Small-Sample Learning for Typology-Constrained Generative Design: Novel Framework and Application Practice in Linpan, China

To address the challenges of ambiguous regional feature identification, high digital modeling costs, and limited regional adaptability of general-purpose AIGC tools in the micro-renewal of Western Sichuan Linpan, this study proposes a typology-constrained small-sample learning framework. A structured dataset of 80 high-quality, semantically annotated images was constructed to extract typological features, including spatial layouts, architectural forms, and environmental relationships. Based on Stable Diffusion, the framework integrates LoRA fine-tuning for style adaptation and ControlNet for structural control. To constrain the number of trainable parameters under the limited-data setting, LoRA rank control, dropout regularization, and early stopping were incorporated into the training procedure. Under the fixed 80-image dataset and experimental configuration, the complete framework achieved lower FID values and higher CLIP and expert-evaluation scores than the prompt-only baseline. Compared with Scheme A, the FID decreased by 22.7% (from 284.52 to 220.00), while the CLIP Score increased by 18.3% (from 0.224 to 0.265). Under the evaluated configuration, the complete framework showed closer correspondence with the specified Linpan architectural characteristics and spatial conditions than the prompt-only baseline. The results support the feasibility of translating architectural typological knowledge into semantic and geometric conditioning signals under the evaluated limited-data setting. However, the influence of training-set size on model stability and generation performance was not examined and requires further investigation.