[kastnerkyle]

Research into "pure" unconditional generation can often lead to gains in the conditional setting. See literally any GAN research, VQ-VAE, VAE, diffusion, etc - all started from "unconditional/low information" pretty much. Both directly (in terms of modeling) and indirectly (by forcing you to really reason about what conditioning is telling you about the modeling, and what's in the data), these approaches really force you to think about what it means to just "make music".

Also, I think artistic uses (such as Dadabots, who heavily used SampleRNN) show clearly that "musicians" like interesting tools, even if uncontrolled in some cases. Tools to exactly execute an idea are important (DAW-like), but so are novelty generating machines like (many) unconditional generators end up being. Jukebox is another nice example of this.

On the "good for elevator music" comment - the stuff I've heard from these models is rarely relaxing enough to be in any elevator I would ride. But there are snippets of inspiration in there for sure.

Generally, I do favor controllable models with lots of input knobs and conditioning for direct use, but there's space for many different approaches in pushing the research forward.

Different creators will work all kind of odd models into their workflows, even things that are objectively less "high quality", and not really controllable. To me, that's a great thing and reason enough to keep pushing unsupervised learning forward.