AI-Generated Content (AIGC), as a novel manner of providing Metaverse services in the forthcoming Internet paradigm, can resolve the obstacles of immersion requirements. Concurrently, edge computing, as an evolutionary paradigm of computing in communication systems, effectively augments real-time interactive services. In pursuit of enhancing the accessibility of AIGC services, the deployment of AIGC models (e.g., diffusion models) to edge servers and local devices has become a prevailing trend. Nevertheless, this approach faces constraints imposed by battery life and computational resources when tasks are offloaded to local devices, limiting the capacity to deliver high-quality content to users while adhering to stringent latency requirements. So there will be a tradeoff between the utility of AIGC models and offloading decisions in the edge computing paradigm. This paper proposes a joint optimization algorithm for offloading decisions, computation time, and diffusion steps of the diffusion models in the reverse diffusion stage. Moreover, we take the average error into consideration as the metric for evaluating the quality of the generated results. Experimental results conclusively demonstrate that the proposed algorithm achieves superior joint optimization performance compared to the baselines.

翻译：AI生成内容（AIGC）作为一种面向未来互联网范式中提供元宇宙服务的新兴方式，能够解决沉浸式需求的障碍。同时，边缘计算作为通信系统中计算模式的演进范式，有效增强了实时交互服务。为提升AIGC服务的可及性，将AIGC模型（例如扩散模型）部署至边缘服务器与本地设备已成为主流趋势。然而，当任务卸载至本地设备时，该方法面临电池寿命与计算资源的制约，在满足严格延迟要求的同时，限制了向用户提供高质量内容的能力。因此在边缘计算范式下，AIGC模型的效用与卸载决策之间将存在权衡。本文提出一种面向扩散模型逆扩散阶段中卸载决策、计算时间及扩散步骤的联合优化算法。同时，我们将平均误差作为评估生成结果质量的指标。实验结果表明，与基线方法相比，所提算法实现了更优的联合优化性能。