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ZorbaTHut  2yr ago (text post)   3691 thread views

Friday Fun Thread for September 30, 2022

Be advised; this thread is not for serious in depth discussion of weighty topics, this thread is not for anything Culture War related. This thread is for Fun. You got jokes? Share 'em. You got silly questions? Ask 'em.

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What are they training it on? Only a few hundred k, so presumably they're not throwing the entire e621 catalog at it yet.

From my understanding, the major trainers have largely downloaded a subset of e621 data, filtered by upvote score, and then by content. Furry and Yiffy to both SFW and less-extreme-kink NSFW, with different thresholds and limits. Zach3d on 'texture' fetish and a few specific species mixed with a small subset of general pictures with a higher upvote score. I think most have also filtered out material that they think is likely to cause artifacting, either technical stuff like severe jpg compression, or many-panel comic pieces.

So it's relatively easy to grab sets and train a model, to the point that groups of amateurs can do it? That's good news: I had a vision of all art production being censored by the kind of political commissars who inevitably take over large projects.

The idea of being able to restrict artists at the canvas level must be making some of them drool.

As far as I know, each of these datasets has been curated by one person, to their respective tastes. Hasuwoof for Yiffy, DirtyApples for Furry, and Zach for Zach3d. e621 is well-enough tagged for high-score posts that it seems fairly automatable, and as long as you're not abusing the download process, it's hard to tell a normal user from an archiver, especially if you filter before download. And the code itself is... not fun, since it's poorly documented python in most parts, but it's nothing ridiculous.

((There's a My Little Pony-specific one that's supposed to have been released recently, but I know less about that.))

There's been some discussion of setting up teams for difficult heavy lifting (eg, improving tagging, building and parsing datasets with more eyes-on-curation), but the big issue for now are cost and technical accessibility. The core model is expensive because it took literally millions of steps in a large dataset, but further tuning is relatively cheap, with most epochs taking less than a day on a single (beefy) cloud GPU server. But getting the data together and onto that machine rapidly enough can be complex to do right, and easy to end up with a staggering AWS bill if done wrong.

That'll be less an issue if newer GPU generations continue to bulk up on VRAM; if done at home, it's mostly an energy (and/or cooling) bill thing. And that might be coming as soon as this winter for people willing to splurge on the higher-VRAM versions of the 4090.

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