The performance of deep learning models for surgical scene understanding is strongly limited by the availability of large-scale, diverse, and well-annotated datasets. In medical imaging, data acquisition is costly and constrained by privacy regulations, motivating the use of synthetic data generation. In this work, we investigate semantic mask-driven image synthesis for cataract surgery using both conditional generative adversarial networks and diffusion-based models. We introduce two complementary semantic augmentation strategies that operate directly at the mask level, enabling the generation of anatomically consistent yet diverse surgical scenes and expanding the effective semantic training distribution. Experimental results on the Cataract-1K dataset demonstrate that the proposed augmentation strategies significantly improve image quality and diversity, allowing generative models to overcome early performance saturation. Our findings highlight the importance of procedural semantic augmentation for scalable synthetic data generation in surgical imaging.