Instructions to use SidXXD/Clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/Clean with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/Clean", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a sks cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0f046a1d158739d8759ca33173ef41f3180d9cab274d6a92d936a9cde2881875
- Size of remote file:
- 76.7 MB
- SHA256:
- 3dfb4f75d0ac6a167809bbf7098891af8097e3dbebc6bd9678363e94acefecc9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.