  Distributed Artificial Intelligence-Generated Content (AIGC) has attracted
increasing attention. However, it faces two significant challenges: how to
maximize the subjective Quality of Experience (QoE) and how to enhance the
energy efficiency, which are particularly pronounced in widely adopted
Generative Diffusion Model (GDM)-based AIGC services for image generation. In
this paper, we propose a novel user-centric Interactive AI (IAI) approach for
service management, with a distributed GDM-based AIGC framework, prioritizing
efficient and collaborative GDM deployment. Specifically, we restructure the
GDM's inference process, i.e., the denoising chain, to enable users'
semantically similar prompts to share a portion of diffusion steps.
Furthermore, to maximize the users' subjective QoE, we propose an IAI approach,
i.e., Reinforcement Learning With Large Language Models Interaction (RLLI),
which utilizes Large Language Model (LLM)-empowered generative agents to
replicate users interaction, providing real-time and subjective QoE feedback
that reflects a spectrum of user personalities. Lastly, we present the
GDM-based Deep Deterministic Policy Gradient (G-DDPG) algorithm, adapted to the
proposed RLLI framework, for effective communication and computing resource
allocation while considering user subjective personalities and dynamic wireless
environments in decision-making. Simulation results show that G-DDPG can
increase the sum QoE by 15%, compared with the conventional DDPG algorithm.
