Re:Compressing Images with Stable Diffusion
by agumonkey on Thursday September 01, 2022 @02:10AM
(#32672625)
Reminds me of the old IBM joke: you can compress everything down to a bit, if you include the data in the decompresser.
Re:Compressing Images with Stable Diffusion
by thrdbndndn on Thursday September 01, 2022 @12:48AM
(#32672127)
> this must already exist as a compression model?
Definitely exist in video compression, not so sure about still image (but most of still image compression advancements lately are just side effect of video compression).
Re:Compressing Images with Stable Diffusion
by Nition on Thursday September 01, 2022 @05:25AM
(#32673998)
I wonder how close to an arbitrary image a truly optimal Stable Diffusion prompt could actually get (the ones on the blog being just a good guess). Like say you magically could try infinite prompt variations to find the closest match. Maybe some of the text would look like encoded gibberish. I imagine the "optimal" prompt would be very long, maybe - probably - larger than the original image.
2. Any image generated with stable diffusion, could be distributed as prompt + seed leading to 99% compression
3. Image -> downsample –> super-resolution model -> render -- this must already exist as a compression model? The only limitation is that super-res models probably can't run on phones yet... but some day soon they'll be good enough.
4. All deep learning is fundamentally a compression system (reduce a lot of input data to a set of finite features and weights). But it might also be a very good lossy compression system because it can identify what's worth keeping high res and what's worth aggressively downsampling.
5. Interestingly, we seem to be compute limited more than bandwidth limited nowadays so it might take a while for an economically useful DL-compression product to emerge.