解决多轮图像编辑中细节丢失问题，保持图像清晰度。
FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing
- 通过注入参考速度场的高频特征，保留图像细粒度细节。
- 在10轮以上连续编辑中，身份保持和指令遵循能力显著优于现有方法。
- 无需训练，适合需要稳定多轮修改的视觉创作场景。
基于自然语言的图像编辑已成为直观视觉操作的强大范式。尽管近期模型在单次编辑上表现优异，但在多轮编辑中会严重退化。系统分析表明，高频信息逐渐丢失是主要原因。我们提出 FreqEdit，一种无需训练的框架，可在10次以上连续迭代中保持稳定编辑。其包含三个协同组件：(1) 从参考速度场注入高频特征以保留细粒度细节；(2) 自适应注入策略，空间调节注入强度以实现区域精准控制；(3) 路径补偿机制，周期性重校正编辑轨迹，防止过度约束。大量实验表明，与七种先进基线相比，FreqEdit在身份保持和指令遵循方面均表现更优。
原文摘要 · Abstract (English)
Instruction-based image editing through natural language has emerged as a powerful paradigm for intuitive visual manipulation. While recent models achieve impressive results on single edits, they suffer from severe quality degradation under multi-turn editing. Through systematic analysis, we identify progressive loss of high-frequency information as the primary cause of this quality degradation. We present FreqEdit, a training-free framework that enables stable editing across 10+ consecutive iterations. Our approach comprises three synergistic components: (1) high-frequency feature injection from reference velocity fields to preserve fine-grained details, (2) an adaptive injection strategy that spatially modulates injection strength for precise region-specific control, and (3) a path compensation mechanism that periodically recalibrates the editing trajectory to prevent over-constraint. Extensive experiments demonstrate that FreqEdit achieves superior performance in both identity preservation and instruction following compared to seven state-of-the-art baselines.
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