Instructions to use teragron/capybara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use teragron/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("teragron/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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from teragron/capybara: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/teragron/capybara/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://teragron/capybara/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/teragron/capybara/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 2cc8c95cf9d66447319299b40caf9b4349f0f8b6804ff2ae4d7493b23f9aead3
- Size of remote file:
- 492 MB
- SHA256:
- cc3750e860fb12836dfd8049449acb0bf774ddf4fbb11aa535869124ece4f150
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.