rectified-flow-pytorch
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lucidrains
Feature request: Switch off Unet for DiT
Hello,
I've been reading a lot of the SOTA papers on audio and video generation using Rectified Flows, and it seems most are using Transformers instead of Unets.
Are there any plans to implement such an architecture change? They seem to improve greatly in performance, as in this implementation: https://github.com/cloneofsimo/minRF
Would be great to see it here, as it's a very clear to understand codebase, thanks again for opensourcing it!
@moiseshorta yes you are correct, pure attention has basically completely taken over
why not use Simo's implementation instead of the one here? is there anything lacking in his? he's a pretty amazing guy in general
As I mentioned, your implementation seems a bit more clear to me than Simo's. Although I will give his a try as well :)
ohh, ok, I've seen him live code before and was very impressed. but yeah sure i can add DiT here, or perhaps just make it x-transformer compatible
yeah, that would be great. I'm currently trying to scale up your implementation but I seem to be either very quickly overfitting or just NaN/Inf gradients while training on a bigger dataset...
@moiseshorta overfitting vs divergence are two very different things, and there are tricks of the trade for both of those issues.
sounds good, give me some time, too many projects but i do want to flesh out this repo a bit more, or dog food other libraries
Yes, definitely. On some occasions there is overfitting, tried mitigating with dropout. On some occasions the divergence has been mitigated with lower LR. Any other advice is greatly appreciated! Looking forward to the implementation.