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
OCS_Models / Conditioning /Image /ControlNet /FLUX-based /SL FLUX.1-dev-ControlNet-Union-Pro-2.0.safetensors
Download Conditioning/Image/ControlNet/FLUX-based/SL FLUX.1-dev-ControlNet-Union-Pro-2.0.safetensors from perilli/OCS_Models: direct link, hf CLI and curl.
- Browser
- Download file 4.28 GB
-
https://huggingface.co/perilli/OCS_Models/resolve/b60e09a77458f2defea945da0bdb9858be8ddee8/Conditioning/Image/ControlNet/FLUX-based/SL%20FLUX.1-dev-ControlNet-Union-Pro-2.0.safetensors
- Command line
-
hf download 'hf://perilli/OCS_Models@b60e09a77458f2defea945da0bdb9858be8ddee8/Conditioning/Image/ControlNet/FLUX-based/SL FLUX.1-dev-ControlNet-Union-Pro-2.0.safetensors'
-
curl -L -o 'SL FLUX.1-dev-ControlNet-Union-Pro-2.0.safetensors' https://huggingface.co/perilli/OCS_Models/resolve/b60e09a77458f2defea945da0bdb9858be8ddee8/Conditioning/Image/ControlNet/FLUX-based/SL%20FLUX.1-dev-ControlNet-Union-Pro-2.0.safetensors
4.28 GB
- Xet hash:
- eaded1f37f5e3160be2705cb12b4d39175e583b5799e0df84b7737c478e9796d
- Size of remote file:
- 4.28 GB
- SHA256:
- 9d03f63f36206bab2f36aed5cfedc8693c2881397534e9d5f9ae9a0a41362517
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.