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.

提出了一种联合优化算法，用于解决在边缘计算范式中对 AI 生成内容模型和卸载决策的优化问题，并通过实验证明所提算法在联合优化性能上优于基准方法。