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
Inpaint
Output
Pose + Inpaint
Output
Pose
Output
Pose
Output
Pose
Output
Canny
Output
HED
Output
Depth
Output
Gray
Output
Low Resolution
High Resolution
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 directorycd VideoX-Fun
# Create model directoriesmkdir -p models/Diffusion_Transformer
mkdir -p models/Personalized_Model
Then download the weights into models/Diffusion_Transformer and models/Personalized_Model.