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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
by 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 agoSo 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 agoIt'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 agoIt 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 agoBut 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 agoThere 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 agoSorry 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...