CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model
This addresses the problem of generating specific content and style in Chinese landscape paintings for art and AI applications, but it is incremental as it builds on existing diffusion models.
The authors tackled controllable Chinese landscape painting generation by proposing CCLAP, a method using a Latent Diffusion Model with content and style modules, which achieved state-of-the-art performance in artful composition and artistic conception.
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.