vllm-omni/recipes/Tongyi-MAI/Z-Image-Turbo.md

Z-Image Turbo

Few-step text-to-image with Tongyi-MAI/Z-Image-Turbo

Summary

• Vendor: Tongyi-MAI
• Model: Tongyi-MAI/Z-Image-Turbo
• Task: Text-to-image
• Mode: Offline inference
• Maintainer: Community

When to use this recipe

Use this recipe to generate 1024x1024 images with the distilled Z-Image Turbo checkpoint through the shared offline text-to-image example.

References

• Model card: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
• Shared runnable example: examples/offline_inference/text_to_image/text_to_image.py
• CPU offload guide: docs/user_guide/diffusion/cpu_offload.md

Hardware Support

This recipe documents one validated Intel XPU configuration. Extend it with more hardware sections as community validation lands.

XPU

1x Intel Arc Pro B70 (32 GB)

BF16 weights with model-level CPU offload and VAE tiling/slicing, 25 steps.

Environment

• OS: Linux
• Python: 3.10+
• torch: 2.13.0+xpu
• vLLM: 0.29.0 (98dff2a8)
• vLLM-Omni: main at 4c7a98c2

Command

python examples/offline_inference/text_to_image/text_to_image.py \
  --model Tongyi-MAI/Z-Image-Turbo \
  --prompt "a cup of coffee on the table" \
  --num-inference-steps 25 \
  --enable-cpu-offload \
  --vae-use-tiling \
  --vae-use-slicing \
  --enforce-eager \
  --output z_image_turbo_output.png

Verification

Confirm z_image_turbo_output.png is written as a 1024x1024 PNG matching the prompt.

Notes

• Memory usage: peak 15.6 GiB, about 58 s per image.
• Known limitations: only offline generation was qualified. Online serving is out of scope for this profile.