| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | pipeline_tag: text-to-image |
| | library_name: diffusers |
| | --- |
| | |
| | **Quantized GGUF version of Z-Image.** |
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| | **Original model link:** [https://huggingface.co/Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image) |
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| | **Watch us at Youtube:** [@VantageWithAI](https://www.youtube.com/@vantagewithai) |
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| | <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> |
| |
|
| | ## 🎨 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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| | ### 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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|
| | ## 📜 Citation |
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|
| | If you find our work useful in your research, please consider citing: |
| |
|
| | ```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} |
| | } |
| | ``` |
