Instructions to use JosephusCheung/RuminationDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JosephusCheung/RuminationDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JosephusCheung/RuminationDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "masterpiece, best quality, anime, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, garden, looking at viewer" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 6e0a504f7bf64f98793b4a161e10da1fd6b8d1d0386dac01923323d8974e73ab
- Size of remote file:
- 1.36 GB
- SHA256:
- b4fa1e9be3f7011b14a78bf082d90b56ec5670e45d09b829ada4283595dfd780
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.