Diffusion models achieve remarkable quality in image generation, but at a
cost. Iterative denoising requires many time steps to produce high fidelity
images. We argue that the denoising process is crucially limited by an
accumulation of the reconstruction error due to an initial inaccurate
reconstruction of the target data. This leads to lower quality outputs, and
slower convergence. To address this issue, we propose compensation sampling to
guide the generation towards the target domain. We introduce a compensation
term, implemented as a U-Net, which adds negligible computation overhead during
training and, optionally, inference. Our approach is flexible and we
demonstrate its application in unconditional generation, face inpainting, and
face de-occlusion using benchmark datasets CIFAR-10, CelebA, CelebA-HQ,
FFHQ-256, and FSG. Our approach consistently yields state-of-the-art results in
terms of image quality, while accelerating the denoising process to converge
during training by up to an order of magnitude.

通过引入补偿采样，我们的方法在图像品质方面始终产生最先进的结果，同时将去噪过程加速了一个数量级。

扩散模型中的补偿采样以提高收敛性

Compensation Sampling for Improved Convergence in Diffusion Models

Diffusion models that are based on iterative denoising have been recently
proposed and leveraged in various generation tasks like image generation.
Whereas, as a way inherently built for continuous data, existing diffusion
models still have some limitations in modeling discrete data, e.g., languages.
For example, the generally used Gaussian noise can not handle the discrete
corruption well, and the objectives in continuous spaces fail to be stable for
textual data in the diffusion process especially when the dimension is high. To
alleviate these issues, we introduce a novel diffusion model for language
modeling, Masked-Diffuse LM, with lower training cost and better performances,
inspired by linguistic features in languages. Specifically, we design a
linguistic-informed forward process which adds corruptions to the text through
strategically soft-masking to better noise the textual data. Also, we directly
predict the categorical distribution with cross-entropy loss function in every
diffusion step to connect the continuous space and discrete space in a more
efficient and straightforward way. Through experiments on 5 controlled
generation tasks, we demonstrate that our Masked-Diffuse LM can achieve better
generation quality than the state-of-the-art diffusion models with better
efficiency.

介绍了一种新的语言建模扩散模型 Masked-Diffuse LM，通过语言学特征和软掩蔽添加文本失真，通过交叉熵损失函数在每个扩散步骤中直接预测分类分布，以更高效和直接的方式连接连续空间和离散空间，优于目前最先进的扩散模型。