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
Upload folder using huggingface_hub
Browse files- ckpt/config.json +22 -0
- ckpt/diffusion_pytorch_model.safetensors +3 -0
ckpt/config.json
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{
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"_class_name": "ViTVAE",
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"_diffusers_version": "0.28.2",
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"ddconfig": {
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"conv_last_layer": true,
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"depth": 24,
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"double_z": true,
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"embed_dim": 1024,
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"in_chans": 3,
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"ln_in_attn": true,
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"mlp_ratio": 4,
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"norm_code": false,
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"num_heads": 16,
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"patch_length": 4,
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"patch_size": 8,
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"qkv_bias": true,
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"video_length": 16,
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"video_size": 256,
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"z_chans": 16
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},
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"model_type": "vit"
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}
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ckpt/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d5092a7bcd112b7a743235bddad17d30b497da48b70eae51c5340bae8294b761
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size 2455072868
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