Progressive Image Synthesis from Semantics to Details with Denoising Diffusion GAN

Although denoising diffusion probabilistic models (DDPMs) have shown remarkable progress in image generation, they typically face two main challenges: the time-expensive sampling process and the semantically meaningless latent space, which are often addressed separately in previous works. In particular, the latest representative work Denoising Diffusion GAN reduces the sampling steps to as few as two but ignores the semantics of the latent space. To address the two challenges simultaneously, we propose a two-stage framework to make the latent space of Denoising Diffusion GAN more semantically meaningful while enjoying its efficiency. Extensive results on three benchmark datasets demonstrate that our proposed diffusion model achieves competitive results with only two sampling steps in unconditional image generation. More importantly, the latent space of our diffusion model trained for unconditional image generation is shown to be semantically meaningful, which can be exploited on various downstream tasks (e.g., attribute editing) without further training.