---
title: Z-Image-Turbo-Fun-Controlnet-Union
canonical_url: "https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union"
md_url: "https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union.md"
repository: PAI/Z-Image-Turbo-Fun-Controlnet-Union
last_updated: 2025-12-12
license: apache-2.0
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
parameters: 1.6B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - Pytorch
downloads: 65787
stars: 38
---

# Z-Image-Turbo-Fun-Controlnet-Union

> Z-Image-Turbo-Fun-Controlnet-Union - PAI 在 ModelScope 开源的模型。Z-Image-Turbo-Fun-Controlnet-Union

PAI/Z-Image-Turbo-Fun-Controlnet-Union 是 ModelScope 魔搭社区上的 1.6B 参数text-to-image-synthesis模型，采用 apache-2.0 许可。

- **Repository**: PAI/Z-Image-Turbo-Fun-Controlnet-Union
- **License**: apache-2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 1.6B
- **Downloads**: 65787
- **Stars**: 38
- **Last updated**: 2025-12-12

Source: https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union

---

# Z-Image-Turbo-Fun-Controlnet-Union

[![Github](https://img.shields.io/badge/🎬%20Code-Github-blue)](https://github.com/aigc-apps/VideoX-Fun)

## News
The new control model with more control blocks and inpaint mode is [released](https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.0).

## Model Features
- This ControlNet is added on 6 blocks.
- The model was trained from scratch for 10,000 steps on a dataset of 1 million high-quality images covering both general and human-centric content. Training was performed at 1328 resolution using BFloat16 precision, with a batch size of 64, a learning rate of 2e-5, and a text dropout ratio of 0.10.
- It supports multiple control conditions—including Canny, HED, Depth, Pose and MLSD can be used like a standard ControlNet.
- You can adjust control_context_scale for stronger control and better detail preservation. For better stability, we highly recommend using a detailed prompt. The optimal range for control_context_scale is from 0.65 to 0.80.

## TODO 
- [ ] Train on more data and for more steps.
- [ ] Support inpaint mode.

## Results

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
  <tr>
    <td>Pose</td>
    <td>Output</td>
  </tr>
  <tr>
    <td><img src="asset/pose2.jpg" width="100%" /></td>
    <td><img src="results/pose2.png" width="100%" /></td>
  </tr>
</table>

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
  <tr>
    <td>Pose</td>
    <td>Output</td>
  </tr>
  <tr>
    <td><img src="asset/pose.jpg" width="100%" /></td>
    <td><img src="results/pose.png" width="100%" /></td>
  </tr>
</table>

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
  <tr>
    <td>Canny</td>
    <td>Output</td>
  </tr>
  <tr>
    <td><img src="asset/canny.jpg" width="100%" /></td>
    <td><img src="results/canny.png" width="100%" /></td>
  </tr>
</table>

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
  <tr>
    <td>HED</td>
    <td>Output</td>
  </tr>
  <tr>
    <td><img src="asset/hed.jpg" width="100%" /></td>
    <td><img src="results/hed.png" width="100%" /></td>
  </tr>
</table>

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
  <tr>
    <td>Depth</td>
    <td>Output</td>
  </tr>
  <tr>
    <td><img src="asset/depth.jpg" width="100%" /></td>
    <td><img src="results/depth.png" width="100%" /></td>
  </tr>
</table>

## Inference
Go to the VideoX-Fun repository for more details.

Please clone the VideoX-Fun repository and create the required directories:

```sh
# Clone the code
git clone https://github.com/aigc-apps/VideoX-Fun.git

# Enter VideoX-Fun's directory
cd VideoX-Fun

# Create model directories
mkdir -p models/Diffusion_Transformer
mkdir -p models/Personalized_Model
```

Then download the weights into models/Diffusion_Transformer and models/Personalized_Model.

```
📦 models/
├── 📂 Diffusion_Transformer/
│   └── 📂 Z-Image-Turbo/
├── 📂 Personalized_Model/
│   └── 📦 Z-Image-Turbo-Fun-Controlnet-Union.safetensors
```

Then run the file `examples/z_image_fun/predict_t2i_control.py`.