@BrianCChao Could you please elaborate? Does A.3 have any proof that one should rely on the distance between velocity and noise (in freq. domain) to define the proper transition time? And not the distance between spectrum of x_t and noise, for example?
@BrianCChao Your derivation from Sec. 4.2 with the Assumption A1 is indeed remarkable! I have a question about it: choosing the transition time based on noise-dominated *velocity* is a design choice. In SwD work the authors did the same analysis but relied on noise-dominated *input* instead
@GordonWetzstein Interestingly, in PyramidalWan paper, a similar approach of injecting noise into high frequencies is described. Before that, PyramidFlow used it implicitly with Haar wavelet
https://t.co/UtMg2Cz9SS
https://t.co/GWi2LK9mHv
@jm_alexia@oguzhannercan We've tried JEDi in our Mobile Video Diffusion project https://t.co/Bdq4FhoVII (see Appendix) and it indeed showed itself more aligned with the process of model pruning than FVD. Thanks for your research!
@LinBin46984 Amazing work! Many thanks for sharing it with the community!
May I ask about the video generation evaluation? Has Latte-L been initialized with the same PixArt-α despite the latent space had changed? Or have pretrained weights for the UCF and SkyTimelapse datasets been used?
@jm_alexia To be honest, I do not really understand what the difference of the proposed CMMD vs well-known KID is (except for the different kernel used). The authors mention KID in the Related Works section but do not evaluate against it
Hello @AmazonHelp, my Amazon DE account is currently on hold, and I cannot log in on the website/in the app. I received an email from account-resolution(AT)amazon(DOT)de and replied with the requested document, but after 4 days still haven't received any response