vllm-omni/docs/getting_started/quickstart.md

Quickstart

This guide will help you quickly get started with vLLM-Omni to perform:

• Offline batched inference
• Online serving using OpenAI-compatible server

Prerequisites

• OS: Linux
• Python: 3.12

Installation

For installation on GPU from source:

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

# On CUDA
uv pip install vllm==0.14.0 --torch-backend=auto

# On ROCm
uv pip install vllm==0.14.0 --extra-index-url https://wheels.vllm.ai/rocm/0.14.0/rocm700

git clone https://github.com/vllm-project/vllm-omni.git
cd vllm-omni
uv pip install -e .

For additional installation methods — please see the installation guide.

Offline Inference

Text-to-image generation quickstart with vLLM-Omni:

from vllm_omni.entrypoints.omni import Omni

if __name__ == "__main__":
    omni = Omni(model="Tongyi-MAI/Z-Image-Turbo")
    prompt = "a cup of coffee on the table"
    outputs = omni.generate(prompt)
    images = outputs[0].request_output[0].images
    images[0].save("coffee.png")

For more usages, please refer to offline inference

Online Serving with OpenAI-Completions API

Text-to-image generation quickstart with vLLM-Omni:

vllm serve Tongyi-MAI/Z-Image-Turbo --omni --port 8091

curl -s http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "a cup of coffee on the table"}
    ],
    "extra_body": {
      "height": 1024,
      "width": 1024,
      "num_inference_steps": 50,
      "guidance_scale": 4.0,
      "seed": 42
    }
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2 | base64 -d > coffee.png

For more details, please refer to online serving.