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I like this because they are trying to show how AI is a copyright laundry. I can see other commenters picking apart its method of heuristically guessing at sou

I like this because they are trying to show how AI is a copyright laundry. I can see other commenters picking apart its method of heuristically guessing at source images from training data. That obviously won't be accurate, or a full picture, but I wonder if it would convince a judge. An interesting challenge for these heuristics would be to take the picture under test along with its prompt, retrain the model without the training pictures it identifies, and regenerate using the same prompt to see whether the output is remotely similar. Obviously that would be hilariously expensive and slow for a casual web service like this, but not beyond the realms of possibility for a wealthy copyright-holder. e.g. if an prompt for an image includes "in the style of Kincade", and you could subtract all of Kinkade's copyrighted images from the training data, would the model still be able to produce anything like his work? If not, Thomas Kinkade might have a copyright case against people who publish AI art "in the style of Kincade", because he could show that his input was the major contributor to any lucrative output, even if nobody could pin down the cause & effect.

kthejoker2 4y ago
Kind of a sidebar, but interesting you chose Thomas Kinkade, who has been dead 10 years, and yet new "Thomas Kinkade Studios" work with his signature (i.e. "in the style of Thomas Kinade") is still being produced by by his family, who are probably stoked to be able to use an AI to quickly create "new" works.
- tough 4y ago
  The point is they will want a moat around that and probably won't be so happy anyone with a GPU or 1$ can do it too
- mattbee 4y ago
  Haha, OK, not greatly in touch with the visual arts me, was just grasping for a monumentally successful modern painter. But I don't think it changes my point - I'm sure his estate would like to use copyright law to keep the exclusive right to produce works in his style.
deleted 4y ago
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clbrmbr 4y ago
Unless there are imitation Kincades in the training set labeled with the name Kincade, I would expect the model to fail to produce anything relevant. It’s a strange situation. Certainly a human painter is free to study copywritten works closely and then produce art in the style of the other artist, provided the resulting work is “in the style of” but does not copy a specific painting. So why is a human SD-artist not allowed to do the same with their preferred tool? Perhaps because we can draw a causal line (a very fuzzy one) between the original and the knock off? But wait… in the human painter case the same fuzzy line exists, it’s just through wet-ware not hardware. Perhaps passing first into a LLM trained to generate good prompts but never output specific artists’ names could get close? Has someone tried this?
- mattbee 4y ago
  If there were a legally sound way to trace inspiration from a copyrighted work through the brain into another work, lawyers would be all over it! They try often enough with music. The line will surely get a lot less fuzzy with AI models.
renonce 4y ago
Without retraining the model, you can often get a different image by generating it again or modifying the prompt slightly. Makes people wonder if changing the training set would have any bigger impact.
throwaway1851 4y ago
Causal attribution would indeed be cost prohibitive. But what about comparing neural activations for pairs or sets of input/output images? It might shed some light on how much “extra” knowledge SD pulls in beyond the memorized features of the suspected source images.
convexfunction 4y ago
It's not quite the same, and might not be possible to make rigorous enough that it really proves anything, but something sort of similar would actually be practical to at least attempt in many cases. Stable Diffusion checkpoints of the same major version, along with other families of model weights, have the IMO fascinating property that you can do element-wise arithmetic with them, and the resulting model will actually sort of function like you'd naively expect. Recent paper on the topic (in LLMs, not diffusion models) here: https://arxiv.org/abs/2212.04089 https://arxiv.org/abs/2212.04089 So, if you take a Stable Diffusion checkpoint (call it "A") which is only lightly trained on some subset of an artist's work, then fine tune it on the full corpus of that artist's work to a point where it's still coherent/"good" and just shy of actually memorizing the fine tuning data (call the resulting model "B"), then define model "C" as 2A-B (i.e. A + (A-B), where A-B is the artist's task vector multiplied by -1), can you still produce qualitatively similar images with model C? Whether with the exact same prompt, or the same prompt with "in the style of Kinkade" removed (which doesn't mean as much if Kinkade's task vector was subtracted), or with any prompt whatsoever? Lots of issues with this as laid out -- it's definitely not quite the same as "forgetting" Kinkade from the training data, and "any prompt whatsoever" introduces tons of leeway, and most good AI-assisted art is not just an unmodified single text-to-image output anyway -- but it might be a promising direction to explore. (Strongly disagree with the "copyright laundry" characterization, by the way.)

kthejoker2 4y ago

Kind of a sidebar, but interesting you chose Thomas Kinkade, who has been dead 10 years, and yet new "Thomas Kinkade Studios" work with his signature (i.e. "in the style of Thomas Kinade") is still being produced by by his family, who are probably stoked to be able to use an AI to quickly create "new" works.

tough 4y ago
The point is they will want a moat around that and probably won't be so happy anyone with a GPU or 1$ can do it too
mattbee 4y ago
Haha, OK, not greatly in touch with the visual arts me, was just grasping for a monumentally successful modern painter. But I don't think it changes my point - I'm sure his estate would like to use copyright law to keep the exclusive right to produce works in his style.

deleted 4y ago

[deleted]

clbrmbr 4y ago

Unless there are imitation Kincades in the training set labeled with the name Kincade, I would expect the model to fail to produce anything relevant. It’s a strange situation. Certainly a human painter is free to study copywritten works closely and then produce art in the style of the other artist, provided the resulting work is “in the style of” but does not copy a specific painting. So why is a human SD-artist not allowed to do the same with their preferred tool? Perhaps because we can draw a causal line (a very fuzzy one) between the original and the knock off? But wait… in the human painter case the same fuzzy line exists, it’s just through wet-ware not hardware. Perhaps passing first into a LLM trained to generate good prompts but never output specific artists’ names could get close? Has someone tried this?

mattbee 4y ago
If there were a legally sound way to trace inspiration from a copyrighted work through the brain into another work, lawyers would be all over it! They try often enough with music. The line will surely get a lot less fuzzy with AI models.

renonce 4y ago

Without retraining the model, you can often get a different image by generating it again or modifying the prompt slightly. Makes people wonder if changing the training set would have any bigger impact.

throwaway1851 4y ago

Causal attribution would indeed be cost prohibitive. But what about comparing neural activations for pairs or sets of input/output images? It might shed some light on how much “extra” knowledge SD pulls in beyond the memorized features of the suspected source images.

convexfunction 4y ago

It's not quite the same, and might not be possible to make rigorous enough that it really proves anything, but something sort of similar would actually be practical to at least attempt in many cases. Stable Diffusion checkpoints of the same major version, along with other families of model weights, have the IMO fascinating property that you can do element-wise arithmetic with them, and the resulting model will actually sort of function like you'd naively expect. Recent paper on the topic (in LLMs, not diffusion models) here: https://arxiv.org/abs/2212.04089 https://arxiv.org/abs/2212.04089 So, if you take a Stable Diffusion checkpoint (call it "A") which is only lightly trained on some subset of an artist's work, then fine tune it on the full corpus of that artist's work to a point where it's still coherent/"good" and just shy of actually memorizing the fine tuning data (call the resulting model "B"), then define model "C" as 2A-B (i.e. A + (A-B), where A-B is the artist's task vector multiplied by -1), can you still produce qualitatively similar images with model C? Whether with the exact same prompt, or the same prompt with "in the style of Kinkade" removed (which doesn't mean as much if Kinkade's task vector was subtracted), or with any prompt whatsoever? Lots of issues with this as laid out -- it's definitely not quite the same as "forgetting" Kinkade from the training data, and "any prompt whatsoever" introduces tons of leeway, and most good AI-assisted art is not just an unmodified single text-to-image output anyway -- but it might be a promising direction to explore. (Strongly disagree with the "copyright laundry" characterization, by the way.)