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") image = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
card: note the future direction (an adapter that assigns compute on its own)
Browse files
README.md
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release while we try something considerably more ambitious that may or may
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not work (an all-in-one adapter that internalises the whole two-pass
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pipeline). If that lands it will replace this file; if it does not, this
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one still does a useful job.
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## What it does
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release while we try something considerably more ambitious that may or may
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not work (an all-in-one adapter that internalises the whole two-pass
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pipeline). If that lands it will replace this file; if it does not, this
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one still does a useful job. Part of that work is finding out whether an
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adapter can be made to actually assign more compute to the burst on its
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own (a variable-rate attention behaviour learned inside the model, rather
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than the nodes stretching the clock for it); this pilot does not do that,
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see the mechanics note below.
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## What it does
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