Guidance in conditional diffusion generation is of great importance for
sample quality and controllability. However, existing guidance schemes are to
be desired. On one hand, mainstream methods such as classifier guidance and
classifier-free guidance both require extra training with labeled data, which
is time-consuming and unable to adapt to new conditions. On the other hand,
training-free methods such as universal guidance, though more flexible, have
yet to demonstrate comparable performance. In this work, through a
comprehensive investigation into the design space, we show that it is possible
to achieve significant performance improvements over existing guidance schemes
by leveraging off-the-shelf classifiers in a training-free fashion, enjoying
the best of both worlds. Employing calibration as a general guideline, we
propose several pre-conditioning techniques to better exploit pretrained
off-the-shelf classifiers for guiding diffusion generation. Extensive
experiments on ImageNet validate our proposed method, showing that
state-of-the-art diffusion models (DDPM, EDM, DiT) can be further improved (up
to 20%) using off-the-shelf classifiers with barely any extra computational
cost. With the proliferation of publicly available pretrained classifiers, our
proposed approach has great potential and can be readily scaled up to
text-to-image generation tasks. The code is available at
this https URL

通过使用现成的预训练分类器在无需额外计算成本的情况下，提出了预处理技术来引导扩散生成，旨在实现比现有方案更显著的性能改进，尤其是在图像生成任务中。

分类器引导扩散生成的设计空间阐明

Elucidating The Design Space of Classifier-Guided Diffusion Generation

Stochastic Gradient Descent (SGD) is one of the many iterative optimization
methods that are widely used in solving machine learning problems. These
methods display valuable properties and attract researchers and industrial
machine learning engineers with their simplicity. However, one of the
weaknesses of this type of methods is the necessity to tune learning rate
(step-size) for every loss function and dataset combination to solve an
optimization problem and get an efficient performance in a given time budget.
Stochastic Gradient Descent with Polyak Step-size (SPS) is a method that offers
an update rule that alleviates the need of fine-tuning the learning rate of an
optimizer. In this paper, we propose an extension of SPS that employs
preconditioning techniques, such as Hutchinson's method, Adam, and AdaGrad, to
improve its performance on badly scaled and/or ill-conditioned datasets.

扩展了 Stochastic Gradient Descent with Polyak Step-size (SPS) 方法，使用 Hutchinson's 方法、Adam 和 AdaGrad 等预处理技术来提高其在糟糕缩放和 / 或病态数据集上的性能。