Instructions to use duja1/tovebw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use duja1/tovebw with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("duja1/tovebw", dtype=torch.bfloat16, device_map="cuda") prompt = "t123ovebw" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from duja1/tovebw: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/duja1/tovebw/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://duja1/tovebw/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/duja1/tovebw/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 58b2ef3edf0ffaee0966f8af3eae88116e7029ba271bb6dc26c51d654c175552
- Size of remote file:
- 492 MB
- SHA256:
- c72dc7fade3ca482b41850195aaac08abd96fa2e07e739e148c78eb314c03ad9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.