Related papers: One-Step Face Restoration via Shortcut-Enhanced Coupling Flow

One-Step Face Restoration via Shortcut-Enhanced Coupling Flow

URL: http://arxiv.org/abs/2603.03648v1
Date: Wed, 04 Mar 2026 02:11:15 GMT
Title: One-Step Face Restoration via Shortcut-Enhanced Coupling Flow
Authors: Xiaohui Sun, Hanlin Wu,
Abstract summary: We propose Shortcut-enhanced Coupling flow for Face Restoration (SCFlowFR)<n>It explicitly models the LQ--HQ dependency, minimizing path crossovers and promoting near-linear transport.<n> Experiments demonstrate that SCFlowFR achieves state-of-the-art one-step face restoration quality with inference speed comparable to traditional non-diffusion methods.
Score: 2.8265172105754104
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Face restoration has advanced significantly with generative models like diffusion models and flow matching (FM), which learn continuous-time mappings between distributions. However, existing FM-based approaches often start from Gaussian noise, ignoring the inherent dependency between low-quality (LQ) and high-quality (HQ) data, resulting in path crossovers, curved trajectories, and multi-step sampling requirements. To address these issues, we propose Shortcut-enhanced Coupling flow for Face Restoration (SCFlowFR). First, it establishes a \textit{data-dependent coupling} that explicitly models the LQ--HQ dependency, minimizing path crossovers and promoting near-linear transport. Second, we employ conditional mean estimation to obtain a coarse prediction that refines the source anchor to tighten coupling and conditions the velocity field to stabilize large-step updates. Third, a shortcut constraint supervises average velocities over arbitrary time intervals, enabling accurate one-step inference. Experiments demonstrate that SCFlowFR achieves state-of-the-art one-step face restoration quality with inference speed comparable to traditional non-diffusion methods.

Related papers

Trajectory Stitching for Solving Inverse Problems with Flow-Based Models [68.36374645801901]
Flow-based generative models have emerged as powerful priors for solving inverse problems.<n>We propose MS-Flow, which represents the trajectory as a sequence of intermediate latent states rather than a single initial code.<n>We demonstrate the effectiveness of MS-Flow over existing methods on image recovery and inverse problems, including inpainting, super-resolution, and computed tomography.
arXiv Detail & Related papers (2026-02-09T11:36:41Z)
FlowConsist: Make Your Flow Consistent with Real Trajectory [99.22869983378062]
We argue that current fast-flow training paradigms suffer from two fundamental issues.<n> conditional velocities constructed from randomly paired noise-data samples introduce systematic trajectory drift.<n>We propose FlowConsist, a training framework designed to enforce trajectory consistency in fast flows.
arXiv Detail & Related papers (2026-02-06T03:24:23Z)
Temporal Pair Consistency for Variance-Reduced Flow Matching [13.328987133593154]
Temporal Pair Consistency (TPC) is a lightweight variance-reduction principle that couples velocity predictions at paired timesteps along the same probability path.<n>Instantiated within flow matching, TPC improves sample quality and efficiency across CIFAR-10 and ImageNet at multiple resolutions.
arXiv Detail & Related papers (2026-02-04T00:05:21Z)
ReDi: Rectified Discrete Flow [17.72385262464804]
We analyze the factorization approximation error using Conditional Total Correlation (TC)<n>We propose Rectified Discrete Flow (ReDi), a novel iterative method that reduces the underlying factorization error.<n> Empirically, ReDi significantly reduces Conditional TC and enables few-step generation.
arXiv Detail & Related papers (2025-07-21T01:18:44Z)
Solving Inverse Problems with FLAIR [68.87167940623318]
We present FLAIR, a training-free variational framework that leverages flow-based generative models as prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
arXiv Detail & Related papers (2025-06-03T09:29:47Z)
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency [97.28511135503176]
We introduce Consistency Flow Matching (Consistency-FM), a novel FM method that explicitly enforces self-consistency in the velocity field. Preliminary experiments demonstrate that our Consistency-FM significantly improves training efficiency by converging 4.4x faster than consistency models.
arXiv Detail & Related papers (2024-07-02T16:15:37Z)
Optimal Flow Matching: Learning Straight Trajectories in Just One Step [89.37027530300617]
We develop and theoretically justify the novel textbf Optimal Flow Matching (OFM) approach. It allows recovering the straight OT displacement for the quadratic transport in just one FM step. The main idea of our approach is the employment of vector field for FM which are parameterized by convex functions.
arXiv Detail & Related papers (2024-03-19T19:44:54Z)
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences [31.210626775505407]
Occlusions between consecutive frames have long posed a significant challenge in optical flow estimation. We present a Streamlined In-batch Multi-frame (SIM) pipeline tailored to video input, attaining a similar level of time efficiency to two-frame networks. StreamFlow not only excels in terms of performance on challenging KITTI and Sintel datasets, with particular improvement in occluded areas.
arXiv Detail & Related papers (2023-11-28T07:53:51Z)
DifFace: Blind Face Restoration with Diffused Error Contraction [62.476329680424975]
DifFace is capable of coping with unseen and complex degradations more gracefully without complicated loss designs. It is superior to current state-of-the-art methods, especially in cases with severe degradations.
arXiv Detail & Related papers (2022-12-13T11:52:33Z)

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