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
Download cached_pipe/text_encoder from Montey/lua-edge: direct link, hf CLI and curl.
- Browser
- Download file 2.73 MB
-
https://huggingface.co/Montey/lua-edge/resolve/main/cached_pipe/text_encoder
- Command line
-
hf download hf://Montey/lua-edge/cached_pipe/text_encoder
-
curl -L -o text_encoder https://huggingface.co/Montey/lua-edge/resolve/main/cached_pipe/text_encoder
2.73 MB
- Xet hash:
- 418a5721037cc66dc57e07324b072022b3c1ed083457b7d70be7ef9dfae6eadd
- Size of remote file:
- 2.73 MB
- SHA256:
- 5e05bb0bdc1e7bb9af8b5524fce5523d4ceadfc12037fce22cf224e9797f9c31
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