Optimization of user association in a densely deployed cellular network is usually challenging and even more complicated due to the dynamic nature of user mobility and fluctuation in user counts. While deep reinforcement learning (DRL) emerges as a promising solution, its application in practice is hindered by high trial-and-error costs in real world and unsatisfactory physical network performance during training. Also, existing DRL-based user association methods are typically applicable to scenarios with a fixed number of users due to convergence and compatibility challenges. To address these limitations, we introduce a large vision model (LVM)-enhanced digital twin (DT) for wireless networks and propose a parallel DT-driven DRL method for user association and load balancing in networks with dynamic user counts, distribution, and mobility patterns. To construct this LVM-enhanced DT for DRL training, we develop a zero-shot generative user mobility model, named Map2Traj, based on the diffusion model. Map2Traj estimates user trajectory patterns and spatial distributions solely from street maps. DRL models undergo training in the DT environment, avoiding direct interactions with physical networks. To enhance the generalization ability of DRL models for dynamic scenarios, a parallel DT framework is further established to alleviate strong correlation and non-stationarity in single-environment training and improve training efficiency. Numerical results show that the developed LVM-enhanced DT achieves closely comparable training efficacy to the real environment, and the proposed parallel DT framework even outperforms the single real-world environment in DRL training with nearly 20\% gain in terms of cell-edge user performance.