted_2$2 = { class: "flex flex-col gap-4" };
+const _hoisted_3$2 = { class: "text-2xl font-semibold text-neutral-100" };
+const _hoisted_4$2 = { class: "text-neutral-400 my-0" };
+const _hoisted_5 = { class: "flex gap-2" };
+const _hoisted_6 = {
+  key: 0,
+  class: "flex flex-col gap-4 bg-neutral-800 p-4 rounded-lg"
+};
+const _hoisted_7 = { class: "text-lg mt-0 font-medium text-neutral-100" };
+const _hoisted_8 = { class: "flex flex-col gap-3" };
+const _hoisted_9 = ["onClick"];
+const _hoisted_10 = ["for"];
+const _hoisted_11 = { class: "text-sm text-neutral-400 my-1" };
+const _hoisted_12 = {
+  key: 1,
+  class: "text-neutral-400 italic"
+};
+const _sfc_main$3 = /* @__PURE__ */ defineComponent({
+  __name: "MigrationPicker",
+  props: {
+    "sourcePath": { required: false },
+    "sourcePathModifiers": {},
+    "migrationItemIds": {
+      required: false
+    },
+    "migrationItemIdsModifiers": {}
+  },
+  emits: ["update:sourcePath", "update:migrationItemIds"],
+  setup(__props) {
+    const { t: t2 } = useI18n();
+    const electron = electronAPI();
+    const sourcePath = useModel(__props, "sourcePath");
+    const migrationItemIds = useModel(__props, "migrationItemIds");
+    const migrationItems = ref(
+      MigrationItems.map((item) => ({
+        ...item,
+        selected: true
+      }))
+    );
+    const pathError = ref("");
+    const isValidSource = computed(
+      () => sourcePath.value !== "" && pathError.value === ""
+    );
+    const validateSource = /* @__PURE__ */ __name(async (sourcePath2) => {
+      if (!sourcePath2) {
+        pathError.value = "";
+        return;
+      }
+      try {
+        pathError.value = "";
+        const validation = await electron.validateComfyUISource(sourcePath2);
+        if (!validation.isValid) pathError.value = validation.error;
+      } catch (error) {
+        console.error(error);
+        pathError.value = t2("install.pathValidationFailed");
+      }
+    }, "validateSource");
+    const browsePath = /* @__PURE__ */ __name(async () => {
+      try {
+        const result = await electron.showDirectoryPicker();
+        if (result) {
+          sourcePath.value = result;
+          await validateSource(result);
+        }
+      } catch (error) {
+        console.error(error);
+        pathError.value = t2("install.failedToSelectDirectory");
+      }
+    }, "browsePath");
+    watchEffect(() => {
+      migrationItemIds.value = migrationItems.value.filter((item) => item.selected).map((item) => item.id);
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1$2, [
+        createBaseVNode("div", _hoisted_2$2, [
+          createBaseVNode("h2", _hoisted_3$2, toDisplayString(_ctx.$t("install.migrateFromExistingInstallation")), 1),
+          createBaseVNode("p", _hoisted_4$2, toDisplayString(_ctx.$t("install.migrationSourcePathDescription")), 1),
+          createBaseVNode("div", _hoisted_5, [
+            createVNode(unref(script$4), {
+              modelValue: sourcePath.value,
+              "onUpdate:modelValue": [
+                _cache[0] || (_cache[0] = ($event) => sourcePath.value = $event),
+                validateSource
+              ],
+              placeholder: "Select existing ComfyUI installation (optional)",
+              class: normalizeClass(["flex-1", { "p-invalid": pathError.value }])
+            }, null, 8, ["modelValue", "class"]),
+            createVNode(unref(script$7), {
+              icon: "pi pi-folder",
+              onClick: browsePath,
+              class: "w-12"
+            })
+          ]),
+          pathError.value ? (openBlock(), createBlock(unref(script$8), {
+            key: 0,
+            severity: "error"
+          }, {
+            default: withCtx(() => [
+              createTextVNode(toDisplayString(pathError.value), 1)
+            ]),
+            _: 1
+          })) : createCommentVNode("", true)
+        ]),
+        isValidSource.value ? (openBlock(), createElementBlock("div", _hoisted_6, [
+          createBaseVNode("h3", _hoisted_7, toDisplayString(_ctx.$t("install.selectItemsToMigrate")), 1),
+          createBaseVNode("div", _hoisted_8, [
+            (openBlock(true), createElementBlock(Fragment, null, renderList(migrationItems.value, (item) => {
+              return openBlock(), createElementBlock("div", {
+                key: item.id,
+                class: "flex items-center gap-3 p-2 hover:bg-neutral-700 rounded",
+                onClick: /* @__PURE__ */ __name(($event) => item.selected = !item.selected, "onClick")
+              }, [
+                createVNode(unref(script$9), {
+                  modelValue: item.selected,
+                  "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => item.selected = $event, "onUpdate:modelValue"),
+                  inputId: item.id,
+                  binary: true,
+                  onClick: _cache[1] || (_cache[1] = withModifiers(() => {
+                  }, ["stop"]))
+                }, null, 8, ["modelValue", "onUpdate:modelValue", "inputId"]),
+                createBaseVNode("div", null, [
+                  createBaseVNode("label", {
+                    for: item.id,
+                    class: "text-neutral-200 font-medium"
+                  }, toDisplayString(item.label), 9, _hoisted_10),
+                  createBaseVNode("p", _hoisted_11, toDisplayString(item.description), 1)
+                ])
+              ], 8, _hoisted_9);
+            }), 128))
+          ])
+        ])) : (openBlock(), createElementBlock("div", _hoisted_12, toDisplayString(_ctx.$t("install.migrationOptional")), 1))
+      ]);
+    };
+  }
+});
+const _hoisted_1$1 = { class: "flex flex-col items-center gap-4" };
+const _hoisted_2$1 = { class: "w-full" };
+const _hoisted_3$1 = { class: "text-lg font-medium text-neutral-100" };
+const _hoisted_4$1 = { class: "text-sm text-neutral-400 mt-1" };
+const _sfc_main$2 = /* @__PURE__ */ defineComponent({
+  __name: "MirrorItem",
+  props: /* @__PURE__ */ mergeModels({
+    item: {}
+  }, {
+    "modelValue": { required: true },
+    "modelModifiers": {}
+  }),
+  emits: /* @__PURE__ */ mergeModels(["state-change"], ["update:modelValue"]),
+  setup(__props, { emit: __emit }) {
+    const emit = __emit;
+    const modelValue = useModel(__props, "modelValue");
+    const validationState = ref(ValidationState.IDLE);
+    const normalizedSettingId = computed(() => {
+      return normalizeI18nKey(__props.item.settingId);
+    });
+    onMounted(() => {
+      modelValue.value = __props.item.mirror;
+    });
+    watch(validationState, (newState) => {
+      emit("state-change", newState);
+      if (newState === ValidationState.INVALID && modelValue.value === __props.item.mirror) {
+        modelValue.value = __props.item.fallbackMirror;
+      }
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1$1, [
+        createBaseVNode("div", _hoisted_2$1, [
+          createBaseVNode("h3", _hoisted_3$1, toDisplayString(_ctx.$t(`settings.${normalizedSettingId.value}.name`)), 1),
+          createBaseVNode("p", _hoisted_4$1, toDisplayString(_ctx.$t(`settings.${normalizedSettingId.value}.tooltip`)), 1)
+        ]),
+        createVNode(_sfc_main$7, {
+          modelValue: modelValue.value,
+          "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => modelValue.value = $event),
+          "validate-url-fn": /* @__PURE__ */ __name((mirror) => unref(checkMirrorReachable)(mirror + (_ctx.item.validationPathSuffix ?? "")), "validate-url-fn"),
+          onStateChange: _cache[1] || (_cache[1] = ($event) => validationState.value = $event)
+        }, null, 8, ["modelValue", "validate-url-fn"])
+      ]);
+    };
+  }
+});
+const _sfc_main$1 = /* @__PURE__ */ defineComponent({
+  __name: "MirrorsConfiguration",
+  props: /* @__PURE__ */ mergeModels({
+    device: {}
+  }, {
+    "pythonMirror": { required: true },
+    "pythonMirrorModifiers": {},
+    "pypiMirror": { required: true },
+    "pypiMirrorModifiers": {},
+    "torchMirror": { required: true },
+    "torchMirrorModifiers": {}
+  }),
+  emits: ["update:pythonMirror", "update:pypiMirror", "update:torchMirror"],
+  setup(__props) {
+    const showMirrorInputs = ref(false);
+    const pythonMirror = useModel(__props, "pythonMirror");
+    const pypiMirror = useModel(__props, "pypiMirror");
+    const torchMirror = useModel(__props, "torchMirror");
+    const getTorchMirrorItem = /* @__PURE__ */ __name((device) => {
+      const settingId = "Comfy-Desktop.UV.TorchInstallMirror";
+      switch (device) {
+        case "mps":
+          return {
+            settingId,
+            mirror: NIGHTLY_CPU_TORCH_URL,
+            fallbackMirror: NIGHTLY_CPU_TORCH_URL
+          };
+        case "nvidia":
+          return {
+            settingId,
+            mirror: CUDA_TORCH_URL,
+            fallbackMirror: CUDA_TORCH_URL
+          };
+        case "cpu":
+        default:
+          return {
+            settingId,
+            mirror: PYPI_MIRROR.mirror,
+            fallbackMirror: PYPI_MIRROR.fallbackMirror
+          };
+      }
+    }, "getTorchMirrorItem");
+    const userIsInChina = ref(false);
+    onMounted(async () => {
+      userIsInChina.value = await isInChina();
+    });
+    const useFallbackMirror = /* @__PURE__ */ __name((mirror) => ({
+      ...mirror,
+      mirror: mirror.fallbackMirror
+    }), "useFallbackMirror");
+    const mirrors = computed(
+      () => [
+        [PYTHON_MIRROR, pythonMirror],
+        [PYPI_MIRROR, pypiMirror],
+        [getTorchMirrorItem(__props.device), torchMirror]
+      ].map(([item, modelValue]) => [
+        userIsInChina.value ? useFallbackMirror(item) : item,
+        modelValue
+      ])
+    );
+    const validationStates = ref(
+      mirrors.value.map(() => ValidationState.IDLE)
+    );
+    const validationState = computed(() => {
+      return mergeValidationStates(validationStates.value);
+    });
+    const validationStateTooltip = computed(() => {
+      switch (validationState.value) {
+        case ValidationState.INVALID:
+          return t("install.settings.mirrorsUnreachable");
+        case ValidationState.VALID:
+          return t("install.settings.mirrorsReachable");
+        default:
+          return t("install.settings.checkingMirrors");
+      }
+    });
+    return (_ctx, _cache) => {
+      const _directive_tooltip = resolveDirective("tooltip");
+      return openBlock(), createBlock(unref(script$a), {
+        header: _ctx.$t("install.settings.mirrorSettings"),
+        toggleable: "",
+        collapsed: !showMirrorInputs.value,
+        "pt:root": "bg-neutral-800 border-none w-[600px]"
+      }, {
+        icons: withCtx(() => [
+          withDirectives(createBaseVNode("i", {
+            class: normalizeClass({
+              "pi pi-spin pi-spinner text-neutral-400": validationState.value === unref(ValidationState).LOADING,
+              "pi pi-check text-green-500": validationState.value === unref(ValidationState).VALID,
+              "pi pi-times text-red-500": validationState.value === unref(ValidationState).INVALID
+            })
+          }, null, 2), [
+            [_directive_tooltip, validationStateTooltip.value]
+          ])
+        ]),
+        default: withCtx(() => [
+          (openBlock(true), createElementBlock(Fragment, null, renderList(mirrors.value, ([item, modelValue], index) => {
+            return openBlock(), createElementBlock(Fragment, {
+              key: item.settingId + item.mirror
+            }, [
+              index > 0 ? (openBlock(), createBlock(unref(script$1), { key: 0 })) : createCommentVNode("", true),
+              createVNode(_sfc_main$2, {
+                item,
+                modelValue: modelValue.value,
+                "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => modelValue.value = $event, "onUpdate:modelValue"),
+                onStateChange: /* @__PURE__ */ __name(($event) => validationStates.value[index] = $event, "onStateChange")
+              }, null, 8, ["item", "modelValue", "onUpdate:modelValue", "onStateChange"])
+            ], 64);
+          }), 128))
+        ]),
+        _: 1
+      }, 8, ["header", "collapsed"]);
+    };
+  }
+});
+const _hoisted_1 = { class: "flex pt-6 justify-end" };
+const _hoisted_2 = { class: "flex pt-6 justify-between" };
+const _hoisted_3 = { class: "flex pt-6 justify-between" };
+const _hoisted_4 = { class: "flex mt-6 justify-between" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "InstallView",
+  setup(__props) {
+    const device = ref(null);
+    const installPath = ref("");
+    const pathError = ref("");
+    const migrationSourcePath = ref("");
+    const migrationItemIds = ref([]);
+    const autoUpdate = ref(true);
+    const allowMetrics = ref(true);
+    const pythonMirror = ref("");
+    const pypiMirror = ref("");
+    const torchMirror = ref("");
+    const highestStep = ref(0);
+    const handleStepChange = /* @__PURE__ */ __name((value) => {
+      setHighestStep(value);
+      electronAPI().Events.trackEvent("install_stepper_change", {
+        step: value
+      });
+    }, "handleStepChange");
+    const setHighestStep = /* @__PURE__ */ __name((value) => {
+      const int = typeof value === "number" ? value : parseInt(value, 10);
+      if (!isNaN(int) && int > highestStep.value) highestStep.value = int;
+    }, "setHighestStep");
+    const hasError = computed(() => pathError.value !== "");
+    const noGpu = computed(() => typeof device.value !== "string");
+    const electron = electronAPI();
+    const router = useRouter();
+    const install = /* @__PURE__ */ __name(() => {
+      const options = {
+        installPath: installPath.value,
+        autoUpdate: autoUpdate.value,
+        allowMetrics: allowMetrics.value,
+        migrationSourcePath: migrationSourcePath.value,
+        migrationItemIds: toRaw(migrationItemIds.value),
+        pythonMirror: pythonMirror.value,
+        pypiMirror: pypiMirror.value,
+        torchMirror: torchMirror.value,
+        device: device.value
+      };
+      electron.installComfyUI(options);
+      const nextPage = options.device === "unsupported" ? "/manual-configuration" : "/server-start";
+      router.push(nextPage);
+    }, "install");
+    onMounted(async () => {
+      if (!electron) return;
+      const detectedGpu = await electron.Config.getDetectedGpu();
+      if (detectedGpu === "mps" || detectedGpu === "nvidia") {
+        device.value = detectedGpu;
+      }
+      electronAPI().Events.trackEvent("install_stepper_change", {
+        step: "0",
+        gpu: detectedGpu
+      });
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$8, { dark: "" }, {
+        default: withCtx(() => [
+          createVNode(unref(script$f), {
+            class: "h-full p-8 2xl:p-16",
+            value: "0",
+            "onUpdate:value": handleStepChange
+          }, {
+            default: withCtx(() => [
+              createVNode(unref(script$b), { class: "select-none" }, {
+                default: withCtx(() => [
+                  createVNode(unref(script$c), { value: "0" }, {
+                    default: withCtx(() => [
+                      createTextVNode(toDisplayString(_ctx.$t("install.gpu")), 1)
+                    ]),
+                    _: 1
+                  }),
+                  createVNode(unref(script$c), {
+                    value: "1",
+                    disabled: noGpu.value
+                  }, {
+                    default: withCtx(() => [
+                      createTextVNode(toDisplayString(_ctx.$t("install.installLocation")), 1)
+                    ]),
+                    _: 1
+                  }, 8, ["disabled"]),
+                  createVNode(unref(script$c), {
+                    value: "2",
+                    disabled: noGpu.value || hasError.value || highestStep.value < 1
+                  }, {
+                    default: withCtx(() => [
+                      createTextVNode(toDisplayString(_ctx.$t("install.migration")), 1)
+                    ]),
+                    _: 1
+                  }, 8, ["disabled"]),
+                  createVNode(unref(script$c), {
+                    value: "3",
+                    disabled: noGpu.value || hasError.value || highestStep.value < 2
+                  }, {
+                    default: withCtx(() => [
+                      createTextVNode(toDisplayString(_ctx.$t("install.desktopSettings")), 1)
+                    ]),
+                    _: 1
+                  }, 8, ["disabled"])
+                ]),
+                _: 1
+              }),
+              createVNode(unref(script$d), null, {
+                default: withCtx(() => [
+                  createVNode(unref(script$e), { value: "0" }, {
+                    default: withCtx(({ activateCallback }) => [
+                      createVNode(GpuPicker, {
+                        device: device.value,
+                        "onUpdate:device": _cache[0] || (_cache[0] = ($event) => device.value = $event)
+                      }, null, 8, ["device"]),
+                      createBaseVNode("div", _hoisted_1, [
+                        createVNode(unref(script$7), {
+                          label: _ctx.$t("g.next"),
+                          icon: "pi pi-arrow-right",
+                          iconPos: "right",
+                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("1"), "onClick"),
+                          disabled: typeof device.value !== "string"
+                        }, null, 8, ["label", "onClick", "disabled"])
+                      ])
+                    ]),
+                    _: 1
+                  }),
+                  createVNode(unref(script$e), { value: "1" }, {
+                    default: withCtx(({ activateCallback }) => [
+                      createVNode(_sfc_main$4, {
+                        installPath: installPath.value,
+                        "onUpdate:installPath": _cache[1] || (_cache[1] = ($event) => installPath.value = $event),
+                        pathError: pathError.value,
+                        "onUpdate:pathError": _cache[2] || (_cache[2] = ($event) => pathError.value = $event)
+                      }, null, 8, ["installPath", "pathError"]),
+                      createBaseVNode("div", _hoisted_2, [
+                        createVNode(unref(script$7), {
+                          label: _ctx.$t("g.back"),
+                          severity: "secondary",
+                          icon: "pi pi-arrow-left",
+                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("0"), "onClick")
+                        }, null, 8, ["label", "onClick"]),
+                        createVNode(unref(script$7), {
+                          label: _ctx.$t("g.next"),
+                          icon: "pi pi-arrow-right",
+                          iconPos: "right",
+                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("2"), "onClick"),
+                          disabled: pathError.value !== ""
+                        }, null, 8, ["label", "onClick", "disabled"])
+                      ])
+                    ]),
+                    _: 1
+                  }),
+                  createVNode(unref(script$e), { value: "2" }, {
+                    default: withCtx(({ activateCallback }) => [
+                      createVNode(_sfc_main$3, {
+                        sourcePath: migrationSourcePath.value,
+                        "onUpdate:sourcePath": _cache[3] || (_cache[3] = ($event) => migrationSourcePath.value = $event),
+                        migrationItemIds: migrationItemIds.value,
+                        "onUpdate:migrationItemIds": _cache[4] || (_cache[4] = ($event) => migrationItemIds.value = $event)
+                      }, null, 8, ["sourcePath", "migrationItemIds"]),
+                      createBaseVNode("div", _hoisted_3, [
+                        createVNode(unref(script$7), {
+                          label: _ctx.$t("g.back"),
+                          severity: "secondary",
+                          icon: "pi pi-arrow-left",
+                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("1"), "onClick")
+                        }, null, 8, ["label", "onClick"]),
+                        createVNode(unref(script$7), {
+                          label: _ctx.$t("g.next"),
+                          icon: "pi pi-arrow-right",
+                          iconPos: "right",
+                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("3"), "onClick")
+                        }, null, 8, ["label", "onClick"])
+                      ])
+                    ]),
+                    _: 1
+                  }),
+                  createVNode(unref(script$e), { value: "3" }, {
+                    default: withCtx(({ activateCallback }) => [
+                      createVNode(_sfc_main$6, {
+                        autoUpdate: autoUpdate.value,
+                        "onUpdate:autoUpdate": _cache[5] || (_cache[5] = ($event) => autoUpdate.value = $event),
+                        allowMetrics: allowMetrics.value,
+                        "onUpdate:allowMetrics": _cache[6] || (_cache[6] = ($event) => allowMetrics.value = $event)
+                      }, null, 8, ["autoUpdate", "allowMetrics"]),
+                      createVNode(_sfc_main$1, {
+                        device: device.value,
+                        pythonMirror: pythonMirror.value,
+                        "onUpdate:pythonMirror": _cache[7] || (_cache[7] = ($event) => pythonMirror.value = $event),
+                        pypiMirror: pypiMirror.value,
+                        "onUpdate:pypiMirror": _cache[8] || (_cache[8] = ($event) => pypiMirror.value = $event),
+                        torchMirror: torchMirror.value,
+                        "onUpdate:torchMirror": _cache[9] || (_cache[9] = ($event) => torchMirror.value = $event),
+                        class: "mt-6"
+                      }, null, 8, ["device", "pythonMirror", "pypiMirror", "torchMirror"]),
+                      createBaseVNode("div", _hoisted_4, [
+                        createVNode(unref(script$7), {
+                          label: _ctx.$t("g.back"),
+                          severity: "secondary",
+                          icon: "pi pi-arrow-left",
+                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("2"), "onClick")
+                        }, null, 8, ["label", "onClick"]),
+                        createVNode(unref(script$7), {
+                          label: _ctx.$t("g.install"),
+                          icon: "pi pi-check",
+                          iconPos: "right",
+                          disabled: hasError.value,
+                          onClick: _cache[10] || (_cache[10] = ($event) => install())
+                        }, null, 8, ["label", "disabled"])
+                      ])
+                    ]),
+                    _: 1
+                  })
+                ]),
+                _: 1
+              })
+            ]),
+            _: 1
+          })
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+const InstallView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-cd6731d2"]]);
+export {
+  InstallView as default
+};
+//# sourceMappingURL=InstallView-DW9xwU_F.js.map

web/assets/InstallView-DbJ2cGfL.css

@@ -1,18 +1,20 @@
 
-:root {
+.p-tag[data-v-79125ff6] {
   --p-tag-gap: 0.5rem;
 }
 .hover-brighten {
+&[data-v-79125ff6] {
   transition-property: color, background-color, border-color, text-decoration-color, fill, stroke;
   transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
   transition-duration: 150ms;
   transition-property: filter, box-shadow;
-&:hover {
+  }
+&[data-v-79125ff6]:hover {
     filter: brightness(107%) contrast(105%);
     box-shadow: 0 0 0.25rem #ffffff79;
 }
 }
-.p-accordioncontent-content {
+.p-accordioncontent-content[data-v-79125ff6] {
   border-radius: 0.5rem;
   --tw-bg-opacity: 1;
   background-color: rgb(23 23 23 / var(--tw-bg-opacity));
@@ -21,14 +23,14 @@
   transition-duration: 150ms;
 }
 div.selected {
-.gpu-button:not(.selected) {
+.gpu-button[data-v-79125ff6]:not(.selected) {
     opacity: 0.5;
 }
-.gpu-button:not(.selected):hover {
+.gpu-button[data-v-79125ff6]:not(.selected):hover {
     opacity: 1;
 }
 }
-.gpu-button {
+.gpu-button[data-v-79125ff6] {
   margin: 0px;
   display: flex;
   width: 50%;
@@ -43,37 +45,37 @@ div.selected {
   transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
   transition-duration: 150ms;
 }
-.gpu-button:hover {
+.gpu-button[data-v-79125ff6]:hover {
   --tw-bg-opacity: 0.75;
 }
 .gpu-button {
-&.selected {
+&.selected[data-v-79125ff6] {
     --tw-bg-opacity: 1;
     background-color: rgb(64 64 64 / var(--tw-bg-opacity));
 }
-&.selected {
+&.selected[data-v-79125ff6] {
     --tw-bg-opacity: 0.5;
 }
-&.selected {
+&.selected[data-v-79125ff6] {
     opacity: 1;
 }
-&.selected:hover {
+&.selected[data-v-79125ff6]:hover {
     --tw-bg-opacity: 0.6;
 }
 }
-.disabled {
+.disabled[data-v-79125ff6] {
   pointer-events: none;
   opacity: 0.4;
 }
-.p-card-header {
+.p-card-header[data-v-79125ff6] {
   flex-grow: 1;
   text-align: center;
 }
-.p-card-body {
+.p-card-body[data-v-79125ff6] {
   padding-top: 0px;
   text-align: center;
 }
 
-[data-v-de33872d] .p-steppanel {
+[data-v-cd6731d2] .p-steppanel {
     background-color: transparent
 }

web/assets/KeybindingPanel-CDYVPYDp.css

@@ -0,0 +1,8 @@
+
+[data-v-8454e24f] .p-datatable-tbody > tr > td {
+  padding: 0.25rem;
+  min-height: 2rem
+}
+[data-v-8454e24f] .p-datatable-row-selected .actions,[data-v-8454e24f] .p-datatable-selectable-row:hover .actions {
+  visibility: visible
+}

web/assets/KeybindingPanel-DvrUYZ4S.css

@@ -1,8 +0,0 @@
-
-[data-v-2554ab36] .p-datatable-tbody > tr > td {
-  padding: 0.25rem;
-  min-height: 2rem
-}
-[data-v-2554ab36] .p-datatable-row-selected .actions,[data-v-2554ab36] .p-datatable-selectable-row:hover .actions {
-  visibility: visible
-}

web/assets/KeybindingPanel-oavhFdkz.js

@@ -1,10 +1,9 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, c as computed, o as openBlock, f as createElementBlock, F as Fragment, E as renderList, N as createVNode, M as withCtx, aE as createTextVNode, X as toDisplayString, j as unref, aI as script, I as createCommentVNode, ab as ref, cn as FilterMatchMode, a$ as useKeybindingStore, a2 as useCommandStore, a1 as useI18n, af as normalizeI18nKey, w as watchEffect, bs as useToast, r as resolveDirective, k as createBlock, co as SearchBox, H as createBaseVNode, l as script$2, av as script$4, bM as withModifiers, bZ as script$5, aP as script$6, i as withDirectives, cp as _sfc_main$2, aL as pushScopeId, aM as popScopeId, cq as KeyComboImpl, cr as KeybindingImpl, _ as _export_sfc } from "./index-DjNHn37O.js";
-import { s as script$1, a as script$3 } from "./index-B5F0uxTQ.js";
-import { u as useKeybindingService } from "./keybindingService-Bx7YdkXn.js";
-import "./index-B-aVupP5.js";
-import "./index-5HFeZax4.js";
+import { d as defineComponent, c as computed, o as openBlock, f as createElementBlock, F as Fragment, D as renderList, k as createVNode, z as withCtx, a8 as createTextVNode, E as toDisplayString, j as unref, a5 as script, B as createCommentVNode, T as ref, dx as FilterMatchMode, ao as useKeybindingStore, J as useCommandStore, I as useI18n, X as normalizeI18nKey, w as watchEffect, aV as useToast, r as resolveDirective, y as createBlock, dy as SearchBox, m as createBaseVNode, l as script$2, bk as script$4, as as withModifiers, bn as script$5, ac as script$6, i as withDirectives, dz as _sfc_main$2, dA as KeyComboImpl, dB as KeybindingImpl, _ as _export_sfc } from "./index-Bv0b06LE.js";
+import { g as script$1, h as script$3 } from "./index-CgMyWf7n.js";
+import { u as useKeybindingService } from "./keybindingService-DyjX-nxF.js";
+import "./index-Dzu9WL4p.js";
 const _hoisted_1$1 = {
   key: 0,
   class: "px-2"
@@ -37,7 +36,6 @@ const _sfc_main$1 = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2554ab36"), n = n(), popScopeId(), n), "_withScopeId");
 const _hoisted_1 = { class: "actions invisible flex flex-row" };
 const _hoisted_2 = ["title"];
 const _hoisted_3 = { key: 1 };
@@ -98,6 +96,16 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     }
     __name(removeKeybinding, "removeKeybinding");
     function captureKeybinding(event) {
+      if (!event.shiftKey && !event.altKey && !event.ctrlKey && !event.metaKey) {
+        switch (event.key) {
+          case "Escape":
+            cancelEdit();
+            return;
+          case "Enter":
+            saveKeybinding();
+            return;
+        }
+      }
       const keyCombo = KeyComboImpl.fromEvent(event);
       newBindingKeyCombo.value = keyCombo;
     }
@@ -153,7 +161,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
             value: commandsData.value,
             selection: selectedCommandData.value,
             "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event),
-            "global-filter-fields": ["id"],
+            "global-filter-fields": ["id", "label"],
             filters: filters.value,
             selectionMode: "single",
             stripedRows: "",
@@ -218,7 +226,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
             visible: editDialogVisible.value,
             "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event),
             modal: "",
-            header: currentEditingCommand.value?.id,
+            header: currentEditingCommand.value?.label,
             onHide: cancelEdit
           }, {
             footer: withCtx(() => [
@@ -248,7 +256,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   severity: "error"
                 }, {
                   default: withCtx(() => [
-                    createTextVNode(" Keybinding already exists on "),
+                    _cache[3] || (_cache[3] = createTextVNode(" Keybinding already exists on ")),
                     createVNode(unref(script), {
                       severity: "secondary",
                       value: existingKeybindingOnCombo.value.commandId
@@ -277,8 +285,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-2554ab36"]]);
+const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-8454e24f"]]);
 export {
   KeybindingPanel as default
 };
-//# sourceMappingURL=KeybindingPanel-Dc3C4lG1.js.map
+//# sourceMappingURL=KeybindingPanel-oavhFdkz.js.map

web/assets/MaintenanceView-DEJCj8SR.css

@@ -0,0 +1,87 @@
+
+.task-card-ok[data-v-c3bd7658] {
+
+  position: absolute;
+
+  right: -1rem;
+
+  bottom: -1rem;
+
+  grid-column: 1 / -1;
+
+  grid-row: 1 / -1;
+
+  --tw-text-opacity: 1;
+
+  color: rgb(150 206 76 / var(--tw-text-opacity));
+
+  opacity: 1;
+
+  transition-property: opacity;
+
+  transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
+
+  transition-duration: 150ms;
+
+  font-size: 4rem;
+  text-shadow: 0.25rem 0 0.5rem black;
+  z-index: 10;
+}
+.p-card {
+&[data-v-c3bd7658] {
+
+  transition-property: opacity;
+
+  transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
+
+  transition-duration: 150ms;
+
+  --p-card-background: var(--p-button-secondary-background);
+  opacity: 0.9;
+  }
+&.opacity-65[data-v-c3bd7658] {
+    opacity: 0.4;
+}
+&[data-v-c3bd7658]:hover {
+    opacity: 1;
+}
+}
+[data-v-c3bd7658] .p-card-header {
+  z-index: 0;
+}
+[data-v-c3bd7658] .p-card-body {
+  z-index: 1;
+  flex-grow: 1;
+  justify-content: space-between;
+}
+.task-div {
+> i[data-v-c3bd7658] {
+    pointer-events: none;
+}
+&:hover > i[data-v-c3bd7658] {
+    opacity: 0.2;
+}
+}
+
+[data-v-dd50a7dd] .p-tag {
+  --p-tag-gap: 0.375rem;
+}
+.backspan[data-v-dd50a7dd]::before {
+  position: absolute;
+  margin: 0px;
+  color: var(--p-text-muted-color);
+  font-family: 'primeicons';
+  top: -2rem;
+  right: -2rem;
+  speak: none;
+  font-style: normal;
+  font-weight: normal;
+  font-variant: normal;
+  text-transform: none;
+  line-height: 1;
+  display: inline-block;
+  -webkit-font-smoothing: antialiased;
+  opacity: 0.02;
+  font-size: min(14rem, 90vw);
+  z-index: 0;
+}

web/assets/ManualConfigurationView-CsirlNfV.css

@@ -1,7 +1,7 @@
 
-:root {
+.p-tag[data-v-dc169863] {
   --p-tag-gap: 0.5rem;
 }
-.comfy-installer {
+.comfy-installer[data-v-dc169863] {
   margin-top: max(1rem, max(0px, calc((100vh - 42rem) * 0.5)));
 }

web/assets/ManualConfigurationView-DTLyJ3VG.js

@@ -1,9 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, a1 as useI18n, ab as ref, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, aI as script, l as script$2, c0 as electronAPI } from "./index-DjNHn37O.js";
-import { s as script$1 } from "./index-jXPKy3pP.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
-import "./index-5HFeZax4.js";
+import { d as defineComponent, I as useI18n, T as ref, p as onMounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, a5 as script, b3 as script$1, l as script$2, b9 as electronAPI, _ as _export_sfc } from "./index-Bv0b06LE.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
 const _hoisted_1 = { class: "comfy-installer grow flex flex-col gap-4 text-neutral-300 max-w-110" };
 const _hoisted_2 = { class: "text-2xl font-semibold text-neutral-100" };
 const _hoisted_3 = { class: "m-1 text-neutral-300" };
@@ -69,7 +67,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
+const ManualConfigurationView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-dc169863"]]);
 export {
-  _sfc_main as default
+  ManualConfigurationView as default
 };
-//# sourceMappingURL=ManualConfigurationView-Bi_qHE-n.js.map
+//# sourceMappingURL=ManualConfigurationView-DTLyJ3VG.js.map

web/assets/MetricsConsentView-C80fk2cl.js

@@ -0,0 +1,86 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
+import { d as defineComponent, aV as useToast, I as useI18n, T as ref, bi as useRouter, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, a8 as createTextVNode, k as createVNode, j as unref, br as script, l as script$1, b9 as electronAPI } from "./index-Bv0b06LE.js";
+const _hoisted_1 = { class: "h-full p-8 2xl:p-16 flex flex-col items-center justify-center" };
+const _hoisted_2 = { class: "bg-neutral-800 rounded-lg shadow-lg p-6 w-full max-w-[600px] flex flex-col gap-6" };
+const _hoisted_3 = { class: "text-3xl font-semibold text-neutral-100" };
+const _hoisted_4 = { class: "text-neutral-400" };
+const _hoisted_5 = { class: "text-neutral-400" };
+const _hoisted_6 = {
+  href: "https://comfy.org/privacy",
+  target: "_blank",
+  class: "text-blue-400 hover:text-blue-300 underline"
+};
+const _hoisted_7 = { class: "flex items-center gap-4" };
+const _hoisted_8 = {
+  id: "metricsDescription",
+  class: "text-neutral-100"
+};
+const _hoisted_9 = { class: "flex pt-6 justify-end" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "MetricsConsentView",
+  setup(__props) {
+    const toast = useToast();
+    const { t } = useI18n();
+    const allowMetrics = ref(true);
+    const router = useRouter();
+    const isUpdating = ref(false);
+    const updateConsent = /* @__PURE__ */ __name(async () => {
+      isUpdating.value = true;
+      try {
+        await electronAPI().setMetricsConsent(allowMetrics.value);
+      } catch (error) {
+        toast.add({
+          severity: "error",
+          summary: t("install.errorUpdatingConsent"),
+          detail: t("install.errorUpdatingConsentDetail"),
+          life: 3e3
+        });
+      } finally {
+        isUpdating.value = false;
+      }
+      router.push("/");
+    }, "updateConsent");
+    return (_ctx, _cache) => {
+      const _component_BaseViewTemplate = _sfc_main$1;
+      return openBlock(), createBlock(_component_BaseViewTemplate, { dark: "" }, {
+        default: withCtx(() => [
+          createBaseVNode("div", _hoisted_1, [
+            createBaseVNode("div", _hoisted_2, [
+              createBaseVNode("h2", _hoisted_3, toDisplayString(_ctx.$t("install.helpImprove")), 1),
+              createBaseVNode("p", _hoisted_4, toDisplayString(_ctx.$t("install.updateConsent")), 1),
+              createBaseVNode("p", _hoisted_5, [
+                createTextVNode(toDisplayString(_ctx.$t("install.moreInfo")) + " ", 1),
+                createBaseVNode("a", _hoisted_6, toDisplayString(_ctx.$t("install.privacyPolicy")), 1),
+                _cache[1] || (_cache[1] = createTextVNode(". "))
+              ]),
+              createBaseVNode("div", _hoisted_7, [
+                createVNode(unref(script), {
+                  modelValue: allowMetrics.value,
+                  "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => allowMetrics.value = $event),
+                  "aria-describedby": "metricsDescription"
+                }, null, 8, ["modelValue"]),
+                createBaseVNode("span", _hoisted_8, toDisplayString(allowMetrics.value ? _ctx.$t("install.metricsEnabled") : _ctx.$t("install.metricsDisabled")), 1)
+              ]),
+              createBaseVNode("div", _hoisted_9, [
+                createVNode(unref(script$1), {
+                  label: _ctx.$t("g.ok"),
+                  icon: "pi pi-check",
+                  loading: isUpdating.value,
+                  iconPos: "right",
+                  onClick: updateConsent
+                }, null, 8, ["label", "loading"])
+              ])
+            ])
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=MetricsConsentView-C80fk2cl.js.map

web/assets/NotSupportedView-B78ZVR9Z.js

@@ -1,21 +1,16 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, bW as useRouter, r as resolveDirective, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, i as withDirectives } from "./index-DjNHn37O.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+import { d as defineComponent, bi as useRouter, r as resolveDirective, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, l as script, i as withDirectives, _ as _export_sfc } from "./index-Bv0b06LE.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
 const _imports_0 = "" + new URL("images/sad_girl.png", import.meta.url).href;
 const _hoisted_1 = { class: "sad-container" };
-const _hoisted_2 = /* @__PURE__ */ createBaseVNode("img", {
-  class: "sad-girl",
-  src: _imports_0,
-  alt: "Sad girl illustration"
-}, null, -1);
-const _hoisted_3 = { class: "no-drag sad-text flex items-center" };
-const _hoisted_4 = { class: "flex flex-col gap-8 p-8 min-w-110" };
-const _hoisted_5 = { class: "text-4xl font-bold text-red-500" };
-const _hoisted_6 = { class: "space-y-4" };
-const _hoisted_7 = { class: "text-xl" };
-const _hoisted_8 = { class: "list-disc list-inside space-y-1 text-neutral-800" };
-const _hoisted_9 = { class: "flex gap-4" };
+const _hoisted_2 = { class: "no-drag sad-text flex items-center" };
+const _hoisted_3 = { class: "flex flex-col gap-8 p-8 min-w-110" };
+const _hoisted_4 = { class: "text-4xl font-bold text-red-500" };
+const _hoisted_5 = { class: "space-y-4" };
+const _hoisted_6 = { class: "text-xl" };
+const _hoisted_7 = { class: "list-disc list-inside space-y-1 text-neutral-800" };
+const _hoisted_8 = { class: "flex gap-4" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "NotSupportedView",
   setup(__props) {
@@ -37,18 +32,22 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       return openBlock(), createBlock(_sfc_main$1, null, {
         default: withCtx(() => [
           createBaseVNode("div", _hoisted_1, [
-            _hoisted_2,
-            createBaseVNode("div", _hoisted_3, [
-              createBaseVNode("div", _hoisted_4, [
-                createBaseVNode("h1", _hoisted_5, toDisplayString(_ctx.$t("notSupported.title")), 1),
-                createBaseVNode("div", _hoisted_6, [
-                  createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("notSupported.message")), 1),
-                  createBaseVNode("ul", _hoisted_8, [
+            _cache[0] || (_cache[0] = createBaseVNode("img", {
+              class: "sad-girl",
+              src: _imports_0,
+              alt: "Sad girl illustration"
+            }, null, -1)),
+            createBaseVNode("div", _hoisted_2, [
+              createBaseVNode("div", _hoisted_3, [
+                createBaseVNode("h1", _hoisted_4, toDisplayString(_ctx.$t("notSupported.title")), 1),
+                createBaseVNode("div", _hoisted_5, [
+                  createBaseVNode("p", _hoisted_6, toDisplayString(_ctx.$t("notSupported.message")), 1),
+                  createBaseVNode("ul", _hoisted_7, [
                     createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.macos")), 1),
                     createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.windows")), 1)
                   ])
                 ]),
-                createBaseVNode("div", _hoisted_9, [
+                createBaseVNode("div", _hoisted_8, [
                   createVNode(unref(script), {
                     label: _ctx.$t("notSupported.learnMore"),
                     icon: "pi pi-github",
@@ -80,7 +79,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
+const NotSupportedView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-ebb20958"]]);
 export {
-  _sfc_main as default
+  NotSupportedView as default
 };
-//# sourceMappingURL=NotSupportedView-Drz3x2d-.js.map
+//# sourceMappingURL=NotSupportedView-B78ZVR9Z.js.map

web/assets/NotSupportedView-RFx6eCkN.css

@@ -1,17 +1,19 @@
 
 .sad-container {
+&[data-v-ebb20958] {
   display: grid;
   align-items: center;
   justify-content: space-evenly;
   grid-template-columns: 25rem 1fr;
-& > * {
+}
+&[data-v-ebb20958] > * {
     grid-row: 1;
 }
 }
-.sad-text {
+.sad-text[data-v-ebb20958] {
   grid-column: 1/3;
 }
-.sad-girl {
+.sad-girl[data-v-ebb20958] {
   grid-column: 2/3;
   width: min(75vw, 100vh);
 }

web/assets/ServerConfigPanel-BYrt6wyr.js

@@ -1,25 +1,23 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { H as createBaseVNode, o as openBlock, f as createElementBlock, Z as markRaw, d as defineComponent, a as useSettingStore, aS as storeToRefs, a5 as watch, cO as useCopyToClipboard, a1 as useI18n, k as createBlock, M as withCtx, j as unref, bZ as script, X as toDisplayString, E as renderList, F as Fragment, N as createVNode, l as script$1, I as createCommentVNode, bQ as script$2, cP as FormItem, cp as _sfc_main$1, c0 as electronAPI } from "./index-DjNHn37O.js";
-import { u as useServerConfigStore } from "./serverConfigStore-CvyKFVuP.js";
+import { o as openBlock, f as createElementBlock, m as createBaseVNode, H as markRaw, d as defineComponent, a as useSettingStore, af as storeToRefs, N as watch, dJ as useCopyToClipboard, I as useI18n, y as createBlock, z as withCtx, j as unref, bn as script, E as toDisplayString, D as renderList, F as Fragment, k as createVNode, l as script$1, B as createCommentVNode, bl as script$2, dK as FormItem, dz as _sfc_main$1, b9 as electronAPI } from "./index-Bv0b06LE.js";
+import { u as useServerConfigStore } from "./serverConfigStore-D2Vr0L0h.js";
 const _hoisted_1$1 = {
   viewBox: "0 0 24 24",
   width: "1.2em",
   height: "1.2em"
 };
-const _hoisted_2$1 = /* @__PURE__ */ createBaseVNode("path", {
-  fill: "none",
-  stroke: "currentColor",
-  "stroke-linecap": "round",
-  "stroke-linejoin": "round",
-  "stroke-width": "2",
-  d: "m4 17l6-6l-6-6m8 14h8"
-}, null, -1);
-const _hoisted_3$1 = [
-  _hoisted_2$1
-];
 function render(_ctx, _cache) {
-  return openBlock(), createElementBlock("svg", _hoisted_1$1, [..._hoisted_3$1]);
+  return openBlock(), createElementBlock("svg", _hoisted_1$1, _cache[0] || (_cache[0] = [
+    createBaseVNode("path", {
+      fill: "none",
+      stroke: "currentColor",
+      "stroke-linecap": "round",
+      "stroke-linejoin": "round",
+      "stroke-width": "2",
+      d: "m4 17l6-6l-6-6m8 14h8"
+    }, null, -1)
+  ]));
 }
 __name(render, "render");
 const __unplugin_components_0 = markRaw({ name: "lucide-terminal", render });
@@ -155,4 +153,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ServerConfigPanel-Be4StJmv.js.map
+//# sourceMappingURL=ServerConfigPanel-BYrt6wyr.js.map

web/assets/ServerStartView-B7TlHxYo.js

@@ -0,0 +1,100 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, I as useI18n, T as ref, bo as ProgressStatus, p as onMounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, a8 as createTextVNode, E as toDisplayString, j as unref, f as createElementBlock, B as createCommentVNode, k as createVNode, l as script, i as withDirectives, v as vShow, bp as BaseTerminal, b9 as electronAPI, _ as _export_sfc } from "./index-Bv0b06LE.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
+const _hoisted_1 = { class: "flex flex-col w-full h-full items-center" };
+const _hoisted_2 = { class: "text-2xl font-bold" };
+const _hoisted_3 = { key: 0 };
+const _hoisted_4 = {
+  key: 0,
+  class: "flex flex-col items-center gap-4"
+};
+const _hoisted_5 = { class: "flex items-center my-4 gap-2" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ServerStartView",
+  setup(__props) {
+    const electron = electronAPI();
+    const { t } = useI18n();
+    const status = ref(ProgressStatus.INITIAL_STATE);
+    const electronVersion = ref("");
+    let xterm;
+    const terminalVisible = ref(true);
+    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
+      status.value = newStatus;
+      if (newStatus === ProgressStatus.ERROR) terminalVisible.value = false;
+      else xterm?.clear();
+    }, "updateProgress");
+    const terminalCreated = /* @__PURE__ */ __name(({ terminal, useAutoSize }, root) => {
+      xterm = terminal;
+      useAutoSize({ root, autoRows: true, autoCols: true });
+      electron.onLogMessage((message) => {
+        terminal.write(message);
+      });
+      terminal.options.cursorBlink = false;
+      terminal.options.disableStdin = true;
+      terminal.options.cursorInactiveStyle = "block";
+    }, "terminalCreated");
+    const reinstall = /* @__PURE__ */ __name(() => electron.reinstall(), "reinstall");
+    const reportIssue = /* @__PURE__ */ __name(() => {
+      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
+    }, "reportIssue");
+    const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs");
+    onMounted(async () => {
+      electron.sendReady();
+      electron.onProgressUpdate(updateProgress);
+      electronVersion.value = await electron.getElectronVersion();
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$1, {
+        dark: "",
+        class: "flex-col"
+      }, {
+        default: withCtx(() => [
+          createBaseVNode("div", _hoisted_1, [
+            createBaseVNode("h2", _hoisted_2, [
+              createTextVNode(toDisplayString(unref(t)(`serverStart.process.${status.value}`)) + " ", 1),
+              status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("span", _hoisted_3, " v" + toDisplayString(electronVersion.value), 1)) : createCommentVNode("", true)
+            ]),
+            status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_4, [
+              createBaseVNode("div", _hoisted_5, [
+                createVNode(unref(script), {
+                  icon: "pi pi-flag",
+                  severity: "secondary",
+                  label: unref(t)("serverStart.reportIssue"),
+                  onClick: reportIssue
+                }, null, 8, ["label"]),
+                createVNode(unref(script), {
+                  icon: "pi pi-file",
+                  severity: "secondary",
+                  label: unref(t)("serverStart.openLogs"),
+                  onClick: openLogs
+                }, null, 8, ["label"]),
+                createVNode(unref(script), {
+                  icon: "pi pi-refresh",
+                  label: unref(t)("serverStart.reinstall"),
+                  onClick: reinstall
+                }, null, 8, ["label"])
+              ]),
+              !terminalVisible.value ? (openBlock(), createBlock(unref(script), {
+                key: 0,
+                icon: "pi pi-search",
+                severity: "secondary",
+                label: unref(t)("serverStart.showTerminal"),
+                onClick: _cache[0] || (_cache[0] = ($event) => terminalVisible.value = true)
+              }, null, 8, ["label"])) : createCommentVNode("", true)
+            ])) : createCommentVNode("", true),
+            withDirectives(createVNode(BaseTerminal, { onCreated: terminalCreated }, null, 512), [
+              [vShow, terminalVisible.value]
+            ])
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-e6ba9633"]]);
+export {
+  ServerStartView as default
+};
+//# sourceMappingURL=ServerStartView-B7TlHxYo.js.map

web/assets/ServerStartView-BZ7uhZHv.css

@@ -1,5 +1,5 @@
 
-[data-v-42c1131d] .xterm-helper-textarea {
+[data-v-e6ba9633] .xterm-helper-textarea {
   /* Hide this as it moves all over when uv is running */
   display: none;
 }

web/assets/ServerStartView-CIDTUh4x.js

@@ -1,98 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, a1 as useI18n, ab as ref, b_ as ProgressStatus, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, aE as createTextVNode, X as toDisplayString, j as unref, f as createElementBlock, I as createCommentVNode, N as createVNode, l as script, i as withDirectives, v as vShow, b$ as BaseTerminal, aL as pushScopeId, aM as popScopeId, c0 as electronAPI, _ as _export_sfc } from "./index-DjNHn37O.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-42c1131d"), n = n(), popScopeId(), n), "_withScopeId");
-const _hoisted_1 = { class: "text-2xl font-bold" };
-const _hoisted_2 = { key: 0 };
-const _hoisted_3 = {
-  key: 0,
-  class: "flex flex-col items-center gap-4"
-};
-const _hoisted_4 = { class: "flex items-center my-4 gap-2" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "ServerStartView",
-  setup(__props) {
-    const electron = electronAPI();
-    const { t } = useI18n();
-    const status = ref(ProgressStatus.INITIAL_STATE);
-    const electronVersion = ref("");
-    let xterm;
-    const terminalVisible = ref(true);
-    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
-      status.value = newStatus;
-      if (newStatus === ProgressStatus.ERROR) terminalVisible.value = false;
-      else xterm?.clear();
-    }, "updateProgress");
-    const terminalCreated = /* @__PURE__ */ __name(({ terminal, useAutoSize }, root) => {
-      xterm = terminal;
-      useAutoSize(root, true, true);
-      electron.onLogMessage((message) => {
-        terminal.write(message);
-      });
-      terminal.options.cursorBlink = false;
-      terminal.options.disableStdin = true;
-      terminal.options.cursorInactiveStyle = "block";
-    }, "terminalCreated");
-    const reinstall = /* @__PURE__ */ __name(() => electron.reinstall(), "reinstall");
-    const reportIssue = /* @__PURE__ */ __name(() => {
-      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
-    }, "reportIssue");
-    const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs");
-    onMounted(async () => {
-      electron.sendReady();
-      electron.onProgressUpdate(updateProgress);
-      electronVersion.value = await electron.getElectronVersion();
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createBlock(_sfc_main$1, {
-        dark: "",
-        class: "flex-col"
-      }, {
-        default: withCtx(() => [
-          createBaseVNode("h2", _hoisted_1, [
-            createTextVNode(toDisplayString(unref(t)(`serverStart.process.${status.value}`)) + " ", 1),
-            status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("span", _hoisted_2, " v" + toDisplayString(electronVersion.value), 1)) : createCommentVNode("", true)
-          ]),
-          status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_3, [
-            createBaseVNode("div", _hoisted_4, [
-              createVNode(unref(script), {
-                icon: "pi pi-flag",
-                severity: "secondary",
-                label: unref(t)("serverStart.reportIssue"),
-                onClick: reportIssue
-              }, null, 8, ["label"]),
-              createVNode(unref(script), {
-                icon: "pi pi-file",
-                severity: "secondary",
-                label: unref(t)("serverStart.openLogs"),
-                onClick: openLogs
-              }, null, 8, ["label"]),
-              createVNode(unref(script), {
-                icon: "pi pi-refresh",
-                label: unref(t)("serverStart.reinstall"),
-                onClick: reinstall
-              }, null, 8, ["label"])
-            ]),
-            !terminalVisible.value ? (openBlock(), createBlock(unref(script), {
-              key: 0,
-              icon: "pi pi-search",
-              severity: "secondary",
-              label: unref(t)("serverStart.showTerminal"),
-              onClick: _cache[0] || (_cache[0] = ($event) => terminalVisible.value = true)
-            }, null, 8, ["label"])) : createCommentVNode("", true)
-          ])) : createCommentVNode("", true),
-          withDirectives(createVNode(BaseTerminal, { onCreated: terminalCreated }, null, 512), [
-            [vShow, terminalVisible.value]
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-42c1131d"]]);
-export {
-  ServerStartView as default
-};
-//# sourceMappingURL=ServerStartView-CIDTUh4x.js.map