  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. The core of Rectified Flow lies in its \emph{reflow} procedure, which
straightens the trajectories of probability flows, refines the coupling between
noises and images, and facilitates the distillation process with student
models. We propose a novel text-conditioned pipeline to turn Stable Diffusion
(SD) into an ultra-fast one-step model, in which we find reflow plays a
critical role in improving the assignment between noise and images. Leveraging
our new pipeline, we create, to the best of our knowledge, the first one-step
diffusion-based text-to-image generator with SD-level image quality, achieving
an FID (Frechet Inception Distance) of $23.3$ on MS COCO 2017-5k, surpassing
the previous state-of-the-art technique, progressive distillation, by a
significant margin ($37.2$ $\rightarrow$ $23.3$ in FID). By utilizing an
expanded network with 1.7B parameters, we further improve the FID to $22.4$. We
call our one-step models \emph{InstaFlow}. On MS COCO 2014-30k, InstaFlow
yields an FID of $13.1$ in just $0.09$ second, the best in $\leq 0.1$ second
regime, outperforming the recent StyleGAN-T ($13.9$ in $0.1$ second). Notably,
the training of InstaFlow only costs 199 A100 GPU days. Codes and pre-trained
models are available at \url{github.com/gnobitab/InstaFlow}.
