title: InstaFlow: One Step is Enough for High-Quality Diffusion-Based
    Text-to-Image Generation
summary: 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 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 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. Project
  page:~https://github.com/gnobitab/InstaFlow.
paper_authors._id: 65011cca70b6b05c5af0c456
paper_authors.name: Xingchao Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-13T07:59:20.422Z
paper_authors._id: 646b0bbdec9a61e871799339
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/646b0bbdec9a61e871799339/Xippv-ajkHkrGGAA7caLn.jpeg
paper_authors.fullname: Xingchao Liu
paper_authors.type: user
paper_authors.user: XCLiu
paper_authors._id: 65011cca70b6b05c5af0c457
paper_authors.name: Xiwen Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-13T07:59:27.772Z
paper_authors._id: 643bf03db409fef15e07b1be
paper_authors.avatarUrl: /avatars/b8cf1bfead9660d4beec21c9082238e4.svg
paper_authors.fullname: Xiwen Zhang
paper_authors.type: user
paper_authors.user: xiwenzhang
paper_authors._id: 65011cca70b6b05c5af0c458
paper_authors.name: Jianzhu Ma
paper_authors._id: 65011cca70b6b05c5af0c459
paper_authors.name: Jian Peng
paper_authors._id: 65011cca70b6b05c5af0c45a
paper_authors.name: Qiang Liu
paper_title: InstaFlow: One Step is Enough for High-Quality Diffusion-Based
    Text-to-Image Generation
paper_summary: 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 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 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. Project
  page:~https://github.com/gnobitab/InstaFlow.
paper_ai_summary: Rectified Flow is used to develop an ultra-fast one-step text-to-image generator named InstaFlow, achieving high image quality with significantly reduced inference time compared to existing methods.
paper_ai_keywords: diffusion models text-to-image generation Rectified Flow reflow procedure probability flows noise images distillation student models text-conditioned pipeline Stable Diffusion FID Frechet Inception Distance MS COCO InstaFlow StyleGAN-T GPU days