AdaEdit: 一种基于流的图像编辑中的自适应时间与通道调节
AdaEdit: Adaptive Temporal and Channel Modulation for Flow-Based Image Editing
AI总结：
本文提出AdaEdit框架，通过自适应的渐进注入策略和通道选择性潜在扰动，解决流匹配模型中源特征注入与目标特征生成的矛盾，提升图像编辑效果。
摘要：
基于流的图像编辑在流匹配模型中已成为一种强大的无训练文本引导图像操作范式。该范式的核心挑战是注入困境：在去噪过程中注入源特征会保留原始图像的背景，但同时抑制模型合成编辑内容的能力。现有方法采用固定注入策略——二进制开/关时间调度、均匀空间混合比率和通道无关的潜在扰动——忽略了注入需求在时间和通道维度上的本质异质性。本文提出AdaEdit，一种无训练的自适应编辑框架，通过两个互补的创新解决这一困境。首先，我们提出渐进注入策略，用连续衰减函数（sigmoid、cosine或线性）取代硬二进制截止，使源特征保留到目标特征生成之间实现平滑过渡，并消除特征不连续伪影。其次，我们引入通道选择性潜在扰动，根据反转和随机潜在之间的分布差距估计每个通道的重要性，并应用不同的扰动强度——强烈扰动编辑相关通道，同时保留结构编码通道。在PIE-Bench基准（700张图像，10种编辑类型）上的广泛实验表明，AdaEdit在LPIPS上比强基线减少8.7%，在SSIM上提高2.6%，在PSNR上提高2.3%，同时保持竞争性的CLIP相似度。AdaEdit完全插拔式，并兼容多种ODE求解器，包括Euler、RF-Solver和FireFlow。代码可在https://github.com/leeguandong/AdaEdit获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
