# Multi-Scale Universal Style-Transfer Network Based on Diffusion Model
## 5 Conclusions  
This paper addresses existing issues in current style-transfer networks for artistic image generation, such as poor fusion between style and content and insufficient smoothness at the edges of the main subject. To overcome these limitations, a multi-scale general style-transfer network based on diffusion models is proposed. The network consists of a coarse style-transfer module and a refined style-transfer module. In the first stage, the coarse style-transfer module quickly processes low-resolution images while ensuring the effectiveness of style transfer. To better handle the edge details of the content subject, the Segment Anything Model (SAM) is employed to segment the main content and generate a corresponding mask image to extract edge information. In the second stage, the preliminary style-transferred image and the mask image are used to refine the edge areas, enabling better fusion of content and style around the subject's edges while preserving more details. To further enhance the overall image quality and improve style fusion along the subject's edges, the initially generated image is upsampled by a factor of two and passed into the style-refinement module. With the aid of ControlNet, content-aware edge refinement is performed, resulting in improved image quality. Comprehensive experiments and analyses were conducted from both qualitative and quantitative perspectives. From a subjective visual standpoint, the proposed method achieves better fusion between the subject and the style compared to existing approaches. This is also supported by pixel-similarity comparison results, which show a higher degree of style-content fusion. In terms of quantitative metrics, the proposed method achieves superior performance in cosine similarity, Gram loss, and SSIM, demonstrating its overall advantage in style-transfer effectiveness. Future work will focus on constructing larger and more diverse datasets to support more comprehensive and in-depth experimental studies, thereby enhancing the effectiveness and generalization ability of style transfer. In addition, more targeted and extensive ablation studies will be designed and conducted to verify the contribution of each module and design choice from multiple perspectives. Furthermore, a wider range of visual evaluation metrics will be introduced to more thoroughly assess the quality of style-content fusion, providing a more detailed quantification of the model's performance in terms of style consistency, structural preservation, and perceptual quality.  
Conceptualization, N.S. and Y.P.; methodology, N.S. and J.W.; data curation, N.S.; writing--original draft preparation, N.S. and J.W.; writing--review and editing, Y.P.; and supervision and project administration, Y.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (62272040, 62201525 and 62172005) and research on the Fundamental Research Funds for the Central Universities CUC24 QT08.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ArtFlow** & **StyTr2** & **CAP-VSTNet** & **Ours** \\ \hline Inference time & 0.17 s & 0.11 s & 0.12 s & 0.14 s \\ SSIM [44] & 0.376 & 0.430 & 0.426 & 0.439 \\ Cosine similarity & 0.803 & 0.822 & 0.826 & 0.834 \\ Gram loss [45] & 0.790 & 0.747 & 0.751 & 0.730 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative analysis of style-transfer methods.  
**Data Availability Statement:** The data presented in this study is available on request from the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Maschine Robot. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.