Instructions to use mit-han-lab/nunchaku-sana with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mit-han-lab/nunchaku-sana with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mit-han-lab/nunchaku-sana", 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:
- ac64631b98b3b74f056cd83ae45cfb0138b5386afe2d13d8b1f75b240be8fdec
- Size of remote file:
- 1.46 GB
- SHA256:
- 320be49f2501c71825b92ab1aeddd2cb589873e73fa1d9a254ca39cd6400b1a4
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