diff --git a/README.md b/README.md index e300b6b8d..e7fc6912e 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,7 @@ Easy, fast, and cheap omni-modality model serving for everyone --- *Latest News* 🔥 +- [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support. - [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni). - [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends. - [2026/03] We released [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments. @@ -31,9 +32,9 @@ Easy, fast, and cheap omni-modality model serving for everyone [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 +- **Omni-modality**: Text, image, audio, video, and action 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 +- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs

@@ -57,9 +58,10 @@ vLLM-Omni is flexible and easy to use with: vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including: -- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos, HunyuanImage, BAGEL) +- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL) - **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3) - **Diffusion models** — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX) +- **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy) ## Getting Started diff --git a/docs/README.md b/docs/README.md index 66fc8ef46..f6263483b 100644 --- a/docs/README.md +++ b/docs/README.md @@ -28,9 +28,9 @@ Easy, fast, and cheap omni-modality model serving for everyone [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 +- **Omni-modality**: Text, image, audio, video, and action 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 +- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs

@@ -55,8 +55,10 @@ vLLM-Omni is flexible and easy to use with: vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including: -- Omni-modality models (e.g. Qwen2.5-Omni, Qwen3-Omni) -- Multi-modality generation models (e.g. Qwen-Image) +- Omni-modality models (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL) +- TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3) +- Diffusion models — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX) +- Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy) For more information, checkout the following: diff --git a/docs/design/architecture_overview.md b/docs/design/architecture_overview.md index 1c38ba671..20c99fd9c 100644 --- a/docs/design/architecture_overview.md +++ b/docs/design/architecture_overview.md @@ -13,7 +13,7 @@ This document outlines the architectural design for vLLM-Omni. The primary goal of the vLLM-Omni project is to build the fastest and easiest-to-use open-source Omni-Modality model inference & serving engine. vLLM-Omni extends the original vLLM, which was created to support large language models for text-based autoregressive (AR) generation tasks. vLLM-Omni is designed to support: -* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, and video, alongside text. +* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, video, and action trajectories, alongside text. * **Non-Autoregressive Structure:** Support model structure beyond autoregressive, especially Diffusion Transformer (DiT), which is widely used in visual and audio generation. * **Integration with vLLM Core:** Maintain compatibility and leverage existing vLLM key modules and optimizations where applicable. * **Extensibility:** Design a modular and flexible architecture that can easily accommodate new modalities, model architectures, and output formats. diff --git a/docs/source/architecture/omni-modality-model-architecture.png b/docs/source/architecture/omni-modality-model-architecture.png index 53978b323..7bbd5b9b3 100644 Binary files a/docs/source/architecture/omni-modality-model-architecture.png and b/docs/source/architecture/omni-modality-model-architecture.png differ