vllm-omni/examples/offline_inference/text_to_image/README.md

# Text-To-Image

This folder provides several entrypoints for experimenting with `Qwen/Qwen-Image` `Tongyi-MAI/Z-Image-Turbo` using vLLM-Omni:

- `text_to_image.py`: command-line script for single image generation with advanced options.
- `web_demo.py`: lightweight Gradio UI for interactive prompt/seed/CFG exploration.

## Basic Usage

```python
from vllm_omni.entrypoints.omni import Omni

if __name__ == "__main__":
    omni = Omni(model="Qwen/Qwen-Image")
    prompt = "a cup of coffee on the table"
    images = omni.generate(prompt)
    images[0].save("coffee.png")
```

## Local CLI Usage

```bash
python text_to_image.py \
  --model Tongyi-MAI/Z-Image-Turbo \
  --prompt "a cup of coffee on the table" \
  --seed 42 \
  --cfg_scale 4.0 \
  --num_images_per_prompt 1 \
  --num_inference_steps 50 \
  --height 1024 \
  --width 1024 \
  --output outputs/coffee.png
```

Key arguments:

- `--prompt`: text description (string).
- `--seed`: integer seed for deterministic sampling.
- `--cfg_scale`: true CFG scale (model-specific guidance strength).
- `--num_images_per_prompt`: number of images to generate per prompt (saves as `output`, `output_1`, ...).
- `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
- `--height/--width`: output resolution (defaults 1024x1024).
- `--output`: path to save the generated PNG.

> ℹ️ Qwen-Image currently publishes best-effort presets at `1328x1328`, `1664x928`, `928x1664`, `1472x1140`, `1140x1472`, `1584x1056`, and `1056x1584`. Adjust `--height/--width` accordingly for the most reliable outcomes.

## Web UI Demo

Launch the gradio demo:

```bash
python gradio_demo.py --port 7862
```

Then open `http://localhost:7862/` on your local browser to interact with the web UI.