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
Change
requirements.txt
to add extra arguments to be used when installing the package
For testing, you need a docker container with pytorch and ubuntu 22.04,
you can download your listed dependencies with
pip install -r requirements.txt -e .
, and then running
start_inference
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