💫 StarVector: Generating Scalable Vector Graphics Code from Images and Text

<a href="https://joanrod.github.io" target="_blank">Juan A. Rodriguez</a>,
<a href="https://abhaypuri.github.io/portfolio/" target="_blank">Abhay Puri</a>,
<a href="https://shubhamagarwal92.github.io/" target="_blank">Shubham Agarwal</a>,
<a href="https://scholar.google.ca/citations?user=8vRS7F0AAAAJ&hl=en" target="_blank">Issam H. Laradji</a>,
<a href="https://scholar.google.es/citations?user=IwBx73wAAAAJ&hl=ca" target="_blank">Pau Rodriguez</a>,
<a href="https://scholar.google.es/citations?user=1jHvtfsAAAAJ&hl=ca" target="_blank">David Vazquez</a>,
<a href="https://scholar.google.com/citations?user=1ScWJOoAAAAJ&hl=en" target="_blank">Chris Pal</a>,
<a href="https://scholar.google.com/citations?user=aVfyPAoAAAAJ&hl=en" target="_blank">Marco Pedersoli</a>

🔥 News

• Sep 2025: RLRF Accepted at NeurIPS 2025,
• Our follow-up work to StarVector, RLRF, has been accepted to NeurIPS 2025! Check out the paper [Link]
• March 2025: StarVector Accepted at CVPR 2025,
• StarVector has been accepted at CVPR 2025! Check out the paper [Link]
• Check out our website for more information [Link]
• StarVector models are now available on HuggingFace! [Link] [Link]
• SVGBench and SVG-Stack datasets are now available on HuggingFace Datasets! [Link] [Link]

🚀 Introduction

StarVector is a multimodal vision-language model for Scalable Vector Graphics (SVG) generation. It can be used to perform image2SVG and text2SVG generation. We pose image generation as a code generation task, using the power of multimodal VLMs

Abstract: Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation methods have focused on curve-based vectorization, lacking semantic understanding, often producing artifacts, and struggling with SVG primitives beyond \textit{path} curves. To address these issues, we introduce StarVector, a multimodal large language model for SVG generation. It performs image vectorization by understanding image semantics and using SVG primitives for compact, precise outputs. Unlike traditional methods, StarVector works directly in the SVG code space, leveraging visual understanding to apply accurate SVG primitives. To train StarVector, we create SVG-Stack, a diverse dataset of 2M samples that enables generalization across vectorization tasks and precise use of primitives like ellipses, polygons, and text. We address challenges in SVG evaluation, showing that pixel-based metrics like MSE fail to capture the unique qualities of vector graphics. We introduce SVG-Bench, a benchmark across 10 datasets, and 3 tasks: Image-to-SVG, Text-to-SVG generation, and diagram generation. Using this setup, StarVector achieves state-of-the-art performance, producing more compact and semantically rich SVGs.

Multimodal Architecture

StarVector uses a multimodal architecture to process images and text. When performing Image-to-SVG (or image vectorization), the image is projected into visual tokens, and SVG code is generated. When performing Text-to-SVG, the model only receives the text instruction (no image is provided), and a novel SVG is created. The LLM is based of StarCoder, which we leverage to transfer coding skills to SVG generation.

📖 Table of Contents

• 💿 Installation
• 🏎️ Quick Start - Image2SVG Generation
• 🎨 Models
• 📊 Datasets
• 🏋️‍♂️ Training
• 🏆 Evaluation on SVG-Bench
• 🧩 Demo
• 📚 Citation
• 📝 License

Installation

1. Clone this repository and navigate to star-vector folder

git clone https://github.com/joanrod/star-vector.git
cd star-vector

1. Install Package

conda create -n starvector python=3.11.3 -y
conda activate starvector
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

1. Install additional packages for training

pip install -e ".[train]"

Upgrade to latest code base

git pull
pip install -e .

Quick Start - Image2SVG Generation

from PIL import Image
from starvector.model.starvector_arch import StarVectorForCausalLM
from starvector.data.util import process_and_rasterize_svg

model_name = "starvector/starvector-8b-im2svg"

starvector = StarVectorForCausalLM.from_pretrained(model_name)

starvector.cuda()
starvector.eval()

image_pil = Image.open('assets/examples/sample-0.png')
image = starvector.process_images([image_pil])[0].cuda()
batch = {"image": image}

raw_svg = starvector.generate_im2svg(batch, max_length=1000)[0]
svg, raster_image = process_and_rasterize_svg(raw_svg)

Use it from HuggingFace AutoModel

from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
from starvector.data.util import process_and_rasterize_svg
import torch

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

starvector.cuda()
starvector.eval()

image_pil = Image.open('assets/examples/sample-18.png')

image = processor(image_pil, return_tensors="pt")['pixel_values'].cuda()
if not image.shape[0] == 1:
    image = image.squeeze(0)
batch = {"image": image}

raw_svg = starvector.generate_im2svg(batch, max_length=4000)[0]
svg, raster_image = process_and_rasterize_svg(raw_svg)

Models

We provide Hugging Face 🤗 model checkpoints for image2SVG vectorization, for 💫 StarVector-8B and 💫 StarVector-1B. These are the results on SVG-Bench, using the DinoScore metric.

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 (🤗 Link)	0.926	0.978	0.975	0.929	0.943
💫 StarVector-8B (🤗 Link)	0.966	0.982	0.984	0.981	0.959

Note: StarVector models will not work for natural images or illustrations, as they have not been trained on those images. They excel in vectorizing icons, logotypes, technical diagrams, graphs, and charts.

Datasets - SVG-Bench

SVG-Bench is a benchmark for evaluating SVG generation models. It contains 10 datasets, and 3 tasks: Image-to-SVG, Text-to-SVG, and Diagram-to-SVG.

See our Huggingface 🤗 Dataset Collection

Dataset	Train	Val	Test	Token Length	SVG Primitives	Annotation
SVG-Stack (🤗 Link)	2.1M	108k	5.7k	1,822 ± 1,808	All	Captions
SVG-Stack_sim (🤗 Link)	601k	30.1k	1.5k	2k ± 918	Vector path	-
SVG-Diagrams (🤗 Link)	-	-	472	3,486 ± 1,918	All	-
SVG-Fonts (🤗 Link)	1.8M	91.5k	4.8k	2,121 ± 1,868	Vector path	Font letter
SVG-Fonts_sim (🤗 Link)	1.4M	71.7k	3.7k	1,722 ± 723	Vector path	Font letter
SVG-Emoji (🤗 Link)	8.7k	667	668	2,551 ± 1,805	All	-
SVG-Emoji_sim (🤗 Link)	580	57	96	2,448 ± 1,026	Vector Path	-
SVG-Icons (🤗 Link)	80.4k	6.2k	2.4k	2,449 ± 1,543	Vector path	-
SVG-Icons_sim (🤗 Link)	80,435	2,836	1,277	2,005 ± 824	Vector path	-
SVG-FIGR (🤗 Link)	270k	27k	3k	5,342 ± 2,345	Vector path	Class, Caption

We offer a summary of statistics about the datasets used in our training and evaluation experiments. This datasets are included in SVG-Bench. The subscript sim stands for the simplified version of the dataset, as required by some baselines.

Training

Confirm dependencies are installed

pip install -e ".[train]"

Set environment variables

We recommend setting the following environment variables:

  export HF_HOME=<path to the folder where you want to store the models>
  export HF_TOKEN=<your huggingface token>
  export WANDB_API_KEY=<your wandb token>
  export OUTPUT_DIR=<path/to/output>

cd the root of the repository.

cd star-vector

Image2SVG Pretraining (Stage 1)

We have different training approaches for StarVector-1B and StarVector-8B. StarVector-1B can be trained using Deepspeed, while StarVector-8B requires FSDP.

StarVector-1B Training

You can use the following command to train StarVector-1B on SVG-Stack for the Image2SVG vectorization task, using Deepspeed and Accelerate

# StarVector-1B
accelerate launch --config_file configs/accelerate/deepspeed-8-gpu.yaml starvector/train/train.py config=configs/models/starvector-1b/im2svg-stack.yaml

StarVector-8B Training

You can use the following command to train StarVector-8B on SVG-Stack for the Image2SVG vectorization task, using FSDP and Accelerate. We provide the torchrun command to support multi-nodes and multi-GPUs.

# StarVector-8B
torchrun \
  --nproc-per-node=8 \
  --nnodes=1 \
  starvector/train/train.py \
  config=configs/models/starvector-8b/im2svg-stack.yaml

Finetuning StarVector (Stage 2)

After pretraining StarVector on image vectorization, we finetune it on additional SVG tasks like Text2SVG, and SVG-Bench datasets.

Text2SVG Finetuning

# StarVector-1B
accelerate launch --config_file config/accelerate/deepspeed-8-gpu.yaml starvector/train/train.py config=configs/models/starvector-1b/text2svg-stack.yaml

# StarVector-8B
torchrun \
  --nproc-per-node=8 \
  --nnodes=1 \
  starvector/train/train.py \
  config=configs/models/starvector-8b/text2svg-stack.yaml

SVG-Bench Finetuning

```bash

StarVector-1B

accelerate launch --config_file config/accelerate/deepspeed-8-gpu.yaml starvector/train/train.py config=configs/models/starvector-1b/im2svg-{