Generative Regeneration of Historic Urban Fabric: A Framework Based on Deep Learning and Multi-Objective Optimization

Amid China’s rapid urbanization, many historic districts face complex challenges, including fragmented traditional fabrics, disordered spatial morphology, and discontinuous street networks. To tackle these issues, this study proposes a multimodal deep learning framework that combines Generative Adversarial Networks (GANs) and Diffusion Models, establishing an integrated generation-optimization workflow for the renewal of historic districts. The methodology begins by using Pix2PixHD to generate high-precision fabric layouts, followed by fine-tuning a Diffusion Model through Low-Rank Adaptation (LoRA) to achieve diversified morphological expansion. The candidate proposals are quantitatively evaluated using a ten-indicator evaluation matrix that covers both architectural fabric and street network dimensions. Afterwards, these proposals undergo iterative optimization with a multi-objective framework to enhance both urban fabric morphology and network performance. The framework was validated through an empirical study of the Yuehe Historic District in Jiaxing. The results indicate that the generated schemes closely align with the original urban fabric. Compared with the existing expanded area (EA), the weighted comprehensive fitness score of the optimized scheme group improved from 0.66 to 0.89 ± 0.02 (a 34.8% increase), with the standard deviation decreasing from 0.07 to 0.02, indicating significantly enhanced stability. Deep learning balances morphological authenticity, generative diversity, and performance in historic district preservation and renewal.