Merge branch 'master' into image-histogram
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This commit is contained in:
guill 2026-04-06 12:56:13 -07:00 committed by GitHub
commit ffec484ca6
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8 changed files with 1119 additions and 174 deletions

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@ -20,29 +20,12 @@ jobs:
git_tag: ${{ inputs.git_tag }}
cache_tag: "cu130"
python_minor: "13"
python_patch: "11"
python_patch: "12"
rel_name: "nvidia"
rel_extra_name: ""
test_release: true
secrets: inherit
release_nvidia_cu128:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release NVIDIA cu128"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "cu128"
python_minor: "12"
python_patch: "10"
rel_name: "nvidia"
rel_extra_name: "_cu128"
test_release: true
secrets: inherit
release_nvidia_cu126:
permissions:
contents: "write"
@ -76,3 +59,20 @@ jobs:
rel_extra_name: ""
test_release: false
secrets: inherit
release_xpu:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release Intel XPU"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "xpu"
python_minor: "13"
python_patch: "12"
rel_name: "intel"
rel_extra_name: ""
test_release: true
secrets: inherit

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@ -61,6 +61,7 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo
## Features
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
- NOTE: There are many more models supported than the list below, if you want to see what is supported see our templates list inside ComfyUI.
- Image Models
- SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
- [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
@ -136,7 +137,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
- Builds a new release using the latest stable core version
3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
- Weekly frontend updates are merged into the core repository
- Every 2+ weeks frontend updates are merged into the core repository
- Features are frozen for the upcoming core release
- Development continues for the next release cycle
@ -275,7 +276,7 @@ Nvidia users should install stable pytorch using this command:
This is the command to install pytorch nightly instead which might have performance improvements.
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu132```
#### Troubleshooting

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@ -3,12 +3,9 @@ from ..diffusionmodules.openaimodel import Timestep
import torch
class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
def __init__(self, *args, timestep_dim=256, **kwargs):
super().__init__(*args, **kwargs)
if clip_stats_path is None:
clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
else:
clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
self.register_buffer("data_std", clip_std[None, :], persistent=False)
self.time_embed = Timestep(timestep_dim)

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@ -1745,6 +1745,8 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable
temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
if len(temp_sd) > 0:
sd = temp_sd
if custom_operations is None:
sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
parameters = comfy.utils.calculate_parameters(sd)
weight_dtype = comfy.utils.weight_dtype(sd)

226
comfy_api_nodes/apis/wan.py Normal file
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@ -0,0 +1,226 @@
from pydantic import BaseModel, Field
class Text2ImageInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: str | None = Field(None)
class Image2ImageInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: str | None = Field(None)
images: list[str] = Field(..., min_length=1, max_length=2)
class Text2VideoInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: str | None = Field(None)
audio_url: str | None = Field(None)
class Image2VideoInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: str | None = Field(None)
img_url: str = Field(...)
audio_url: str | None = Field(None)
class Reference2VideoInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: str | None = Field(None)
reference_video_urls: list[str] = Field(...)
class Txt2ImageParametersField(BaseModel):
size: str = Field(...)
n: int = Field(1, description="Number of images to generate.") # we support only value=1
seed: int = Field(..., ge=0, le=2147483647)
prompt_extend: bool = Field(True)
watermark: bool = Field(False)
class Image2ImageParametersField(BaseModel):
size: str | None = Field(None)
n: int = Field(1, description="Number of images to generate.") # we support only value=1
seed: int = Field(..., ge=0, le=2147483647)
watermark: bool = Field(False)
class Text2VideoParametersField(BaseModel):
size: str = Field(...)
seed: int = Field(..., ge=0, le=2147483647)
duration: int = Field(5, ge=5, le=15)
prompt_extend: bool = Field(True)
watermark: bool = Field(False)
audio: bool = Field(False, description="Whether to generate audio automatically.")
shot_type: str = Field("single")
class Image2VideoParametersField(BaseModel):
resolution: str = Field(...)
seed: int = Field(..., ge=0, le=2147483647)
duration: int = Field(5, ge=5, le=15)
prompt_extend: bool = Field(True)
watermark: bool = Field(False)
audio: bool = Field(False, description="Whether to generate audio automatically.")
shot_type: str = Field("single")
class Reference2VideoParametersField(BaseModel):
size: str = Field(...)
duration: int = Field(5, ge=5, le=15)
shot_type: str = Field("single")
seed: int = Field(..., ge=0, le=2147483647)
watermark: bool = Field(False)
class Text2ImageTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Text2ImageInputField = Field(...)
parameters: Txt2ImageParametersField = Field(...)
class Image2ImageTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Image2ImageInputField = Field(...)
parameters: Image2ImageParametersField = Field(...)
class Text2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Text2VideoInputField = Field(...)
parameters: Text2VideoParametersField = Field(...)
class Image2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Image2VideoInputField = Field(...)
parameters: Image2VideoParametersField = Field(...)
class Reference2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Reference2VideoInputField = Field(...)
parameters: Reference2VideoParametersField = Field(...)
class Wan27MediaItem(BaseModel):
type: str = Field(...)
url: str = Field(...)
class Wan27ReferenceVideoInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: str | None = Field(None)
media: list[Wan27MediaItem] = Field(...)
class Wan27ReferenceVideoParametersField(BaseModel):
resolution: str = Field(...)
ratio: str | None = Field(None)
duration: int = Field(5, ge=2, le=10)
watermark: bool = Field(False)
seed: int = Field(..., ge=0, le=2147483647)
class Wan27ReferenceVideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Wan27ReferenceVideoInputField = Field(...)
parameters: Wan27ReferenceVideoParametersField = Field(...)
class Wan27ImageToVideoInputField(BaseModel):
prompt: str | None = Field(None)
negative_prompt: str | None = Field(None)
media: list[Wan27MediaItem] = Field(...)
class Wan27ImageToVideoParametersField(BaseModel):
resolution: str = Field(...)
duration: int = Field(5, ge=2, le=15)
prompt_extend: bool = Field(True)
watermark: bool = Field(False)
seed: int = Field(..., ge=0, le=2147483647)
class Wan27ImageToVideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Wan27ImageToVideoInputField = Field(...)
parameters: Wan27ImageToVideoParametersField = Field(...)
class Wan27VideoEditInputField(BaseModel):
prompt: str = Field(...)
media: list[Wan27MediaItem] = Field(...)
class Wan27VideoEditParametersField(BaseModel):
resolution: str = Field(...)
ratio: str | None = Field(None)
duration: int = Field(0)
audio_setting: str = Field("auto")
watermark: bool = Field(False)
seed: int = Field(..., ge=0, le=2147483647)
class Wan27VideoEditTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Wan27VideoEditInputField = Field(...)
parameters: Wan27VideoEditParametersField = Field(...)
class Wan27Text2VideoParametersField(BaseModel):
resolution: str = Field(...)
ratio: str | None = Field(None)
duration: int = Field(5, ge=2, le=15)
prompt_extend: bool = Field(True)
watermark: bool = Field(False)
seed: int = Field(..., ge=0, le=2147483647)
class Wan27Text2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Text2VideoInputField = Field(...)
parameters: Wan27Text2VideoParametersField = Field(...)
class TaskCreationOutputField(BaseModel):
task_id: str = Field(...)
task_status: str = Field(...)
class TaskCreationResponse(BaseModel):
output: TaskCreationOutputField | None = Field(None)
request_id: str = Field(...)
code: str | None = Field(None, description="Error code for the failed request.")
message: str | None = Field(None, description="Details about the failed request.")
class TaskResult(BaseModel):
url: str | None = Field(None)
code: str | None = Field(None)
message: str | None = Field(None)
class ImageTaskStatusOutputField(TaskCreationOutputField):
task_id: str = Field(...)
task_status: str = Field(...)
results: list[TaskResult] | None = Field(None)
class VideoTaskStatusOutputField(TaskCreationOutputField):
task_id: str = Field(...)
task_status: str = Field(...)
video_url: str | None = Field(None)
code: str | None = Field(None)
message: str | None = Field(None)
class ImageTaskStatusResponse(BaseModel):
output: ImageTaskStatusOutputField | None = Field(None)
request_id: str = Field(...)
class VideoTaskStatusResponse(BaseModel):
output: VideoTaskStatusOutputField | None = Field(None)
request_id: str = Field(...)

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@ -1,5 +1,5 @@
comfyui-frontend-package==1.42.8
comfyui-workflow-templates==0.9.39
comfyui-workflow-templates==0.9.44
comfyui-embedded-docs==0.4.3
torch
torchsde

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@ -146,6 +146,10 @@ def is_loopback(host):
def create_origin_only_middleware():
@web.middleware
async def origin_only_middleware(request: web.Request, handler):
if 'Sec-Fetch-Site' in request.headers:
sec_fetch_site = request.headers['Sec-Fetch-Site']
if sec_fetch_site == 'cross-site':
return web.Response(status=403)
#this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason.
#in that case the Host and Origin hostnames won't match
#I know the proper fix would be to add a cookie but this should take care of the problem in the meantime