Exploring Collaborative Diffusion Model Inferring for AIGC-Enabled Edge Services

With the advancements in AI-generated content (AIGC) technologies and edge cloud networks, the demand for AIGC services is increasing. The diffusion model stands out for its ability to generate images from text. However, its high computational requirements challenge user devices, leading to ongoing efforts to improve inferring speed while preserving image quality. In this study, we propose a novel edge-user collaborative inferring framework. By capitalizing on collaboration among the devices in edge and user, the proposed framework optimizes the service delay, network resource consumption, and user device computing resource consumption of the user’s AIGC service, and improves the user’s QoE. To demonstrate the efficiency of our proposed framework, we conduct comparative experiments and ablation experiments on a variety of datasets. Experimental results show that the proposed framework achieves better-generated image quality and reduces a large number of computing resources and network consumption, improving user QoE.