LayoutFlow: Flow Matching for Layout Generation
Julian Jorge Andrade Guerreiro Naoto Inoue Kento Masui Mayu Otani Hideki Nakayama
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.