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_canny.safetensors
- Xet hash:
- 97ba5167aa3337ee6b1379e15165c7a6b6c76fa48a7752ec9283a377ca976ca5
- Size of remote file:
- 8.65 GB
- SHA256:
- 4bc5cf949f6501a4bd125c6c1190e8fba0f1471f5ce36e9ebd1114867abedd4c
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