Instructions to use Efficient-Large-Model/SANA-Streaming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Efficient-Large-Model/SANA-Streaming with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Efficient-Large-Model/SANA-Streaming", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download source/00_local_editing_source.mp4 from Efficient-Large-Model/SANA-Streaming: direct link, hf CLI and curl.
- Browser
- Download file 77.4 MB
-
https://huggingface.co/Efficient-Large-Model/SANA-Streaming/resolve/main/source/00_local_editing_source.mp4
- Command line
-
hf download hf://Efficient-Large-Model/SANA-Streaming/source/00_local_editing_source.mp4
-
curl -L -o 00_local_editing_source.mp4 https://huggingface.co/Efficient-Large-Model/SANA-Streaming/resolve/main/source/00_local_editing_source.mp4
77.4 MB
- Xet hash:
- aa0e38253310340897fa16ca81871338a26242f28212907c47a3d9859565372b
- Size of remote file:
- 77.4 MB
- SHA256:
- d162eaa4ba50c0391bac83bbf4d5a813e32a1c7d49d387b6113dd9bcad96a501
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