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Improved Vector Quantized Diffusion Models
[article]
2023
arXiv
pre-print
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
arXiv:2205.16007v2
fatcat:qxzvhx4ounaojndi5qimhcw33e