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geekfolk t1_je3io3b wrote

Reply to comment by Beautiful-Gur-9456 in [P] Consistency: Diffusion in a Single Forward Pass 🚀 by Beautiful-Gur-9456

using pretrained models is kind of cheating, some GANs use this trick too (projected GANs). But as a standalone model, it does not seem to work as well as SOTA GANs (judged by the numbers in the paper)

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>Still, it's a lot easier than trying to solve any kind of minimax problem.

This is true for GANs in the early days; however, modern GANs are proved to not have mode collapse and the training is proved to converge.

>It's actually reminiscent of GANs since it uses pre-trained networks

I assume you mean distilling a diffusion model in the paper. There have been some attempts to combine diffusion and GANs to get the best of both worlds but afaik none involved distillation, I'm curious if anyone has tried distilling diffusion models into GANs.

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Beautiful-Gur-9456 OP t1_je3qsdu wrote

Nope. I mean the LPIPS loss, which kinda acts like a discriminator in GANs. We can replace it to MSE without much degradation.

Distilling SOTA diffusion model is obviously cheating 😂, so I didn't even think of it. In my view, they are just apples and oranges. We can augment diffusion models with GANs and vice versa to get the most out of them, but what's the point? That would make things way more complex. It's clear that diffusion models cannot beat SOTA GANs for one-step generation; they've been tailored for that particular task for years. But we're just exploring possibilities, right?

Aside from the complexity, I think it's worth a shot to replace LPIPS loss and adversarially train it as a discriminator. Using pre-trained VGG is cheating anyway. That would be an interesting direction to see!

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