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>if I write a style parody of William Shakespeare and add a '--Willy Shakespeare' at the end to round it off, have I revealed that I have secretly copied his work? //

I doubt you're suggesting SD, Dall-E, etc., are producing parodies so bringing in parody considerations muddies the water a lot. Also, Shakespeare's works are out of copyright.

If you sell a painting signed with a [facsimile] signature of Dali then it's pretty hard to say you didn't copy the signature, as a minimum. Thats likely to be a trademark violation too. Now, suppose you include aspects in the image specifically associated with the artist, and a signature, ... there's no way to genuinely deny that is a derivative.

> a painting signed with a [facsimile] signature of Dali

That's not what's happening here though.

If you look at the original tweet (https://twitter.com/LaurynIpsum/status/1599953586699767808) it seems that the complaint is about the "mangled remains of an artist’s signature". I don't see any examples where it's actually copying the signature of a specific artist.

(Please do share an example of that if there is one.)

I do respect artist's concerns. I have a hard time getting this one though. The ai learned that humans usually put squiggly lines in the corners, and it does, too. What is wrong with this?

It's reasoning based on a false assumption. If you start from the assumption that AI imagery is based on specific source images, which had signatures, then it can make sense to assume that the squiggly lines in the corner are evidence that it copied-and-mangled some such specific pictures.

That's not what it did, of course. But if you start from the assumption...

Nobody is complaining about William Shakespeare or public domain works that are out of copyright. The issue is that there is clearly copyrighted works in the model that living artists have not consented to being in the model.

> SD et al are defended on the grounds of being transformative use they do not distribute (ie copy) the original training images, and they are not a derivative work due to transformativeness, so the license of the original images is completely irrelevant.

If this is irrelevant, why is Stability.AI creating an "opt out" system for artists? It's almost like they know they are copying digital artists works without their explicit consent. This wouldn't be an issue if they only used images in the public domain.

https://www.technologyreview.com/2022/12/16/1065247/artists-...

And we both know that Stable Diffusion have Dreambooth which you have to pay to use the platform, so this is clearly not fair use.

https://platform.stability.ai/docs/getting-started/credits-a...

> If this is irrelevant, why is Stability.AI creating an "opt out" system for artists? It's almost like they know they are copying digital artists works without their explicit consent. This wouldn't be an issue if they only used images in the public domain.

It might also be because there's a huge backlash against the concept of stable diffusion in certain circles. Whether or not that backlash matches the legalities, or if their logic is sound at all, has absolutely nothing to do with it.

> Nobody is complaining about William Shakespeare or public domain works that are out of copyright.

They definitely are, and I deliberately chose a public domain writer for my example to get away from the narrow procedural grounds and focus on the issue: does generating a 'signature' or 'watermark' prove copying? No, of course not, and that's true whether you are in or out of the public domain.

> If this is irrelevant, why is Stability.AI creating an "opt out" system for artists?

What is prudent or nice or moral or good PR != legal. (And vice-versa, of course.)

Does anyone know if attempts have been made to trick these ML models into reproducing original copyrighted inputs verbatim (edit: or close enough)?

Edit: Asking about verbatim copies wasn't really a great question. I should have asked about producing things that are "close enough to cause legal trouble" (whether that be due to copyright, trademark, or something else).

There's been a lot of work on memorization, yes, and you can also do nearest-neighbor lookups in the original data to gutcheck 'memorization'. As usual, the answer is "it's complicated" but for most practical purposes, the answer is 'no': you will get the Mona Lisa if you ask for it, absolutely, but the odds of a randomly generated image being a doppelganger is near-zero. (If you've seen stuff on social media to the contrary, then you may have been misled by various people peddling img2img or 'variation' functions, or prompting for it, or other ways of lying/ignorance.)

But you certainly can get things like watermarks without any real memorization. Watermarks have been a nuisance in GANS, for example - the StyleGAN CATS model was filled with watermarks or attempted meme text captions, even though the cats were so nightmarish that they obviously weren't 'plagiarized' so nobody made a big deal about it back then and they understood the GAN had simply learned that watermarks were a thing in many real images and it would try to imitate them where plausible in a sample.

If there's a specific prompt that makes the model produce such an image (without img2img), why shouldn't it count? The question isn't whether the model produces such things randomly, but rather whether it's capable of producing them in principle, even if it requires a very elaborate prompt.

> If there's a specific prompt that makes the model produce such an image (without img2img), why shouldn't it count?

There's a specific prompt which makes a human artist produce such an image without img2img too: "Please draw the Mona Lisa". There - you just did it in your head while reading this comment!

So? Humans aren't copyrightable works. But a bunch of weights that constitutes the model is just data, and that data can very well contain copyrighted works. The question is whether it does in any meaningful sense. And if it can reproduce them verbatim with the right prompt, I don't see how the answer could be "no", anymore so than a password-protected archive of a copyrighted JPEG.

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.

> 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.

Or maybe the compressed file is the model, and prompt + tweakable params is a "path" inside that compressed file?

I edited my post a while ago, but I shouldn't have ask about "verbatim" copies. See reply to sibling for a more interesting question.

That's not really how memorization in neutral networks works. For classifiers, memorization is more like learning a hash function and a lookup table; no need to store the full image at all. Even for very large models, the weights are a tiny fraction of the size of the original data.

It's probably helpful to think of embeddings for generative models in a similar way; it's a very specific embedding function, like a locality sensitive hash, which doesn't require actually storing the data.

Thanks. Yes, I shouldn't have asked about "verbatim" copies -- I should have asked about something more like "close enough to cause legal trouble". Obviously copying verbatim is a violation of copyright, but there must be some threshold of "close enough" that is still problematic. E.g. compressed MP3s of copyrighted songs aren't a verbatim reproduction, but as far as I'm aware they're still covered by copyright.

Trademarks are even broader.

A band playing publicly a reinterpretation of a famous band will get sued even if its not verbatim.

That’s an even better example.

That's a known failure mode called "overfitting" or "memorization", where a specific input text is very accurately reproduced.

I'm not aware of it occurring for any copyrighted inputs, but it occurs for many famous artworks-- it's nearly impossible to convince Stable Diffusion to style "Mona Lisa" at all.

> distributing the original image violates the non-commercial clause of the original images because someone, somewhere, might somehow have made money

I agree with the rest of your post, but this point seems a bit uncharitable.

I think the claims would be:

1. It's a breach of copyright for Megaface to share the images in any case without attribution & replicating the CC-NC license. It would (presumably) be OK assuming Megaface were to correctly apply the CC-NC licenses to the dataset.

2. It's a breach of copyright for anyone consuming Megaface (e.g. Google) to use those images for commercial purposes.

And your argument for SD applies to 2. that regardless of license, it's OK to create a transformative work. But it still doesn't get Megaface off the hook for 1. - distributing those images without the license.