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Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation
March 1, 2024, 5:43 a.m. | Minghui Hu, Yujie Wang, Tat-Jen Cham, Jianfei Yang, P. N. Suganthan
cs.LG updates on arXiv.org arxiv.org
Abstract: The integration of Vector Quantised Variational AutoEncoder (VQ-VAE) with autoregressive models as generation part has yielded high-quality results on image generation. However, the autoregressive models will strictly follow the progressive scanning order during the sampling phase. This leads the existing VQ series models to hardly escape the trap of lacking global information. Denoising Diffusion Probabilistic Models (DDPM) in the continuous domain have shown a capability to capture the global context, while generating high-quality images. In …
abstract arxiv autoencoder autoregressive models context cs.cv cs.lg diffusion global image image generation integration leads modelling part quality results sampling series type vae vector will
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