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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
  • HuggingFace models should be specified in the models array in the pyproject.toml file, and will be downloaded before benchmarking
    • The pipeline does not have internet access so all dependencies and models must be included in the pyproject.toml
    • Compiled models should be hosted on HuggingFace and included in the models array in the pyproject.toml (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
  • Ensure the entire repository (excluding dependencies and HuggingFace models) is under 16MB

For testing, you need a docker container with pytorch and ubuntu 22.04. You can download your listed dependencies with uv, installed with:

pipx ensurepath
pipx install uv

You can then relock with uv lock, and then run with uv run start_inference