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 …

Abstract Generative Adversarial Networks (GAN) have led to the generation of very realistic
face images, which have been used in fake social media accounts and other disinformation …

Large-scale text-to-image generative models have shown their remarkable ability to
synthesize diverse, high-quality images. However, directly applying these models for real …

In addition to the unprecedented ability in imaginary creation, large text-to-image models are
expected to take customized concepts in image generation. Existing works generally learn …

While generative models produce high-quality images of concepts learned from a large-
scale database, a user often wishes to synthesize instantiations of their own concepts (for …

We propose a method for editing images from human instructions: given an input image and
a written instruction that tells the model what to do, our model follows these instructions to …

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 …

This survey reviews text-to-image diffusion models in the context that diffusion models have
emerged to be popular for a wide range of generative tasks. As a self-contained work, this …

Recent large-scale text-guided diffusion models provide powerful image generation
capabilities. Currently, a massive effort is given to enable the modification of these images …

During image editing, existing deep generative models tend to re-synthesize the entire
output from scratch, including the unedited regions. This leads to a significant waste of …