Diffusion models in vision: A survey
Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …
Diffusion models: A comprehensive survey of methods and applications
Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …
record-breaking performance in many applications, including image synthesis, video …
Imagic: Text-based real image editing with diffusion models
Text-conditioned image editing has recently attracted considerable interest. However, most
methods are currently limited to one of the following: specific editing types (eg, object …
methods are currently limited to one of the following: specific editing types (eg, object …
ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers
Large-scale diffusion-based generative models have led to breakthroughs in text-
conditioned high-resolution image synthesis. Starting from random noise, such text-to-image …
conditioned high-resolution image synthesis. Starting from random noise, such text-to-image …
Better diffusion models further improve adversarial training
It has been recognized that the data generated by the denoising diffusion probabilistic
model (DDPM) improves adversarial training. After two years of rapid development in …
model (DDPM) improves adversarial training. After two years of rapid development in …
Physdiff: Physics-guided human motion diffusion model
Denoising diffusion models hold great promise for generating diverse and realistic human
motions. However, existing motion diffusion models largely disregard the laws of physics in …
motions. However, existing motion diffusion models largely disregard the laws of physics in …
Diffusion models as plug-and-play priors
We consider the problem of inferring high-dimensional data $ x $ in a model that consists of
a prior $ p (x) $ and an auxiliary differentiable constraint $ c (x, y) $ on $ x $ given some …
a prior $ p (x) $ and an auxiliary differentiable constraint $ c (x, y) $ on $ x $ given some …
Genie: Higher-order denoising diffusion solvers
Denoising diffusion models (DDMs) have emerged as a powerful class of generative
models. A forward diffusion process slowly perturbs the data, while a deep model learns to …
models. A forward diffusion process slowly perturbs the data, while a deep model learns to …
Fast sampling of diffusion models via operator learning
Diffusion models have found widespread adoption in various areas. However, their
sampling process is slow because it requires hundreds to thousands of network evaluations …
sampling process is slow because it requires hundreds to thousands of network evaluations …
Visual adversarial examples jailbreak aligned large language models
Warning: this paper contains data, prompts, and model outputs that are offensive in nature.
Recently, there has been a surge of interest in integrating vision into Large Language …
Recently, there has been a surge of interest in integrating vision into Large Language …