metadata

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".

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.

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.

TiM Illustration

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	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	6.14	1.65
TiM-C2I-512	664M	DC-AE	4.79	1.69

Citation

If you find this project useful, please kindly cite:

@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.