Instructions to use CSWRY/VOSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CSWRY/VOSR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CSWRY/VOSR", 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
- Xet hash:
- c98cfcc1757c1c8ea72811bacafe0e12e2718933b4a5b29c16e0cfc0b77b048a
- Size of remote file:
- 178 MB
- SHA256:
- 1dd2c67f3f29734d48709513ed181d4f351966d76ba95f77a5cff8e1daf547fa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.