Metadata-Version: 2.4
Name: starvector
Version: 1.0
Summary: Generating Scalable Vector Graphics Code from Images and Text
Project-URL: Homepage, https://starvector.github.io
Project-URL: Bug Tracker, https://github.com/joanrod/starvector/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch==2.5.1
Requires-Dist: torchvision==0.20.1
Requires-Dist: transformers==4.49.0
Requires-Dist: tokenizers==0.21.1
Requires-Dist: sentencepiece==0.2.0
Requires-Dist: accelerate
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Requires-Dist: markdown2[all]
Requires-Dist: numpy<2.0.0
Requires-Dist: scikit-learn==1.2.2
Requires-Dist: gradio==3.36.1
Requires-Dist: gradio_client==0.2.9
Requires-Dist: requests
Requires-Dist: httpx==0.24.0
Requires-Dist: uvicorn
Requires-Dist: fastapi
Requires-Dist: svgpathtools==1.6.1
Requires-Dist: seaborn==0.12.2
Requires-Dist: taming-transformers
Requires-Dist: lpips
Requires-Dist: cairosvg
Requires-Dist: beautifulsoup4
Requires-Dist: webcolors
Requires-Dist: tqdm
Requires-Dist: omegaconf
Requires-Dist: open-clip-torch
Requires-Dist: noise
Requires-Dist: datasets
Requires-Dist: scikit-image
Requires-Dist: fairscale
Requires-Dist: lxml
Requires-Dist: torch-fidelity
Requires-Dist: clip-openai
Requires-Dist: scipy==1.11.1
Requires-Dist: sentence-transformers
Requires-Dist: reportlab
Requires-Dist: svglib
Requires-Dist: Pillow
Requires-Dist: protobuf
Requires-Dist: openai
Provides-Extra: train
Requires-Dist: deepspeed; extra == "train"
Requires-Dist: ninja; extra == "train"
Requires-Dist: wandb; extra == "train"
Dynamic: license-file
💫 StarVector: Generating Scalable Vector Graphics Code from Images and Text
## 🔥 News
- March 2025: **StarVector Accepted at CVPR 2025**,
- StarVector has been accepted at CVPR 2025! [[Link](https://arxiv.org/abs/2312.11556)]
- Check out our website for more information [[Link](https://starvector.github.io/)]
- StarVector models are now available on Hugging Face Model Hub! [[Link](https://huggingface.co/starvector/starvector-1b-im2svg)] [[Link](https://huggingface.co/starvector/starvector-8b-im2svg)]
- SVGBench and SVG-Stack datasets are now available on Hugging Face Datasets Hub! [[Link](https://huggingface.co/datasets/starvector/svg-bench)] [[Link](https://huggingface.co/datasets/starvector/svg-stack)]
## 🚀 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 recieves 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](#installation)
- [🏎️ Quick Start - Image2SVG Generation](#quick-start---image2svg-generation)
- [🎨 Models](#models)
- [📊 Datasets](#datasets---svg-bench)
- [🏋️♂️ Training](#training)
- [🏆 Evaluation on SVG-Bench](#validation-on-svg-benchmarks-svg-bench)
- [🧩 Demo](#starvector-demo)
- [📚 Citation](#citation)
- [📝 License](#license)
## Installation
1. Clone this repository and navigate to star-vector folder
```bash
git clone https://github.com/joanrod/star-vector.git
cd star-vector
```
2. Install Package
```Shell
conda create -n starvector python=3.11.3 -y
conda activate starvector
pip install --upgrade pip # enable PEP 660 support
pip install -e .
```
3. Install additional packages for training
```
pip install -e ".[train]"
```
### Upgrade to latest code base
```Shell
git pull
pip install -e .
```
## Quick Start - Image2SVG Generation
```Python
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
```Python
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](https://huggingface.co/collections/starvector/starvector-models-6783b22c7bd4b43d13cb5289) 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](https://huggingface.co/starvector/starvector-1b-im2svg)) | 0.926 | 0.978 | 0.975 | 0.929 | 0.943 |
| 💫 StarVector-8B (🤗 [Link](https://huggingface.co/starvector/starvector-8b-im2svg)) | **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](https://huggingface.co/collections/starvector/starvector-svg-datasets-67811204a76475be4dd66d09)
| Dataset | Train | Val | Test | Token Length | SVG Primitives | Annotation |
|-----------------|--------|-------|------|------------------|----------------|----------------|
| SVG-Stack (🤗 [Link](https://huggingface.co/datasets/starvector/svg-stack)) | 2.1M | 108k | 5.7k | 1,822 ± 1,808 | All | [Captions](https://huggingface.co/datasets/starvector/text2svg-stack) |
| SVG-Stack_sim (🤗 [Link](https://huggingface.co/datasets/starvector/svg-stack-simple)) | 601k | 30.1k | 1.5k | 2k ± 918 | Vector path | - |
| SVG-Diagrams (🤗 [Link](https://huggingface.co/datasets/starvector/svg-diagrams)) | - | - | 472 | 3,486 ± 1,918 | All | - |
| SVG-Fonts (🤗 [Link](https://huggingface.co/datasets/starvector/svg-fonts)) | 1.8M | 91.5k | 4.8k | 2,121 ± 1,868 | Vector path | Font letter |
| SVG-Fonts_sim (🤗 [Link](https://huggingface.co/datasets/starvector/svg-fonts-simple)) | 1.4M | 71.7k | 3.7k | 1,722 ± 723 | Vector path | Font letter |
| SVG-Emoji (🤗 [Link](https://huggingface.co/datasets/starvector/svg-emoji)) | 8.7k | 667 | 668 | 2,551 ± 1,805 | All | - |
| SVG-Emoji_sim (🤗 [Link](https://huggingface.co/datasets/starvector/svg-emoji-simple)) | 580 | 57 | 96 | 2,448 ± 1,026 | Vector Path | - |
| SVG-Icons (🤗 [Link](https://huggingface.co/datasets/starvector/svg-icons)) | 80.4k | 6.2k | 2.4k | 2,449 ± 1,543 | Vector path | - |
| SVG-Icons_sim (🤗 [Link](https://huggingface.co/datasets/starvector/svg-icons-simple)) | 80,435 | 2,836 | 1,277| 2,005 ± 824 | Vector path | - |
| SVG-FIGR (🤗 [Link](https://huggingface.co/datasets/starvector/FIGR-SVG)) | 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
```bash
pip install -e ".[train]"
```
### Set environment variables
We recommend setting the following environment variables:
```bash
export HF_HOME=
export HF_TOKEN=
export WANDB_API_KEY=
export OUTPUT_DIR=
```
cd the root of the repository.
```Shell
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
```bash
# 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.
```bash
# 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
```bash
# 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-{fonts,icons,emoji}.yaml
# StarVector-8B
torchrun \
--nproc-per-node=8 \
--nnodes=1 \
starvector/train/train.py \
config=configs/models/starvector-8b/im2svg-{fonts,icons,emoji}.yaml
```
We also provide shell scripts in `scripts/train/*`
## Validation on SVG Benchmarks (⭐ SVG-Bench)
We validate StarVector on ⭐ SVG-Bench Benchmark. We provide the SVGValidator class that allows you to run StarVector using **1) the HuggingFace generation backend** or **2) the VLLM backend**. The later is substantially faster thanks to the use of Paged Attention.
### HuggingFace Generation Backend
Let's start with the evaluation for StarVector-1B and StarVector-8B on SVG-Stack, using the HuggingFace generation backend (StarVectorHFAPIValidator). To override the input arguments, you can add cli args following the yaml file structure.
```bash
# StarVector-1B on SVG-Stack, using the HuggingFace backend
python starvector/validation/validate.py \
config=configs/generation/hf/starvector-1b/im2svg.yaml \
dataset.name=starvector/svg-stack
# StarVector-8B on SVG-Stack, using the vanilla HuggingFace generation API
python starvector/validation/validate.py \
config=configs/generation/hf/starvector-8b/im2svg.yaml \
dataset.name=starvector/svg-stack
```
### vLLM Backend
For using the vLLM backend (StarVectorVLLMAPIValidator), first install our StarVector fork of VLLM, [here](https://github.com/starvector/vllm).
```bash
git clone https://github.com/starvector/vllm.git
cd vllm
pip install -e .
```
Then, launch the using the vllm config file (it uses StarVectorVLLMValidator):
```bash
# StarVector-1B
python starvector/validation/validate.py \
config=configs/generation/vllm/starvector-1b/im2svg.yaml \
dataset.name=starvector/svg-stack
# StarVector-8B
python starvector/validation/validate.py \
config=configs/generation/vllm/starvector-8b/im2svg.yaml \
dataset.name=starvector/svg-stack
```
#### Generate using Temperature Sweep
Temperature sweep is an evaluation technique where we:
1. Generate multiple SVG candidates using different temperature values (controlling randomness in generation)
2. Evaluate each candidate using the DinoScore metric
3. Select the best performing SVG as the final output
This approach improves result quality by exploring multiple generation possibilities, though it requires more computation time.
```bash
# StarVector-1B (vLLM)
python starvector/validation/run_validator.py \
config=configs/generation/vllm/starvector-1b/im2svg.yaml \
dataset.name=svg-stack \
generation_params.generation_sweep=True \
generation_params.num_generations_different_temp=5 \
generation_params.min_temperature=0.0 \
generation_params.max_temperature=0.5
# StarVector-8B (vLLM)
python starvector/validation/run_validator.py \
config=configs/generation/vllm/starvector-8b/im2svg.yaml \
dataset.name=svg-stack \
generation_params.generation_sweep=True \
generation_params.num_generations_different_temp=10 \
generation_params.min_temperature=0.0 \
generation_params.max_temperature=0.5
```
We provide evaluation scripts in `scripts/eval/*`
## StarVector Demo
The demo provides two options for converting images to SVG code:
1. HuggingFace generation functionality
2. VLLM (recommended) - offers faster generation speed
### Option 1: HuggingFace Generation with Gradio Web UI
We provide a Gradio web UI for you to play with our model.
#### Launch a controller
```Shell
python -m starvector.serve.controller --host 0.0.0.0 --port 10000
```
#### Launch a gradio web server.
```Shell
python -m starvector.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload --port 7000
```
You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker.
#### Launch a model worker
This is the actual *worker* that performs the inference on the GPU. Each worker is responsible for a single model specified in `--model-path`.
```Shell
python -m starvector.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path joanrodai/starvector-1.4b
```
Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list.
You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker.
```Shell
vllm serve starvector/starvector-8b-im2svg --chat-template configs/chat-template.jinja --trust-remote-code --port 8001 --max-model-len 16000
python -m starvector.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port --worker http://localhost: --model-path
```
#### Option 2: Launch VLLM
0. Remember to clone the starvector/vllm fork (it has modifications for starvector).
```Shell
git clone https://github.com/starvector/vllm.git
cd vllm
pip install -e .
```
1. Call this to launch the VLLM endpoint
```Shell
vllm serve starvector/starvector-1b-im2svg --chat-template configs/chat-template.jinja --trust-remote-code --port 8000 --max-model-len 8192
```
2. Create the demo for VLLM
```Shell
python -m starvector.serve.vllm_api_gradio.controller --host 0.0.0.0 --port 10000
python -m starvector.serve.vllm_api_gradio.gradio_web_server --controller http://localhost:10000 --model-list-mode reload --port 7000
python -m starvector.serve.vllm_api_gradio.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-name starvector/starvector-1b-im2svg --vllm-base-url http://localhost:8000
```
3. Add more models by serving them with VLLM and calling a new model worker
```Shell
vllm serve starvector/starvector-8b-im2svg --chat-template configs/chat-template.jinja --trust-remote-code --port 8001 --max-model-len 16384
python -m starvector.serve.vllm_api_gradio.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40001 --worker http://localhost:40001 --model-name starvector/starvector-8b-im2svg --vllm-base-url http://localhost:8001
```
## Citation
```
@misc{rodriguez2024starvector,
title={StarVector: Generating Scalable Vector Graphics Code from Images and Text},
author={Juan A. Rodriguez and Abhay Puri and Shubham Agarwal and Issam H. Laradji and Pau Rodriguez and Sai Rajeswar and David Vazquez and Christopher Pal and Marco Pedersoli},
year={2024},
eprint={2312.11556},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2312.11556},
}
```
## License
This project is licensed under the Apache License, Version 2.0 - see the [LICENSE](LICENSE) file for details.