Instructions to use Jiabooo/diffusionsat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jiabooo/diffusionsat with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jiabooo/diffusionsat", 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:
- 65f4e3b3d78a866daa337638fcb6f113baaa6ec09bd799499a1f3900412c1246
- Size of remote file:
- 681 MB
- SHA256:
- 3d1116cef12c6da2aa5b1e2269d2bcf839cd493b3c9cc68941b69690af5630c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.