Recent diffusion probabilistic models (DPMs) have shown remarkable abilities
of generated content, however, they often suffer from complex forward
processes, resulting in inefficient solutions for the reversed process and
prolonged sampling times. In this paper, we aim to address the aforementioned
challenges by focusing on the diffusion process itself that we propose to
decouple the intricate diffusion process into two comparatively simpler process
to improve the generative efficacy and speed. In particular, we present a novel
diffusion paradigm named DDM (\textbf{D}ecoupled \textbf{D}iffusion
\textbf{M}odels) based on the It\^{o} diffusion process, in which the image
distribution is approximated by an explicit transition probability while the
noise path is controlled by the standard Wiener process. We find that
decoupling the diffusion process reduces the learning difficulty and the
explicit transition probability improves the generative speed significantly. We
prove a new training objective for DPM, which enables the model to learn to
predict the noise and image components separately. Moreover, given the novel
forward diffusion equation, we derive the reverse denoising formula of DDM that
naturally supports fewer steps of generation without ordinary differential
equation (ODE) based accelerators. Our experiments demonstrate that DDM
outperforms previous DPMs by a large margin in fewer function evaluations
setting and gets comparable performances in long function evaluations setting.
We also show that our framework can be applied to image-conditioned generation
and high-resolution image synthesis, and that it can generate high-quality
images with only 10 function evaluations.

本文提出了一种名为 DDM 的扩散模型，通过将复杂的扩散过程分解为两个相对简单的过程，来提高生成效果和速度，它通过显式转移概率近似图像分布，并通过标准维纳过程控制噪声路径；文章还提出了一个新的 DPM 训练目标，能够分别预测噪声和图像成分，同时，DDM 的逆向去噪公式可以自然地支持少数的生成步骤（不需要基于 ODE 的加速器），实验结果表明，DDM 在更少的函数评估方面优于以前的 DPM。