  As Metaverse emerges as the next-generation Internet paradigm, the ability to
efficiently generate content is paramount. AIGenerated Content (AIGC) emerges
as a key solution, yet the resource intensive nature of large Generative AI
(GAI) models presents challenges. To address this issue, we introduce an
AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless
edge networks to ensure broad AIGC services accessibility for Metaverse users.
Nonetheless, an important aspect of providing personalized user experiences
requires carefully selecting AIGC Service Providers (ASPs) capable of
effectively executing user tasks, which is complicated by environmental
uncertainty and variability. Addressing this gap in current research, we
introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion
model-based approach for generating the optimal ASP selection decisions.
Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep
Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and
effectiveness of ASP selection. Our comprehensive experiments demonstrate that
D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD
algorithm has the potential for extension to various optimization problems in
wireless networks, positioning it as a promising approach for future research
on AIGC-driven services. The implementation of our proposed method is available
at: https://github.com/Lizonghang/AGOD.
