Add task category and update license metadata

README.md

@@ -1,9 +1,11 @@
 ---
-license: mit
-datasets:
-- internlm/EndoCoT-Data
 language:
 - en
+license: cc-by-nc-4.0
+task_categories:
+- image-to-image
+datasets:
+- internlm/EndoCoT-Data
 base_model:
 - Qwen/Qwen-Image-Edit-2511
 ---
@@ -25,36 +27,21 @@ base_model:
   <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
 
-## 📝TODO
-
-- [x] Open source the training code
-- [ ] Open source the training data
-- [x] Open source the main task ckpt
-- [ ] Open source the edit model ckpt
-- [ ] Refactor the codebase for better usability and maintainability
-
-## 📰News
-
-- 🚀 [2026/3/12] We have released the EndoCoT [repository](https://github.com/InternLM/EndoCoT) and [ckpts](https://huggingface.co/internlm/EndoCoT).
-
-## 🌟Highlight
-
-![main](fig/main.jpg)
+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.
 
-- EndoCoT is a reasoning paradigm for diffusion models that enables step-by-step inference. It outperforms conventional training methods on Qwen-Image-Edit-2511.
+- **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)
 
-![exp](fig/exp.png)
+## 🌟 Highlights
 
-- And provide transparent, intermediate reasoning trajectories.
+- **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.
 
-![case](fig/case.jpg)
-
-## ⚡Quick Start
+## ⚡ Quick Start
 
 ### Setup environment
 
@@ -63,141 +50,51 @@ git clone https://github.com/InternLM/EndoCoT
 cd EndoCoT
 conda create -n EndoCoT python=3.10
 conda activate EndoCot
-# Please install the version of torch compatible with your machine.
 pip install -r requirements.txt
-# Please install the version of vLLM compatible with your machine.
 ```
 
-### Inference
-
-1. Download the ckpt:
-
-   - You may find our pretrained weights at: [**EndoCoT**](https://huggingface.co/InternLM/EndoCoT)
+### Sample Usage (Inference)
 
-   >  Following the configuration of *[**Diffthinker**](https://github.com/lcqysl/DiffThinker)*, we provide a customized checkpoint for **Qwen-Image-Edit**. This checkpoint has been merged from the original `safetensors` to ensure compatibility with*[**Diffsynth-Studio**](https://github.com/modelscope/DiffSynth-Studio)* training. Please use the checkpoint provided in this repository instead of the official version for correct loading and inference.
+To test a single case using the codebase:
 
-2. Test Single Case
-
-   ```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
-   ```
+```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
+```
 
-3. Eval Our Ckpt
+### Training
 
-   > We follow the exact same setting as *[**Diffthinker**](https://github.com/lcqysl/DiffThinker)*
+1. Download the datasets & `metadata.csv` and ensure they are placed in the same directory.
+2. Run the training scripts:
 
-   ```bash
-   cd Maze
-   bash eval/gen_and_parse.sh
-   bash eval/eval_path.sh
-   ```
+```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
+```
 
-### Training
+## 📰 News
 
-1. Download the datasets & metadata.csv
-
-   - You may find our training data at: [**EndoCoT dataset**](https://huggingface.co/datasets/InternLM/EndoCoT)
-
-   > Since the metadata uses relative paths, please ensure the dataset files are placed in the same directory as `metadata.csv`
-
-2. Train your model
-
-   ```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
-   ```
-
-### How to change the latent reasoning steps?
-
-> **Note on Customization:** Since the current implementation is straightforward, you can only manually adjust the latent reasoning steps in `DiffSynth-Studio/diffsynth/pipelines/qwen_image.py`:
->
-> - **Line 442:** Modify `infer_steps`.
-> - **Line 471:** Modify `training_steps`.
->
-> ##### **We plan to optimize this in future releases.**
-
-```python
-def encode_prompt_edit(self, pipe: QwenImagePipeline, prompt, edit_image, is_final, gt_prompt=None, idx=None):
-
-        drop_idx = 64
-        if type(prompt[0])==str:
-            template =  "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
-            txt = template.format(prompt[0])
-            model_inputs = pipe.processor(text=txt, images=edit_image, padding=True, return_tensors="pt").to(pipe.device)
-            embedding_layers = pipe.text_encoder.model.language_model.get_input_embeddings()
-            with torch.no_grad():
-                inputs_embeds = embedding_layers(model_inputs.input_ids)
-            self.attention_mask = model_inputs.attention_mask
-            self.pixel_values = model_inputs.pixel_values
-            self.image_grid_thw = model_inputs.image_grid_thw
-        else:
-            inputs_embeds= prompt[0]
-            
-        # dxl: test use
-        if is_final==None or idx!=None:
-            print("现在在inference。或者stage2训练")
-            if idx!=None:
-                iter_times = idx-2
-            else:
-                # infer step
-                iter_times = 50
-                
-            with torch.no_grad():
-                inputs_embeds = self.manual_generate_eval(
-                    pipe, 
-                    inputs_embeds=inputs_embeds,
-                    max_new_tokens=iter_times,
-                ).detach()
-            
-            # dxl: only update the last 2 tokens
-            if idx!=None:
-                inputs_embeds = self.manual_generate_eval(
-                    pipe,
-                    inputs_embeds=inputs_embeds,
-                    max_new_tokens=2,
-                )
-
-            generated_embeds = inputs_embeds
-
-		... ... 
-        
-        # dxl：training
-        if is_final!=None and idx==None:
-            try:
-                generated_embeds, _ = self.manual_generate(
-                    pipe,
-                    inputs_embeds=inputs_embeds,
-                    is_final=is_final,
-                    # training steps
-                    max_new_tokens=2,
-                )
-            except Exception as e:
-                print(f"Error!: {type(e).__name__} - {e}")
-                print(inputs_embeds.shape)
-                assert False
-
-        try: 
-            return split_hidden_states, generated_embeds, eos_loss
-        except:
-            print(f"[WARNING] Prompt was not updated correctly for inference.")
-            return split_hidden_states
-```
+- 🚀 [2026/3/12] We have released the EndoCoT [repository](https://github.com/InternLM/EndoCoT) and [ckpts](https://huggingface.co/internlm/EndoCoT).
 
 ## 📖 Citation
 
 ```
-Coming Soon
+@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
 
-![Code License](https://img.shields.io/badge/Code%20License-MIT-green.svg) ![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg) 
+The code in the associated repository is licensed under the **MIT License**. The dataset is licensed under the **CC BY-NC 4.0 License**.