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docs/source/en/_toctree.yml

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title: WanAnimateTransformer3DModel
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- local: api/models/wan_transformer_3d
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title: WanTransformer3DModel
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- local: z_image_transformer2d
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- local: api/models/z_image_transformer2d
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title: ZImageTransformer2DModel
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title: Transformers
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- sections:

docs/source/en/api/pipelines/z_image.md

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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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</div>
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Z-Image is a powerful and highly efficient image generation model with 6B parameters. Currently there's only one model with two more to be released:
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[Z-Image](https://huggingface.co/papers/2511.22699) is a powerful and highly efficient image generation model with 6B parameters. Currently there's only one model with two more to be released:
2020

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|Model|HuggingFace|
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[[autodoc]] ZImagePipeline
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- all
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- __call__
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## Citation
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```bibtex
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@article{team2025zimage,
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title={Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer},
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author={Z-Image Team},
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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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- __call__

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