TwinFlow

KO | EN

gitlite — search

#hacktoberfest #react #typescript #android #machine-learning #docker #macos #nodejs #windows #cli #pytorch #security #ios #mcp

(★ 537)

[ICLR 2026] Taming large-scale few-step training with self-adversarial flows! 👏🏻

TwinFlow Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<h1 align="center"><sub><sup>TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows</sup></sub></h1>

<p align="center">
  <a href="https://zhenglin-cheng.com/" target="_blank">Zhenglin&nbsp;Cheng</a><sup>*</sup> &ensp; <b>&middot;</b> &ensp;
  <a href="https://scholar.google.com/citations?user=-8XvRRIAAAAJ" target="_blank">Peng&nbsp;Sun</a><sup>*</sup> &ensp; <b>&middot;</b> &ensp;
  <a href="https://sites.google.com/site/leeplus/" target="_blank">Jianguo&nbsp;Li</a> &ensp; <b>&middot;</b> &ensp;
  <a href="https://lins-lab.github.io/" target="_blank">Tao&nbsp;Lin</a>
</p>

[![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;
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Model-TwinFlow--Z--Image--Turbo--exp-yellow)](https://huggingface.co/inclusionAI/TwinFlow-Z-Image-Turbo)&#160;
[![Github Repo](https://img.shields.io/badge/inclusionAI%2FTwinFlow-black?logo=github)](https://github.com/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>
<a href="https://deepwiki.com/inclusionAI/TwinFlow"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>&#160;
[![WeChat](https://img.shields.io/badge/WeChat-green?logo=wechat&amp&color=white)](https://raw.githubusercontent.com/inclusionAI/TwinFlow/main/assets/wechat.png)

</div>

**Join the WeChat Group, feel free to reach out anytime if you have any questions!👇**

<p>👇 WeChat Group QR Code/微信群二维码 👇</p>

- Technical Discussion Group 1 is full, please join Technical Discussion Group 2

| Technical Discussion Group/技术讨论群 | Model Users Discussion Group/AIGC模型使用讨论群 |
|----------------------------|------------------------------------------|
| <img src="./assets/wechat.png" style="width: 70%;" /> | <img src="./assets/wechat2.png" style="width: 70%;" /> |

</details>

## 🧭 Table of Contents

- [🔥 Codebase Usage 🔥](src/README.md)
- [Inference Demo](#inference-demo)
- [Tutorials on MNIST](tutorials/README.md)

## 📰 News

- TwinFlow is accepted to ICLR 2026, see you in 🇧🇷!
- Thanks to [@mengqin](https://github.com/mengqin) for adapting more compatible [TwinFlow-models workflows in ComfyUI](https://github.com/mengqin/ComfyUI-TwinFlow)! 👏🏻
- Thanks to [@smthemex](https://github.com/smthemex) for adapting [TwinFlow-models workflows in ComfyUI](https://github.com/smthemex/ComfyUI_TwinFlow)! 👏🏻
- We release experimental version of faster Z-Image-Turbo!
- We release training code and better TwinFlow implementation on SD3.5 and OpenUni under `src` directory 👏🏻.
- 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

![](https://raw.githubusercontent.com/inclusionAI/TwinFlow/main/assets/twinflow_feats.jpg)

## 💪 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:

```bash
pip install git+https://github.com/huggingface/diffusers
```

Run inference demo `inference.py`:

```python
python inference.py
```

We recommend to sample for 2~4 NFEs:

```python
# 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

```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}
}

@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](https://github.com/LINs-lab/RCGM) and [UCGM](https://github.com/LINs-lab/UCGM), with much support from [InclusionAI](https://github.com/inclusionAI).

Note: The [LINs Lab](https://lins-lab.github.io/) has openings for PhD students for the Fall 2026/2027 intake. Interested candidates are encouraged to reach out.

TwinFlow

TwinFlow Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

TwinFlow

TwinFlow Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README