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.
翻译:近些年来,艺术绘画取得了显著进步。利用自动编码器将原始图像与压缩潜质空间和交叉关注增强的U-Net连接起来,作为传播的支柱,潜在扩散模型(LDMs)实现了稳定高生育率的图像生成。在本文中,我们侧重于提高当前LDM的创造性绘画能力,朝两个方向发展,即文本状态扩展和用维基亚数据集进行模型再培训。通过文本条件扩展,用户输入提示扩大,拥有丰富的背景知识,以加深理解和解释提示。维基亚数据集包含近400年来由1,000多名富有风格和风格的著名艺术家所画的80K著名作品。通过再培训,我们可以要求这些艺术家在现代主题上画出新颖和创造性的绘画。与原始模型的直接比较表明,创造力和艺术家是丰富的。