5 ms·

The difference is that JPEG does store the real image, at least close enough to within the given tolerance (determined by the compression factor). That image is

The difference is that JPEG does store the real image, at least close enough to within the given tolerance (determined by the compression factor). That image is as real as say an image on film (also not exact, nor in "original" form). With Stable Diffusion it's storing the style, but can't reproduce any single input image-- there aren't enough bits [0]. (except by luck, but that's really true for any storage). [0] https://en.wikipedia.org/wiki/Shannon–Hartley_theorem https://en.wikipedia.org/wiki/Shannon–Hartley_theorem

peoplefromibiza 4y ago
> there aren't enough bits you can create compressed copy of a file containing 100TB of the letter "A" in much less than 4GB there could be enough bits in there to reproduce some of the inputs.
- CuriouslyC 4y ago
  What this analogy is saying is that if an image is generic and derivative enough (or massively overrepresented in the training data) it may be possible to reconstruct a very close approximation from the model. If the training data is unbiased, I question the validity of copyright claims on an image that is sufficiently derivative that it can be reproduced in this manner.
  - gort19 4y ago
mjburgess 4y ago
The weights of a NN are just a compressed representation of the training data, think lossy zip. Rank all generated images by similarity to the training data (etc.) and you can see what's stored. The Shannon-Hartley theorem isnt relevant. A 4GB zip of 100TB text data can exactly reproduce the initial 100TB for some distributions of that initial dataset.
- chii 4y ago
  if you reproduced an exact image (to the same lossy degree as jpeg) using the NN, then you are violating copyright. But if you reproduced an image whose style matches another copyrighted image (e.g., blah in the style of starry night), then how does that new image (which didn't exist before) violate existing copyright? You cannot copyright a style. The NN containing information which _could_ be used to reconstruct an exact image doesn't itself constitute copyright violation - because the right to use information for training NN is not an exclusive right that the original holder of the training set has. So either a new law has to come into existence, vis a vis the right to use copyrighted works to train a NN, or the current copyright laws should apply (which implies that NN generated images which are not "exact" copies of existing works don't violate copyright).
  - int_19h 4y ago
    If a given model can consistently reproduce an exact image given the same input prompt, why shouldn't the model itself be considered a compressed form of that image?
    - chii 4y ago
      so does the number pi constitute a copyright infringement?
      - int_19h 4y ago
        No, since it's not a derived work. And if you can come up with a model that can reproduce images exactly without first getting trained on them, it wouldn't be a derived work, either.
        chii 4y ago
        > No, since it's not a derived work. but why is the set of numbers in a matrix considered derived? I can trivially derive the number pi. My original point is that just because something contains the information does not imply that it violates copyright.

peoplefromibiza 4y ago

> there aren't enough bits you can create compressed copy of a file containing 100TB of the letter "A" in much less than 4GB there could be enough bits in there to reproduce some of the inputs.

CuriouslyC 4y ago
What this analogy is saying is that if an image is generic and derivative enough (or massively overrepresented in the training data) it may be possible to reconstruct a very close approximation from the model. If the training data is unbiased, I question the validity of copyright claims on an image that is sufficiently derivative that it can be reproduced in this manner.
- gort19 4y ago

mjburgess 4y ago

The weights of a NN are just a compressed representation of the training data, think lossy zip. Rank all generated images by similarity to the training data (etc.) and you can see what's stored. The Shannon-Hartley theorem isnt relevant. A 4GB zip of 100TB text data can exactly reproduce the initial 100TB for some distributions of that initial dataset.

chii 4y ago
if you reproduced an exact image (to the same lossy degree as jpeg) using the NN, then you are violating copyright. But if you reproduced an image whose style matches another copyrighted image (e.g., blah in the style of starry night), then how does that new image (which didn't exist before) violate existing copyright? You cannot copyright a style. The NN containing information which _could_ be used to reconstruct an exact image doesn't itself constitute copyright violation - because the right to use information for training NN is not an exclusive right that the original holder of the training set has. So either a new law has to come into existence, vis a vis the right to use copyrighted works to train a NN, or the current copyright laws should apply (which implies that NN generated images which are not "exact" copies of existing works don't violate copyright).
- int_19h 4y ago
  If a given model can consistently reproduce an exact image given the same input prompt, why shouldn't the model itself be considered a compressed form of that image?
  - chii 4y ago
    so does the number pi constitute a copyright infringement?
    - int_19h 4y ago
      No, since it's not a derived work. And if you can come up with a model that can reproduce images exactly without first getting trained on them, it wouldn't be a derived work, either.
      - chii 4y ago
        > No, since it's not a derived work. but why is the set of numbers in a matrix considered derived? I can trivially derive the number pi. My original point is that just because something contains the information does not imply that it violates copyright.