Denoising diffusion probabilistic models (DDPMs) have achieved impressive
performance on various image generation tasks, including image
super-resolution. By learning to reverse the process of gradually diffusing the
data distribution into Gaussian noise, DDPMs generate new data by iteratively
denoising from random noise. Despite their impressive performance,
diffusion-based generative models suffer from high computational costs due to
the large number of denoising steps.In this paper, we first observed that the
intermediate latent states gradually converge and become indistinguishable when
diffusing a pair of low- and high-resolution images. This observation inspired
us to propose the Partial Diffusion Model (PartDiff), which diffuses the image
to an intermediate latent state instead of pure random noise, where the
intermediate latent state is approximated by the latent of diffusing the
low-resolution image. During generation, Partial Diffusion Models start
denoising from the intermediate distribution and perform only a part of the
denoising steps. Additionally, to mitigate the error caused by the
approximation, we introduce "latent alignment", which aligns the latent between
low- and high-resolution images during training. Experiments on both magnetic
resonance imaging (MRI) and natural images show that, compared to plain
diffusion-based super-resolution methods, Partial Diffusion Models
significantly reduce the number of denoising steps without sacrificing the
quality of generation.

通过学习逐渐将数据分布扩散为高斯噪声的过程，并以随机噪声进行迭代去噪处理，去噪扩散概率模型（DDPMs）在各种图像生成任务中取得了令人印象深刻的性能。本文提出了部分扩散模型（PartDiff），通过将图像扩散到中间潜在状态而不是纯随机噪声来生成新数据，并采用近似于低分辨率图像扩散的潜在状态来逼近中间潜在状态。实验证明，与纯扩散基础的超分辨率方法相比，部分扩散模型可以显著减少去噪步骤的数量而不损失生成的质量。