Fashion Design Support for Lolita Brands Using Stable Diffusion

Abstract

With advancements in deep learning, image generation has been widely explored in fashion such as virtual try-on and attribute editing. However, understanding brand-specific clothing features through deep learning models remains underexplored. It is necessary to capture designers' creative intentions and brand-specific features. In this study, we explored image generation which reflects brand-specific features to understand clothing features for deep learning. With the support of a fashion brand specializing in Lolita fashion, we utilized Stable Diffusion to generate high-quality images. We trained LoRA models on clothing images with backgrounds and facial features removed, using prompts that reflect brand-specific attributes such as color, shape, and impression. Furthermore, we explored merging multiple LoRA models to create hybrid clothing designs that blend different elements. We developed a web application based on the proposed system and evaluated its effectiveness as a fashion design support tool by having actual design-related users test it.