标题： 通过噪声优化实现粘性校正流动的直线化 显示英文标题

标题： Straighten Viscous Rectified Flow via Noise Optimization

摘要： 重流操作旨在通过在噪声和图像之间构建确定性耦合，使校正流在训练期间的推理轨迹更加直线化，从而提高单步或少步生成中生成图像的质量。 然而，我们发现重流存在关键限制，特别是由于其构建的确定性耦合中的图像与真实图像之间的分布差距，导致无法快速生成高质量图像。 为解决这些不足，我们提出了一种新的替代方法，称为通过噪声优化的直化粘性校正流（VRFNO），这是一种结合编码器和神经速度场的联合训练框架。 VRFNO引入了两项关键创新：(1) 历史速度项，增强了轨迹区分度，使模型能够更准确地预测当前轨迹的速度，(2) 通过重新参数化进行噪声优化，形成与真实图像的优化耦合，然后用于训练，有效缓解了重流局限性导致的误差。 在合成数据和不同分辨率的真实数据集上的综合实验表明，VRFNO显著缓解了重流的局限性，在单步和少步生成任务中均达到了最先进的性能。 显示英文摘要

摘要： The Reflow operation aims to straighten the inference trajectories of the rectified flow during training by constructing deterministic couplings between noises and images, thereby improving the quality of generated images in single-step or few-step generation. However, we identify critical limitations in Reflow, particularly its inability to rapidly generate high-quality images due to a distribution gap between images in its constructed deterministic couplings and real images. To address these shortcomings, we propose a novel alternative called Straighten Viscous Rectified Flow via Noise Optimization (VRFNO), which is a joint training framework integrating an encoder and a neural velocity field. VRFNO introduces two key innovations: (1) a historical velocity term that enhances trajectory distinction, enabling the model to more accurately predict the velocity of the current trajectory, and (2) the noise optimization through reparameterization to form optimized couplings with real images which are then utilized for training, effectively mitigating errors caused by Reflow's limitations. Comprehensive experiments on synthetic data and real datasets with varying resolutions show that VRFNO significantly mitigates the limitations of Reflow, achieving state-of-the-art performance in both one-step and few-step generation tasks.