| 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. |