# SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step Diffusion

arXiv: https://arxiv.org/abs/2412.04301 · HTML: https://mlsanity.com/paper/2412.04301/

## Key facts

- Cited by 28 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 36 of its 43 references that are in the map.
- First posted on arXiv 5 Dec 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 36)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)
- [Zero-shot Image-to-Image Translation](https://mlsanity.com/paper/2302.03027/) (2023)
- [In-Domain GAN Inversion for Real Image Editing](https://mlsanity.com/paper/2004.00049/) (2020)
- [InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation](https://mlsanity.com/paper/2309.06380/) (2023)
- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [Generative Visual Manipulation on the Natural Image Manifold](https://mlsanity.com/paper/1609.03552/) (2016)
- [Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation](https://mlsanity.com/paper/2403.12015/) (2024)
- [JourneyDB: A Benchmark for Generative Image Understanding](https://mlsanity.com/paper/2307.00716/) (2023)
- [High-Fidelity GAN Inversion for Image Attribute Editing](https://mlsanity.com/paper/2109.06590/) (2021)
- [Seeing What a GAN Cannot Generate](https://mlsanity.com/paper/1910.11626/) (2019)
- [Inverting The Generator Of A Generative Adversarial Network](https://mlsanity.com/paper/1611.05644/) (2016)
- [ReNoise: Real Image Inversion Through Iterative Noising](https://mlsanity.com/paper/2403.14602/) (2024)
- [Invertible Conditional GANs for image editing](https://mlsanity.com/paper/1611.06355/) (2016)
- [SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation](https://mlsanity.com/paper/2312.05239/) (2023)

## Built on by (most cited first, 28 of 28)

- [ChordEdit: One-Step Low-Energy Transport for Image Editing](https://mlsanity.com/paper/2602.19083/) (2026)
- [MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing](https://mlsanity.com/paper/2506.01921/) (2025)
- [Dragging with Geometry: From Pixels to Geometry-Guided Image Editing](https://mlsanity.com/paper/2509.25740/) (2025)
- [DFVEdit: Conditional Delta Flow Vector for Zero-shot Video Editing](https://mlsanity.com/paper/2506.20967/) (2025)
- [FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing](https://mlsanity.com/paper/2509.22244/) (2025)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [DDiT: Dynamic Patch Scheduling for Efficient Diffusion Transformers](https://mlsanity.com/paper/2602.16968/) (2026)
- [Text-guided Controllable Diffusion for Realistic Camouflage Images Generation](https://mlsanity.com/paper/2511.20218/) (2025)
- [Semantic-Aware Reconstruction Error for Detecting AI-Generated Images](https://mlsanity.com/paper/2508.09487/) (2025)
- [3D-Consistent Multi-View Editing by Correspondence Guidance](https://mlsanity.com/paper/2511.22228/) (2025)
- [CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models](https://mlsanity.com/paper/2601.15441/) (2026)
- [InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting](https://mlsanity.com/paper/2603.23463/) (2026)
- [Training-free image inversion for one-step diffusion models](https://mlsanity.com/paper/2606.01380/) (2026)
- [WhereEdit: Mask-aware Local Latent Editing for One-Step Image Editing](https://mlsanity.com/paper/2607.20883/) (2026)
- [DeepInv: A Novel Self-supervised Learning Approach for Fast and Accurate Diffusion Inversion](https://mlsanity.com/paper/2601.01487/) (2026)
- [FBSDiff++: Improved Frequency Band Substitution of Diffusion Features for Efficient and Highly Controllable Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2601.19115/) (2026)
- [TexEditor: Structure-Preserving Text-Driven Texture Editing](https://mlsanity.com/paper/2603.18488/) (2026)
- [BiFM: Bidirectional Flow Matching for Few-Step Image Editing and Generation](https://mlsanity.com/paper/2603.24942/) (2026)
- [Prompt-Guided Image Editing with Masked Logit Nudging in Visual Autoregressive Models](https://mlsanity.com/paper/2604.14591/) (2026)
- [From Competition to Coopetition: Coopetitive Training-Free Image Editing Based on Text Guidance](https://mlsanity.com/paper/2604.15948/) (2026)
- [Bridging the Manifold Gap: Riemannian Residual Line Search for One-Step Image Editing](https://mlsanity.com/paper/2606.24844/) (2026)
- [ReconPlusGen: Injecting Reconstruction Prior into Multi-view 3D Generation through Noise Inversion and Modulation](https://mlsanity.com/paper/2609.11129/) (2026)
- [EditCrafter: Tuning-free High-Resolution Image Editing via Pretrained Diffusion Model](https://mlsanity.com/paper/2604.10268/) (2026)
- [SwiftPie: Lightning-fast Subject-driven Image Personalization via One step Diffusion](https://mlsanity.com/paper/2605.01510/) (2026)
- [ResEdit: Residual embeddings for precise generative image editing](https://mlsanity.com/paper/2606.16457/) (2026)
- [Compression Asymmetry and Trajectory Binding in Noise-Anchored Diffusion Inversion](https://mlsanity.com/paper/2607.09784/) (2026)
- [GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation](https://mlsanity.com/paper/2609.07006/) (2026)
- [Beyond Temporal Smoothing: Spatial Energy Budgets Stabilize One-Step Diffusion Editing](https://mlsanity.com/paper/2609.32841/) (2026)

## Abstract (arXiv)

Recent advances in text-guided image editing enable users to perform image edits through simple text inputs, leveraging the extensive priors of multi-step diffusion-based text-to-image models. However, these methods often fall short of the speed demands required for real-world and on-device applications due to the costly multi-step inversion and sampling process involved. In response to this, we introduce SwiftEdit, a simple yet highly efficient editing tool that achieve instant text-guided image editing (in 0.23s). The advancement of SwiftEdit lies in its two novel contributions: a one-step inversion framework that enables one-step image reconstruction via inversion and a mask-guided editing technique with our proposed attention rescaling mechanism to perform localized image editing. Extensive experiments are provided to demonstrate the effectiveness and efficiency of SwiftEdit. In particular, SwiftEdit enables instant text-guided image editing, which is extremely faster than previous multi-step methods (at least 50 times faster) while maintain a competitive performance in editing results. Our project page is at: https://swift-edit.github.io/