Improve dataset card: Add metadata, paper link, code link, and sample usage

README.md

@@ -1 +1,49 @@
-arxiv.org/abs/2509.04394
+---
+task_categories:
+- text-to-image
+license: apache-2.0
+language:
+- en
+tags:
+- diffusion-model
+- generative-ai
+- image-generation
+- toy-dataset
+---
+
+# TiM-Toy-T2I-Dataset
+
+This repository contains the toy dataset for text-to-image (T2I) generation used in the paper [Transition Models: Rethinking the Generative Learning Objective](https://huggingface.co/papers/2509.04394).
+
+Transition Models (TiM) introduce an exact, continuous-time dynamics equation that analytically defines state transitions across any finite time interval. This leads to a novel generative paradigm, TiM, which adapts to arbitrary-step transitions, seamlessly traversing the generative trajectory from single leaps to fine-grained refinement with more steps. This specific dataset serves as a toy dataset for training and experimentation with TiM for text-to-image generation.
+
+Code: [https://github.com/WZDTHU/TiM](https://github.com/WZDTHU/TiM)
+
+## Sample Usage
+
+To download this toy text-to-image dataset for training TiM models, you can follow the instructions from the official GitHub repository under the "Dataset Setup" section:
+
+```bash
+bash tools/download_toy_t2i_dataset.sh
+```
+
+For more details on setting up the environment, downloading models, and other training/sampling scripts, please refer to the [official GitHub repository](https://github.com/WZDTHU/TiM).
+
+## Citation
+
+If you find the project useful, please kindly cite:
+
+```bibtex
+@article{wang2025transition,
+  title={Transition Models: Rethinking the Generative Learning Objective}, 
+  author={Wang, Zidong and Zhang, Yiyuan and Yue, Xiaoyu and Yue, Xiangyu and Li, Yangguang and Ouyang, Wanli and Bai, Lei},
+  year={2025},
+  eprint={2509.04394},
+  archivePrefix={arXiv},
+  primaryClass={cs.LG}
+}
+```
+
+## License
+
+This project is licensed under the Apache-2.0 license.