mlsanityData to 7 Oct 2026

OpenCOLE: Towards Reproducible Automatic Graphic Design Generation

arXiv 2406.08232 ↗First posted 12 Jun 2024cs.CV cs.GRcited by 36

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Abstract (arXiv)

Automatic generation of graphic designs has recently received considerable attention. However, the state-of-the-art approaches are complex and rely on proprietary datasets, which creates reproducibility barriers. In this paper, we propose an open framework for automatic graphic design called OpenCOLE, where we build a modified version of the pioneering COLE and train our model exclusively on publicly available datasets. Based on GPT4V evaluations, our model shows promising performance comparable to the original COLE. We release the pipeline and training results to encourage open development.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 20 of its 29 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

20 of 29 references.

References · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2023Visual Instruction Tuningcited by 7,792
  4. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  5. 2023LayoutDM: Discrete Diffusion Model for Controllable Layout Generationcited by 141
  6. 2019LayoutGAN: Generating Graphic Layouts with Wireframe Discriminatorscited by 130
  7. 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 122
  8. 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
  9. 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 93
  10. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  11. 2022LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restrictioncited by 43
  12. 2023AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generationcited by 41
8 more
  1. 2023LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Modelscited by 40
  2. 2023Towards Flexible Multi-modal Document Modelscited by 30
  3. 2021InfoColorizer: Interactive Recommendation of Color Palettes for Infographicscited by 15
  4. 2023Towards Diverse and Consistent Typography Generationcited by 11
  5. 2022Color Recommendation for Vector Graphic Documents based on Multi-Palette Representationcited by 9
  6. 2018Visual Font Pairingcited by 8
  7. 2022Generative Colorization of Structured Mobile Web Pagescited by 8
  8. 2023Multimodal Color Recommendation in Vector Graphic Documentscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 18; 2026: 14.

36 papers.

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  1. 2025ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generationcited by 37
  2. 2025POSTA: A Go-to Framework for Customized Artistic Poster Generationcited by 36
  3. 2025LayerD: Decomposing Raster Graphic Designs into Layerscited by 20
  4. 2025BizGen: Advancing Article-level Visual Text Rendering for Infographics Generationcited by 18
  5. 2025CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generationcited by 17
  6. 2024Multimodal Markup Document Models for Graphic Design Completioncited by 15
  7. 2024Can GPTs Evaluate Graphic Design Based on Design Principles?cited by 14
  8. 2025PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Modelscited by 14
  9. 2024From Elements to Design: A Layered Approach for Automatic Graphic Design Compositioncited by 11
  10. 2025BannerAgency: Advertising Banner Design with Multimodal LLM Agentscited by 11
  11. 2025OmniPSD: Layered PSD Generation with Diffusion Transformercited by 11
  12. 2025PhotoArtAgent: Intelligent Photo Retouching with Language Model-Based Artist Agentscited by 9
24 more
  1. 2025Rethinking Layered Graphic Design Generation with a Top-Down Approachcited by 9
  2. 2025IGD: Instructional Graphic Design with Multimodal Layer Generationcited by 6
  3. 2025UTDesign: A Unified Framework for Stylized Text Editing and Generation in Graphic Design Imagescited by 6
  4. 2024Type-R: Automatically Retouching Typos for Text-to-Image Generationcited by 5
  5. 2026LaDe: Unified Multi-Layered Graphic Media Generation and Decompositioncited by 5
  6. 2026DesignAsCode: Bridging Structural Editability and Visual Fidelity in Graphic Design Generationcited by 3
  7. 2025From Fragment to One Piece: A Survey on AI-Driven Graphic Designcited by 2
  8. 2025Agentic Design Review Systemcited by 2
  9. 2026Seeing is Improving: Visual Feedback for Iterative Text Layout Refinementcited by 2
  10. 2025Mirror in the Model: Ad Banner Image Generation via Reflective Multi-LLM and Multi-modal Agentscited by 1
  11. 2025DesignLab: Designing Slides Through Iterative Detection and Correctioncited by 1
  12. 2026PosterIQ: A Design Perspective Benchmark for Poster Understanding and Generationcited by 1
  13. 2026AnyDoc: Enhancing Document Generation via Large-Scale HTML/CSS Data Synthesis and Height-Aware Reinforcement Optimizationcited by 1
  14. 2026PSDesigner: Automated Graphic Design with a Human-Like Creative Workflowcited by 1
  15. 2026Does Synthetic Layered Design Data Benefit Layered Design Decomposition?cited by 1
  16. 2025Step-by-step Layered Design Generationcited by 0
  17. 2025Text-Conditioned Background Generation for Editable Multi-Layer Documentscited by 0
  18. 2026CPT: Controllable and Editable Design Variations with Language Modelscited by 0
  19. 2026ReContraster: Making Your Posters Stand Out with Regional Contrastcited by 0
  20. 2026Brief2Design: A Multi-phased, Compositional Approach to Prompt-based Graphic Designcited by 0
  21. 2026Editable Visual Designcited by 0
  22. 2026Learning Interaction between Image and Layout Priors for Joint Image-Layout Generation in Design Templatescited by 0
  23. 2026Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Designcited by 0
  24. 2026PixelART: Image-to-Layer Decomposition without Latents or Text-to-Image Pretrainingcited by 0