I think what I'm saying is that I don't understand why the embedding exists. I assume it's some kind of training and inference cost issue? But why can't the Gem

I think what I'm saying is that I don't understand why the embedding exists. I assume it's some kind of training and inference cost issue? But why can't the Gemma architecture linked above just learn to represent pixels in the LLM model's embedding space directly, rather than having the embedding from 48 x 48 pixel chunks? Or rather, give the embedding model some context to produce the embedding? (Which, as you note, wouldn't really be an embedding anymore, but seems like it would better understand fine detail)