Frequency-Guided Acceleration of Diffusion-Based Interactive Denoising in Mobile Edge Networks

Abstract

Artificial Intelligence-Generated Content is reshaping interactive content creation. However, provisioning diffusion-based interactive denoising services in mobile edge networks remains challenging, due to the demanding computation and communication resources. To address these challenges, in this paper, we investigate the innate features of latent diffusion models, which have been widely used for image generation. We first observe that, 1) latent states are compact in data size compared to generated images, and 2) image attributes can be differentiated in frequency domain across the denoising stages. Motivated by these insights, we propose an end-edge collaborative denoising system to accelerate progressive image generation through hybrid latent state reuse and denoising tasks offloading. At the core of this system design are two synergistic algorithms configuring generation quality and service latency. The Adaptive Reuse Configuration Selection algorithm properly configures the synthesis of latent states by identifying reusable latent states, while preserving consistent image attributes between progressive tasks, thus minimizing computational redundancy with guaranteed generation quality. Subsequently, the Distributed Denoising Management algorithm optimizes the offloading ratio between the edge server and mobile users based on their computing resource availability. This paradigm enables the transmission of lightweight latent tensors instead of high-resolution images, thereby alleviating transmission burden. Extensive experimental comparisons against baselines demonstrate that, the proposed system can reduce service latency by up to 66.9% and enhance generation quality by up to 62.3%.