Instructions to use Tongyi-MAI/Z-Image-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Tongyi-MAI/Z-Image-Turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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@@ -180,4 +180,18 @@ If you find our work useful in your research, please consider citing:
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journal={arXiv preprint arXiv:2511.22699},
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year={2025}
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}
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```
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journal={arXiv preprint arXiv:2511.22699},
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year={2025}
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}
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@article{liu2025decoupled,
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title={Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield},
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author={Dongyang Liu and Peng Gao and David Liu and Ruoyi Du and Zhen Li and Qilong Wu and Xin Jin and Sihan Cao and Shifeng Zhang and Hongsheng Li and Steven Hoi},
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journal={arXiv preprint arXiv:2511.22677},
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year={2025}
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}
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@article{jiang2025distribution,
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title={Distribution Matching Distillation Meets Reinforcement Learning},
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author={Jiang, Dengyang and Liu, Dongyang and Wang, Zanyi and Wu, Qilong and Jin, Xin and Liu, David and Li, Zhen and Wang, Mengmeng and Gao, Peng and Yang, Harry},
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journal={arXiv preprint arXiv:2511.13649},
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year={2025}
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}
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```
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