Reducing Spatial Fitting Error in Distillation of Denoising Diffusion Models

• URL: http://arxiv.org/abs/2311.03830v2
• Date: Thu, 21 Dec 2023 15:18:34 GMT
• Title: Reducing Spatial Fitting Error in Distillation of Denoising Diffusion Models
• Authors: Shengzhe Zhou, Zejian Lee, Shengyuan Zhang, Lefan Hou, Changyuan Yang, Guang Yang, Zhiyuan Yang, Lingyun Sun
• Abstract summary: Knowledge distillation for diffusion models is an effective method to address this limitation with a shortened sampling process. We attribute the degradation to the spatial fitting error occurring in the training of both the teacher and student model. SFERD utilizes attention guidance from the teacher model and a designed semantic gradient predictor to reduce the student's fitting error. We achieve an FID of 5.31 on CIFAR-10 and 9.39 on ImageNet 64$times$64 with only one step, outperforming existing diffusion methods.
• Score: 13.364271265023953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Denoising Diffusion models have exhibited remarkable capabilities in image generation. However, generating high-quality samples requires a large number of iterations. Knowledge distillation for diffusion models is an effective method to address this limitation with a shortened sampling process but causes degraded generative quality. Based on our analysis with bias-variance decomposition and experimental observations, we attribute the degradation to the spatial fitting error occurring in the training of both the teacher and student model. Accordingly, we propose $\textbf{S}$patial $\textbf{F}$itting-$\textbf{E}$rror $\textbf{R}$eduction $\textbf{D}$istillation model ($\textbf{SFERD}$). SFERD utilizes attention guidance from the teacher model and a designed semantic gradient predictor to reduce the student's fitting error. Empirically, our proposed model facilitates high-quality sample generation in a few function evaluations. We achieve an FID of 5.31 on CIFAR-10 and 9.39 on ImageNet 64$\times$64 with only one step, outperforming existing diffusion methods. Our study provides a new perspective on diffusion distillation by highlighting the intrinsic denoising ability of models. Project link: \url{https://github.com/Sainzerjj/SFERD}.

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