OmniConsistency is a universal consistency enhancement plug-in based on the diffusion transformer released by the Show Lab of the National University of Singapore on May 28, 2025. OmniConsistency significantly improves visual coherence and aesthetic quality, achieving performance comparable to the most advanced commercial model GPT-4o. It fills the performance gap in style consistency between open source models and commercial models (such as GPT-4o), provides a low-cost, highly controllable solution for AI creation, and promotes the democratization of image generation technology. Its compatibility and plug-and-play features also lower the threshold for developers and creators to use it. The relevant paper results are "OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data".
The computing resources used in this tutorial are a single RTX A6000 card.
2. Effect display
3. Operation steps
1. Start the container
If "Bad Gateway" is displayed, it means the model is initializing. Since the model is large, please wait about 2-3 minutes and refresh the page.
2. Usage Examples
Once you enter the web page, you can interact with the model.
If you use Custom LoRA, the model needs time to be downloaded online, so it takes longer to generate. Please wait patiently. In addition, the model download may fail due to network problems during the model download process. It is recommended to restart the container and download the model again.
Generate results
4. Discussion
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Citation Information
Thanks to Github user SuperYang Deployment of this tutorial. The reference information of this project is as follows:
@inproceedings{Song2025OmniConsistencyLS,
title={OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data},
author={Yiren Song and Cheng Liu and Mike Zheng Shou},
year={2025},
url={https://api.semanticscholar.org/CorpusID:278905729}
}
