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--- |
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license: apache-2.0 |
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library_name: videox_fun |
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--- |
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# Z-Image-Turbo-Fun-Controlnet-Union |
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[](https://github.com/aigc-apps/VideoX-Fun) |
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## News |
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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). |
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## Model Features |
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- This ControlNet is added on 6 blocks. |
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- 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. |
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- It supports multiple control conditions—including Canny, HED, Depth, Pose and MLSD can be used like a standard ControlNet. |
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- 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. |
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## TODO |
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- [ ] Train on more data and for more steps. |
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- [ ] Support inpaint mode. |
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## Results |
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<table border="0" style="width: 100%; text-align: left; margin-top: 20px;"> |
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<tr> |
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<td>Pose</td> |
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<td>Output</td> |
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</tr> |
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<tr> |
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<td><img src="asset/pose2.jpg" width="100%" /></td> |
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<td><img src="results/pose2.png" width="100%" /></td> |
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</tr> |
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</table> |
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<table border="0" style="width: 100%; text-align: left; margin-top: 20px;"> |
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<tr> |
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<td>Pose</td> |
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<td>Output</td> |
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</tr> |
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<tr> |
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<td><img src="asset/pose.jpg" width="100%" /></td> |
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<td><img src="results/pose.png" width="100%" /></td> |
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</tr> |
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</table> |
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<table border="0" style="width: 100%; text-align: left; margin-top: 20px;"> |
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<tr> |
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<td>Canny</td> |
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<td>Output</td> |
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</tr> |
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<tr> |
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<td><img src="asset/canny.jpg" width="100%" /></td> |
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<td><img src="results/canny.png" width="100%" /></td> |
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</tr> |
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</table> |
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<table border="0" style="width: 100%; text-align: left; margin-top: 20px;"> |
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<tr> |
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<td>HED</td> |
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<td>Output</td> |
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</tr> |
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<tr> |
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<td><img src="asset/hed.jpg" width="100%" /></td> |
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<td><img src="results/hed.png" width="100%" /></td> |
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</tr> |
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</table> |
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<table border="0" style="width: 100%; text-align: left; margin-top: 20px;"> |
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<tr> |
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<td>Depth</td> |
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<td>Output</td> |
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</tr> |
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<tr> |
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<td><img src="asset/depth.jpg" width="100%" /></td> |
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<td><img src="results/depth.png" width="100%" /></td> |
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</tr> |
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</table> |
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## Inference |
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Go to the VideoX-Fun repository for more details. |
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Please clone the VideoX-Fun repository and create the required directories: |
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```sh |
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# Clone the code |
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git clone https://github.com/aigc-apps/VideoX-Fun.git |
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# Enter VideoX-Fun's directory |
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cd VideoX-Fun |
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# Create model directories |
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mkdir -p models/Diffusion_Transformer |
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mkdir -p models/Personalized_Model |
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``` |
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Then download the weights into models/Diffusion_Transformer and models/Personalized_Model. |
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``` |
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📦 models/ |
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├── 📂 Diffusion_Transformer/ |
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│ └── 📂 Z-Image-Turbo/ |
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├── 📂 Personalized_Model/ |
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│ └── 📦 Z-Image-Turbo-Fun-Controlnet-Union.safetensors |
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``` |
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Then run the file `examples/z_image_fun/predict_t2i_control.py`. |
