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
Download OCS_13.0.webp from perilli/OCS_Models: direct link, hf CLI and curl.
- Browser
- Download file 417 kB
-
https://huggingface.co/perilli/OCS_Models/resolve/main/OCS_13.0.webp
- Command line
-
hf download hf://perilli/OCS_Models/OCS_13.0.webp
-
curl -L -o OCS_13.0.webp https://huggingface.co/perilli/OCS_Models/resolve/main/OCS_13.0.webp
417 kB

- Xet hash:
- 251f2fce7bbb13a7e3dd4badb811b1cd8f33ee8a89a3ebd1c25cfca87ebbeece
- Size of remote file:
- 417 kB
- SHA256:
- a07576eaf2f6aa82713490c94c8dfb3bbb976fc65c42d5ed172501d8e616d4e5
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