| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-to-image |
| library_name: diffusers |
| --- |
| |
| <h1 align="center">⚡️- Image<br><sub><sup>An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer</sup></sub></h1> |
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| <div align="center"> |
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| [](https://tongyi-mai.github.io/Z-Image-blog/)  |
| [](https://github.com/Tongyi-MAI/Z-Image)  |
| [](https://huggingface.co/Tongyi-MAI/Z-Image)  |
| [](https://huggingface.co/spaces/Tongyi-MAI/Z-Image)  |
| [](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)  |
| [](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=569345&modelType=Checkpoint&sdVersion=Z_IMAGE&modelUrl=modelscope%3A%2F%2FTongyi-MAI%2FZ-Image%3Frevision%3Dmaster)  |
| <a href="https://arxiv.org/abs/2511.22699" target="_blank"><img src="https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv" height="21px"></a> |
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| Welcome to the official repository for the Z-Image（造相）project! |
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| </div> |
|
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| ## 🎨 Z-Image |
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| **Z-Image** is the foundation model of the ⚡️- Image family, engineered for good quality, robust generative diversity, broad stylistic coverage, and precise prompt adherence. |
| While Z-Image-Turbo is built for speed, |
| Z-Image is a full-capacity, undistilled transformer designed to be the backbone for creators, researchers, and developers who require the highest level of creative freedom. |
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| ### 🌟 Key Features |
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| - **Undistilled Foundation**: As a non-distilled base model, Z-Image preserves the complete training signal. It supports full Classifier-Free Guidance (CFG), providing the precision required for complex prompt engineering and professional workflows. |
| - **Aesthetic Versatility**: Z-Image masters a vast spectrum of visual languages—from hyper-realistic photography and cinematic digital art to intricate anime and stylized illustrations. It is the ideal engine for scenarios requiring rich, multi-dimensional expression. |
| - **Enhanced Output Diversity**: Built for exploration, Z-Image delivers significantly higher variability in composition, facial identity, and lighting across different seeds, ensuring that multi-person scenes remain distinct and dynamic. |
| - **Built for Development**: The ideal starting point for the community. Its non-distilled nature makes it a good base for LoRA training, structural conditioning (ControlNet) and semantic conditioning. |
| - **Robust Negative Control**: Responds with high fidelity to negative prompting, allowing users to reliably suppress artifacts and adjust compositions. |
|
|
| ### 🆚 Z-Image vs Z-Image-Turbo |
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| | Aspect | Z-Image | Z-Image-Turbo | |
| |------|------|------| |
| | CFG | ✅ | ❌ | |
| | Steps | 28~50 | 8 | |
| | Fintunablity | ✅ | ❌ | |
| | Negative Prompting | ✅ | ❌ | |
| | Diversity | High | Low | |
| | Visual Quality | High | Very High | |
| | RL | ❌ | ✅ | |
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| ## 🚀 Quick Start |
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| ### Installation & Download |
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| Install the latest version of diffusers: |
| ```bash |
| pip install git+https://github.com/huggingface/diffusers |
| ``` |
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| Download the model: |
| ```bash |
| pip install -U huggingface_hub |
| HF_XET_HIGH_PERFORMANCE=1 hf download Tongyi-MAI/Z-Image |
| ``` |
|
|
| ### Recommended Parameters |
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| - **Resolution:** 512×512 to 2048×2048 (total pixel area, any aspect ratio) |
| - **Guidance scale:** 3.0 – 5.0 |
| - **Inference steps:** 28 – 50 |
|
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| ### Usage Example |
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|
| ```python |
| import torch |
| from diffusers import ZImagePipeline |
| |
| # Load the pipeline |
| pipe = ZImagePipeline.from_pretrained( |
| "Tongyi-MAI/Z-Image", |
| torch_dtype=torch.bfloat16, |
| low_cpu_mem_usage=False, |
| ) |
| pipe.to("cuda") |
| |
| # Generate image |
| prompt = "两名年轻亚裔女性紧密站在一起，背景为朴素的灰色纹理墙面，可能是室内地毯地面。左侧女性留着长卷发，身穿藏青色毛衣，左袖有奶油色褶皱装饰，内搭白色立领衬衫，下身白色裤子；佩戴小巧金色耳钉，双臂交叉于背后。右侧女性留直肩长发，身穿奶油色卫衣，胸前印有“Tun the tables”字样，下方为“New ideas”，搭配白色裤子；佩戴银色小环耳环，双臂交叉于胸前。两人均面带微笑直视镜头。照片，自然光照明，柔和阴影，以藏青、奶油白为主的中性色调，休闲时尚摄影，中等景深，面部和上半身对焦清晰，姿态放松，表情友好，室内环境，地毯地面，纯色背景。" |
| negative_prompt = "" # Optional, but would be powerful when you want to remove some unwanted content |
| |
| image = pipe( |
| prompt=prompt, |
| negative_prompt=negative_prompt, |
| height=1280, |
| width=720, |
| cfg_normalization=False, |
| num_inference_steps=50, |
| guidance_scale=4, |
| generator=torch.Generator("cuda").manual_seed(42), |
| ).images[0] |
| |
| image.save("example.png") |
| ``` |
|
|
| ## 📜 Citation |
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| If you find our work useful in your research, please consider citing: |
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| ```bibtex |
| @article{team2025zimage, |
| title={Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer}, |
| author={Z-Image Team}, |
| journal={arXiv preprint arXiv:2511.22699}, |
| year={2025} |
| } |
| ``` |
