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 /SD3-based /SAI sd3.5_large_controlnet_depth.safetensors
- Xet hash:
- 463f7890d171ad35a767232bd17a847a38e90d71e1a57cb1255be9a6a5df8d17
- Size of remote file:
- 8.65 GB
- SHA256:
- d6ded6aa4f60eda74ae48a8fdc1a9fa11b36f05488975916f1ecd4834fddffd0
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