[Paper Review] LayoutDM: Transformer-based Diffusion Model for Layout Generation

Shang Chai, Liansheng Zhuang+1 more|arXiv (Cornell University)|May 4, 2023

Generative Adversarial Networks and Image Synthesis4 citations

TL;DR

This paper proposes LayoutDM, a transformer-based diffusion model for conditional layout generation that replaces convolutional networks with a pure transformer architecture to denoise layout data. By leveraging self-attention mechanisms, LayoutDM achieves state-of-the-art performance in layout quality, diversity, and training stability, outperforming GANs and VAEs on benchmark datasets like PublayNet and COCO.

ABSTRACT

Automatic layout generation that can synthesize high-quality layouts is an important tool for graphic design in many applications. Though existing methods based on generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) have progressed, they still leave much room for improving the quality and diversity of the results. Inspired by the recent success of diffusion models in generating high-quality images, this paper explores their potential for conditional layout generation and proposes Transformer-based Layout Diffusion Model (LayoutDM) by instantiating the conditional denoising diffusion probabilistic model (DDPM) with a purely transformer-based architecture. Instead of using convolutional neural networks, a transformer-based conditional Layout Denoiser is proposed to learn the reverse diffusion process to generate samples from noised layout data. Benefitting from both transformer and DDPM, our LayoutDM is of desired properties such as high-quality generation, strong sample diversity, faithful distribution coverage, and stationary training in comparison to GANs and VAEs. Quantitative and qualitative experimental results show that our method outperforms state-of-the-art generative models in terms of quality and diversity.

Motivation & Objective

• To address the limitations of GANs and VAEs in layout generation, such as mode collapse, unstable training, and suboptimal sample quality.
• To explore the potential of diffusion models in conditional layout generation, where layouts are structured as variable-length, mixed-modal data with discrete and continuous elements.
• To design a purely transformer-based architecture that can model complex relationships between layout elements and handle variable-length inputs effectively.
• To achieve high-fidelity layout generation with strong diversity and faithful distribution coverage, overcoming the shortcomings of existing generative models.
• To demonstrate the generalization capability of the model across diverse layout generation tasks, including scene layouts and text logo generation.

Proposed method

• The model uses a conditional denoising diffusion probabilistic model (DDPM) framework, where the reverse denoising process is learned by a transformer-based conditional Layout Denoiser (cLayoutDenoiser).
• The cLayoutDenoiser processes noised layout data using multi-head self-attention to capture long-range dependencies and relationships between elements, without relying on positional encodings for element order.
• Input layout elements are represented as a sequence of tokens combining categorical labels and 2D coordinates, enabling the transformer to process mixed-modal, variable-length layouts.
• Conditional input is provided via attribute embeddings (e.g., category labels), which are concatenated with the layout tokens to guide the denoising process.
• The model is trained using a variational lower bound objective that minimizes the reconstruction loss between the original and denoised layouts across diffusion steps.
• During inference, the model iteratively denoises a random noise vector into a coherent layout, conditioned on user-specified attributes.

Experimental results

Research questions

• RQ1 Can a purely transformer-based architecture effectively replace convolutional networks in the reverse denoising process of diffusion models for layout generation?
• RQ2 Can the self-attention mechanism in transformers model complex spatial and semantic relationships between layout elements more effectively than convolutional layers?
• RQ3 Does the proposed LayoutDM achieve better sample diversity and distribution coverage than GANs and VAEs in conditional layout generation?
• RQ4 Can LayoutDM generalize to diverse layout generation tasks beyond standard document layouts, such as text logo and scene layout generation?
• RQ5 How does LayoutDM perform in terms of training stability and inference quality compared to existing state-of-the-art models?

Key findings

• LayoutDM outperforms state-of-the-art models like LayoutGAN++ in both visual quality and diversity on the PublayNet dataset, as confirmed by both quantitative metrics and qualitative comparisons.
• The model generates layouts with better alignment and reduced element overlap compared to LayoutGAN++, demonstrating improved spatial reasoning and layout coherence.
• On the COCO dataset, LayoutDM successfully generates plausible natural scene layouts, correctly placing objects like boats in rivers and clouds in the sky, indicating strong understanding of semantic and spatial relationships.
• In text logo generation, LayoutDM produces more flexible and aesthetically pleasing layouts than LogoGAN, especially when handling longer text sequences, avoiding rigid left-to-right arrangements.
• The model exhibits stationary training and avoids mode collapse, achieving strong distribution coverage and consistent performance across multiple random seeds.
• Despite its iterative denoising process, LayoutDM maintains competitive inference speed and generalization across diverse layout types, including document, logo, and scene layouts.