Instructions to use Glanty/Capybara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Glanty/Capybara with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Glanty/Capybara", 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:
- b7b9f9a4aa6f6530ae6676ccd208ab7a185c22e4acc5d17e34f067ccc42b3191
- Size of remote file:
- 1.2 GB
- SHA256:
- b3aafee96d60e98aa18b3c7f73a2c5a2360f1f2f6df79361190a4c9e05c5ab21
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.