From 74eedd3ef1fef6f9a865975abd6f9d260ed59c2c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zeyu=20Huang=20=7C=20=E9=BB=83=E6=BE=A4=E5=AE=87?=
<11222265+fhfuih@users.noreply.github.com>
Date: Wed, 11 Mar 2026 18:22:45 +0800
Subject: [PATCH] [Frontend] ComfyUI video & LoRA support (#1596)
Signed-off-by: Huang, Zeyu <11222265+fhfuih@users.noreply.github.com>
---
apps/ComfyUI-vLLM-Omni/README.md | 17 +-
apps/ComfyUI-vLLM-Omni/__init__.py | 9 +
.../comfyui_vllm_omni/nodes.py | 166 +++++-
.../comfyui_vllm_omni/utils/api_client.py | 88 ++-
.../comfyui_vllm_omni/utils/format.py | 70 +++
.../comfyui_vllm_omni/utils/types.py | 4 +
.../comfyui_vllm_omni/utils/validators.py | 2 +-
.../docs/images/comfyui-video-generation.jpg | Bin 0 -> 132038 bytes
.../vLLM-Omni Video Generation.json | 513 ++++++++++++++++++
docs/features/comfyui.md | 5 +-
tests/comfyui/conftest.py | 7 +
tests/comfyui/test_comfyui_integration.py | 171 +++++-
.../openai_api/test_image_server.py | 1 -
13 files changed, 1038 insertions(+), 15 deletions(-)
create mode 100644 apps/ComfyUI-vLLM-Omni/docs/images/comfyui-video-generation.jpg
create mode 100644 apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Video Generation.json
diff --git a/apps/ComfyUI-vLLM-Omni/README.md b/apps/ComfyUI-vLLM-Omni/README.md
index 23bbe5b72..54f2fdf2e 100644
--- a/apps/ComfyUI-vLLM-Omni/README.md
+++ b/apps/ComfyUI-vLLM-Omni/README.md
@@ -47,6 +47,7 @@ If no, check your shell running the ComfyUI process. There may be some error mes
This extension offers the following nodes based on the output modalities (at **ComfyUI sidebar -> Node Library**):
- **Generate Image** for text-to-image and image-to-image tasks
+- **Generate Video** for text-to-video and image-to-video tasks
- **Multimodality Understanding** for multimodality-to-text and multimodality-to-audio tasks
- **TTS** and **TTS Voice Clone** for TTS tasks
@@ -59,7 +60,7 @@ To build a simple workflow yourself,
- Drag a generation node onto the canvas.
- Depending on your need, grab built-in multimedia file loader nodes, such as **image->Load Image**, **image->video->Load Video**, **audio->Load Audio**
-- Depending on your need, grab built-in multimedia file preview nodes, such as **image->Preview Image**, **image->video->Save Video**, **audio->Preview Audio**. For text output, you can install [ComfyUI-Custom-Scripts plugin](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/) and grab its **utils->Show Text 🐍** node.
+- Depending on your need, grab built-in multimedia file preview nodes, such as **image->Preview Image**, **image->video->Save Video**, **audio->Preview Audio**, **utils->Preview as Text**.
- If you want to tune sampling parameters, grab corresponding nodes from **vLLM-Omni-> Sampling Params**.
- For multi-stage models, you can connect multiple **AR Sampling Params** and **Diffusion Sampling Params** nodes to a **Multi-Stage Sampling Params List** node, and connect this node to the generation node.
- For some multi-stage models like BAGEL, [only one stage's sampling parameters are exposed and tunable via vLLM-Omni's online serving API](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/examples/online_serving/bagel/). Thus, these models are treated as single-stage ones. Please check the vLLM-Omni documentation on how to correctly set each model's sampling parameters.
@@ -104,6 +105,20 @@ You can configure per-stage sampling parameters for multi-stage models.
> [!TIP]
> The node automatically choose text-to-image or image-to-image API endpoints depending on whether you connect an image input or not.
+### Text-to-video and image-to-video generation (e.g., Wan)
+
+(Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni Video Generation**)
+
+
+
+
+
+
+
+> [!TIP]
+> The node automatically choose text-to-video or image-to-video API endpoints depending on whether you connect an image input or not.
+
### TTS (e.g., Qwen TTS series)
(Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni TTS**)
diff --git a/apps/ComfyUI-vLLM-Omni/__init__.py b/apps/ComfyUI-vLLM-Omni/__init__.py
index 641824add..e89b2ca1e 100644
--- a/apps/ComfyUI-vLLM-Omni/__init__.py
+++ b/apps/ComfyUI-vLLM-Omni/__init__.py
@@ -14,17 +14,21 @@ from .comfyui_vllm_omni.nodes import (
VLLMOmniARSampling,
VLLMOmniDiffusionSampling,
VLLMOmniGenerateImage,
+ VLLMOmniGenerateVideo,
VLLMOmniQwenTTSParams,
+ VLLMOmniRemoteLoRA,
VLLMOmniSamplingParamsList,
VLLMOmniTTS,
VLLMOmniUnderstanding,
VLLMOmniVoiceClone,
+ VLLMOmniWanParams,
)
# A dictionary that contains all nodes you want to export with their names
NODE_CLASS_MAPPINGS = {
# === Generation ===
"VLLMOmniGenerateImage": VLLMOmniGenerateImage,
+ "VLLMOmniGenerateVideo": VLLMOmniGenerateVideo,
"VLLMOmniUnderstanding": VLLMOmniUnderstanding,
"VLLMOmniTTS": VLLMOmniTTS,
"VLLMOmniVoiceClone": VLLMOmniVoiceClone,
@@ -32,13 +36,16 @@ NODE_CLASS_MAPPINGS = {
"VLLMOmniARSampling": VLLMOmniARSampling,
"VLLMOmniDiffusionSampling": VLLMOmniDiffusionSampling,
"VLLMOmniSamplingParamsList": VLLMOmniSamplingParamsList,
+ "VLLMOmniRemoteLoRA": VLLMOmniRemoteLoRA,
"VLLMOmniQwenTTSParams": VLLMOmniQwenTTSParams,
+ "VLLMOmniWanParams": VLLMOmniWanParams,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
# === Generation ===
"VLLMOmniGenerateImage": "Generate Image",
+ "VLLMOmniGenerateVideo": "Generate Video",
"VLLMOmniUnderstanding": "Multimodality Understanding",
"VLLMOmniTTS": "TTS (Text to Speech)",
"VLLMOmniVoiceClone": "TTS Voice Cloning",
@@ -46,7 +53,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"VLLMOmniARSampling": "AR Sampling Params",
"VLLMOmniDiffusionSampling": "Diffusion Sampling Params",
"VLLMOmniSamplingParamsList": "Multi-Stage Sampling Params List",
+ "VLLMOmniRemoteLoRA": "LoRA",
"VLLMOmniQwenTTSParams": "Qwen TTS Params",
+ "VLLMOmniWanParams": "Wan Video Params",
}
WEB_DIRECTORY = "./web"
diff --git a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/nodes.py b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/nodes.py
index 5caa3869e..bfea93998 100644
--- a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/nodes.py
+++ b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/nodes.py
@@ -11,6 +11,7 @@ from .utils.types import (
AutoregressionSamplingParams,
DiffusionSamplingParams,
QwenTTSModelSpecificParams,
+ WanModelSpecificParams,
)
from .utils.validators import (
add_sampling_parameters_to_stage,
@@ -56,6 +57,7 @@ class VLLMOmniGenerateImage(_VLLMOmniGenerateBase):
# "video": ("VIDEO",),
# "audio": ("AUDIO",),
"sampling_params": ("SAMPLING_PARAMS",),
+ "lora": ("REMOTE_LORA",),
},
}
@@ -76,9 +78,11 @@ class VLLMOmniGenerateImage(_VLLMOmniGenerateBase):
audio: AudioInput | None = None, # Hidden & unused
video: VideoInput | None = None, # Hidden & unused
sampling_params: dict | list[dict] | None = None,
+ lora: dict | None = None,
**kwargs,
):
- logger.info("Uncaught kwargs: %s", kwargs)
+ if kwargs:
+ logger.info("Uncaught kwargs: %s", kwargs)
logger.debug("Got sampling params: %s", sampling_params)
validate_model_and_sampling_params_types(model, sampling_params)
if image is None and mask is not None:
@@ -105,6 +109,7 @@ class VLLMOmniGenerateImage(_VLLMOmniGenerateBase):
height=height,
negative_prompt=negative_prompt,
sampling_params=sampling_params,
+ lora=lora,
)
return (output,)
elif image is not None and audio is None and video is None:
@@ -119,6 +124,7 @@ class VLLMOmniGenerateImage(_VLLMOmniGenerateBase):
negative_prompt=negative_prompt,
mask=mask,
sampling_params=sampling_params,
+ lora=lora,
)
return (output,)
@@ -136,11 +142,91 @@ class VLLMOmniGenerateImage(_VLLMOmniGenerateBase):
audio=audio,
video=video,
sampling_params=sampling_params,
+ lora=lora,
)
return (output,)
+class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "url": ("STRING", {"default": "http://localhost:8000/v1"}),
+ "model": ("STRING", {"default": "Wan-AI/Wan2.2-T2V-A14B-Diffusers"}),
+ "prompt": ("STRING", {"multiline": True}),
+ "negative_prompt": ("STRING", {"multiline": True, "default": ""}),
+ "width": ("INT", {"default": 832, "min": 1}),
+ "height": ("INT", {"default": 480, "min": 1}),
+ "fps": ("INT", {"default": 16, "min": 1}),
+ "num_frames": ("INT", {"default": 41, "min": 1}),
+ },
+ "optional": {
+ "image": ("IMAGE",),
+ "sampling_params": ("SAMPLING_PARAMS",),
+ "lora": ("REMOTE_LORA",),
+ "model_params": ("VIDEO_PARAMS",),
+ },
+ }
+
+ RETURN_TYPES = ("VIDEO",)
+ RETURN_NAMES = ("video",)
+ FUNCTION = "generate"
+
+ async def generate(
+ self,
+ url: str,
+ model: str,
+ prompt: str,
+ width: int,
+ height: int,
+ fps: int,
+ num_frames: int,
+ negative_prompt: str | None = None,
+ image: torch.Tensor | None = None,
+ sampling_params: dict | list[dict] | None = None,
+ model_params: dict | None = None,
+ lora: dict | None = None,
+ **kwargs,
+ ):
+ if kwargs:
+ logger.info("Uncaught kwargs: %s", kwargs)
+ logger.debug("Got sampling params: %s", sampling_params)
+ logger.debug("Got model params: %s", model_params)
+ validate_model_and_sampling_params_types(model, sampling_params)
+
+ # Currently, all video generation models are single-stage diffusion models
+ if isinstance(sampling_params, list):
+ if len(sampling_params) != 1:
+ raise ValueError(
+ "Video generation expects a single sampling params group. "
+ "Please provide one Diffusion sampling node."
+ )
+ sampling_params = sampling_params[0]
+
+ if sampling_params is not None:
+ sampling_params.pop("type", None) # internal fields
+ if model_params is not None:
+ model_params.pop("type", None) # internal fields
+
+ client = VLLMOmniClient(url)
+ output = await client.generate_video(
+ model=model,
+ prompt=prompt,
+ image=image, # image present => i2v, absent => t2v
+ width=width,
+ height=height,
+ num_frames=num_frames,
+ fps=fps,
+ negative_prompt=negative_prompt,
+ sampling_params=sampling_params,
+ lora=lora,
+ model_params=model_params,
+ )
+ return (output,)
+
+
class VLLMOmniUnderstanding(_VLLMOmniGenerateBase):
@classmethod
def INPUT_TYPES(cls):
@@ -188,7 +274,8 @@ class VLLMOmniUnderstanding(_VLLMOmniGenerateBase):
use_audio_in_video: bool = True,
**kwargs,
) -> tuple[str, AudioInput]:
- logger.info("Uncaught kwargs: %s", kwargs)
+ if kwargs:
+ logger.info("Uncaught kwargs: %s", kwargs)
logger.debug("Got sampling params: %s", sampling_params)
validate_model_and_sampling_params_types(model, sampling_params)
@@ -547,6 +634,52 @@ class VLLMOmniSamplingParamsList:
return (params,)
+class VLLMOmniRemoteLoRA:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "local_path": ("STRING", {"default": ""}),
+ "name": ("STRING", {"default": ""}),
+ "scale": (
+ "FLOAT",
+ {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1},
+ ),
+ "int_id": (
+ "INT",
+ {
+ "default": 0,
+ "min": 0,
+ "step": 1,
+ "tooltip": "0 means it is not set and the server can derive it.",
+ },
+ ),
+ }
+ }
+
+ RETURN_TYPES = ("REMOTE_LORA",)
+ RETURN_NAMES = ("lora",)
+ FUNCTION = "get_lora"
+ CATEGORY = "vLLM-Omni"
+
+ @classmethod
+ def VALIDATE_INPUTS(cls, local_path, name) -> str | Literal[True]:
+ if not local_path.strip() or not name.strip():
+ return "Both local_path and name must be provided."
+ return True
+
+ def get_lora(self, local_path: str, name: str, scale: float, int_id: int):
+ local_path = local_path.strip()
+ name = name.strip()
+ lora = {
+ "local_path": local_path or None,
+ "name": name or None,
+ "scale": float(scale),
+ "int_id": int(int_id) if int_id > 0 else None,
+ }
+ return (lora,)
+
+
class VLLMOmniQwenTTSParams:
@classmethod
def INPUT_TYPES(cls):
@@ -572,3 +705,32 @@ class VLLMOmniQwenTTSParams:
def get_params(self, **kwargs):
return (QwenTTSModelSpecificParams(kwargs),)
+
+
+class VLLMOmniWanParams:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "guidance_scale_2": (
+ "FLOAT",
+ {"default": 4.0, "min": 0.0, "max": 20.0, "step": 0.1},
+ ),
+ "boundary_ratio": (
+ "FLOAT",
+ {"default": 0.875, "min": 0.0, "max": 1.0, "step": 0.001},
+ ),
+ "flow_shift": (
+ "FLOAT",
+ {"default": 5.0, "min": 0.0, "max": 100.0, "step": 0.1},
+ ),
+ }
+ }
+
+ RETURN_TYPES = ("VIDEO_PARAMS",)
+ RETURN_NAMES = ("Wan video params",)
+ FUNCTION = "get_params"
+ CATEGORY = "vLLM-Omni/Video Params"
+
+ def get_params(self, **kwargs):
+ return (WanModelSpecificParams(kwargs),)
diff --git a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/api_client.py b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/api_client.py
index d340073bc..2c936d33f 100644
--- a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/api_client.py
+++ b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/api_client.py
@@ -6,6 +6,7 @@ The image generation part is derived from dougbtv/comfyui-vllm-omni by Doug (@do
Original source at https://github.com/dougbtv/comfyui-vllm-omni, distributed under the MIT License.
"""
+import json
from typing import Any
import aiohttp
@@ -17,6 +18,7 @@ from .format import (
audio_to_base64,
base64_to_audio,
base64_to_image_tensor,
+ base64_to_video,
bytes_to_audio,
image_tensor_to_base64,
image_tensor_to_png_bytes,
@@ -30,7 +32,7 @@ logger = get_logger(__name__)
class VLLMOmniClient:
- def __init__(self, base_url: str, timeout: float = 300.0):
+ def __init__(self, base_url: str, timeout: float | None = None):
self.base_url = base_url
self.timeout = aiohttp.ClientTimeout(total=timeout)
@@ -43,12 +45,13 @@ class VLLMOmniClient:
height: int,
negative_prompt: str | None = None,
sampling_params: dict | None = None,
+ lora: dict | None = None,
) -> torch.Tensor:
"""Run text-to-image generation via DALLE API"""
await self._check_model_exist(model)
size = f"{width}x{height}"
- payload = {
+ payload: dict[str, Any] = {
"model": model,
"prompt": prompt,
"size": size,
@@ -58,6 +61,8 @@ class VLLMOmniClient:
payload["negative_prompt"] = negative_prompt
if sampling_params is not None:
payload.update(sampling_params)
+ if lora is not None:
+ payload["lora"] = lora
logger.debug("img gen payload: %s", payload)
url = self.base_url + "/images/generations"
@@ -110,6 +115,7 @@ class VLLMOmniClient:
negative_prompt: str | None = None,
mask: torch.Tensor | None = None,
sampling_params: dict | None = None,
+ lora: dict | None = None,
) -> torch.Tensor:
"""Run image editing via DALLE API"""
await self._check_model_exist(model)
@@ -131,6 +137,8 @@ class VLLMOmniClient:
if sampling_params is not None:
for k, v in sampling_params.items():
form.add_field(k, str(v))
+ if lora is not None:
+ form.add_field("lora", json.dumps(lora, ensure_ascii=False))
if mask is not None:
mask_filename = "mask.png"
form.add_field(
@@ -183,6 +191,7 @@ class VLLMOmniClient:
audio: AudioInput | None = None,
video: VideoInput | None = None,
sampling_params: dict | list[dict] | None = None,
+ lora: dict | None = None,
) -> torch.Tensor:
payload = VLLMOmniClient._prepare_chat_completion_messages(
model=model,
@@ -193,6 +202,8 @@ class VLLMOmniClient:
video=video,
sampling_params=sampling_params,
modalities=["image"],
+ # === below are additional `extra_body` fields, handled by **kwargs ===
+ lora=lora,
)
choices = await self._generate_base_chat_completion(model, payload)
@@ -206,6 +217,79 @@ class VLLMOmniClient:
return torch.stack(image_tensors, dim=0)
+ async def generate_video(
+ self,
+ *,
+ model: str,
+ prompt: str,
+ width: int,
+ height: int,
+ num_frames: int,
+ fps: int,
+ negative_prompt: str | None = None,
+ image: torch.Tensor | None = None,
+ sampling_params: dict | None = None,
+ model_params: dict | None = None,
+ lora: dict | None = None,
+ **extra_body,
+ ) -> VideoInput:
+ form = aiohttp.FormData()
+ form.add_field("model", model)
+ form.add_field("prompt", prompt)
+ form.add_field("width", str(width))
+ form.add_field("height", str(height))
+ form.add_field("num_frames", str(num_frames))
+ form.add_field("fps", str(fps))
+ if negative_prompt:
+ form.add_field("negative_prompt", negative_prompt)
+ if sampling_params is not None:
+ for k, v in sampling_params.items():
+ form.add_field(k, str(v))
+ if model_params is not None:
+ for k, v in model_params.items():
+ form.add_field(k, str(v))
+ if lora is not None:
+ form.add_field("lora", json.dumps(lora, ensure_ascii=False))
+ if extra_body:
+ form.add_field("extra_body", json.dumps(extra_body, ensure_ascii=False))
+
+ if image is not None:
+ image_filename = "image.png" # Required for multipart form
+ form.add_field(
+ "input_reference",
+ image_tensor_to_png_bytes(image, image_filename),
+ filename=image_filename,
+ content_type="image/png",
+ )
+
+ url = self.base_url + "/videos"
+ async with aiohttp.ClientSession(timeout=self.timeout) as session:
+ try:
+ async with session.post(url, data=form) as response:
+ if not response.ok:
+ error_text = await response.text()
+ raise (ValueError if response.status < 500 else RuntimeError)(
+ f"vLLM-Omni API returned status {response.status}: {error_text}"
+ )
+
+ try:
+ data = await response.json()
+ except aiohttp.ContentTypeError as e:
+ raise RuntimeError(f"Invalid JSON response from vLLM-Omni: {e}")
+ except aiohttp.ClientError as e:
+ raise RuntimeError(f"Network error connecting to vLLM-Omni at {url}: {e}")
+
+ if "data" not in data:
+ raise RuntimeError("API response missing 'data' field - expected OpenAI DALL-E format")
+ if not data["data"]:
+ raise RuntimeError("API returned empty data array")
+ try:
+ base64_str = data["data"][0]["b64_json"]
+ except (KeyError, IndexError):
+ raise RuntimeError("API response missing 'b64_json' field in first data item")
+
+ return base64_to_video(base64_str)
+
async def generate_understanding_chat_completion(
self,
*,
diff --git a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/format.py b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/format.py
index 0ddf86f9c..642c83c8d 100644
--- a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/format.py
+++ b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/format.py
@@ -6,12 +6,17 @@ Original source at https://github.com/dougbtv/comfyui-vllm-omni, distributed und
import base64
import mimetypes
+from fractions import Fraction
from io import BytesIO
import av
import numpy as np
import torch
+from av.audio.frame import AudioFrame
+from av.audio.resampler import AudioResampler
+from av.video.frame import VideoFrame
from comfy_api.input import AudioInput, VideoInput
+from comfy_api.latest import InputImpl, Types
from comfy_extras import nodes_audio
from PIL import Image
@@ -138,6 +143,71 @@ def video_to_base64(video: VideoInput, filename: str = "video.mp4") -> str:
return f"data:{mime_type};base64,{base64_str}"
+def base64_to_video(base64_str: str) -> VideoInput:
+ if base64_str.startswith("data:video"):
+ _, base64_str = base64_str.split(",", 1)
+
+ try:
+ video_bytes = base64.b64decode(base64_str)
+ except Exception as e:
+ raise ValueError(f"Invalid base64 string: {e}")
+
+ video_buffer = BytesIO(video_bytes)
+
+ try:
+ with av.open(video_buffer, mode="r") as container:
+ video_stream = next((s for s in container.streams if s.type == "video"), None)
+ if video_stream is None:
+ raise ValueError("No video stream found in decoded payload.")
+
+ frames = []
+ for frame in container.decode(video_stream):
+ if not isinstance(frame, VideoFrame):
+ continue
+ image = frame.to_ndarray(format="rgb24")
+ frames.append(torch.from_numpy(image).float() / 255.0)
+
+ if len(frames) == 0:
+ raise ValueError("No video frames found in decoded payload.")
+
+ images = torch.stack(frames, dim=0)
+ frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
+
+ audio: AudioInput | None = None
+ if len(container.streams.audio):
+ audio_stream = container.streams.audio[-1]
+ audio_frames = []
+ resampler = AudioResampler(format="fltp")
+ for frame in container.decode(audio_stream):
+ if not isinstance(frame, AudioFrame):
+ continue
+ resampled = resampler.resample(frame)
+ if not isinstance(resampled, list):
+ resampled = [resampled]
+ for audio_frame in resampled:
+ if audio_frame is not None:
+ audio_frames.append(audio_frame.to_ndarray())
+
+ if len(audio_frames) > 0:
+ audio_data = np.concatenate(audio_frames, axis=1)
+ sample_rate = int(audio_stream.sample_rate) if audio_stream.sample_rate else 1
+ audio = {
+ "waveform": torch.from_numpy(audio_data).unsqueeze(0),
+ "sample_rate": sample_rate,
+ }
+
+ components = Types.VideoComponents(
+ images=images,
+ frame_rate=frame_rate,
+ audio=audio,
+ metadata=container.metadata if container.metadata else None,
+ )
+ except Exception as e:
+ raise RuntimeError(f"Failed to decode video: {e}")
+
+ return InputImpl.VideoFromComponents(components)
+
+
def audio_to_bytes(audio: AudioInput, filename: str = "audio.mp3", quality: str = "128k") -> BytesIO:
waveform = audio["waveform"][0] # Shape: (C, T)
sample_rate = audio["sample_rate"]
diff --git a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/types.py b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/types.py
index 6cc79f3ed..c7d254eb9 100644
--- a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/types.py
+++ b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/types.py
@@ -23,6 +23,10 @@ class QwenTTSModelSpecificParams(dict):
pass
+class WanModelSpecificParams(dict):
+ pass
+
+
class ModelMode(Enum):
IMAGE_GENERATION = auto()
VIDEO_GENERATION = auto()
diff --git a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/validators.py b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/validators.py
index 2abdd2100..c5ac4bdf4 100644
--- a/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/validators.py
+++ b/apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/validators.py
@@ -16,7 +16,7 @@ def validate_model_and_sampling_params_types(
# Skip if no spec or no sampling params
pipeline_spec, _ = lookup_model_spec(model_name)
if pipeline_spec is None:
- logger.info("skipping sampling params check because spec is not found")
+ logger.info(f"skipping sampling params check because spec for {model_name} is not found")
return
if sampling_param_list is None:
return
diff --git a/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-video-generation.jpg b/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-video-generation.jpg
new file mode 100644
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