The difference is an image generation algorithm does not consume images the way a human does, nor reproduce them that way. If you show a human several Rembrandt's and ask them to duplicate them, you won't get exact copies, no matter how brilliant the human is: the human doesn't know how Rembrandt painted, and especially if you don't permit them to keep references, you won't get the exact painting: you'll get the elements of the original that most stuck out to them, combined with an ethereal but detectable sense of their original tastes leaking through. That's how inspiration works.
If on the other hand you ask an image generator for a Rembrandt, you'll get several usable images, and good odds a few them will be outright copies, and decent odds a few of them will be configured into an etsy or ebay product image despite you not asking for that. And the better the generator is, the better it's going to do at making really good Rembrandt style paintings, which ironically, increases the odds of it just copying a real one that appeared many times in it's training data.
People try and excuse this with explanations about how it doesn't store the images in it's model, which is true, it doesn't. However if you have a famous painting by any artist, or any work really, it's going to show up in the training data many, many times, and the more popular the artist, the more times it's going to be averaged. So if the same piece appears in lots and lots of places, it creates a "rut" in the data if you will, where the algorithm is likely going to strike repeatedly. This is why it's possible to get full copied artworks out of image generators with the right prompts.