If we are going to go technical I would argue that artists generate a highly derivative work, i.e. they input the "training data" via their eyes, experience of the world, ruminating (is that the word?) about art... and then output motions of their hands, words, and so on which create new artistic work.

Diffusion models tune a series of numbers via a complicated but essentially straightforward and "pipeline-like" process from the training data, so their work is significantly less derivative, even if visually it looks different. I guess we all agree that say, a encrypted zip file of a song is not a derivative, it's the song after some data processing.

Maybe we should improve the definition of derivative work to express how many layers of indirection lie between training material and output? If I trace over a painting I guess most would agree I've barely done any art, and I think that the process is more or less "pipeline-like" (even if complicated): input data is immediately converted to "art" by my brain following the lines. An artist that studies Goya for years and slowly improves his Goya-like portraits is another kind of level of indirection between training material and output!

Many of the great artists also produced art that fit right in with the zeitgeist. Impressionists, realists, surrealists, etc. Classic rock songs as variations on a theme. There were very few pioneers of each new style and hundreds of inspired (copycat) artists. Do we discount their art as well? Certainly not!

Just go look at $_SECTION in your local bookstore. Most of the books have covers that fit right in with all the other covers on the shelf.

So my question is whether a computer doing the same thing is a difference in degree only, or in kind? But I think most people with strong opinions (and itchy downvote fingers) are coming at this from a moral outrage perspective (massive theft of IP / disregard for humans).