为实现智能交通数字孪生（ITDT），需调度无人机（UAV）处理路侧传感器采集的感知数据。此时，扩散模型等生成式人工智能（GAI）技术被部署于无人机上，将原始感知数据转化为高质量、高价值的信息。为此，我们提出GAI赋能的ITDT架构。一组扩散模型推理（DMI）任务在具有动态移动性的无人机上进行动态处理，同时影响数字孪生（DT）更新的保真度与时延。本文将DMI任务卸载、推理优化与无人机轨迹规划建模为联合优化问题，并以系统效用最大化（SUM）为目标，以应对GAI赋能ITDT中的保真度-时延权衡挑战。为在动态网络环境下求解该问题，我们将SUM问题建模为异构智能体马尔可夫决策过程，并提出基于顺序更新的异构智能体双延迟深度确定性策略梯度（SU-HATD3）算法，可快速学习近似最优解。数值结果表明，相较于若干基线算法，所提算法在提升系统效用与收敛速度方面具有显著优势。

To implement the intelligent transportation digital twin (ITDT), unmanned aerial vehicles (UAVs) are scheduled to process the sensing data from the roadside sensors. At this time, generative artificial intelligence (GAI) technologies such as diffusion models are deployed on the UAVs to transform the raw sensing data into the high-quality and valuable. Therefore, we propose the GAI-empowered ITDT. The dynamic processing of a set of diffusion model inference (DMI) tasks on the UAVs with dynamic mobility simultaneously influences the DT updating fidelity and delay. In this paper, we investigate a joint optimization problem of DMI task offloading, inference optimization and UAV trajectory planning as the system utility maximization (SUM) problem to address the fidelity-delay tradeoff for the GAI-empowered ITDT. To seek a solution to the problem under the network dynamics, we model the SUM problem as the heterogeneous-agent Markov decision process, and propose the sequential update-based heterogeneous-agent twin delayed deep deterministic policy gradient (SU-HATD3) algorithm, which can quickly learn a near-optimal solution. Numerical results demonstrate that compared with several baseline algorithms, the proposed algorithm has great advantages in improving the system utility and convergence rate.