QoS-Driven Hybrid Inference Scheme for Generative Diffusion Models in MEC-Enabled AI-Generated Content Networks
Xinyi Zhuang, Jiaqi Wu, Hanming Wu, Ming Tang, Lin Gao · 2025
AI-Generated Content (AIGC) based on Generative Diffusion Models (GDMs) is revolutionizing content creation and promoting substantial advancements in domains like autonomous driving and robotics. Leveraging progress in Mobile Edge Computing (MEC) and model compression techniques, GDMs are increasingly being deployed on Edge Servers (ESs) and User Equipments (UEs), which typically face resource limitations. In such MEC-enabled scenarios, designing an efficient inference scheme for GDMs still remains a significant challenge, due to the resource constraints on ESs and UEs as well as the personalized demands of AIGC users. In this work, we propose a novel hybrid inference scheme, which consists of two stages: public prompt generation and common-to-personalized inference. In the first stage, a Large Language Model (LLM) is adopted to generate public prompts derived from the common features of users' personal prompts. In the second stage, a common inference phase based on public prompts is first executed for all users (to produce common intermediate results), and then a personalized inference phase based on each user's personal prompts is performed for each individual user (to generate final contents). Clearly, by introducing the common inference phase, the total inference steps can be significantly reduced. In such a scheme, we further study a hybrid inference optimization problem to optimize both common and personalized inference steps, aiming to maximize the total Quality of Service (QoS), while minimizing delay and energy consumption. Simulation results show that our proposed scheme significantly outperforms existing benchmarks, with the performance gains ranging from 12.6 % to 102.2 %.