Two possible reasons, off the top of my head. First, it probably wouldn't work. Stable Diffusion is going to be a lot better at telling whether two images are

Two possible reasons, off the top of my head. First, it probably wouldn't work. Stable Diffusion is going to be a lot better at telling whether two images are the same than a perceptual hash check. E.g. I bet if you removed all pictures of the Mona Lisa from the training data, it could still produce a pixel perfect copy of it, just from the many times it appeared in the background of pictures, or under weird adjustments or lighting that fooled the perceptual hash. Second, I would guess they wanted common images to appear in the training data more than once. It should be trained on the Mona Lisa more than someone's snapchat of their dinner. Common images are more salient. Reminds me of asking Chat-GPT for an obfuscated C contest entry. It produced one verbatim, though it couldn't explain what it did (it looked like syntactic nonsense, but it printed the Twelve Days of Christmas when run). I can only imagine it saw that inscrutable sequence of characters enough times that it memorized it.