ProReflow: Progressive Reflow with Decomposed Velocity

• URL: http://arxiv.org/abs/2503.04824v1
• Date: Wed, 05 Mar 2025 04:50:53 GMT
• Title: ProReflow: Progressive Reflow with Decomposed Velocity
• Authors: Lei Ke, Haohang Xu, Xuefei Ning, Yu Li, Jiajun Li, Haoling Li, Yuxuan Lin, Dongsheng Jiang, Yujiu Yang, Linfeng Zhang,
• Abstract summary: Flow matching aims to reflow the diffusion process of diffusion models into a straight line for a few-step and even one-step generation.<n>We introduce progressive reflow, which progressively reflows the diffusion models in local timesteps until the whole diffusion progresses.<n>We also introduce aligned v-prediction, which highlights the importance of direction matching in flow matching over magnitude matching.
• Score: 52.249464542399636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have achieved significant progress in both image and video generation while still suffering from huge computation costs. As an effective solution, flow matching aims to reflow the diffusion process of diffusion models into a straight line for a few-step and even one-step generation. However, in this paper, we suggest that the original training pipeline of flow matching is not optimal and introduce two techniques to improve it. Firstly, we introduce progressive reflow, which progressively reflows the diffusion models in local timesteps until the whole diffusion progresses, reducing the difficulty of flow matching. Second, we introduce aligned v-prediction, which highlights the importance of direction matching in flow matching over magnitude matching. Experimental results on SDv1.5 and SDXL demonstrate the effectiveness of our method, for example, conducting on SDv1.5 achieves an FID of 10.70 on MSCOCO2014 validation set with only 4 sampling steps, close to our teacher model (32 DDIM steps, FID = 10.05).

Related papers

• FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems [51.99765487172328]
  Posterior sampling for inverse problem solving can be effectively achieved using flows. Flow-Driven Posterior Sampling (FlowDPS) outperforms state-of-the-art alternatives.
  arXiv  Detail & Related papers  (2025-03-11T07:56:14Z)
• One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation [60.54811860967658]
  FluxSR is a novel one-step diffusion Real-ISR based on flow matching models.<n>First, we introduce Flow Trajectory Distillation (FTD) to distill a multi-step flow matching model into a one-step Real-ISR.<n>Second, to improve image realism and address high-frequency artifact issues in generated images, we propose TV-LPIPS as a perceptual loss.
  arXiv  Detail & Related papers  (2025-02-04T04:11:29Z)
• Solving Inverse Problems using Diffusion with Iterative Colored Renoising [11.179585999627353]
  We show that the approximations produced by existing methods are relatively poor, especially early in the reverse process. We propose a new approach that iteratively reestimates and "renoises" the estimate several times per diffusion step. This iterative approach, which we call Fast Iterative REnoising (FIRE), injects colored noise that is shaped to ensure that the pre-trained diffusion model always sees white noise.
  arXiv  Detail & Related papers  (2025-01-29T08:20:05Z)
• Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations [53.180374639531145]
  Self-Refining Diffusion Samplers (SRDS) retain sample quality and can improve latency at the cost of additional parallel compute.<n>We take inspiration from the Parareal algorithm, a popular numerical method for parallel-in-time integration of differential equations.
  arXiv  Detail & Related papers  (2024-12-11T11:08:09Z)
• Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow [65.51671121528858]
  Diffusion models have greatly improved visual generation but are hindered by slow generation speed due to the computationally intensive nature of solving generative ODEs. Rectified flow, a widely recognized solution, improves generation speed by straightening the ODE path. We propose Rectified Diffusion, which generalizes the design space and application scope of rectification to encompass the broader category of diffusion models.
  arXiv  Detail & Related papers  (2024-10-09T17:43:38Z)
• SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow [24.213303324584906]
  We develop small, efficient one-step diffusion models based on the powerful rectified flow framework. We train a one-step diffusion model with an FID of 5.02 and 15.7M parameters, outperforming the previous state-of-the-art one-step diffusion model.
  arXiv  Detail & Related papers  (2024-07-17T16:38:45Z)
• Improving the Training of Rectified Flows [14.652876697052156]
  Diffusion models have shown great promise for image and video generation, but sampling from state-of-the-art models requires expensive numerical integration of a generative ODE. One approach for tackling this problem is rectified flows, which iteratively learn smooth ODE paths that are less susceptible to truncation error. We propose improved techniques for training rectified flows, allowing them to compete with emphknowledge distillation methods even in the low NFE setting. Our improved rectified flow outperforms the state-of-the-art distillation methods such as consistency distillation and progressive distillation in both one-step and two
  arXiv  Detail & Related papers  (2024-05-30T17:56:04Z)
• PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator [73.80050807279461]
  Piecewise Rectified Flow (PeRFlow) is a flow-based method for accelerating diffusion models. PeRFlow achieves superior performance in a few-step generation.
  arXiv  Detail & Related papers  (2024-05-13T07:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.