vllm-omni

vLLM-omni is designed to extend vLLM capabilities to support multi-modality model inference and serving, particularly focusing on non-autoregressive structures and non-textual outputs.

🎯 Overview

Traditional vLLM systems are limited to text-based, autoregressive generation. vLLM-omni addresses this limitation by enabling support for:

Multi-modal Models: Text, image, video, audio, and sensor data processing
Non-autoregressive Architectures: Diffusion Transformers (DiT) and other parallel generation models
Heterogeneous Outputs: Beyond traditional text generation to multimodal outputs

🏗️ Architecture

vLLM-omni is built on a modular architecture that extends vLLM's core functionality:

🚀 Key Features

Multi-Engine Support

Autoregressive Engine: Traditional text generation with enhanced KV-caching
Diffusion Engine: Support for DiT models and iterative generation
Hybrid Engine: Combined AR+DiT processing pipelines

Modality Processing

Text: Advanced tokenization and embedding generation
Image: Vision encoder integration (CLIP, etc.)
Audio: Speech processing and audio embedding

📋 Supported Models

AR + Diffusion Transformer (DiT) Models

Qwen-omni (Thinker-Talker-Codec structure)
HunyunaImage 3.0 (Ongoing)
Qwen-Image (Ongoing)

🛠️ Installation

Environment setup

Use Docker to keep consistent basic environment (Optional, Recommended)

docker run --gpus all --ipc=host --network=host -v $source_dir:$container_dir --name $container_name -it nvcr.io/nvidia/pytorch:25.01-py3 bash

Set up basic uv environment

pip install uv
uv venv --python 3.12 --seed
source .venv/bin/activate

Installation of vLLM (for users)

Now we build it based on vLLM v0.11.0. Please install it with command below.

uv pip install vllm==0.11.0 --torch-backend=auto

Installation of vLLM (for developers)

If you want to check or debug with source code of vLLM, install stable release version of vllm with 0.11.0 from source with pre-built wheel file.

git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout v0.11.0

Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set VLLM_PRECOMPILED_WHEEL_LOCATION as your local absolute path of whl file.

export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.11.0/vllm-0.11.0-cp38-abi3-manylinux1_x86_64.whl

Install vllm with command below (If you have no existing PyTorch).

uv pip install --editable .

Install vllm with command below (If you already have PyTorch).

python use_existing_torch.py
uv pip install -r requirements/build.txt
uv pip install --no-build-isolation --editable .

Verification for successful installation of vLLM

Just run the command below. If no error, it demonstrates that the installation is successfull.

python -c "import vllm._C"

Installation of vLLM-omni

Install additional requirements for vllm-omni

git clone https://github.com/vllm-project/vllm-omni.git
cd vllm_omni
uv pip install -r requirements.txt

Run examples (Qwen2.5-omni)

Get into the example folder

cd examples/offline_inference/qwen_2_5_omni

Modify PYTHONPATH in run.sh as your path of vllm_omni. Then run.

bash run.sh

The output audio is saved in ./output_audio

Further details

For detailed architecture design, see vllm_omni_design.md and high_level_arch_design.md.

README.md

vLLM-omni: Multi-modal Extension for vLLM