Quite right, but…
> That argument also makes little sense when you consider that the model is a couple gigabytes itself, it can't memorize 240TB of data, so it "learned".
The matter is really very nuanced and trivialising it that way is unhelpful.
If I recompress 240TB as super low quality jpgs and manage to zip them up as single file that is significantly smaller than 240TB (because you can), does the fact they are not pixel perfect matches for the original images mean you’re not violating copyright?
If an AI model can generate statistically significantly similar images from the training data, with a trivial guessable prompt (“a picture by xxx” or whatever) then it’s entirely arguable that the model is similarly infringing.
The exact compression algorithm, be it model or jpg or zip is irrelevant to that point.
It’s entirely reasonable to say, if this is so good at learning, why don’t you train it without the art station dataset.
…because if it’s just learning techniques, generic public domain art should be fine right? Can’t you just engineer the prompting better so that it generates “by Greg Rutkowski“ images without being trained on actual images by Greg?
If not, then it’s not just learning technique, it’s copying.
So; tldr: there’s plenty of scope for trying to train a model on an ethically sourced dataset, and investigation of techniques vs copying in generative models.
It is 100% not something we can just brush off.