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 Vision/Segmentation/YOLO/efficient_sam_s_gpu.jit from perilli/OCS_Models: direct link, hf CLI and curl.
- Browser
- Download file 106 MB
-
https://huggingface.co/perilli/OCS_Models/resolve/7e875d0c84a4cc61ba83e039df0d813cf74456fd/Vision/Segmentation/YOLO/efficient_sam_s_gpu.jit
- Command line
-
hf download hf://perilli/OCS_Models@7e875d0c84a4cc61ba83e039df0d813cf74456fd/Vision/Segmentation/YOLO/efficient_sam_s_gpu.jit
-
curl -L -o efficient_sam_s_gpu.jit https://huggingface.co/perilli/OCS_Models/resolve/7e875d0c84a4cc61ba83e039df0d813cf74456fd/Vision/Segmentation/YOLO/efficient_sam_s_gpu.jit
106 MB
- Xet hash:
- c4aa7662451789412e1e5f4691b19d9a1839917a6499677d4dc8de7c1ea32817
- Size of remote file:
- 106 MB
- SHA256:
- e47c589ead2c6a80d38050ce63083a551e288db27113d534e0278270fc7cba26
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