Creative painting with latent diffusion models
Xianchao Wu
Computer science - computer vision and pattern recognition, computer science - artificial intelligence, computer science - computation and language, computer science - graphics, computer science - machine learning
Abstract
Artistic painting has achieved significant progress during recent years. Using an autoencoder to connect the original images with compressed latent spaces and a cross attention enhanced U-Net as the backbone of diffusion, latent diffusion models (LDMs) have achieved stable and high fertility image generation. In this paper, we focus on enhancing the creative painting ability of current LDMs in two directions, textual condition extension and model retraining with Wikiart dataset. Through textual
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Paper ID: 4caa3c3e-7b60-43ba-8d2f-858ecfd51787Added: 10/26/2025