论文标题
锥体降解扩散概率模型
Pyramidal Denoising Diffusion Probabilistic Models
论文作者
论文摘要
最近，扩散模型已显示出令人印象深刻的图像产生性能，并且已经在各种计算机视觉任务中进行了广泛的研究。不幸的是，培训和评估扩散模型会消耗大量时间和计算资源。为了解决这个问题，我们提出了一个新型的金字塔扩散模型，该模型可以生成高分辨率图像，从使用位置嵌入训练的{\ em单}得分函数从更粗的分辨率图像开始。这使神经网络变得更轻，还可以在不损害其性能的情况下产生及时的图像。此外，我们表明使用单个分数函数也可以有效地用于多尺度的超分辨率问题。
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.
