Laplacian Multi-scale Flow Matching for Generative Modeling

• URL: http://arxiv.org/abs/2602.19461v1
• Date: Mon, 23 Feb 2026 03:09:56 GMT
• Title: Laplacian Multi-scale Flow Matching for Generative Modeling
• Authors: Zelin Zhao, Petr Molodyk, Haotian Xue, Yongxin Chen,
• Abstract summary: We present Laplacian multiscale flow matching (LapFlow), a novel framework that enhances flow matching by leveraging multi-scale representations for image generative modeling.<n>Our approach decomposes images into Laplacian pyramid residuals and processes different scales in parallel through a mixture-of-transformers (MoT) architecture with causal attention mechanisms.
• Score: 23.408491192194926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present Laplacian multiscale flow matching (LapFlow), a novel framework that enhances flow matching by leveraging multi-scale representations for image generative modeling. Our approach decomposes images into Laplacian pyramid residuals and processes different scales in parallel through a mixture-of-transformers (MoT) architecture with causal attention mechanisms. Unlike previous cascaded approaches that require explicit renoising between scales, our model generates multi-scale representations in parallel, eliminating the need for bridging processes. The proposed multi-scale architecture not only improves generation quality but also accelerates the sampling process and promotes scaling flow matching methods. Through extensive experimentation on CelebA-HQ and ImageNet, we demonstrate that our method achieves superior sample quality with fewer GFLOPs and faster inference compared to single-scale and multi-scale flow matching baselines. The proposed model scales effectively to high-resolution generation (up to 1024$\times$1024) while maintaining lower computational overhead.

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