InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation

arXiv:2309.06380v2 Announce Type: replace
Abstract: Diffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attempts to improve its sampling speed and reduce computational costs through distillation have been unsuccessful in achieving a functional one-step model. In this paper, we explore a recent method called Rectified Flow, which, thus far, has only been applied to small datasets. …

abstract arxiv computational costs creativity cs.cv cs.lg diffusion diffusion models distillation however image image generation inference process quality reduce results sampling speed text text-to-image through type