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Great. Now the defence shows an artist that can recreate an image. Cool, now people who look at images get copyright suits filed against them for encoding those

Great. Now the defence shows an artist that can recreate an image. Cool, now people who look at images get copyright suits filed against them for encoding those images in their heads.

dylan604 4y ago
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.
- realusername 4y ago
  > 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.
  - dylan604 4y ago
    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.
    - astrange 4y ago
      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/ https://openai.com/blog/dall-e-2-pre-training-mitigations/ "Preventing Image Regurgitation".
      - ghaff 4y ago
        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.
        astrange 4y ago
        That's extremely not how it works. If there's only one training example it's not going to remember anything like actual visual details of it.
        ghaff 4y ago
        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.
        SillyUsername 4y ago
        Actually that's partly how it works. 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.
    - realusername 4y ago
      > 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.
    - hhjinks 4y ago
      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.
      - ghaff 4y ago
        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. [1] https://en.wikipedia.org/wiki/Barack_Obama_%22Hope%22_poster https://en.wikipedia.org/wiki/Barack_Obama_%22Hope%22_poster
  - bluefirebrand 4y ago
    This just sounds like really fancy, really lossy compression to me. Compression that returns something different from the original most of the time, but still could return the original.
    - anothernewdude 4y ago
      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.
- XorNot 4y ago
  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.
  - yazaddaruvala 4y ago
    > 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.
    - astrange 4y ago
      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.
    - techdragon 4y ago
      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.
- turtleyacht 4y ago
  If you spent a decade trying to draw it, wouldn't your brain have the right "weights" to execute it pretty exactly going forward? 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?)
smusamashah 4y ago
Don't think stable Diffusion can reproduce any single image its trained on, not matter what prompts you use. 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.
- Aerroon 4y ago
  I think there's a chance they might be able to recreate some simpler work if they make the prompts specific enough. When you set up a prompt you're essentially telling the system what you want it to generate - if you prompt it with enough specificity you might be able to just recreate the image you had. Kind of like recreating your image one object at a time. It might not be exact, but close enough.
  - smusamashah 4y ago
    People have tried, unless the thing you want to recreat has been seen by it a lot (over trained) you won't get the same image. You don't have that much fine grained control via text only. Best you can do is to mask and keep inpainting the area that looks different until it doesn't.
  - rlt 4y ago
    > if you prompt it with enough specificity you might be able to just recreate the image you had 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.
- neuah 4y ago
  Does SD have to recreate the entire image for it to violate copyright? 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.

Great. Now the defence shows an artist that can recreate an image. Cool, now people who look at images get copyright suits filed against them for encoding those images in their heads.

dylan604 4y ago

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.

realusername 4y ago
> 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.
- dylan604 4y ago
  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.
  - astrange 4y ago
    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/ https://openai.com/blog/dall-e-2-pre-training-mitigations/ "Preventing Image Regurgitation".
    - ghaff 4y ago
      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.
      - astrange 4y ago
        That's extremely not how it works. If there's only one training example it's not going to remember anything like actual visual details of it.
        ghaff 4y ago
        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.
        SillyUsername 4y ago
        Actually that's partly how it works. 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.
  - realusername 4y ago
    > 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.
  - hhjinks 4y ago
    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.
    - ghaff 4y ago
      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. [1] https://en.wikipedia.org/wiki/Barack_Obama_%22Hope%22_poster https://en.wikipedia.org/wiki/Barack_Obama_%22Hope%22_poster
- bluefirebrand 4y ago
  This just sounds like really fancy, really lossy compression to me. Compression that returns something different from the original most of the time, but still could return the original.
  - anothernewdude 4y ago
    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.
XorNot 4y ago
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.
- yazaddaruvala 4y ago
  > 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.
  - astrange 4y ago
    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.
  - techdragon 4y ago
    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.
turtleyacht 4y ago
If you spent a decade trying to draw it, wouldn't your brain have the right "weights" to execute it pretty exactly going forward? 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?)

smusamashah 4y ago

Don't think stable Diffusion can reproduce any single image its trained on, not matter what prompts you use. 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.

Aerroon 4y ago
I think there's a chance they might be able to recreate some simpler work if they make the prompts specific enough. When you set up a prompt you're essentially telling the system what you want it to generate - if you prompt it with enough specificity you might be able to just recreate the image you had. Kind of like recreating your image one object at a time. It might not be exact, but close enough.
- smusamashah 4y ago
  People have tried, unless the thing you want to recreat has been seen by it a lot (over trained) you won't get the same image. You don't have that much fine grained control via text only. Best you can do is to mask and keep inpainting the area that looks different until it doesn't.
- rlt 4y ago
  > if you prompt it with enough specificity you might be able to just recreate the image you had 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.
neuah 4y ago
Does SD have to recreate the entire image for it to violate copyright? 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.