Edit-GRPO: A Locality-Preserving Policy Optimization Framework for Image Editing

• URL: http://arxiv.org/abs/2605.16951v1
• Date: Sat, 16 May 2026 12:05:39 GMT
• Title: Edit-GRPO: A Locality-Preserving Policy Optimization Framework for Image Editing
• Abstract summary: We propose Edit-GRPO, preserving Locality while optimizing image editing.<n>By assigning region-specific optimization signals to edit and non-edit areas, Edit-GRPO aligns policy updates with the spatial structure of editing tasks.<n>This design effectively suppresses common artifacts such as context distortion and boundary inconsistency.
• Score: 95.29911956874389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A fundamental challenge in image editing lies in preserving spatial locality: edits should improve targeted content without inadvertently altering surrounding regions. However, most optimization-based editing approaches treat images as holistic entities, causing global policy updates that undermine locality and introduce undesired context changes. We observe that this issue stems from a mismatch between localized editing intent and globally applied optimization signals. Motivated by this insight, we propose Edit-GRPO, preserving Locality while optimizing image editing, a locality-preserving policy optimization framework that explicitly decouples editing and preservation objectives. By assigning region-specific optimization signals to edit and non-edit areas, Edit-GRPO aligns policy updates with the spatial structure of editing tasks, enabling localized improvements while maintaining global visual coherence. This design effectively suppresses common artifacts such as context distortion and boundary inconsistency. Extensive experiments across diverse image editing scenarios demonstrate that Edit-GRPO significantly improves locality preservation while maintaining strong editing performance compared to existing optimization-based methods, validating the generality and effectiveness of the proposed framework.

Related papers

• WhereEdit: Mask-aware Local Latent Editing for One-Step Image Editing [16.758428220880734]
  One-step text-to-image (T2I) models enable efficient image synthesis and provide new opportunities for real-time image editing.<n>Existing one-step editing methods rely on text conditioning for semantic transformation, lacking explicit spatial control over textitwhere to edit.<n>We propose textbfWhereEdit, a framework that reformulates one-step editing as localized adaptive editing.
  arXiv  Detail & Related papers  (2026-07-23T03:11:25Z)
• HierEdit: Region-Aware Hierarchical Diffusion for Efficient High-Resolution Editing [83.1290629939693]
  High-resolution image editing is essential for professional and creative applications.<n>Current approaches redundantly process the entire image canvas or rely on large-scale high-resolution datasets.<n>We introduce HierEdit, a region-aware hierarchical diffusion framework for efficient and scalable high-resolution image editing.
  arXiv  Detail & Related papers  (2026-05-17T07:14:15Z)
• Rethinking Where to Edit: Task-Aware Localization for Instruction-Based Image Editing [30.23368654238746]
  We propose a training-free, task-aware edit localization framework for instruction-based image editing.<n>Our framework consistently improves non-edit region consistency while maintaining strong instruction-following performance.
  arXiv  Detail & Related papers  (2026-04-22T07:08:01Z)
• Region-Constrained Group Relative Policy Optimization for Flow-Based Image Editing [2.096755686662369]
  We propose RC-GRPO-Editing, a region-constrained GRPO post-training framework for flow-based image editing under deterministic ODE sampling.<n>It suppresses background-induced nuisance variance to enable cleaner localized credit assignment, improving region instruction adherence and preserving non-target content.<n> Experiments on CompBench show consistent improvements in editing region instruction adherence and non-target preservation.
  arXiv  Detail & Related papers  (2026-04-10T14:58:15Z)
• LocateEdit-Bench: A Benchmark for Instruction-Based Editing Localization [21.62979058692505]
  We propose a large-scale dataset comprising $231$K edited images to benchmark forgery localization methods.<n>Our dataset incorporates four cutting-edge editing models and covers three common edit types.<n>Our work establishes a foundation to keep pace with the evolving landscape of image editing, thereby facilitating the development of effective methods for future forgery localization.
  arXiv  Detail & Related papers  (2026-02-05T12:01:09Z)
• 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)
• LORE: Latent Optimization for Precise Semantic Control in Rectified Flow-based Image Editing [0.276240219662896]
  We introduce LORE, a training-free and efficient image editing method.<n>LORE directly optimize the inverted noise, addressing the core limitations in generalization and controllability of existing approaches.<n> Experimental results show that LORE significantly outperforms strong baselines in terms of semantic alignment, image quality, and background fidelity.
  arXiv  Detail & Related papers  (2025-08-05T06:45:04Z)
• DCEdit: Dual-Level Controlled Image Editing via Precisely Localized Semantics [71.78350994830885]
  We present a novel approach to improving text-guided image editing using diffusion-based models.<n>Our method uses visual and textual self-attention to enhance the cross-attention map, which can serve as a regional cues to improve editing performance.<n>To fully compare our methods with other DiT-based approaches, we construct the RW-800 benchmark, featuring high resolution images, long descriptive texts, real-world images, and a new text editing task.
  arXiv  Detail & Related papers  (2025-03-21T02:14:03Z)
• Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training [61.984277261016146]
  We propose a CustomNeRF model that unifies a text description or a reference image as the editing prompt. To tackle the first challenge, we propose a Local-Global Iterative Editing (LGIE) training scheme that alternates between foreground region editing and full-image editing. For the second challenge, we also design a class-guided regularization that exploits class priors within the generation model to alleviate the inconsistency problem.
  arXiv  Detail & Related papers  (2023-12-04T06:25:06Z)
• Optimisation-Based Multi-Modal Semantic Image Editing [58.496064583110694]
  We propose an inference-time editing optimisation to accommodate multiple editing instruction types. By allowing to adjust the influence of each loss function, we build a flexible editing solution that can be adjusted to user preferences. We evaluate our method using text, pose and scribble edit conditions, and highlight our ability to achieve complex edits.
  arXiv  Detail & Related papers  (2023-11-28T15:31:11Z)

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.