DLAS is PIPified (but I'm still cloning it as a submodule to make updating it easier)

fixes #134

;)

simplified spawning the training process by having it spawn the distributed training processes in the train.py script, so it should work on Windows too

rewrote how AIVC gets training metrics (need to clean up later)

while I'm breaking things, migrating dependencies to modules folder for tidiness

big cleanup to make my life easier when i add more parameters

actually make using adamw_zero optimizer for multi-gpus work

I forgot about the changelog and never kept up with it, so I'll just not use a changelog

use torchrun instead for multigpu

experimental multi-gpu training (Linux only, because I can't into batch files)

Added option to disable bitsandbytesoptimizations for systems that do not support it (systems without a Turing-onward Nvidia card), saves use of float16 and bitsandbytes for training into the config json

fixed some files not copying for bitsandbytes (I was wrong to assume it copied folders too), fixed stopping generating and training, some other thing that I forgot since it's been slowly worked on in my small free times

ugh

removed the logic to toggle BNB capabilities, since I guess I can't do that from outside the module

huge success

handled paths that contain spaces because python for whatever god forsaken reason will always split on spaces even if wrapping an argument in quotes

a bit of UI cleanup, import multiple audio files at once, actually shows progress when importing voices, hides audio metadata / latents if no generated settings are detected, preparing datasets shows its progress, saving a training YAML shows a message when done, training now works within the web UI, training output shows to web UI, provided notebook is cleaned up and uses a venv, etc.

cleanup, "injected" dvae.pth to download through tortoise's model loader, so I don't need to keep copying it

training added, seems to work, need to test it more

updated notebooks to use the new "main" setup

tab to generate the training YAML