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
- Xet hash:
- eaded1f37f5e3160be2705cb12b4d39175e583b5799e0df84b7737c478e9796d
- Size of remote file:
- 4.28 GB
- SHA256:
- 9d03f63f36206bab2f36aed5cfedc8693c2881397534e9d5f9ae9a0a41362517
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