  Vector quantized diffusion (VQ-Diffusion) is a powerful generative model for
text-to-image synthesis, but sometimes can still generate low-quality samples
or weakly correlated images with text input. We find these issues are mainly
due to the flawed sampling strategy. In this paper, we propose two important
techniques to further improve the sample quality of VQ-Diffusion. 1) We explore
classifier-free guidance sampling for discrete denoising diffusion model and
propose a more general and effective implementation of classifier-free
guidance. 2) We present a high-quality inference strategy to alleviate the
joint distribution issue in VQ-Diffusion. Finally, we conduct experiments on
various datasets to validate their effectiveness and show that the improved
VQ-Diffusion suppresses the vanilla version by large margins. We achieve an
8.44 FID score on MSCOCO, surpassing VQ-Diffusion by 5.42 FID score. When
trained on ImageNet, we dramatically improve the FID score from 11.89 to 4.83,
demonstrating the superiority of our proposed techniques.
