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 Conditioning/Image/ControlNet/SD3-based/InstantX SD3-Controlnet-Tile.safetensors from perilli/OCS_Models: direct link, hf CLI and curl.
- Browser
- Download file 1.19 GB
-
https://huggingface.co/perilli/OCS_Models/resolve/b60e09a77458f2defea945da0bdb9858be8ddee8/Conditioning/Image/ControlNet/SD3-based/InstantX%20SD3-Controlnet-Tile.safetensors
- Command line
-
hf download 'hf://perilli/OCS_Models@b60e09a77458f2defea945da0bdb9858be8ddee8/Conditioning/Image/ControlNet/SD3-based/InstantX SD3-Controlnet-Tile.safetensors'
-
curl -L -o 'InstantX SD3-Controlnet-Tile.safetensors' https://huggingface.co/perilli/OCS_Models/resolve/b60e09a77458f2defea945da0bdb9858be8ddee8/Conditioning/Image/ControlNet/SD3-based/InstantX%20SD3-Controlnet-Tile.safetensors
1.19 GB
- Xet hash:
- 921dcee9865274d7b1c18ac98f2a4dcb43fbe42dda8a7fcb7703813844c6dc18
- Size of remote file:
- 1.19 GB
- SHA256:
- dbbdfcc3b15e27f69c5fa3eaa0451c4d114ca6a88e5748c20c2639647f18705a
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