Look-Ahead and Look-Back Flows: Training-Free Image Generation with Trajectory Smoothing

• URL: http://arxiv.org/abs/2602.09449v1
• Date: Tue, 10 Feb 2026 06:34:47 GMT
• Title: Look-Ahead and Look-Back Flows: Training-Free Image Generation with Trajectory Smoothing
• Authors: Yan Luo, Henry Huang, Todd Y. Zhou, Mengyu Wang,
• Abstract summary: Various training-free flow matching approaches have been developed to improve image generation through flow velocity field adjustment.<n>We propose two training-free trajectory smoothing schemes: emphLook-Ahead, which averages the current and next-step latents using a curvature-gated weight, and emphLook-Back, which smoothes latents using an exponential moving average with decay.
• Score: 3.77130368225397
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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