Free-Lunch Color-Texture Disentanglement for Stylized Image Generation

• URL: http://arxiv.org/abs/2503.14275v2
• Date: Fri, 21 Mar 2025 08:42:51 GMT
• Title: Free-Lunch Color-Texture Disentanglement for Stylized Image Generation
• Authors: Jiang Qin, Senmao Li, Alexandra Gomez-Villa, Shiqi Yang, Yaxing Wang, Kai Wang, Joost van de Weijer,
• Abstract summary: This paper introduces the first tuning-free approach to achieve free-lunch color-texture disentanglement in stylized T2I generation.<n>We develop techniques for separating and extracting Color-Texture Embeddings (CTE) from individual color and texture reference images.<n>To ensure that the color palette of the generated image aligns closely with the color reference, we apply a whitening and coloring transformation.
• Score: 58.406368812760256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Text-to-Image (T2I) diffusion models have transformed image generation, enabling significant progress in stylized generation using only a few style reference images. However, current diffusion-based methods struggle with fine-grained style customization due to challenges in controlling multiple style attributes, such as color and texture. This paper introduces the first tuning-free approach to achieve free-lunch color-texture disentanglement in stylized T2I generation, addressing the need for independently controlled style elements for the Disentangled Stylized Image Generation (DisIG) problem. Our approach leverages the Image-Prompt Additivity property in the CLIP image embedding space to develop techniques for separating and extracting Color-Texture Embeddings (CTE) from individual color and texture reference images. To ensure that the color palette of the generated image aligns closely with the color reference, we apply a whitening and coloring transformation to enhance color consistency. Additionally, to prevent texture loss due to the signal-leak bias inherent in diffusion training, we introduce a noise term that preserves textural fidelity during the Regularized Whitening and Coloring Transformation (RegWCT). Through these methods, our Style Attributes Disentanglement approach (SADis) delivers a more precise and customizable solution for stylized image generation. Experiments on images from the WikiArt and StyleDrop datasets demonstrate that, both qualitatively and quantitatively, SADis surpasses state-of-the-art stylization methods in the DisIG task.Code will be released at https://deepffff.github.io/sadis.github.io/.

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