Creative Painting with Latent Diffusion Models. (arXiv:2209.14697v1 [cs.CV])

Artistic painting has achieved significant progress during recent years by
applying hundreds of GAN variants. However, adversarial training has been
reported to be notoriously unstable and can lead to mode collapse. Recently,
diffusion models have achieved GAN-level sample quality without adversarial
training. Using autoencoders to project the original images into compressed
latent spaces and cross attention enhanced U-Net as the backbone of diffusion,
latent diffusion models have achieved stable and high fertility image
generation. In this paper, we focus on …

arxiv diffusion diffusion models painting