CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model

TL;DR

This paper introduces CCLAP, a controllable Chinese landscape painting generation method using Latent Diffusion Models, enabling specific content and style generation with a new dataset and achieving state-of-the-art results.

Contribution

The paper presents a novel controllable generation framework with cascaded modules and a new Chinese landscape painting dataset, enhancing controllability and quality.

Findings

Achieves state-of-the-art performance in Chinese landscape painting generation.

Effectively controls content and style based on input text and reference images.

Demonstrates superior artistic quality and composition.

Abstract

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.