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| title: README | |
| emoji: 🏢 | |
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| <div align="center"> | |
| <img src="https://huggingface.co/spaces/diffusion-cot/README/resolve/main/208273488.png"/ width=400> | |
| </div> | |
| This organization holds the artifacts for our research conducted on enabling reasoning in diffusion-based image synthesis models. Our first | |
| effort in this line of research is **ReflectionFlow**, where we introduce the first ever large-scale dataset, **GenRef**, suitable for | |
| reflection-tuning. | |
| Below, we provide the links related to ReflectionFlow: | |
| * [ReflectionFlow paper](https://arxiv.org/abs/2504.16080) | |
| * [Projection website](https://diffusion-cot.github.io/reflection2perfection/) | |
| * [Models and datasets](https://huggingface.co/collections/diffusion-cot/reflectionflow-release-6803e14352b1b13a16aeda44) | |
| * [Code](https://github.com/Diffusion-CoT/ReflectionFlow) | |
| Citation | |
| ```bibtex | |
| misc{zhuo2025reflectionperfectionscalinginferencetime, | |
| title={From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning}, | |
| author={Le Zhuo and Liangbing Zhao and Sayak Paul and Yue Liao and Renrui Zhang and Yi Xin and Peng Gao and Mohamed Elhoseiny and Hongsheng Li}, | |
| year={2025}, | |
| eprint={2504.16080}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2504.16080}, | |
| } | |
| ``` | |
| Enjoy 🤗 |