Integrating artificial intelligence (AI) into residential architectural design faces challenges due to fragmented workflows and the lack of localized datasets. This study proposes the CorbuAI framework, hypothesizing that a multimodal AI system integrating Pix2pix-GAN and Stable Diffusion (SD) can streamline the transition from floor plan generation to elevation and interior design within a specific regional context. We developed a custom dataset featuring 2335 manually refined Chinese residential floor plans and 1570 elevation images. The methodology employs a specialized U-Net V2.0 generator for functional layout synthesis and an SD-based model for stylistic transfer and elevation rendering. Evaluation was conducted through both subjective professional scoring and objective metrics, including the Perceptual Hash Algorithm (pHash). Results demonstrate that CorbuAI achieves high accuracy in spatial allocation (scoring 0.88/1.0) and high structural consistency in elevation generation (mean pHash similarity of 0.82). The framework significantly reduces design iteration time while maintaining professional aesthetic standards. This research provides a scalable AI-driven methodology for automated residential design, bridging the gap between schematic layouts and visual representation in the Chinese architectural context.

Artificial Intelligence (AI) has emerged as a transformative force in the field of architectural design. This study aims to systematically analyze the influence mechanisms of Artificial Intelligence-based Architectural Design (AIAD) by constructing a comprehensive hybrid model that integrates the Analytic Hierarchy Process (AHP), Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). Based on the previous quantitative literature review, 6 primary categories and 18 secondary influencing factors were identified. Data were collected from a panel of fifteen experts representing the architecture industry, academia, and computer science. Through weighting analysis, causal mapping, hierarchical structuring, and driving–dependence classification, the study clarifies the complex interrelationships among influencing factors and reveals the underlying drivers that accelerate or constrain AI adoption in architectural design. By quantifying the hierarchical and causal influence of factors, this research provides theoretical findings and practical insights for design firms undergoing digital transformation. The results extend previous meta-analytical studies, offering a decision-support system that bridges academic research and real-world applications, thereby guiding stakeholders toward informed adoption of artificial intelligence for future cultural tourism development and regional spatial innovation.