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README.md
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</p>
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## 📖 Introduction
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**UniPic2-Metaquery-9B** is an unified multimodal model built on Qwen2.5-VL-Instruct and SD3.5-Medium. It delivers end-to-end image understanding, text-to-image (T2I) generation, and image editing, and runs smoothly on a single 16 GB consumer GPU.
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<div align="center"> <img src="teaser.png" alt="Model Teaser" width="720"> </div>
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## 📊 Benchmarks
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**UniPic2-Metaquery-9B** w/o GRPO achieves competitive results across a variety of vision-language tasks:
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| ✂️ **GEditBench-EN** | 6.90 |
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| 🧪 **ImgEdit-Bench** | 4.10 |
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## 🧠 Usage
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edited_image.save("image_editing.png")
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```
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## 📄 License
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This model is released under the MIT License.
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## Citation
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If you use Skywork-UniPic in your research, please cite:
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```
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</p>
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## 📖 Introduction
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**UniPic2-Metaquery-9B** is an unified multimodal model built on Qwen2.5-VL-Instruct and SD3.5-Medium. It delivers end-to-end image understanding, text-to-image (T2I) generation, and image editing, and runs smoothly on a single 16 GB consumer GPU.
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<div align="center"> <img src="teaser.png" alt="Model Teaser" width="720"> </div>
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## 📊 Benchmarks
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**UniPic2-Metaquery-9B** w/o GRPO achieves competitive results across a variety of vision-language tasks:
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| ✂️ **GEditBench-EN** | 6.90 |
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| 🧪 **ImgEdit-Bench** | 4.10 |
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---
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## 🧠 Usage
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edited_image.save("image_editing.png")
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```
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## 📄 License
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This model is released under the MIT License.
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## Citation
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If you use Skywork-UniPic in your research, please cite:
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```
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