Wax Printing Pattern Generation Design Based on Diffusion Model Fine-tuning Paradigm

WANG Xinyue; LYU Jian; HOU Yukang; ZHOU Xin; LIN Junxi

doi:10.19554/j.cnki.1001-3563.2026.02.021

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (2) : 223-231. DOI: 10.19554/j.cnki.1001-3563.2026.02.021

Visual Communication Design

Wax Printing Pattern Generation Design Based on Diffusion Model Fine-tuning Paradigm

WANG Xinyue^a,b, LYU Jian^{b, c,*}, HOU Yukang^c, ZHOU Xin^b, LIN Junxi^b

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Abstract

The work aims to propose a wax printing pattern generation design method based on diffusion model fine-tuning paradigm with the wax printing pattern as an example, to explore the intelligent innovative design methods and strategies of national patterns from the perspective of artificial intelligence. Firstly, the wax printing stylized LoRA model was trained with a small-scale physical wax printing sample data set with text annotation. Secondly, the stable diffusion model SDXL was used as the baseline model for wax printing pattern generation, and the SDXL was injected into the trainable layer for fine-tuning training using the low-rank adaptive LoRA method. Finally, in order to solve the randomness of the image generated by the traditional diffusion model, the control network ControlNet was introduced to extract the edge feature information of the input image as a condition to guide the control diffusion model to generate personalized wax printing patterns. The experimental results indicated that innovative wax printing patterns with high realism could be generated through text or image conditions. Compared with the two image generation models of SD1.5 and CycleGAN, it achieved better results in FID and SSIM indexes, which verified the feasibility and effectiveness of the method. Taking the intelligent innovative design of wax printing patterns as an example, the application of generative AI in the field of traditional ethnic patterns is of great significance to the transformation and development of modern design of ethnic patterns.

Key words

generative AI / diffusion model / low rank adaptive / intelligent design / national pattern / wax printing pattern

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WANG Xinyue, LYU Jian, HOU Yukang, ZHOU Xin, LIN Junxi. Wax Printing Pattern Generation Design Based on Diffusion Model Fine-tuning Paradigm[J]. Packaging Engineering. 2026, 47(2): 223-231 https://doi.org/10.19554/j.cnki.1001-3563.2026.02.021

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