79 lines
4.0 KiB
Markdown
79 lines
4.0 KiB
Markdown
<p align="center">
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<picture>
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<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">
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<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%>
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</picture>
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</p>
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<h3 align="center">
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Easy, fast, and cheap omni-modality model serving for everyone
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</h3>
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<p align="center">
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| <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> |
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</p>
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---
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*Latest News* 🔥
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- [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).
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- [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.
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---
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## About
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[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:
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- **Omni-modality**: Text, image, video, and audio data processing
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- **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
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- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
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<p align="center">
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<picture>
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<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%>
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</picture>
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</p>
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vLLM-Omni is fast with:
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- State-of-the-art AR support by leveraging efficient KV cache management from vLLM
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- Pipelined stage execution overlapping for high throughput performance
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- Fully disaggregation based on OmniConnector and dynamic resource allocation across stages
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vLLM-Omni is flexible and easy to use with:
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- Heterogeneous pipeline abstraction to manage complex model workflows
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- Seamless integration with popular Hugging Face models
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- Tensor, pipeline, data and expert parallelism support for distributed inference
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- Streaming outputs
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- OpenAI-compatible API server
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vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
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- Omni-modality models (e.g. Qwen-Omni)
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- Multi-modality generation models (e.g. Qwen-Image)
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## Getting Started
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Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn more.
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- [Installation](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/)
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- [Quickstart](https://vllm-omni.readthedocs.io/en/latest/getting_started/quickstart/)
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- [List of Supported Models](https://vllm-omni.readthedocs.io/en/latest/models/supported_models/)
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## Contributing
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We welcome and value any contributions and collaborations.
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Please check out [Contributing to vLLM-Omni](https://vllm-omni.readthedocs.io/en/latest/contributing/) for how to get involved.
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## Join the Community
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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).
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## Star History
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[](https://www.star-history.com/#vllm-project/vllm-omni&type=date&legend=top-left)
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## License
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Apache License 2.0, as found in the [LICENSE](./LICENSE) file.
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