# Target-aware Image Editing via Cycle-consistent Constraints

Source: [Target-aware Image Editing via Cycle-consistent Constraints](<https://www.sota2.com/research/paper/68fc59603ef7ccd74e452529>)

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

Data updated: 2026-04-01T05:00:12.612Z

Authors: Yanghao Wang, Zhen Wang, Long Chen

Published: 2025-10-23T04:58:29.000Z

## Abstract

Recent pre-trained text-to-image flow models have enabled remarkable progress in text-based image editing. Mainstream approaches adopt a corruption-then-restoration paradigm, where the source image is first corrupted into an editable \`\`intermediate state'' and then restored to the target image under the prompt guidance. However, current methods construct this intermediate state in a target-agnostic manner, i.e., they mainly focus on realizing source image reconstruction while neglecting the semantic gaps towards the specific editing target. This design inherently results in limited editability or inconsistency when the desired modifications substantially deviate from the source. In this paper, we argue that the intermediate state should be target-aware, i.e., selectively corrupting editing-relevant contents while preserving editing-irrelevant ones. Thus, we propose FlowCycle, an inversion-free and flow-based editing framework that parameterizes corruption with learnable noises and optimizes them through a cycle-consistent process. By iteratively editing the source to the target and recovering back to the source with dual consistency constraints, FlowCycle learns to produce a target-aware intermediate state, enabling faithful modifications while preserving source consistency. For efficiency, we further accelerate the optimization by dynamically adjusting the sampling steps. Extensive ablations demonstrated that FlowCycle achieves superior editing performance.

## 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: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.20212>)

[PDF](<https://arxiv.org/pdf/2510.20212.pdf>)