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arXiv · 2512.01755

FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing

Yucheng Liao·Jiajun Liang·Kaiqian Cui·Baoquan Zhao·Haoran Xie·Wei Liu·Qing Li·Xudong Mao
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Abstract

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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Yucheng Liao, Jiajun Liang, Kaiqian Cui, Baoquan Zhao, Haoran Xie, Wei Liu, Qing Li, Xudong Mao. 2025-12-01. FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing. https://arxiv.org/abs/2512.01755

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