Instructions to use m-usab/cat-image-diffusion-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use m-usab/cat-image-diffusion-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("m-usab/cat-image-diffusion-model", 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:
- 485f572e6cdd1403410fd576e6a419ad31aa795c66f3a12f1ed5789580010159
- Size of remote file:
- 74.2 MB
- SHA256:
- 71a6f2a60024b00077e582c919cb85b1034cbd6ad525a6e5440c6140615bf082
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.