Structure-Aware Latent Diffusion for High-Quality Line Art Colorization

To address the limitations of existing line art colorization methods in structural preservation, color mapping accuracy, and semantic consistency, this study proposes a structure-aware multi-instance constrained line art colorization method based on latent diffusion. Built upon the latent diffusion framework, the proposed method introduces a structure-aware constraint mechanism to enhance the preservation of line contours and edge details during generation. Meanwhile, instance-level semantic modeling and feature fusion strategies are incorporated to achieve coherent local color representation and optimize global semantic consistency. In addition, a unified optimization objective is constructed by jointly integrating structural constraints, color consistency constraints, and regularization terms, thereby improving the visual quality and naturalness of the generated results through collaborative multi-constraint learning. Experimental results on public datasets demonstrate that the proposed method outperforms comparative approaches in terms of FID, PSNR, SSIM, and LPIPS, producing high-quality colorization results with clear structures, natural colors, and strong semantic consistency, which verifies its effectiveness and superiority.

@article{Xu2026,
  title     = {Structure-Aware Latent Diffusion for High-Quality Line Art Colorization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Shuhua Xu and Qiang Ai and An Zhao and Guan Yang and Bo Chen},
  doi       = {10.14569/IJACSA.2026.0170674},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170674}
}