AI RESEARCH
EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing
arXiv CS.CV
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ArXi:2512.11715v2 Announce Type: replace Recent advances in diffusion models (DMs) have achieved exceptional visual quality in image editing tasks. However, the global denoising dynamics of DMs inherently conflate local editing targets with the full-image context, leading to unintended modifications in non-target regions. In this paper, we shift our attention beyond DMs and turn to Masked Generative Transformers (MGTs) as an alternative approach to tackle this challenge.