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

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

Figures (7)

• Figure 1: Comparison of injection schedule functions. The Progressive Injection Schedule (sigmoid, cosine, linear) provides smooth decay from full injection to zero, eliminating the discontinuity artifact of the binary schedule.
• Figure 2: Pipeline of AdaEdit. Our method consists of three phases: (1) Inversion with feature caching, where source attention features $K_s^l, V_s^l$ are cached and an editing mask $M$ is extracted; (2) Channel-Selective Latent Perturbation, which estimates per-channel importance and applies differentiated AdaIN strengths---edit-relevant channels receive strong perturbation while structure channels are preserved; (3) Sampling with Progressive Injection, where cached source features are mixed with target features using a smoothly decaying weight $w(t)$. Sub-diagrams show (b) the progressive sigmoid schedule vs. binary cutoff, and (c) channel-selective perturbation strengths.
• Figure 3: Main results comparison on PIE-Bench (700 images). AdaEdit achieves significant improvements in background preservation metrics (LPIPS, SSIM, PSNR) with minimal impact on editing quality (CLIP).
• Figure 4: Per-type LPIPS comparison. AdaEdit consistently improves background preservation across all 10 editing types, with the largest gains on spatially localized edits.
• Figure 5: Radar chart showing SSIM scores across all editing types. AdaEdit (orange) consistently outperforms ProEdit (blue) across all categories.