SNR-Edit: Structure-Aware Noise Rectification for Inversion-Free Flow-Based Editing

• URL: http://arxiv.org/abs/2601.19180v1
• Date: Tue, 27 Jan 2026 04:24:21 GMT
• Title: SNR-Edit: Structure-Aware Noise Rectification for Inversion-Free Flow-Based Editing
• Authors: Lifan Jiang, Boxi Wu, Yuhang Pei, Tianrun Wu, Yongyuan Chen, Yan Zhao, Shiyu Yu, Deng Cai,
• Abstract summary: Inversion-free image editing using flow-based generative models challenges the prevailing inversion-based pipelines.<n>We introduce SNR-Edit, a training-free framework achieving faithful Latent Trajectory Correction via adaptive noise control.
• Score: 18.5465888954825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inversion-free image editing using flow-based generative models challenges the prevailing inversion-based pipelines. However, existing approaches rely on fixed Gaussian noise to construct the source trajectory, leading to biased trajectory dynamics and causing structural degradation or quality loss. To address this, we introduce SNR-Edit, a training-free framework achieving faithful Latent Trajectory Correction via adaptive noise control. Mechanistically, SNR-Edit uses structure-aware noise rectification to inject segmentation constraints into the initial noise, anchoring the stochastic component of the source trajectory to the real image's implicit inversion position and reducing trajectory drift during source--target transport. This lightweight modification yields smoother latent trajectories and ensures high-fidelity structural preservation without requiring model tuning or inversion. Across SD3 and FLUX, evaluations on PIE-Bench and SNR-Bench show that SNR-Edit delivers performance on pixel-level metrics and VLM-based scoring, while adding only about 1s overhead per image.

Related papers

• Free Lunch for Stabilizing Rectified Flow Inversion [11.80912018629953]
  Rectified-Flow (RF)-based generative models have emerged as strong alternatives to traditional diffusion models.<n>We propose Proximal-Mean Inversion (PMI), a training-free gradient correction method.<n>We also introduce mimic-CFG, a lightweight velocity correction scheme for editing tasks.
  arXiv  Detail & Related papers  (2026-02-12T11:42:36Z)
• Guided Path Sampling: Steering Diffusion Models Back on Track with Principled Path Guidance [5.814544128372275]
  We propose Guided Path Sampling (GPS) as a new paradigm for iterative refinement.<n>GPS replaces unstable extrapolation with a principled, manifold-constrained, ensuring the sampling path remains on the data manifold.<n>GPS outperforms existing methods in both perceptual quality and complex prompt adherence.
  arXiv  Detail & Related papers  (2025-12-28T11:12:56Z)
• Reversible Inversion for Training-Free Exemplar-guided Image Editing [127.97756928865032]
  Existing approaches often require large-scale pre-training to learn relationships between the source and reference images.<n>Standard inversion is sub-optimal for EIE, leading to poor quality and inefficiency.<n>We introduce textbfReversible Inversion (ReInversion) for effective and efficient EIE.
  arXiv  Detail & Related papers  (2025-12-01T07:56:06Z)
• One-Step Diffusion Transformer for Controllable Real-World Image Super-Resolution [54.485573602613535]
  We present ODTSR, a one-step diffusion transformer that performs Real-ISR considering fidelity and controllability simultaneously.<n>ODTSR employs Fidelity-aware Adversarial Training (FAA) to enhance controllability and achieve one-step inference.<n>Experiments demonstrate that ODTSR achieves state-of-the-art (SOTA) performance on generic Real-ISR.
  arXiv  Detail & Related papers  (2025-11-21T11:00:59Z)
• Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance [52.705112811734566]
  A novel diffusion-based framework is introduced for solving inverse problems using a piecewise guidance scheme.<n>The proposed method is problem-agnostic and readily adaptable to a variety of inverse problems.<n>The framework achieves a reduction in inference time of (25%) for inpainting with both random and center masks, and (23%) and (24%) for (4times) and (8times) super-resolution tasks.
  arXiv  Detail & Related papers  (2025-07-22T19:35:14Z)
• Straighten Viscous Rectified Flow via Noise Optimization [24.065483360595458]
  The Reflow operation aims to straighten the inference trajectories of the rectified flow during training by constructing deterministic couplings between noises and images.<n>We identify critical limitations in Reflow, particularly its inability to rapidly generate high-quality images due to a distribution gap between images in its constructed deterministic couplings and real images.<n>We propose a novel alternative called Straighten Viscous Rectified Flow via Noise Optimization (VRFNO), which is a joint training framework integrating an encoder and a neural velocity field.
  arXiv  Detail & Related papers  (2025-07-14T12:35:17Z)
• FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing [47.908940130654535]
  FlowAlign is an inversion-free flow-based framework for consistent image editing with optimal control-based trajectory control.<n>Our terminal point regularization is shown to balance semantic alignment with the edit prompt and structural consistency with the source image along the trajectory.<n>FlowAlign outperforms existing methods in both source preservation and editing controllability.
  arXiv  Detail & Related papers  (2025-05-29T06:33:16Z)
• Taming Rectified Flow for Inversion and Editing [57.3742655030493]
  Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation.<n>Despite their robust generative capabilities, these models often struggle with inaccuracies.<n>We propose RF-r, a training-free sampler that effectively enhances inversion precision by mitigating the errors in the inversion process of rectified flow.
  arXiv  Detail & Related papers  (2024-11-07T14:29:02Z)
• Rolling Shutter Correction with Intermediate Distortion Flow Estimation [55.59359977619609]
  This paper proposes to correct the rolling shutter (RS) distorted images by estimating the distortion flow from the global shutter (GS) to RS directly. Existing methods usually perform correction using the undistortion flow from the RS to GS. We introduce a new framework that directly estimates the distortion flow and rectifies the RS image with the backward warping operation.
  arXiv  Detail & Related papers  (2024-04-09T14:40:54Z)

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.