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-ZeroRelevance- t1_iwbijk5 wrote

Reply to comment by visarga in Ai art is a mixed bag by Nintell

Artefacts from the original model will be amplified, and the generations will gradually become more divorced from reality with each loop. This can only really be solved with either some kind of teacher agent, or giving the AI much more real training data than synthetic training data.

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visarga t1_iwdz0jc wrote

But if you have a selection process it might become a virtuous cycle. An evolutionary art system based on humans and AI.

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-ZeroRelevance- t1_iwe0va9 wrote

I suppose you could train a GAN to differentiate between real and artificial art, and then only train the next model on the art it thought was real, but it probably won’t mitigate the problem entirely. It’ll definitely help though.

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visarga t1_iwh8n84 wrote

I would first collect examples of frequent issues: double heads, noodle hands, deformities. These are the negative examples. I would collect positive examples from the training set because those images are supposedly normal, but match them as well as possible to the negative examples with cosine similarity. Train a rejection model.

To generate prompts I would finetune gpt-2 on a large collection of prompts crawled from the net. Put the prompts into SD, reject deformed images. Rank the images with an image quality model (probably easy to find), keep only the high quality ones.

You can generate as many images as you like. They would be un-copyrightable because they have been generated end-to-end without human supervision. So just great for making a huge training set for AI art.

You could also replace all known artist names with semantic hashes to keep the capability of selecting styles without needing to name anyone. We would have style codes or style embeddings instead of artist names.

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