AI Generative Design of Dunhuang Ceiling Patterns Based on LoRA Label Optimization

MENG Xinyi; YIN Xiaochen; ZHAO Yilong

doi:10.19554/j.cnki.1001-3563.2026.02.019

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (2) : 197-210. DOI: 10.19554/j.cnki.1001-3563.2026.02.019

Visual Communication Design

AI Generative Design of Dunhuang Ceiling Patterns Based on LoRA Label Optimization

MENG Xinyi, YIN Xiaochen^*, ZHAO Yilong

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Abstract

The work aims to integrate design methodologies into AI generative design processes, and enhance LoRA style transfer effectiveness by optimizing label content through feature extraction from LoRA training sets. With Dunhuang ceiling patterns as an example, a training set was constructed. Firstly, the established image features of the samples and perceptual keywords summarizing the styles were extracted through structural analysis, pattern element analysis, color clustering, and Kansei engineering experiments. Secondly, eye-tracking technology was employed to quantify the characteristic features of pattern elements in the ceiling patterns, ranking them based on visual importance. Finally, the extracted features were converted into natural language for manual annotation of the training set. A comparison of generative content before and after label optimization using the LoRA model demonstrated that the optimized model more effectively reproduced the style characteristics and pattern content of the training set samples. By combining qualitative and quantitative methods, the study objectively and comprehensively extracts training set features, effectively optimizes label content, and improves the stability of content generated by the LoRA model. This approach promotes the innovation and development of traditional patterns through intelligent design.

Key words

Low-Rank Adaptation (LoRA) / image annotation / eye movement experiments / kansei engineering / Dunhuang ceiling patterns

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MENG Xinyi, YIN Xiaochen, ZHAO Yilong. AI Generative Design of Dunhuang Ceiling Patterns Based on LoRA Label Optimization[J]. Packaging Engineering. 2026, 47(2): 197-210 https://doi.org/10.19554/j.cnki.1001-3563.2026.02.019

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