Instructions to use can34/Modill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use can34/Modill with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("can34/Modill", 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:
- dbb4e0cae0a42fcf0ef1f130e2aa56a691513052d5f5e95ca93573bac7cc66d4
- Size of remote file:
- 4.85 MB
- SHA256:
- d6350211b5e2cd93e2e243edd4e35a4210d1256c2eabeca054fb4a89cfb5b532
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.