Improve model card for DiffThinker: Add metadata, links, and usage details (#1)

- Improve model card for DiffThinker: Add metadata, links, and usage details (938263e73b8c21b82d1875067db6287304281cd5)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,17 +1,45 @@
 ---
-license: apache-2.0
-language:
-- en
 base_model:
 - Qwen/Qwen-Image-Edit-2509
+language:
+- en
+license: apache-2.0
+library_name: diffusers
+pipeline_tag: image-to-image
 ---
+
 # DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
+
 <a href="https://diffthinker-project.github.io/"><img src="https://img.shields.io/badge/%F0%9F%8C%90%20Project-Page-2563eb" alt="Project Page"></a>
-<br>
-This model is based on the paper: https://arxiv.org/abs/2512.24165
+<a href="https://github.com/lcqysl/DiffThinker"><img src="https://img.shields.io/badge/GitHub-Code-blue?logo=github" alt="GitHub"></a>
+<a href="https://huggingface.co/papers/2512.24165"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b" alt="Paper"></a>
+
+DiffThinker introduces a novel Generative Multimodal Reasoning paradigm, establishing a diffusion-based reasoning framework. It reformulates multimodal reasoning as a native generative image-to-image task, achieving superior logical consistency and spatial precision in vision-centric tasks compared to traditional text-centric Multimodal Large Language Models (MLLMs).
+
+### Features
+DiffThinker exhibits four core properties in its approach to vision-centric reasoning:
+-   **Efficiency**: Streamlined reasoning process.
+-   **Controllability**: Precise spatial and logical generation.
+-   **Native Parallelism**: Advantageous for complex reasoning steps.
+-   **Collaboration**: Works effectively across multiple domains (sequential planning, combinatorial optimization, constraint satisfaction, and spatial configuration).
+
+### Quick Start
+To get started with DiffThinker, clone the official repository and install the necessary dependencies:
+```bash
+git clone https://github.com/lcqysl/DiffThinker.git
+cd DiffThinker/DiffSynth-Studio
+pip install -e .
+pip install gymnasium
+
+# (Optional) Install vLLM for OCR tasks
+# we recommend installing it in a SEPARATE environment to avoid conflicts.
+# pip install vllm
+```
+
 ### Inference & Evaluation
-The test datasets used in our experiments is provided within each task's directory. We recommend using the same data to ensure the reproducibility of our results and to facilitate comparison with other models. If you wish to generate your own test data, please refer to the ```gen.txt``` file in each task directory.
-```code
+The test datasets used in our experiments are provided within each task's directory. We recommend using the same data to ensure the reproducibility of our results and to facilitate comparison with other models. If you wish to generate your own test data, please refer to the `gen.txt` file in each task directory.
+
+```bash
 cd Maze
 
 # 1. Inference and Parsing
@@ -23,4 +51,14 @@ bash eval/eval_path.sh
 # 3. Individual Inference
 python ../DiffSynth-Studio/add/infer/infer.py
 python ../DiffSynth-Studio/add/infer/infer_with_middle.py
+```
+
+### Citation
+```bibtex
+@article{he2024diffthinker,
+  title={DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models},
+  author={He, Zefeng and Qu, Xiaoye and Li, Yafu and Zhu, Tong and Huang, Siyuan and Cheng, Yu},
+  journal={arXiv preprint arXiv:2512.24165},
+  year={2024}
+}
 ```