blueSGL t1_iu4z8up wrote on October 28, 2022 at 4:16 PM

Reply to comment by Education-Sea in new physics-inspired Deep Learning method generates images with electrodynamics by SleekEagle

skimmed the paper and might have missed it, does it say if this is more or less VRAM efficient?

dasnihil t1_iu51u8k wrote on October 28, 2022 at 4:33 PM

skimmed the paper and figured most of this math is beyond me, but it's exciting nonetheless.

SleekEagle OP t1_iu55u04 wrote on October 28, 2022 at 4:59 PM

The deep dive section gives an overview of Green's functions! Don't be intimidated by the verbiage, the central ideas are not too complicated :)

If you have taken a multivariable calculus class then most of it should make sense

dasnihil t1_iu59271 wrote on October 28, 2022 at 5:20 PM

ahh found a dear fellow scholar in the wild!!

SleekEagle OP t1_iu5zfj5 wrote on October 28, 2022 at 8:19 PM

👋 hello friend!

SleekEagle OP t1_iu569xx wrote on October 28, 2022 at 5:02 PM

I don't think the paper explicitly says anything about this, but I would expect them to be similar. If anything I would imagine they would require less memory, but not more. That having been said, if you're thinking of e.g. DALL-E 2 or Stable Diffusion, those models also have other parts that PFGMs don't (like text encoding networks), so it is completely fair that they are larger!

Education-Sea t1_iu50jst wrote on October 28, 2022 at 4:24 PM

It didn't, from my understanding.

HydrousIt t1_iu6m0g8 wrote on October 28, 2022 at 11:07 PM

A flow model has more VRAM efficiency and is quicker at image generation, although this is sometimes at the cost of having an inferior image quality to GANs in terms of realism.