May 10, 2024, 4:42 a.m. | Minguk Kang, Richard Zhang, Connelly Barnes, Sylvain Paris, Suha Kwak, Jaesik Park, Eli Shechtman, Jun-Yan Zhu, Taesung Park

cs.LG updates on

arXiv:2405.05967v1 Announce Type: cross
Abstract: We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image quality. Our approach interprets diffusion distillation as a paired image-to-image translation task, using noise-to-image pairs of the diffusion model's ODE trajectory. For efficient regression loss computation, we propose E-LatentLPIPS, a perceptual loss operating directly in diffusion model's latent space, utilizing an ensemble of augmentations. Furthermore, we adapt a diffusion model to …

abstract arxiv cs.lg diffusion diffusion model diffusion models distillation gan gans image image-to-image image-to-image translation inference loss noise quality regression trajectory translation type while

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