EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing

ArXi:2605.07455v1 Announce Type: new Visual-prompt-guided edit transfer aims to learn image transformations directly from example pairs, offering precise and controllable editing than purely text-driven approaches. However, existing diffusion transformer-based methods often fail to faithfully reproduce the nstrated edits due to structural mismatches between the task and the backbone, including a pretrained bias toward textual conditioning and inherent stochastic instability during sampling. To bridge this gap, we present EditTransfer++, a framework that combines progressively structured