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_v60B1VAE.safetensors
- Xet hash:
- 112bc792a67b1d7aab2cde97e5ec3e2c1cac9f1a9d032bc21ff85d0b355f4e8d
- Size of remote file:
- 2.13 GB
- SHA256:
- fe7578cb5ee0be63aa15baa894ab5d1751ff9b5b25ef611d5fafb2186d930c30
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