vllm-project--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==0.25.0 --torch-backend=auto

# On ROCm
uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723

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.

!!! note It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni.

If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.25.0` and vLLM Omni `0.25.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.

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.images
    images[0].save("coffee.png")

You can pass a list of prompts and wait for the independent requests to finish, as shown below.

!!! info

For diffusion pipelines, each prompt becomes a separate logical request.
The runtime may automatically batch compatible in-flight requests through
the scheduler and runner.

from vllm_omni.entrypoints.omni import Omni

if __name__ == "__main__":
    omni = Omni(
        model="Tongyi-MAI/Z-Image-Turbo",
        # stage_configs_path="./stage-config.yaml",  # See below
    )
    prompts = [
        "a cup of coffee on a table",
        "a toy dinosaur on a sandy beach",
        "a fox waking up in bed and yawning",
    ]
    omni_outputs = omni.generate(prompts)
    for i_prompt, prompt_output in enumerate(omni_outputs):
        this_request_output = prompt_output.request_output
        this_images = this_request_output.images
        for i_image, image in enumerate(this_images):
            image.save(f"p{i_prompt}-img{i_image}.jpg")
            print("saved to", f"p{i_prompt}-img{i_image}.jpg")
            # saved to p0-img0.jpg
            # saved to p1-img0.jpg
            # saved to p2-img0.jpg

!!! info

For diffusion request-level batching controls such as `max_num_seqs` and
`request_batch_max_wait_ms`, see
[Request-Level Batching](../user_guide/diffusion/request_batching.md).

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.