We study generative diffusion models in the regime where both the data dimension and the
sample size are large, and the score function is trained optimally. Using statistical physics …

In this work, we investigate an intriguing and prevalent phenomenon of diffusion models
which we term as" consistent model reproducibility'': given the same starting noise input and …

Understanding the structure of real data is paramount in advancing modern deep-learning
methodologies. Natural data such as images are believed to be composed of features …

High-quality samples generated with score-based reverse diffusion algorithms provide
evidence that deep neural networks (DNN) trained for denoising can learn high-dimensional …

Recent empirical studies have demonstrated that diffusion models can effectively learn the
image distribution and generate new samples. Remarkably, these models can achieve this …

As deep generative models have progressed, recent work has shown them to be capable of
memorizing and reproducing training datapoints when deployed. These findings call into …

Diffusion models, known for their tremendous ability to generate novel and high-quality
samples, have recently raised concerns due to their data memorization behavior, which …

Score-based generative models (SGMs) sample from a target distribution by iteratively
transforming noise using the score function of the perturbed target. For any finite training set …

Recently, deep generative modelling has become an increasingly powerful tool with seminal
work in a myriad of disciplines. This powerful modelling approach is supposed to not only …

Visual diffusion models have revolutionized the field of creative AI, producing high-quality
and diverse content. However, they inevitably memorize training images or videos …