The positive solution is to scrape Wikimedia Commons for everything in "Category: PD-Art-old-100" and train from scratch on that data. Wikimedia Commons is well

The positive solution is to scrape Wikimedia Commons for everything in "Category: PD-Art-old-100" and train from scratch on that data. Wikimedia Commons is well-moderated, the image data is public domain[0], and the labels can be filtered down to CC-BY or CC-BY-SA subsets[1]. Your resulting model will be CC-BY-SA licensed and the output completely copyright-free. For the record, that's what I've been trying to do[2]; my stumbling blocks have been training time and a bug where my resulting pipeline seems to do the opposite of what I ask[3]. I'm assuming it's because my wikitext parser was broken and CLIP didn't have enough text data to train on; I'll have the answer tomorrow when I have a fully-trained U-Net to play with. If I can ever get this working, I want to also build a CLIP pipeline that can attribute generated images against the training set. This would make it possible to safely use CC-BY and CC-BY-SA datasets: after generating [0] At least in the US. Other jurisdictions think that scanning an image recopyrights it, see https://en.wikipedia.org/wiki/National_Portrait_Gallery_and_Wikimedia_Foundation_copyright_dispute https://en.wikipedia.org/wiki/National_Portrait_Gallery_and_... [1] Watch out for anything tagged with https://commons.wikimedia.org/wiki/Template:Royal_Museums_Greenwich https://commons.wikimedia.org/wiki/Template:Royal_Museums_Gr... as that will taint your model. [2] https://github.com/kmeisthax/PD-Diffusion https://github.com/kmeisthax/PD-Diffusion [3] https://pooper.fantranslation.org/@kmeisthax/109486435508334777 https://pooper.fantranslation.org/@kmeisthax/109486435508334...