Stable Diffusion found to emit some images from training at generation time

Re:Stable Diffusion found to emit some images from training ... by Kye on Wednesday February 01, 2023 @10:22AM (#34611713)

Now imagine you're an expert witness on the stand in the case that eventually settles this. I don't think this will be persuasive even if it's technically sound. How you end up with a copyrighted work doesn't seem relevant: the model still produced something eerily similar to a copyrighted work.

"Copyright laundering" will be the phrase of the era. Throw Picasso in with a thousand reproductions, wash it with DeviantArt, get Picasso back out. Does it matter than it's algorithmically derived rather than a stroke-by-stroke reproduction? There's probably already ample case law around reproductions to deal with this.

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Re:Stable Diffusion found to emit some images from training ... by kilgnad on Wednesday February 01, 2023 @11:49AM (#34613281)

It's technically sound though. Let me give you an example.

   y = f(x) = x + 2

This is what's memorized. But with that equation you can plug in a specific x. to get:

   3 = f(1) = 1 + 2

The training set here would be (1,3). What is memorized is y = f(x) = x + 2. You can literally see that (1,3) is NOT in the model EVEN though that model CAN produce a (1,3) given the right input.

I think the technical part of this is sound. People will just take the face value explanation which is I see a 3! therefore a THREE was memorized. But technically a 3 WAS not memorized. This is categorically true.

You are Right. The persuasive part of this argument is not very good though as you can see by this thread.

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