Instructions to use cvnberk/cat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cvnberk/cat with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cvnberk/cat", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 966c42d6ce15d787f0f5b9a75cfc68cdc5d0af5e26956bb2105a254a11d9e68c
- Size of remote file:
- 457 MB
- SHA256:
- b5ef1d5f05f3cf7b18dd1eaa7e3c437255039ec93150b4e3039858e7424494cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.