Instructions to use Efficient-Large-Model/SANA-WM_bidirectional-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Efficient-Large-Model/SANA-WM_bidirectional-diffusers 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-WM_bidirectional-diffusers", 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 tokenizer/tokenizer.json from Efficient-Large-Model/SANA-WM_bidirectional-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/Efficient-Large-Model/SANA-WM_bidirectional-diffusers/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://Efficient-Large-Model/SANA-WM_bidirectional-diffusers/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Efficient-Large-Model/SANA-WM_bidirectional-diffusers/resolve/main/tokenizer/tokenizer.json
34.4 MB
- Xet hash:
- eb284ec540544a161910fea60e399e9e0f1ae76eec2744468afc560111f0134f
- Size of remote file:
- 34.4 MB
- SHA256:
- 5f7eee611703c5ce5d1eee32d9cdcfe465647b8aff0c1dfb3bed7ad7dbb05060
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