# ChordEdit: One-Step Low-Energy Transport for Image Editing

Source: [ChordEdit: One-Step Low-Energy Transport for Image Editing](<https://www.sota2.com/research/paper/699d30756017c4460ffab33a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-27T05:00:04.193Z

Authors: Liangsi Lu, Xuhang Chen, Minzhe Guo, Shichu Li, Jingchao Wang, Yang Shi

Published: 2026-02-22T07:40:50.000Z

## Abstract

The advent of one-step text-to-image (T2I) models offers unprecedented synthesis speed. However, their application to text-guided image editing remains severely hampered, as forcing existing training-free editors into a single inference step fails. This failure manifests as severe object distortion and a critical loss of consistency in non-edited regions, resulting from the high-energy, erratic trajectories produced by naive vector arithmetic on the models' structured fields. To address this problem, we introduce ChordEdit, a model agnostic, training-free, and inversion-free method that facilitates high-fidelity one-step editing. We recast editing as a transport problem between the source and target distributions defined by the source and target text prompts. Leveraging dynamic optimal transport theory, we derive a principled, low-energy control strategy. This strategy yields a smoothed, variance-reduced editing field that is inherently stable, facilitating the field to be traversed in a single, large integration step. A theoretically grounded and experimentally validated approach allows ChordEdit to deliver fast, lightweight and precise edits, finally achieving true real-time editing on these challenging models.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Editing on PIE Bench (Including Efficiency Metrics)](<https://www.sota2.com/research/sota/image-editing-on-pie-bench-including-efficiency-metrics.md>): Image Editing; PIE-Bench; PSNR: 23.89.

- [Image Editing on PIE-Bench (test)](<https://www.sota2.com/research/sota/image-editing-on-pie-bench-test.md>): Image Editing; PIE-Bench (test); PSNR: 23.89.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.19083>)

[PDF](<https://arxiv.org/pdf/2602.19083.pdf>)