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---
license: apache-2.0
library_name: videox_fun
---
# Z-Image-Turbo-Fun-Controlnet-Union
[](https://github.com/aigc-apps/VideoX-Fun)
## 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`. |