Chiang Mai Journal of Science

     Thai textile designs from user-provided partial sketches. Our method incorporates two key innovations: (1) training augmentation with synthetically generated partial sketches that mimic human drawings, and (2) text-based color control through user descriptions. To enhance robustness and generalization, we employ a multi-stage training pipeline. First, we leverage a pre-trained Stable Diffusion model. We then finetune the model on a larger dataset of Indonesian fabrics (which shares stylistic similarities with Thai textiles) before specializing in a dataset of Thai textiles. Additionally, we implement curriculum learning, where the model starts with complete sketches and gradually progresses to more challenging partial sketches. Ablation studies demonstrate the effectiveness of our approach, yielding robust and colorful designs that adapt to various sketch styles and color instructions. This work opens exciting avenues for future research in AI-assisted textile design, fostering a seamless blend of human creativity and machine intelligence.