Improve model card for Transition Models (TiM)

README.md

@@ -1 +1,58 @@
-arxiv.org/abs/2509.04394
+---
+license: apache-2.0
+pipeline_tag: text-to-image
+---
+
+# Transition Models: Rethinking the Generative Learning Objective
+
+This repository contains the official implementation of **Transition Models (TiM)**, a novel generative model presented in the paper "[Transition Models: Rethinking the Generative Learning Objective](https://huggingface.co/papers/2509.04394)".
+
+TiM addresses the dilemma in generative modeling by introducing an exact, continuous-time dynamics equation that analytically defines state transitions across any finite time interval. This enables a novel generative paradigm that adapts to arbitrary-step transitions, seamlessly traversing the generative trajectory from single leaps to fine-grained refinement with more steps.
+
+For more detailed information, code, and usage instructions, please refer to the official [GitHub repository](https://github.com/WZDTHU/TiM).
+
+## Highlights
+
+*   **Arbitrary-Step Generation**: TiM learns to master arbitrary state-to-state transitions, unifying few-step and many-step regimes within a single, powerful model. This approach allows it to learn the entire solution manifold of the generative process.
+*   **State-of-the-Art Performance**: Despite having only 865M parameters, TiM achieves state-of-the-art performance, surpassing leading models such as SD3.5 (8B parameters) and FLUX.1 (12B parameters) across all evaluated step counts on the GenEval benchmark.
+*   **Monotonic Quality Improvement**: Unlike previous few-step generators, TiM demonstrates consistent quality improvement as the sampling budget increases.
+*   **High-Resolution Fidelity**: When employing its native-resolution strategy, TiM delivers exceptional fidelity at resolutions up to 4096x4096.
+
+<p align="center">
+  <img src="https://github.com/WZDTHU/TiM/raw/main/assets/illustration.png" width="800" alt="TiM Illustration">
+</p>
+
+## Model Zoo
+
+A single TiM model can perform any-step generation (one-step, few-step, and multi-step) and demonstrate monotonic quality improvement as the sampling budget increases.
+
+### Text-to-Image Generation
+
+| Model   | Model Size | VAE                                                                    | 1-NFE GenEval | 8-NFE GenEval | 128-NFE GenEval |
+|---------|------------|------------------------------------------------------------------------|---------------|---------------|-----------------|
+| TiM-T2I | 865M       | [DC-AE](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers) | 0.67          | 0.76          | 0.83            |
+
+### Class-guided Image Generation
+
+| Model     | Model Size | VAE                                                                    | 2-NFE FID | 500-NFE FID |
+|-----------|------------|------------------------------------------------------------------------|-----------|-------------|
+| TiM-C2I-256 | 664M       | [SD-VAE](https://huggingface.co/stabilityai/sd-vae-ft-ema)             | 6.14      | 1.65        |
+| TiM-C2I-512 | 664M       | [DC-AE](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers) | 4.79      | 1.69        |
+
+## Citation
+
+If you find this 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.