Pytorch is a pretty basic building block when you get to some degree of model complexity. It wouldn't really be interesting to implement autograd or some other things pytorch provides imo when the goal is to show a reimplantation of something as "higher" level as SD. It's similar to how I don't mind it when someone doesn't reimplement an OS, or a JavaScript engine when writing a web app from scratch.
And there's been a recent surge in abstractions over pytorch, and even standalone packages for models that you are just expected to import and use as an API (which are very useful, don't get me wrong!). So it's nice to see an implementation that doesn't have 10 different dependencies that each abstract over something pytorch does.
I agree, great series of videos, but there's a dependent clause:
> ...when the goal is to show a reimplantation of something as "higher" level as SD.
Implementing autograd is interesting, but it's not directly in service to our main subject (Stable Diffusion) and would be a major yak shave. Comparable in complexity to the original project.