LoRA-Fine-Tuned Latent Diffusion for High-Fidelity Digitization of Classic Mongolian Patterns
Year: 2025
Type: article
Abstract: Mongolian patterns represent an important component of Mongolian cultural heritage, characterized by their dual structure of geometric symmetry and dynamic ornamental motifs. However, existing artificial intelligence-based generative methods struggle to preserve both low-frequency structural regularity and high-frequency decorative detail under limited data conditions. This study proposes a parameter-efficient digitization framework based on latent diffusion models (LDMs) fine-tuned with low-rank adaptation (LoRA) to achieve high-fidelity reconstruction of classic Mongolian patterns. A curated few-shot dataset and a low-rank constraint enable effective learning from only eight representative samples, while a dual-prompt mechanism and MSE-driven optimization improve geometric stability and semantic consistency. Integrated within a transparent ComfyUI workflow, the method supports controllable generation and reproducible experimentation. Experimental evaluations demonstrate that the proposed LoRA-LDM model achieves superior structural accuracy, reduced visual distortion, and enhanced motif preservation compared with baseline models. The results confirm the method’s applicability for digital preservation, reconstruction, and derivative design of structured cultural heritage motifs.
Source: Applied Sciences
DOI: 10.3390/app16010011
Authors: Jiatong Liu, Yue Huang
Institution: Beijing University of Posts and Telecommunications
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4417495571