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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

#161

Post by foundart »

An AI "student" which can be used to infinitely scale production of derivative works can easily be used to create works that are copyright violations.

It can also be used to create works that are probably not copyright violations and yet seem to be unfair because they deliver large economic benefits to the deployers of the AI while relying on the uncompensated creativity of the original artists.

The interesting questions to me here are:

1. Should we attempt to modify copyright law to reflect this sense of unfairness?

2. Is it even possible to do so?

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#162

Post by PartiallyTyped »

8n4vidtmkvmk wrote: post #130
realusername wrote: post #19

Earlier quoted context omitted.

The opposite way, the training images are there to support the model to generalize features. Reproducing parts of existing images in the dataset is called overfitting and is considered a failure of the model.

how do you measure success? i wrote an OCR program in college. we split the data set in half. you train it on one half then test it against the other half. you can train stable diffusion on half the images, but then what? you use the image descriptions of the other half and measure how similar they are? in essence, attempting to reproduce exact replicas. but i guess even then it wouldn't be copyright if those images…

FID score is a measure of success.

Instead of aiming to reproduce exact replicas, you use a classifier and retrieve the input of the last layer. Do it for both generated and original inputs, and then measure the differences in the statistics.

Wikipedia has a good article on this.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#163

Post by XorNot »

TheDong wrote: post #143
hgomersall wrote: post #132

Earlier quoted context omitted.

Some years ago I had an idea to have a method of file sharing with strong plausible deniability from the sharer. The idea, in stage one, was to split a file into chunks and xor those with other random chunks (equivalent to a one-time pad), those chunks as well as the created random chunks then got shared around the networks, with nobody hosting both parts of a pair. The next stage is that future files inserted into t…

I think this touches on the core mismatch between the legal perspective and technical perspective. Yes, on a technical level, those chunks are random data. On the legal side, however, those chunks are illegal copyright infringement because that is their intent, and there is a process that allows the intent to happen. I can't really say it better than this post does, so I highly recommend reading it: https://ansuz.soo…

Except you've a heckin' problem with Stable Diffusion because you have to argue that the intent is to steal the copyright by copying already existing artworks.

But that's not what people use Stable Diffusion for: people use Stable Diffusion to create new works which don't previously exist as that combination of colors/bytes/etc.

Artists don't have copyright on their artistic style, process, technique or subject matter - only on the actual artwork they output or reasonable similarities. But "reasonable similarity" covers exactly that intent - an intent to simply recreate the original.

People keep talking about copyright, but no one's trying to rip off actual existing work. They're doing things like "Pixar style, ultra detailed gundam in a flower garden". So you're rocking up in court saying "the intent is to steal my clients work" - but where is the clients line of gundam horticultural representations? It doesn't exist.

You can't copyright artistic style, only actual output. Artists are fearful that the ability to emulate style means commissions will dry up (this is true) but you've never had copyright protection over style, and it's not even remotely clear how that would work (and, IMO, it would be catastrophic if it was - there's exactly one group of megacorps who would now be in a position to sue everyone because try defining "style" in a legal sense).

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#164

Post by yazaddaruvala »

XorNot wrote: post #149
dylan604 wrote: post #108

Earlier quoted context omitted.

Just because I look at an image does not mean that I can recreate it. storing it in the training data means the AI can recreate it. There's a world of difference that you are just writing off.

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 it has already become a fun debate to watch.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#165

Post by synu »

forgotusername6 wrote: post #147
visarga wrote: post #88

Earlier quoted context omitted.

That's a huge understatement. 5 billion images to a model of 5GB. 1 byte per image. Let's see if one byte per image would constitute a copyright violation in other fields than neural networks.

The distribution of the bytes matters a bit here. In theory the model could be over trained against one copyrighted work such that it is almost perfectly preserved within the model.

You can see this with the Mona Lisa. You can get pretty close reproductions back by asking for it (or at least you could in one of the iterations). Likely it overfit due to it being such a ubiquitous image.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#166

Post by dylan604 »

realusername wrote: post #119
dylan604 wrote: post #108

Earlier quoted context omitted.

Just because I look at an image does not mean that I can recreate it. storing it in the training data means the AI can recreate it. There's a world of difference that you are just writing off.

> 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.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#167

Post by IncRnd »

realusername wrote: post #160
IncRnd wrote: post #146

Earlier quoted context omitted.

Go to stablediffusionweb.com and enter "a person like biden" into the box. You will see a picture exactly like President Biden. That picture will have been derived from the trained images of Joe Biden. That cannot be in dispute.

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

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#168

Post by astrange »

yazaddaruvala wrote: post #158
magnat wrote: post #60

Earlier quoted context omitted.

And how that's different from gzip or base64, which can re-create original image when given appropriate input?

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.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#169

Post by visarga »

8n4vidtmkvmk wrote: post #116
limitedsupply wrote: post #56

Earlier quoted context omitted.

These models produce a lot of “in the style of” content, which is different from an exact copy. Is that different enough? I guess that’s what this lawsuit is going to be about.

I've seen some overtrained models. they keep showing the same face over and over again. surely from the training data. i don't think you can argue against stable diffusion as a whole, but maybe specific models that haven't muddled the data enough to become something unique

It's a small industry to fine-tune a model on your photos to generate fantasy images of yourself / to see yourself in a different way.

Re: We’ve filed a law­suit chal­leng­ing Sta­ble Dif­fu­sion

#170

Post by astrange »

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.

That is not the point of using the training data. It's specifically trained to not do that.

See https://openai.com/blog/dall-e-2-pre-training-mitigations/ "Preventing Image Regurgitation".

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