# FastDrag: Manipulate Anything in One Step

arXiv: https://arxiv.org/abs/2405.15769 · HTML: https://mlsanity.com/paper/2405.15769/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 32 references that are in the map.
- First posted on arXiv 24 May 2024.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (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)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [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)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (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)
- [DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models](https://mlsanity.com/paper/2307.02421/) (2023)
- [Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code](https://mlsanity.com/paper/2310.01506/) (2023)
- [Inversion-Free Image Editing with Natural Language](https://mlsanity.com/paper/2312.04965/) (2023)
- [DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing](https://mlsanity.com/paper/2402.02583/) (2024)
- [The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing](https://mlsanity.com/paper/2311.01410/) (2023)
- [Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagation](https://mlsanity.com/paper/2404.01050/) (2024)
- [GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models](https://mlsanity.com/paper/2404.07206/) (2024)
- [FreeDrag: Feature Dragging for Reliable Point-based Image Editing](https://mlsanity.com/paper/2307.04684/) (2023)
- [StableDrag: Stable Dragging for Point-based Image Editing](https://mlsanity.com/paper/2403.04437/) (2024)
- [Noise Map Guidance: Inversion with Spatial Context for Real Image Editing](https://mlsanity.com/paper/2402.04625/) (2024)

## Built on by (most cited first, 21 of 21)

- [MotionStream: Real-Time Video Generation with Interactive Motion Controls](https://mlsanity.com/paper/2511.01266/) (2025)
- [SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation](https://mlsanity.com/paper/2411.04989/) (2024)
- [EEdit: Rethinking the Spatial and Temporal Redundancy for Efficient Image Editing](https://mlsanity.com/paper/2503.10270/) (2025)
- [DragFlow: Unleashing DiT Priors with Region Based Supervision for Drag Editing](https://mlsanity.com/paper/2510.02253/) (2025)
- [Image Editing with Diffusion Models: A Survey](https://mlsanity.com/paper/2504.13226/) (2025)
- [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)
- [3D-Consistent Multi-View Editing by Correspondence Guidance](https://mlsanity.com/paper/2511.22228/) (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)
- [DynaDrag: Dynamic Drag-Style Image Editing by Motion Prediction](https://mlsanity.com/paper/2601.00542/) (2026)
- [TopoEdit: Fast Post-Optimization Editing of Topology Optimized Structures](https://mlsanity.com/paper/2602.22430/) (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)
- [Drag as Evidence: Motion-Grounded Latent Recomposition for Drag-Based Editing](https://mlsanity.com/paper/2609.36755/) (2026)
- [ThinkDrag: Semantic Drag-Based Image Editing with Visual Reasoning](https://mlsanity.com/paper/2610.13316/) (2026)

## Abstract (arXiv)

Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks. However, prevailing methods typically adopt n-step iterations for latent semantic optimization to achieve drag-based image editing, which is time-consuming and limits practical applications. In this paper, we introduce a novel one-step drag-based image editing method, i.e., FastDrag, to accelerate the editing process. Central to our approach is a latent warpage function (LWF), which simulates the behavior of a stretched material to adjust the location of individual pixels within the latent space. This innovation achieves one-step latent semantic optimization and hence significantly promotes editing speeds. Meanwhile, null regions emerging after applying LWF are addressed by our proposed bilateral nearest neighbor interpolation (BNNI) strategy. This strategy interpolates these regions using similar features from neighboring areas, thus enhancing semantic integrity. Additionally, a consistency-preserving strategy is introduced to maintain the consistency between the edited and original images by adopting semantic information from the original image, saved as key and value pairs in self-attention module during diffusion inversion, to guide the diffusion sampling. Our FastDrag is validated on the DragBench dataset, demonstrating substantial improvements in processing time over existing methods, while achieving enhanced editing performance. Project page: https://fastdrag-site.github.io/ .