Flow matching has emerged as a promising framework for training generative models, demonstrating impressive empirical performance while offering relative ease of training compared to diffusion-based models. However, this method still requires numerous function evaluations in the sampling process. To address these limitations, we introduce a self-corrected flow distillation method that effectively integrates consistency models and adversarial training within the flow-matching framework. This work is a pioneer in achieving consistent generation quality in both few-step and one-step sampling. Our extensive experiments validate the effectiveness of our method, yielding superior results both quantitatively and qualitatively on CelebA-HQ and zero-shot benchmarks on the COCO dataset. Our implementation is released atthis https URL
View on arXiv@article{dao2025_2412.16906,
title={ Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image Generation },
author={ Quan Dao and Hao Phung and Trung Dao and Dimitris Metaxas and Anh Tran },
journal={arXiv preprint arXiv:2412.16906},
year={ 2025 }
}