3 ms·

It's because it's easy to see how it takes an existing artwork and modifies it. For example when asked to paint Noah it used a stereotypical image of last cent

• dougmwne 4y ago
  This is where it gets interesting. The model is just 4.5gb. That’s too small to contain the actual trillion images it was trained on. So in all likelihood, it doesn’t have an actual specific image of Noah to work from, but a web of concepts, connections, correlations. It’s probably analogous to if you were asked to draw a picture in the style of Da Vinci. Most peoples’ brains can’t reproduce the exact image, but a low fidelity representation of it that they can fill in with imagination. If you run the same prompt multiple times you can start to see evidence of this. The images are similar but also clearly different, not at all a quick transformation of an existing image.
  • ars 4y ago
    It was trained with 400 million images, not a trillion. That's 11 bytes per image, and since most images are quite duplicative that actually is enough to more or less store every image permutation.
    • ryankrage77 4y ago
      So it can store a little over 400 million images that are 3 pixels by 3 pixels in black and white. Or 2 by 2 pixels with three colours.
      • visarga 4y ago
        It's not even pixels the model is storing ... it's gradients from the loss function. Gradients that have the shape of the network, not of the image it is training on.
      • ars 4y ago
        It doesn't have to store every image - most are essentially duplicates of each other and only serve to reinforce which is the most representative image when multiple choices are linked with some reference text. It only needs to store a fraction of the images to have a never ending supply of source material (I guess that's kind of obvious since this program works :)
        • dougmwne 4y ago
          But again, I think the idea that the model weights store the source images as if it were a library is fallacious. There. Is no known way to get it to spit back out a copy of a training image.
    • synu 4y ago
      There is some flexible encoding of terms to images there, too, that would take up some space. Though I’m not really sure you could decompress the weights in some way to regenerate the 400 million images and metadata.
    • dougmwne 4y ago
      Sorry it was not a trillion. It was first trained on 2 billion, then fine tuned on smaller sets of highly aesthetic images. https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/Stable_Diffusion_v1_Model_Card.md https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a...
• aaroninsf 4y ago
  Not to your point, I advocate for being more precise in the language we use when talking about how these systems function. These systems do not in any literal way "encode" or index or otherwise store specific images; they have incomprehensibly (to us) distributed representations representing aggregations over their training set. The thrust of the observation is correct; but the specifics are very different, it's probably a more accurate "lay person" description to say something like, "the system was trained with only a few references for what Noah looked like, and doesn't have much initiative by default to improvise or make inferences, so if it tries in good faith to render Noah it is limited by what it's seen." The point just being, it's less about making a collage or modifying a source, more about say 'doing its best to recapitulate a set of learned higher-level properties which are specific to the "Noah" concept.'
  • visarga 4y ago
    > it's less about making a collage or modifying a source, more about say 'doing its best to recapitulate a set of learned higher-level properties That sounds almost ... humane. I bet it will trigger some people who only see copyright violation in it. They don't understand the image is being created "from first principles".