Diffusion models (DMs) have disrupted the image super-resolution (SR) field and further
closed the gap between image quality and human perceptual preferences. They are easy to …

Generative AI (GenAI) represents a shift from AI's ability to “recognize” to its ability to
“generate” solutions for a wide range of tasks. As generated solutions and applications grow …

Image diffusion models, trained on massive image collections, have emerged as the most
versatile image generator model in terms of quality and diversity. They support inverting real …

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 …

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 text-to-image diffusion models are able to generate convincing results of
unprecedented quality. However, it is nearly impossible to control the shapes of different …

Recent works on diffusion models have demonstrated a strong capability for conditioning
image generation, eg, text-guided image synthesis. Such success inspires many efforts …

Recent advances in text-to-image generation with diffusion models present transformative
capabilities in image quality. However, user controllability of the generated image, and fast …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

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 …