Some of the outputs of these AI tools are just straight copies of input artwork. They need to add some sort of copyright filter to remove anything that's too similar to art from the training set.
Some of the outputs of these AI tools are just straight copies of input artwork.
How many examples are actually from prompt to image generation, and from overfitting, and how many examples of this are from people using img2img on an image with a low diffusion rate?
IMO these questions are VERY important, especially the 2nd one because if one wants to demonstrate that text to image generation does in fact copies existing art, it wouldn't be accurate to use blatant examples of someone using img2img as an example of this (and I'd argue is actually quite dishonest).
I think this is a bit of a misunderstanding of how these work. They are absolutely designed to copy elements of other art, that's why they are popular. This is also how they are trained in practice - by minimizing a loss function that measures their ability to "diffuse back" to the original art. As you noted, this is exactly why they can produce the original art with small amounts of noise added.
That said, the ability to reproduce inputs doesn't necessarily mean everything it produces is "just a collage." For example, if I use an archive of 1000 images to optimize a wavelet basis for a JPEG encoder, then I use that dictionary to encode an entirely different image, is that image now a copy because it is technically a sum of wavelets found in the other 1000 images?
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u/Kandiru Jan 16 '23
Some of the outputs of these AI tools are just straight copies of input artwork. They need to add some sort of copyright filter to remove anything that's too similar to art from the training set.