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 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.
Exactly, the quarrel here is between the users of Stable Diffusion, some of which are deliberately, legally speaking with intent (prompt crafting to get a specific output demonstrates clear intent), trying to use Stable Diffusion to produce images that are highly derivative of and may or may not be declared legally infringing works of another artist, and the artists who’s works are being potentially infringed upon.
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.
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.
I would assert (with no legal backing, since this is the first suit that actually attempts to address the issue either way) that the trained model is a copyright infringement in itself. It is a novel kind of copyright infringement, to be sure, but I believe that use of copyrighted material in a neural net's training set without the creator's permission should be considered copyright infringement without any further act required to make it so.
I think this is a reasonable question for the uninitiated—those for whom "training a neural network" seems like it would be a lot like "teaching a human"—but for those with deeper understanding (tbh, I would only describe my knowledge in both these areas as that of an interested amateur), it is a) a poor analogy, and b) already a settled question in law.
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.
Stable Diffusion is essentially a Compression Codec though. It's one optimised to compress real world images and art, by using statistics gathered from real world images and art.
It's like the compression that occurs when I say "Mona Lisa" and you read it, and can know many aspects of that painting.
I will admit to knowing the overall underlying technology better than the details of what specific implementations consist of. My understanding is, though, that "Stable Diffusion" is both a specific refinement (or set of refinements) of the same ML techniques that created DALL-E, Midjourney, and other ML art generators, and the trained model that the group working on it created to go with it.
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.
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's probably a very relevant point. (I'm guessing.) If I ask for an image of a red dragon in the style of $ARTIST, and the algorithm goes off and says "Oh, I've got the perfect one already in my data"--or even "I've got a few like that, I'll just paste them together"--that's a problem.
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.
I realize that's not how it works. My point was that they're apparently taking deliberate steps to try to make sure the model trains over a large number of images and doesn't overfit on a small sample given a sufficiently specific "in the style of," etc.
> 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.
Why does this argument apply to an Artificial Intelligence, but not a human one? A human is not breaking copyright just by being able recreate a copyrighted work they've studied.
It depends to what degree it's literal copying. See e.g. the Obama "Hope" poster. [1] Though that case is muddied by the fact that the artist lied about the source of his inspiration. Had it in fact been an older photo of JFK in a similar pose, there probably wouldn't have been a controversy.
It's not really. It's more like making an entire compression scheme that is very good at compressing images encountered in real life, rather than say, noisy images.
There's a world of difference that you are just writing off.