Instructions to use timduck8/catv9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use timduck8/catv9 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("timduck8/catv9", torch_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
- Xet hash:
- df8995441e2547aec06b858413161e7adb52c4a3c2603cdb4ffeaa158e1562f2
- Size of remote file:
- 5.14 GB
- SHA256:
- 21ee7eaa05b9e55c14489c3b78bfe4aadb5b384916ee904ff411c541aa8fea01
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.