Rethinking One-Step Image Editing through ChordEdit: Reproduction, Simplification, and New Insights
- URL: http://arxiv.org/abs/2606.14042v1
- Date: Fri, 12 Jun 2026 02:35:45 GMT
- Title: Rethinking One-Step Image Editing through ChordEdit: Reproduction, Simplification, and New Insights
- Abstract summary: One-step image editing is important for making text-guided editing fast, practical, and easy to deploy.<n>We revisit ChordEdit through reproduction, ablation, and simplification.
- Score: 3.9337184784112917
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: One-step image editing is important for making text-guided editing fast, practical, and easy to deploy, but its underlying mechanism is still not fully understood. We revisit ChordEdit through reproduction, ablation, and simplification. Our analysis shows that a) the chord window $δ$ largely acts as an effective timestep shift from $t$ to $t - δ$; b) chord transport acts on high-noise images and mainly performs low-frequency semantic editing; and c) proximal alignment acts on low-noise images and complements it by adding high-frequency target details. In this view, ChordEdit naturally decomposes editing into a coarse low-frequency transport stage and a fine high-frequency alignment stage. These findings suggest a path toward prompt-conditioned dynamic timestep selection for adaptive image editing. All code and results can be found at \href{https://github.com/Harvard-AI-and-Robotics-Lab/ChordEdit-Reproduction}{link}.
Related papers
- BeatEdit: Symbolic Music Generation as Explicit Editing [8.63659638468049]
Edit-based methods have proven effective for text transformation tasks, but remain largely unexplored for symbolic music.<n>We propose BeatEdit, the first framework for symbolic music generation based on explicit edit operations.<n>BeatEdit recasts generation as producing new content by editing a draft rather than synthesizing from scratch.
arXiv Detail & Related papers (2026-07-13T06:00:06Z) - DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing [51.56484100374058]
We propose DirectEdit, a training-free editing method for pre-trained text-to-image (T2I) models.<n>DirectEdit eliminates the inherent reconstruction error without introducing additional neural function evaluations (NFEs)<n>Experiments demonstrate that DirectEdit achieves efficient and accurate image editing, delivering superior performance that outperforms state-of-the-art methods.
arXiv Detail & Related papers (2026-05-04T10:09:18Z) - SpotEdit: Selective Region Editing in Diffusion Transformers [66.44912649206553]
SpotEdit is a training-free diffusion editing framework that selectively updates only the modified regions.<n>By reducing unnecessary computation and maintaining high fidelity in unmodified areas, SpotEdit achieves efficient and precise image editing.
arXiv Detail & Related papers (2025-12-26T14:59:41Z) - FlowDC: Flow-Based Decoupling-Decay for Complex Image Editing [52.54102743380658]
We propose FlowDC, which decouples the complex editing into multiple sub-editing effects and superposes them in parallel during the editing process.<n>FlowDC shows superior results compared with existing methods.
arXiv Detail & Related papers (2025-12-12T09:08:39Z) - Group Relative Attention Guidance for Image Editing [38.299491082179905]
Group Relative Attention Guidance (GRAG) is a simple yet effective method that modulates the focus of the model on the input image relative to the editing instruction.<n>Our code will be released at https://www.littlemisfit.com/little-misfit/GRAG-Image-Editing.
arXiv Detail & Related papers (2025-10-28T17:22:44Z) - Visual Autoregressive Modeling for Instruction-Guided Image Editing [97.04821896251681]
We present a visual autoregressive framework that reframes image editing as a next-scale prediction problem.<n>VarEdit generates multi-scale target features to achieve precise edits.<n>It completes a $512times512$ editing in 1.2 seconds, making it 2.2$times$ faster than the similarly sized UltraEdit.
arXiv Detail & Related papers (2025-08-21T17:59:32Z) - Exploring Iterative Manifold Constraint for Zero-shot Image Editing [38.7483790652481]
We propose a novel zero-shot editing paradigm dubbed ZZEdit.<n>It locates a qualified intermediate-inverted latent marked as $z_p$ as a better editing pivot.<n>Our ZZEdit performs iterative manifold constraint between the manifold of $M_p$ and $M_p-1$, leading to fewer fidelity errors.
arXiv Detail & Related papers (2025-01-07T09:00:36Z) - TurboEdit: Instant text-based image editing [32.06820085957286]
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models.
We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image.
Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion and 4 NFEs per edit.
arXiv Detail & Related papers (2024-08-14T18:02:24Z) - TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models [53.757752110493215]
We focus on a popular line of text-based editing frameworks - the edit-friendly'' DDPM-noise inversion approach.
We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength.
We propose a pseudo-guidance approach that efficiently increases the magnitude of edits without introducing new artifacts.
arXiv Detail & Related papers (2024-08-01T17:27:28Z) - FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion Models [44.26371926512843]
We introduce a novel free approach that employs progressive $textbfFre$qu$textbfe$ncy truncation to refine the guidance of $textbfDiff$usion models for universal editing tasks.
Our method achieves comparable results with state-of-the-art methods across a variety of editing tasks and on a diverse set of images.
arXiv Detail & Related papers (2024-04-18T04:47:28Z) - EditGAN: High-Precision Semantic Image Editing [120.49401527771067]
EditGAN is a novel method for high quality, high precision semantic image editing.
We show that EditGAN can manipulate images with an unprecedented level of detail and freedom.
We can also easily combine multiple edits and perform plausible edits beyond EditGAN training data.
arXiv Detail & Related papers (2021-11-04T22:36:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.