There's a world of difference that you are just writing off.
No it doesn't, it means that abstract facts related to this image might be stored.
Which of course then arrives at the problem: the original data plainly isn't stored in a byte exact form, and you can only recover it by providing an astounding specific input string (the 512 bit latent space vector). But that's not data which is contained within Stable Diffusion. 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.
See https://openai.com/blog/dall-e-2-pre-training-mitigations/ "Preventing Image Regurgitation".
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.
You can’t sue Canon for helping a user take better infringing copies of a painting, nor can you sue Apple or Nikon or Sony or Samsung… you can sue the user making an infringing image, not the tools they used to make the infringing image… the tools have no mens rea.
That's plainly untrue, as Stable Diffusion is not just the algorithm, but the trained model—trained on millions of copyrighted images.
Compression that returns something different from the original most of the time, but still could return the original.
Except with computers, they don't need to eat or sleep, converse or attend stand-ups.
And once you're able to draw that one picture, you could probably draw similar ones. Your own style may emerge too.
Just thinking. Copywriters, students, and scribes used to copy stuff verbatim, sometimes just to "learn" it.
The product of that study could be published works, a synthesis of ideas from elsewhere, and so on. We would say it belonged to the executor, though.
So the AI learned, and what it has created belongs to it. Maybe.
Or, once we acknowledge AI can "see" images, precedent opens the way to citizenship (humanship?)
[1] https://en.wikipedia.org/wiki/Barack_Obama_%22Hope%22_poster
It does have Mona lisa because of over fitting. But that's because there is too much Mona lisa on internet.
These artist taking part in suit won't be able to recreat any of their work.
SD might know how to violate copyright but is that enough to sue it? Or can you only sue violations it helps create?
Kind of like recreating your image one object at a time. It might not be exact, but close enough.
Best you can do is to mask and keep inpainting the area that looks different until it doesn't.
That’s said, it does raise the question, “should this precedent be extended to humans?”
i.e. Can humans be taught something based on copyrighted materials in the training set/curriculum?
To address (b) first: Fair Use has long held that educational purposes are a valid reason for using copyrighted materials without express permission—for instance, showing a whole class a VHS or DVD, which would technically require a separate release otherwise.
For (a): I don't know anything about your background in ML, so pardon if this is all obvious, but at least current neural nets and other ML programs are not "AI" in anything like the kind of sense where "teaching" is an apt word to describe the process of creating the model. Certainly the reasoning behind the Fair Use exception for educating humans does not apply—there is no mind there to better; no person to improve the life, understanding, or skills of.
At some point the input must be considered part of the work. At the limit you could just describe every pixel, but that certainly wouldn’t mean the model contained the work.
As a thought experiment, imagine a variant of something like SD was used for music generation rather than images. It was trained on all music on spotify and it is marketed as a paid tool for producers and artists. If the model reproduces specific sounds from certain songs, e.g. the specific beat from a song, hook, or melody, it would seem pretty straightforward that the generated content was derivative, even though only a feature of it was precisely reproduced. I could be wrong but as far as i am aware you need to get permission to use samples. Even if the content is not published those sounds are being sold by the company as inspiration, and therefore that should violate copyright. The training data is paramount because if you trained the model on stuff you generated yourself or on stuff with appropriate CC license, the resulting work would not violate copyright, or you could at least argue independent creation.
In the feature space of images and art, SD is doing something very similar, so i can see the argument that it violates copyright even without reproducing the whole training data.
Overall, i think we will ultimately need to decide how we want these technologies used, what restrictions should be on the training data, etc, and then create new laws specifically for the new technology, rather than trying to shoehorn it into existing copyright law.
A trained model holds relationships between patterns/colours in artwork and their affinity to the other images in the model (ignoring the English tagging of images data within this model for a minute). To this degree, it holds relationships between millions of images and the degree of similarities (i.e. affinity weighting of the patterns within them) in a big blob (the model).
When you ask for a dragon by $ARTIST it will find within it's model an area of data with high affinity to a dragon and that of $ARTIST. What has been glossed over in discussion here is that there are millions of other bits of related images - that have lower affinity - from lots of unrelated artwork which gives the generated image uniqueness. Because of this, you can never recreate 1:1 the original image, it's always diluted by the relationships from the huge mass of other training data, e.g. a colour from a dinosaur exhibit in a museum may also be incorporated as it looks like a dragon, along with many other minor traits from millions of other images, chosen at random (and other seed values).
Another interesting point is that a picture of a smiling dark haired woman would have high affinity with Mona Lisa, but when you prompt for Mona Lisa you may get parts of that back and not the patterns from the Mona Lisa*, even though it looks the same. That arguably (not getting Mona Lisa) is no longer the copyrighted data.
* Nb. this is a contrived example, since in SD the real Mona Lisa weightings will out number the individual dark haired woman's many times, however this concept might be (more) appropriate for minor artists whose work is not popular enough to form a significantly large amount of weighting in the training data.
It's like the compression that occurs when I say "Mona Lisa" and you read it, and can know many aspects of that painting.
So while it would be possible to create a "Public Diffusion" that took the Stable Diffusion refinements of the ML techniques and created a model built solely out of public-domain art, as it stands, "Stable Diffusion" includes by definition the model that is built from the copyrighted works in question.