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 …

Large text-guided diffusion models, such as DALLE-2, are able to generate stunning
photorealistic images given natural language descriptions. While such models are highly …

Since their introduction, diffusion models have quickly become the prevailing approach to
generative modeling in many domains. They can be interpreted as learning the gradients of …

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

Metrics for evaluating generative models aim to measure the discrepancy between real and
generated images. The oftenused Frechet Inception Distance (FID) metric, for example …

We introduce a new generative model where samples are produced via Langevin dynamics
using gradients of the data distribution estimated with score matching. Because gradients …

Many applications of text generation require incorporating different constraints to control the
semantics or style of generated text. These constraints can be hard (eg, ensuring certain …

We propose to reinterpret a standard discriminative classifier of p (y| x) as an energy based
model for the joint distribution p (x, y). In this setting, the standard class probabilities can be …

In this work, we propose a novel technique to generate shapes from point cloud data. A point
cloud can be viewed as samples from a distribution of 3D points whose density is …