InstaFlow: One Step is Enough for High-Quality Diffusion-Based
  Text-to-Image Generation
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.
65011cca70b6b05c5af0c456
Xingchao Liu
admin_assigned
2023-09-13T07:59:20.422Z
646b0bbdec9a61e871799339
https://cdn-avatars.huggingface.co/v1/production/uploads/646b0bbdec9a61e871799339/Xippv-ajkHkrGGAA7caLn.jpeg
Xingchao Liu
user
XCLiu
65011cca70b6b05c5af0c457
Xiwen Zhang
admin_assigned
2023-09-13T07:59:27.772Z
643bf03db409fef15e07b1be
/avatars/b8cf1bfead9660d4beec21c9082238e4.svg
Xiwen Zhang
user
xiwenzhang
65011cca70b6b05c5af0c458
Jianzhu Ma
65011cca70b6b05c5af0c459
Jian Peng
65011cca70b6b05c5af0c45a
Qiang Liu
InstaFlow: One Step is Enough for High-Quality Diffusion-Based
  Text-to-Image Generation
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.
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.
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