Pyramidal Denoising Diffusion Probabilistic Models

Dohoon Ryu; Jong Chul Ye

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arXiv · 2208.01864

Pyramidal Denoising Diffusion Probabilistic Models

Dohoon Ryu·Jong Chul Ye

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Abstract

Recently, diffusion model have demonstrated impressive image generation performances, and have been extensively studied in various computer vision tasks. Unfortunately, training and evaluating diffusion models consume a lot of time and computational resources. To address this problem, here we present a novel pyramidal diffusion model that can generate high resolution images starting from much coarser resolution images using a {\em single} score function trained with a positional embedding. This enables a neural network to be much lighter and also enables time-efficient image generation without compromising its performances. Furthermore, we show that the proposed approach can be also efficiently used for multi-scale super-resolution problem using a single score function.

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Dohoon Ryu, Jong Chul Ye. 2022-08-03. Pyramidal Denoising Diffusion Probabilistic Models. https://arxiv.org/abs/2208.01864

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