Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. Although current style-transfer methods have achieved promising results when processing photorealistic images, they often struggle with brushstroke preservation in artworks, especially in styles such as oil painting and pointillism. In such cases, the extracted style and content features tend to include redundant information, leading to issues such as blurred edges and a loss of fine details in the transferred images. To address this problem, this paper proposes a multi-scale general style-transfer network based on diffusion models. The proposed network consists of a coarse style-transfer module and a refined style-transfer module. First, the coarse style-transfer module is designed to perform mainstream style-transfer tasks more efficiently by operating on downsampled images, enabling faster processing with satisfactory results. Next, to further enhance edge fidelity, a refined style-transfer module is introduced. This module utilizes a segmentation component to generate a mask of the main subject in the image and performs edge-aware refinement. This enhances the fusion between the subject’s edges and the target style while preserving more detailed features. To improve overall image quality and better integrate the style along the content boundaries, the output from the coarse module is upsampled by a factor of two and combined with the subject mask. With the assistance of ControlNet and Stable Diffusion, the model performs content-aware edge redrawing to enhance the overall visual quality of the stylized image. Compared with state-of-the-art style-transfer methods, the proposed model preserves more edge details and achieves more natural fusion between style and content.

The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between content and style features after transfer, making effective separation challenging. To address this issue, we propose a Dual-Branch Decoupled Image Style Transfer Network (DBDST-Net) to better disentangle content and style representations. The network consists of two branches: a Content Feature Decoupling Branch, which captures fine-grained content structures for more precise content separation, and a Style Feature Decoupling Branch, which enhances sensitivity to style-specific attributes. To further improve the decoupling performance, we introduce a dense-regressive loss that minimizes the discrepancy between the original content image and the content reconstructed from the stylized output, thereby promoting the independence of content and style features while enhancing image quality. Additionally, to mitigate the limited availability of style data, we employ the Stable Diffusion model to generate stylized samples for data augmentation. Extensive experiments demonstrate that our method achieves a better balance between content preservation and style rendering compared to existing approaches.