Model reference · open weights
MiniMax-H3-Fun-Controlnet-Union is an open-weight video model from alibaba-pai, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
MiniMax-H3-Fun-Controlnet-Union [](https://github.com/aigc-apps/VideoX-Fun) 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 guidancescale = 1.0, one forward pass per step, no classifier-free guidance needed. - Inpainting is supported: the control input is widened to controlindim = 49 (latent + masked latent + mask channels); use examples/minimaxh3fun/predictv2vcontrolinpaint.py. - controlcontextscale 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 numinferencesteps = 40, guidancescale = 1.0, controlcontextscale = 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: Then download the base MiniMax-H3 model and this checkpoint into models/DiffusionTransformer. Then edit the settings at the top of examples/minimaxh3fun/predictv2vcontrol.py and run it. Notes: - configpath must build the control branch exactly as trained (controlblocksplaces: [0, 10, 20, 3
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | alibaba-pai |
|---|---|
| Type | Video models |
| Variants | 1 |
| Runs with | videox_fun |
| Released | 2026-08-24 |
| Popularity | 6k downloads / month |
| Likes | 165 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| MiniMax-H3-Fun-Controlnet-Union | — | BF16 | — | — | Weights ↗ |
Using it via the API
Once AxForge deploys minimax-h3-fun-controlnet-union for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (minimax-h3-fun-controlnet-union below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/videos/generations \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"minimax-h3-fun-controlnet-union","prompt":"a drone shot over a forest"}'
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Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗
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