Instructions to use mohammeddevibe/cat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mohammeddevibe/cat-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("mohammeddevibe/cat-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
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- c7eaaa9b0727a28144b562b04e986f56e858cf77df1007d423410a991f530661
- Size of remote file:
- 335 MB
- SHA256:
- 9d3af2790f491ba035286a801bf72f7e59c8dd35d05de4563587cbcb0290982f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.