Trace-guided fine-grained memory control and offline joint planning raise diffusion serving SLO attainment by up to 3.7× while cutting configuration search from hours to minutes.
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion
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abstract
Layout design is an important task in various design fields, including user interface, document, and graphic design. As this task requires tedious manual effort by designers, prior works have attempted to automate this process using generative models, but commonly fell short of providing intuitive user controls and achieving design objectives. In this paper, we build a conditional latent diffusion model, PLay, that generates parametrically conditioned layouts in vector graphic space from user-specified guidelines, which are commonly used by designers for representing their design intents in current practices. Our method outperforms prior works across three datasets on metrics including FID and FD-VG, and in user study. Moreover, it brings a novel and interactive experience to professional layout design processes.
fields
cs.DC 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration
Trace-guided fine-grained memory control and offline joint planning raise diffusion serving SLO attainment by up to 3.7× while cutting configuration search from hours to minutes.