FreqEdit: 为鲁棒多轮图像编辑保留高频特征
FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing
AI总结：
本文提出FreqEdit框架，通过高频特征注入、自适应注入策略和路径补偿机制，解决多轮图像编辑中高频信息丢失问题，提升编辑稳定性和指令遵循能力。
摘要：
基于自然语言的图像编辑已发展为直观视觉操作的强大范式。尽管最近的模型在单次编辑中表现优异，但在多轮编辑中却面临严重的质量退化。通过系统分析，我们发现高频信息的逐步丢失是质量退化的主要原因。我们提出了FreqEdit，一种无需训练的框架，能够在10多次迭代中实现稳定的编辑。我们的方法包含三个协同组件：(1) 从参考速度场注入高频特征以保留细粒度细节，(2) 一种自适应注入策略，通过空间调节注入强度以实现精确区域控制，(3) 一种路径补偿机制，通过定期重新校准编辑轨迹以防止过度约束。大量实验表明，FreqEdit在身份保持和指令遵循方面优于七个最先进的基线模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
