DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer

• URL: http://arxiv.org/abs/2303.03755v1
• Date: Tue, 7 Mar 2023 09:30:43 GMT
• Title: DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer
• Authors: Elad Levi, Eli Brosh, Mykola Mykhailych, Meir Perez
• Abstract summary: We introduce DLT, a joint discrete-continuous diffusion model. DLT has a flexible conditioning mechanism that allows for conditioning on any given subset of all the layout component classes, locations, and sizes. Our method outperforms state-of-the-art generative models on various layout generation datasets with respect to different metrics and conditioning settings.
• Score: 2.0483033421034142
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generating visual layouts is an essential ingredient of graphic design. The ability to condition layout generation on a partial subset of component attributes is critical to real-world applications that involve user interaction. Recently, diffusion models have demonstrated high-quality generative performances in various domains. However, it is unclear how to apply diffusion models to the natural representation of layouts which consists of a mix of discrete (class) and continuous (location, size) attributes. To address the conditioning layout generation problem, we introduce DLT, a joint discrete-continuous diffusion model. DLT is a transformer-based model which has a flexible conditioning mechanism that allows for conditioning on any given subset of all the layout component classes, locations, and sizes. Our method outperforms state-of-the-art generative models on various layout generation datasets with respect to different metrics and conditioning settings. Additionally, we validate the effectiveness of our proposed conditioning mechanism and the joint continuous-diffusion process. This joint process can be incorporated into a wide range of mixed discrete-continuous generative tasks.

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