vllm-omni/docs/user_guide/examples/offline_inference/text_to_image.md

Text-To-Image

Source https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/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.

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")

!!! 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.

!!! 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.

Apart from string prompt, vLLM-Omni also supports dictionary prompts in the same style as vLLM. This is useful for models that support negative prompts.

from vllm_omni.entrypoints.omni import Omni

if __name__ == "__main__":
    omni = Omni(model="Qwen/Qwen-Image")
    outputs = omni.generate([
      {
        "prompt": "a cup of coffee on a table"，
        "negative_prompt": "low resolution"
      },
      {
        "prompt": "a toy dinosaur on a sandy beach"，
        "negative_prompt": "cinematic, realistic"
      }
    ])
    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.

Example materials

??? abstract "gradio_demo.py" py --8<-- "examples/offline_inference/text_to_image/gradio_demo.py" ??? abstract "text_to_image.py" py --8<-- "examples/offline_inference/text_to_image/text_to_image.py"