Bump ComfyUI version to v0.3.22

This reverts commit a80bc822a2.

.github/workflows/stable-release.yml

@@ -22,7 +22,7 @@ on:
         description: 'Python patch version'
         required: true
         type: string
-        default: "8"
+        default: "9"
 
 
 jobs:

.github/workflows/update-frontend.yml

@@ -1,58 +0,0 @@
-name: Update Frontend Release
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: "Frontend version to update to (e.g., 1.0.0)"
-        required: true
-        type: string
-
-jobs:
-  update-frontend:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-      pull-requests: write
-
-    steps:
-      - name: Checkout ComfyUI
-        uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
-        with:
-          python-version: '3.10'
-      - name: Install requirements
-        run: |
-          python -m pip install --upgrade pip
-          pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-          pip install -r requirements.txt
-          pip install wait-for-it
-      # Frontend asset will be downloaded to ComfyUI/web_custom_versions/Comfy-Org_ComfyUI_frontend/{version}
-      - name: Start ComfyUI server
-        run: |
-          python main.py --cpu --front-end-version Comfy-Org/ComfyUI_frontend@${{ github.event.inputs.version }} 2>&1 | tee console_output.log &
-          wait-for-it --service 127.0.0.1:8188 -t 30
-      - name: Configure Git
-        run: |
-          git config --global user.name "GitHub Action"
-          git config --global user.email "action@github.com"
-      # Replace existing frontend content with the new version and remove .js.map files
-      # See https://github.com/Comfy-Org/ComfyUI_frontend/issues/2145 for why we remove .js.map files
-      - name: Update frontend content
-        run: |
-          rm -rf web/
-          cp -r web_custom_versions/Comfy-Org_ComfyUI_frontend/${{ github.event.inputs.version }} web/
-          rm web/**/*.js.map
-      - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
-        with:
-          token: ${{ secrets.PR_BOT_PAT }}
-          commit-message: "Update frontend to v${{ github.event.inputs.version }}"
-          title: "Frontend Update: v${{ github.event.inputs.version }}"
-          body: |
-            Automated PR to update frontend content to version ${{ github.event.inputs.version }}
-
-            This PR was created automatically by the frontend update workflow.
-          branch: release-${{ github.event.inputs.version }}
-          base: master
-          labels: Frontend,dependencies

.github/workflows/windows_release_dependencies.yml

@@ -29,7 +29,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "8"
+        default: "9"
 #  push:
 #    branches:
 #      - master

.github/workflows/windows_release_package.yml

@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "8"
+        default: "9"
 #  push:
 #    branches:
 #      - master

CODEOWNERS

@@ -11,14 +11,13 @@
 /notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
 /script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
 /.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
+/requirements.txt @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
+/pyproject.toml @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink
 
 # Python web server
 /api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 /app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 /utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 
-# Frontend assets
-/web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang
-
 # Extra nodes
 /comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink

README.md

@@ -46,7 +46,7 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 #### [Manual Install](#manual-install-windows-linux)
 Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
 
-## Examples
+## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
 See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
 
 
@@ -68,6 +68,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [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.
@@ -260,6 +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
 

app/frontend_management.py

@@ -3,8 +3,10 @@ import argparse
 import logging
 import os
 import re
+import sys
 import tempfile
 import zipfile
+import importlib
 from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
@@ -12,9 +14,19 @@ from typing import TypedDict, Optional
 
 import requests
 from typing_extensions import NotRequired
+
 from comfy.cli_args import DEFAULT_VERSION_STRING
 
 
+try:
+    import comfyui_frontend_package
+except ImportError:
+    # TODO: Remove the check after roll out of 0.3.16
+    req_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'requirements.txt'))
+    logging.error(f"\n\n********** ERROR ***********\n\ncomfyui-frontend-package is not installed. Please install the updated requirements.txt file by running:\n{sys.executable} -m pip install -r {req_path}\n\nThis error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.\n\nIf you are on the portable package you can run: update\\update_comfyui.bat to solve this problem\n********** ERROR **********\n")
+    exit(-1)
+
+
 REQUEST_TIMEOUT = 10  # seconds
 
 
@@ -109,7 +121,7 @@ def download_release_asset_zip(release: Release, destination_path: str) -> None:
 
 
 class FrontendManager:
-    DEFAULT_FRONTEND_PATH = str(Path(__file__).parents[1] / "web")
+    DEFAULT_FRONTEND_PATH = str(importlib.resources.files(comfyui_frontend_package) / "static")
     CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
 
     @classmethod

comfy/cli_args.py

@@ -130,7 +130,12 @@ parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha
 
 parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
 parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.")
-parser.add_argument("--fast", action="store_true", help="Enable some untested and potentially quality deteriorating optimizations.")
+
+class PerformanceFeature(enum.Enum):
+    Fp16Accumulation = "fp16_accumulation"
+    Fp8MatrixMultiplication = "fp8_matrix_mult"
+
+parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult")
 
 parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
 parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
@@ -194,3 +199,14 @@ if args.disable_auto_launch:
 
 if args.force_fp16:
     args.fp16_unet = True
+
+
+# '--fast' is not provided, use an empty set
+if args.fast is None:
+    args.fast = set()
+# '--fast' is provided with an empty list, enable all optimizations
+elif args.fast == []:
+    args.fast = set(PerformanceFeature)
+# '--fast' is provided with a list of performance features, use that list
+else:
+    args.fast = set(args.fast)

comfy/controlnet.py

@@ -418,10 +418,7 @@ def controlnet_config(sd, model_options={}):
         weight_dtype = comfy.utils.weight_dtype(sd)
 
         supported_inference_dtypes = list(model_config.supported_inference_dtypes)
-        if weight_dtype is not None:
-            supported_inference_dtypes.append(weight_dtype)
-
-        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
+        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype)
 
     load_device = comfy.model_management.get_torch_device()
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
@@ -689,10 +686,7 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}):
         if supported_inference_dtypes is None:
             supported_inference_dtypes = [comfy.model_management.unet_dtype()]
 
-        if weight_dtype is not None:
-            supported_inference_dtypes.append(weight_dtype)
-
-        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
+        unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype)
 
     load_device = comfy.model_management.get_torch_device()
 

comfy/ldm/lightricks/model.py

@@ -7,7 +7,7 @@ from einops import rearrange
 import math
 from typing import Dict, Optional, Tuple
 
-from .symmetric_patchifier import SymmetricPatchifier
+from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
 
 
 def get_timestep_embedding(
@@ -377,12 +377,16 @@ class LTXVModel(torch.nn.Module):
 
                  positional_embedding_theta=10000.0,
                  positional_embedding_max_pos=[20, 2048, 2048],
+                 causal_temporal_positioning=False,
+                 vae_scale_factors=(8, 32, 32),
                  dtype=None, device=None, operations=None, **kwargs):
         super().__init__()
         self.generator = None
+        self.vae_scale_factors = vae_scale_factors
         self.dtype = dtype
         self.out_channels = in_channels
         self.inner_dim = num_attention_heads * attention_head_dim
+        self.causal_temporal_positioning = causal_temporal_positioning
 
         self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device)
 
@@ -416,42 +420,23 @@ class LTXVModel(torch.nn.Module):
 
         self.patchifier = SymmetricPatchifier(1)
 
-    def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, guiding_latent_noise_scale=0, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs):
         patches_replace = transformer_options.get("patches_replace", {})
 
-        indices_grid = self.patchifier.get_grid(
-            orig_num_frames=x.shape[2],
-            orig_height=x.shape[3],
-            orig_width=x.shape[4],
-            batch_size=x.shape[0],
-            scale_grid=((1 / frame_rate) * 8, 32, 32),
-            device=x.device,
-        )
-
-        if guiding_latent is not None:
-            ts = torch.ones([x.shape[0], 1, x.shape[2], x.shape[3], x.shape[4]], device=x.device, dtype=x.dtype)
-            input_ts = timestep.view([timestep.shape[0]] + [1] * (x.ndim - 1))
-            ts *= input_ts
-            ts[:, :, 0] = guiding_latent_noise_scale * (input_ts[:, :, 0] ** 2)
-            timestep = self.patchifier.patchify(ts)
-            input_x = x.clone()
-            x[:, :, 0] = guiding_latent[:, :, 0]
-            if guiding_latent_noise_scale > 0:
-                if self.generator is None:
-                    self.generator = torch.Generator(device=x.device).manual_seed(42)
-                elif self.generator.device != x.device:
-                    self.generator = torch.Generator(device=x.device).set_state(self.generator.get_state())
-
-                noise_shape = [guiding_latent.shape[0], guiding_latent.shape[1], 1, guiding_latent.shape[3], guiding_latent.shape[4]]
-                scale = guiding_latent_noise_scale * (input_ts ** 2)
-                guiding_noise = scale * torch.randn(size=noise_shape, device=x.device, generator=self.generator)
-
-                x[:, :, 0] = guiding_noise[:, :, 0] + x[:, :, 0] *  (1.0 - scale[:, :, 0])
-
-
         orig_shape = list(x.shape)
 
-        x = self.patchifier.patchify(x)
+        x, latent_coords = self.patchifier.patchify(x)
+        pixel_coords = latent_to_pixel_coords(
+            latent_coords=latent_coords,
+            scale_factors=self.vae_scale_factors,
+            causal_fix=self.causal_temporal_positioning,
+        )
+
+        if keyframe_idxs is not None:
+            pixel_coords[:, :, -keyframe_idxs.shape[2]:] = keyframe_idxs
+
+        fractional_coords = pixel_coords.to(torch.float32)
+        fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)
 
         x = self.patchify_proj(x)
         timestep = timestep * 1000.0
@@ -459,7 +444,7 @@ class LTXVModel(torch.nn.Module):
         if attention_mask is not None and not torch.is_floating_point(attention_mask):
             attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max
 
-        pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
+        pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype)
 
         batch_size = x.shape[0]
         timestep, embedded_timestep = self.adaln_single(
@@ -519,8 +504,4 @@ class LTXVModel(torch.nn.Module):
             out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
         )
 
-        if guiding_latent is not None:
-            x[:, :, 0] = (input_x[:, :, 0] - guiding_latent[:, :, 0]) / input_ts[:, :, 0]
-
-        # print("res", x)
         return x

comfy/ldm/lightricks/symmetric_patchifier.py

@@ -6,16 +6,29 @@ from einops import rearrange
 from torch import Tensor
 
 
-def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor:
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(
-            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
-        )
-    elif dims_to_append == 0:
-        return x
-    return x[(...,) + (None,) * dims_to_append]
+def latent_to_pixel_coords(
+    latent_coords: Tensor, scale_factors: Tuple[int, int, int], causal_fix: bool = False
+) -> Tensor:
+    """
+    Converts latent coordinates to pixel coordinates by scaling them according to the VAE's
+    configuration.
+    Args:
+        latent_coords (Tensor): A tensor of shape [batch_size, 3, num_latents]
+        containing the latent corner coordinates of each token.
+        scale_factors (Tuple[int, int, int]): The scale factors of the VAE's latent space.
+        causal_fix (bool): Whether to take into account the different temporal scale
+            of the first frame. Default = False for backwards compatibility.
+    Returns:
+        Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates.
+    """
+    pixel_coords = (
+        latent_coords
+        * torch.tensor(scale_factors, device=latent_coords.device)[None, :, None]
+    )
+    if causal_fix:
+        # Fix temporal scale for first frame to 1 due to causality
+        pixel_coords[:, 0] = (pixel_coords[:, 0] + 1 - scale_factors[0]).clamp(min=0)
+    return pixel_coords
 
 
 class Patchifier(ABC):
@@ -44,29 +57,26 @@ class Patchifier(ABC):
     def patch_size(self):
         return self._patch_size
 
-    def get_grid(
-        self, orig_num_frames, orig_height, orig_width, batch_size, scale_grid, device
+    def get_latent_coords(
+        self, latent_num_frames, latent_height, latent_width, batch_size, device
     ):
-        f = orig_num_frames // self._patch_size[0]
-        h = orig_height // self._patch_size[1]
-        w = orig_width // self._patch_size[2]
-        grid_h = torch.arange(h, dtype=torch.float32, device=device)
-        grid_w = torch.arange(w, dtype=torch.float32, device=device)
-        grid_f = torch.arange(f, dtype=torch.float32, device=device)
-        grid = torch.meshgrid(grid_f, grid_h, grid_w, indexing='ij')
-        grid = torch.stack(grid, dim=0)
-        grid = grid.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
-
-        if scale_grid is not None:
-            for i in range(3):
-                if isinstance(scale_grid[i], Tensor):
-                    scale = append_dims(scale_grid[i], grid.ndim - 1)
-                else:
-                    scale = scale_grid[i]
-                grid[:, i, ...] = grid[:, i, ...] * scale * self._patch_size[i]
-
-        grid = rearrange(grid, "b c f h w -> b c (f h w)", b=batch_size)
-        return grid
+        """
+        Return a tensor of shape [batch_size, 3, num_patches] containing the
+            top-left corner latent coordinates of each latent patch.
+        The tensor is repeated for each batch element.
+        """
+        latent_sample_coords = torch.meshgrid(
+            torch.arange(0, latent_num_frames, self._patch_size[0], device=device),
+            torch.arange(0, latent_height, self._patch_size[1], device=device),
+            torch.arange(0, latent_width, self._patch_size[2], device=device),
+            indexing="ij",
+        )
+        latent_sample_coords = torch.stack(latent_sample_coords, dim=0)
+        latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+        latent_coords = rearrange(
+            latent_coords, "b c f h w -> b c (f h w)", b=batch_size
+        )
+        return latent_coords
 
 
 class SymmetricPatchifier(Patchifier):
@@ -74,6 +84,8 @@ class SymmetricPatchifier(Patchifier):
         self,
         latents: Tensor,
     ) -> Tuple[Tensor, Tensor]:
+        b, _, f, h, w = latents.shape
+        latent_coords = self.get_latent_coords(f, h, w, b, latents.device)
         latents = rearrange(
             latents,
             "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)",
@@ -81,7 +93,7 @@ class SymmetricPatchifier(Patchifier):
             p2=self._patch_size[1],
             p3=self._patch_size[2],
         )
-        return latents
+        return latents, latent_coords
 
     def unpatchify(
         self,

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -15,6 +15,7 @@ class CausalConv3d(nn.Module):
         stride: Union[int, Tuple[int]] = 1,
         dilation: int = 1,
         groups: int = 1,
+        spatial_padding_mode: str = "zeros",
         **kwargs,
     ):
         super().__init__()
@@ -38,7 +39,7 @@ class CausalConv3d(nn.Module):
             stride=stride,
             dilation=dilation,
             padding=padding,
-            padding_mode="zeros",
+            padding_mode=spatial_padding_mode,
             groups=groups,
         )
 

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -1,13 +1,15 @@
+from __future__ import annotations
 import torch
 from torch import nn
 from functools import partial
 import math
 from einops import rearrange
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 from .conv_nd_factory import make_conv_nd, make_linear_nd
 from .pixel_norm import PixelNorm
 from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings
 import comfy.ops
+
 ops = comfy.ops.disable_weight_init
 
 class Encoder(nn.Module):
@@ -32,7 +34,7 @@ class Encoder(nn.Module):
         norm_layer (`str`, *optional*, defaults to `group_norm`):
             The normalization layer to use. Can be either `group_norm` or `pixel_norm`.
         latent_log_var (`str`, *optional*, defaults to `per_channel`):
-            The number of channels for the log variance. Can be either `per_channel`, `uniform`, or `none`.
+            The number of channels for the log variance. Can be either `per_channel`, `uniform`, `constant` or `none`.
     """
 
     def __init__(
@@ -40,12 +42,13 @@ class Encoder(nn.Module):
         dims: Union[int, Tuple[int, int]] = 3,
         in_channels: int = 3,
         out_channels: int = 3,
-        blocks=[("res_x", 1)],
+        blocks: List[Tuple[str, int | dict]] = [("res_x", 1)],
         base_channels: int = 128,
         norm_num_groups: int = 32,
         patch_size: Union[int, Tuple[int]] = 1,
         norm_layer: str = "group_norm",  # group_norm, pixel_norm
         latent_log_var: str = "per_channel",
+        spatial_padding_mode: str = "zeros",
     ):
         super().__init__()
         self.patch_size = patch_size
@@ -65,6 +68,7 @@ class Encoder(nn.Module):
             stride=1,
             padding=1,
             causal=True,
+            spatial_padding_mode=spatial_padding_mode,
         )
 
         self.down_blocks = nn.ModuleList([])
@@ -82,6 +86,7 @@ class Encoder(nn.Module):
                     resnet_eps=1e-6,
                     resnet_groups=norm_num_groups,
                     norm_layer=norm_layer,
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "res_x_y":
                 output_channel = block_params.get("multiplier", 2) * output_channel
@@ -92,6 +97,7 @@ class Encoder(nn.Module):
                     eps=1e-6,
                     groups=norm_num_groups,
                     norm_layer=norm_layer,
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_time":
                 block = make_conv_nd(
@@ -101,6 +107,7 @@ class Encoder(nn.Module):
                     kernel_size=3,
                     stride=(2, 1, 1),
                     causal=True,
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_space":
                 block = make_conv_nd(
@@ -110,6 +117,7 @@ class Encoder(nn.Module):
                     kernel_size=3,
                     stride=(1, 2, 2),
                     causal=True,
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_all":
                 block = make_conv_nd(
@@ -119,6 +127,7 @@ class Encoder(nn.Module):
                     kernel_size=3,
                     stride=(2, 2, 2),
                     causal=True,
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_all_x_y":
                 output_channel = block_params.get("multiplier", 2) * output_channel
@@ -129,6 +138,34 @@ class Encoder(nn.Module):
                     kernel_size=3,
                     stride=(2, 2, 2),
                     causal=True,
+                    spatial_padding_mode=spatial_padding_mode,
+                )
+            elif block_name == "compress_all_res":
+                output_channel = block_params.get("multiplier", 2) * output_channel
+                block = SpaceToDepthDownsample(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    stride=(2, 2, 2),
+                    spatial_padding_mode=spatial_padding_mode,
+                )
+            elif block_name == "compress_space_res":
+                output_channel = block_params.get("multiplier", 2) * output_channel
+                block = SpaceToDepthDownsample(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    stride=(1, 2, 2),
+                    spatial_padding_mode=spatial_padding_mode,
+                )
+            elif block_name == "compress_time_res":
+                output_channel = block_params.get("multiplier", 2) * output_channel
+                block = SpaceToDepthDownsample(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    stride=(2, 1, 1),
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             else:
                 raise ValueError(f"unknown block: {block_name}")
@@ -152,10 +189,18 @@ class Encoder(nn.Module):
             conv_out_channels *= 2
         elif latent_log_var == "uniform":
             conv_out_channels += 1
+        elif latent_log_var == "constant":
+            conv_out_channels += 1
         elif latent_log_var != "none":
             raise ValueError(f"Invalid latent_log_var: {latent_log_var}")
         self.conv_out = make_conv_nd(
-            dims, output_channel, conv_out_channels, 3, padding=1, causal=True
+            dims,
+            output_channel,
+            conv_out_channels,
+            3,
+            padding=1,
+            causal=True,
+            spatial_padding_mode=spatial_padding_mode,
         )
 
         self.gradient_checkpointing = False
@@ -197,6 +242,15 @@ class Encoder(nn.Module):
                 sample = torch.cat([sample, repeated_last_channel], dim=1)
             else:
                 raise ValueError(f"Invalid input shape: {sample.shape}")
+        elif self.latent_log_var == "constant":
+            sample = sample[:, :-1, ...]
+            approx_ln_0 = (
+                -30
+            )  # this is the minimal clamp value in DiagonalGaussianDistribution objects
+            sample = torch.cat(
+                [sample, torch.ones_like(sample, device=sample.device) * approx_ln_0],
+                dim=1,
+            )
 
         return sample
 
@@ -231,7 +285,7 @@ class Decoder(nn.Module):
         dims,
         in_channels: int = 3,
         out_channels: int = 3,
-        blocks=[("res_x", 1)],
+        blocks: List[Tuple[str, int | dict]] = [("res_x", 1)],
         base_channels: int = 128,
         layers_per_block: int = 2,
         norm_num_groups: int = 32,
@@ -239,6 +293,7 @@ class Decoder(nn.Module):
         norm_layer: str = "group_norm",
         causal: bool = True,
         timestep_conditioning: bool = False,
+        spatial_padding_mode: str = "zeros",
     ):
         super().__init__()
         self.patch_size = patch_size
@@ -264,6 +319,7 @@ class Decoder(nn.Module):
             stride=1,
             padding=1,
             causal=True,
+            spatial_padding_mode=spatial_padding_mode,
         )
 
         self.up_blocks = nn.ModuleList([])
@@ -283,6 +339,7 @@ class Decoder(nn.Module):
                     norm_layer=norm_layer,
                     inject_noise=block_params.get("inject_noise", False),
                     timestep_conditioning=timestep_conditioning,
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "attn_res_x":
                 block = UNetMidBlock3D(
@@ -294,6 +351,7 @@ class Decoder(nn.Module):
                     inject_noise=block_params.get("inject_noise", False),
                     timestep_conditioning=timestep_conditioning,
                     attention_head_dim=block_params["attention_head_dim"],
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "res_x_y":
                 output_channel = output_channel // block_params.get("multiplier", 2)
@@ -306,14 +364,21 @@ class Decoder(nn.Module):
                     norm_layer=norm_layer,
                     inject_noise=block_params.get("inject_noise", False),
                     timestep_conditioning=False,
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_time":
                 block = DepthToSpaceUpsample(
-                    dims=dims, in_channels=input_channel, stride=(2, 1, 1)
+                    dims=dims,
+                    in_channels=input_channel,
+                    stride=(2, 1, 1),
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_space":
                 block = DepthToSpaceUpsample(
-                    dims=dims, in_channels=input_channel, stride=(1, 2, 2)
+                    dims=dims,
+                    in_channels=input_channel,
+                    stride=(1, 2, 2),
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_all":
                 output_channel = output_channel // block_params.get("multiplier", 1)
@@ -323,6 +388,7 @@ class Decoder(nn.Module):
                     stride=(2, 2, 2),
                     residual=block_params.get("residual", False),
                     out_channels_reduction_factor=block_params.get("multiplier", 1),
+                    spatial_padding_mode=spatial_padding_mode,
                 )
             else:
                 raise ValueError(f"unknown layer: {block_name}")
@@ -340,7 +406,13 @@ class Decoder(nn.Module):
 
         self.conv_act = nn.SiLU()
         self.conv_out = make_conv_nd(
-            dims, output_channel, out_channels, 3, padding=1, causal=True
+            dims,
+            output_channel,
+            out_channels,
+            3,
+            padding=1,
+            causal=True,
+            spatial_padding_mode=spatial_padding_mode,
         )
 
         self.gradient_checkpointing = False
@@ -433,6 +505,12 @@ class UNetMidBlock3D(nn.Module):
         resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
         resnet_groups (`int`, *optional*, defaults to 32):
             The number of groups to use in the group normalization layers of the resnet blocks.
+        norm_layer (`str`, *optional*, defaults to `group_norm`):
+            The normalization layer to use. Can be either `group_norm` or `pixel_norm`.
+        inject_noise (`bool`, *optional*, defaults to `False`):
+            Whether to inject noise into the hidden states.
+        timestep_conditioning (`bool`, *optional*, defaults to `False`):
+            Whether to condition the hidden states on the timestep.
 
     Returns:
         `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
@@ -451,6 +529,7 @@ class UNetMidBlock3D(nn.Module):
         norm_layer: str = "group_norm",
         inject_noise: bool = False,
         timestep_conditioning: bool = False,
+        spatial_padding_mode: str = "zeros",
     ):
         super().__init__()
         resnet_groups = (
@@ -476,13 +555,17 @@ class UNetMidBlock3D(nn.Module):
                     norm_layer=norm_layer,
                     inject_noise=inject_noise,
                     timestep_conditioning=timestep_conditioning,
+                    spatial_padding_mode=spatial_padding_mode,
                 )
                 for _ in range(num_layers)
             ]
         )
 
     def forward(
-        self, hidden_states: torch.FloatTensor, causal: bool = True, timestep: Optional[torch.Tensor] = None
+        self,
+        hidden_states: torch.FloatTensor,
+        causal: bool = True,
+        timestep: Optional[torch.Tensor] = None,
     ) -> torch.FloatTensor:
         timestep_embed = None
         if self.timestep_conditioning:
@@ -507,9 +590,62 @@ class UNetMidBlock3D(nn.Module):
         return hidden_states
 
 
+class SpaceToDepthDownsample(nn.Module):
+    def __init__(self, dims, in_channels, out_channels, stride, spatial_padding_mode):
+        super().__init__()
+        self.stride = stride
+        self.group_size = in_channels * math.prod(stride) // out_channels
+        self.conv = make_conv_nd(
+            dims=dims,
+            in_channels=in_channels,
+            out_channels=out_channels // math.prod(stride),
+            kernel_size=3,
+            stride=1,
+            causal=True,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+    def forward(self, x, causal: bool = True):
+        if self.stride[0] == 2:
+            x = torch.cat(
+                [x[:, :, :1, :, :], x], dim=2
+            )  # duplicate first frames for padding
+
+        # skip connection
+        x_in = rearrange(
+            x,
+            "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w",
+            p1=self.stride[0],
+            p2=self.stride[1],
+            p3=self.stride[2],
+        )
+        x_in = rearrange(x_in, "b (c g) d h w -> b c g d h w", g=self.group_size)
+        x_in = x_in.mean(dim=2)
+
+        # conv
+        x = self.conv(x, causal=causal)
+        x = rearrange(
+            x,
+            "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w",
+            p1=self.stride[0],
+            p2=self.stride[1],
+            p3=self.stride[2],
+        )
+
+        x = x + x_in
+
+        return x
+
+
 class DepthToSpaceUpsample(nn.Module):
     def __init__(
-        self, dims, in_channels, stride, residual=False, out_channels_reduction_factor=1
+        self,
+        dims,
+        in_channels,
+        stride,
+        residual=False,
+        out_channels_reduction_factor=1,
+        spatial_padding_mode="zeros",
     ):
         super().__init__()
         self.stride = stride
@@ -523,6 +659,7 @@ class DepthToSpaceUpsample(nn.Module):
             kernel_size=3,
             stride=1,
             causal=True,
+            spatial_padding_mode=spatial_padding_mode,
         )
         self.residual = residual
         self.out_channels_reduction_factor = out_channels_reduction_factor
@@ -558,7 +695,7 @@ class DepthToSpaceUpsample(nn.Module):
 class LayerNorm(nn.Module):
     def __init__(self, dim, eps, elementwise_affine=True) -> None:
         super().__init__()
-        self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine)
+        self.norm = ops.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine)
 
     def forward(self, x):
         x = rearrange(x, "b c d h w -> b d h w c")
@@ -591,6 +728,7 @@ class ResnetBlock3D(nn.Module):
         norm_layer: str = "group_norm",
         inject_noise: bool = False,
         timestep_conditioning: bool = False,
+        spatial_padding_mode: str = "zeros",
     ):
         super().__init__()
         self.in_channels = in_channels
@@ -617,6 +755,7 @@ class ResnetBlock3D(nn.Module):
             stride=1,
             padding=1,
             causal=True,
+            spatial_padding_mode=spatial_padding_mode,
         )
 
         if inject_noise:
@@ -641,6 +780,7 @@ class ResnetBlock3D(nn.Module):
             stride=1,
             padding=1,
             causal=True,
+            spatial_padding_mode=spatial_padding_mode,
         )
 
         if inject_noise:
@@ -801,9 +941,44 @@ class processor(nn.Module):
         return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)
 
 class VideoVAE(nn.Module):
-    def __init__(self, version=0):
+    def __init__(self, version=0, config=None):
         super().__init__()
 
+        if config is None:
+            config = self.guess_config(version)
+
+        self.timestep_conditioning = config.get("timestep_conditioning", False)
+        double_z = config.get("double_z", True)
+        latent_log_var = config.get(
+            "latent_log_var", "per_channel" if double_z else "none"
+        )
+
+        self.encoder = Encoder(
+            dims=config["dims"],
+            in_channels=config.get("in_channels", 3),
+            out_channels=config["latent_channels"],
+            blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))),
+            patch_size=config.get("patch_size", 1),
+            latent_log_var=latent_log_var,
+            norm_layer=config.get("norm_layer", "group_norm"),
+            spatial_padding_mode=config.get("spatial_padding_mode", "zeros"),
+        )
+
+        self.decoder = Decoder(
+            dims=config["dims"],
+            in_channels=config["latent_channels"],
+            out_channels=config.get("out_channels", 3),
+            blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))),
+            patch_size=config.get("patch_size", 1),
+            norm_layer=config.get("norm_layer", "group_norm"),
+            causal=config.get("causal_decoder", False),
+            timestep_conditioning=self.timestep_conditioning,
+            spatial_padding_mode=config.get("spatial_padding_mode", "zeros"),
+        )
+
+        self.per_channel_statistics = processor()
+
+    def guess_config(self, version):
         if version == 0:
             config = {
                 "_class_name": "CausalVideoAutoencoder",
@@ -830,7 +1005,7 @@ class VideoVAE(nn.Module):
                 "use_quant_conv": False,
                 "causal_decoder": False,
             }
-        else:
+        elif version == 1:
             config = {
                 "_class_name": "CausalVideoAutoencoder",
                 "dims": 3,
@@ -866,37 +1041,47 @@ class VideoVAE(nn.Module):
                 "causal_decoder": False,
                 "timestep_conditioning": True,
             }
-
-        double_z = config.get("double_z", True)
-        latent_log_var = config.get(
-            "latent_log_var", "per_channel" if double_z else "none"
-        )
-
-        self.encoder = Encoder(
-            dims=config["dims"],
-            in_channels=config.get("in_channels", 3),
-            out_channels=config["latent_channels"],
-            blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))),
-            patch_size=config.get("patch_size", 1),
-            latent_log_var=latent_log_var,
-            norm_layer=config.get("norm_layer", "group_norm"),
-        )
-
-        self.decoder = Decoder(
-            dims=config["dims"],
-            in_channels=config["latent_channels"],
-            out_channels=config.get("out_channels", 3),
-            blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))),
-            patch_size=config.get("patch_size", 1),
-            norm_layer=config.get("norm_layer", "group_norm"),
-            causal=config.get("causal_decoder", False),
-            timestep_conditioning=config.get("timestep_conditioning", False),
-        )
-
-        self.timestep_conditioning = config.get("timestep_conditioning", False)
-        self.per_channel_statistics = processor()
+        else:
+            config = {
+                "_class_name": "CausalVideoAutoencoder",
+                "dims": 3,
+                "in_channels": 3,
+                "out_channels": 3,
+                "latent_channels": 128,
+                "encoder_blocks": [
+                    ["res_x", {"num_layers": 4}],
+                    ["compress_space_res", {"multiplier": 2}],
+                    ["res_x", {"num_layers": 6}],
+                    ["compress_time_res", {"multiplier": 2}],
+                    ["res_x", {"num_layers": 6}],
+                    ["compress_all_res", {"multiplier": 2}],
+                    ["res_x", {"num_layers": 2}],
+                    ["compress_all_res", {"multiplier": 2}],
+                    ["res_x", {"num_layers": 2}]
+                ],
+                "decoder_blocks": [
+                    ["res_x", {"num_layers": 5, "inject_noise": False}],
+                    ["compress_all", {"residual": True, "multiplier": 2}],
+                    ["res_x", {"num_layers": 5, "inject_noise": False}],
+                    ["compress_all", {"residual": True, "multiplier": 2}],
+                    ["res_x", {"num_layers": 5, "inject_noise": False}],
+                    ["compress_all", {"residual": True, "multiplier": 2}],
+                    ["res_x", {"num_layers": 5, "inject_noise": False}]
+                ],
+                "scaling_factor": 1.0,
+                "norm_layer": "pixel_norm",
+                "patch_size": 4,
+                "latent_log_var": "uniform",
+                "use_quant_conv": False,
+                "causal_decoder": False,
+                "timestep_conditioning": True
+            }
+        return config
 
     def encode(self, x):
+        frames_count = x.shape[2]
+        if ((frames_count - 1) % 8) != 0:
+            raise ValueError("Invalid number of frames: Encode input must have 1 + 8 * x frames (e.g., 1, 9, 17, ...). Please check your input.")
         means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
         return self.per_channel_statistics.normalize(means)
 

comfy/ldm/lightricks/vae/conv_nd_factory.py

@@ -17,7 +17,11 @@ def make_conv_nd(
     groups=1,
     bias=True,
     causal=False,
+    spatial_padding_mode="zeros",
+    temporal_padding_mode="zeros",
 ):
+    if not (spatial_padding_mode == temporal_padding_mode or causal):
+        raise NotImplementedError("spatial and temporal padding modes must be equal")
     if dims == 2:
         return ops.Conv2d(
             in_channels=in_channels,
@@ -28,6 +32,7 @@ def make_conv_nd(
             dilation=dilation,
             groups=groups,
             bias=bias,
+            padding_mode=spatial_padding_mode,
         )
     elif dims == 3:
         if causal:
@@ -40,6 +45,7 @@ def make_conv_nd(
                 dilation=dilation,
                 groups=groups,
                 bias=bias,
+                spatial_padding_mode=spatial_padding_mode,
             )
         return ops.Conv3d(
             in_channels=in_channels,
@@ -50,6 +56,7 @@ def make_conv_nd(
             dilation=dilation,
             groups=groups,
             bias=bias,
+            padding_mode=spatial_padding_mode,
         )
     elif dims == (2, 1):
         return DualConv3d(
@@ -59,6 +66,7 @@ def make_conv_nd(
             stride=stride,
             padding=padding,
             bias=bias,
+            padding_mode=spatial_padding_mode,
         )
     else:
         raise ValueError(f"unsupported dimensions: {dims}")

comfy/ldm/lightricks/vae/dual_conv3d.py

@@ -18,11 +18,13 @@ class DualConv3d(nn.Module):
         dilation: Union[int, Tuple[int, int, int]] = 1,
         groups=1,
         bias=True,
+        padding_mode="zeros",
     ):
         super(DualConv3d, self).__init__()
 
         self.in_channels = in_channels
         self.out_channels = out_channels
+        self.padding_mode = padding_mode
         # Ensure kernel_size, stride, padding, and dilation are tuples of length 3
         if isinstance(kernel_size, int):
             kernel_size = (kernel_size, kernel_size, kernel_size)
@@ -108,6 +110,7 @@ class DualConv3d(nn.Module):
             self.padding1,
             self.dilation1,
             self.groups,
+            padding_mode=self.padding_mode,
         )
 
         if skip_time_conv:
@@ -122,6 +125,7 @@ class DualConv3d(nn.Module):
             self.padding2,
             self.dilation2,
             self.groups,
+            padding_mode=self.padding_mode,
         )
 
         return x
@@ -137,7 +141,16 @@ class DualConv3d(nn.Module):
         stride1 = (self.stride1[1], self.stride1[2])
         padding1 = (self.padding1[1], self.padding1[2])
         dilation1 = (self.dilation1[1], self.dilation1[2])
-        x = F.conv2d(x, weight1, self.bias1, stride1, padding1, dilation1, self.groups)
+        x = F.conv2d(
+            x,
+            weight1,
+            self.bias1,
+            stride1,
+            padding1,
+            dilation1,
+            self.groups,
+            padding_mode=self.padding_mode,
+        )
 
         _, _, h, w = x.shape
 
@@ -154,7 +167,16 @@ class DualConv3d(nn.Module):
         stride2 = self.stride2[0]
         padding2 = self.padding2[0]
         dilation2 = self.dilation2[0]
-        x = F.conv1d(x, weight2, self.bias2, stride2, padding2, dilation2, self.groups)
+        x = F.conv1d(
+            x,
+            weight2,
+            self.bias2,
+            stride2,
+            padding2,
+            dilation2,
+            self.groups,
+            padding_mode=self.padding_mode,
+        )
         x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w)
 
         return x

comfy/ldm/wan/model.py

@@ -212,14 +212,10 @@ class WanAttentionBlock(nn.Module):
 
         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)
+        # cross-attention & ffn
+        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
 
 
@@ -421,7 +417,7 @@ class WanModel(torch.nn.Module):
         e0 = self.time_projection(e).unflatten(1, (6, self.dim))
 
         # context
-        context = self.text_embedding(torch.cat([context, context.new_zeros(context.size(0), self.text_len - context.size(1), context.size(2))], dim=1))
+        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
@@ -442,7 +438,6 @@ class WanModel(torch.nn.Module):
         # 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

comfy/model_base.py

@@ -161,9 +161,13 @@ class BaseModel(torch.nn.Module):
                     extra = extra.to(dtype)
             extra_conds[o] = extra
 
+        t = self.process_timestep(t, x=x, **extra_conds)
         model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
         return self.model_sampling.calculate_denoised(sigma, model_output, x)
 
+    def process_timestep(self, timestep, **kwargs):
+        return timestep
+
     def get_dtype(self):
         return self.diffusion_model.dtype
 
@@ -185,6 +189,11 @@ class BaseModel(torch.nn.Module):
 
             if concat_latent_image.shape[1:] != noise.shape[1:]:
                 concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
+                if noise.ndim == 5:
+                    if concat_latent_image.shape[-3] < noise.shape[-3]:
+                        concat_latent_image = torch.nn.functional.pad(concat_latent_image, (0, 0, 0, 0, 0, noise.shape[-3] - concat_latent_image.shape[-3]), "constant", 0)
+                    else:
+                        concat_latent_image = concat_latent_image[:, :, :noise.shape[-3]]
 
             concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])
 
@@ -213,6 +222,11 @@ class BaseModel(torch.nn.Module):
                         cond_concat.append(self.blank_inpaint_image_like(noise))
                     elif ck == "mask_inverted":
                         cond_concat.append(torch.zeros_like(noise)[:, :1])
+                if ck == "concat_image":
+                    if concat_latent_image is not None:
+                        cond_concat.append(concat_latent_image.to(device))
+                    else:
+                        cond_concat.append(torch.zeros_like(noise))
             data = torch.cat(cond_concat, dim=1)
             return data
         return None
@@ -845,17 +859,26 @@ class LTXV(BaseModel):
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
-        guiding_latent = kwargs.get("guiding_latent", None)
-        if guiding_latent is not None:
-            out['guiding_latent'] = comfy.conds.CONDRegular(guiding_latent)
-
-        guiding_latent_noise_scale = kwargs.get("guiding_latent_noise_scale", None)
-        if guiding_latent_noise_scale is not None:
-            out["guiding_latent_noise_scale"] = comfy.conds.CONDConstant(guiding_latent_noise_scale)
-
         out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
+
+        denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if denoise_mask is not None:
+            out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask)
+
+        keyframe_idxs = kwargs.get("keyframe_idxs", None)
+        if keyframe_idxs is not None:
+            out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs)
+
         return out
 
+    def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
+        if denoise_mask is None:
+            return timestep
+        return self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0]
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        return latent_image
+
 class HunyuanVideo(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo)
@@ -872,20 +895,17 @@ 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]))
         return out
 
+class HunyuanVideoSkyreelsI2V(HunyuanVideo):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device)
+        self.concat_keys = ("concat_image",)
+
+
 class CosmosVideo(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT)

comfy/model_detection.py

@@ -1,3 +1,4 @@
+import json
 import comfy.supported_models
 import comfy.supported_models_base
 import comfy.utils
@@ -33,7 +34,7 @@ def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
         return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
     return None
 
-def detect_unet_config(state_dict, key_prefix):
+def detect_unet_config(state_dict, key_prefix, metadata=None):
     state_dict_keys = list(state_dict.keys())
 
     if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model
@@ -210,6 +211,8 @@ def detect_unet_config(state_dict, key_prefix):
     if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
         dit_config = {}
         dit_config["image_model"] = "ltxv"
+        if metadata is not None and "config" in metadata:
+            dit_config.update(json.loads(metadata["config"]).get("transformer", {}))
         return dit_config
 
     if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt
@@ -454,8 +457,8 @@ def model_config_from_unet_config(unet_config, state_dict=None):
     logging.error("no match {}".format(unet_config))
     return None
 
-def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False):
-    unet_config = detect_unet_config(state_dict, unet_key_prefix)
+def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None):
+    unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata)
     if unet_config is None:
         return None
     model_config = model_config_from_unet_config(unet_config, state_dict)

comfy/model_management.py

@@ -19,7 +19,7 @@
 import psutil
 import logging
 from enum import Enum
-from comfy.cli_args import args
+from comfy.cli_args import args, PerformanceFeature
 import torch
 import sys
 import platform
@@ -95,6 +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
 
@@ -112,6 +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
@@ -127,6 +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())
 
@@ -153,6 +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']
@@ -232,7 +253,7 @@ try:
         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:
@@ -259,9 +280,10 @@ if ENABLE_PYTORCH_ATTENTION:
 
 PRIORITIZE_FP16 = False  # TODO: remove and replace with something that shows exactly which dtype is faster than the other
 try:
-    if is_nvidia() and args.fast:
+    if is_nvidia() and PerformanceFeature.Fp16Accumulation in args.fast:
         torch.backends.cuda.matmul.allow_fp16_accumulation = True
         PRIORITIZE_FP16 = True  # TODO: limit to cards where it actually boosts performance
+        logging.info("Enabled fp16 accumulation.")
 except:
     pass
 
@@ -316,6 +338,8 @@ 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))
 
@@ -651,7 +675,7 @@ def unet_inital_load_device(parameters, dtype):
 def maximum_vram_for_weights(device=None):
     return (get_total_memory(device) * 0.88 - minimum_inference_memory())
 
-def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
+def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32], weight_dtype=None):
     if model_params < 0:
         model_params = 1000000000000000000000
     if args.fp32_unet:
@@ -669,10 +693,8 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
 
     fp8_dtype = None
     try:
-        for dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
-            if dtype in supported_dtypes:
-                fp8_dtype = dtype
-                break
+        if weight_dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
+            fp8_dtype = weight_dtype
     except:
         pass
 
@@ -684,7 +706,7 @@ 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 PRIORITIZE_FP16 or weight_dtype == torch.float16:
         if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params):
             return torch.float16
 
@@ -720,6 +742,9 @@ def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.flo
         return None
 
     fp16_supported = should_use_fp16(inference_device, prioritize_performance=True)
+    if PRIORITIZE_FP16 and fp16_supported and torch.float16 in supported_dtypes:
+        return torch.float16
+
     for dt in supported_dtypes:
         if dt == torch.float16 and fp16_supported:
             return torch.float16
@@ -905,6 +930,8 @@ 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
@@ -936,6 +963,8 @@ 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
@@ -984,6 +1013,13 @@ 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']
@@ -1053,6 +1089,9 @@ 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
 
@@ -1121,6 +1160,11 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
             return False
 
     props = torch.cuda.get_device_properties(device)
+
+    if is_mlu():
+        if props.major > 3:
+            return True
+
     if props.major >= 8:
         return True
 

comfy/ops.py

@@ -18,7 +18,7 @@
 
 import torch
 import comfy.model_management
-from comfy.cli_args import args
+from comfy.cli_args import args, PerformanceFeature
 import comfy.float
 
 cast_to = comfy.model_management.cast_to #TODO: remove once no more references
@@ -360,7 +360,11 @@ def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_
     if scaled_fp8 is not None:
         return scaled_fp8_ops(fp8_matrix_mult=fp8_compute, scale_input=True, override_dtype=scaled_fp8)
 
-    if fp8_compute and (fp8_optimizations or args.fast) and not disable_fast_fp8:
+    if (
+        fp8_compute and
+        (fp8_optimizations or PerformanceFeature.Fp8MatrixMultiplication in args.fast) and
+        not disable_fast_fp8
+    ):
         return fp8_ops
 
     if compute_dtype is None or weight_dtype == compute_dtype:

comfy/samplers.py

@@ -19,6 +19,12 @@ import comfy.hooks
 import scipy.stats
 import numpy
 
+
+def add_area_dims(area, num_dims):
+    while (len(area) // 2) < num_dims:
+        area = [2147483648] + area[:len(area) // 2] + [0] + area[len(area) // 2:]
+    return area
+
 def get_area_and_mult(conds, x_in, timestep_in):
     dims = tuple(x_in.shape[2:])
     area = None
@@ -34,6 +40,10 @@ def get_area_and_mult(conds, x_in, timestep_in):
             return None
     if 'area' in conds:
         area = list(conds['area'])
+        area = add_area_dims(area, len(dims))
+        if (len(area) // 2) > len(dims):
+            area = area[:len(dims)] + area[len(area) // 2:(len(area) // 2) + len(dims)]
+
     if 'strength' in conds:
         strength = conds['strength']
 
@@ -50,7 +60,7 @@ def get_area_and_mult(conds, x_in, timestep_in):
         if "mask_strength" in conds:
             mask_strength = conds["mask_strength"]
         mask = conds['mask']
-        assert(mask.shape[1:] == x_in.shape[2:])
+        assert (mask.shape[1:] == x_in.shape[2:])
 
         mask = mask[:input_x.shape[0]]
         if area is not None:
@@ -64,16 +74,17 @@ def get_area_and_mult(conds, x_in, timestep_in):
     mult = mask * strength
 
     if 'mask' not in conds and area is not None:
-        rr = 8
+        fuzz = 8
         for i in range(len(dims)):
+            rr = min(fuzz, mult.shape[2 + i] // 4)
             if area[len(dims) + i] != 0:
                 for t in range(rr):
                     m = mult.narrow(i + 2, t, 1)
-                    m *= ((1.0/rr) * (t + 1))
+                    m *= ((1.0 / rr) * (t + 1))
             if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]:
                 for t in range(rr):
                     m = mult.narrow(i + 2, area[i] - 1 - t, 1)
-                    m *= ((1.0/rr) * (t + 1))
+                    m *= ((1.0 / rr) * (t + 1))
 
     conditioning = {}
     model_conds = conds["model_conds"]
@@ -548,25 +559,37 @@ def resolve_areas_and_cond_masks(conditions, h, w, device):
     logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
     return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)
 
-def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2
+def create_cond_with_same_area_if_none(conds, c):
     if 'area' not in c:
         return
 
+    def area_inside(a, area_cmp):
+        a = add_area_dims(a, len(area_cmp) // 2)
+        area_cmp = add_area_dims(area_cmp, len(a) // 2)
+
+        a_l = len(a) // 2
+        area_cmp_l = len(area_cmp) // 2
+        for i in range(min(a_l, area_cmp_l)):
+            if a[a_l + i] < area_cmp[area_cmp_l + i]:
+                return False
+        for i in range(min(a_l, area_cmp_l)):
+            if (a[i] + a[a_l + i]) > (area_cmp[i] + area_cmp[area_cmp_l + i]):
+                return False
+        return True
+
     c_area = c['area']
     smallest = None
     for x in conds:
         if 'area' in x:
             a = x['area']
-            if c_area[2] >= a[2] and c_area[3] >= a[3]:
-                if a[0] + a[2] >= c_area[0] + c_area[2]:
-                    if a[1] + a[3] >= c_area[1] + c_area[3]:
-                        if smallest is None:
-                            smallest = x
-                        elif 'area' not in smallest:
-                            smallest = x
-                        else:
-                            if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]:
-                                smallest = x
+            if area_inside(c_area, a):
+                if smallest is None:
+                    smallest = x
+                elif 'area' not in smallest:
+                    smallest = x
+                else:
+                    if math.prod(smallest['area'][:len(smallest['area']) // 2]) > math.prod(a[:len(a) // 2]):
+                        smallest = x
         else:
             if smallest is None:
                 smallest = x

comfy/sd.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+import json
 import torch
 from enum import Enum
 import logging
@@ -134,8 +135,8 @@ class CLIP:
     def clip_layer(self, layer_idx):
         self.layer_idx = layer_idx
 
-    def tokenize(self, text, return_word_ids=False):
-        return self.tokenizer.tokenize_with_weights(text, return_word_ids)
+    def tokenize(self, text, return_word_ids=False, **kwargs):
+        return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs)
 
     def add_hooks_to_dict(self, pooled_dict: dict[str]):
         if self.apply_hooks_to_conds:
@@ -249,7 +250,7 @@ class CLIP:
         return self.patcher.get_key_patches()
 
 class VAE:
-    def __init__(self, sd=None, device=None, config=None, dtype=None):
+    def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
@@ -357,7 +358,12 @@ class VAE:
                     version = 0
                 elif tensor_conv1.shape[0] == 1024:
                     version = 1
-                self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version)
+                    if "encoder.down_blocks.1.conv.conv.bias" in sd:
+                        version = 2
+                vae_config = None
+                if metadata is not None and "config" in metadata:
+                    vae_config = json.loads(metadata["config"]).get("vae", None)
+                self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config)
                 self.latent_channels = 128
                 self.latent_dim = 3
                 self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
@@ -873,13 +879,13 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
     return (model, clip, vae)
 
 def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}):
-    sd = comfy.utils.load_torch_file(ckpt_path)
-    out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options)
+    sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)
+    out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata)
     if out is None:
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
     return out
 
-def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}):
+def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None):
     clip = None
     clipvision = None
     vae = None
@@ -891,19 +897,19 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
     load_device = model_management.get_torch_device()
 
-    model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix)
+    model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata)
     if model_config is None:
         return None
 
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
-    if weight_dtype is not None and model_config.scaled_fp8 is None:
-        unet_weight_dtype.append(weight_dtype)
+    if model_config.scaled_fp8 is not None:
+        weight_dtype = None
 
     model_config.custom_operations = model_options.get("custom_operations", None)
     unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
 
     if unet_dtype is None:
-        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
 
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
@@ -920,7 +926,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     if output_vae:
         vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
         vae_sd = model_config.process_vae_state_dict(vae_sd)
-        vae = VAE(sd=vae_sd)
+        vae = VAE(sd=vae_sd, metadata=metadata)
 
     if output_clip:
         clip_target = model_config.clip_target(state_dict=sd)
@@ -994,11 +1000,11 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse
 
     offload_device = model_management.unet_offload_device()
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
-    if weight_dtype is not None and model_config.scaled_fp8 is None:
-        unet_weight_dtype.append(weight_dtype)
+    if model_config.scaled_fp8 is not None:
+        weight_dtype = None
 
     if dtype is None:
-        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
     else:
         unet_dtype = dtype
 

comfy/sd1_clip.py

@@ -482,7 +482,7 @@ class SDTokenizer:
         return (embed, leftover)
 
 
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         '''
         Takes a prompt and converts it to a list of (token, weight, word id) elements.
         Tokens can both be integer tokens and pre computed CLIP tensors.
@@ -596,7 +596,7 @@ class SD1Tokenizer:
         tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
         setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
 
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids)
         return out

comfy/sdxl_clip.py

@@ -26,7 +26,7 @@ class SDXLTokenizer:
         self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
         self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
 
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
         out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)

comfy/supported_models.py

@@ -762,7 +762,7 @@ class LTXV(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.LTXV
 
-    memory_usage_factor = 2.7
+    memory_usage_factor = 5.5 # TODO: img2vid is about 2x vs txt2vid
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
@@ -826,6 +826,16 @@ class HunyuanVideo(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect))
 
+class HunyuanVideoSkyreelsI2V(HunyuanVideo):
+    unet_config = {
+        "image_model": "hunyuan_video",
+        "in_channels": 32,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.HunyuanVideoSkyreelsI2V(self, device=device)
+        return out
+
 class CosmosT2V(supported_models_base.BASE):
     unet_config = {
         "image_model": "cosmos",
@@ -939,6 +949,6 @@ class WAN21_I2V(WAN21_T2V):
         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 = [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, HunyuanVideoSkyreelsI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V]
 
 models += [SVD_img2vid]

comfy/text_encoders/flux.py

@@ -18,7 +18,7 @@ class FluxTokenizer:
         self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
 
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
         out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)

comfy/text_encoders/hunyuan_video.py

@@ -41,11 +41,14 @@ class HunyuanVideoTokenizer:
         self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"""  # 95 tokens
         self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1)
 
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, llama_template=None, **kwargs):
         out = {}
         out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
 
-        llama_text = "{}{}".format(self.llama_template, text)
+        if llama_template is None:
+            llama_text = "{}{}".format(self.llama_template, text)
+        else:
+            llama_text = "{}{}".format(llama_template, text)
         out["llama"] = self.llama.tokenize_with_weights(llama_text, return_word_ids)
         return out
 

comfy/text_encoders/hydit.py

@@ -37,7 +37,7 @@ class HyditTokenizer:
         self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory)
         self.mt5xl = MT5XLTokenizer(tokenizer_data={"spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory)
 
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids)
         out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids)

comfy/text_encoders/sd3_clip.py

@@ -43,7 +43,7 @@ class SD3Tokenizer:
         self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
 
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
         out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
         out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)

comfy/text_encoders/wan.py

@@ -11,7 +11,7 @@ class UMT5XXlModel(sd1_clip.SDClipModel):
 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=1, pad_token=0)
+        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()}

comfy/utils.py

@@ -46,12 +46,18 @@ if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in
 else:
     logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.")
 
-def load_torch_file(ckpt, safe_load=False, device=None):
+def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
     if device is None:
         device = torch.device("cpu")
+    metadata = None
     if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
         try:
-            sd = safetensors.torch.load_file(ckpt, device=device.type)
+            with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f:
+                sd = {}
+                for k in f.keys():
+                    sd[k] = f.get_tensor(k)
+                if return_metadata:
+                    metadata = f.metadata()
         except Exception as e:
             if len(e.args) > 0:
                 message = e.args[0]
@@ -77,7 +83,7 @@ def load_torch_file(ckpt, safe_load=False, device=None):
                     sd = pl_sd
             else:
                 sd = pl_sd
-    return sd
+    return (sd, metadata) if return_metadata else sd
 
 def save_torch_file(sd, ckpt, metadata=None):
     if metadata is not None:

comfy_extras/nodes_lt.py

@@ -1,9 +1,14 @@
+import io
 import nodes
 import node_helpers
 import torch
 import comfy.model_management
 import comfy.model_sampling
+import comfy.utils
 import math
+import numpy as np
+import av
+from comfy.ldm.lightricks.symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
 
 class EmptyLTXVLatentVideo:
     @classmethod
@@ -33,7 +38,6 @@ class LTXVImgToVideo:
                              "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
                              "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                             "image_noise_scale": ("FLOAT", {"default": 0.15, "min": 0, "max": 1.0, "step": 0.01, "tooltip": "Amount of noise to apply on conditioning image latent."})
                              }}
 
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
@@ -42,16 +46,217 @@ class LTXVImgToVideo:
     CATEGORY = "conditioning/video_models"
     FUNCTION = "generate"
 
-    def generate(self, positive, negative, image, vae, width, height, length, batch_size, image_noise_scale):
+    def generate(self, positive, negative, image, vae, width, height, length, batch_size):
         pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
         encode_pixels = pixels[:, :, :, :3]
         t = vae.encode(encode_pixels)
-        positive = node_helpers.conditioning_set_values(positive, {"guiding_latent": t, "guiding_latent_noise_scale": image_noise_scale})
-        negative = node_helpers.conditioning_set_values(negative, {"guiding_latent": t, "guiding_latent_noise_scale": image_noise_scale})
 
         latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
         latent[:, :, :t.shape[2]] = t
-        return (positive, negative, {"samples": latent}, )
+
+        conditioning_latent_frames_mask = torch.ones(
+            (batch_size, 1, latent.shape[2], 1, 1),
+            dtype=torch.float32,
+            device=latent.device,
+        )
+        conditioning_latent_frames_mask[:, :, :t.shape[2]] = 0
+
+        return (positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}, )
+
+
+def conditioning_get_any_value(conditioning, key, default=None):
+    for t in conditioning:
+        if key in t[1]:
+            return t[1][key]
+    return default
+
+
+def get_noise_mask(latent):
+    noise_mask = latent.get("noise_mask", None)
+    latent_image = latent["samples"]
+    if noise_mask is None:
+        batch_size, _, latent_length, _, _ = latent_image.shape
+        noise_mask = torch.ones(
+            (batch_size, 1, latent_length, 1, 1),
+            dtype=torch.float32,
+            device=latent_image.device,
+        )
+    else:
+        noise_mask = noise_mask.clone()
+    return noise_mask
+
+def get_keyframe_idxs(cond):
+    keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None)
+    if keyframe_idxs is None:
+        return None, 0
+    num_keyframes = torch.unique(keyframe_idxs[:, 0]).shape[0]
+    return keyframe_idxs, num_keyframes
+
+class LTXVAddGuide:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE",),
+                             "latent": ("LATENT",),
+                             "image": ("IMAGE", {"tooltip": "Image or video to condition the latent video on. Must be 8*n + 1 frames." \
+                                                 "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames."}),
+                             "frame_idx": ("INT", {"default": 0, "min": -9999, "max": 9999,
+                                                   "tooltip": "Frame index to start the conditioning at. Must be divisible by 8. " \
+                                                   "If a frame is not divisible by 8, it will be rounded down to the nearest multiple of 8. " \
+                                                   "Negative values are counted from the end of the video."}),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                             }
+            }
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+
+    CATEGORY = "conditioning/video_models"
+    FUNCTION = "generate"
+
+    def __init__(self):
+        self._num_prefix_frames = 2
+        self._patchifier = SymmetricPatchifier(1)
+
+    def encode(self, vae, latent_width, latent_height, images, scale_factors):
+        time_scale_factor, width_scale_factor, height_scale_factor = scale_factors
+        images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1]
+        pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="disabled").movedim(1, -1)
+        encode_pixels = pixels[:, :, :, :3]
+        t = vae.encode(encode_pixels)
+        return encode_pixels, t
+
+    def get_latent_index(self, cond, latent_length, frame_idx, scale_factors):
+        time_scale_factor, _, _ = scale_factors
+        _, num_keyframes = get_keyframe_idxs(cond)
+        latent_count = latent_length - num_keyframes
+        frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * 8 + 1 + frame_idx, 0)
+        frame_idx = frame_idx // time_scale_factor * time_scale_factor # frame index must be divisible by 8
+
+        latent_idx = (frame_idx + time_scale_factor - 1) // time_scale_factor
+
+        return frame_idx, latent_idx
+
+    def add_keyframe_index(self, cond, frame_idx, guiding_latent, scale_factors):
+        keyframe_idxs, _ = get_keyframe_idxs(cond)
+        _, latent_coords = self._patchifier.patchify(guiding_latent)
+        pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, True)
+        pixel_coords[:, 0] += frame_idx
+        if keyframe_idxs is None:
+            keyframe_idxs = pixel_coords
+        else:
+            keyframe_idxs = torch.cat([keyframe_idxs, pixel_coords], dim=2)
+        return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs})
+
+    def append_keyframe(self, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors):
+        positive = self.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors)
+        negative = self.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors)
+
+        mask = torch.full(
+            (noise_mask.shape[0], 1, guiding_latent.shape[2], 1, 1),
+            1.0 - strength,
+            dtype=noise_mask.dtype,
+            device=noise_mask.device,
+        )
+
+        latent_image = torch.cat([latent_image, guiding_latent], dim=2)
+        noise_mask = torch.cat([noise_mask, mask], dim=2)
+        return positive, negative, latent_image, noise_mask
+
+    def replace_latent_frames(self, latent_image, noise_mask, guiding_latent, latent_idx, strength):
+        cond_length = guiding_latent.shape[2]
+        assert latent_image.shape[2] >= latent_idx + cond_length, "Conditioning frames exceed the length of the latent sequence."
+
+        mask = torch.full(
+            (noise_mask.shape[0], 1, cond_length, 1, 1),
+            1.0 - strength,
+            dtype=noise_mask.dtype,
+            device=noise_mask.device,
+        )
+
+        latent_image = latent_image.clone()
+        noise_mask = noise_mask.clone()
+
+        latent_image[:, :, latent_idx : latent_idx + cond_length] = guiding_latent
+        noise_mask[:, :, latent_idx : latent_idx + cond_length] = mask
+
+        return latent_image, noise_mask
+
+    def generate(self, positive, negative, vae, latent, image, frame_idx, strength):
+        scale_factors = vae.downscale_index_formula
+        latent_image = latent["samples"]
+        noise_mask = get_noise_mask(latent)
+
+        _, _, latent_length, latent_height, latent_width = latent_image.shape
+        image, t = self.encode(vae, latent_width, latent_height, image, scale_factors)
+
+        frame_idx, latent_idx = self.get_latent_index(positive, latent_length, frame_idx, scale_factors)
+        assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence."
+
+        num_prefix_frames = min(self._num_prefix_frames, t.shape[2])
+
+        positive, negative, latent_image, noise_mask = self.append_keyframe(
+            positive,
+            negative,
+            frame_idx,
+            latent_image,
+            noise_mask,
+            t[:, :, :num_prefix_frames],
+            strength,
+            scale_factors,
+        )
+
+        latent_idx += num_prefix_frames
+
+        t = t[:, :, num_prefix_frames:]
+        if t.shape[2] == 0:
+            return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
+
+        latent_image, noise_mask = self.replace_latent_frames(
+            latent_image,
+            noise_mask,
+            t,
+            latent_idx,
+            strength,
+        )
+
+        return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
+
+
+class LTXVCropGuides:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "latent": ("LATENT",),
+                             }
+            }
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+
+    CATEGORY = "conditioning/video_models"
+    FUNCTION = "crop"
+
+    def __init__(self):
+        self._patchifier = SymmetricPatchifier(1)
+
+    def crop(self, positive, negative, latent):
+        latent_image = latent["samples"].clone()
+        noise_mask = get_noise_mask(latent)
+
+        _, num_keyframes = get_keyframe_idxs(positive)
+        if num_keyframes == 0:
+            return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
+
+        latent_image = latent_image[:, :, :-num_keyframes]
+        noise_mask = noise_mask[:, :, :-num_keyframes]
+
+        positive = node_helpers.conditioning_set_values(positive, {"keyframe_idxs": None})
+        negative = node_helpers.conditioning_set_values(negative, {"keyframe_idxs": None})
+
+        return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
 
 
 class LTXVConditioning:
@@ -174,6 +379,77 @@ class LTXVScheduler:
 
         return (sigmas,)
 
+def encode_single_frame(output_file, image_array: np.ndarray, crf):
+    container = av.open(output_file, "w", format="mp4")
+    try:
+        stream = container.add_stream(
+            "h264", rate=1, options={"crf": str(crf), "preset": "veryfast"}
+        )
+        stream.height = image_array.shape[0]
+        stream.width = image_array.shape[1]
+        av_frame = av.VideoFrame.from_ndarray(image_array, format="rgb24").reformat(
+            format="yuv420p"
+        )
+        container.mux(stream.encode(av_frame))
+        container.mux(stream.encode())
+    finally:
+        container.close()
+
+
+def decode_single_frame(video_file):
+    container = av.open(video_file)
+    try:
+        stream = next(s for s in container.streams if s.type == "video")
+        frame = next(container.decode(stream))
+    finally:
+        container.close()
+    return frame.to_ndarray(format="rgb24")
+
+
+def preprocess(image: torch.Tensor, crf=29):
+    if crf == 0:
+        return image
+
+    image_array = (image[:(image.shape[0] // 2) * 2, :(image.shape[1] // 2) * 2] * 255.0).byte().cpu().numpy()
+    with io.BytesIO() as output_file:
+        encode_single_frame(output_file, image_array, crf)
+        video_bytes = output_file.getvalue()
+    with io.BytesIO(video_bytes) as video_file:
+        image_array = decode_single_frame(video_file)
+    tensor = torch.tensor(image_array, dtype=image.dtype, device=image.device) / 255.0
+    return tensor
+
+
+class LTXVPreprocess:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "img_compression": (
+                    "INT",
+                    {
+                        "default": 35,
+                        "min": 0,
+                        "max": 100,
+                        "tooltip": "Amount of compression to apply on image.",
+                    },
+                ),
+            }
+        }
+
+    FUNCTION = "preprocess"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("output_image",)
+    CATEGORY = "image"
+
+    def preprocess(self, image, img_compression):
+        if img_compression > 0:
+            output_images = []
+            for i in range(image.shape[0]):
+                output_images.append(preprocess(image[i], img_compression))
+        return (torch.stack(output_images),)
+
 
 NODE_CLASS_MAPPINGS = {
     "EmptyLTXVLatentVideo": EmptyLTXVLatentVideo,
@@ -181,4 +457,7 @@ NODE_CLASS_MAPPINGS = {
     "ModelSamplingLTXV": ModelSamplingLTXV,
     "LTXVConditioning": LTXVConditioning,
     "LTXVScheduler": LTXVScheduler,
+    "LTXVAddGuide": LTXVAddGuide,
+    "LTXVPreprocess": LTXVPreprocess,
+    "LTXVCropGuides": LTXVCropGuides,
 }

comfy_extras/nodes_video_model.py

@@ -4,6 +4,7 @@ import comfy.utils
 import comfy.sd
 import folder_paths
 import comfy_extras.nodes_model_merging
+import node_helpers
 
 
 class ImageOnlyCheckpointLoader:
@@ -121,12 +122,38 @@ class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
         comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
         return {}
 
+
+class ConditioningSetAreaPercentageVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+                             "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+                             "temporal": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+                             "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
+                             "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
+                             "z": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning"
+
+    def append(self, conditioning, width, height, temporal, x, y, z, strength):
+        c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x),
+                                                                "strength": strength,
+                                                                "set_area_to_bounds": False})
+        return (c, )
+
+
 NODE_CLASS_MAPPINGS = {
     "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader,
     "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning,
     "VideoLinearCFGGuidance": VideoLinearCFGGuidance,
     "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance,
     "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave,
+    "ConditioningSetAreaPercentageVideo": ConditioningSetAreaPercentageVideo,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {

comfy_extras/nodes_wan.py

@@ -11,9 +11,9 @@ class WanImageToVideo:
         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}),
+                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
                 },
                 "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.17"
+__version__ = "0.3.22"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.17"
+version = "0.3.22"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,3 +1,4 @@
+comfyui-frontend-package==1.10.17
 torch
 torchsde
 torchvision

web/assets/BaseViewTemplate-BTbuZf5t.js

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

web/assets/CREDIT.txt

@@ -1 +0,0 @@
-Thanks to OpenArt (https://openart.ai) for providing the sorted-custom-node-map data, captured in September 2024.

web/assets/DesktopStartView-D9r53Bue.js

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-const _sfc_main = /* @__PURE__ */ defineComponent({
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-  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 {
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-//# sourceMappingURL=DesktopStartView-D9r53Bue.js.map

web/assets/DesktopUpdateView-C-R0415K.js

@@ -1,58 +0,0 @@
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-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;
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-    onUnmounted(() => electron.Validation.dispose());
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-              createVNode(_sfc_main$1, {
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-export {
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-//# sourceMappingURL=DesktopUpdateView-C-R0415K.js.map

web/assets/DesktopUpdateView-CxchaIvw.css

@@ -1,20 +0,0 @@
-
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-  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

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-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" };
-const _hoisted_4 = { class: "text-xl" };
-const _hoisted_5 = { class: "text-xl" };
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-const _hoisted_7 = { class: "flex gap-4 flex-row-reverse" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
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-  setup(__props) {
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-        _: 1
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-  }
-});
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-//# sourceMappingURL=DownloadGitView-PWqK5ke4.js.map

web/assets/ExtensionPanel-Ba57xrmg.js

@@ -1,182 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
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-import { g as script$2, h as script$6 } from "./index-CgMyWf7n.js";
-import "./index-Dzu9WL4p.js";
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-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "ExtensionPanel",
-  setup(__props) {
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-    const extensionStore = useExtensionStore();
-    const settingStore = useSettingStore();
-    const editingEnabledExtensions = ref({});
-    onMounted(() => {
-      extensionStore.extensions.forEach((ext) => {
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-      });
-    });
-    const changedExtensions = computed(() => {
-      return extensionStore.extensions.filter(
-        (ext) => editingEnabledExtensions.value[ext.name] !== extensionStore.isExtensionEnabled(ext.name)
-      );
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-    const hasChanges = computed(() => {
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-    const updateExtensionStatus = /* @__PURE__ */ __name(() => {
-      const editingDisabledExtensionNames = Object.entries(
-        editingEnabledExtensions.value
-      ).filter(([_, enabled]) => !enabled).map(([name]) => name);
-      settingStore.set("Comfy.Extension.Disabled", [
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web/assets/GraphView-Bo28XDd0.css

@@ -1,383 +0,0 @@
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-.comfy-menu-hamburger[data-v-82120b51] {
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-    z-index: 9999;
-    display: flex;
-    flex-direction: row
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-
-[data-v-e50caa15] .p-splitter-gutter {
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-.side-bar-panel[data-v-e50caa15] {
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-.bottom-panel[data-v-e50caa15] {
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-.splitter-overlay[data-v-e50caa15] {
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-  background-color: transparent;
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-.splitter-overlay-root[data-v-e50caa15] {
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-  top: 0px;
-  left: 0px;
-  height: 100%;
-  width: 100%;
-
-  /* Set it the same as the ComfyUI menu */
-  /* Note: Lite-graph DOM widgets have the same z-index as the node id, so
-  999 should be sufficient to make sure splitter overlays on node's DOM
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-  z-index: 999;
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-.p-buttongroup-vertical[data-v-27a9500c] {
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-  flex-direction: column;
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-.p-buttongroup-vertical .p-button[data-v-27a9500c] {
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web/assets/KeybindingPanel-CDYVPYDp.css

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web/assets/KeybindingPanel-oavhFdkz.js

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-  setup(__props) {
-    const filters = ref({
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-    });
-    const keybindingStore = useKeybindingStore();
-    const keybindingService = useKeybindingService();
-    const commandStore = useCommandStore();
-    const { t } = useI18n();
-    const commandsData = computed(() => {
-      return Object.values(commandStore.commands).map((command) => ({
-        id: command.id,
-        label: t(`commands.${normalizeI18nKey(command.id)}.label`, command.label),
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-      }));
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-    const selectedCommandData = ref(null);
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-    const newBindingKeyCombo = ref(null);
-    const currentEditingCommand = ref(null);
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-    const existingKeybindingOnCombo = computed(() => {
-      if (!currentEditingCommand.value) {
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-      if (currentEditingCommand.value.keybinding?.combo?.equals(
-        newBindingKeyCombo.value
-      )) {
-        return null;
-      }
-      if (!newBindingKeyCombo.value) {
-        return null;
-      }
-      return keybindingStore.getKeybinding(newBindingKeyCombo.value);
-    });
-    function editKeybinding(commandData) {
-      currentEditingCommand.value = commandData;
-      newBindingKeyCombo.value = commandData.keybinding ? commandData.keybinding.combo : null;
-      editDialogVisible.value = true;
-    }
-    __name(editKeybinding, "editKeybinding");
-    watchEffect(() => {
-      if (editDialogVisible.value) {
-        setTimeout(() => {
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-        }, 300);
-      }
-    });
-    function removeKeybinding(commandData) {
-      if (commandData.keybinding) {
-        keybindingStore.unsetKeybinding(commandData.keybinding);
-        keybindingService.persistUserKeybindings();
-      }
-    }
-    __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;
-    }
-    __name(captureKeybinding, "captureKeybinding");
-    function cancelEdit() {
-      editDialogVisible.value = false;
-      currentEditingCommand.value = null;
-      newBindingKeyCombo.value = null;
-    }
-    __name(cancelEdit, "cancelEdit");
-    function saveKeybinding() {
-      if (currentEditingCommand.value && newBindingKeyCombo.value) {
-        const updated = keybindingStore.updateKeybindingOnCommand(
-          new KeybindingImpl({
-            commandId: currentEditingCommand.value.id,
-            combo: newBindingKeyCombo.value
-          })
-        );
-        if (updated) {
-          keybindingService.persistUserKeybindings();
-        }
-      }
-      cancelEdit();
-    }
-    __name(saveKeybinding, "saveKeybinding");
-    const toast = useToast();
-    async function resetKeybindings() {
-      keybindingStore.resetKeybindings();
-      await keybindingService.persistUserKeybindings();
-      toast.add({
-        severity: "info",
-        summary: "Info",
-        detail: "Keybindings reset",
-        life: 3e3
-      });
-    }
-    __name(resetKeybindings, "resetKeybindings");
-    return (_ctx, _cache) => {
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-      return openBlock(), createBlock(_sfc_main$2, {
-        value: "Keybinding",
-        class: "keybinding-panel"
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-            "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event),
-            placeholder: _ctx.$t("g.searchKeybindings") + "..."
-          }, null, 8, ["modelValue", "placeholder"])
-        ]),
-        default: withCtx(() => [
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-            selection: selectedCommandData.value,
-            "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event),
-            "global-filter-fields": ["id", "label"],
-            filters: filters.value,
-            selectionMode: "single",
-            stripedRows: "",
-            pt: {
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-              createVNode(unref(script$1), {
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-                header: ""
-              }, {
-                body: withCtx((slotProps) => [
-                  createBaseVNode("div", _hoisted_1, [
-                    createVNode(unref(script$2), {
-                      icon: "pi pi-pencil",
-                      class: "p-button-text",
-                      onClick: /* @__PURE__ */ __name(($event) => editKeybinding(slotProps.data), "onClick")
-                    }, null, 8, ["onClick"]),
-                    createVNode(unref(script$2), {
-                      icon: "pi pi-trash",
-                      class: "p-button-text p-button-danger",
-                      onClick: /* @__PURE__ */ __name(($event) => removeKeybinding(slotProps.data), "onClick"),
-                      disabled: !slotProps.data.keybinding
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-                ]),
-                _: 1
-              }),
-              createVNode(unref(script$1), {
-                field: "id",
-                header: _ctx.$t("g.command"),
-                sortable: "",
-                class: "max-w-64 2xl:max-w-full"
-              }, {
-                body: withCtx((slotProps) => [
-                  createBaseVNode("div", {
-                    class: "overflow-hidden text-ellipsis whitespace-nowrap",
-                    title: slotProps.data.id
-                  }, toDisplayString(slotProps.data.label), 9, _hoisted_2)
-                ]),
-                _: 1
-              }, 8, ["header"]),
-              createVNode(unref(script$1), {
-                field: "keybinding",
-                header: _ctx.$t("g.keybinding")
-              }, {
-                body: withCtx((slotProps) => [
-                  slotProps.data.keybinding ? (openBlock(), createBlock(_sfc_main$1, {
-                    key: 0,
-                    keyCombo: slotProps.data.keybinding.combo,
-                    isModified: unref(keybindingStore).isCommandKeybindingModified(slotProps.data.id)
-                  }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_3, "-"))
-                ]),
-                _: 1
-              }, 8, ["header"])
-            ]),
-            _: 1
-          }, 8, ["value", "selection", "filters"]),
-          createVNode(unref(script$6), {
-            class: "min-w-96",
-            visible: editDialogVisible.value,
-            "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event),
-            modal: "",
-            header: currentEditingCommand.value?.label,
-            onHide: cancelEdit
-          }, {
-            footer: withCtx(() => [
-              createVNode(unref(script$2), {
-                label: "Save",
-                icon: "pi pi-check",
-                onClick: saveKeybinding,
-                disabled: !!existingKeybindingOnCombo.value,
-                autofocus: ""
-              }, null, 8, ["disabled"])
-            ]),
-            default: withCtx(() => [
-              createBaseVNode("div", null, [
-                createVNode(unref(script$4), {
-                  class: "mb-2 text-center",
-                  ref_key: "keybindingInput",
-                  ref: keybindingInput,
-                  modelValue: newBindingKeyCombo.value?.toString() ?? "",
-                  placeholder: "Press keys for new binding",
-                  onKeydown: withModifiers(captureKeybinding, ["stop", "prevent"]),
-                  autocomplete: "off",
-                  fluid: "",
-                  invalid: !!existingKeybindingOnCombo.value
-                }, null, 8, ["modelValue", "invalid"]),
-                existingKeybindingOnCombo.value ? (openBlock(), createBlock(unref(script$5), {
-                  key: 0,
-                  severity: "error"
-                }, {
-                  default: withCtx(() => [
-                    _cache[3] || (_cache[3] = createTextVNode(" Keybinding already exists on ")),
-                    createVNode(unref(script), {
-                      severity: "secondary",
-                      value: existingKeybindingOnCombo.value.commandId
-                    }, null, 8, ["value"])
-                  ]),
-                  _: 1
-                })) : createCommentVNode("", true)
-              ])
-            ]),
-            _: 1
-          }, 8, ["visible", "header"]),
-          withDirectives(createVNode(unref(script$2), {
-            class: "mt-4",
-            label: _ctx.$t("g.reset"),
-            icon: "pi pi-trash",
-            severity: "danger",
-            fluid: "",
-            text: "",
-            onClick: resetKeybindings
-          }, null, 8, ["label"]), [
-            [_directive_tooltip, _ctx.$t("g.resetKeybindingsTooltip")]
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-8454e24f"]]);
-export {
-  KeybindingPanel as default
-};
-//# sourceMappingURL=KeybindingPanel-oavhFdkz.js.map

web/assets/MaintenanceView-DEJCj8SR.css

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

web/assets/ManualConfigurationView-CsirlNfV.css

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

web/assets/ManualConfigurationView-DTLyJ3VG.js

@@ -1,74 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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" };
-const _hoisted_4 = { class: "ml-2" };
-const _hoisted_5 = { class: "m-1 mb-4" };
-const _hoisted_6 = { class: "m-0" };
-const _hoisted_7 = { class: "m-1" };
-const _hoisted_8 = { class: "font-mono" };
-const _hoisted_9 = { class: "m-1" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "ManualConfigurationView",
-  setup(__props) {
-    const { t } = useI18n();
-    const electron = electronAPI();
-    const basePath = ref(null);
-    const sep = ref("/");
-    const restartApp = /* @__PURE__ */ __name((message) => electron.restartApp(message), "restartApp");
-    onMounted(async () => {
-      basePath.value = await electron.getBasePath();
-      if (basePath.value.indexOf("/") === -1) sep.value = "\\";
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
-        default: withCtx(() => [
-          createBaseVNode("div", _hoisted_1, [
-            createBaseVNode("h2", _hoisted_2, toDisplayString(_ctx.$t("install.manualConfiguration.title")), 1),
-            createBaseVNode("p", _hoisted_3, [
-              createVNode(unref(script), {
-                icon: "pi pi-exclamation-triangle",
-                severity: "warn",
-                value: unref(t)("icon.exclamation-triangle")
-              }, null, 8, ["value"]),
-              createBaseVNode("strong", _hoisted_4, toDisplayString(_ctx.$t("install.gpuSelection.customComfyNeedsPython")), 1)
-            ]),
-            createBaseVNode("div", null, [
-              createBaseVNode("p", _hoisted_5, toDisplayString(_ctx.$t("install.manualConfiguration.requirements")) + ": ", 1),
-              createBaseVNode("ul", _hoisted_6, [
-                createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customManualVenv")), 1),
-                createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customInstallRequirements")), 1)
-              ])
-            ]),
-            createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("install.manualConfiguration.createVenv")) + ":", 1),
-            createVNode(unref(script$1), {
-              header: unref(t)("install.manualConfiguration.virtualEnvironmentPath")
-            }, {
-              default: withCtx(() => [
-                createBaseVNode("span", _hoisted_8, toDisplayString(`${basePath.value}${sep.value}.venv${sep.value}`), 1)
-              ]),
-              _: 1
-            }, 8, ["header"]),
-            createBaseVNode("p", _hoisted_9, toDisplayString(_ctx.$t("install.manualConfiguration.restartWhenFinished")), 1),
-            createVNode(unref(script$2), {
-              class: "place-self-end",
-              label: unref(t)("menuLabels.Restart"),
-              severity: "warn",
-              icon: "pi pi-refresh",
-              onClick: _cache[0] || (_cache[0] = ($event) => restartApp("Manual configuration complete"))
-            }, null, 8, ["label"])
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-const ManualConfigurationView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-dc169863"]]);
-export {
-  ManualConfigurationView as default
-};
-//# sourceMappingURL=ManualConfigurationView-DTLyJ3VG.js.map

web/assets/MetricsConsentView-C80fk2cl.js

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

web/assets/NotSupportedView-B78ZVR9Z.js

@@ -1,86 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, bi as useRouter, r as resolveDirective, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, l as script, i as withDirectives, _ as _export_sfc } from "./index-Bv0b06LE.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
-const _imports_0 = "" + new URL("images/sad_girl.png", import.meta.url).href;
-const _hoisted_1 = { class: "sad-container" };
-const _hoisted_2 = { 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) {
-    const openDocs = /* @__PURE__ */ __name(() => {
-      window.open(
-        "https://github.com/Comfy-Org/desktop#currently-supported-platforms",
-        "_blank"
-      );
-    }, "openDocs");
-    const reportIssue = /* @__PURE__ */ __name(() => {
-      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
-    }, "reportIssue");
-    const router = useRouter();
-    const continueToInstall = /* @__PURE__ */ __name(() => {
-      router.push("/install");
-    }, "continueToInstall");
-    return (_ctx, _cache) => {
-      const _directive_tooltip = resolveDirective("tooltip");
-      return openBlock(), createBlock(_sfc_main$1, null, {
-        default: withCtx(() => [
-          createBaseVNode("div", _hoisted_1, [
-            _cache[0] || (_cache[0] = createBaseVNode("img", {
-              class: "sad-girl",
-              src: _imports_0,
-              alt: "Sad girl illustration"
-            }, null, -1)),
-            createBaseVNode("div", _hoisted_2, [
-              createBaseVNode("div", _hoisted_3, [
-                createBaseVNode("h1", _hoisted_4, toDisplayString(_ctx.$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.$t("notSupported.supportedDevices.windows")), 1)
-                  ])
-                ]),
-                createBaseVNode("div", _hoisted_8, [
-                  createVNode(unref(script), {
-                    label: _ctx.$t("notSupported.learnMore"),
-                    icon: "pi pi-github",
-                    onClick: openDocs,
-                    severity: "secondary"
-                  }, null, 8, ["label"]),
-                  createVNode(unref(script), {
-                    label: _ctx.$t("notSupported.reportIssue"),
-                    icon: "pi pi-flag",
-                    onClick: reportIssue,
-                    severity: "secondary"
-                  }, null, 8, ["label"]),
-                  withDirectives(createVNode(unref(script), {
-                    label: _ctx.$t("notSupported.continue"),
-                    icon: "pi pi-arrow-right",
-                    iconPos: "right",
-                    onClick: continueToInstall,
-                    severity: "danger"
-                  }, null, 8, ["label"]), [
-                    [_directive_tooltip, _ctx.$t("notSupported.continueTooltip")]
-                  ])
-                ])
-              ])
-            ])
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-const NotSupportedView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-ebb20958"]]);
-export {
-  NotSupportedView as default
-};
-//# sourceMappingURL=NotSupportedView-B78ZVR9Z.js.map

web/assets/NotSupportedView-RFx6eCkN.css

@@ -1,19 +0,0 @@
-
-.sad-container {
-&[data-v-ebb20958] {
-  display: grid;
-  align-items: center;
-  justify-content: space-evenly;
-  grid-template-columns: 25rem 1fr;
-}
-&[data-v-ebb20958] > * {
-    grid-row: 1;
-}
-}
-.sad-text[data-v-ebb20958] {
-  grid-column: 1/3;
-}
-.sad-girl[data-v-ebb20958] {
-  grid-column: 2/3;
-  width: min(75vw, 100vh);
-}

web/assets/ServerConfigPanel-BYrt6wyr.js

@@ -1,156 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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"
-};
-function render(_ctx, _cache) {
-  return openBlock(), createElementBlock("svg", _hoisted_1$1, _cache[0] || (_cache[0] = [
-    createBaseVNode("path", {
-      fill: "none",
-      stroke: "currentColor",
-      "stroke-linecap": "round",
-      "stroke-linejoin": "round",
-      "stroke-width": "2",
-      d: "m4 17l6-6l-6-6m8 14h8"
-    }, null, -1)
-  ]));
-}
-__name(render, "render");
-const __unplugin_components_0 = markRaw({ name: "lucide-terminal", render });
-const _hoisted_1 = { class: "flex flex-col gap-2" };
-const _hoisted_2 = { class: "flex justify-end gap-2" };
-const _hoisted_3 = { class: "flex items-center justify-between" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "ServerConfigPanel",
-  setup(__props) {
-    const settingStore = useSettingStore();
-    const serverConfigStore = useServerConfigStore();
-    const {
-      serverConfigsByCategory,
-      serverConfigValues,
-      launchArgs,
-      commandLineArgs,
-      modifiedConfigs
-    } = storeToRefs(serverConfigStore);
-    const revertChanges = /* @__PURE__ */ __name(() => {
-      serverConfigStore.revertChanges();
-    }, "revertChanges");
-    const restartApp = /* @__PURE__ */ __name(() => {
-      electronAPI().restartApp();
-    }, "restartApp");
-    watch(launchArgs, (newVal) => {
-      settingStore.set("Comfy.Server.LaunchArgs", newVal);
-    });
-    watch(serverConfigValues, (newVal) => {
-      settingStore.set("Comfy.Server.ServerConfigValues", newVal);
-    });
-    const { copyToClipboard } = useCopyToClipboard();
-    const copyCommandLineArgs = /* @__PURE__ */ __name(async () => {
-      await copyToClipboard(commandLineArgs.value);
-    }, "copyCommandLineArgs");
-    const { t } = useI18n();
-    const translateItem = /* @__PURE__ */ __name((item) => {
-      return {
-        ...item,
-        name: t(`serverConfigItems.${item.id}.name`, item.name),
-        tooltip: item.tooltip ? t(`serverConfigItems.${item.id}.tooltip`, item.tooltip) : void 0
-      };
-    }, "translateItem");
-    return (_ctx, _cache) => {
-      const _component_i_lucide58terminal = __unplugin_components_0;
-      return openBlock(), createBlock(_sfc_main$1, {
-        value: "Server-Config",
-        class: "server-config-panel"
-      }, {
-        header: withCtx(() => [
-          createBaseVNode("div", _hoisted_1, [
-            unref(modifiedConfigs).length > 0 ? (openBlock(), createBlock(unref(script), {
-              key: 0,
-              severity: "info",
-              "pt:text": "w-full"
-            }, {
-              default: withCtx(() => [
-                createBaseVNode("p", null, toDisplayString(_ctx.$t("serverConfig.modifiedConfigs")), 1),
-                createBaseVNode("ul", null, [
-                  (openBlock(true), createElementBlock(Fragment, null, renderList(unref(modifiedConfigs), (config) => {
-                    return openBlock(), createElementBlock("li", {
-                      key: config.id
-                    }, toDisplayString(config.name) + ": " + toDisplayString(config.initialValue) + " → " + toDisplayString(config.value), 1);
-                  }), 128))
-                ]),
-                createBaseVNode("div", _hoisted_2, [
-                  createVNode(unref(script$1), {
-                    label: _ctx.$t("serverConfig.revertChanges"),
-                    onClick: revertChanges,
-                    outlined: ""
-                  }, null, 8, ["label"]),
-                  createVNode(unref(script$1), {
-                    label: _ctx.$t("serverConfig.restart"),
-                    onClick: restartApp,
-                    outlined: "",
-                    severity: "danger"
-                  }, null, 8, ["label"])
-                ])
-              ]),
-              _: 1
-            })) : createCommentVNode("", true),
-            unref(commandLineArgs) ? (openBlock(), createBlock(unref(script), {
-              key: 1,
-              severity: "secondary",
-              "pt:text": "w-full"
-            }, {
-              icon: withCtx(() => [
-                createVNode(_component_i_lucide58terminal, { class: "text-xl font-bold" })
-              ]),
-              default: withCtx(() => [
-                createBaseVNode("div", _hoisted_3, [
-                  createBaseVNode("p", null, toDisplayString(unref(commandLineArgs)), 1),
-                  createVNode(unref(script$1), {
-                    icon: "pi pi-clipboard",
-                    onClick: copyCommandLineArgs,
-                    severity: "secondary",
-                    text: ""
-                  })
-                ])
-              ]),
-              _: 1
-            })) : createCommentVNode("", true)
-          ])
-        ]),
-        default: withCtx(() => [
-          (openBlock(true), createElementBlock(Fragment, null, renderList(Object.entries(unref(serverConfigsByCategory)), ([label, items], i) => {
-            return openBlock(), createElementBlock("div", { key: label }, [
-              i > 0 ? (openBlock(), createBlock(unref(script$2), { key: 0 })) : createCommentVNode("", true),
-              createBaseVNode("h3", null, toDisplayString(_ctx.$t(`serverConfigCategories.${label}`, label)), 1),
-              (openBlock(true), createElementBlock(Fragment, null, renderList(items, (item) => {
-                return openBlock(), createElementBlock("div", {
-                  key: item.name,
-                  class: "mb-4"
-                }, [
-                  createVNode(FormItem, {
-                    item: translateItem(item),
-                    formValue: item.value,
-                    "onUpdate:formValue": /* @__PURE__ */ __name(($event) => item.value = $event, "onUpdate:formValue"),
-                    id: item.id,
-                    labelClass: {
-                      "text-highlight": item.initialValue !== item.value
-                    }
-                  }, null, 8, ["item", "formValue", "onUpdate:formValue", "id", "labelClass"])
-                ]);
-              }), 128))
-            ]);
-          }), 128))
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-export {
-  _sfc_main as default
-};
-//# sourceMappingURL=ServerConfigPanel-BYrt6wyr.js.map

web/assets/ServerStartView-B7TlHxYo.js

@@ -1,100 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, I as useI18n, T as ref, bo as ProgressStatus, p as onMounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, a8 as createTextVNode, E as toDisplayString, j as unref, f as createElementBlock, B as createCommentVNode, k as createVNode, l as script, i as withDirectives, v as vShow, bp as BaseTerminal, b9 as electronAPI, _ as _export_sfc } from "./index-Bv0b06LE.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
-const _hoisted_1 = { class: "flex flex-col w-full h-full items-center" };
-const _hoisted_2 = { class: "text-2xl font-bold" };
-const _hoisted_3 = { key: 0 };
-const _hoisted_4 = {
-  key: 0,
-  class: "flex flex-col items-center gap-4"
-};
-const _hoisted_5 = { class: "flex items-center my-4 gap-2" };
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-      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
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-    const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs");
-    onMounted(async () => {
-      electron.sendReady();
-      electron.onProgressUpdate(updateProgress);
-      electronVersion.value = await electron.getElectronVersion();
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-            status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_4, [
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-                createVNode(unref(script), {
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-                  label: unref(t)("serverStart.reinstall"),
-                  onClick: reinstall
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-                key: 0,
-                icon: "pi pi-search",
-                severity: "secondary",
-                label: unref(t)("serverStart.showTerminal"),
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-const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-e6ba9633"]]);
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web/assets/ServerStartView-BZ7uhZHv.css

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-[data-v-e6ba9633] .xterm-helper-textarea {
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web/assets/UserSelectView-C703HOyO.js

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-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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$1 } from "./BaseViewTemplate-BTbuZf5t.js";
-const _hoisted_1 = {
-  id: "comfy-user-selection",
-  class: "min-w-84 relative rounded-lg bg-[var(--comfy-menu-bg)] p-5 px-10 shadow-lg"
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-const _hoisted_2 = { class: "flex w-full flex-col items-center" };
-const _hoisted_3 = { class: "flex w-full flex-col gap-2" };
-const _hoisted_4 = { for: "new-user-input" };
-const _hoisted_5 = { class: "flex w-full flex-col gap-2" };
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-const _hoisted_7 = { class: "mt-5" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "UserSelectView",
-  setup(__props) {
-    const userStore = useUserStore();
-    const router = useRouter();
-    const selectedUser = ref(null);
-    const newUsername = ref("");
-    const loginError = ref("");
-    const createNewUser = computed(() => newUsername.value.trim() !== "");
-    const newUserExistsError = computed(() => {
-      return userStore.users.find((user) => user.username === newUsername.value) ? `User "${newUsername.value}" already exists` : "";
-    });
-    const error = computed(() => newUserExistsError.value || loginError.value);
-    const login = /* @__PURE__ */ __name(async () => {
-      try {
-        const user = createNewUser.value ? await userStore.createUser(newUsername.value) : selectedUser.value;
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-          throw new Error("No user selected");
-        }
-        userStore.login(user);
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-    }, "login");
-    onMounted(async () => {
-      if (!userStore.initialized) {
-        await userStore.initialize();
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-      return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
-        default: withCtx(() => [
-          createBaseVNode("main", _hoisted_1, [
-            _cache[2] || (_cache[2] = createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1)),
-            createBaseVNode("div", _hoisted_2, [
-              createBaseVNode("div", _hoisted_3, [
-                createBaseVNode("label", _hoisted_4, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
-                createVNode(unref(script), {
-                  id: "new-user-input",
-                  modelValue: newUsername.value,
-                  "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => newUsername.value = $event),
-                  placeholder: _ctx.$t("userSelect.enterUsername"),
-                  onKeyup: withKeys(login, ["enter"])
-                }, null, 8, ["modelValue", "placeholder"])
-              ]),
-              createVNode(unref(script$1)),
-              createBaseVNode("div", _hoisted_5, [
-                createBaseVNode("label", _hoisted_6, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
-                createVNode(unref(script$2), {
-                  modelValue: selectedUser.value,
-                  "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => selectedUser.value = $event),
-                  class: "w-full",
-                  inputId: "existing-user-select",
-                  options: unref(userStore).users,
-                  "option-label": "username",
-                  placeholder: _ctx.$t("userSelect.selectUser"),
-                  disabled: createNewUser.value
-                }, null, 8, ["modelValue", "options", "placeholder", "disabled"]),
-                error.value ? (openBlock(), createBlock(unref(script$3), {
-                  key: 0,
-                  severity: "error"
-                }, {
-                  default: withCtx(() => [
-                    createTextVNode(toDisplayString(error.value), 1)
-                  ]),
-                  _: 1
-                })) : createCommentVNode("", true)
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-              createBaseVNode("footer", _hoisted_7, [
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-                  onClick: login
-                }, null, 8, ["label"])
-              ])
-            ])
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
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web/assets/WelcomeView-Brz3-luE.css

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-  animation: fadeInUp-7dfaf74c 1.5s ease-out;
-  animation-fill-mode: both;
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web/assets/WelcomeView-DIFvbWc2.js

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-var __defProp = Object.defineProperty;
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-import { _ as _sfc_main$1 } from "./BaseViewTemplate-BTbuZf5t.js";
-const _hoisted_1 = { class: "flex flex-col items-center justify-center gap-8 p-8" };
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-            createBaseVNode("h1", _hoisted_2, toDisplayString(_ctx.$t("welcome.title")), 1),
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-              label: _ctx.$t("welcome.getStarted"),
-              icon: "pi pi-arrow-right",
-              iconPos: "right",
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-              rounded: "",
-              onClick: _cache[0] || (_cache[0] = ($event) => navigateTo("/install")),
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-        _: 1
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-  }
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-export {
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-};
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web/assets/images/manual-configuration.svg

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web/assets/index-BRhY6FpL.css

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-	font-family: Arial, Helvetica, sans-serif;
-	border-color: black;
-	margin: 20vh auto;
-	max-height: 60vh;
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-.comfy-group-manage-outer {
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-.comfy-group-manage-outer > header {
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-	align-items: center;
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-	background: var(--comfy-menu-bg);
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-.comfy-group-manage-outer > header select {
-	background: var(--comfy-input-bg);
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-.comfy-group-manage h2 {
-	margin: 0;
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-.comfy-group-manage main {
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-.comfy-group-manage .drag-handle {
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-.comfy-group-manage-list {
-	border-right: 1px solid var(--comfy-menu-bg);
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-.comfy-group-manage-list ul {
-	margin: 40px 0 0;
-	padding: 0;
-	list-style: none;
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-.comfy-group-manage-list-items {
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-.comfy-group-manage-list li {
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-.comfy-group-manage-list li:not(.selected):hover div {
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-.comfy-group-manage-node {
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-import { bX as script$1, o as openBlock, f as createElementBlock, at as mergeProps, m as createBaseVNode } from "./index-Bv0b06LE.js";
-var script = {
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-  "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"
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-    "clip-rule": "evenodd",
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-__name(render, "render");
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web/assets/keybindingService-DyjX-nxF.js

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-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { ao as useKeybindingStore, J as useCommandStore, a as useSettingStore, dA as KeyComboImpl, dB as KeybindingImpl } from "./index-Bv0b06LE.js";
-const CORE_KEYBINDINGS = [
-  {
-    combo: {
-      ctrl: true,
-      key: "Enter"
-    },
-    commandId: "Comfy.QueuePrompt"
-  },
-  {
-    combo: {
-      ctrl: true,
-      shift: true,
-      key: "Enter"
-    },
-    commandId: "Comfy.QueuePromptFront"
-  },
-  {
-    combo: {
-      ctrl: true,
-      alt: true,
-      key: "Enter"
-    },
-    commandId: "Comfy.Interrupt"
-  },
-  {
-    combo: {
-      key: "r"
-    },
-    commandId: "Comfy.RefreshNodeDefinitions"
-  },
-  {
-    combo: {
-      key: "q"
-    },
-    commandId: "Workspace.ToggleSidebarTab.queue"
-  },
-  {
-    combo: {
-      key: "w"
-    },
-    commandId: "Workspace.ToggleSidebarTab.workflows"
-  },
-  {
-    combo: {
-      key: "n"
-    },
-    commandId: "Workspace.ToggleSidebarTab.node-library"
-  },
-  {
-    combo: {
-      key: "m"
-    },
-    commandId: "Workspace.ToggleSidebarTab.model-library"
-  },
-  {
-    combo: {
-      key: "s",
-      ctrl: true
-    },
-    commandId: "Comfy.SaveWorkflow"
-  },
-  {
-    combo: {
-      key: "o",
-      ctrl: true
-    },
-    commandId: "Comfy.OpenWorkflow"
-  },
-  {
-    combo: {
-      key: "Backspace"
-    },
-    commandId: "Comfy.ClearWorkflow"
-  },
-  {
-    combo: {
-      key: "g",
-      ctrl: true
-    },
-    commandId: "Comfy.Graph.GroupSelectedNodes"
-  },
-  {
-    combo: {
-      key: ",",
-      ctrl: true
-    },
-    commandId: "Comfy.ShowSettingsDialog"
-  },
-  // For '=' both holding shift and not holding shift
-  {
-    combo: {
-      key: "=",
-      alt: true
-    },
-    commandId: "Comfy.Canvas.ZoomIn",
-    targetElementId: "graph-canvas"
-  },
-  {
-    combo: {
-      key: "+",
-      alt: true,
-      shift: true
-    },
-    commandId: "Comfy.Canvas.ZoomIn",
-    targetElementId: "graph-canvas"
-  },
-  // For number pad '+'
-  {
-    combo: {
-      key: "+",
-      alt: true
-    },
-    commandId: "Comfy.Canvas.ZoomIn",
-    targetElementId: "graph-canvas"
-  },
-  {
-    combo: {
-      key: "-",
-      alt: true
-    },
-    commandId: "Comfy.Canvas.ZoomOut",
-    targetElementId: "graph-canvas"
-  },
-  {
-    combo: {
-      key: "."
-    },
-    commandId: "Comfy.Canvas.FitView",
-    targetElementId: "graph-canvas"
-  },
-  {
-    combo: {
-      key: "p"
-    },
-    commandId: "Comfy.Canvas.ToggleSelected.Pin",
-    targetElementId: "graph-canvas"
-  },
-  {
-    combo: {
-      key: "c",
-      alt: true
-    },
-    commandId: "Comfy.Canvas.ToggleSelectedNodes.Collapse",
-    targetElementId: "graph-canvas"
-  },
-  {
-    combo: {
-      key: "b",
-      ctrl: true
-    },
-    commandId: "Comfy.Canvas.ToggleSelectedNodes.Bypass",
-    targetElementId: "graph-canvas"
-  },
-  {
-    combo: {
-      key: "m",
-      ctrl: true
-    },
-    commandId: "Comfy.Canvas.ToggleSelectedNodes.Mute",
-    targetElementId: "graph-canvas"
-  },
-  {
-    combo: {
-      key: "`",
-      ctrl: true
-    },
-    commandId: "Workspace.ToggleBottomPanelTab.logs-terminal"
-  },
-  {
-    combo: {
-      key: "f"
-    },
-    commandId: "Workspace.ToggleFocusMode"
-  }
-];
-const useKeybindingService = /* @__PURE__ */ __name(() => {
-  const keybindingStore = useKeybindingStore();
-  const commandStore = useCommandStore();
-  const settingStore = useSettingStore();
-  const keybindHandler = /* @__PURE__ */ __name(async function(event) {
-    const keyCombo = KeyComboImpl.fromEvent(event);
-    if (keyCombo.isModifier) {
-      return;
-    }
-    const target = event.composedPath()[0];
-    if (keyCombo.isReservedByTextInput && (target.tagName === "TEXTAREA" || target.tagName === "INPUT" || target.tagName === "SPAN" && target.classList.contains("property_value"))) {
-      return;
-    }
-    const keybinding = keybindingStore.getKeybinding(keyCombo);
-    if (keybinding && keybinding.targetElementId !== "graph-canvas") {
-      event.preventDefault();
-      await commandStore.execute(keybinding.commandId);
-      return;
-    }
-    if (event.ctrlKey || event.altKey || event.metaKey) {
-      return;
-    }
-    if (event.key === "Escape") {
-      const modals = document.querySelectorAll(".comfy-modal");
-      for (const modal of modals) {
-        const modalDisplay = window.getComputedStyle(modal).getPropertyValue("display");
-        if (modalDisplay !== "none") {
-          modal.style.display = "none";
-          break;
-        }
-      }
-      for (const d of document.querySelectorAll("dialog")) d.close();
-    }
-  }, "keybindHandler");
-  const registerCoreKeybindings = /* @__PURE__ */ __name(() => {
-    for (const keybinding of CORE_KEYBINDINGS) {
-      keybindingStore.addDefaultKeybinding(new KeybindingImpl(keybinding));
-    }
-  }, "registerCoreKeybindings");
-  function registerUserKeybindings() {
-    const unsetBindings = settingStore.get("Comfy.Keybinding.UnsetBindings");
-    for (const keybinding of unsetBindings) {
-      keybindingStore.unsetKeybinding(new KeybindingImpl(keybinding));
-    }
-    const newBindings = settingStore.get("Comfy.Keybinding.NewBindings");
-    for (const keybinding of newBindings) {
-      keybindingStore.addUserKeybinding(new KeybindingImpl(keybinding));
-    }
-  }
-  __name(registerUserKeybindings, "registerUserKeybindings");
-  async function persistUserKeybindings() {
-    await settingStore.set(
-      "Comfy.Keybinding.NewBindings",
-      Object.values(keybindingStore.getUserKeybindings())
-    );
-    await settingStore.set(
-      "Comfy.Keybinding.UnsetBindings",
-      Object.values(keybindingStore.getUserUnsetKeybindings())
-    );
-  }
-  __name(persistUserKeybindings, "persistUserKeybindings");
-  return {
-    keybindHandler,
-    registerCoreKeybindings,
-    registerUserKeybindings,
-    persistUserKeybindings
-  };
-}, "useKeybindingService");
-export {
-  useKeybindingService as u
-};
-//# sourceMappingURL=keybindingService-DyjX-nxF.js.map

web/assets/serverConfigStore-D2Vr0L0h.js

@@ -1,90 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a1 as defineStore, T as ref, c as computed } from "./index-Bv0b06LE.js";
-const useServerConfigStore = defineStore("serverConfig", () => {
-  const serverConfigById = ref({});
-  const serverConfigs = computed(() => {
-    return Object.values(serverConfigById.value);
-  });
-  const modifiedConfigs = computed(
-    () => {
-      return serverConfigs.value.filter((config) => {
-        return config.initialValue !== config.value;
-      });
-    }
-  );
-  const revertChanges = /* @__PURE__ */ __name(() => {
-    for (const config of modifiedConfigs.value) {
-      config.value = config.initialValue;
-    }
-  }, "revertChanges");
-  const serverConfigsByCategory = computed(() => {
-    return serverConfigs.value.reduce(
-      (acc, config) => {
-        const category = config.category?.[0] ?? "General";
-        acc[category] = acc[category] || [];
-        acc[category].push(config);
-        return acc;
-      },
-      {}
-    );
-  });
-  const serverConfigValues = computed(() => {
-    return Object.fromEntries(
-      serverConfigs.value.map((config) => {
-        return [
-          config.id,
-          config.value === config.defaultValue || config.value === null || config.value === void 0 ? void 0 : config.value
-        ];
-      })
-    );
-  });
-  const launchArgs = computed(() => {
-    const args = Object.assign(
-      {},
-      ...serverConfigs.value.map((config) => {
-        if (config.value === config.defaultValue || config.value === null || config.value === void 0) {
-          return {};
-        }
-        return config.getValue ? config.getValue(config.value) : { [config.id]: config.value };
-      })
-    );
-    return Object.fromEntries(
-      Object.entries(args).map(([key, value]) => {
-        if (value === true) {
-          return [key, ""];
-        }
-        return [key, value.toString()];
-      })
-    );
-  });
-  const commandLineArgs = computed(() => {
-    return Object.entries(launchArgs.value).map(([key, value]) => [`--${key}`, value]).flat().filter((arg) => arg !== "").join(" ");
-  });
-  function loadServerConfig(configs, values) {
-    for (const config of configs) {
-      const value = values[config.id] ?? config.defaultValue;
-      serverConfigById.value[config.id] = {
-        ...config,
-        value,
-        initialValue: value
-      };
-    }
-  }
-  __name(loadServerConfig, "loadServerConfig");
-  return {
-    serverConfigById,
-    serverConfigs,
-    modifiedConfigs,
-    serverConfigsByCategory,
-    serverConfigValues,
-    launchArgs,
-    commandLineArgs,
-    revertChanges,
-    loadServerConfig
-  };
-});
-export {
-  useServerConfigStore as u
-};
-//# sourceMappingURL=serverConfigStore-D2Vr0L0h.js.map

web/assets/uvMirrors-B-HKMf6X.js

@@ -1,16 +0,0 @@
-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/extensions/core/clipspace.js

@@ -1,2 +0,0 @@
-// Shim for extensions/core/clipspace.ts
-export const ClipspaceDialog = window.comfyAPI.clipspace.ClipspaceDialog;

web/extensions/core/groupNode.js

@@ -1,3 +0,0 @@
-// Shim for extensions/core/groupNode.ts
-export const GroupNodeConfig = window.comfyAPI.groupNode.GroupNodeConfig;
-export const GroupNodeHandler = window.comfyAPI.groupNode.GroupNodeHandler;

web/extensions/core/groupNodeManage.js

@@ -1,2 +0,0 @@
-// Shim for extensions/core/groupNodeManage.ts
-export const ManageGroupDialog = window.comfyAPI.groupNodeManage.ManageGroupDialog;

web/extensions/core/maskEditorOld.js

@@ -1,2 +0,0 @@
-// Shim for extensions/core/maskEditorOld.ts
-export const MaskEditorDialogOld = window.comfyAPI.maskEditorOld.MaskEditorDialogOld;

web/extensions/core/widgetInputs.js

@@ -1,5 +0,0 @@
-// Shim for extensions/core/widgetInputs.ts
-export const getWidgetConfig = window.comfyAPI.widgetInputs.getWidgetConfig;
-export const convertToInput = window.comfyAPI.widgetInputs.convertToInput;
-export const setWidgetConfig = window.comfyAPI.widgetInputs.setWidgetConfig;
-export const mergeIfValid = window.comfyAPI.widgetInputs.mergeIfValid;

web/index.html

@@ -1,15 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8">
-    <title>ComfyUI</title>
-    <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-Bv0b06LE.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-CBxvvAzM.css">
-  </head>
-  <body class="litegraph grid">
-    <div id="vue-app"></div>
-  </body>
-</html>

web/scripts/api.js

@@ -1,3 +0,0 @@
-// Shim for scripts/api.ts
-export const ComfyApi = window.comfyAPI.api.ComfyApi;
-export const api = window.comfyAPI.api.api;

web/scripts/app.js

@@ -1,4 +0,0 @@
-// Shim for scripts/app.ts
-export const ANIM_PREVIEW_WIDGET = window.comfyAPI.app.ANIM_PREVIEW_WIDGET;
-export const ComfyApp = window.comfyAPI.app.ComfyApp;
-export const app = window.comfyAPI.app.app;

web/scripts/changeTracker.js

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