InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow
Yiming Gong, Zhen Zhu, Minjia Zhang
Abstract
We propose a fast text-guided image editing method called InstantEdit based on the RectifiedFlow framework, which is structured as a few-step editing process that preserves critical content while following closely to textual instructions. Our approach leverages the straight sampling trajectories of RectifiedFlow by introducing a specialized inversion strategy called PerRFI. To maintain consistent while editable results for RectifiedFlow model, we further propose a novel regeneration method, Inversion Latent Injection, which effectively reuses latent information obtained during inversion to facilitate more coherent and detailed regeneration. Additionally, we propose a Disentangled Prompt Guidance technique to balance editability with detail preservation, and integrate a Canny-conditioned ControlNet to incorporate structural cues and suppress artifacts. Evaluation on the PIE image editing dataset demonstrates that InstantEdit is not only fast but also achieves better qualitative and quantitative results compared to state-of-the-art few-step editing methods.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext acb74ce2-d386-45d1-9c30-2e531d991eb9Cited by top-tier papers3
- ChordEdit: One-Step Low-Energy Transport for Image EditingLiangsi Lu, Xuhang Chen, Minzhe Guo, Shichu Li et al.CVPR 2026 · 22 citations
- RewardFlow: Generate Images by Optimizing What You RewardOnkar Susladkar, Dong-Hwan Jang, Tushar Prakash, Adheesh Sunil Juvekar et al.CVPR 2026 · 2 citations
- BiFM: Bidirectional Flow Matching for Few-Step Image Editing and GenerationYasong Dai, Zeeshan Hayder, David Ahmedt-Aristizabal, Hongdong LiCVPR 2026 · 1 citation
Builds on25
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 13,211 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 11,743 citations
- BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsJunnan Li, Dongxu Li, Silvio Savarese, Steven C. H. HoiICML 2023 · 7,873 citations
Related papers
- Training-Free Text-Guided Image Editing with Visual Autoregressive ModelYufei Wang, Lanqing Guo, Zhihao Li, Jiaxing Huang et al.ICCV 2025
- Delta Rectified Flow Sampling for Text-to-Image EditingGaspard Beaudouin, Minghan Li, Jaeyeon Kim, Sung-Hoon Yoon et al.CVPR 2026 · 4 citations
- Describe, Don't Dictate: Semantic Image Editing with Natural Language IntentEn Ci, Shanyan Guan, Yanhao Ge, Yilin Zhang et al.ICCV 2025
- SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step DiffusionTrong-Tung Nguyen, Quang Nguyen, Khoi Nguyen, Anh Tuan Tran et al.CVPR 2025
- Inversion-Free Image Editing with Language-Guided Diffusion ModelsSihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma et al.CVPR 2024 · 12 citations
