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
Download figures/inhouse_human_evaluation.png from sand-ai/MAGI-1: direct link, hf CLI and curl.
- Browser
- Download file 304 kB
-
https://huggingface.co/sand-ai/MAGI-1/resolve/main/figures/inhouse_human_evaluation.png
- Command line
-
hf download hf://sand-ai/MAGI-1/figures/inhouse_human_evaluation.png
-
curl -L -o inhouse_human_evaluation.png https://huggingface.co/sand-ai/MAGI-1/resolve/main/figures/inhouse_human_evaluation.png
304 kB

- Xet hash:
- 6c75f7e4c465a4e349863a3a23c190cde363c89badf402b9c6fa5116f9f925e8
- Size of remote file:
- 304 kB
- SHA256:
- 657aa4a189f7db325a5acc967fad6b40ad22d55855ecbe038f27235abf9be3aa
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