Bump ComfyUI version to v0.3.18

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

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.
@@ -47,12 +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.
@@ -120,7 +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).
 
@@ -130,6 +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.
@@ -138,9 +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.
 
@@ -152,7 +179,7 @@ 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 6.3 which might have some performance improvements:
 
@@ -186,7 +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:
 
@@ -234,6 +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
 
@@ -290,6 +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/)
@@ -306,7 +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:
@@ -323,7 +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)

comfy/cli_args.py

@@ -179,6 +179,8 @@ parser.add_argument(
 
 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()
 else:
@@ -189,3 +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/k_diffusion/sampling.py

@@ -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
@@ -1289,53 +1289,60 @@ 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.):

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/hunyuan_video/model.py

@@ -310,7 +310,7 @@ 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=None, attention_mask=None, control=None, transformer_options={}, **kwargs):

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:

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
@@ -165,9 +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
 
@@ -873,6 +872,15 @@ class HunyuanVideo(BaseModel):
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
+        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]))
@@ -904,3 +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 = 0.1
+    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))
 
@@ -665,6 +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:
@@ -882,6 +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
@@ -898,6 +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:
@@ -908,6 +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():
@@ -920,11 +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
 
@@ -954,6 +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']
@@ -990,21 +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
@@ -1018,6 +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
 
@@ -1075,13 +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
@@ -1100,11 +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/samplers.py

@@ -686,7 +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", "gradient_estimation"]
+                  "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

@@ -58,7 +58,7 @@ def load_torch_file(ckpt, safe_load=False, device=None):
                 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 incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt))
+                    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:

comfy_extras/nodes_load_3d.py

@@ -20,10 +20,7 @@ class Load3D():
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "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")
@@ -35,22 +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
@@ -67,11 +56,7 @@ class Load3DAnimation():
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "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")
@@ -83,20 +68,14 @@ 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
@@ -104,10 +83,27 @@ class Preview3D():
         return {"required": {
             "model_file": ("STRING", {"default": "", "multiline": False}),
             "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
@@ -124,11 +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__ = "0.3.13"
+__version__ = "0.3.18"

folder_paths.py

@@ -9,7 +9,7 @@ from collections.abc import Collection
 
 from comfy.cli_args import args
 
-supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
+supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
 
 folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {}
 

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

@@ -914,7 +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}),
@@ -924,7 +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":
@@ -941,6 +941,10 @@ class CLIPLoader:
             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
 
@@ -1062,10 +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]
@@ -1760,6 +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"]
@@ -1946,6 +1981,7 @@ NODE_CLASS_MAPPINGS = {
     "PreviewImage": PreviewImage,
     "LoadImage": LoadImage,
     "LoadImageMask": LoadImageMask,
+    "LoadImageOutput": LoadImageOutput,
     "ImageScale": ImageScale,
     "ImageScaleBy": ImageScaleBy,
     "ImageInvert": ImageInvert,
@@ -2046,6 +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)",
@@ -2230,6 +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 = "0.3.13"
+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:

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'."

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)

web/assets/BaseViewTemplate-BTbuZf5t.js

@@ -1,4 +1,4 @@
-import { d as defineComponent, ad as ref, t as onMounted, bT as isElectron, bV as electronAPI, af as nextTick, o as openBlock, f as createElementBlock, i as withDirectives, v as vShow, m as createBaseVNode, M as renderSlot, V as normalizeClass } from "./index-QvfM__ze.js";
+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",
@@ -16,11 +16,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       symbolColor: "#171717"
     };
     const topMenuRef = ref(null);
-    const isNativeWindow = ref(false);
     onMounted(async () => {
       if (isElectron()) {
-        const windowStyle = await electronAPI().Config.getWindowStyle();
-        isNativeWindow.value = windowStyle === "custom";
         await nextTick();
         electronAPI().changeTheme({
           ...props.dark ? darkTheme : lightTheme,
@@ -30,7 +27,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     });
     return (_ctx, _cache) => {
       return openBlock(), createElementBlock("div", {
-        class: normalizeClass(["font-sans w-screen h-screen flex flex-col pointer-events-auto", [
+        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"
         ]])
       }, [
@@ -39,7 +36,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
           ref: topMenuRef,
           class: "app-drag w-full h-[var(--comfy-topbar-height)]"
         }, null, 512), [
-          [vShow, isNativeWindow.value]
+          [vShow, unref(isNativeWindow)()]
         ]),
         createBaseVNode("div", _hoisted_1, [
           renderSlot(_ctx.$slots, "default")
@@ -51,4 +48,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as _
 };
-//# sourceMappingURL=BaseViewTemplate-BhQMaVFP.js.map
+//# 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/DesktopStartView-le6AjGZr.js

@@ -1,22 +0,0 @@
-import { d as defineComponent, o as openBlock, J as createBlock, P as withCtx, m as createBaseVNode, k as createVNode, j as unref, ch as script } from "./index-QvfM__ze.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BhQMaVFP.js";
-const _hoisted_1 = { class: "max-w-screen-sm w-screen p-8" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "DesktopStartView",
-  setup(__props) {
-    return (_ctx, _cache) => {
-      return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
-        default: withCtx(() => [
-          createBaseVNode("div", _hoisted_1, [
-            createVNode(unref(script), { mode: "indeterminate" })
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-export {
-  _sfc_main as default
-};
-//# sourceMappingURL=DesktopStartView-le6AjGZr.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, J as createBlock, P as withCtx, m as createBaseVNode, Z as toDisplayString, k as createVNode, j as unref, l as script, c2 as useRouter } from "./index-QvfM__ze.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BhQMaVFP.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-rPK_vYgU.js.map
+//# sourceMappingURL=DownloadGitView-PWqK5ke4.js.map

web/assets/ExtensionPanel-Ba57xrmg.js

@@ -1,8 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, ad as ref, cu as FilterMatchMode, cz as useExtensionStore, a as useSettingStore, t as onMounted, c as computed, o as openBlock, J as createBlock, P as withCtx, k as createVNode, cv as SearchBox, j as unref, c6 as script, m as createBaseVNode, f as createElementBlock, I as renderList, Z as toDisplayString, aG as createTextVNode, H as Fragment, l as script$1, L as createCommentVNode, aK as script$3, b8 as script$4, cc as script$5, cw as _sfc_main$1 } from "./index-QvfM__ze.js";
-import { s as script$2, a as script$6 } from "./index-DpF-ptbJ.js";
-import "./index-Q1cQr26V.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",
@@ -179,4 +179,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ExtensionPanel-3jWrm6Zi.js.map
+//# sourceMappingURL=ExtensionPanel-Ba57xrmg.js.map

web/assets/GraphView-Bo28XDd0.css

@@ -1,6 +1,5 @@
 
-.comfy-menu-hamburger[data-v-7ed57d1a] {
-    pointer-events: auto;
+.comfy-menu-hamburger[data-v-82120b51] {
     position: fixed;
     z-index: 9999;
     display: flex;
@@ -41,19 +40,19 @@
   z-index: 999;
 }
 
-.p-buttongroup-vertical[data-v-cb8f9a1a] {
+.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-cb8f9a1a] {
+.p-buttongroup-vertical .p-button[data-v-27a9500c] {
   margin: 0;
   border-radius: 0;
 }
 
-.node-tooltip[data-v-46859edf] {
+.node-tooltip[data-v-f03142eb] {
   background: var(--comfy-input-bg);
   border-radius: 5px;
   box-shadow: 0 0 5px rgba(0, 0, 0, 0.4);
@@ -133,13 +132,11 @@
   border-right: 4px solid var(--p-button-text-primary-color);
 }
 
-.side-tool-bar-container[data-v-33cac83a] {
+.side-tool-bar-container[data-v-04875455] {
   display: flex;
   flex-direction: column;
   align-items: center;
 
-  pointer-events: auto;
-
   width: var(--sidebar-width);
   height: 100%;
 
@@ -150,16 +147,16 @@
   --sidebar-width: 4rem;
   --sidebar-icon-size: 1.5rem;
 }
-.side-tool-bar-container.small-sidebar[data-v-33cac83a] {
+.side-tool-bar-container.small-sidebar[data-v-04875455] {
   --sidebar-width: 2.5rem;
   --sidebar-icon-size: 1rem;
 }
-.side-tool-bar-end[data-v-33cac83a] {
+.side-tool-bar-end[data-v-04875455] {
   align-self: flex-end;
   margin-top: auto;
 }
 
-.status-indicator[data-v-8d011a31] {
+.status-indicator[data-v-fd6ae3af] {
   position: absolute;
   font-weight: 700;
   font-size: 1.5rem;
@@ -221,7 +218,7 @@
   border-radius: 0px
 }
 
-[data-v-38831d8e] .workflow-tabs {
+[data-v-6ab68035] .workflow-tabs {
   background-color: var(--comfy-menu-bg);
 }
 
@@ -230,36 +227,41 @@
   border-bottom-left-radius: 0;
 }
 
-.comfyui-queue-button[data-v-e9044686] .p-splitbutton-dropdown {
+.comfyui-queue-button[data-v-91a628af] .p-splitbutton-dropdown {
   border-top-right-radius: 0;
   border-bottom-right-radius: 0;
 }
 
-.actionbar[data-v-915e5456] {
+.actionbar[data-v-ebd56d51] {
   pointer-events: all;
   position: fixed;
   z-index: 1000;
 }
-.actionbar.is-docked[data-v-915e5456] {
+.actionbar.is-docked[data-v-ebd56d51] {
   position: static;
   border-style: none;
   background-color: transparent;
   padding: 0px;
 }
-.actionbar.is-dragging[data-v-915e5456] {
+.actionbar.is-dragging[data-v-ebd56d51] {
   -webkit-user-select: none;
      -moz-user-select: none;
           user-select: none;
 }
-[data-v-915e5456] .p-panel-content {
+[data-v-ebd56d51] .p-panel-content {
   padding: 0.25rem;
 }
-.is-docked[data-v-915e5456] .p-panel-content {
+.is-docked[data-v-ebd56d51] .p-panel-content {
   padding: 0px;
 }
-[data-v-915e5456] .p-panel-header {
+[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;
@@ -275,7 +277,7 @@
   border-style: solid;
 }
 
-.comfyui-menu[data-v-6e35440f] {
+.comfyui-menu[data-v-68d3b5b9] {
   width: 100vw;
   height: var(--comfy-topbar-height);
   background: var(--comfy-menu-bg);
@@ -288,19 +290,94 @@
   order: 0;
   grid-column: 1/-1;
 }
-.comfyui-menu.dropzone[data-v-6e35440f] {
+.comfyui-menu.dropzone[data-v-68d3b5b9] {
   background: var(--p-highlight-background);
 }
-.comfyui-menu.dropzone-active[data-v-6e35440f] {
+.comfyui-menu.dropzone-active[data-v-68d3b5b9] {
   background: var(--p-highlight-background-focus);
 }
-[data-v-6e35440f] .p-menubar-item-label {
+[data-v-68d3b5b9] .p-menubar-item-label {
   line-height: revert;
 }
-.comfyui-logo[data-v-6e35440f] {
+.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/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

@@ -2,11 +2,13 @@
 .p-tag[data-v-79125ff6] {
   --p-tag-gap: 0.5rem;
 }
-.hover-brighten[data-v-79125ff6] {
+.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;
+  }
 &[data-v-79125ff6]:hover {
     filter: brightness(107%) contrast(105%);
     box-shadow: 0 0 0.25rem #ffffff79;
@@ -20,7 +22,7 @@
   transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
   transition-duration: 150ms;
 }
-div.selected[data-v-79125ff6] {
+div.selected {
 .gpu-button[data-v-79125ff6]:not(.selected) {
     opacity: 0.5;
 }
@@ -46,7 +48,7 @@ div.selected[data-v-79125ff6] {
 .gpu-button[data-v-79125ff6]:hover {
   --tw-bg-opacity: 0.75;
 }
-.gpu-button[data-v-79125ff6] {
+.gpu-button {
 &.selected[data-v-79125ff6] {
     --tw-bg-opacity: 1;
     background-color: rgb(64 64 64 / var(--tw-bg-opacity));
@@ -74,6 +76,6 @@ div.selected[data-v-79125ff6] {
   text-align: center;
 }
 
-[data-v-0a97b0ae] .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

@@ -1,9 +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, H as Fragment, I as renderList, k as createVNode, P as withCtx, aG as createTextVNode, Z as toDisplayString, j as unref, aK as script, L as createCommentVNode, ad as ref, cu as FilterMatchMode, a$ as useKeybindingStore, a4 as useCommandStore, a3 as useI18n, ah as normalizeI18nKey, w as watchEffect, bz as useToast, r as resolveDirective, J as createBlock, cv as SearchBox, m as createBaseVNode, l as script$2, ax as script$4, b3 as withModifiers, c6 as script$5, aP as script$6, i as withDirectives, cw as _sfc_main$2, p as pushScopeId, q as popScopeId, cx as KeyComboImpl, cy as KeybindingImpl, _ as _export_sfc } from "./index-QvfM__ze.js";
-import { s as script$1, a as script$3 } from "./index-DpF-ptbJ.js";
-import { u as useKeybindingService } from "./keybindingService-Cak1En5n.js";
-import "./index-Q1cQr26V.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"
@@ -36,7 +36,6 @@ const _sfc_main$1 = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2554ab36"), n = n(), popScopeId(), n), "_withScopeId");
 const _hoisted_1 = { class: "actions invisible flex flex-row" };
 const _hoisted_2 = ["title"];
 const _hoisted_3 = { key: 1 };
@@ -97,6 +96,16 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     }
     __name(removeKeybinding, "removeKeybinding");
     function captureKeybinding(event) {
+      if (!event.shiftKey && !event.altKey && !event.ctrlKey && !event.metaKey) {
+        switch (event.key) {
+          case "Escape":
+            cancelEdit();
+            return;
+          case "Enter":
+            saveKeybinding();
+            return;
+        }
+      }
       const keyCombo = KeyComboImpl.fromEvent(event);
       newBindingKeyCombo.value = keyCombo;
     }
@@ -152,7 +161,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
             value: commandsData.value,
             selection: selectedCommandData.value,
             "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event),
-            "global-filter-fields": ["id"],
+            "global-filter-fields": ["id", "label"],
             filters: filters.value,
             selectionMode: "single",
             stripedRows: "",
@@ -217,7 +226,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
             visible: editDialogVisible.value,
             "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event),
             modal: "",
-            header: currentEditingCommand.value?.id,
+            header: currentEditingCommand.value?.label,
             onHide: cancelEdit
           }, {
             footer: withCtx(() => [
@@ -247,7 +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
@@ -276,8 +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-D6O16W_1.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-DTLyJ3VG.js

@@ -1,8 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, a3 as useI18n, ad as ref, t as onMounted, o as openBlock, J as createBlock, P as withCtx, m as createBaseVNode, Z as toDisplayString, k as createVNode, j as unref, aK as script, bN as script$1, l as script$2, p as pushScopeId, q as popScopeId, bV as electronAPI, _ as _export_sfc } from "./index-QvfM__ze.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BhQMaVFP.js";
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-dc169863"), n = n(), popScopeId(), n), "_withScopeId");
+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" };
@@ -72,4 +71,4 @@ const ManualConfigurationView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scop
 export {
   ManualConfigurationView as default
 };
-//# sourceMappingURL=ManualConfigurationView-enyqGo0M.js.map
+//# sourceMappingURL=ManualConfigurationView-DTLyJ3VG.js.map

web/assets/MetricsConsentView-C80fk2cl.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BhQMaVFP.js";
-import { d as defineComponent, bz as useToast, a3 as useI18n, ad as ref, c2 as useRouter, o as openBlock, J as createBlock, P as withCtx, m as createBaseVNode, Z as toDisplayString, aG as createTextVNode, k as createVNode, j as unref, cc as script, l as script$1, bV as electronAPI } from "./index-QvfM__ze.js";
+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$1, b9 as electronAPI } from "./index-Bv0b06LE.js";
 const _hoisted_1 = { class: "h-full p-8 2xl:p-16 flex flex-col items-center justify-center" };
 const _hoisted_2 = { class: "bg-neutral-800 rounded-lg shadow-lg p-6 w-full max-w-[600px] flex flex-col gap-6" };
 const _hoisted_3 = { class: "text-3xl font-semibold text-neutral-100" };
@@ -53,7 +53,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
               createBaseVNode("p", _hoisted_5, [
                 createTextVNode(toDisplayString(_ctx.$t("install.moreInfo")) + " ", 1),
                 createBaseVNode("a", _hoisted_6, toDisplayString(_ctx.$t("install.privacyPolicy")), 1),
-                createTextVNode(". ")
+                _cache[1] || (_cache[1] = createTextVNode(". "))
               ]),
               createBaseVNode("div", _hoisted_7, [
                 createVNode(unref(script), {
@@ -83,4 +83,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=MetricsConsentView-lSfLu4nr.js.map
+//# sourceMappingURL=MetricsConsentView-C80fk2cl.js.map

web/assets/NotSupportedView-B78ZVR9Z.js

@@ -1,22 +1,16 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, c2 as useRouter, r as resolveDirective, o as openBlock, J as createBlock, P as withCtx, m as createBaseVNode, Z as toDisplayString, k as createVNode, j as unref, l as script, i as withDirectives, p as pushScopeId, q as popScopeId, _ as _export_sfc } from "./index-QvfM__ze.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BhQMaVFP.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 _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-ebb20958"), n = n(), popScopeId(), n), "_withScopeId");
 const _hoisted_1 = { class: "sad-container" };
-const _hoisted_2 = /* @__PURE__ */ _withScopeId(() => /* @__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) {
@@ -38,18 +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",
@@ -85,4 +83,4 @@ const NotSupportedView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "
 export {
   NotSupportedView as default
 };
-//# sourceMappingURL=NotSupportedView-Vc8_xWgH.js.map
+//# sourceMappingURL=NotSupportedView-B78ZVR9Z.js.map

web/assets/NotSupportedView-RFx6eCkN.css

@@ -1,9 +1,11 @@
 
-.sad-container[data-v-ebb20958] {
+.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;
 }

web/assets/ServerConfigPanel-BYrt6wyr.js

@@ -1,25 +1,23 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { m as createBaseVNode, o as openBlock, f as createElementBlock, a0 as markRaw, d as defineComponent, a as useSettingStore, aS as storeToRefs, a7 as watch, cW as useCopyToClipboard, a3 as useI18n, J as createBlock, P as withCtx, j as unref, c6 as script, Z as toDisplayString, I as renderList, H as Fragment, k as createVNode, l as script$1, L as createCommentVNode, c4 as script$2, cX as FormItem, cw as _sfc_main$1, bV as electronAPI } from "./index-QvfM__ze.js";
-import { u as useServerConfigStore } from "./serverConfigStore-DCme3xlV.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-B-w0HFlz.js.map
+//# sourceMappingURL=ServerConfigPanel-BYrt6wyr.js.map

web/assets/ServerStartView-B7TlHxYo.js

@@ -1,8 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, a3 as useI18n, ad as ref, c7 as ProgressStatus, t as onMounted, o as openBlock, J as createBlock, P as withCtx, m as createBaseVNode, aG as createTextVNode, Z as toDisplayString, j as unref, f as createElementBlock, L as createCommentVNode, k as createVNode, l as script, i as withDirectives, v as vShow, c8 as BaseTerminal, p as pushScopeId, q as popScopeId, bV as electronAPI, _ as _export_sfc } from "./index-QvfM__ze.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BhQMaVFP.js";
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-4140d62b"), n = n(), popScopeId(), n), "_withScopeId");
+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 };
@@ -94,8 +93,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-4140d62b"]]);
+const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-e6ba9633"]]);
 export {
   ServerStartView as default
 };
-//# sourceMappingURL=ServerStartView-48wfE1MS.js.map
+//# sourceMappingURL=ServerStartView-B7TlHxYo.js.map

web/assets/ServerStartView-BZ7uhZHv.css

@@ -1,5 +1,5 @@
 
-[data-v-4140d62b] .xterm-helper-textarea {
+[data-v-e6ba9633] .xterm-helper-textarea {
   /* Hide this as it moves all over when uv is running */
   display: none;
 }

web/assets/UserSelectView-C703HOyO.js

@@ -1,18 +1,17 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, aX as useUserStore, c2 as useRouter, ad as ref, c as computed, t as onMounted, o as openBlock, J as createBlock, P as withCtx, m as createBaseVNode, Z as toDisplayString, k as createVNode, c3 as withKeys, j as unref, ax as script, c4 as script$1, c5 as script$2, c6 as script$3, aG as createTextVNode, L as createCommentVNode, l as script$4 } from "./index-QvfM__ze.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BhQMaVFP.js";
+import { d as defineComponent, ak as useUserStore, bi as useRouter, T as ref, c as computed, p as onMounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, bj as withKeys, j as unref, bk as script, bl as script$1, bm as script$2, bn as script$3, a8 as createTextVNode, B as createCommentVNode, l as script$4 } from "./index-Bv0b06LE.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
 const _hoisted_1 = {
   id: "comfy-user-selection",
   class: "min-w-84 relative rounded-lg bg-[var(--comfy-menu-bg)] p-5 px-10 shadow-lg"
 };
-const _hoisted_2 = /* @__PURE__ */ createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1);
-const _hoisted_3 = { class: "flex w-full flex-col items-center" };
-const _hoisted_4 = { class: "flex w-full flex-col gap-2" };
-const _hoisted_5 = { for: "new-user-input" };
-const _hoisted_6 = { class: "flex w-full flex-col gap-2" };
-const _hoisted_7 = { for: "existing-user-select" };
-const _hoisted_8 = { class: "mt-5" };
+const _hoisted_2 = { class: "flex w-full flex-col items-center" };
+const _hoisted_3 = { class: "flex w-full flex-col gap-2" };
+const _hoisted_4 = { for: "new-user-input" };
+const _hoisted_5 = { class: "flex w-full flex-col gap-2" };
+const _hoisted_6 = { for: "existing-user-select" };
+const _hoisted_7 = { class: "mt-5" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "UserSelectView",
   setup(__props) {
@@ -47,10 +46,10 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
         default: withCtx(() => [
           createBaseVNode("main", _hoisted_1, [
-            _hoisted_2,
-            createBaseVNode("div", _hoisted_3, [
-              createBaseVNode("div", _hoisted_4, [
-                createBaseVNode("label", _hoisted_5, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
+            _cache[2] || (_cache[2] = createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1)),
+            createBaseVNode("div", _hoisted_2, [
+              createBaseVNode("div", _hoisted_3, [
+                createBaseVNode("label", _hoisted_4, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
                 createVNode(unref(script), {
                   id: "new-user-input",
                   modelValue: newUsername.value,
@@ -60,8 +59,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                 }, null, 8, ["modelValue", "placeholder"])
               ]),
               createVNode(unref(script$1)),
-              createBaseVNode("div", _hoisted_6, [
-                createBaseVNode("label", _hoisted_7, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
+              createBaseVNode("div", _hoisted_5, [
+                createBaseVNode("label", _hoisted_6, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
                 createVNode(unref(script$2), {
                   modelValue: selectedUser.value,
                   "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => selectedUser.value = $event),
@@ -82,7 +81,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   _: 1
                 })) : createCommentVNode("", true)
               ]),
-              createBaseVNode("footer", _hoisted_8, [
+              createBaseVNode("footer", _hoisted_7, [
                 createVNode(unref(script$4), {
                   label: _ctx.$t("userSelect.next"),
                   onClick: login
@@ -99,4 +98,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=UserSelectView-CXmVKOeK.js.map
+//# sourceMappingURL=UserSelectView-C703HOyO.js.map

web/assets/WelcomeView-DIFvbWc2.js

@@ -1,8 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, c2 as useRouter, o as openBlock, J as createBlock, P as withCtx, m as createBaseVNode, Z as toDisplayString, k as createVNode, j as unref, l as script, p as pushScopeId, q as popScopeId, _ as _export_sfc } from "./index-QvfM__ze.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BhQMaVFP.js";
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-7dfaf74c"), n = n(), popScopeId(), n), "_withScopeId");
+import { d as defineComponent, bi as useRouter, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, l as script, _ as _export_sfc } from "./index-Bv0b06LE.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
 const _hoisted_1 = { class: "flex flex-col items-center justify-center gap-8 p-8" };
 const _hoisted_2 = { class: "animated-gradient-text text-glow select-none" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
@@ -37,4 +36,4 @@ const WelcomeView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-
 export {
   WelcomeView as default
 };
-//# sourceMappingURL=WelcomeView-C8whKl15.js.map
+//# sourceMappingURL=WelcomeView-DIFvbWc2.js.map

web/assets/index-CBxvvAzM.css

@@ -306,6 +306,7 @@
 .litegraph .dialog .dialog-footer {
   height: 50px;
   padding: 10px;
+  margin: 0;
   border-top: 1px solid #1a1a1a;
 }
 
@@ -442,63 +443,6 @@
   color: black;
 }
 
-.litegraph .subgraph_property {
-  padding: 4px;
-}
-
-.litegraph .subgraph_property:hover {
-  background-color: #333;
-}
-
-.litegraph .subgraph_property.extra {
-  margin-top: 8px;
-}
-
-.litegraph .subgraph_property span.name {
-  font-size: 1.3em;
-  padding-left: 4px;
-}
-
-.litegraph .subgraph_property span.type {
-  opacity: 0.5;
-  margin-right: 20px;
-  padding-left: 4px;
-}
-
-.litegraph .subgraph_property span.label {
-  display: inline-block;
-  width: 60px;
-  padding: 0px 10px;
-}
-
-.litegraph .subgraph_property input {
-  width: 140px;
-  color: #999;
-  background-color: #1a1a1a;
-  border-radius: 4px;
-  border: 0;
-  margin-right: 10px;
-  padding: 4px;
-  padding-left: 10px;
-}
-
-.litegraph .subgraph_property button {
-  background-color: #1c1c1c;
-  color: #aaa;
-  border: 0;
-  border-radius: 2px;
-  padding: 4px 10px;
-  cursor: pointer;
-}
-
-.litegraph .subgraph_property.extra {
-  color: #ccc;
-}
-
-.litegraph .subgraph_property.extra input {
-  background-color: #111;
-}
-
 .litegraph .bullet_icon {
   margin-left: 10px;
   border-radius: 10px;
@@ -661,21 +605,6 @@
 .litegraph .dialog .dialog-content {
   display: block;
 }
-.litegraph .dialog .dialog-content .subgraph_property {
-  padding: 5px;
-}
-.litegraph .dialog .dialog-footer {
-  margin: 0;
-}
-.litegraph .dialog .dialog-footer .subgraph_property {
-  margin-top: 0;
-  display: flex;
-  align-items: center;
-  padding: 5px;
-}
-.litegraph .dialog .dialog-footer .subgraph_property .name {
-  flex: 1;
-}
 .litegraph .graphdialog {
   display: flex;
   align-items: center;
@@ -2101,6 +2030,18 @@
   .inset-0{
     inset: 0px;
   }
+  .-bottom-4{
+    bottom: -1rem;
+  }
+  .-right-14{
+    right: -3.5rem;
+  }
+  .-right-4{
+    right: -1rem;
+  }
+  .bottom-0{
+    bottom: 0px;
+  }
   .bottom-\[10px\]{
     bottom: 10px;
   }
@@ -2110,6 +2051,15 @@
   .left-0{
     left: 0px;
   }
+  .left-1\/2{
+    left: 50%;
+  }
+  .left-12{
+    left: 3rem;
+  }
+  .left-2{
+    left: 0.5rem;
+  }
   .left-\[-350px\]{
     left: -350px;
   }
@@ -2119,6 +2069,9 @@
   .top-0{
     top: 0px;
   }
+  .top-2{
+    top: 0.5rem;
+  }
   .top-\[50px\]{
     top: 50px;
   }
@@ -2128,12 +2081,21 @@
   .z-10{
     z-index: 10;
   }
+  .z-20{
+    z-index: 20;
+  }
   .z-\[1000\]{
     z-index: 1000;
   }
   .z-\[9999\]{
     z-index: 9999;
   }
+  .col-span-full{
+    grid-column: 1 / -1;
+  }
+  .row-span-full{
+    grid-row: 1 / -1;
+  }
   .m-0{
     margin: 0px;
   }
@@ -2146,6 +2108,9 @@
   .m-2{
     margin: 0.5rem;
   }
+  .m-8{
+    margin: 2rem;
+  }
   .mx-1{
     margin-left: 0.25rem;
     margin-right: 0.25rem;
@@ -2178,6 +2143,10 @@
     margin-top: 1rem;
     margin-bottom: 1rem;
   }
+  .my-8{
+    margin-top: 2rem;
+    margin-bottom: 2rem;
+  }
   .mb-2{
     margin-bottom: 0.5rem;
   }
@@ -2226,6 +2195,9 @@
   .mt-5{
     margin-top: 1.25rem;
   }
+  .mt-6{
+    margin-top: 1.5rem;
+  }
   .block{
     display: block;
   }
@@ -2259,15 +2231,24 @@
   .h-1{
     height: 0.25rem;
   }
+  .h-1\/2{
+    height: 50%;
+  }
   .h-16{
     height: 4rem;
   }
+  .h-48{
+    height: 12rem;
+  }
   .h-6{
     height: 1.5rem;
   }
   .h-64{
     height: 16rem;
   }
+  .h-8{
+    height: 2rem;
+  }
   .h-96{
     height: 26rem;
   }
@@ -2292,12 +2273,21 @@
   .max-h-full{
     max-height: 100%;
   }
+  .min-h-52{
+    min-height: 13rem;
+  }
   .min-h-8{
     min-height: 2rem;
   }
+  .min-h-full{
+    min-height: 100%;
+  }
   .min-h-screen{
     min-height: 100vh;
   }
+  .w-0{
+    width: 0px;
+  }
   .w-1\/2{
     width: 50%;
   }
@@ -2310,12 +2300,21 @@
   .w-16{
     width: 4rem;
   }
+  .w-24{
+    width: 6rem;
+  }
   .w-28{
     width: 7rem;
   }
+  .w-3{
+    width: 0.75rem;
+  }
   .w-3\/12{
     width: 25%;
   }
+  .w-32{
+    width: 8rem;
+  }
   .w-44{
     width: 11rem;
   }
@@ -2356,15 +2355,24 @@
   .min-w-110{
     min-width: 32rem;
   }
+  .min-w-32{
+    min-width: 8rem;
+  }
   .min-w-84{
     min-width: 22rem;
   }
   .min-w-96{
     min-width: 26rem;
   }
+  .min-w-full{
+    min-width: 100%;
+  }
   .max-w-110{
     max-width: 32rem;
   }
+  .max-w-48{
+    max-width: 12rem;
+  }
   .max-w-64{
     max-width: 16rem;
   }
@@ -2395,6 +2403,9 @@
   .grow{
     flex-grow: 1;
   }
+  .border-collapse{
+    border-collapse: collapse;
+  }
   .-translate-y-40{
     --tw-translate-y: -10rem;
     transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));
@@ -2413,6 +2424,9 @@
   .cursor-pointer{
     cursor: pointer;
   }
+  .touch-none{
+    touch-action: none;
+  }
   .select-none{
     -webkit-user-select: none;
        -moz-user-select: none;
@@ -2463,9 +2477,15 @@
   .justify-around{
     justify-content: space-around;
   }
+  .justify-evenly{
+    justify-content: space-evenly;
+  }
   .gap-0{
     gap: 0px;
   }
+  .gap-1{
+    gap: 0.25rem;
+  }
   .gap-2{
     gap: 0.5rem;
   }
@@ -2481,6 +2501,11 @@
   .gap-8{
     gap: 2rem;
   }
+  .space-x-1 > :not([hidden]) ~ :not([hidden]){
+    --tw-space-x-reverse: 0;
+    margin-right: calc(0.25rem * var(--tw-space-x-reverse));
+    margin-left: calc(0.25rem * calc(1 - var(--tw-space-x-reverse)));
+  }
   .space-y-1 > :not([hidden]) ~ :not([hidden]){
     --tw-space-y-reverse: 0;
     margin-top: calc(0.25rem * calc(1 - var(--tw-space-y-reverse)));
@@ -2528,9 +2553,6 @@
   .whitespace-pre-line{
     white-space: pre-line;
   }
-  .whitespace-pre-wrap{
-    white-space: pre-wrap;
-  }
   .text-wrap{
     text-wrap: wrap;
   }
@@ -2560,6 +2582,10 @@
     border-left-width: 0px;
     border-right-width: 0px;
   }
+  .border-y{
+    border-top-width: 1px;
+    border-bottom-width: 1px;
+  }
   .border-b{
     border-bottom-width: 1px;
   }
@@ -2575,9 +2601,16 @@
   .border-solid{
     border-style: solid;
   }
+  .border-hidden{
+    border-style: hidden;
+  }
   .border-none{
     border-style: none;
   }
+  .border-neutral-700{
+    --tw-border-opacity: 1;
+    border-color: rgb(64 64 64 / var(--tw-border-opacity));
+  }
   .bg-\[var\(--comfy-menu-bg\)\]{
     background-color: var(--comfy-menu-bg);
   }
@@ -2732,6 +2765,9 @@
   .text-center{
     text-align: center;
   }
+  .text-right{
+    text-align: right;
+  }
   .font-mono{
     font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
   }
@@ -2826,24 +2862,44 @@
     --tw-text-opacity: 1;
     color: rgb(239 68 68 / var(--tw-text-opacity));
   }
+  .text-white{
+    --tw-text-opacity: 1;
+    color: rgb(255 255 255 / var(--tw-text-opacity));
+  }
   .underline{
     text-decoration-line: underline;
   }
   .no-underline{
     text-decoration-line: none;
   }
+  .antialiased{
+    -webkit-font-smoothing: antialiased;
+    -moz-osx-font-smoothing: grayscale;
+  }
   .opacity-0{
     opacity: 0;
   }
   .opacity-100{
     opacity: 1;
   }
+  .opacity-15{
+    opacity: 0.15;
+  }
+  .opacity-25{
+    opacity: 0.25;
+  }
   .opacity-40{
     opacity: 0.4;
   }
   .opacity-50{
     opacity: 0.5;
   }
+  .opacity-65{
+    opacity: 0.65;
+  }
+  .opacity-75{
+    opacity: 0.75;
+  }
   .shadow-lg{
     --tw-shadow: 0 10px 15px -3px rgb(0 0 0 / 0.1), 0 4px 6px -4px rgb(0 0 0 / 0.1);
     --tw-shadow-colored: 0 10px 15px -3px var(--tw-shadow-color), 0 4px 6px -4px var(--tw-shadow-color);
@@ -2891,6 +2947,9 @@
   .duration-100{
     transition-duration: 100ms;
   }
+  .duration-200{
+    transition-duration: 200ms;
+  }
   .duration-300{
     transition-duration: 300ms;
   }
@@ -2949,8 +3008,6 @@ body {
   height: 100vh;
   margin: 0;
   overflow: hidden;
-  grid-template-columns: auto 1fr auto;
-  grid-template-rows: auto 1fr auto;
   background: var(--bg-color) var(--bg-img);
   color: var(--fg-color);
   min-height: -webkit-fill-available;
@@ -2960,87 +3017,6 @@ body {
   font-family: Arial, sans-serif;
 }
 
-/**
-  +------------------+------------------+------------------+
-  |                                                        |
-  |  .comfyui-body-                                        |
-  |       top                                              |
-  | (spans all cols)                                       |
-  |                                                        |
-  +------------------+------------------+------------------+
-  |                  |                  |                  |
-  | .comfyui-body-   |   #graph-canvas  | .comfyui-body-   |
-  |      left        |                  |      right       |
-  |                  |                  |                  |
-  |                  |                  |                  |
-  +------------------+------------------+------------------+
-  |                                                        |
-  |  .comfyui-body-                                        |
-  |      bottom                                            |
-  | (spans all cols)                                       |
-  |                                                        |
-  +------------------+------------------+------------------+
-*/
-
-.comfyui-body-top {
-  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 {
-  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 {
-  width: 100%;
-  height: 100%;
-  order: -3;
-  grid-column: 2;
-  grid-row: 2;
-  position: relative;
-  overflow: hidden;
-}
-
-#graph-canvas {
-  width: 100%;
-  height: 100%;
-  touch-action: none;
-}
-
-.comfyui-body-right {
-  order: -2;
-  z-index: 10;
-  grid-column: 3;
-  grid-row: 2;
-}
-
-.comfyui-body-bottom {
-  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;
-}
-
 .comfy-multiline-input {
   background-color: var(--comfy-input-bg);
   color: var(--input-text);
@@ -3455,84 +3431,6 @@ dialog::backdrop {
   justify-content: center;
 }
 
-#comfy-settings-dialog {
-  padding: 0;
-  width: 41rem;
-}
-
-#comfy-settings-dialog tr > td:first-child {
-  text-align: right;
-}
-
-#comfy-settings-dialog tbody button,
-#comfy-settings-dialog table > button {
-  background-color: var(--bg-color);
-  border: 1px var(--border-color) solid;
-  border-radius: 0;
-  color: var(--input-text);
-  font-size: 1rem;
-  padding: 0.5rem;
-}
-
-#comfy-settings-dialog button:hover {
-  background-color: var(--tr-odd-bg-color);
-}
-
-/* General CSS for tables */
-
-.comfy-table {
-  border-collapse: collapse;
-  color: var(--input-text);
-  font-family: Arial, sans-serif;
-  width: 100%;
-}
-
-.comfy-table caption {
-  position: sticky;
-  top: 0;
-  background-color: var(--bg-color);
-  color: var(--input-text);
-  font-size: 1rem;
-  font-weight: bold;
-  padding: 8px;
-  text-align: center;
-  border-bottom: 1px solid var(--border-color);
-}
-
-.comfy-table caption .comfy-btn {
-  position: absolute;
-  top: -2px;
-  right: 0;
-  bottom: 0;
-  cursor: pointer;
-  border: none;
-  height: 100%;
-  border-radius: 0;
-  aspect-ratio: 1/1;
-  -webkit-user-select: none;
-     -moz-user-select: none;
-          user-select: none;
-  font-size: 20px;
-}
-
-.comfy-table caption .comfy-btn:focus {
-  outline: none;
-}
-
-.comfy-table tr:nth-child(even) {
-  background-color: var(--tr-even-bg-color);
-}
-
-.comfy-table tr:nth-child(odd) {
-  background-color: var(--tr-odd-bg-color);
-}
-
-.comfy-table td,
-.comfy-table th {
-  border: 1px solid var(--border-color);
-  padding: 8px;
-}
-
 /* Context menu */
 
 .litegraph .dialog {
@@ -3632,24 +3530,6 @@ dialog::backdrop {
   will-change: transform;
 }
 
-@media only screen and (max-width: 450px) {
-  #comfy-settings-dialog .comfy-table tbody {
-    display: grid;
-  }
-  #comfy-settings-dialog .comfy-table tr {
-    display: grid;
-  }
-  #comfy-settings-dialog tr > td:first-child {
-    text-align: center;
-    border-bottom: none;
-    padding-bottom: 0;
-  }
-  #comfy-settings-dialog tr > td:not(:first-child) {
-    text-align: center;
-    border-top: none;
-  }
-}
-
 audio.comfy-audio.empty-audio-widget {
   display: none;
 }
@@ -3660,7 +3540,6 @@ audio.comfy-audio.empty-audio-widget {
   left: 0;
   width: 100%;
   height: 100%;
-  pointer-events: none;
 }
 
 /* Set auto complete panel's width as it is not accessible within vue-root */
@@ -3672,6 +3551,30 @@ audio.comfy-audio.empty-audio-widget {
   padding: var(--comfy-tree-explorer-item-padding) !important;
 }
 
+/* Load3d styles */
+.comfy-load-3d,
+.comfy-load-3d-animation,
+.comfy-preview-3d,
+.comfy-preview-3d-animation{
+  display: flex;
+  flex-direction: column;
+  background: transparent;
+  flex: 1;
+  position: relative;
+  overflow: hidden;
+}
+
+.comfy-load-3d canvas,
+.comfy-load-3d-animation canvas,
+.comfy-preview-3d canvas,
+.comfy-preview-3d-animation canvas{
+  display: flex;
+  width: 100% !important;
+  height: 100% !important;
+}
+
+/* End of Load3d styles */
+
 /* [Desktop] Electron window specific styles */
 .app-drag {
   app-region: drag;
@@ -3699,6 +3602,42 @@ audio.comfy-audio.empty-audio-widget {
 .hover\:opacity-100:hover{
   opacity: 1;
 }
+@media (prefers-reduced-motion: no-preference){
+
+  .motion-safe\:w-0{
+    width: 0px;
+  }
+
+  .motion-safe\:opacity-0{
+    opacity: 0;
+  }
+
+  .group\/sidebar-tab:focus-within .motion-safe\:group-focus-within\/sidebar-tab\:w-auto{
+    width: auto;
+  }
+
+  .group\/sidebar-tab:focus-within .motion-safe\:group-focus-within\/sidebar-tab\:opacity-100{
+    opacity: 1;
+  }
+
+  .group\/sidebar-tab:hover .motion-safe\:group-hover\/sidebar-tab\:w-auto{
+    width: auto;
+  }
+
+  .group\/sidebar-tab:hover .motion-safe\:group-hover\/sidebar-tab\:opacity-100{
+    opacity: 1;
+  }
+
+  .group\/tree-node:hover .motion-safe\:group-hover\/tree-node\:opacity-100{
+    opacity: 1;
+  }
+}
+@media not all and (min-width: 640px){
+
+  .max-sm\:hidden{
+    display: none;
+  }
+}
 @media (min-width: 768px){
 
   .md\:flex{
@@ -3780,7 +3719,7 @@ audio.comfy-audio.empty-audio-widget {
     padding-top: 0px
 }
 
-.prompt-dialog-content[data-v-3df70997] {
+.prompt-dialog-content[data-v-4f1e3bbe] {
   white-space: pre-wrap;
 }
 
@@ -3798,17 +3737,17 @@ audio.comfy-audio.empty-audio-widget {
   margin-bottom: 1rem;
 }
 
-.comfy-error-report[data-v-09b72a20] {
+.comfy-error-report[data-v-e5000be2] {
   display: flex;
   flex-direction: column;
   gap: 1rem;
 }
-.action-container[data-v-09b72a20] {
+.action-container[data-v-e5000be2] {
   display: flex;
   gap: 1rem;
   justify-content: flex-end;
 }
-.wrapper-pre[data-v-09b72a20] {
+.wrapper-pre[data-v-e5000be2] {
   white-space: pre-wrap;
   word-wrap: break-word;
 }
@@ -3826,7 +3765,7 @@ audio.comfy-audio.empty-audio-widget {
   margin-left: auto;
 }
 
-.comfy-missing-models[data-v-ebf9fccc] {
+.comfy-missing-models[data-v-f8d63775] {
   max-height: 300px;
   overflow-y: auto;
 }
@@ -3868,22 +3807,22 @@ audio.comfy-audio.empty-audio-widget {
     background-color: rgb(234 179 8 / var(--tw-bg-opacity))
 }
 
-[data-v-ba13476b] .p-inputtext {
+[data-v-b3ab067d] .p-inputtext {
   --p-form-field-padding-x: 0.625rem;
 }
-.p-button.p-inputicon[data-v-ba13476b] {
+.p-button.p-inputicon[data-v-b3ab067d] {
   width: auto;
   border-style: none;
   padding: 0px;
 }
 
-.form-input[data-v-e4e3022d] .input-slider .p-inputnumber input,
-.form-input[data-v-e4e3022d] .input-slider .slider-part {
+.form-input[data-v-a29c257f] .input-slider .p-inputnumber input,
+.form-input[data-v-a29c257f] .input-slider .slider-part {
 
     width: 5rem
 }
-.form-input[data-v-e4e3022d] .p-inputtext,
-.form-input[data-v-e4e3022d] .p-select {
+.form-input[data-v-a29c257f] .p-inputtext,
+.form-input[data-v-a29c257f] .p-select {
 
     width: 11rem
 }
@@ -4173,26 +4112,26 @@ audio.comfy-audio.empty-audio-widget {
     position: relative;
 }
 
-[data-v-250ab9af] .p-terminal .xterm {
+[data-v-873a313f] .p-terminal .xterm {
   overflow-x: auto;
 }
-[data-v-250ab9af] .p-terminal .xterm-screen {
+[data-v-873a313f] .p-terminal .xterm-screen {
   background-color: black;
   overflow-y: hidden;
 }
 
-[data-v-90a7f075] .p-terminal .xterm {
+[data-v-14fef2e4] .p-terminal .xterm {
   overflow-x: auto;
 }
-[data-v-90a7f075] .p-terminal .xterm-screen {
+[data-v-14fef2e4] .p-terminal .xterm-screen {
   background-color: black;
   overflow-y: hidden;
 }
 
-[data-v-03daf1c8] .p-terminal .xterm {
+[data-v-cf0c7d52] .p-terminal .xterm {
   overflow-x: auto;
 }
-[data-v-03daf1c8] .p-terminal .xterm-screen {
+[data-v-cf0c7d52] .p-terminal .xterm-screen {
   background-color: black;
   overflow-y: hidden;
 }
@@ -4504,28 +4443,28 @@ audio.comfy-audio.empty-audio-widget {
   box-sizing: border-box;
 }
 
-.tree-node[data-v-a6457774] {
+.tree-node[data-v-a945b5a8] {
   width: 100%;
   display: flex;
   align-items: center;
   justify-content: space-between;
 }
-.leaf-count-badge[data-v-a6457774] {
+.leaf-count-badge[data-v-a945b5a8] {
   margin-left: 0.5rem;
 }
-.node-content[data-v-a6457774] {
+.node-content[data-v-a945b5a8] {
   display: flex;
   align-items: center;
   flex-grow: 1;
 }
-.leaf-label[data-v-a6457774] {
+.leaf-label[data-v-a945b5a8] {
   margin-left: 0.5rem;
 }
-[data-v-a6457774] .editable-text span {
+[data-v-a945b5a8] .editable-text span {
   word-break: break-all;
 }
 
-[data-v-31d518da] .tree-explorer-node-label {
+[data-v-e3a237e6] .tree-explorer-node-label {
   width: 100%;
   display: flex;
   align-items: center;
@@ -4538,10 +4477,10 @@ audio.comfy-audio.empty-audio-widget {
  * By setting the position to relative on the parent and using an absolutely positioned pseudo-element,
  * we can create a visual indicator for the drop target without affecting the layout of other elements.
  */
-[data-v-31d518da] .p-tree-node-content:has(.tree-folder) {
+[data-v-e3a237e6] .p-tree-node-content:has(.tree-folder) {
   position: relative;
 }
-[data-v-31d518da] .p-tree-node-content:has(.tree-folder.can-drop)::after {
+[data-v-e3a237e6] .p-tree-node-content:has(.tree-folder.can-drop)::after {
   content: '';
   position: absolute;
   top: 0;
@@ -4552,21 +4491,21 @@ audio.comfy-audio.empty-audio-widget {
   pointer-events: none;
 }
 
-[data-v-5e759e25] .p-toolbar-end .p-button {
+[data-v-0061c432] .p-toolbar-end .p-button {
 
     padding-top: 0.25rem;
 
     padding-bottom: 0.25rem
 }
 @media (min-width: 1536px) {
-[data-v-5e759e25] .p-toolbar-end .p-button {
+[data-v-0061c432] .p-toolbar-end .p-button {
 
         padding-top: 0.5rem;
 
         padding-bottom: 0.5rem
 }
 }
-[data-v-5e759e25] .p-toolbar-start {
+[data-v-0061c432] .p-toolbar-start {
 
     min-width: 0px;
 
@@ -4644,36 +4583,11 @@ audio.comfy-audio.empty-audio-widget {
   vertical-align: top;
 }
 
-[data-v-0bb2ac55] .pi-fake-spacer {
+[data-v-3be51840] .pi-fake-spacer {
   height: 1px;
   width: 16px;
 }
 
-._content[data-v-c4279e6b] {
-
-    display: flex;
-
-    flex-direction: column
-}
-._content[data-v-c4279e6b] > :not([hidden]) ~ :not([hidden]) {
-
-    --tw-space-y-reverse: 0;
-
-    margin-top: calc(0.5rem * calc(1 - var(--tw-space-y-reverse)));
-
-    margin-bottom: calc(0.5rem * var(--tw-space-y-reverse))
-}
-._footer[data-v-c4279e6b] {
-
-    display: flex;
-
-    flex-direction: column;
-
-    align-items: flex-end;
-
-    padding-top: 1rem
-}
-
 .slot_row[data-v-d9792337] {
   padding: 2px;
 }
@@ -4801,34 +4715,61 @@ audio.comfy-audio.empty-audio-widget {
   color: var(--error-text);
 }
 
+._content[data-v-c4279e6b] {
+
+    display: flex;
+
+    flex-direction: column
+}
+._content[data-v-c4279e6b] > :not([hidden]) ~ :not([hidden]) {
+
+    --tw-space-y-reverse: 0;
+
+    margin-top: calc(0.5rem * calc(1 - var(--tw-space-y-reverse)));
+
+    margin-bottom: calc(0.5rem * var(--tw-space-y-reverse))
+}
+._footer[data-v-c4279e6b] {
+
+    display: flex;
+
+    flex-direction: column;
+
+    align-items: flex-end;
+
+    padding-top: 1rem
+}
+
 .node-lib-node-container[data-v-da9a8962] {
     height: 100%;
     width: 100%
 }
 
-.p-selectbutton .p-button[data-v-05364174] {
+.p-selectbutton .p-button[data-v-bd06e12b] {
   padding: 0.5rem;
 }
-.p-selectbutton .p-button .pi[data-v-05364174] {
+.p-selectbutton .p-button .pi[data-v-bd06e12b] {
   font-size: 1.5rem;
 }
-.field[data-v-05364174] {
+.field[data-v-bd06e12b] {
   display: flex;
   flex-direction: column;
   gap: 0.5rem;
 }
-.color-picker-container[data-v-05364174] {
+.color-picker-container[data-v-bd06e12b] {
   display: flex;
   align-items: center;
   gap: 0.5rem;
 }
 
-.scroll-container[data-v-ad33a347] {
+.scroll-container {
+&[data-v-ad33a347] {
   height: 100%;
   overflow-y: auto;
 
   /* Firefox */
   scrollbar-width: none;
+}
 &[data-v-ad33a347]::-webkit-scrollbar {
     width: 1px;
 }

web/assets/index-Dzu9WL4p.js

@@ -0,0 +1,27 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { bX as script$1, o as openBlock, f as createElementBlock, at as mergeProps, m as createBaseVNode } from "./index-Bv0b06LE.js";
+var script = {
+  name: "BarsIcon",
+  "extends": script$1
+};
+function render(_ctx, _cache, $props, $setup, $data, $options) {
+  return openBlock(), createElementBlock("svg", mergeProps({
+    width: "14",
+    height: "14",
+    viewBox: "0 0 14 14",
+    fill: "none",
+    xmlns: "http://www.w3.org/2000/svg"
+  }, _ctx.pti()), _cache[0] || (_cache[0] = [createBaseVNode("path", {
+    "fill-rule": "evenodd",
+    "clip-rule": "evenodd",
+    d: "M13.3226 3.6129H0.677419C0.497757 3.6129 0.325452 3.54152 0.198411 3.41448C0.0713707 3.28744 0 3.11514 0 2.93548C0 2.75581 0.0713707 2.58351 0.198411 2.45647C0.325452 2.32943 0.497757 2.25806 0.677419 2.25806H13.3226C13.5022 2.25806 13.6745 2.32943 13.8016 2.45647C13.9286 2.58351 14 2.75581 14 2.93548C14 3.11514 13.9286 3.28744 13.8016 3.41448C13.6745 3.54152 13.5022 3.6129 13.3226 3.6129ZM13.3226 7.67741H0.677419C0.497757 7.67741 0.325452 7.60604 0.198411 7.479C0.0713707 7.35196 0 7.17965 0 6.99999C0 6.82033 0.0713707 6.64802 0.198411 6.52098C0.325452 6.39394 0.497757 6.32257 0.677419 6.32257H13.3226C13.5022 6.32257 13.6745 6.39394 13.8016 6.52098C13.9286 6.64802 14 6.82033 14 6.99999C14 7.17965 13.9286 7.35196 13.8016 7.479C13.6745 7.60604 13.5022 7.67741 13.3226 7.67741ZM0.677419 11.7419H13.3226C13.5022 11.7419 13.6745 11.6706 13.8016 11.5435C13.9286 11.4165 14 11.2442 14 11.0645C14 10.8848 13.9286 10.7125 13.8016 10.5855C13.6745 10.4585 13.5022 10.3871 13.3226 10.3871H0.677419C0.497757 10.3871 0.325452 10.4585 0.198411 10.5855C0.0713707 10.7125 0 10.8848 0 11.0645C0 11.2442 0.0713707 11.4165 0.198411 11.5435C0.325452 11.6706 0.497757 11.7419 0.677419 11.7419Z",
+    fill: "currentColor"
+  }, null, -1)]), 16);
+}
+__name(render, "render");
+script.render = render;
+export {
+  script as s
+};
+//# sourceMappingURL=index-Dzu9WL4p.js.map

web/assets/index-Q1cQr26V.js

@@ -1,29 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { cA as script$1, m as createBaseVNode, o as openBlock, f as createElementBlock, G as mergeProps } from "./index-QvfM__ze.js";
-var script = {
-  name: "BarsIcon",
-  "extends": script$1
-};
-var _hoisted_1 = /* @__PURE__ */ createBaseVNode("path", {
-  "fill-rule": "evenodd",
-  "clip-rule": "evenodd",
-  d: "M13.3226 3.6129H0.677419C0.497757 3.6129 0.325452 3.54152 0.198411 3.41448C0.0713707 3.28744 0 3.11514 0 2.93548C0 2.75581 0.0713707 2.58351 0.198411 2.45647C0.325452 2.32943 0.497757 2.25806 0.677419 2.25806H13.3226C13.5022 2.25806 13.6745 2.32943 13.8016 2.45647C13.9286 2.58351 14 2.75581 14 2.93548C14 3.11514 13.9286 3.28744 13.8016 3.41448C13.6745 3.54152 13.5022 3.6129 13.3226 3.6129ZM13.3226 7.67741H0.677419C0.497757 7.67741 0.325452 7.60604 0.198411 7.479C0.0713707 7.35196 0 7.17965 0 6.99999C0 6.82033 0.0713707 6.64802 0.198411 6.52098C0.325452 6.39394 0.497757 6.32257 0.677419 6.32257H13.3226C13.5022 6.32257 13.6745 6.39394 13.8016 6.52098C13.9286 6.64802 14 6.82033 14 6.99999C14 7.17965 13.9286 7.35196 13.8016 7.479C13.6745 7.60604 13.5022 7.67741 13.3226 7.67741ZM0.677419 11.7419H13.3226C13.5022 11.7419 13.6745 11.6706 13.8016 11.5435C13.9286 11.4165 14 11.2442 14 11.0645C14 10.8848 13.9286 10.7125 13.8016 10.5855C13.6745 10.4585 13.5022 10.3871 13.3226 10.3871H0.677419C0.497757 10.3871 0.325452 10.4585 0.198411 10.5855C0.0713707 10.7125 0 10.8848 0 11.0645C0 11.2442 0.0713707 11.4165 0.198411 11.5435C0.325452 11.6706 0.497757 11.7419 0.677419 11.7419Z",
-  fill: "currentColor"
-}, null, -1);
-var _hoisted_2 = [_hoisted_1];
-function render(_ctx, _cache, $props, $setup, $data, $options) {
-  return openBlock(), createElementBlock("svg", mergeProps({
-    width: "14",
-    height: "14",
-    viewBox: "0 0 14 14",
-    fill: "none",
-    xmlns: "http://www.w3.org/2000/svg"
-  }, _ctx.pti()), _hoisted_2, 16);
-}
-__name(render, "render");
-script.render = render;
-export {
-  script as s
-};
-//# sourceMappingURL=index-Q1cQr26V.js.map

web/assets/keybindingService-DyjX-nxF.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a$ as useKeybindingStore, a4 as useCommandStore, a as useSettingStore, cx as KeyComboImpl, cy as KeybindingImpl } from "./index-QvfM__ze.js";
+import { ao as useKeybindingStore, J as useCommandStore, a as useSettingStore, dA as KeyComboImpl, dB as KeybindingImpl } from "./index-Bv0b06LE.js";
 const CORE_KEYBINDINGS = [
   {
     combo: {
@@ -186,7 +186,7 @@ const useKeybindingService = /* @__PURE__ */ __name(() => {
       return;
     }
     const target = event.composedPath()[0];
-    if (!keyCombo.hasModifier && (target.tagName === "TEXTAREA" || target.tagName === "INPUT" || target.tagName === "SPAN" && target.classList.contains("property_value"))) {
+    if (keyCombo.isReservedByTextInput && (target.tagName === "TEXTAREA" || target.tagName === "INPUT" || target.tagName === "SPAN" && target.classList.contains("property_value"))) {
       return;
     }
     const keybinding = keybindingStore.getKeybinding(keyCombo);
@@ -247,4 +247,4 @@ const useKeybindingService = /* @__PURE__ */ __name(() => {
 export {
   useKeybindingService as u
 };
-//# sourceMappingURL=keybindingService-Cak1En5n.js.map
+//# sourceMappingURL=keybindingService-DyjX-nxF.js.map

web/assets/serverConfigStore-D2Vr0L0h.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a1 as defineStore, ad as ref, c as computed } from "./index-QvfM__ze.js";
+import { a1 as defineStore, T as ref, c as computed } from "./index-Bv0b06LE.js";
 const useServerConfigStore = defineStore("serverConfig", () => {
   const serverConfigById = ref({});
   const serverConfigs = computed(() => {
@@ -87,4 +87,4 @@ const useServerConfigStore = defineStore("serverConfig", () => {
 export {
   useServerConfigStore as u
 };
-//# sourceMappingURL=serverConfigStore-DCme3xlV.js.map
+//# sourceMappingURL=serverConfigStore-D2Vr0L0h.js.map

web/assets/uvMirrors-B-HKMf6X.js

@@ -0,0 +1,16 @@
+const PYTHON_MIRROR = {
+  settingId: "Comfy-Desktop.UV.PythonInstallMirror",
+  mirror: "https://github.com/astral-sh/python-build-standalone/releases/download",
+  fallbackMirror: "https://bgithub.xyz/astral-sh/python-build-standalone/releases/download",
+  validationPathSuffix: "/20250115/cpython-3.10.16+20250115-aarch64-apple-darwin-debug-full.tar.zst.sha256"
+};
+const PYPI_MIRROR = {
+  settingId: "Comfy-Desktop.UV.PypiInstallMirror",
+  mirror: "https://pypi.org/simple/",
+  fallbackMirror: "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
+};
+export {
+  PYTHON_MIRROR as P,
+  PYPI_MIRROR as a
+};
+//# sourceMappingURL=uvMirrors-B-HKMf6X.js.map

web/index.html

@@ -6,8 +6,8 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
     <link rel="stylesheet" type="text/css" href="user.css" />
     <link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
-    <script type="module" crossorigin src="./assets/index-QvfM__ze.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-Cf-n7v0V.css">
+    <script type="module" crossorigin src="./assets/index-Bv0b06LE.js"></script>
+    <link rel="stylesheet" crossorigin href="./assets/index-CBxvvAzM.css">
   </head>
   <body class="litegraph grid">
     <div id="vue-app"></div>

web/scripts/domWidget.js

@@ -0,0 +1,2 @@
+// Shim for scripts/domWidget.ts
+export const DOMWidgetImpl = window.comfyAPI.domWidget.DOMWidgetImpl;

web/templates/default.json

@@ -266,7 +266,7 @@
       ],
       "properties": {},
       "widgets_values": [
-        "v1-5-pruned-emaonly.safetensors"
+        "v1-5-pruned-emaonly-fp16.safetensors"
       ]
     }
   ],
@@ -349,8 +349,8 @@
   "extra": {},
   "version": 0.4,
   "models": [{
-    "name": "v1-5-pruned-emaonly.safetensors",
-    "url": "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly.safetensors?download=true",
+    "name": "v1-5-pruned-emaonly-fp16.safetensors",
+    "url": "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors?download=true",
     "directory": "checkpoints"
   }]
 }

web/templates/image2image.json

@@ -330,7 +330,7 @@
         "Node name for S&R": "CheckpointLoaderSimple"
       },
       "widgets_values": [
-        "v1-5-pruned-emaonly.safetensors"
+        "v1-5-pruned-emaonly-fp16.safetensors"
       ]
     }
   ],
@@ -440,8 +440,8 @@
   "extra": {},
   "version": 0.4,
   "models": [{
-    "name": "v1-5-pruned-emaonly.safetensors",
-    "url": "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly.safetensors?download=true",
+    "name": "v1-5-pruned-emaonly-fp16.safetensors",
+    "url": "https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors?download=true",
     "directory": "checkpoints"
   }]
 }