Co-Painter: Fine-Grained Controllable Image Stylization via Implicit Decoupling and Adaptive Injection

Bowen Fu, Wei Wei, Jiaqi Tang, Jiangtao Nie, Yanyu Ye, Xiaogang Xu, Ying-Cong Chen, Lei Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 16830-16839

Controllable diffusion models have been widely applied in image stylization. However, existing methods often treat the style in the reference image as a single, indivisible entity, which makes it difficult to transfer specific stylistic attributes. To address this issue, we propose a fine-grained controllable image stylization framework, Co-Painter, to decouple multiple attributes embedded in the reference image and adaptively inject it into the diffusion model. We first build a multi-condition image stylization framework based on the text-to-image generation model. Then, to drive it, we develop a fine-grained decoupling mechanism to implicitly separate the attributes from the image. Finally, we design a gated feature injection mechanism to adaptively regulate the importance of multiple attributes. To support the above procedure, we also build a dataset with fine-grained styles. It comprises nearly 48,000 image-text pairs samples. Extensive experiments demonstrate that the proposed model achieves an optimal balance between text alignment and style similarity to reference images, both in standard and fine-grained settings.