---
license: apache-2.0
language:
- en
pipeline_tag: text-to-image
tags:
- mlx
- apple-silicon
- z-image
- diffusion
---

# Z-Image-Turbo-MLX

**Unofficial** MLX-format weights converted from [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) for native Apple Silicon inference.

> **This is NOT the official Z-Image repository.**
> For the original model and official resources, please visit:
> - Official model: [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo)
> - Official GitHub: [github.com/Tongyi-MAI/Z-Image](https://github.com/Tongyi-MAI/Z-Image)

## About

Z-Image-Turbo is a powerful and efficient 6B-parameter image generation model developed by the **Tongyi-MAI team (Alibaba)**. It achieves sub-second inference on enterprise GPUs with only 8 NFEs, excelling at photorealistic generation, bilingual text rendering, and instruction adherence.

This repository contains the same weights converted to **MLX format** (Apple's ML framework) so they can be loaded natively on Apple Silicon (M1/M2/M3/M4) without PyTorch or CUDA dependencies.

## Conversion Details

| Item | Detail |
|------|--------|
| Source model | [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) |
| Target format | MLX (safetensors with MLX-compatible layout) |
| Precision | bfloat16 |
| License | Apache-2.0 (inherited from the original model) |
| Converted by | [illusion615](https://huggingface.co/illusion615) |

## Intended Use

These weights are designed for use with a local project's zimage_mlx_service backend, enabling local image generation on Mac with Apple Silicon.

## Citation

If you use these weights, please cite the original Z-Image work:

```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}
}
```

## Acknowledgments

All credit for the Z-Image model architecture, training, and research goes to the [Tongyi-MAI team](https://github.com/Tongyi-MAI). This repository only provides a format conversion for Apple Silicon compatibility.