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
| { | |
| "_class_name": "ViTVAE", | |
| "_diffusers_version": "0.28.2", | |
| "ddconfig": { | |
| "conv_last_layer": true, | |
| "depth": 24, | |
| "double_z": true, | |
| "embed_dim": 1024, | |
| "in_chans": 3, | |
| "ln_in_attn": true, | |
| "mlp_ratio": 4, | |
| "norm_code": false, | |
| "num_heads": 16, | |
| "patch_length": 4, | |
| "patch_size": 8, | |
| "qkv_bias": true, | |
| "video_length": 16, | |
| "video_size": 256, | |
| "z_chans": 16 | |
| }, | |
| "model_type": "vit" | |
| } | |