80 lines
4.4 KiB
Markdown
80 lines
4.4 KiB
Markdown
<p align="center">
|
||
<picture>
|
||
<source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/logos/vllm-omni-logo.png">
|
||
<img alt="vllm-omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/logos/vllm-omni-logo.png" width=55%>
|
||
</picture>
|
||
</p>
|
||
<h3 align="center">
|
||
Easy, fast, and cheap omni-modality model serving for everyone
|
||
</h3>
|
||
|
||
<p align="center">
|
||
| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> | <a href="docs/assets/WeChat.jpg"><b>WeChat</b></a> |
|
||
</p>
|
||
|
||
---
|
||
|
||
*Latest News* 🔥
|
||
- [2026/02] We released [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0) - This is the first **stable release** of vLLM-Omni that expands Omni’s diffusion / image-video generation and audio / TTS stack, improves distributed execution and memory efficiency, and broadens platform/backend coverage (GPU/ROCm/NPU/XPU). It also brings meaningful upgrades to serving APIs, profiling & benchmarking, and overall stability.
|
||
- [2026/01] We released [0.12.0rc1](https://github.com/vllm-project/vllm-omni/releases/tag/v0.12.0rc1) - a major RC milestone focused on maturing the diffusion stack, strengthening OpenAI-compatible serving, expanding omni-model coverage, and improving stability across platforms (GPU/NPU/ROCm), please check our latest [design](https://docs.google.com/presentation/d/1qv4qMW1rKAqDREMXiUDLIgqqHQe7TDPj/edit?usp=sharing&ouid=110473603432222024453&rtpof=true&sd=true).
|
||
- [2025/11] vLLM community officially released [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni) in order to support omni-modality models serving.
|
||
|
||
---
|
||
|
||
## About
|
||
|
||
[vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
|
||
|
||
- **Omni-modality**: Text, image, video, and audio data processing
|
||
- **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
|
||
- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
|
||
|
||
<p align="center">
|
||
<picture>
|
||
<img alt="vllm-omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/omni-modality-model-architecture.png" width=55%>
|
||
</picture>
|
||
</p>
|
||
|
||
vLLM-Omni is fast with:
|
||
|
||
- State-of-the-art AR support by leveraging efficient KV cache management from vLLM
|
||
- Pipelined stage execution overlapping for high throughput performance
|
||
- Fully disaggregation based on OmniConnector and dynamic resource allocation across stages
|
||
|
||
vLLM-Omni is flexible and easy to use with:
|
||
|
||
- Heterogeneous pipeline abstraction to manage complex model workflows
|
||
- Seamless integration with popular Hugging Face models
|
||
- Tensor, pipeline, data and expert parallelism support for distributed inference
|
||
- Streaming outputs
|
||
- OpenAI-compatible API server
|
||
|
||
vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
|
||
|
||
- Omni-modality models (e.g. Qwen-Omni)
|
||
- Multi-modality generation models (e.g. Qwen-Image)
|
||
|
||
## Getting Started
|
||
|
||
Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn more.
|
||
|
||
- [Installation](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/)
|
||
- [Quickstart](https://vllm-omni.readthedocs.io/en/latest/getting_started/quickstart/)
|
||
- [List of Supported Models](https://vllm-omni.readthedocs.io/en/latest/models/supported_models/)
|
||
|
||
## Contributing
|
||
|
||
We welcome and value any contributions and collaborations.
|
||
Please check out [Contributing to vLLM-Omni](https://vllm-omni.readthedocs.io/en/latest/contributing/) for how to get involved.
|
||
|
||
## Join the Community
|
||
Feel free to ask questions, provide feedbacks and discuss with fellow users of vLLM-Omni in `#sig-omni` slack channel at [slack.vllm.ai](https://slack.vllm.ai) or vLLM user forum at [discuss.vllm.ai](https://discuss.vllm.ai).
|
||
|
||
## Star History
|
||
|
||
[](https://www.star-history.com/#vllm-project/vllm-omni&type=date&legend=top-left)
|
||
|
||
## License
|
||
|
||
Apache License 2.0, as found in the [LICENSE](./LICENSE) file.
|