Z Image Turbo Fun ControlNet Union

--- license: apache-2.0 library_name: videox_fun --- # Z-Image-Turbo-Fun-Controlnet-Union [](https://github.com/aigc-apps/VideoX-Fun) ## News 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). ## 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 Pose Output Pose Output Canny Output HED Output Depth Output ## 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`.