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One, the diffusion model's possible output space contains every RGB image ever. But two, it cannot ever possibly contain the original inputs verbatim, because (
by polygamous_bat 4y ago
One, the diffusion model's possible output space contains every RGB image ever. But two, it cannot ever possibly contain the original inputs verbatim, because (the size of the model)/(the size of the training set) comes out to be something like 0.2 KB per image. Unless it's an incredible compression algorithm, diffusion necessarily have learned something from the input rather than copy-pasting things, as claimed upthread.
- schemescape 4y agoI edited my post a while ago, but I shouldn't have ask about "verbatim" copies. See reply to sibling for a more interesting question.
- TeMPOraL 4y ago> Unless it's an incredible compression algorithm, diffusion necessarily have learned something from the input rather than copy-pasting things, as claimed upthread. Arguably, "learning" and "compression" are the same thing. In this sense, you can view SD as a compression algorithm where the decoder is the model, the compressed file is the prompt + tweakable params, and there aren't any error checks made, so you can feed random data into the decoder and get something out.
- int_19h 4y agoOr maybe the compressed file is the model, and prompt + tweakable params is a "path" inside that compressed file?