Integrating Domain Knowledge into Image Generation Models for Textile Design

We propose a prompt augmentation method for textile design image generation that integrates textile-specific domain knowledge, such as patterns, historical context, and design constraints. Recent advances in text-to-image generative models enable designers to create images from natural-language instructions. However, the quality of generated images largely depends on the precision of the prompt, and crafting appropriate prompts remains challenging for designers. To address this issue, our method incorporates domain knowledge about textiles to expand prompts in a way that is suitable for textile design image synthesis. Through qualitative and quantitative evaluations, we confirm that prompts expanded by the proposed method yield better results than those produced from simple prompts or from prompts expanded using only the large language model's internal parametric knowledge.