LayoutFlow: Flow Matching for Layout Generation

• URL: http://arxiv.org/abs/2403.18187v2
• Date: Sat, 13 Jul 2024 02:30:20 GMT
• Title: LayoutFlow: Flow Matching for Layout Generation
• Authors: Julian Jorge Andrade Guerreiro, Naoto Inoue, Kento Masui, Mayu Otani, Hideki Nakayama,
• Abstract summary: We propose an efficient flow-based model capable of generating high-quality layouts. Our method learns to gradually move, or flow, the elements of an initial sample until it reaches its final prediction.
• Score: 23.045325684880957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finding a suitable layout represents a crucial task for diverse applications in graphic design. Motivated by simpler and smoother sampling trajectories, we explore the use of Flow Matching as an alternative to current diffusion-based layout generation models. Specifically, we propose LayoutFlow, an efficient flow-based model capable of generating high-quality layouts. Instead of progressively denoising the elements of a noisy layout, our method learns to gradually move, or flow, the elements of an initial sample until it reaches its final prediction. In addition, we employ a conditioning scheme that allows us to handle various generation tasks with varying degrees of conditioning with a single model. Empirically, LayoutFlow performs on par with state-of-the-art models while being significantly faster.

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