Generative artificial intelligence (AI) has emerged as a powerful technology with numerous
applications in various domains. There is a need to identify the requirements and evaluation …

Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

Despite the success in large-scale text-to-image generation and text-conditioned image
editing, existing methods still struggle to produce consistent generation and editing results …

Generating and editing images from open domain text prompts is a challenging task that
heretofore has required expensive and specially trained models. We demonstrate a novel …

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 …

Inspired by the ability of StyleGAN to generate highly re-alistic images in a variety of
domains, much recent work hasfocused on understanding how to use the latent spaces …

Instruction-based image editing improves the controllability and flexibility of image
manipulation via natural commands without elaborate descriptions or regional masks …

In this work, we propose TediGAN, a novel framework for multi-modal image generation and
manipulation with textual descriptions. The proposed method consists of three components …

Controllable image synthesis models allow creation of diverse images based on text
instructions or guidance from a reference image. Recently, denoising diffusion probabilistic …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …