Instructions to use levihsu/OOTDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use levihsu/OOTDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("levihsu/OOTDiffusion", 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
- Xet hash:
- 8eea36a84e57e6617853d8011ebdbf2df119daaa1c8510e6fbcaf2f2c23591ce
- Size of remote file:
- 1.71 GB
- SHA256:
- 156f677ed4495acd1ec7197249c091b85c240267c82f2f7f2e4eae4177931fed
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