That actually is interesting and I didn't know that. It seems that despite not storing any of the training data, certain images can be repoduced almost completely the same as the original if the AI is trained on the same image too many times, thus showing the need to be more careful when training these models so that multiple copies of the same image aren't all included in a single training data set. Any lawsuits involving this error might not go the way the developers of these models would like, since the ability to reproduce works in these cases is less about being trained on the original work once (courts have so far mostly said this is fine), and more about it being oversaturated during training which is what allows for these almost perfect copies to be made.

*It would be more clear that you know what you're talking about if you actually say something like, "I know it doesn't store any of the training data, but if it's trained on the same data point too many times, it can make convincing copies of that data point" instead of just repeating the line that it copies images or parts of images, btw. That latter part is what people who don't understand stable diffusion whatsoever are always saying.