HN user
... sardines in a can ...
Better analogy: suppose you take 10 pieces of A4 paper and scrunch them up into 10 separate balls. Now suppose you scrunch them all up together into one ball. One manifold, taking ten times less space.
In the same way, you don't need to store each pixel of each image separately, or in its own sub-space. Yes? It's really good compression, that's all.
It doesn't compress each image separately. Rather, it compresses its entire training set into one dense region of cartesian space. Each pixel of each training image is in there, scurnched up next to every other pixel of every other training image. It's like sardines in a can, except in thousands (?) of dimensions. You have no hope of calculating how much space it needs for that just by dividing the size of the model by the dimensions of images in pixels, like you did.
You can see that it has memorised all its training material very clearly in its ability to perform style transfer. You can ask it to geneate an image of Pikachu in the style of Vermeer. It can do that because it has memorised images of Pikachu and images by Vermeer in its weird compressed form and it can combine them by finding a point on a gradient between their representation in its memory.
If it didn't have memorised images of Pikachu, or images by Vermeer, how do you guess it could generate images of Pikachu or in the style of Vermeer? It's not magick.