With the development of deep generative models, recent years have seen great success of Chinese landscape painting generation. However, few works focus on controllable Chinese landscape painting generation due to the lack of data and limited modeling capabilities. In this work, we propose a controllable Chinese landscape painting generation method named CCLAP, which can generate painting with specific content and style based on Latent Diffusion Model. Specifically, it consists of two cascaded modules, i.e., content generator and style aggregator. The content generator module guarantees the content of generated paintings specific to the input text. While the style aggregator module is to generate paintings of a style corresponding to a reference image. Moreover, a new dataset of Chinese landscape paintings named CLAP is collected for comprehensive evaluation. Both the qualitative and quantitative results demonstrate that our method achieves state-of-the-art performance, especially in artfully-composed and artistic conception. Codes are available at https://github.com/Robin-WZQ/CCLAP.

本文提出了一种名为CCLAP的可控制的中国山水画生成方法，它可以基于潜在扩散模型生成具有特定内容和风格的画作，其中包括内容生成器和样式聚合器两个级联模块，并收集了名为CLAP的新数据集进行全面评估。我们的方法在艺术构图和艺术概念方面取得了最先进的表现。

CCLAP: 基于潜在扩散模型的可控中华山水画生成