Denoising Probabilistic Models (DPMs) represent an emerging domain of
generative models that excel in generating diverse and high-quality images.
However, most current training methods for DPMs often neglect the correlation
between timesteps, limiting the model's performance in generating images
effectively. Notably, we theoretically point out that this issue can be caused
by the cumulative estimation gap between the predicted and the actual
trajectory. To minimize that gap, we propose a novel \textit{sequence-aware}
loss that aims to reduce the estimation gap to enhance the sampling quality.
Furthermore, we theoretically show that our proposed loss function is a tighter
upper bound of the estimation loss in comparison with the conventional loss in
DPMs. Experimental results on several benchmark datasets including CIFAR10,
CelebA, and CelebA-HQ consistently show a remarkable improvement of our
proposed method regarding the image generalization quality measured by FID and
Inception Score compared to several DPM baselines. Our code and pre-trained
checkpoints are available at https://github.com/viettmab/SA-DPM.

提出了一种新的序列感知损失函数，以减小估计误差从而提高采样质量，并通过实验证明了该方法相比于传统损失函数在图像泛化质量方面的显著改进。