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

Text-To-Image

This folder provides several entrypoints for experimenting with Qwen/Qwen-Image Qwen/Qwen-Image-2512 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.

Note that when you pass in multiple independent prompts, they will be processed sequentially. Batching requests is currently not supported.

Basic Usage

from vllm_omni.entrypoints.omni import Omni

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

Or put more than one prompt in a request, processing them sequentially.

from vllm_omni.entrypoints.omni import Omni

if __name__ == "__main__":
    omni = Omni(model="Qwen/Qwen-Image")
    prompts = [
      "a cup of coffee on a table",
      "a toy dinosaur on a sandy beach",
      "a fox waking up in bed and yawning",
    ]
    outputs = omni.generate(prompts)
    for i, output in enumerate(outputs):
      image = output.request_output[0].images[0].save(f"{i}.jpg")

Local CLI Usage

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:

python gradio_demo.py --port 7862

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