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+---
+base_model:
+- Qwen/Qwen-Image
+---
+
+<h1 align="center">TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows</h1>
+
+<div align="center">
+
+[![Project Page](https://img.shields.io/badge/Project%20Page-133399.svg?logo=homepage)](https://zhenglin-cheng.com/twinflow)&#160;
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Model-TwinFlow--Qwen--Image-yellow)](https://huggingface.co/inclusionAI/TwinFlow)&#160;
+<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>
+
+
+</div>
+
+## News
+
+- We release **TwinFlow-Qwen-Image-v1.0**! And we are also working on **Z-Image-Turbo to make it more faster**!
+
+## TwinFlow
+
+Checkout 2-NFE visualization of TwinFlow-Qwen-Image 👇
+
+![](https://raw.githubusercontent.com/inclusionAI/TwinFlow/refs/heads/main/assets/demo.jpg)
+
+
+### Overview
+
+We introduce TwinFlow, a framework that realizes high-quality 1-step and few-step generation without the pipeline bloat.
+
+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.
+
+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.
+
+![](https://raw.githubusercontent.com/inclusionAI/TwinFlow/refs/heads/main/assets/twinflow.png)
+
+Key Advantages:
+- **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.
+- **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.
+
+### Inference Demo
+
+Install the latest diffusers:
+
+```bash
+pip install git+https://github.com/huggingface/diffusers
+```
+
+Run inference demo `inference.py`:
+
+```python
+python inference.py
+```
+
+## Citation
+
+```bibtex
+@article{cheng2025twinflow,
+  title={TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows},
+  author={Cheng, Zhenglin and Sun, Peng and Li, Jianguo and Lin, Tao},
+  journal={arXiv preprint arXiv:2512.05150},
+  year={2025}
+}
+```
+
+## Acknowledgement
+
+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).