I'm going to try to plead my case for images generated using sophisticated prompt engineering to be copyrightable. For example, at the point that I've written a prompt with 20 tags, 10 negative prompt tags, some loras, custom weights, embeddings merges, and prompt editing, I'm now writing what is effectively a "program", which should be copyrightable and so should its outputs. It's total BS to me that a book of midjo…
Now let's reduce it by another image. Again, less options for what to display, fewer images to take pixels from, but still a lot of options, output may sound copyrightable.
Now lets do that N-1 times. What output will we get when the model was trained on a single image, let's say an image that is labeled 'dog'. If your prompt is "an image of a dog" you will get that image, the only image in the training set. When going from latent space to image space, taking pixels from that image in the output, despite it being done in convoluted ways, is that not an obvious copyright infringement? I think it is. There's a cloud of mumbo jumbo about latent space, but after the dust settles and it needs to generate pixels in the output image, Stable Diffusion has a step that is essentially copying pixels from the source image into the output. When there's only 1 image, it will reproduce large portions of that image, necessarily infringing on copyright.
So then adding back images one by one into the training set, each one being used as source for the pixels being copied, what makes that model OK? Just because the output is 50% image A and 50% image B, or 0.1% image A and 0.1% image B and 99.8% image C, doesn't suddenly make it OK.
Once there are millions of images, you end up with just tiny blobs of pixels being copied from many different images. That's still infringes on the copyright of all those images, because it's essentially a map-reduce process that maps pixels from copyrighted images and reduces them into a single image.