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Hey, we tried our method on DiT (PixArt alpha and sigma) and MMDiT (FLUX.1-dev) and found that while CleanDIFT does lead to an overall performance increase, the feature maps still underperform compared to Stable Diffusion 1 and 2, which is why we did not include it.
Thanks for your reply.
I also tried using DiT model feature maps for related perception research, and the results were similar to yours—not as good as Stable Diffusion 1 and 2. Did you consider the possible reasons for these results? (like feature maps from Transformer blocks not being suitable for dense prediction tasks)
Hi. Thanks for your interesting work!
Did you try other stable diffusion like stable diffusion 3 with DiT architectures?
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