自适应推理时间缩放：基于早期步骤潜在验证的图像编辑
Adaptive Inference-Time Scaling via Early-Step Latent Verification for Image Editing
AI总结：
提出VeriLatent框架，通过早期步骤潜在空间编辑激活图验证初始噪声，实现自适应推理时间缩放，提升图像编辑质量和效率。
摘要：
基于指令的图像编辑随着生成模型的最新进展取得了显著进步。然而，编辑结果的质量仍受随机采样的初始噪声影响，特别是在复杂编辑场景中。不合适的初始噪声可能导致不满意的编辑结果。最近的推理时间缩放方法通过采样多个初始噪声并选择更好的候选者来解决这一问题。然而，大多数方法遵循解码-验证方案，引入了效率与准确性的权衡。当在有限的推理步骤后进行解码时，解码后的图像通常噪声过大，无法进行可靠评估，而充分去噪的图像则需要更高的计算成本。为了解决这个问题，我们提出了VeriLatent，一种即插即用的自适应推理时间缩放框架，用于图像编辑的早期步骤潜在验证。具体来说，我们提出了一种新颖的验证器，通过在早期阶段通过潜在空间编辑激活图对每个初始噪声进行评分。它通过评估候选者是否能在正确区域引发有效编辑来识别有希望的候选者。这使得无需将潜在变量解码为图像即可进行高效的早期剪枝。在此基础上，我们进一步开发了一种用于推理时间缩放的自适应搜索策略。它根据编辑难度分配推理预算，从而减少函数评估次数（NFE）。在多个基准测试和不同基础模型上的大量实验表明，VeriLatent持续提高了编辑性能和推理时间缩放效率。
英文摘要：
Instruction-based image editing has made notable progress with recent advances in generative models. However, the quality of the edited result is still influenced by the randomly sampled initial noise, particularly in complex editing scenarios. An unsuitable initial noise may lead to unsatisfactory editing results. Recent inference-time scaling methods address this issue by sampling multiple initial noises and selecting better candidates. Nevertheless, most of them follow a decode-then-verify scheme which introduces an efficiency-accuracy trade-off. When decoding is performed after limited inference steps, the decoded images often remain too noisy for reliable assessment, whereas sufficiently denoised images require much higher computational cost. To address this issue, we propose VeriLatent, a plug-and-play adaptive inference-time scaling framework with early-step latent verification for image editing. Specifically, we propose a novel verifier that scores each initial noise through a latent-space editing activation map at an early stage. It identifies promising candidates by assessing whether they can induce an effective edit in the correct region. This enables efficient early pruning without decoding latents into images. Building on this, we further develop an adaptive search strategy for inference-time scaling. It allocates inference budgets according to editing difficulty, thereby reducing the number of function evaluations (NFE). Extensive experiments on multiple benchmarks and different base models demonstrate that VeriLatent consistently improves both editing performance and inference-time scaling efficiency.
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。
