TwinFlow
[ICLR 2026] Taming large-scale few-step training with self-adversarial flows! 👏🏻
Install / Use
/learn @inclusionAI/TwinFlowREADME
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</details>🧭 Table of Contents
📰 News
- TwinFlow is accepted to ICLR 2026, see you in 🇧🇷!
- Thanks to @mengqin for adapting more compatible TwinFlow-models workflows in ComfyUI! 👏🏻
- Thanks to @smthemex for adapting TwinFlow-models workflows in ComfyUI! 👏🏻
- We release experimental version of faster Z-Image-Turbo!
- We release training code and better TwinFlow implementation on SD3.5 and OpenUni under
srcdirectory 👏🏻. - We release tutorials on MNIST to provide core implementation of TwinFlow!
- We release TwinFlow-Qwen-Image-v1.0! And we are also working on Z-Image-Turbo to make it faster!
⚙️ Key Features
💪 Open-source Plans
- [x] Release inference and sampler code for TwinFlow-Qwen-Image-v1.0.
- [x] Release training tutorials on MNIST for understanding.
- [x] Release training code on SD3.5 and OpenUni.
- [x] Release faster experimental version of Z-Image-Turbo.
- [ ] Release large-scale training code.
TwinFlow
TwinFlow-Z-Image-Turbo-exp Visualization
<div align="center"> <img src="assets/twinflow_z_2step.jpg" width="1000" /> <p style="margin-top: 8px; font-size: 14px; color: #666; font-weight: bold;"> 2-NFE visualization of TwinFlow-Z-Image-Turbo-exp </p> </div> <details> <summary>👀 Original Z-Image-Turbo 2-NFE</summary> <div align="center"> <img src="assets/z_turbo_2step.jpg" width="1000" /> <p style="margin-top: 8px; font-size: 14px; color: #666; font-weight: bold;"> 2-NFE visualization of Z-Image-Turbo </p> </div> </details>TwinFlow-Qwen-Image Visualization
<div align="center"> <img src="assets/demo.jpg" width="1000" /> <p style="margin-top: 8px; font-size: 14px; color: #666; font-weight: bold;"> 2-NFE visualization of TwinFlow-Qwen-Image </p> </div>Comparison with Qwen-Image and Qwen-Image-Lightning
<div align="center"> <img src="assets/case1.jpg" width="1000" /> <p style="margin-top: 16px; font-size: 14px; color: #666; font-weight: bold; max-width: 1000px;"> Case 1: 万里长城秋景，蜿蜒盘踞于层峦叠嶂的山脉之上，砖石城墙与烽火台在暖阳下呈现古朴的土黄色，山间枫叶如火般绚烂，游客点缀其间，远山薄雾缭绕，天空湛蓝飘着几朵白云，高角度全景构图，细节丰富，光影柔和。 </p> </div><div align="center"> <img src="assets/case2.jpg" width="1000" /> <p style="margin-top: 16px; font-size: 14px; color: #666; font-weight: bold; max-width: 1000px;"> Case2: 超高清壁纸, 梦幻光影, 少女在元宵灯会中回眸一笑, 提着一盏兔子花灯, 周围挂满明亮的灯笼, 暖色调灯光映照在脸上, 华丽的唐装, 繁复的头饰, 热闹的背景虚化, 焦外光斑美丽, 中景镜头。<br> Same prompt but different noise (left to right). Top to bottom shown are: Qwen-Image (50×2 NFE), TwinFlow-Qwen-Image (1-NFE), and Qwen-Image-Lightning-v2.0 (1-NFE).<br>TwinFlow-Qwen-Image generates high-quality images at 1-NFE while preserving strong diversity. </p> </div>
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.
<div align="center"> <img src="assets/twinflow.png" alt="TwinFlow method overview" width="500" /> <p style="margin-top: 8px; font-size: 14px; color: #666; font-weight: bold;"> TwinFlow method overview </p> </div>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
For ComfyUI users, please see https://github.com/smthemex/ComfyUI_TwinFlow.
Install the latest diffusers:
pip install git+https://github.com/huggingface/diffusers
Run inference demo inference.py:
python inference.py
We recommend to sample for 2~4 NFEs:
# 4 NFE config
sampler_config = {
"sampling_steps": 4,
"stochast_ratio": 1.0,
"extrapol_ratio": 0.0,
"sampling_order": 1,
"time_dist_ctrl": [1.0, 1.0, 1.0],
"rfba_gap_steps": [0.001, 0.5],
}
# 2 NFE config
sampler_config = {
"sampling_steps": 2,
"stochast_ratio": 1.0,
"extrapol_ratio": 0.0,
"sampling_order": 1,
"time_dist_ctrl": [1.0, 1.0, 1.0],
"rfba_gap_steps": [0.001, 0.6],
}
📖 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}
}
@misc{sun2025anystep,
author = {Sun, Peng and Lin, Tao},
note = {GitHub repository},
title = {Any-step Generation via N-th Order Recursive Consistent Velocity Field Estimation},
url = {https://github.com/LINs-lab/RCGM},
year = {2025}
}
@article{sun2025unified,
title = {Unified continuous generative models},
author = {Sun, Peng and Jiang, Yi and Lin, Tao},
journal = {arXiv preprint arXiv:2505.07447},
year = {2025},
url = {https://arxiv.org/abs/2505.07447},
archiveprefix = {arXiv},
eprint = {2505.07447},
primaryclass = {cs.LG}
}
🤗 Acknowledgement
TwinFlow is built upon RCGM and UCGM, with much support from InclusionAI.
Note: The LINs Lab has openings for PhD students for the Fall 2026/2027 intake. Interested candidates are encouraged to reach out.
