If there is a paired dataset, I guess this could be "easy". The stylegan "input" is essentially used to control parameters at various stages within the network, so you could adjust them one at a time, or on varying schedules or something, to get the sort of gradual effect.
I know that randomly instantiated neural networks can produce some pretty trippy image transformations as well, so maybe there is a way to bootstrap without paired data.
Lastly, I doubt this is on the right track but it would be cool if you could produce appropriately styled images with a "compression" approach. ie, trying to fit the audio information into some small visually meaningful latent space, and then using that to generate images.
edit: ok just watched the first example, back to square 1 for me. Its literally pulling stuff from paintings.
My co-founder has a YouTube channel where he will be going into technical detail how this all works. He will be posting a video in the coming weeks. https://www.youtube.com/channel/UCNIkB2IeJ-6AmZv7bQ1oBYg
Also out of curiosity, did you determine anything about the legality of training the GAN on copyrighted images and decidings its output is its own creative work, or using public domain images?