# PLay: Parametrically Conditioned Layout Generation using Latent Diffusion

arXiv: https://arxiv.org/abs/2301.11529 · HTML: https://mlsanity.com/paper/2301.11529/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 43 of its 59 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [Generative Visual Manipulation on the Natural Image Manifold](https://mlsanity.com/paper/1609.03552/) (2016)
- [DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation](https://mlsanity.com/paper/2007.11301/) (2020)
- [Symbolic Music Generation with Diffusion Models](https://mlsanity.com/paper/2103.16091/) (2021)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks](https://mlsanity.com/paper/2207.04632/) (2022)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [Self-conditioned Embedding Diffusion for Text Generation](https://mlsanity.com/paper/2211.04236/) (2022)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [Variational Transformer Networks for Layout Generation](https://mlsanity.com/paper/2104.02416/) (2021)

## Built on by (most cited first, 24 of 24)

- [Hierarchical Neural Coding for Controllable CAD Model Generation](https://mlsanity.com/paper/2307.00149/) (2023)
- [Graphic Design with Large Multimodal Model](https://mlsanity.com/paper/2404.14368/) (2024)
- [ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation](https://mlsanity.com/paper/2502.18364/) (2025)
- [UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback](https://mlsanity.com/paper/2406.07739/) (2024)
- [Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints](https://mlsanity.com/paper/2402.04754/) (2024)
- [Dolfin: Diffusion Layout Transformers without Autoencoder](https://mlsanity.com/paper/2310.16305/) (2023)
- [LayoutFlow: Flow Matching for Layout Generation](https://mlsanity.com/paper/2403.18187/) (2024)
- [UICrit: Enhancing Automated Design Evaluation with a UICritique Dataset](https://mlsanity.com/paper/2407.08850/) (2024)
- [LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models](https://mlsanity.com/paper/2309.09506/) (2023)
- [UI Layout Generation with LLMs Guided by UI Grammar](https://mlsanity.com/paper/2310.15455/) (2023)
- [Boosting GUI Prototyping with Diffusion Models](https://mlsanity.com/paper/2306.06233/) (2023)
- [LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation](https://mlsanity.com/paper/2407.12356/) (2024)
- [AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models](https://mlsanity.com/paper/2503.00591/) (2025)
- [What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design](https://mlsanity.com/paper/2406.13436/) (2024)
- [SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation](https://mlsanity.com/paper/2507.11579/) (2025)
- [Revision Matters: Generative Design Guided by Revision Edits](https://mlsanity.com/paper/2406.18559/) (2024)
- [WebRPG: Automatic Web Rendering Parameters Generation for Visual Presentation](https://mlsanity.com/paper/2407.15502/) (2024)
- [CANVAS: A Benchmark for Vision-Language Models on Tool-Based User Interface Design](https://mlsanity.com/paper/2511.20737/) (2025)
- [LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformer](https://mlsanity.com/paper/2407.15233/) (2024)
- [ControlGUI: Guiding Generative GUI Exploration through Perceptual Visual Flow](https://mlsanity.com/paper/2502.03330/) (2025)
- [Sketch-to-Layout: Sketch-Guided Multimodal Layout Generation](https://mlsanity.com/paper/2510.27632/) (2025)
- [Step-by-step Layered Design Generation](https://mlsanity.com/paper/2512.03335/) (2025)
- [DesignSense: A Human Preference Dataset and Reward Modeling Framework for Graphic Layout Generation](https://mlsanity.com/paper/2602.23438/) (2026)
- [Learning Interaction between Image and Layout Priors for Joint Image-Layout Generation in Design Templates](https://mlsanity.com/paper/2609.11519/) (2026)

## Abstract (arXiv)

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.