Wasserstein Style Distribution Analysis and Transform for Stylized Image Generation

tion of different features to style. Based on our findings, we propose a novel Wasserstein Style Distribution Transform (WSDT) method, which generates stylized images by transforming the distribution of style-sensitive features to align with that of style features. WSDT applies channel adaptive distribution transform to ensure that information not related to the style is not introduced. Our approach is simple yet efficient, optimization-free, and can be seamlessly integrated into attention-based text-to-image diffusion models. Extensive experiments demonstrate the effectiveness of our approach in stylized image generation tasks.