Uncovering the Text Embedding in Text-to-Image Diffusion Models

arXiv:2404.01154v1 Announce Type: new
Abstract: The correspondence between input text and the generated image exhibits opacity, wherein minor textual modifications can induce substantial deviations in the generated image. While, text embedding, as the pivotal intermediary between text and images, remains relatively underexplored. In this paper, we address this research gap by delving into the text embedding space, unleashing its capacity for controllable image editing and explicable semantic direction attributes within a learning-free framework. Specifically, we identify two critical insights regarding …

abstract arxiv cs.ai cs.cv diffusion diffusion models embedding gap generated image image diffusion images paper pivotal research text text embedding text-to-image textual type