Creative Painting with Latent Diffusion Models
This work addresses the problem of limited artistic creativity in AI-generated paintings for users and artists, representing an incremental improvement.
The paper tackled enhancing creative painting in latent diffusion models by extending textual conditions and retraining on the Wikiart dataset, resulting in enriched creativity and artistry as shown in direct comparisons with the original model.
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 condition extension, users' input prompts are expanded with rich contextual knowledge for deeper understanding and explaining the prompts. Wikiart dataset contains 80K famous artworks drawn during recent 400 years by more than 1,000 famous artists in rich styles and genres. Through the retraining, we are able to ask these artists to draw novel and creative painting on modern topics. Direct comparisons with the original model show that the creativity and artistry are enriched.