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Classical garden visualization commonly suffers from high production cost, time-consuming production processes, and low efficiency in iterative design. To address these limitations, this study proposes a domain-oriented AI-assisted design framework for Jiangnan classical garden architectural scenes based on a large language model (LLM) and a latent diffusion model (LDM). Rather than emphasizing the generic combination of LLMs and diffusion models, the study focuses on constructing a workflow that links natural-language parsing, structured semantic representation, distributed LoRA-based coordinated generation, and multi-level evaluation within a strongly domain-constrained design context. Specifically, the LLM is fine-tuned through supervised fine-tuning using classical garden literature and standardized samples to generate structured prompts aligned with professional design semantics, while a distributed hierarchical LoRA strategy is applied to the LDM to separate architectural morphology control from environment-level scene expression. A trigger-word-based coordination mechanism further supports rule-driven invocation and dynamic weighting of architectural and environmental LoRA models. Through this framework, the study seeks to address the unstable architectural type recognition, insufficient spatial-semantic mapping, and stylistic deviation often observed when general-purpose generation models are applied to traditional garden architectural scenes. Evaluation results indicate that more than 75% of the generated images reach a professionally usable level in spatial organization and artistic-conception restoration, while over 80% exhibit traditional garden aesthetics and visual authenticity. The proposed framework improves generation efficiency and provides a domain-oriented technical pathway for controllable generation, design iteration, design communication, and literature-informed reconstruction of Jiangnan classical garden scenes.
Li et al. (Wed,) studied this question.