Collection of nitpicks

See below for a small collection of nitpicks; I suppose those will be adressed before the first release, but wanted to write them down somewhere:

Documentation seems to be in a different format so will need to be refactored to work with the doc-builder
The logging class still has mentions to 🤗 Transformers
You have some tqdm instances in schedulers, but they're not using the utility defined in logging in order to toggle bars on and off
Some copyrights have wrong attribution (e.g., configuration_utils which mentions NVIDIA)
In some places you define a global variable for configuratio name (e.g., SCHEDULER_CONFIG_NAME = "scheduler_config.json"), in other places you define them as a string (config_name = "model_index.json", in DiffusionPipeline)
We have some conventions in transformers that IMO make sense for this library as well, wondering if it is an oversight or a decision to do differently. Those that I can see are: single letter variables, message-less asserts
The register_modules doesn't seem to play well with code completion tools
The CLIP model is entirely copy/pasted from transformers, is the goal to upstream the change at some point or to keep it separate in that repo?

All very good points!

Doc: Yes we need to change that our style
CLIP: idea is to merge it upstream into transformers
other points: All yes, we need to adapt this

Made a small pull request to fix the logging class issues.
Not a huge contribution, just wanted to get my feet wet with contributing 🙂

It does change the environmental variables too, but I think this shouldn't have any effects elsewhere - I checked for other occurrences of the variables using ripgrep

@vvvm23

Super nice of you @vvvm23 - thanks a lot :-)

We want to clean up the code and add appropriate licensing / links to original code early next week and we'll soon release a list of todo's for contributions as well if you're interested in contributing more in the coming weeks

Hi yes, would be interested, feel free to tag me when that happens~ I don't have much experience with continuous diffusion and contributing to big projects, but fairly good at pytorch in general.