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

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 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.