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 / Generation /Image /SD1-based /RealisticVision /realisticVisionV60B1_v60B1InpaintingVAE.safetensors
- Xet hash:
- f7795c06a6f61d675d26f8cda796fe013ef1143225ac9301e8fe54776bb136e8
- Size of remote file:
- 2.13 GB
- SHA256:
- 346e4b5a73cc02c8e9aa619ab44bf3afde2badc24dc158c7eae3f8b1c5edc334
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