Using a conditional diffusion model for traffic scene generation presents certain challenges and limitations that need to be addressed to enhance overall performance. One challenge is the complexity of modeling interactions between various traffic agents accurately. The diffusion process may struggle to capture intricate dependencies and interactions in dynamic traffic scenarios. To mitigate this, incorporating attention mechanisms or graph neural networks can help capture long-range dependencies and complex interactions among agents. Another limitation is the potential for mode collapse, where the model generates similar trajectories regardless of input conditions. To address this, techniques like curriculum learning or adversarial training can be employed to encourage diversity in generated samples. Furthermore, ensuring the diffusion model's scalability and efficiency as the dataset size grows is crucial. Implementing parallel processing and optimizing computational resources can help overcome scalability challenges. By addressing these challenges, the conditional diffusion model in Dragtraffic can achieve higher accuracy, diversity, and realism in traffic scene generation.