- [vLLM-Omni installed](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/) on either the same device or another device discoverable via the internet.
- No need to install additional packages apart from those already required by ComfyUI.
> [!TIP]
> If you run both ComfyUI and vLLM-Omni on the same device, you can create separate virtual environments and use different Python versions for them.
## Installation
Copy this folder to the `custom_nodes` subfolder of your ComfyUI installation. Your directory should look like `ComfyUI/custom_nodes/ComfyUI-vLLM-Omni`.
If you are running ComfyUI during copying, you should restart ComfyUI to load this extension.
> You can use utility websites such as <https://download-directory.github.io/> to download a subdirectory of a repo. Also checkout community discussions (e.g., <https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repository>) for more info.
Check **ComfyUI's sidebar -> Node Library**. There should be a new folder named **vLLM-Omni**.
If no, check your shell running the ComfyUI process. There may be some error messages before the line `Import times for custom nodes:` and the line `To see the GUI go to: http://127.0.0.1:8188`.
## Quickstart
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
Every node carries the vLLM-Omni mark in its title bar and is tinted by what it outputs, so a graph is readable at a glance:
| Colour | Nodes | What they produce |
| --- | --- | --- |
| Blue | Generate Image, Generate Video, Multimodality Understanding, TTS, TTS Voice Clone | A generated image, video, audio, or text. These are the only nodes that reach a server. |
| Amber | AR / Diffusion / Multi-Stage Sampling Params | Sampling parameters that apply to any model |
| Purple | Qwen TTS Params, Wan Video Params, MiniMax-H3 Video Params | Parameters that only one model family accepts |
| Red | LoRA, FastH3 Deployment | Which weights the server is expected to have loaded |
| Teal | Video References | Reference media |
Recolouring a node by hand (right click -> Colors) overrides its tint, and the choice is kept.
**Generate Video** takes a clip length in seconds (`duration`), not a frame count. Frames stay the wire unit and are derived with the node's `fps`, so the length is always measured against the rate that is actually served; models that accept only certain frame counts still round to their own lattice server-side. Graphs saved before this widget existed stored `num_frames` in its place and are converted on load, using the fps recorded alongside it -- the browser console names every node it rewrites.
- 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**, **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.
- For multi-stage models where all stages are either autoregression or diffusion, you can also connect only a single Sampling Params node, indicating that this set of sampling parameters will be used for all stages.
> Although this node enables all-modality input, you should check whether the specific model you host and request for supports the modalities you connect to the node.
You can configure per-stage sampling parameters for multi-stage models.
The **vLLM-Omni MiniMax H3 Video Upscale** template generates video remotely, upscales it with SeedVR2, and saves the original and upscaled videos with the generated audio and FPS. See [workflow setup](docs/wf07-h3-upscale.md).
FastH3 is fused into MiniMax-H3 when the vLLM-Omni server starts; it is not a request-switchable LoRA. Download one adapter variant and start a non-offloaded FL2VA server. For the Dense / Data-Free profile:
For the VSA / Data-Free profile, download `vsa-datafree/adapter_model.safetensors`, use that file as `--lora-path`, and add:
```bash
--diffusion-attention-backend FASTVIDEO_VSA \
--fastvideo-vsa-topk 64
```
The VSA profile also requires a compatible `fastvideo-kernel` installation. See the [MiniMax-H3 FastH3 recipe](../../recipes/MiniMaxAI/MiniMax-H3.md#fasth3-adapter) for the full serving contract, supported parallel layouts, and kernel requirements.
Open the **vLLM-Omni FastH3 Text to Video** template, then:
- Set the server URL and served model name on **FastH3 Deployment**.
- Connect its output to **Generate Video → fast_h3**. When connected, the deployment node's URL and model take precedence over the corresponding Generate Video widgets.
- Keep `frame`, `references`, **LoRA**, and **MiniMax-H3 Video Params** disconnected. FastH3 Preview v1 supports T2VA only, is already fused, and owns both flow shifts.
- A connected **Diffusion Sampling Params** node may set seed and other ordinary sampling options. The integration always enforces four denoising steps and 24 FPS for FastH3.
The node records which server the workflow targets; it does not start one, nor switch adapters or attention backends on a running server.
Node tints and the title-bar mark are applied in `web/main.js`, keyed off each node's declared output types rather than a list of node names. A new node that returns an existing type is themed with no front-end change; a new output type needs one entry in `FAMILY_BY_OUTPUT` there.
- Single server mode only. No automatic load balancing or failover.
- Features set is bounded to vLLM-Omni's online service capability, including
- The types of models supported in online mode,
- The types of sampling parameters supported in the online mode,
- The ways to send files (primarily through full-length base64 in JSON payload),
- Figuring out errors in the payload (such as unsupported fields by a specific model) if the endpoint does not explicitly return an error,
- (The lack of) Authentication
- (The lack of) Progress indicator
## Support
If you are new to ComfyUI, please check out [its documentation](https://docs.comfy.org/) for usage instructions.
If you are new to vLLM-Omni, please also check out [its documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/) for usage instructions.
Whenever you find an issue or problem, please
- First find out if this is an upstream limitation of vLLM-Omni's online serving mode, by [checking their documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/examples/).
- [Open an issue](https://github.com/vllm-project/vllm-omni/issues) that clearly describes this ComfyUI or online service problem.
- <https://github.com/dougbtv/comfyui-vllm-omni/> The official reference implementation for ComfyUI integration with vLLM-Omni's DALL-E compatible image generation API.
