As an emerging paradigm of content creation, AI-Generated Content (AIGC) has been widely adopted by a large number of edge end users. However, the requests for generated content from AIGC users have obvious diversity, and there remains a notable lack of research addressing the variance in user demands for AIGC services. This gap underscores a critical need for suitable AIGC service selection mechanisms satisfying various AIGC user requirements under resource-constrained edge environments. To address this challenge, this paper proposes a novel Attention-based Diffusion Soft Actor-Critic (ADSAC) algorithm to select the appropriate AIGC model in response to heterogeneous AIGC user requests. Specifically, the ADSAC algorithm integrates a diffusion model as the policy network in the off-policy reinforcement learning (RL) framework, to capture the intricate relationships between the characteristics of AIGC tasks and the integrated edge network states. Furthermore, an attention mechanism is utilized to harness the contextual long-range dependencies present in state feature vectors, enhancing the decision-making process. Extensive experiments validate the effectiveness of our algorithm in enhancing the overall user utility and reducing the crash rate of servers. Compared to the existing methods, the proposed ADSAC algorithm outperforms existing methods, reducing the overall user utility loss and the server crash rate by at least 58.3% and 58.4%, respectively. These results demonstrate our ADSAC algorithm is a robust solution to the challenges of diverse and dynamic user requirements in edge-based AIGC application environments.

翻译：作为内容创作的新兴范式，AI生成内容已被大量边缘终端用户广泛采用。然而，AIGC用户对生成内容的请求具有显著多样性，目前缺乏针对AIGC服务中用户需求差异性的研究。这一空白凸显了在资源受限的边缘环境下，亟需建立满足各类AIGC用户需求的适当服务选择机制。为应对这一挑战，本文提出一种新颖的基于注意力的扩散软演员-评论家算法，用于响应用户异构的AIGC请求。具体而言，ADSAC算法将扩散模型作为离策略强化学习框架中的策略网络，以捕捉AIGC任务特征与集成边缘网络状态之间的复杂关系。此外，利用注意力机制提取状态特征向量中的上下文长程依赖关系，从而增强决策过程。大量实验验证了所提算法在提升整体用户效用和降低服务器崩溃率方面的有效性。与现有方法相比，所提ADSAC算法将整体用户效用损失和服务器崩溃率分别降低至少58.3%和58.4%。这些结果表明，ADSAC算法能稳健应对边缘AIGC应用环境中多样化且动态的用户需求所带来的挑战。