AdaEdit: Adaptive Temporal and Channel Modulation for Flow-Based Image Editing

2026-03-23

AI Summary

This work addresses the trade-off between background preservation and content generation in flow-matching-based image editing, where feature injection often leads to mutual suppression of these objectives. To resolve this, the authors propose a training-free, adaptive editing framework that dynamically modulates feature fusion and editing strength across both temporal and channel dimensions. Specifically, a progressive injection scheduling mechanism employs continuous decay functions—such as sigmoid or cosine—to gradually attenuate source feature injection over time, while a channel-selective latent perturbation strategy estimates channel importance based on latent distribution discrepancies and applies differentiated perturbations accordingly. The framework is compatible with various ODE solvers and achieves improved performance on PIE-Bench, reducing LPIPS by 8.7% and increasing SSIM and PSNR by 2.6% and 2.3%, respectively, all while maintaining high CLIP similarity, thereby enabling plug-and-play applicability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Feature Construction/Reformulation
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Inversion-based image editing in flow matching models has emerged as a powerful paradigm for training-free, text-guided image manipulation. A central challenge in this paradigm is the injection dilemma: injecting source features during denoising preserves the background of the original image but simultaneously suppresses the model's ability to synthesize edited content. Existing methods address this with fixed injection strategies -- binary on/off temporal schedules, uniform spatial mixing ratios, and channel-agnostic latent perturbation -- that ignore the inherently heterogeneous nature of injection demand across both the temporal and channel dimensions. In this paper, we present AdaEdit, a training-free adaptive editing framework that resolves this dilemma through two complementary innovations. First, we propose a Progressive Injection Schedule that replaces hard binary cutoffs with continuous decay functions (sigmoid, cosine, or linear), enabling a smooth transition from source-feature preservation to target-feature generation and eliminating feature discontinuity artifacts. Second, we introduce Channel-Selective Latent Perturbation, which estimates per-channel importance based on the distributional gap between the inverted and random latents and applies differentiated perturbation strengths accordingly -- strongly perturbing edit-relevant channels while preserving structure-encoding channels. Extensive experiments on the PIE-Bench benchmark (700 images, 10 editing types) demonstrate that AdaEdit achieves an 8.7% reduction in LPIPS, a 2.6% improvement in SSIM, and a 2.3% improvement in PSNR over strong baselines, while maintaining competitive CLIP similarity. AdaEdit is fully plug-and-play and compatible with multiple ODE solvers including Euler, RF-Solver, and FireFlow. Code is available at https://github.com/leeguandong/AdaEdit

Problem

• flow-based image editing
• inversion-based editing
• feature injection dilemma
• temporal modulation
• channel modulation

Innovation

• Adaptive Injection
• Progressive Injection Schedule
• Channel-Selective Perturbation
• Flow-Based Editing
• Training-Free Image Manipulation