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 …

Recent progress in large-scale zero-shot speech synthesis has been significantly advanced
by language models and diffusion models. However, the generation process of both …

Large generative diffusion models have revolutionized text-to-image generation and offer
immense potential for conditional generation tasks such as image enhancement restoration …

Super-resolution methods are increasingly being specialized for both real-world and face-
specific tasks. However, many existing approaches rely on simplistic degradation models …

Score function estimation is the cornerstone of both training and sampling from diffusion
generative models. Despite this fact, the most commonly used estimators are either biased …

In this paper we identify the source of a singularity in the training loss of key denoising
models, that causes the denoiser's predictions to collapse towards the mean of the source or …

We propose the characteristic generator, a novel one-step generative model that combines
the efficiency of sampling in Generative Adversarial Networks (GANs) with the stable …

We propose a semi-supervised masked auxiliary classifier generative adversarial network
(SM-ACGAN) that has good classification performance in situations where labeled training …

The recently introduced Consistency models pose an efficient alternative to diffusion
algorithms, enabling rapid and good quality image synthesis. These methods overcome the …