Diffusion models have demonstrated exceptional efficacy in various generative
applications. While existing models focus on minimizing a weighted sum of
denoising score matching losses for data distribution modeling, their training
primarily emphasizes instance-level optimization, overlooking valuable
structural information within each mini-batch, indicative of pair-wise
relationships among samples. To address this limitation, we introduce
Structure-guided Adversarial training of Diffusion Models (SADM). In this
pioneering approach, we compel the model to learn manifold structures between
samples in each training batch. To ensure the model captures authentic manifold
structures in the data distribution, we advocate adversarial training of the
diffusion generator against a novel structure discriminator in a minimax game,
distinguishing real manifold structures from the generated ones. SADM
substantially improves existing diffusion transformers (DiT) and outperforms
existing methods in image generation and cross-domain fine-tuning tasks across
12 datasets, establishing a new state-of-the-art FID of 1.58 and 2.11 on
ImageNet for class-conditional image generation at resolutions of 256x256 and
512x512, respectively.

通过对批处理内样本之间的流形结构进行结构引导的对抗训练，我们引入了一种新的 SADM 扩散模型，显著提高了现有扩散模型的性能，并在图像生成和跨域微调任务中优于现有方法，在 12 个数据集上建立了分辨率为 256x256 和 512x512 的图像网上新的 FID 技术，分别为 1.58 和 2.11。

结构引导的扩散模型对抗训练

Structure-Guided Adversarial Training of Diffusion Models

This paper is concerned with a rate-distortion theory for sequences of i.i.d.
random variables with general distribution supported on general sets including
manifolds and fractal sets. Manifold structures are prevalent in data science,
e.g., in compressed sensing, machine learning, image processing, and
handwritten digit recognition. Fractal sets find application in image
compression and in modeling of Ethernet traffic. We derive a lower bound on the
(single-letter) rate-distortion function that applies to random variables X of
general distribution and for continuous X reduces to the classical Shannon
lower bound. Moreover, our lower bound is explicit up to a parameter obtained
by solving a convex optimization problem in a nonnegative real variable. The
only requirement for the bound to apply is the existence of a sigma-finite
reference measure for X satisfying a certain subregularity condition. This
condition is very general and prevents the reference measure from being highly
concentrated on balls of small radii. To illustrate the wide applicability of
our result, we evaluate the lower bound for a random variable distributed
uniformly on a manifold, namely, the unit circle, and a random variable
distributed uniformly on a self-similar set, namely, the middle third Cantor
set.

该论文探讨了一种适用于具有一般分布的随机变量序列的率失真理论，其中包括流形和分形集合，引入了一种下界和一种广泛适用的子规整条件。