Taming Rectified Flow for Inversion and Editing
On the map: Diffusion models → Image generation, text-to-image and editing
Abstract (arXiv)
Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation. Despite their robust generative capabilities, these models often struggle with inversion inaccuracies, which could further limit their effectiveness in downstream tasks such as image and video editing. To address this issue, we propose RF-Solver, a novel training-free sampler that effectively enhances inversion precision by mitigating the errors in the ODE-solving process of rectified flow. Specifically, we derive the exact formulation of the rectified flow ODE and apply the high-order Taylor expansion to estimate its nonlinear components, significantly enhancing the precision of ODE solutions at each timestep. Building upon RF-Solver, we further propose RF-Edit, a general feature-sharing-based framework for image and video editing. By incorporating self-attention features from the inversion process into the editing process, RF-Edit effectively preserves the structural information of the source image or video while achieving high-quality editing results. Our approach is compatible with any pre-trained rectified-flow-based models for image and video tasks, requiring no additional training or optimization. Extensive experiments across generation, inversion, and editing tasks in both image and video modalities demonstrate the superiority and versatility of our method. The source code is available at https://github.com/wangjiangshan0725/RF-Solver-Edit.
Key facts. Cited by 200 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 74 of its 95 references that are in the map. First posted on arXiv 7 Nov 2024.
Builds on
74 of 95 references.
Closest ancestors · 14
Same direction, newest first
- 2024HART: Efficient Visual Generation with Hybrid Autoregressive Transformer14 Oct
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- 2022Null-text Inversion for Editing Real Images using Guided Diffusion Models17 Nov
- 2022Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Models15 Nov
2 more
Other references · 60
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
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- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
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- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
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- 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 897
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- 2023Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videoscited by 312
- 2022Rectified Flow: A Marginal Preserving Approach to Optimal Transportcited by 277
- 2022EDICT: Exact Diffusion Inversion via Coupled Transformationscited by 256
- 2023Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translationcited by 253
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- 2024COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editingcited by 59
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- 2023On Exact Inversion of DPM-Solverscited by 47
- 2024MultiBooth: Towards Generating All Your Concepts in an Image from Textcited by 45
- 2024Diffusion Model-Based Video Editing: A Surveycited by 44
50 most cited of 60 All 60 (JSON)
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Citing papers per year: 2024: 3; 2025: 99; 2026: 98.
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