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--- |
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base_model: |
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- Qwen/Qwen-Image |
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pipeline_tag: text-to-image |
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library_name: diffusers |
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license: apache-2.0 |
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--- |
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<h1 align="center">TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows</h1> |
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<div align="center"> |
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[](https://zhenglin-cheng.com/twinflow)  |
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[](https://huggingface.co/inclusionAI/TwinFlow)  |
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[](https://github.com/inclusionAI/TwinFlow)  |
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<a href="https://arxiv.org/abs/2512.05150" target="_blank"><img src="https://img.shields.io/badge/Paper-b5212f.svg?logo=arxiv" height="21px"></a> |
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</div> |
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## News |
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- We release **TwinFlow-Qwen-Image-v1.0**! And we are also working on **Z-Image-Turbo to make it more faster**! |
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## TwinFlow |
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Checkout 2-NFE visualization of TwinFlow-Qwen-Image 👇 |
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### Overview |
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We introduce TwinFlow, a framework that realizes high-quality 1-step and few-step generation without the pipeline bloat. |
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Instead of relying on external discriminators or frozen teachers, TwinFlow creates an internal "twin trajectory". By extending the time interval to $t\in[−1,1]$, we utilize the negative time branch to map noise to "fake" data, creating a self-adversarial signal directly within the model. |
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Then, the model can rectify itself by minimizing the difference of the velocity fields between real trajectory and fake trajectory, i.e. the $\Delta_\mathrm{v}$. The rectification performs distribution matching as velocity matching, which gradually transforms the model into a 1-step/few-step generator. |
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Key Advantages: |
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- **One-model Simplicity.** We eliminate the need for any auxiliary networks. The model learns to rectify its own flow field, acting as the generator, fake/real score. No extra GPU memory is wasted on frozen teachers or discriminators during training. |
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- **Scalability on Large Models.** TwinFlow is **easy to scale on 20B full-parameter training** due to the one-model simplicity. In contrast, methods like VSD, SiD, and DMD/DMD2 require maintaining three separate models for distillation, which not only significantly increases memory consumption—often leading OOM, but also introduces substantial complexity when scaling to large-scale training regimes. |
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### Inference Demo |
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Install the latest diffusers: |
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```bash |
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pip install git+https://github.com/huggingface/diffusers |
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``` |
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Run inference demo `inference.py`: |
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```python |
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python inference.py |
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``` |
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## Citation |
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```bibtex |
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@article{cheng2025twinflow, |
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title={TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows}, |
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author={Cheng, Zhenglin and Sun, Peng and Li, Jianguo and Lin, Tao}, |
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journal={arXiv preprint arXiv:2512.05150}, |
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year={2025} |
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} |
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``` |
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## Acknowledgement |
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TwinFlow is built upon [RCGM](https://github.com/LINs-lab/RCGM) and [UCGM](https://github.com/LINs-lab/UCGM), with much support from [InclusionAI](https://github.com/inclusionAI). |
