A Feature-Tag-Driven Semantic Control Framework for AIGC-Based Furniture Design Using LoRA Fine-Tuning

To address challenges such as semantic distortion, poor controllability, and the lack of domain-specific structural knowledge in AIGC-driven furniture design, this study proposes a feature-tag-driven semantic control framework based on LoRA (Low-Rank Adaptation) fine-tuning. First, Facet Analysis Theory is introduced to construct a structured representation system, deconstructing furniture into multi-dimensional components (e.g., silhouette, base, backrest, and armrests) and establishing a standardized feature-tag dictionary for deep annotation. Subsequently, domain design knowledge is precisely embedded into the latent space of a diffusion model via LoRA training to reinforce structural consistency. The framework was evaluated through single- and multi-feature comparative tests using a hybrid metric of Feature Hit Rate (FHR) and CLIP-based semantic similarity. Results indicate that the proposed method significantly outperforms general-purpose models in feature-level controllability and structural logic. This research provides a transferable methodology for integrating domain knowledge into generative models, offering significant value for the digital modular design and intelligent manufacturing of upholstered furniture systems.