Accurate and controllable image editing is a challenging task that has attracted significant
attention recently. Notably DragGAN developed by Pan et al.(2023) is an interactive point …

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 …

Text-to-image models give rise to workflows which often begin with an exploration step,
where users sift through a large collection of generated images. The global nature of the text …

Incorporating human feedback has been shown to be crucial to align text generated by large
language models to human preferences. We hypothesize that state-of-the-art instructional …

In this paper we propose the Matting Anything Model (MAM) an efficient and versatile
framework for estimating the alpha matte of any instance in an image with flexible and …

Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

A key aspect of text-to-image personalization methods is the manner in which the target
concept is represented within the generative process. This choice greatly affects the visual …

Recent advancements in text-to-image generative models have demonstrated a remarkable
ability to capture a deep semantic understanding of images. In this work, we leverage this …

Diffusion models gain increasing popularity for their generative capabilities. Recently, there
have been surging needs to generate customized images by inverting diffusion models from …

While diffusion-based text-to-image (T2I) models provide a simple and powerful way to
generate images, guiding this generation remains a challenge. For concepts that are difficult …