Skip to main content

Research Repository

Advanced Search

CLPFusion: A Latent Diffusion Model Framework for Realistic Chinese Landscape Painting Style Transfer

Pan, Jiahui; Li, Frederick W. B.; Yang, Bailin; Nan, Fangzhe

CLPFusion: A Latent Diffusion Model Framework for Realistic Chinese Landscape Painting Style Transfer Thumbnail


Authors

Jiahui Pan

Bailin Yang

Fangzhe Nan



Abstract

This study focuses on transforming real-world scenery into Chinese landscape painting masterpieces through style transfer. Traditional methods using convolutional neural networks (CNNs) and generative adversarial networks (GANs) often yield inconsistent patterns and artifacts. The rise of diffusion models (DMs) presents new opportunities for realistic image generation, but their inherent noise characteristics make it challenging to synthesize pure white or black images. Consequently, existing DM-based methods struggle to capture the unique style and color information of Chinese landscape paintings. To overcome these limitations, we propose CLPFusion, a novel framework that leverages pre-trained diffusion models for artistic style transfer. A key innovation is the Bidirectional State Space Models-CrossAttention (BiSSM-CA) module, which efficiently learns and retains the distinct styles of Chinese landscape paintings. Additionally, we introduce two latent space feature adjustment methods, Latent-AdaIN and Latent-WCT, to enhance style modulation during inference. Experiments demonstrate that CLPFusion produces more realistic and artistic Chinese landscape paintings than existing approaches, showcasing its effectiveness and uniqueness in the field.

Citation

Pan, J., Li, F. W. B., Yang, B., & Nan, F. (2025). CLPFusion: A Latent Diffusion Model Framework for Realistic Chinese Landscape Painting Style Transfer. Computer Animation and Virtual Worlds, 36(3), Article e70053. https://doi.org/10.1002/cav.70053

Journal Article Type Article
Acceptance Date May 12, 2025
Online Publication Date Jun 7, 2025
Publication Date 2025-06
Deposit Date May 28, 2025
Publicly Available Date Jun 26, 2025
Journal Computer Animation and Virtual Worlds
Print ISSN 1546-4261
Electronic ISSN 1546-427X
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 36
Issue 3
Article Number e70053
DOI https://doi.org/10.1002/cav.70053
Public URL https://durham-repository.worktribe.com/output/3965752

Files





You might also like



Downloadable Citations