News
- We release experimental version of faster Z-Image-Turbo!
- 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 👇
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.
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:
pip install git+https://github.com/huggingface/diffusers
Run inference demo inference.py:
python inference.py
Citation
@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 and UCGM, with much support from InclusionAI.