ACEdit: Stable Multi-Turn Image Editing via Asymmetric Conditioning

arXiv:2610.22328v1 Announce Type: cross Abstract: Image editing models produce impressive results for individual edits, but their quality often deteriorates over multiple editing turns. Each editing turn introduces small distribution shifts that accumulate over successive edits, creating a gap between the input distributions encountered during the editor's original training and multi-turn inference. We introduce a training scheme that makes multi-turn image editing stable under this accumulating distribution shift. A key is to force the editor to operate on its own outputs from previous editin

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