Instructions to use KamCastle/jugg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use KamCastle/jugg with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("KamCastle/jugg", 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:
- ecce153bd3dd3d8f9177c04f8915542bd91f238d0ad27608489df73900909564
- Size of remote file:
- 335 MB
- SHA256:
- 714520b9ea539f5d30e6073de61439def371f1520908180463af4ff5432a4b16
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