Instructions to use sand-ai/MAGI-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sand-ai/MAGI-1 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("sand-ai/MAGI-1", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - MAGI-1
How to use sand-ai/MAGI-1 with MAGI-1:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 73486630c61c5a15a12a8204ee7dcf83d20c5a7ab657d9f23ae52113fbce1657
- Size of remote file:
- 3.56 MB
- SHA256:
- 7a44e9b01116d3207d8e190119464e1e49cf62d4ad67acd30767bc6984724e95
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