| | --- |
| | language: |
| | - en |
| | license: cc-by-nc-4.0 |
| | task_categories: |
| | - image-to-image |
| | datasets: |
| | - internlm/EndoCoT-Data |
| | base_model: |
| | - Qwen/Qwen-Image-Edit-2511 |
| | --- |
| | |
| | <p align="center"> <img src="fig/banner.svg" alt="EndoCoT" width="900"/> </p> |
| |
|
| | <p align="center"> |
| | <a href="https://github.com/InternLM/EndoCoT"><img src="https://img.shields.io/github/stars/InternLM/EndoCoT?style=flat-square&logo=github&label=Stars&color=FFB300"></a> |
| | <a href="https://github.com/InternLM/EndoCoT/forks"><img src="https://img.shields.io/github/forks/InternLM/EndoCoT?style=flat-square&logo=github&label=Forks&color=2196F3"></a> |
| | <a href="https://github.com/InternLM/EndoCoT/issues"><img src="https://img.shields.io/github/issues/InternLM/EndoCoT?style=flat-square&logo=github&label=Issues&color=4CAF50"></a> |
| | <a href="https://github.com/InternLM/EndoCoT/blob/main/LICENSE"><img src="https://img.shields.io/github/license/InternLM/EndoCoT?style=flat-square&label=License&color=9C27B0"></a> |
| | <br> |
| | <a href="https://arxiv.org/abs/2603.12252"><img src="https://img.shields.io/badge/Paper-arXiv-B31B1B?style=flat-square"></a> |
| | <a href="https://internlm.github.io/EndoCoT/"><img src="https://img.shields.io/badge/Homepage-Project-blue?style=flat-square"></a> |
| | <a href="https://huggingface.co/internlm/EndoCoT"><img src="https://img.shields.io/badge/Model-HuggingFace-yellow?style=flat-square"></a> |
| | <a href="https://huggingface.co/datasets/internlm/EndoCoT-Data"><img src="https://img.shields.io/badge/Dataset-HuggingFace-orange?style=flat-square"></a> |
| | <br> |
| | <br> |
| | <img src="fig/teaser.jpg" alt="Teaser" width="100%" style="border-radius: 10px; box-shadow: 0 6px 20px rgba(0,0,0,0.2);"> |
| | </p> |
| |
|
| | # EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models |
| |
|
| | This repository contains the training data for **EndoCoT**, a novel framework that activates the reasoning potential of Multimodal Large Language Models (MLLMs) within diffusion frameworks through an iterative thought guidance module. |
| |
|
| | - **Paper:** [EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models](https://arxiv.org/abs/2603.12252) |
| | - **Project Page:** [https://internlm.github.io/EndoCoT/](https://internlm.github.io/EndoCoT/) |
| | - **Repository:** [https://github.com/InternLM/EndoCoT](https://github.com/InternLM/EndoCoT) |
| |
|
| | ## 🌟 Highlights |
| |
|
| | - **EndoCoT** is a reasoning paradigm for diffusion models that enables step-by-step inference. |
| | - It outperforms conventional training methods on complex tasks like Maze, TSP, VSP, and Sudoku. |
| | - Provides transparent, intermediate reasoning trajectories. |
| |
|
| | ## ⚡ Quick Start |
| |
|
| | ### Setup environment |
| |
|
| | ```bash |
| | git clone https://github.com/InternLM/EndoCoT |
| | cd EndoCoT |
| | conda create -n EndoCoT python=3.10 |
| | conda activate EndoCot |
| | pip install -r requirements.txt |
| | ``` |
| |
|
| | ### Sample Usage (Inference) |
| |
|
| | To test a single case using the codebase: |
| |
|
| | ```bash |
| | cd test |
| | python test.py \ |
| | --task Maze \ |
| | --model_root /path/to/merged_ckpts \ |
| | --lora_path /path/to/your_lora_weight.safetensors \ |
| | --input_image ./data/sudoku_sample.png \ |
| | --output_dir ./outputs/sudoku_results |
| | ``` |
| |
|
| | ### Training |
| |
|
| | 1. Download the datasets & `metadata.csv` and ensure they are placed in the same directory. |
| | 2. Run the training scripts: |
| |
|
| | ```bash |
| | cd DiffSynth-Studio |
| | bash add/Maze/stage1.sh |
| | python change_ckpt_prefix.py --src /path/to/the/Maze/save/dir/Maze_stage1 |
| | bash add/Maze/stage2.sh |
| | python change_ckpt_prefix.py --src /path/to/the/Maze/save/dir/Maze_stage2 |
| | ``` |
| |
|
| | ## 📰 News |
| |
|
| | - 🚀 [2026/3/12] We have released the EndoCoT [repository](https://github.com/InternLM/EndoCoT) and [ckpts](https://huggingface.co/internlm/EndoCoT). |
| |
|
| | ## 📖 Citation |
| |
|
| | ``` |
| | @article{dai2026endocot, |
| | title={EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models}, |
| | author={Dai, Xuanlang and Zhou, Yujie and Xing, Long and Bu, Jiazi and Wei, Xilin and Liu, Yuhong and Zhang, Beichen and Chen, Kai and Zang, Yuhang}, |
| | journal={arXiv preprint arXiv:2603.12252}, |
| | year={2026} |
| | } |
| | ``` |
| |
|
| | ## ⚖️ License |
| |
|
| | The code in the associated repository is licensed under the **MIT License**. The dataset is licensed under the **CC BY-NC 4.0 License**. |