Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Abstract Generative Adversarial Networks (GANs) are a type of deep learning architecture
that uses two networks namely a generator and a discriminator that, by competing against …

Large-scale text-to-image generative models have been a revolutionary breakthrough in the
evolution of generative AI, synthesizing diverse images with highly complex visual concepts …

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 …

Large-scale generative models are capable of producing high-quality images from detailed
prompts. However, many aspects of an image are difficult or impossible to convey through …

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-
the-art video models are still lagging behind image models in terms of visual quality and …

With generative models proliferating at a rapid rate, there is a growing need for general
purpose fake image detectors. In this work, we first show that the existing paradigm, which …

We present a method for zero-shot, text-driven editing of natural images and videos. Given
an image or a video and a text prompt, our goal is to edit the appearance of existing objects …

Diffusion models generate high-quality images but require dozens of forward passes. We
introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model …

Score-based diffusion models (SBDMs) have achieved the SOTA FID results in unpaired
image-to-image translation (I2I). However, we notice that existing methods totally ignore the …