# Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models

arXiv: https://arxiv.org/abs/2411.16602 · HTML: https://mlsanity.com/paper/2411.16602/

## Key facts

- Cited by 38 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 40 of its 65 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## 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 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018)
- [CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders](https://mlsanity.com/paper/2106.14843/) (2021)
- [Semantic-SAM: Segment and Recognize Anything at Any Granularity](https://mlsanity.com/paper/2307.04767/) (2023)
- [DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation](https://mlsanity.com/paper/2007.11301/) (2020)
- [GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation](https://mlsanity.com/paper/2401.04092/) (2024)
- [SceneCraft: An LLM Agent for Synthesizing 3D Scene as Blender Code](https://mlsanity.com/paper/2403.01248/) (2024)
- [VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models](https://mlsanity.com/paper/2211.11319/) (2022)
- [3D-GPT: Procedural 3D Modeling with Large Language Models](https://mlsanity.com/paper/2310.12945/) (2023)
- [A Learned Representation for Scalable Vector Graphics](https://mlsanity.com/paper/1904.02632/) (2019)
- [AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes](https://mlsanity.com/paper/2312.06644/) (2023)
- [AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ](https://mlsanity.com/paper/2310.00367/) (2023)
- [Sketchformer: Transformer-based Representation for Sketched Structure](https://mlsanity.com/paper/2002.10381/) (2020)
- [SVGDreamer: Text Guided SVG Generation with Diffusion Model](https://mlsanity.com/paper/2312.16476/) (2023)
- [IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers](https://mlsanity.com/paper/2304.14400/) (2023)
- [FIGR: Few-shot Image Generation with Reptile](https://mlsanity.com/paper/1901.02199/) (2019)
- [DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models](https://mlsanity.com/paper/2306.14685/) (2023)
- [Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases](https://mlsanity.com/paper/2403.09675/) (2024)
- [PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM](https://mlsanity.com/paper/2406.02884/) (2024)
- [Graphic Design with Large Multimodal Model](https://mlsanity.com/paper/2404.14368/) (2024)

## Built on by (most cited first, 30 of 38)

- [OmniSVG: A Unified Scalable Vector Graphics Generation Model](https://mlsanity.com/paper/2504.06263/) (2025)
- [MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Mesh](https://mlsanity.com/paper/2508.01242/) (2025)
- [SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation](https://mlsanity.com/paper/2506.03139/) (2025)
- [GeoGramBench: Benchmarking the Geometric Program Reasoning in Modern LLMs](https://mlsanity.com/paper/2505.17653/) (2025)
- [Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback](https://mlsanity.com/paper/2604.20730/) (2026)
- [EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection](https://mlsanity.com/paper/2603.00155/) (2026)
- [SVGThinker: Instruction-Aligned and Reasoning-Driven Text-to-SVG Generation](https://mlsanity.com/paper/2509.24299/) (2025)
- [DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance](https://mlsanity.com/paper/2512.10894/) (2025)
- [Symbolic Graphics Programming with Large Language Models](https://mlsanity.com/paper/2509.05208/) (2025)
- [VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation](https://mlsanity.com/paper/2605.01517/) (2026)
- [ViewCraft3D: High-Fidelity and View-Consistent 3D Vector Graphics Synthesis](https://mlsanity.com/paper/2505.19492/) (2025)
- [Automatic Method Illustration Generation for AI Scientific Papers via Drawing Middleware Creation, Evolution, and Orchestration](https://mlsanity.com/paper/2603.29590/) (2026)
- [LottieGPT: Tokenizing Vector Animation for Autoregressive Generation](https://mlsanity.com/paper/2604.11792/) (2026)
- [From Pixels to Paths: A Multi-Agent Framework for Editable Scientific Illustration](https://mlsanity.com/paper/2510.27452/) (2025)
- [IntroSVG: Learning from Rendering Feedback for Text-to-SVG Generation via an Introspective Generator-Critic Framework](https://mlsanity.com/paper/2603.09312/) (2026)
- [Stroke of Surprise: Progressive Semantic Illusions in Vector Sketching](https://mlsanity.com/paper/2602.12280/) (2026)
- [VecGlypher: Unified Vector Glyph Generation with Language Models](https://mlsanity.com/paper/2602.21461/) (2026)
- [AmodalSVG: Amodal Image Vectorization via Semantic Layer Peeling](https://mlsanity.com/paper/2604.10940/) (2026)
- [Deltaynamics: Language-Based Representation for Inferring Rigid-Body Dynamics From Videos](https://mlsanity.com/paper/2605.20576/) (2026)
- [GeoLoom: High-quality Geometric Diagram Generation from Textual Input](https://mlsanity.com/paper/2512.08180/) (2025)
- [Unlocking the Latent Canvas: Eliciting and Benchmarking Symbolic Visual Expression in LLMs](https://mlsanity.com/paper/2603.14505/) (2026)
- [Figures as Programs: Recursive Generation of Editable Scientific Figures](https://mlsanity.com/paper/2609.01006/) (2026)
- [SVGauge: Towards Human-Aligned Evaluation for SVG Generation](https://mlsanity.com/paper/2509.07127/) (2025)
- [Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling](https://mlsanity.com/paper/2604.05072/) (2026)
- [mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval](https://mlsanity.com/paper/2604.17054/) (2026)
- [AnchorFlow: Learning Anchor Placement for Faithful and Editable SVG Reconstruction](https://mlsanity.com/paper/2605.19551/) (2026)
- [Math-Vision Diagrams: A Comprehensive Benchmark for Evaluating LLM Mathematical Diagram Generation Capabilities](https://mlsanity.com/paper/2608.08964/) (2026)
- [EdiTikZ: Scientific Figure Editing from Revision Trajectories](https://mlsanity.com/paper/2609.01409/) (2026)
- [PPTBench: Can Coding Agents Reconstruct the Visual World through Structured, Editable Slides](https://mlsanity.com/paper/2609.29718/) (2026)
- [See it. Say it. Sorted: Agentic System for Compositional Diagram Generation](https://mlsanity.com/paper/2508.15222/) (2025)

## Abstract (arXiv)

Scalable Vector Graphics (SVG) has become the de facto standard for vector graphics in digital design, offering resolution independence and precise control over individual elements. Despite their advantages, creating high-quality SVG content remains challenging, as it demands technical expertise with professional editing software and a considerable time investment to craft complex shapes. Recent text-to-SVG generation methods aim to make vector graphics creation more accessible, but they still encounter limitations in shape regularity, generalization ability, and expressiveness. To address these challenges, we introduce Chat2SVG, a hybrid framework that combines the strengths of Large Language Models (LLMs) and image diffusion models for text-to-SVG generation. Our approach first uses an LLM to generate semantically meaningful SVG templates from basic geometric primitives. Guided by image diffusion models, a dual-stage optimization pipeline refines paths in latent space and adjusts point coordinates to enhance geometric complexity. Extensive experiments show that Chat2SVG outperforms existing methods in visual fidelity, path regularity, and semantic alignment. Additionally, our system enables intuitive editing through natural language instructions, making professional vector graphics creation accessible to all users.