Masked Generative Transformer Is What You Need for Image Editing

• URL: http://arxiv.org/abs/2605.10859v1
• Date: Mon, 11 May 2026 17:05:52 GMT
• Title: Masked Generative Transformer Is What You Need for Image Editing
• Abstract summary: Diffusion models entangle edited regions with surrounding context, causing modifications to propagate into areas that should remain intact.<n>We propose a fundamentally different approach by leveraging Masked Generative Transformers (MGTs), whose localized token-prediction paradigm naturally confines changes to intended regions.<n>We present EditMGT, an MGT-based editing framework that is the first of its kind.
• Score: 91.7627902312967
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models dominate image editing, yet their global denoising mechanism entangles edited regions with surrounding context, causing modifications to propagate into areas that should remain intact. We propose a fundamentally different approach by leveraging Masked Generative Transformers (MGTs), whose localized token-prediction paradigm naturally confines changes to intended regions. We present EditMGT, an MGT-based editing framework that is the first of its kind. Our approach employs multi-layer attention consolidation to aggregate cross-attention maps into precise edit localization signals, and region-hold sampling to explicitly prevent token flipping in non-target areas. To support training, we construct CrispEdit-2M, a 2M-sample high-resolution (>1024) editing dataset spanning seven categories. With only 960M parameters, EditMGT achieves state-of-the-art image similarity on multiple benchmarks while delivering 6x faster editing, demonstrating that MGTs offer a compelling alternative to diffusion-based editing.

Related papers

• SpotEdit: Selective Region Editing in Diffusion Transformers [66.44912649206553]
  SpotEdit is a training-free diffusion editing framework that selectively updates only the modified regions.<n>By reducing unnecessary computation and maintaining high fidelity in unmodified areas, SpotEdit achieves efficient and precise image editing.
  arXiv  Detail & Related papers  (2025-12-26T14:59:41Z)
• EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing [84.7089707244905]
  Masked Generative Transformers (MGTs) exhibit a localized decoding paradigm that endows them with the inherent capacity to preserve non-relevant regions during the editing process.<n>We introduce the first MGT-based image editing framework, termed EditMGT.<n>We demonstrate that EditMGT's cross-attention maps provide informative localization signals for localizing edit-relevant regions.<n>We also introduce region-hold sampling, which restricts token flipping within low-attention areas to suppress spurious edits.
  arXiv  Detail & Related papers  (2025-12-12T16:51:19Z)
• NEP: Autoregressive Image Editing via Next Editing Token Prediction [16.69384738678215]
  We propose to formulate image editing as Next Editing-token Prediction (NEP) based on autoregressive image generation.<n>Our model naturally supports test-time scaling (TTS) through iteratively refining its generation in a zero-shot manner.
  arXiv  Detail & Related papers  (2025-08-08T06:06:34Z)
• EditScout: Locating Forged Regions from Diffusion-based Edited Images with Multimodal LLM [50.054404519821745]
  We present a novel framework that integrates a multimodal Large Language Model for enhanced reasoning capabilities.<n>Our framework achieves promising results on MagicBrush, AutoSplice, and PerfBrush datasets.<n> Notably, our method excels on the PerfBrush dataset, a self-constructed test set featuring previously unseen types of edits.
  arXiv  Detail & Related papers  (2024-12-05T02:05:33Z)
• Enhancing Text-to-Image Editing via Hybrid Mask-Informed Fusion [61.42732844499658]
  This paper systematically improves the text-guided image editing techniques based on diffusion models. We incorporate human annotation as an external knowledge to confine editing within a Mask-informed'' region.
  arXiv  Detail & Related papers  (2024-05-24T07:53:59Z)
• MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance [28.212908146852197]
  We develop MAG-Edit, a training-free, inference-stage optimization method, which enables localized image editing in complex scenarios. In particular, MAG-Edit optimize the noise latent feature in diffusion models by maximizing two mask-based cross-attention constraints.
  arXiv  Detail & Related papers  (2023-12-18T17:55:44Z)
• LIME: Localized Image Editing via Attention Regularization in Diffusion Models [69.33072075580483]
  This paper introduces LIME for localized image editing in diffusion models.<n>LIME does not require user-specified regions of interest (RoI) or additional text input, but rather employs features from pre-trained methods and a straightforward clustering method to obtain precise editing mask.<n>We propose a novel cross-attention regularization technique that penalizes unrelated cross-attention scores in the RoI during the denoising steps, ensuring localized edits.
  arXiv  Detail & Related papers  (2023-12-14T18:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.