Instructions to use MATLOWAI/MiniMax-H3-Motion-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MATLOWAI/MiniMax-H3-Motion-Adapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("MATLOWAI/MiniMax-H3-Motion-Adapter") prompt = "A man with short gray hair plays a red electric guitar." input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") output = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Commit History
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card media: two comparison quads and their posters
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card: note the future direction (an adapter that assigns compute on its own)
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card: state sample sizes (Ref2VA two clips, anime one clip), add the 0.75 fewer-objects trade, add the rooftop smoke test with its caveat
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card: say plainly that the LoRA adds no compute; the nodes stretch the clock, the LoRA changes behaviour on it
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add the MiniMax H3 Community License alongside MIT; credit MiniMax and mark the adapter relation
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model card: colour muting is about strong colour and particle effects, seen on both checkpoints
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model card: link the demo page anchor and the node pack
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Motion adapter pilot r16: weights, model card, ComfyUI graph
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initial commit
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