I like to think of stable diffusion as decompressing a text prompt into a 512x512 image. In some sense the 10 word text prompt and seed contains the same amount of information as a 262k pixel image. You could say to a transformer a picture is only worth a few words.

By extension a if every image in the stable diffusion dataset compresses down to a sentence of text, then stable diffusion needs "only" 260M images * 10 words worth of information to train. Gpt3 on the other hand was trained on 45TB of text data.