StarVector is a multimodal vision-language model (VLM) designed specifically for Scalable Vector Graphics (SVG) generation. It can produce high-precision, semantically rich SVG code through both Image-to-SVG and Text-to-SVG methods. Unlike traditional curve vectorization techniques, StarVector operates directly at the SVG code level, allowing it to accurately utilize SVG primitives (such as ellipses, rectangles, polygons, and text), thus avoiding common distortions and artifacts seen in conventional methods.
StarVector employs a multimodal architecture capable of processing both images and text as inputs:
The model is built upon StarCoder, enabling it to transfer coding capabilities to the SVG generation domain, ensuring concise and syntactically correct code.
Traditional SVG generation methods, such as AutoTrace, Potrace, and VTracer, primarily rely on curve fitting and lack semantic understanding of images. This often results in distorted or overly complex path data, making it difficult to handle complex SVG elements.
StarVector’s advantages:
<path> data.Many previous SVG generation methods relied on pixel-level evaluation metrics (e.g., MSE), which fail to measure the true semantic accuracy of SVGs. To address this, the StarVector team developed SVG-Bench, a benchmark specifically designed to assess SVG generation quality, covering 10 datasets and 3 types of SVG generation tasks:
Currently, StarVector offers two model versions, both available for download on Hugging Face:
In SVG-Bench testing, StarVector outperformed all baseline models in DinoScore metrics:
| Method | SVG-Stack | SVG-Fonts | SVG-Icons | SVG-Emoji | SVG-Diagrams |
|---|---|---|---|---|---|
| AutoTrace | 0.942 | 0.954 | 0.946 | 0.975 | 0.874 |
| Potrace | 0.898 | 0.967 | 0.972 | 0.882 | 0.875 |
| VTracer | 0.954 | 0.964 | 0.940 | 0.981 | 0.882 |
| Im2Vec | 0.692 | 0.733 | 0.754 | 0.732 | - |
| LIVE | 0.934 | 0.956 | 0.959 | 0.969 | 0.870 |
| DiffVG | 0.810 | 0.821 | 0.952 | 0.814 | 0.822 |
| GPT-4-V | 0.852 | 0.842 | 0.848 | 0.850 | - |
| 💫 StarVector-1B | 0.926 | 0.978 | 0.975 | 0.929 | 0.943 |
| 💫 StarVector-8B | 0.966 | 0.982 | 0.984 | 0.981 | 0.959 |
Note: StarVector is not designed for natural images or illustrations since its training data primarily consists of icons, technical diagrams, charts, and logos.
StarVector’s training data comes from SVG-Bench, a specialized dataset for SVG generation models, covering 10 sub-datasets, each targeting different SVG generation scenarios:
| Dataset | Training Set | Validation Set | Test Set | Avg. Token Length | Supported SVG Primitives | Annotation Type |
|---|---|---|---|---|---|---|
| SVG-Stack | 2.1M | 108k | 5.7k | 1,822 ± 1,808 | All SVG Primitives | Image Annotation |
| SVG-Stack_sim | 601k | 30.1k | 1.5k | 2,000 ± 918 | Vector path | - |
| SVG-Diagrams | - | - | 472 | 3,486 ± 1,918 | All SVG Primitives | - |
| SVG-Fonts | 1.8M | 91.5k | 4.8k | 2,121 ± 1,868 | Vector path | Font Annotation |
| SVG-Fonts_sim | 1.4M | 71.7k | 3.7k | 1,722 ± 723 | Vector path | Font Annotation |
| SVG-Emoji | 8.7k | 667 | 668 | 2,551 ± 1,805 | All SVG Primitives | - |
| SVG-Emoji_sim | 580 | 57 | 96 | 2,448 ± 1,026 | Vector path | - |
| SVG-Icons | 80.4k | 6.2k | 2.4k | 2,449 ± 1,543 | Vector path | - |
| SVG-Icons_sim | 80.4k | 2.8k | 1.2k | 2,005 ± 824 | Vector path | - |
| SVG-FIGR | 270k | 27k | 3k | 5,342 ± 2,345 | Vector path | Image Classification & Annotation |
SVGs play a crucial role in icons, branding, technical diagrams, and map design. StarVector is currently the most advanced Image-to-SVG and Text-to-SVG generation model. Compared to traditional curve-fitting methods, it offers:
✅ Semantic Understanding, ensuring accurate image structure recognition
✅ Concise Code, generating more efficient SVGs
✅ More Accurate Evaluation Metrics, overcoming pixel-based limitations
✅ Support for Hugging Face training & testing, available for developers
StarVector makes AI-generated SVGs more precise and reliable, opening up new possibilities for vector graphics applications. 💡
👉 Resources:
DMflow.chat: Step into the future of customer service. Enjoy persistent memory, customizable fields, and effortless database integration—no extra setup required. Connect multiple platforms to elevate your efficiency, service, and marketing.
Free to Use in Ghibli Style! EasyControl_Ghibli Model Arrives, Instantly Transforming Photos into...
OpenAI Launches GPT-4o Image Generation with Multi-Turn Editing On March 25, 2025, OpenAI announ...
Google AI Studio Enhances Image Generation: Lower False Positives, Greater Usability Major Updat...
Free AI Drawing Tool Arrives! Unlimited Creativity with Raphael AI In this age of boundless c...
Meta Leffa: AI Virtual Fitting Breakthrough, Realistic Details Create Immersive Shopping Experien...
RMBG 2.0: Revolutionary AI Background Removal Technology, Free and Open Source Outperforms Paid S...
Meta Motivo: A Breakthrough AI Full-Body Humanoid Control Model | Full Analysis and Applications ...
DeepSeek’s Open-Source Week: Five Repos, One Mission—Community Innovation The world of artifi...
OpenAI Day 2: Reinforcement Fine-Tuning and Model Customization: The Future of AI Description Ex...