Earlier quoted context omitted.
> Also, ML models (probably) can't be compressed because they already are compressed; learning and compression are the same thing! I feel like they're kind of two sides of the same coin: learning is about putting more information in the same data, while compression is about putting the same information in less data. I'm wondering if some lossy floating-point compressor (such as zfp) would work.
> I'm wondering if some lossy floating-point compressor (such as zfp) would work. Well apparently this can work; StableDiffusion comes as 32-bit and 16-bit float versions. I'm kind of surprised they both work, but that's lossy compression.
I would like to see if the zfp can be applied to something like Stable Diffusion (or other ML models) and give better results than regular floats at the same size.