# RegionDrag: Fast Region-Based Image Editing with Diffusion Models

arXiv: https://arxiv.org/abs/2407.18247 · HTML: https://mlsanity.com/paper/2407.18247/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 26 of its 30 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Scaling up GANs for Text-to-Image Synthesis](https://mlsanity.com/paper/2303.05511/) (2023)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [Diffusion Self-Guidance for Controllable Image Generation](https://mlsanity.com/paper/2306.00986/) (2023)
- [DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing](https://mlsanity.com/paper/2306.14435/) (2023)
- [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://mlsanity.com/paper/2305.10973/) (2023)
- [High-Fidelity GAN Inversion for Image Attribute Editing](https://mlsanity.com/paper/2109.06590/) (2021)
- [DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models](https://mlsanity.com/paper/2307.02421/) (2023)
- [Inverting The Generator Of A Generative Adversarial Network](https://mlsanity.com/paper/1611.05644/) (2016)
- [DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing](https://mlsanity.com/paper/2402.02583/) (2024)
- [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://mlsanity.com/paper/2210.05559/) (2022)
- [The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing](https://mlsanity.com/paper/2311.01410/) (2023)
- [User-Controllable Latent Transformer for StyleGAN Image Layout Editing](https://mlsanity.com/paper/2208.12408/) (2022)
- [RotationDrag: Point-based Image Editing with Rotated Diffusion Features](https://mlsanity.com/paper/2401.06442/) (2024)

## Built on by (most cited first, 30 of 32)

- [EEdit: Rethinking the Spatial and Temporal Redundancy for Efficient Image Editing](https://mlsanity.com/paper/2503.10270/) (2025)
- [Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing](https://mlsanity.com/paper/2411.15843/) (2024)
- [DragFlow: Unleashing DiT Priors with Region Based Supervision for Drag Editing](https://mlsanity.com/paper/2510.02253/) (2025)
- [Generative Physical AI in Vision: A Survey](https://mlsanity.com/paper/2501.10928/) (2025)
- [VINCIE: Unlocking In-context Image Editing from Video](https://mlsanity.com/paper/2506.10941/) (2025)
- [MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentanglement](https://mlsanity.com/paper/2509.01977/) (2025)
- [CLIPDrag: Combining Text-based and Drag-based Instructions for Image Editing](https://mlsanity.com/paper/2410.03097/) (2024)
- [Training-free Geometric Image Editing on Diffusion Models](https://mlsanity.com/paper/2507.23300/) (2025)
- [Text-to-Image Synthesis: A Decade Survey](https://mlsanity.com/paper/2411.16164/) (2024)
- [DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion Model](https://mlsanity.com/paper/2505.12427/) (2025)
- [AdaptiveDrag: Semantic-Driven Dragging on Diffusion-Based Image Editing](https://mlsanity.com/paper/2410.12696/) (2024)
- [Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing via Bidirectional Warping](https://mlsanity.com/paper/2509.04582/) (2025)
- [DragNeXt: Rethinking Drag-Based Image Editing](https://mlsanity.com/paper/2506.07611/) (2025)
- [Dragging with Geometry: From Pixels to Geometry-Guided Image Editing](https://mlsanity.com/paper/2509.25740/) (2025)
- [LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence](https://mlsanity.com/paper/2509.12203/) (2025)
- [3DGS-Drag: Dragging Gaussians for Intuitive Point-Based 3D Editing](https://mlsanity.com/paper/2601.07963/) (2026)
- [Semantic Correspondence: Unified Benchmarking and a Strong Baseline](https://mlsanity.com/paper/2505.18060/) (2025)
- [RewardFlow: Generate Images by Optimizing What You Reward](https://mlsanity.com/paper/2604.08536/) (2026)
- [DragScene: Interactive 3D Scene Editing with Single-view Drag Instructions](https://mlsanity.com/paper/2412.13552/) (2024)
- [BlobCtrl: Taming Controllable Blob for Element-level Image Editing](https://mlsanity.com/paper/2503.13434/) (2025)
- [Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes](https://mlsanity.com/paper/2601.02356/) (2026)
- [CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers](https://mlsanity.com/paper/2507.15260/) (2025)
- [ContextDrag: Precise Drag-Based Image Editing via Context-Preserving Token Injection and Position-Aligned Attention](https://mlsanity.com/paper/2512.08477/) (2025)
- [RealDrag: The First Dragging Benchmark with Real Target Image](https://mlsanity.com/paper/2512.12287/) (2025)
- [Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation](https://mlsanity.com/paper/2602.03448/) (2026)
- [Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers](https://mlsanity.com/paper/2602.08388/) (2026)
- [DynaDrag: Dynamic Drag-Style Image Editing by Motion Prediction](https://mlsanity.com/paper/2601.00542/) (2026)
- [Editing Away the Evidence: Diffusion-Based Image Manipulation and the Failure Modes of Robust Watermarking](https://mlsanity.com/paper/2603.12949/) (2026)
- [Drag within Prior Distribution: Text-Conditioned Point-Based Image Editing within Distribution Constraints](https://mlsanity.com/paper/2605.13349/) (2026)
- [In-context Region-based Drag: Drag Any Region to Any Shape](https://mlsanity.com/paper/2606.25907/) (2026)

## Abstract (arXiv)

Point-drag-based image editing methods, like DragDiffusion, have attracted significant attention. However, point-drag-based approaches suffer from computational overhead and misinterpretation of user intentions due to the sparsity of point-based editing instructions. In this paper, we propose a region-based copy-and-paste dragging method, RegionDrag, to overcome these limitations. RegionDrag allows users to express their editing instructions in the form of handle and target regions, enabling more precise control and alleviating ambiguity. In addition, region-based operations complete editing in one iteration and are much faster than point-drag-based methods. We also incorporate the attention-swapping technique for enhanced stability during editing. To validate our approach, we extend existing point-drag-based datasets with region-based dragging instructions. Experimental results demonstrate that RegionDrag outperforms existing point-drag-based approaches in terms of speed, accuracy, and alignment with user intentions. Remarkably, RegionDrag completes the edit on an image with a resolution of 512x512 in less than 2 seconds, which is more than 100x faster than DragDiffusion, while achieving better performance. Project page: https://visual-ai.github.io/regiondrag.