# WhereEdit: Mask-aware Local Latent Editing for One-Step Image Editing

arXiv: https://arxiv.org/abs/2607.20883 · HTML: https://mlsanity.com/paper/2607.20883/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 23 of 23)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Mean Flows for One-step Generative Modeling](https://mlsanity.com/paper/2505.13447/) (2025)
- [SDXL-Lightning: Progressive Adversarial Diffusion Distillation](https://mlsanity.com/paper/2402.13929/) (2024)
- [FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models](https://mlsanity.com/paper/2412.08629/) (2024)
- [Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models](https://mlsanity.com/paper/2305.16807/) (2023)
- [Improving Tuning-Free Real Image Editing with Proximal Guidance](https://mlsanity.com/paper/2306.05414/) (2023)
- [Effective Real Image Editing with Accelerated Iterative Diffusion Inversion](https://mlsanity.com/paper/2309.04907/) (2023)
- [TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models](https://mlsanity.com/paper/2408.00735/) (2024)
- [SwiftBrush v2: Make Your One-step Diffusion Model Better Than Its Teacher](https://mlsanity.com/paper/2408.14176/) (2024)
- [SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step Diffusion](https://mlsanity.com/paper/2412.04301/) (2024)
- [Source Prompt Disentangled Inversion for Boosting Image Editability with Diffusion Models](https://mlsanity.com/paper/2403.11105/) (2024)
- [MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance](https://mlsanity.com/paper/2312.11396/) (2023)
- [ChordEdit: One-Step Low-Energy Transport for Image Editing](https://mlsanity.com/paper/2602.19083/) (2026)
- [Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps](https://mlsanity.com/paper/2406.14539/) (2024)
- [LocInv: Localization-aware Inversion for Text-Guided Image Editing](https://mlsanity.com/paper/2405.01496/) (2024)
- [InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow](https://mlsanity.com/paper/2508.06033/) (2025)
- [LayerEdit: Disentangled Multi-Object Editing via Conflict-Aware Multi-Layer Learning](https://mlsanity.com/paper/2511.08251/) (2025)

## Built on by (most cited first, 2 of 2)

- [FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation](https://mlsanity.com/paper/2609.03384/) (2026)
- [Beyond Temporal Smoothing: Spatial Energy Budgets Stabilize One-Step Diffusion Editing](https://mlsanity.com/paper/2609.32841/) (2026)

## Abstract (arXiv)

Recent one-step text-to-image (T2I) models enable efficient image synthesis and provide new opportunities for real-time image editing. However, existing one-step editing methods primarily rely on text conditioning for semantic transformation, lacking explicit spatial control over textitwhere to edit. More importantly, even when spatial constraints are introduced, these methods often struggle to achieve strong and stable semantic modifications within the target regions. In this work, we revisit one-step image editing from a spatially controlled perspective and identify two key challenges: discovering editable regions and achieving effective localized semantic transformation. We reveal that existing methods perform global semantic transport, which limits high-intensity local editing under the one-step setting. To address this issue, we propose WhereEdit, a framework that reformulates one-step editing as localized adaptive editing. WhereEdit automatically identifies semantically relevant regions from internal model features and applies adaptive local modulation to enhance target-region editing while preserving non-target areas and structural consistency. Experiments on the PIE-Bench benchmark demonstrate that WhereEdit consistently outperforms existing one-step image editing methods, achieving superior editing quality while maintaining the efficiency of one-step generation. Additional experiments with region-level supervision further highlight the importance of explicit spatial reasoning for high-quality one-step image editing.