Instructions to use perilli/OCS_Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use perilli/OCS_Models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("perilli/OCS_Models", 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:
- b395967d286f622c4f41fa19ba5639216d52915ee05da7de4162481dfbd6cc69
- Size of remote file:
- 2.5 GB
- SHA256:
- 66a6813e6bd7270ecfe68206a59ddd605a011ae85321188376605c66e0a4f303
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.