Generative artificial intelligence (AI) applications in structural design face persistent challenges due to training data limitations, particularly datasets that lack compliance with critical physical and material requirements. This study proposes a structural optimization-based data enhancement method to address quality deficiencies in generative AI training data, specifically targeting shear wall layout design through diffusion-based generative models. The proposed method introduces three key innovations: (1) A novel generative AI design workflow incorporating data enhancement phase and modifying data preparation and model evaluation phases; (2) Regression formulas enabling data enhancement with incomplete design information through feature distribution analysis; (3) A shear wall layout optimization method for simultaneously improving physical and material metrics. Experimental validation reveals marked improvements in design outcomes through enhanced training data. Specifically, physically non-compliant structural designs show a 67% reduction in occurrence frequency, while material costs for compliant designs decrease by 0.5%. Additionally, performance consistency improves significantly. By addressing data quality limitations through structural optimization, this approach enhances the practical viability of diffusion models in structural engineering applications while preserving adaptability to advanced AI algorithms. The proposed method is modular and scalable, offering potential for extension to other structural systems (e.g., steel frames, composite structures) and design challenges, thereby advancing AI-driven innovation in structural engineering.