---
title: Qwen-Image-2512-Fun-Controlnet-Union
canonical_url: "https://www.modelscope.cn/models/PAI/Qwen-Image-2512-Fun-Controlnet-Union"
md_url: "https://www.modelscope.cn/models/PAI/Qwen-Image-2512-Fun-Controlnet-Union.md"
repository: PAI/Qwen-Image-2512-Fun-Controlnet-Union
last_updated: 2026-02-13
license: apache-2.0
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
parameters: 3.5B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - Pytorch
downloads: 1954
stars: 13
---

# Qwen-Image-2512-Fun-Controlnet-Union

> Qwen-Image-2512-Fun-Controlnet-Union - PAI 在 ModelScope 开源的模型。Qwen-Image-2512-Fun-Controlnet-Union

PAI/Qwen-Image-2512-Fun-Controlnet-Union 是 ModelScope 魔搭社区上的 3.5B 参数text-to-image-synthesis模型，采用 apache-2.0 许可。

- **Repository**: PAI/Qwen-Image-2512-Fun-Controlnet-Union
- **License**: apache-2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 3.5B
- **Downloads**: 1954
- **Stars**: 13
- **Last updated**: 2026-02-13

Source: https://www.modelscope.cn/models/PAI/Qwen-Image-2512-Fun-Controlnet-Union

---

# Qwen-Image-2512-Fun-Controlnet-Union

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

## Model Card

| Name | Description |
|--|--|
| Qwen-Image-2512-Fun-Controlnet-Union-2602.safetensors | Compared to the previous version of the model, we added Gray control to the model. The model was trained for a longer time than before. |
| Qwen-Image-2512-Fun-Controlnet-Union.safetensors | ControlNet weights for Qwen-Image-2512. The model supports multiple control conditions such as Canny, HED, Depth, Pose, MLSD and Scribble. |

## Model Features
- This ControlNet is added on 5 layer blocks. It supports multiple control conditions—including Canny, HED, Depth, Pose, MLSD, Scribble and Gray. It can be used like a standard ControlNet. 
- Inpainting mode is also supported.
- When obtaining control images, acquiring them in a multi-resolution manner results in better generalization.
- 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 0.70 to 0.95. 

## Results
<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
  <tr>
    <td>Pose + Inpaint</td>
    <td>Output</td>
  </tr>
  <tr>
    <td><img src="asset/inpaint.jpg" width="100%" /><img src="asset/mask.jpg" width="100%" /><img src="asset/pose.jpg" width="100%" /></td>
    <td><img src="results/pose_inpaint.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/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>Scribble</td>
    <td>Output</td>
  </tr>
  <tr>
    <td><img src="asset/scribble.jpg" width="100%" /></td>
    <td><img src="results/scribble.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>

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
  <tr>
    <td>Gray</td>
    <td>Output</td>
  </tr>
  <tr>
    <td><img src="asset/gray.jpg" width="100%" /></td>
    <td><img src="results/gray.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/
│   └── 📂 Qwen-Image-2512/
├── 📂 Personalized_Model/
│   └── 📦 Qwen-Image-2512-Fun-Controlnet-Union.safetensors
```

Then run the file `examples/qwenimage_fun/predict_t2i_control.py` and `examples/qwenimage_fun/predict_i2i_inpaint.py`.