StarVector
Generate SVG code from images and text
Listed in categories:
Design ToolsGitHubArtificial Intelligence
Description
StarVector is a groundbreaking foundation model designed for generating Scalable Vector Graphics (SVG) code from images and text instructions. Utilizing a Vision-Language Modeling architecture, it excels at vectorizing a wide range of visual inputs, from simple icons and logos to complex technical diagrams. By reframing vectorization as a code generation task, StarVector integrates visual and textual inputs seamlessly, allowing for the generation of high-quality SVG elements with intricate details and structural relationships.
How to use StarVector?
To use StarVector, load the pretrained model using the Transformers library, process your input image, and generate SVG code with just a few lines of code. The model handles the complexity of understanding visual elements and translating them into structured vector graphics code.
Core features of StarVector:
1️⃣
Advanced Multimodal Architecture
2️⃣
Unparalleled Complexity Handling
3️⃣
Robust Data Foundation
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Leading-Edge Performance
5️⃣
Open Source Accessibility
Why could be used StarVector?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Generating SVG code from images for web design | ✅ | |
| # 2 | Creating vector graphics for technical documentation | ✅ | |
| # 3 | Transforming text descriptions into vector illustrations | ✅ | |
Who developed StarVector?
StarVector is developed by a collaborative team of researchers from ServiceNow Research, Mila, and various Canadian institutions, including ETS Montreal and UBC Vancouver. The team is dedicated to advancing the field of vector graphics generation through innovative machine learning techniques.