Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …

Diffusion Models (DMs) have demonstrated state-of-the-art performance in content
generation without requiring adversarial training. These models are trained using a two-step …

Consistency Models (CM)(Song et al., 2023) accelerate score-based diffusion model
sampling at the cost of sample quality but lack a natural way to trade-off quality for speed. To …

The primary axes of interest in image-generating diffusion models are image quality, the
amount of variation in the results, and how well the results align with a given condition, eg, a …

Consistency models are a nascent family of generative models that can sample high quality
data in one step without the need for adversarial training. Current consistency models …

Text-to-music generation models are now capable of generating high-quality music audio in
broad styles. However, text control is primarily suitable for the manipulation of global musical …

Diffusion models have demonstrated exceptional efficacy in various generative applications.
While existing models focus on minimizing a weighted sum of denoising score matching …

This paper revisits the simple, long-studied, yet still unsolved problem of making image
classifiers robust to imperceptible perturbations. Taking CIFAR10 as an example, SOTA …

Score-based generative models (SGMs) learn a family of noise-conditional score functions
corresponding to the data density perturbed with increasingly large amounts of noise. These …

Deepfake or synthetic images produced using deep generative models pose serious risks to
online platforms. This has triggered several research efforts to accurately detect deepfake …