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license: apache-2.0
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<h1 align="center">Seg2Any: Open-set Segmentation-Mask-to-Image Generation with Precise Shape and Semantic Control</h1>
<div align="center">
<!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
<a href='https://seg2any.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a href='https://arxiv.org/abs/2506.00596'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
<a href='https://github.com/0xLDF/Seg2Any'><img src='https://img.shields.io/badge/⭐_GitHub-Code-blue' alt='GitHub'></a>
<a href="https://huggingface.co/datasets/0xLDF/SACap-1M"><img src="https://img.shields.io/badge/🤗_HuggingFace-Dataset-ffbd45.svg" alt="HuggingFace"></a>
<a href="https://huggingface.co/datasets/0xLDF/SACap-eval"><img src="https://img.shields.io/badge/🤗_HuggingFace-Benchmark-ffbd45.svg" alt="HuggingFace"></a>
</div>
We release model weights trained on three distinct datasets: ADE20K, COCO-Stuff, and SACap-1M. The SACap-1M version is the most popular, offering fine-grained regional text prompts.
For detailed usage instructions, please refer to the [GitHub](https://github.com/0xLDF/Seg2Any).
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<p align="center">
<img src="assets/demo.png" width="90%" height="90%">
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