Isn't this just quantization with extra steps? Can converting the text to an image really be a better way to lossily compress it? (Not that I have any idea what I'm talking about on this topic.)
I also have no idea what I'm talking about, but to me this seems closer to the "caveman mode" that some people use to compress info into fewer tokens. Going through the image tokenizer allows you to leave the source text untouched while still gaining (some of?) the benefits
No, quantization is applied to model weights or the KV cache (the model activations of all past tokens), and is just storing everything with lower precision (carefully, so that it doesn't hurt performance much).
Sending an image of text instead of text reduces the number of input tokens, but they're still being processed by the model at the same precision. This probably also hurts performance in some way – the question is by how much.