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

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. Supplementary Material File (cavw (2).pdf) - Download - 18.34 MB Information & Authors Information Version history Peer review timeline Published Computer Animation and Virtual Worlds Version of Record7 Jun 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection

Authors Metrics & Citations Metrics Article Usage 350views 213downloads Citations Download citation Jiahui Pan, Frederick W. B. Li, Bailin Yang, et al. CLPFusion: A Latent Diffusion Model Framework for Realistic Chinese Landscape Painting Style Transfer. Authorea. 19 April 2025. DOI: https://doi.org/10.22541/au.174505168.87606568/v1 DOI: https://doi.org/10.22541/au.174505168.87606568/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.