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alibaba-pai/Qwen-Image-2512-Fun-Controlnet-Union

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face

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Model Card

Qwen-Image-2512-Fun-Controlnet-Union

![Github](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 controlcontextscale for stronger control and better detail preservation. For better stability, we highly recommend using a detailed prompt. The optimal range for controlcontextscale 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:

# 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/DiffusionTransformer and models/PersonalizedModel.

📦 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.