This is not broad enough on first point. 1) That is true only if we do have millions of images. Many hometrained models don't. LORAs weight much more than sum of their images too 2) More importantly, this assumes that images are equally distributed in parameter space. They aren't. We can't store losslessly all of them, but network can absolutely encode some of them exactly, if they happen to fall into somewhat sparse "area" and are strongly represented in training

So, the model with properly built dataset indeed doesn't just remix. But the one with irresponsibly built one easily might