Existing text-to-image diffusion models struggle to synthesize realistic images given dense
captions, where each text prompt provides a detailed description for a specific image region …

Text-to-image diffusion models have an unprecedented ability to generate diverse and high-
quality images. However, they often struggle to faithfully capture the intended semantics of …

In the rapidly advancing realm of visual generation, diffusion models have revolutionized the
landscape, marking a significant shift in capabilities with their impressive text-guided …

Recent text-to-image diffusion models have reached an unprecedented level in generating
high-quality images. However, their exclusive reliance on textual prompts often falls short in …

Multi-modal generative AI has received increasing attention in both academia and industry.
Particularly, two dominant families of techniques are: i) The multi-modal large language …

We present personalized residuals and localized attention-guided sampling for efficient
concept-driven generation using text-to-image diffusion models. Our method first represents …

Diffusion models have recently emerged as powerful generative models, producing high-
fidelity samples across domains. Despite this, they have two key challenges, including …

This paper introduces innovative solutions to enhance spatial controllability in diffusion
models reliant on text queries. We present two key innovations: Vision Guidance and the …

We introduce a method for composing object-level visual prompts within a text-to-image
diffusion model. Our approach addresses the task of generating semantically coherent …

Recent developments in diffusion models have demonstrated an exceptional capacity to
generate high-quality, prompt-conditioned image edits. Nevertheless, previous approaches …