GAN与Diffusion在传统纹样设计中的实验研究

An experimental study on the application of GAN and Diffusion models in traditional pattern design

传统纹样是中国优秀传统文化的重要组成部分,传统人工设计已经无法满足纹样的现代设计需求,生成式AI为传统纹样设计提供了新的设计路径和方法。文章将生成式AI应用于传统纹样设计中,通过适配实验优选基于GAN的Style GAN和基于Diffusion的Stable Diffusion两种主流图像生成模型进行实验,采用技术分析与艺术分析相结合,对实验结果进行多角度、多维度对比分析,为设计师选择生成设计方法提供参照。实验结果表明,两个模型均能满足基本的艺术设计需求。Style GAN模型生成的纹样图像更接近真实图像的分布,具有更高的图像质量和多样性;Stable Diffusion模型能较好地传承传统纹样的基因,艺术性与创造性兼具,更加符合传统纹样的艺术设计需求。

Traditional patterns come as one of the vital components of China’s rich cultural heritage,embodying the wisdom and aesthetic memory of China.These patterns have been extensively used in various design fields.Artists and designers can draw nourishment and inspiration from the beautiful graphic decorations,the rich implications of forms,and the unique pattern designs.However,traditional manual design methods can no longer meet the diverse and efficient demands of the modern pattern design.Current research on computer-aided pattern design primarily focuses on traditional methods and generative AI approaches.Traditional methods mainly generate new patterns by simulating image morphological features and quantifying image organizational characteristics.Generative AI methods,on the other hand,use deep neural networks for transfer learning to simulate the distribution of image data,thus creating new pattern images and offering new paths and methods for traditional pattern design.While there is already a certain foundational body of research on the generative design of traditional patterns,there are still issues in the field of generative technology application research.These include a lack of research from the perspective of universal generative design of traditional patterns,neglect of the cultural and artistic foundations of these patterns,insufficient attention to the practical application needs of generated patterns,and a lack of comprehensive evaluation of generated patterns.To facilitate deep co-creation between designers and AI,this paper explores the potential and application of image generation models in the innovative design of traditional patterns from an artistic design perspective.Four mainstream image generation models were initially selected through preliminary experiments on traditional pattern generation.Among these,StyleGAN(based on GAN)and Stable Diffusion(based on Diffusion)were chosen for further experimentation.The technical aspects of the datasets,training processes,and model parameters were analyzed,and pattern images were evaluated based on diversity,clarity,and text-image matching.Additionally,a survey was conducted to assess the experimental results on five artistic design elements:form,color,aesthetics,innovation,and application.Combining technical and artistic analyses,the experimental results underwent comprehensive multidimensional evaluation.Finally,the experimental results were validated from the perspective of design requirements,and the superior performance of the two generative design methods in various aspects was explored.This provides case references for designers in selecting and using generative design methods and offers new research perspectives for traditional pattern design studies.The experimental results indicate that both models meet the basic requirements of artistic design.The StyleGAN model produces pattern images closer to the distribution of real images,with higher image quality and diversity,making it suitable for generating line patterns,individual patterns,and continuous patterns,and meeting the needs for quick generation emphasizing formal beauty.In contrast,the Stable Diffusion model better preserves the essence of traditional patterns,balancing artistry and creativity,and is more aligned with the artistic design needs of traditional patterns,suitable for diversified and precise generation requirements,and for cultural content emphasizing inheritance and innovation.This study provides an experimental analysis of the application of image generation models in traditional pattern design,offering new research perspectives and methods for traditional pattern artistic creation.The findings will contribute to the deep application of generative AI in the design of ethnic and traditional patterns,so as to promote the modern transformation of traditional pattern design.