[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?

HF is a great place to play with transformer-based or diffusion models. They have a set of consistent set of APIs that works across a wide range of models. But things go downhill really fast if you want to tweak something or maybe just look under the hood. Some examples:

- `diffusers` did a good job of improving on CompVis's implementation of LDMs and modularizing different components while maintaining the consistency of APIs. But over time the introduction of things …

apis code code quality complexity consistent diffusion diffusion models face good hugging face libraries machinelearning quality readability scaling scaling up set transformer