68 lines
1.7 KiB
Markdown
68 lines
1.7 KiB
Markdown
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# Z-Image Turbo
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> Few-step text-to-image with `Tongyi-MAI/Z-Image-Turbo`
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## Summary
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- Vendor: Tongyi-MAI
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- Model: `Tongyi-MAI/Z-Image-Turbo`
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- Task: Text-to-image
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- Mode: Offline inference
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- Maintainer: Community
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## When to use this recipe
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Use this recipe to generate 1024x1024 images with the distilled Z-Image Turbo
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checkpoint through the shared offline text-to-image example.
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## References
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- Model card: <https://huggingface.co/Tongyi-MAI/Z-Image-Turbo>
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- Shared runnable example:
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[`examples/offline_inference/text_to_image/text_to_image.py`](../../examples/offline_inference/text_to_image/text_to_image.py)
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- CPU offload guide:
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[`docs/user_guide/diffusion/cpu_offload.md`](../../docs/user_guide/diffusion/cpu_offload.md)
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## Hardware Support
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This recipe documents one validated Intel XPU configuration. Extend it with
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more hardware sections as community validation lands.
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## XPU
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### 1x Intel Arc Pro B70 (32 GB)
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BF16 weights with model-level CPU offload and VAE tiling/slicing, 25 steps.
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#### Environment
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- OS: Linux
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- Python: 3.10+
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- torch: 2.13.0+xpu
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- vLLM: 0.29.0 (`98dff2a8`)
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- vLLM-Omni: `main` at `4c7a98c2`
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#### Command
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```bash
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python examples/offline_inference/text_to_image/text_to_image.py \
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--model Tongyi-MAI/Z-Image-Turbo \
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--prompt "a cup of coffee on the table" \
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--num-inference-steps 25 \
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--enable-cpu-offload \
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--vae-use-tiling \
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--vae-use-slicing \
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--enforce-eager \
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--output z_image_turbo_output.png
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```
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#### Verification
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Confirm `z_image_turbo_output.png` is written as a 1024x1024 PNG matching the
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prompt.
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#### Notes
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- Memory usage: peak 15.6 GiB, about 58 s per image.
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- Known limitations: only offline generation was qualified. Online serving is
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out of scope for this profile.
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