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license: apache-2.0
language:
- en
pipeline_tag: text-to-image
library_name: diffusers
---
**Quantized GGUF version of Z-Image.**
**Original model link:** [https://huggingface.co/Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image)
**Watch us at Youtube:** [@VantageWithAI](https://www.youtube.com/@vantagewithai)
<h1 align="center">⚡️- Image<br><sub><sup>An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer</sup></sub></h1>
<div align="center">
[](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>
Welcome to the official repository for the Z-Image（造相）project!
</div>
## 🎨 Z-Image
**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.
### 🌟 Key Features
- **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
| Aspect | Z-Image | Z-Image-Turbo |
|------|------|------|
| CFG | ✅ | ❌ |
| Steps | 28~50 | 8 |
| Fintunablity | ✅ | ❌ |
| Negative Prompting | ✅ | ❌ |
| Diversity | High | Low |
| Visual Quality | High | Very High |
| RL | ❌ | ✅ |
### Recommended Parameters
- **Resolution:** 512×512 to 2048×2048 (total pixel area, any aspect ratio)
- **Guidance scale:** 3.0 – 5.0
- **Inference steps:** 28 – 50
## 📜 Citation
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}
}
``` |