mlsanityData to 13 Oct 2026

Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints

arXiv 2402.04754 ↗First posted 7 Feb 2024cs.CV cs.LGcited by 26

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

Controllable layout generation refers to the process of creating a plausible visual arrangement of elements within a graphic design (e.g., document and web designs) with constraints representing design intentions. Although recent diffusion-based models have achieved state-of-the-art FID scores, they tend to exhibit more pronounced misalignment compared to earlier transformer-based models. In this work, we propose the LAyout Constraint diffusion modEl (LACE), a unified model to handle a broad range of layout generation tasks, such as arranging elements with specified attributes and refining or completing a coarse layout design. The model is based on continuous diffusion models. Compared with existing methods that use discrete diffusion models, continuous state-space design can enable the incorporation of differentiable aesthetic constraint functions in training. For conditional generation, we introduce conditions via masked input. Extensive experiment results show that LACE produces high-quality layouts and outperforms existing state-of-the-art baselines.

Key facts. Cited by 26 papers in the map (data through 13 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 25 of its 38 references that are in the map. First posted on arXiv 7 Feb 2024.

Builds on

25 of 38 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023LayoutDM: Transformer-based Diffusion Model for Layout Generation4 May
  2. 2023LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models21 Mar
  3. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generation14 Mar
  4. 2023Unifying Layout Generation with a Decoupled Diffusion Model9 Mar
  5. 2023PLay: Parametrically Conditioned Layout Generation using Latent Diffusion27 Jan

Other references · 20

  1. 2017Attention Is All You Needcited by 60,106
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  3. 2020Denoising Diffusion Implicit Modelscited by 9,718
  4. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,685
  5. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,425
  6. 2022MaskGIT: Masked Generative Image Transformercited by 1,090
  7. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
  8. 2019PubLayNet: largest dataset ever for document layout analysiscited by 349
  9. 2016A Learned Representation For Artistic Stylecited by 340
  10. 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 122
  11. 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
  12. 2022CARD: Classification and Regression Diffusion Modelscited by 102
8 more
  1. 2021Variational Transformer Networks for Layout Generationcited by 88
  2. 2019Neural Design Network: Graphic Layout Generation with Constraintscited by 83
  3. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  4. 2023Diffusion Models as Masked Autoencoderscited by 71
  5. 2021BLT: Bidirectional Layout Transformer for Controllable Layout Generationcited by 69
  6. 2020Attribute-conditioned Layout GAN for Automatic Graphic Designcited by 67
  7. 2022LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restrictioncited by 43
  8. 2023Diffusion-based Document Layout Generationcited by 21

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 9; 2025: 12; 2026: 5.

26 papers.

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  1. 2024DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perceptioncited by 81
  2. 2024LayoutFlow: Flow Matching for Layout Generationcited by 21
  3. 2024Alfie: Democratising RGBA Image Generation With No cited by 20
  4. 2024Constrained Diffusion Models via Dual Trainingcited by 19
  5. 2024VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correctioncited by 14
  6. 2025Uni-Layout: Integrating Human Feedback in Unified Layout Generation and Evaluationcited by 14
  7. 2025Composition and Alignment of Diffusion Models using Constrained Learningcited by 9
  8. 2024TextLap: Customizing Language Models for Text-to-Layout Planningcited by 8
  9. 2025AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Modelscited by 8
  10. 2024Layout-Corrector: Alleviating Layout Sticking Phenomenon in Discrete Diffusion Modelcited by 7
  11. 2024Design-o-meter: Towards Evaluating and Refining Graphic Designscited by 5
  12. 2025SciPostGen: Bridging the Gap between Scientific Papers and Poster Layoutscited by 4
14 more
  1. 2025LayoutRAG: Retrieval-Augmented Model for Content-agnostic Conditional Layout Generationcited by 3
  2. 2025Style Customization of Text-to-Vector Generation with Image Diffusion Priorscited by 2
  3. 2025Identity-Preserving Text-to-Image Generation via Dual-Level Feature Decoupling and Expert-Guided Fusioncited by 2
  4. 2025OmniDocLayout: Towards Diverse Document Layout Generation via Coarse-to-Fine LLM Learningcited by 2
  5. 2024LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformercited by 1
  6. 2025Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraintscited by 1
  7. 2025DocReward: A Document Reward Model for Structuring and Stylizingcited by 1
  8. 2026PSDesigner: Automated Graphic Design with a Human-Like Creative Workflowcited by 1
  9. 2025Generating Animated Layouts as Structured Text Representationscited by 0
  10. 2025UniLayDiff: A Unified Diffusion Transformer for Content-Aware Layout Generationcited by 0
  11. 2026DesignSense: A Human Preference Dataset and Reward Modeling Framework for Graphic Layout Generationcited by 0
  12. 2026Towards Design Compositingcited by 0
  13. 2026See Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generationcited by 0
  14. 2026Learned End-to-End Guidance Schedules for Diffusion Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer
  2. 2024CoLay: Controllable Layout Generation through Multi-conditional Latent Diffusion
  3. 2024PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM
  4. 2025CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design
  5. 2026Mise-en-Sc`ene: Implicit Layout Emergence in Diffusion Transformers for Human-AI Design Co-Creation
  6. 2026iPoster: Content-Aware Layout Generation for Interactive Poster Design via Graph-Enhanced Diffusion Models
  7. 2024DiffDesign: Controllable Diffusion with Meta Prior for Efficient Interior Design Generation
  8. 2025DiffPattern-Flex: Efficient Layout Pattern Generation via Discrete Diffusion