| --- |
| 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. |
|
|
