[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72466":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":9,"pushedAt":9,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":15,"starSnapshotCount":15,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},72466,"OmniSVG","OmniSVG\u002FOmniSVG","[NeurIPS 2025] OmniSVG is the first family of end-to-end multimodal SVG generators that leverage pre-trained Vision-Language Models (VLMs), capable of generating complex and detailed SVGs, from simple icons to intricate anime characters.","",null,"Python",2521,94,76,36,0,3,13,37,9,74.13,"Apache License 2.0",false,"main",true,[],"2026-06-12 04:01:05","\u003C!-- \u003Cdiv align= \"center\">\n    \u003Ch1> Official repo for OmniSVG\u003C\u002Fh1>\n\n\u003C\u002Fdiv> -->\n\n\u003Ch3 align=\"center\">\u003Cstrong>OmniSVG: A Unified Scalable Vector Graphics Generation Model\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\u003Cdiv align=\"center\">\n\u003Ca href='https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.06263'>\u003Cimg src='https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FarXiv-2504.06263-b31b1b.svg'>\u003C\u002Fa>     \n \u003Ca href='https:\u002F\u002Fomnisvg.github.io\u002F'>\u003Cimg src='https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FProject-Page-Green'>\u003C\u002Fa>     \n\u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002FOmniSVG\u002FOmniSVG1.1_8B\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Weights-HF-orange\">\u003C\u002Fa>     \n\u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002FOmniSVG\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Dataset%20-HF-orange\">\u003C\u002Fa>     \n\u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FOmniSVG\u002FMMSVGBench\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Bench-HF-orange\">\u003C\u002Fa>     \n\u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FOmniSVG\u002FOmniSVG-3B\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Demo%20-HF-orange\">\u003C\u002Fa>     \n\u003Ca href='https:\u002F\u002Fgithub.com\u002FOpenVGLab\u002FOmniSVG-train'>\u003Cimg src='https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTraining-Code-blue?logo=github'>\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## 🔥🔥🔥 News !!\n- [2026\u002F03\u002F02] 🔥 We have released the first Lottie generation model -- **OmniLottie**, checkout [OpenVGLab\u002FOmniLottie](https:\u002F\u002Fgithub.com\u002FOpenVGLab\u002FOmniLottie)\n- [2025\u002F12\u002F31] 👋 We have released the training code of OmniSVG， Check out [OpenVGLab\u002FOmniSVG-Train](https:\u002F\u002Fgithub.com\u002FOpenVGLab\u002FOmniSVG-train)\n- [2025\u002F12\u002F22] We have updated **MMSVG-Icon** (264K→904K) and **MMSVG-Illustration** (66K→255K) datasets with enhanced captions and PNG previews! Check out [MMSVG-Icon](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FOmniSVG\u002FMMSVG-Icon) and [MMSVG-Illustration](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FOmniSVG\u002FMMSVG-Illustration).\n- [2025\u002F12\u002F02] We have released the **OmniSVG1.1_8B** weights and updated **OmniSVG1.1_4B** model weights! Check out [OmniSVG1.1_8B](https:\u002F\u002Fhuggingface.co\u002FOmniSVG\u002FOmniSVG1.1_8B) and [OmniSVG1.1_4B](https:\u002F\u002Fhuggingface.co\u002FOmniSVG\u002FOmniSVG1.1_4B).\n- [2025\u002F12\u002F02] We have released **MMSVGBench** benchmark dataset and evaluation code! Check out [MMSVGBench](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FOmniSVG\u002FMMSVGBench) and [Evaluation](https:\u002F\u002Fgithub.com\u002FOmniSVG\u002FOmniSVG?tab=readme-ov-file#5-evaluation).\n- [2025\u002F09\u002F18] OmniSVG is accepted to **NeurIPS 2025**🔥! See you in San Diego!\n- [2025\u002F07\u002F22] 👋 We have released the Huggingface Demo. 🤗[Demo](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FOmniSVG\u002FOmniSVG-3B).\n- [2025\u002F07\u002F22] 👋 We have released the inference code and model weight of MMSVG-Icon and MMSVG-Illustration dataset. 🤗[Weight](https:\u002F\u002Fhuggingface.co\u002FOmniSVG\u002FOmniSVG).\n- [2025\u002F04\u002F09] 👋 Release MMSVG-Icon and MMSVG-Illustration 🤗[Dataset](https:\u002F\u002Fhuggingface.co\u002FOmniSVG).\n- [2025\u002F04\u002F09] 👋 Upload paper and init project. [Read](https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.06263)\n\n\n\u003Cp align=\"center\">\n    \u003Cimg src=\"assets\u002FOmniSVG-demo-gen-proc-anime-1080.gif\" alt=\"Demo GIF\" width=\"720px\" \u002F>\n\u003C\u002Fp>\n\n## 🧩 Community Contributions\nIf you are developing \u002F using OmniSVG in your projects, or you want to contribute to OmniSVG, please let us know 🎉.\n\n- If you find data issues when using MMSVG dataset, please drop an issue in this [form](https:\u002F\u002Fnpqawhh9ht.feishu.cn\u002Fwiki\u002FKHv2wDqAxiSV8skpkANcbmlwnqc?from=from_copylink).\n- 👋 OmniSVG ComfyUI Plugin by [@smthemex](https:\u002F\u002Fgithub.com\u002Fsmthemex) [ComfyUI_OmniSVG](https:\u002F\u002Fgithub.com\u002Fsmthemex\u002FComfyUI_OmniSVG).\n\n## 📑 Open-source Plan\n- [x] Project Page & Technical Report\n- [x] MMSVG-Icon and MMSVG-Illustration Dataset Release\n- [x] Inference Code & Model Weight of MMSVG-Icon and MMSVG-Illustration Dataset\n- [x] Online Demo (Gradio deployed on Huggingface)\n- [x] Model Weight of OmniSVG1.1_8B Release\n- [x] Model Weight of OmniSVG1.1_4B Release\n- [x] MMSVGBench Benchmark & Evaluation Code Release\n\n\n\n## 1. Introduction\n\n**OmniSVG** is the first family of end-to-end multimodal SVG generators that leverage pre-trained Vision-Language Models (VLMs), capable of generating complex and detailed SVGs, from simple icons to intricate anime characters. We also introduce MMSVG-2M, a multimodal dataset with two million richly annotated SVG assets, along with a standardized evaluation protocol for conditional SVG generation tasks. \n\n\n## 2. Models Downloading\n| Model                       | Download link                   | Size       | Update date |                                                                                     \n|-----------------------------|-------------------------------|------------|------|\n| OmniSVG1.1_8B | [Huggingface](https:\u002F\u002Fhuggingface.co\u002FOmniSVG\u002FOmniSVG1.1_8B)    | 17.2 GB | 2025-12-02  |\n| OmniSVG1.1_4B | [Huggingface](https:\u002F\u002Fhuggingface.co\u002FOmniSVG\u002FOmniSVG1.1_4B)    | 7.69 GB | 2025-12-02  |\n| OmniSVG-3B | [Huggingface](https:\u002F\u002Fhuggingface.co\u002FOmniSVG\u002FOmniSVG)    | 8.49 GB | 2025-07-22  | \n\n\n\n##  3. Dependencies and Installation\nThe dependencies configured according to the following instructions provide an environment equipped for inference\n\n### 3.1 Clone the Repository\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FOmniSVG\u002FOmniSVG.git\ncd OmniSVG\n```\n\n### 3.2 Create Conda Environment\nCreate and activate a new conda environment with Python 3.10:\n```bash\nconda create -n omnisvg python=3.10\nconda activate omnisvg\n```\n\n### 3.3 Install Dependencies\n\n#### System Dependencies\nBefore installing Python packages, you need to install Cairo library which is required by `CairoSVG` in our dependencies:\n\n**macOS:**\n```bash\nbrew install cairo\n```\n\n**Linux (Ubuntu\u002FDebian):**\n```bash\nsudo apt update\nsudo apt install libcairo2 libcairo2-dev\n```\n\n> **Note:** Installing Cairo system library beforehand helps prevent potential build errors when installing `CairoSVG` via pip.\n\n#### Python Dependencies\nWe have tested our environment with CUDA 12.1. You can install CUDA 12.1 by following the [CUDA Toolkit installation guide](https:\u002F\u002Fdeveloper.nvidia.com\u002Fcuda-12-1-0-download-archive).\n\nInstall PyTorch with CUDA 12.1 support:\n```bash\npip install torch==2.3.0+cu121 torchvision==0.18.0+cu121 --index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu121\n```\n\nInstall remaining dependencies:\n```bash\npip install -r requirements.txt\n```\n\n## 4. Inference Script\n\n|                                                  | GPU Memory Usage | Time per 256\u002F512\u002F1024\u002F2048\u002F4096 tokens |\n| ------------------------------------------------ | ---------------- | ----------------- |\n| OmniSVG1.1_8B     | 26G              | 5.38\u002F9.02\u002F20.11\u002F40.34\u002F98.11 seconds       |\n| OmniSVG1.1_4B     | 17G              | 4.08\u002F8.68\u002F18.07\u002F37.51\u002F82.70 seconds       |\n| OmniSVG-3B     | 17G              | 4.08\u002F8.68\u002F18.07\u002F37.51\u002F82.70 seconds       |\n\n\n\u003Cfont color=\"red\">**Note: The inference time shown here is measured per OmniSVG SVG tokens, while the inference time reported in our paper is measured per XML code tokens for fair comparison with baseline methods.**\u003C\u002Ffont> \n\n### Quick Start\n\n**Download Model Weights**\n\nFirst, install the Hugging Face CLI tool:\n```bash\npip install huggingface-hub\n```\n\n**Download the model from Hugging Face:**\n```bash\n# Download OmniSVG1.1-8B\nhuggingface-cli download OmniSVG\u002FOmniSVG1.1_8B --local-dir \u002FPATH\u002FTO\u002FOmniSVG1.1_8B\n\n# Download OmniSVG1.1-4B\nhuggingface-cli download OmniSVG\u002FOmniSVG1.1_4B --local-dir \u002FPATH\u002FTO\u002FOmniSVG1.1_4B\n\n# Download OmniSVG-3B (legacy)\nhuggingface-cli download OmniSVG\u002FOmniSVG --local-dir \u002FPATH\u002FTO\u002FOmniSVG-3B\n```\n\n### Text-to-SVG Generation\n\n**Basic usage - Generate SVG from txt file:**\n```bash\npython inference.py --task text-to-svg --input prompts.txt --output .\u002Foutput_text --save-all-candidates\n```\n\n**Use 4B model:**\n```bash\npython inference.py --task text-to-svg --input prompts.txt --output .\u002Foutput_text --model-size 4B --save-all-candidates\n```\n\n**Generate more candidates and save PNG:**\n```bash\npython inference.py --task text-to-svg --input prompts.txt --output .\u002Foutput_text \\\n    --num-candidates 8 --save-png --save-all-candidates\n```\n\n**Custom generation parameters:**\n```bash\npython inference.py --task text-to-svg --input prompts.txt --output .\u002Foutput_text \\\n    --temperature 0.5 --top-p 0.9 --top-k 50 --repetition-penalty 1.05\n```\n\n**Use local model:**\n```bash\npython inference.py --task text-to-svg --input prompts.txt --output .\u002Foutput_text \\\n    --model-path \u002Fpath\u002Fto\u002Fqwen --weight-path \u002Fpath\u002Fto\u002Fomnisvg\n```\n\n### Image-to-SVG Generation\n\n```bash\npython inference.py --task image-to-svg --input .\u002Fexamples --output .\u002Foutput_image --save-all-candidates\n```\n\n### Interactive Demo\n\nWe provide an interactive generation interface using Gradio:\n\n- **Local Deployment**\n  ```bash\n  python app.py\n  ```\n\n- **Online Demo**\n  \n  Try our live demo on [Hugging Face Spaces](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FOmniSVG\u002FOmniSVG-3B)\n\n\u003Cdiv align=\"center\">\n  \u003Cimg src=\"assets\u002Fcommands.png\" alt=\"cmd\" height=\"256px\" \u002F>\n  \u003Cimg src=\"assets\u002Fomnisvg-teaser.gif\" alt=\"Demo GIF\" height=\"256px\" style=\"margin-right: 10px;\" \u002F>\n\u003C\u002Fdiv>\n\n\n\n## 5. Evaluation\n\nWe provide **MMSVGBench** for standardized evaluation of SVG generation models.\n\n**Download MMSVGBench:**\n```bash\nhuggingface-cli download OmniSVG\u002FMMSVGBench --repo-type dataset --local-dir \u002FPATH\u002FTO\u002FMMSVGBench\n```\n\n### Benchmark Overview\n\nMMSVGBench is a **purely synthetic benchmark** where all prompts and images are generated using GPT models, ensuring the data is **unseen** during model training for fair generalization evaluation. The generation procedure MMSVGBench's prompt is logged, for example the [text2svg prompt log](https:\u002F\u002Fchatgpt.com\u002Fshare\u002F68f773e9-2814-8002-99ed-5e2980e9b9bf). \n\n| Task | Complexity Level | Samples | Description |\n|------|------------------|---------|-------------|\n| Text-to-SVG | Icon | 150 | Simple icons (1-2 elements) |\n| Text-to-SVG | Illustration | 150 | Complex illustrations (1-3 interacting elements) |\n| Image-to-SVG | Icon | 150 | GPT-4o generated icon images |\n| Image-to-SVG | Illustration | 150 | GPT-4o generated illustration images |\n\n**Key Advantages of Synthetic Design:**\n- ✅ **True generalization test** — models cannot have seen these samples during training\n- ✅ **Controlled diversity** — systematic coverage of styles and semantic categories  \n- ✅ **Fairness** — no model has unfair advantage from training data overlap\n\nThe evaluation code is available in the `metrics` directory. For more details about MMSVGBench construction and evaluation metrics, please check [MMSVGBench](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FOmniSVG\u002FMMSVGBench\u002Fblob\u002Fmain\u002FREADME.md).\n\n\n\n## 6. License\nOmniSVG is licensed under the [**Apache License 2.0**](https:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0), while MMSVG dataset is under [**Creative Commons Attribution Non Commercial Share Alike 4.0 License**](https:\u002F\u002Fspdx.org\u002Flicenses\u002FCC-BY-NC-SA-4.0). You can find the license files in the respective github and HuggingFace repositories.\n\n\n\n## Citation\n\n```bibtex\n@article{yang2025omnisvg,\n  title={OmniSVG: A Unified Scalable Vector Graphics Generation Model}, \n  author={Yiying Yang and Wei Cheng and Sijin Chen and Xianfang Zeng and Jiaxu Zhang and Liao Wang and Gang Yu and Xinjun Ma and Yu-Gang Jiang},\n  journal={arXiv preprint arxiv:2504.06263},\n  year={2025}\n}\n```\n\n## Acknowledgments\nWe thank the following excellent open-source works:\n\n[IconShop](https:\u002F\u002Ficon-shop.github.io\u002F): is the first advanced work that leverages LLMs to generate monochrome, icon-level SVGs. We referred to its parametric implementation.\n\nHere is the list of highly related concurrent works:\n\n[LLM4SVG](https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.11102): treats SVG coordinates as number strings and predicts decimal part for higher spatial accuracy.\n\n[StarVector](https:\u002F\u002Fstarvector.github.io\u002F): equips LLM with an image encoder for Image-to-SVG generation.\n\n## Star History\n\n\n[![Star History Chart](https:\u002F\u002Fapi.star-history.com\u002Fsvg?repos=OmniSVG\u002FOmniSVG&type=Date)](https:\u002F\u002Fwww.star-history.com\u002F#OmniSVG\u002FOmniSVG&Date)\n\n","OmniSVG 是一个端到端的多模态 SVG 生成器，利用预训练的视觉-语言模型（VLMs），能够从简单的图标到复杂的动漫角色生成复杂且详细的 SVG 图像。其核心功能包括基于文本描述生成高质量 SVG 矢量图形，并支持多种风格和细节级别的图像生成。该项目采用 Python 语言开发，通过深度学习技术实现高效、准确的图像生成。OmniSVG 适用于需要高质量矢量图形生成的各种场景，如设计工具、动画制作、网页开发等，尤其适合对图像质量和细节有高要求的应用。",2,"2026-06-11 03:42:11","high_star"]