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:

```bash
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](installation/README.md).

## Offline Inference

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

```python
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")
```

You can pass a list of prompts and wait for them to process altogether, shown below.

!!! info

    However, it is not currently recommended to do so
    because not all models support batch inference,
    and batch requesting mostly does not provide significant performance improvement (despite the impression that it does).
    This feature is primarily for the sake of interface compatibility with vLLM and to allow for future improvements.

```python
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[0]
        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 pipelines, the stage config field `stage_args.[].runtime.max_batch_size` is 1 by default, and the input
    list is sliced into single-item requests before feeding into the diffusion pipeline. For models that do internally support
    batched inputs, you can [modify this configuration](../configuration/stage_configs.md) to let the model accept a longer batch of prompts.

For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)

## Online Serving with OpenAI-Completions API

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

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

```bash
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](../user_guide/examples/online_serving/text_to_image.md).