StarVector
StarVector is a foundation model that transforms vectorization into a code generation task by using a vision-language modeling architecture to generate high-quality SVG code directly from images and text inputs.
https://starvector.github.io/?ref=aipure
Product Information
Updated:Jun 16, 2025
StarVector Monthly Traffic Trends
StarVector received 7.8k visits last month, demonstrating a Slight Decline of -19.5%. Based on our analysis, this trend aligns with typical market dynamics in the AI tools sector.
View history trafficWhat is StarVector
StarVector represents a breakthrough in Scalable Vector Graphics (SVG) generation, offering a novel approach that reframes vectorization as a code generation task rather than a traditional image processing problem. It is a multimodal large language model that seamlessly integrates visual and textual inputs into a unified foundation SVG model. Unlike previous methods that focused mainly on curve-based vectorization and lacked semantic understanding, StarVector works directly in the SVG code space and leverages visual understanding to apply accurate SVG primitives, enabling the generation of more complex and semantically rich vector graphics.
Key Features of StarVector
StarVector is a breakthrough foundation model that transforms image vectorization into a code generation task using a multimodal vision-language architecture. It can generate high-quality SVG code directly from both images and text inputs, handling complex vector graphics elements including paths, shapes, text, and other SVG primitives. The model leverages a comprehensive dataset (SVG-Stack) and evaluation framework (SVG-Bench) to produce semantically rich and compact vector graphics that outperform traditional vectorization methods.
Advanced Multimodal Architecture: Integrates both vision and language processing capabilities to understand visual content and generate precise SVG code, combining an image encoder with a language model for comprehensive graphics understanding
Primitive-Aware Vectorization: Intelligently recognizes and generates various SVG primitives (paths, circles, polygons, text) without being limited to just curve-based vectorization
Large-Scale Training: Built on SVG-Stack dataset containing over 2 million SVG samples, enabling robust performance across diverse graphic styles and complexities
Code Generation Approach: Treats vectorization as a code generation task rather than traditional image processing, allowing for more precise and editable SVG outputs
Use Cases of StarVector
Logo Vectorization: Converting bitmap logo images into scalable vector formats for professional branding and design work
Technical Diagram Conversion: Transforming raster technical diagrams and charts into editable vector graphics for documentation and engineering purposes
Icon Design Automation: Automating the process of converting icon designs from pixels to vector format for web and application development
Font and Typography Processing: Converting typography and font designs into vector format for scalable text and character representations
Pros
State-of-the-art performance in SVG generation across multiple benchmarks
Handles complex graphics elements beyond simple curves
Produces more compact and semantically meaningful SVG code
Cons
Not suitable for natural images or illustrations
Requires significant computational resources due to large model size
Limited to specific types of graphics (icons, logos, diagrams, charts)
How to Use StarVector
Install Required Libraries: Install the necessary libraries including transformers and starvector. Visit the StarVector repository (https://github.com/joanrod/star-vector/tree/main) for complete installation instructions.
Import Required Modules: Import the necessary Python modules:
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
from starvector.data.util import process_and_rasterize_svg
import torch
Load the Pre-trained Model: Load the StarVector model using:
model_name = 'starvector/starvector-8b-im2svg'
starvector = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, trust_remote_code=True)
processor = starvector.model.processor
tokenizer = starvector.model.svg_transformer.tokenizer
Prepare the Model: Move the model to GPU and set it to evaluation mode:
starvector.cuda()
starvector.eval()
Load and Process Input Image: Load and process your input image:
image_pil = Image.open('your_image.png')
image = processor(image_pil, return_tensors='pt')['pixel_values'].cuda()
if not image.shape[0] == 1:
image = image.squeeze(0)
batch = {'image': image}
Generate SVG: Generate SVG code from the processed image:
raw_svg = starvector.generate_im2svg(batch, max_length=4000)[0]
svg, raster_image = process_and_rasterize_svg(raw_svg)
Use Generated SVG: The generated SVG code can now be saved to a file or used in your application. The raster_image variable contains a rasterized version of the SVG for preview purposes.
StarVector FAQs
StarVector is a foundation model for SVG generation that transforms vectorization into a code generation task. It uses a vision-language modeling architecture to process both visual and textual inputs to produce high-quality SVG code. The model can understand image semantics and use SVG primitives for compact, precise outputs.
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Analytics of StarVector Website
StarVector Traffic & Rankings
7.8K
Monthly Visits
#1886136
Global Rank
-
Category Rank
Traffic Trends: Mar 2025-May 2025
StarVector User Insights
00:00:07
Avg. Visit Duration
3.37
Pages Per Visit
31.97%
User Bounce Rate
Top Regions of StarVector
HK: 29.59%
ID: 24.9%
GB: 10.58%
IN: 10.21%
PL: 9.56%
Others: 15.16%
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