FlowDC: Flow-Based Decoupling-Decay for Complex Image Editing

TL;DR

FlowDC introduces a flow-based decoupling-decay method for complex image editing, effectively handling multiple targets while maintaining source consistency, outperforming existing solutions on new benchmarks.

Contribution

The paper proposes FlowDC, a novel approach that decouples complex edits into sub-effects and applies velocity decay, improving multi-target editing performance and source preservation.

Findings

FlowDC outperforms existing methods on Complex-PIE-Bench.

Decoupling and decay mechanisms enhance source consistency.

Ablation studies validate module effectiveness.

Abstract

With the surge of pre-trained text-to-image flow matching models, text-based image editing performance has gained remarkable improvement, especially for \underline{simple editing} that only contains a single editing target. To satisfy the exploding editing requirements, the \underline{complex editing} which contains multiple editing targets has posed as a more challenging task. However, current complex editing solutions: single-round and multi-round editing are limited by long text following and cumulative inconsistency, respectively. Thus, they struggle to strike a balance between semantic alignment and source consistency. In this paper, we propose \textbf{FlowDC}, which decouples the complex editing into multiple sub-editing effects and superposes them in parallel during the editing process. Meanwhile, we observed that the velocity quantity that is orthogonal to the editing displacement harms the source structure preserving. Thus, we decompose the velocity and decay the orthogonal part for better source consistency. To evaluate the effectiveness of complex editing settings, we construct a complex editing benchmark: Complex-PIE-Bench. On two benchmarks, FlowDC shows superior results compared with existing methods. We also detail the ablations of our module designs.