---
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.