Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

We present high quality image synthesis results using diffusion probabilistic models, a class
of latent variable models inspired by considerations from nonequilibrium thermodynamics …

Seismic advances in generative AI algorithms for imagery, text, and other data types has led
to the temptation to use synthetic data to train next-generation models. Repeating this …

Abstract Conditional Generative Adversarial Networks (cGAN) generate realistic images by
incorporating class information into GAN. While one of the most popular cGANs is an …

Dynamics and conformational sampling are essential for linking protein structure to
biological function. While challenging to probe experimentally, computer simulations are …

The proposed method, Discriminator Guidance, aims to improve sample generation of pre-
trained diffusion models. The approach introduces a discriminator that gives explicit …

Controllable generation is one of the key requirements for successful adoption of deep
generative models in real-world applications, but it still remains as a great challenge. In …

We introduce manifold-learning flows (ℳ-flows), a new class of generative models that
simultaneously learn the data manifold as well as a tractable probability density on that …