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.

翻译：随着元宇宙作为下一代互联网范式的兴起，高效生成内容的能力至关重要。人工智能生成内容（AIGC）成为关键解决方案，然而大型生成式人工智能（GAI）模型资源密集的特性带来了挑战。为解决这一问题，我们引入了AIGC即服务（AaaS）架构，该架构将AIGC模型部署在无线边缘网络中，以确保元宇宙用户能够广泛访问AIGC服务。然而，提供个性化用户体验的一个重要方面需要精心选择能够有效执行用户任务的AIGC服务提供商（ASP），而环境的不确定性和变化性使这一问题复杂化。针对当前研究的不足，我们提出了人工智能生成最优决策（AGOD）算法，这是一种基于扩散模型的方法，用于生成最优ASP选择决策。将AGOD与深度强化学习（DRL）相结合，我们开发了深度扩散软演员-评论家（D2SAC）算法，提高了ASP选择的效果和效率。我们的综合实验表明，D2SAC优于七种领先的DRL算法。此外，所提出的AGOD算法有望扩展到无线网络中的各种优化问题，使其成为未来AIGC驱动服务研究的一种有前景的方法。我们提出的方法的实现可在以下网址获得：https://github.com/Lizonghang/AGOD。