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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.01755 |
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| _version_ | 1866915879705378816 |
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| author | Liao, Yucheng Liang, Jiajun Cui, Kaiqian Zhao, Baoquan Xie, Haoran Liu, Wei Li, Qing Mao, Xudong |
| author_facet | Liao, Yucheng Liang, Jiajun Cui, Kaiqian Zhao, Baoquan Xie, Haoran Liu, Wei Li, Qing Mao, Xudong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing Liao, Yucheng Liang, Jiajun Cui, Kaiqian Zhao, Baoquan Xie, Haoran Liu, Wei Li, Qing Mao, Xudong Computer Vision and Pattern Recognition 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. |
| title | FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.01755 |