前瞻与回顾流：基于轨迹平滑的无训练图像生成
Look-Ahead and Look-Back Flows: Training-Free Image Generation with Trajectory Smoothing
AI总结：
本文提出两种无训练轨迹平滑方法，通过优化潜在空间中的生成路径，在多个数据集上显著提升图像生成性能。
摘要：
最近的进展将扩散模型重新表述为确定性常微分方程（ODEs），通过流匹配框架提供了一个统一的噪声到数据生成过程的公式。各种无训练流匹配方法已被开发出来，通过调整流速度场来提高图像生成，从而消除了昂贵重新训练的需要。然而，修改速度场v会引入误差，这些误差会通过整个生成路径传播，而对潜在轨迹z的调整则由预训练的速度网络自然纠正，从而减少误差积累。在本文中，我们提出了两种互补的无训练潜在轨迹调整方法，基于未来和过去速度v和潜在轨迹z的信息，直接在潜在空间中优化生成路径。我们提出了两种无训练轨迹平滑方案：前瞻（Look-Ahead），它使用曲率门控权重平均当前和下一步的潜在表示；回顾（Look-Back），它使用指数移动平均和衰减来平滑潜在表示。通过广泛的实验和全面的评估指标，我们证明所提出的无训练轨迹平滑模型在多个数据集上，包括COCO17、CUB-200和Flickr30K，均显著优于各种最先进的模型。
英文摘要：
Recent advances have reformulated diffusion models as deterministic ordinary differential equations (ODEs) through the framework of flow matching, providing a unified formulation for the noise-to-data generative process. Various training-free flow matching approaches have been developed to improve image generation through flow velocity field adjustment, eliminating the need for costly retraining. However, Modifying the velocity field $v$ introduces errors that propagate through the full generation path, whereas adjustments to the latent trajectory $z$ are naturally corrected by the pretrained velocity network, reducing error accumulation. In this paper, we propose two complementary training-free latent-trajectory adjustment approaches based on future and past velocity $v$ and latent trajectory $z$ information that refine the generative path directly in latent space. We propose two training-free trajectory smoothing schemes: \emph{Look-Ahead}, which averages the current and next-step latents using a curvature-gated weight, and \emph{Look-Back}, which smoothes latents using an exponential moving average with decay. We demonstrate through extensive experiments and comprehensive evaluation metrics that the proposed training-free trajectory smoothing models substantially outperform various state-of-the-art models across multiple datasets including COCO17, CUB-200, and Flickr30K.
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。
