README.md

---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-to-image
tags:
- comfyui
- diffusion-single-file
base_model:
- Tongyi-MAI/Z-Image-Turbo
base_model_relation: quantized
---
For more information (including how to compress models yourself), check out https://huggingface.co/DFloat11 and https://github.com/LeanModels/DFloat11

Feel free to request for other models for compression as well, although models whose architecture I am unfamiliar with might be slightly tricky for me.

### How to Use

#### ComfyUI
Install my own fork of the DF11 ComfyUI custom node: https://github.com/mingyi456/ComfyUI-DFloat11-Extended. After installing the DF11 custom node, use the provided workflow [json](z_image_turbo_bf16-DF11-workflow.json), or simply replace the "Load Diffusion Model" node of an existing workflow with the "Load Diffusion Model" node. If you run into any issues, feel free to leave a comment. The workflow is also embedded in the below [png](z_image_turbo_bf16-DF11-workflow.png) image.

![](z_image_turbo_bf16-DF11-workflow.png)

#### `diffusers`
Refer to this [model](https://huggingface.co/mingyi456/Z-Image-Turbo-DF11) instead.

### Compression Details

This is the `pattern_dict` for compressing Z-Image-based models in ComfyUI:

```python
pattern_dict_comfyui = {
    r"noise_refiner\.\d+": (
        "attention.qkv",
        "attention.out",
        "feed_forward.w1",
        "feed_forward.w2",
        "feed_forward.w3",
        "adaLN_modulation.0"
    ),
    r"context_refiner\.\d+": (
        "attention.qkv",
        "attention.out",
        "feed_forward.w1",
        "feed_forward.w2",
        "feed_forward.w3",
    ),
    r"layers\.\d+": (
        "attention.qkv",
        "attention.out",
        "feed_forward.w1",
        "feed_forward.w2",
        "feed_forward.w3",
        "adaLN_modulation.0"
    )
}
```