Instructions to use Montey/lua-edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Montey/lua-edge with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Montey/lua-edge", 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
- Xet hash:
- c4401ead4fe4be44856d1dbd218a55d539ffb5579568169e9eb282f9b74b0d4e
- Size of remote file:
- 9.36 MB
- SHA256:
- 59f85126611ab3b28ea283da1b1a77eeae6937d3ed3c5fc9413c8b40418165fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.