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
| # edge-maxxing-newdream-sdxl | |
| This holds the baseline for the SDXL Nvidia GeForce RTX 4090 contest, which can be forked freely and optimized | |
| Some recommendations are as follows: | |
| - Installing dependencies should be done in pyproject.toml, including git dependencies | |
| - Compiled models should be included directly in the repository(rather than compiling during loading), loading time matters far more than file sizes | |
| - Avoid changing `src/main.py`, as that includes mostly protocol logic. Most changes should be in `models` and `src/pipeline.py` | |
| For testing, you need a docker container with pytorch and ubuntu 22.04. | |
| You can download your listed dependencies with `uv` installed with: | |
| ```bash | |
| pipx install uv | |
| pipx ensurepath | |
| ``` | |
| You can then relock with `uv lock`, and then run with `uv run start_inference` | |