# GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models

arXiv: https://arxiv.org/abs/2404.07206 · HTML: https://mlsanity.com/paper/2404.07206/

## Key facts

- Cited by 34 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 47 of its 65 references that are in the map.
- First posted on arXiv 10 Apr 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, 30 of 47)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2201.11793/) (2022)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [Photorealistic Video Generation with Diffusion Models](https://mlsanity.com/paper/2312.06662/) (2023)
- [DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing](https://mlsanity.com/paper/2306.14435/) (2023)
- [DreamBooth3D: Subject-Driven Text-to-3D Generation](https://mlsanity.com/paper/2303.13508/) (2023)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models](https://mlsanity.com/paper/2307.06949/) (2023)
- [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://mlsanity.com/paper/2305.10973/) (2023)
- [Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models](https://mlsanity.com/paper/2207.14626/) (2022)

## Built on by (most cited first, 30 of 34)

- [SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation](https://mlsanity.com/paper/2411.04989/) (2024)
- [InstantDrag: Improving Interactivity in Drag-based Image Editing](https://mlsanity.com/paper/2409.08857/) (2024)
- [DragFlow: Unleashing DiT Priors with Region Based Supervision for Drag Editing](https://mlsanity.com/paper/2510.02253/) (2025)
- [FastDrag: Manipulate Anything in One Step](https://mlsanity.com/paper/2405.15769/) (2024)
- [DragText: Rethinking Text Embedding in Point-based Image Editing](https://mlsanity.com/paper/2407.17843/) (2024)
- [CLIPDrag: Combining Text-based and Drag-based Instructions for Image Editing](https://mlsanity.com/paper/2410.03097/) (2024)
- [MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors](https://mlsanity.com/paper/2410.16272/) (2024)
- [Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training](https://mlsanity.com/paper/2412.06029/) (2024)
- [DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion Model](https://mlsanity.com/paper/2505.12427/) (2025)
- [Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner](https://mlsanity.com/paper/2406.00432/) (2024)
- [FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields](https://mlsanity.com/paper/2507.08285/) (2025)
- [DragNeXt: Rethinking Drag-Based Image Editing](https://mlsanity.com/paper/2506.07611/) (2025)
- [ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior](https://mlsanity.com/paper/2504.12788/) (2025)
- [Dragging with Geometry: From Pixels to Geometry-Guided Image Editing](https://mlsanity.com/paper/2509.25740/) (2025)
- [Streaming Drag-Oriented Interactive Video Manipulation: Drag Anything, Anytime!](https://mlsanity.com/paper/2510.03550/) (2025)
- [MIND-Edit: MLLM Insight-Driven Editing via Language-Vision Projection](https://mlsanity.com/paper/2505.19149/) (2025)
- [LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence](https://mlsanity.com/paper/2509.12203/) (2025)
- [Generative Blocks World: Moving Things Around in Pictures](https://mlsanity.com/paper/2506.20703/) (2025)
- [GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation](https://mlsanity.com/paper/2510.22337/) (2025)
- [AttentionDrag: Exploiting Latent Correlation Knowledge in Pre-trained Diffusion Models for Image Editing](https://mlsanity.com/paper/2506.13301/) (2025)
- [TDEdit: A Unified Diffusion Framework for Text-Drag Guided Image Manipulation](https://mlsanity.com/paper/2509.21905/) (2025)
- [DirectDrag: High-Fidelity, Mask-Free, Prompt-Free Drag-based Image Editing via Readout-Guided Feature Alignment](https://mlsanity.com/paper/2512.03981/) (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)
- [Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers](https://mlsanity.com/paper/2602.08388/) (2026)
- [TrajLoom: Dense Future Trajectory Generation from Video](https://mlsanity.com/paper/2603.22606/) (2026)
- [PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing](https://mlsanity.com/paper/2604.07230/) (2026)
- [DynaDrag: Dynamic Drag-Style Image Editing by Motion Prediction](https://mlsanity.com/paper/2601.00542/) (2026)
- [Drag within Prior Distribution: Text-Conditioned Point-Based Image Editing within Distribution Constraints](https://mlsanity.com/paper/2605.13349/) (2026)
- [Text-Vision Co-Instructed Image Editing](https://mlsanity.com/paper/2606.16767/) (2026)

## Abstract (arXiv)

In this paper, we introduce GoodDrag, a novel approach to improve the stability and image quality of drag editing. Unlike existing methods that struggle with accumulated perturbations and often result in distortions, GoodDrag introduces an AlDD framework that alternates between drag and denoising operations within the diffusion process, effectively improving the fidelity of the result. We also propose an information-preserving motion supervision operation that maintains the original features of the starting point for precise manipulation and artifact reduction. In addition, we contribute to the benchmarking of drag editing by introducing a new dataset, Drag100, and developing dedicated quality assessment metrics, Dragging Accuracy Index and Gemini Score, utilizing Large Multimodal Models. Extensive experiments demonstrate that the proposed GoodDrag compares favorably against the state-of-the-art approaches both qualitatively and quantitatively. The project page is https://gooddrag.github.io.