CCLAP: Controllable Chinese Landscape Painting Generation Via Latent Diffusion Model (2023-04-09T00:00:00.000000Z)

TL;DR

A controllable Chinese landscape painting generation method named CCLAP, which can generate painting with specific content and style based on Latent Diffusion Model, which achieves state-of-the-art performance, especially in artfully-composed and artistic conception.

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.

Authors

S. Shan

9 papers

Zhilong Ji

3 papers

Jinfeng Bai

3 papers

TL;DR

Abstract

Authors

References27 items

Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) on Melanoma Skin Cancer Detection

LAION-5B: An open large-scale dataset for training next generation image-text models

GlueGAN: Gluing Two Images as a Panorama with Adversarial Learning

Learning to Generate Poetic Chinese Landscape Painting with Calligraphy

High-Resolution and Arbitrary-Sized Chinese Landscape Painting Creation Based on Generative Adversarial Networks

Hierarchical Text-Conditional Image Generation with CLIP Latents

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

High-Resolution Image Synthesis with Latent Diffusion Models

Attentional Wavelet Network for Traditional Chinese Painting Transfer

End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks

Denoising Diffusion Probabilistic Models

Analyzing and Improving the Image Quality of StyleGAN

Real-time Scene Text Detection with Differentiable Binarization

Generating Chinese Classical Landscape Paintings Based on Cycle-Consistent Adversarial Networks

An Interactive and Generative Approach for Chinese Shanshui Painting Document

Neural Abstract Style Transfer for Chinese Traditional Painting

ChipGAN: A Generative Adversarial Network for Chinese Ink Wash Painting Style Transfer

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network

Wasserstein GAN

Improved Techniques for Training GANs

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Microsoft COCO: Common Objects in Context

An Image Inpainting Technique Based on the Fast Marching Method

Progressive Attentional Manifold Alignment for Arbitrary Style Transfer

GENERATIVE ADVERSARIAL NETS

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