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Recently most successful image synthesis models are multi stage process tocombine the advantages of different methods, which always includes a VAE-likemodel for faithfully reconstructing embedding to image and a prior model togenerate image embedding. At the same time, diffusion models have shown becapacity to generate high-quality synthetic images. Our work proposes a VQ-VAEarchitecture model with a diffusion decoder (DiVAE) to work as thereconstructing component in image synthesis. We explore how to input imageembedding into diffusion model for excellent performance and find that simplemodification on diffusion's UNet can achieve it. Training on ImageNet, Ourmodel achieves state-of-the-art results and generates more photorealisticimages specifically. In addition, we apply the DiVAE with an Auto-regressivegenerator on conditional synthesis tasks to perform more human-feeling anddetailed samples.

DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder