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Z-Image-Fun-Controlnet-Union-2.1
alibaba-pai/Z-Image-Fun-Controlnet-Union-2.1
Z-Image-Fun-Controlnet-Union-2.1 is a machine learning model from alibaba-pai. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for videox_fun. The card lists the license as apache-2.0.
[](https://github.com/aigc-apps/VideoX-Fun)
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17.5 GB
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.safetensors17.5 GB · 100%
At a glance
- Library
- videox_fun
- License
- apache-2.0
- Access
- Public
- Created
- Feb 2, 2026
- Updated
- Feb 24, 2026
- SHA
- 755999a9
Spaces
Model card
From the Hugging Face model README
Z-Image-Fun-Controlnet-Union-2.1
Model Card
| Name | Description |
|---|---|
| Z-Image-Fun-Controlnet-Union-2.1.safetensors | ControlNet weights for Z-Image. The model supports multiple control conditions such as Canny, Depth, Pose, MLSD, Scribble, Hed and Gray. This ControlNet is added on 15 layer blocks and 2 refiner layer blocks. |
| Z-Image-Fun-Controlnet-Union-2.1-lite.safetensors | Compared to the large version of the model, fewer layers have control added, resulting in weaker control conditions. This makes it suitable for larger control_context_scale values, and the generation results appear more natural. It is also suitable for lower-spec machines. |
| Z-Image-Fun-Controlnet-Tile-2.1.safetensors | A Tile model trained on high-definition datasets (up to 2048×2048) for super-resolution. |
| Z-Image-Fun-Controlnet-Tile-2.1-lite.safetensors | Applied control latents to fewer layers, resulting in weaker control. This allows for larger control_context_scale values with more natural results, and is also better suited for lower-spec machines. |
Model Features
- This ControlNet is added on 15 layer blocks and 2 refiner layer blocks (Lite models are added on 3 layer blocks and 2 refiner blocks). It supports multiple control conditions—including Canny, Depth, Pose, MLSD, Scribble, Hed and Gray can be used like a standard ControlNet.
- Inpainting mode is also supported. When using inpaint mode, please use a larger control_context_scale, as this will result in better image continuity.
- 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 1.00.
Results
<table border="0" style="width: 100%; text-align: left; margin-top: 20px;"> <tr> <td>Inpaint</td> <td>Output</td> </tr> <tr> <td><img src="asset/inpaint.jpg" width="100%" /><img src="asset/mask.jpg" width="100%" /></td> <td><img src="results/inpaint.png" width="100%" /></td> </tr> </table> <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>Pose</td> <td>Output</td> </tr> <tr> <td><img src="asset/pose3.jpg" width="100%" /></td> <td><img src="results/pose3.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> <table border="0" style="width: 100%; text-align: left; margin-top: 20px;"> <tr> <td>Low Resolution</td> <td>High Resolution</td> </tr> <tr> <td><img src="asset/low_res.jpg" width="100%" /></td> <td><img src="results/high_res.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/Diffusion_Transformer and models/Personalized_Model.
📦 models/
├── 📂 Diffusion_Transformer/
│ └── 📂 Z-Image/
├── 📂 Personalized_Model/
│ ├── 📦 Z-Image-Fun-Controlnet-Union-2.1.safetensors
│ └── 📦 Z-Image-Fun-Controlnet-Union-2.1-lite.safetensors
Then run the file examples/z_image_fun/predict_t2i_control_2.1.py and examples/z_image_fun/predict_i2i_inpaint_2.1.py.
- Library
- videox_fun
- License
- apache-2.0
- Created
- Feb 2, 2026
- Updated
- Feb 24, 2026