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We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

stablediffusionlitigation.com

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Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#171

Post by astrange »

yazaddaruvala wrote: post #164
XorNot wrote: post #149

Earlier quoted context omitted.

No, it means there is a 512 bit number you can combine with the training data to reproduce a reasonable though not exact likeness (attempts to use SD and others as compression algorithms show they're pretty bad at it, because while they can get "similar" they'll outright confabulate details in a plausible looking way - i.e. redrawing the streets of San Francisco in images of the golden gate bridge). Which of course t…

> It's equivalent to trying to sue a compression codec because a specific archive contains a copyrighted image. This is the most salient point in this whole HN thread! You can’t sue Stable Diffusion or the creators of it! That just seems silly. But (I don’t know I’m not a lawyer) there might be an argument to sue an instance of Stable Diffusion and the creators of it. I haven’t picked a side of this debate yet, but i…

You can't (successfully) sue the creators of Stable Diffusion because they're an academic group in Germany, a country that has an explicit allowance in copyright law for training non-commercial models.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#172

Post by visarga »

realusername wrote: post #151
idle_zealot wrote: post #17

Earlier quoted context omitted.

It's a pretty funny assertion. The whole point of ML models is to take training data and learn something general from it, the common threads, such that it can identify/generate more things like the training examples. If the model were, as they assert, just compressing and reproducing/collaging training images then that would just indicate that the engineers of the model failed to prevent overfitting. So basically the…

As a side discussion, is there any research model which tries to do what they describe? Like overfitting to the maximum possible to create a way to compress data. It might be useful in different ways.

Yes, look at NeRF (neural radiance fields) and SIREN (Implicit Neural Representations with Periodic Activation Functions)

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#173

Post by yazaddaruvala »

forgotusername6 wrote: post #134
manholio wrote: post #84

Earlier quoted context omitted.

But they are not original works, they are wholly derived works of the training data set. Take that data set away and the algorithm is unable to produce a single original pixel. The fact that the derivation involves millions of works as opposed to a single one is immaterial for the copyright issue.

If I were to take the first word from a thousand books and use it to write my own would I be guilty of copyright violations?

Words have a special carve out in copyright law / precedent. So much so that a whole other category of Intellectual Property exists called Trademarks to protect special words.

But back to your point “if you were to take the first sentence from a thousand books and use it in your own book”, then yes based on my understanding (I am not a lawyer) of copyright you would be in violation of IP laws.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#174

Post by astrange »

akjetma wrote: post #35
Salgat wrote: post #26

Earlier quoted context omitted.

The difference here is that the images aren't stored, but rather an extremely abstract description of the image was used to very slightly adjust a network of millions of nodes in a tiny direction. No semblance of the original image even remotely exists in the model.

there are some artists with very strong, recognizable styles. if you provide one of these artists' name in your prompt and get a result back that employs their strong, recognizable style, i think that demonstrates that the network has a latent representation of the artists work stored inside of it.

Or it means their style is so easy to recognize that you can see it even when it doesn't exist.

The most common example of this (Greg Rutkowski) is not in StableDiffusion's training set.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#175

Post by realusername »

IncRnd wrote: post #167
realusername wrote: post #160

Earlier quoted context omitted.

Just because it generates you an image like Biden still does not make it a derivative either. You can draw Biden yourself if you're talented and it's not considered a derivative of anything.

There is no need for rhetorical games. The actual issue is that Stable Diffusion does create derivatives of copyrighted works. In some cases the produced images contain pixel level details from the originals. [1] [1] https://arxiv.org/pdf/2212.03860.pdf

> The actual issue is that Stable Diffusion does create derivatives of copyrighted works.

Nothing points to that, in fact even in this website they had to lie on how stablediffusion actually works, maybe a sign that their argument isn't really solid enough.

> [1] https://arxiv.org/pdf/2212.03860.pdf

You realize those are considered defects of the model right? Sure, this model isn't perfect and will be improved.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#176

Post by realusername »

dylan604 wrote: post #166
realusername wrote: post #119

Earlier quoted context omitted.

> storing it in the training data means the AI can recreate it. No it doesn't, it means that abstract facts related to this image might be stored.

The pedantry gets tiring. If the AI can't recreate it exactly, it can recreate a likeness that is compelling enough that the average person would think it was the same. If it can't now, it will as it gets better. That's the point of using the training data.

> If the AI can't recreate it exactly, it can recreate a likeness that is compelling enough that the average person would think it was the same

That's the opposite goal of this image model. Sure you might find other types of research models which are meant to do that but that's not stablediffusion and the likes.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#177

Post by visarga »

PartiallyTyped wrote: post #154
fruit2020 wrote: post #16

Earlier quoted context omitted.

How does it work then? :)

Diffusion models learn a transformation operator. The parameters are adjusted such that the operator maximises the evidence lower bound, or in other words, increasing the likelihood of observing a slightly less noisy version of the input. The guidance component is a vector representation of the text that changes where we are in the sample space. A change in the sample space changes likelihood so for the different pro…

> A good way to think about it is this: given a classifier, I can select a target class and compute the derivative of the input with respect to the target class, and apply the derivative to the input. This puts it closer to my target class.

excellent description, thanks

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#178

Post by lelanthran »

iamacyborg wrote: post #157
lelanthran wrote: post #51

Earlier quoted context omitted.

That's a lie, sure, but if they had instead claimed: The output of stable diffusion isn't possible without first examining millions of copyrighted images Then the suit looks a little more solid, because (as you pointed out) it isn't possible for the stable diffusion owner to know which of those copyright images had clauses that prevents stable diffusion trading and similar usage. The whole problem goes away once arti…

> The whole problem goes away once artists and photographers starting using a license that explicitly removes any use of the work as training data for any automated training. A license which should be opt-in, not opt-out. Of course, it’s opt-out because they know, fundamentally, that most artists would not want to opt-in.

> A license which should be opt-in, not opt-out.

I dunno if it matters that the opt-in has to be at the legislation level.

After all, once Creative Commons adds that clause to their most popular license, it's game over for training things like Stable Diffusion.

I'm thinking that maybe the most popular software licenses can be extended with a single clause like "usage as training data not allowed".

Of course, we cannot retroactively apply these licenses so the current model will still be able to generate images/code; they just won't be able to easily use any new ones without getting into trouble.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#179

Post by synu »

astrange wrote: post #168
yazaddaruvala wrote: post #158

Earlier quoted context omitted.

That’s my point, Diffusion[1] does seem to be “just like” gzip or base64. And it would be illegal for me to sell or distribute zipped copies of images without the copyright holder’s consent. Similarly there might be an argument for why Diffusion[1] specifically can’t be built with copyrighted images. [1] which is just one part of something like Stable Diffusion

A lossy compressor isn't just like a lossless compressor. Especially not one that has ~2 bytes for each input image.

How many bytes make it an original work vs a compressed copy?

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#180

Post by astrange »

minimaxir wrote: post #52

Oddly, there's no mention of CLIP in the post and filing, where CLIPText is the real secret of Stable Diffusion's ability to correlate text to image for all the iterations targeted by the plantiffs. I suspect adding OpenAI as a defendant would make things a tad harder legally.

SD 2.x doesn't use CLIPText anymore, although if they don't know the difference between 1.x and 2.x their argument isn't going to be very good.
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