MiniMax-H3-Fun-Controlnet-Union

This is a legacy version. A newer, better version is now available: MiniMax-H3-Fun-Controlnet-Union-2.0. The 2.0 model produces improved results and is the recommended checkpoint for new usage. Please prefer it over this one.

MiniMax-H3-Fun-Controlnet-Union is a ControlNet-Union for MiniMax-H3, trained with the VideoX-Fun pipeline. A single checkpoint conditions the MiniMax-H3 video generator on Canny, Depth, HED, MLSD or Pose control videos, and also runs video inpainting.

Model Card

Model Features

• Union control: one checkpoint handles Canny, Depth, HED, MLSD and Pose control videos for video-to-video generation, no per-condition checkpoint switching.
• The control branch attaches to 5 of the 50 transformer blocks (layers 0, 10, 20, 30, 40); every control skip is added to the main branch through a zero-gated projection.
• Guidance-distilled: run with guidance_scale = 1.0, one forward pass per step, no classifier-free guidance needed.
• Inpainting is supported: the control input is widened to control_in_dim = 49 (latent + masked latent + mask channels); use examples/minimax_h3_fun/predict_v2v_control_inpaint.py.
• control_context_scale scales every control skip before it is added to the main branch: 1.0 gives the strongest control (used for all results below), values below 1.0 weaken the guidance of the control video, 0.0 switches the control branch off.
• The generation follows the control video: the frame count snaps down to the largest 17 * n + 5 the video VAE can decode (duration capped at 15 seconds), the canvas keeps the control video's own aspect ratio at the height * width pixel budget (both multiples of 32), at a fixed 24 fps.
• Detailed prompts give better stability; we recommend describing the scene, the subject and the camera in the prompt.

Results

All samples below are generated with num_inference_steps = 40, guidance_scale = 1.0, control_context_scale = 1.00, seed 43.

Canny

Depth

HED

MLSD

Pose

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

Then download the base MiniMax-H3 model and this checkpoint into models/Diffusion_Transformer.

📦 models/
├── 📂 Diffusion_Transformer/
│   ├── 📂 MiniMax-H3/
│   └── 📂 MiniMax-H3-Fun-Controlnet-Union/
│       └── 📦 MiniMax-H3-Fun-Controlnet-Union.safetensors

Then edit the settings at the top of examples/minimax_h3_fun/predict_v2v_control.py and run it.

model_name          = "models/Diffusion_Transformer/MiniMax-H3"
config_path         = "config/minimax_h3/minimax_h3_control.yaml"
transformer_path    = "models/Diffusion_Transformer/MiniMax-H3-Fun-Controlnet-Union/MiniMax-H3-Fun-Controlnet-Union.safetensors"
control_video       = "your_control_video.mp4"
prompt              = "your prompt"

python examples/minimax_h3_fun/predict_v2v_control.py

Notes:

• config_path must build the control branch exactly as trained (control_blocks_places: [0, 10, 20, 30, 40], control_in_dim: 49, control_apply_audio: false); a mismatched layout makes the checkpoint fail to load.
• The checkpoint is guidance-distilled: keep guidance_scale = 1.0; a value above 1 applies guidance twice and degrades the output.
• The control checkpoint carries only the control branch; the base MiniMax-H3 weights must be present in model_name.
• Memory: the transformer (about 62 GB) plus the Qwen3-VL text encoder (about 62 GB) do not fit one 80 GB GPU fully loaded; use model_group_offload (fastest) or model_cpu_offload_and_qfloat8 on a single 80 GB GPU.

License

This model is a derivative of MiniMax-H3 and is released under the MiniMax H3 Community License Agreement. Please read the license carefully, especially the territorial restrictions and the Acceptable Use Policy, before use.