AI RESEARCH
FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing
arXiv CS.CV
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ArXi:2512.01755v2 Announce Type: replace 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