In this work, we propose FastDPM, a unified framework for fast sampling in
diffusion probabilistic models. FastDPM generalizes previous methods and gives
rise to new algorithms with improved sample quality. We systematically
investigate the fast sampling methods under this framework across different
domains, on different datasets, and with different amount of conditional
information provided for generation. We find the performance of a particular
method depends on data domains (e.g., image or audio), the trade-off between
sampling speed and sample quality, and the amount of conditional information.
We further provide insights and recipes on the choice of methods for
practitioners.

FastDPM 是一种用于快速抽样扩散概率模型的统一框架，通过在不同领域、不同数据集上系统地研究该框架下的快速抽样方法，探讨了特定方法在不同数据领域、抽样速度与质量权衡以及条件信息的数量上的性能差异，并为从业者提供了选择方法的洞见和配方。

关于扩散概率模型的快速采样

On Fast Sampling of Diffusion Probabilistic Models

We propose a novel, projection based way to incorporate the conditional
information into the discriminator of GANs that respects the role of the
conditional information in the underlining probabilistic model. This approach
is in contrast with most frameworks of conditional GANs used in application
today, which use the conditional information by concatenating the (embedded)
conditional vector to the feature vectors. With this modification, we were able
to significantly improve the quality of the class conditional image generation
on ILSVRC2012 (ImageNet) 1000-class image dataset from the current
state-of-the-art result, and we achieved this with a single pair of a
discriminator and a generator. We were also able to extend the application to
super-resolution and succeeded in producing highly discriminative
super-resolution images. This new structure also enabled high quality category
transformation based on parametric functional transformation of conditional
batch normalization layers in the generator.

本文提出了一种新的投影方法来将条件信息纳入 GAN 的鉴别器中，以尊重条件信息在概率模型中的作用，这种方法与当前应用中使用嵌入向量将条件信息与特征向量拼接的大多数条件 GAN 框架形成对比。通过这种修改，我们能够显著提高基于 ILSVRC2012（ImageNet）1000 类图像数据集的类别条件图像生成质量，并且使用单个鉴别器和生成器对此进行了实现。我们还将这种应用扩展到超分辨率，并成功地生成了高度有区别的超分辨率图像。这种新的结构还使基于参数化功能转换的条件批归一化层的高质量类别转换成为可能。