Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image Generation

• URL: http://arxiv.org/abs/2412.16906v2
• Date: Tue, 25 Mar 2025 03:47:02 GMT
• Title: Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image Generation
• Authors: Quan Dao, Hao Phung, Trung Dao, Dimitris Metaxas, Anh Tran,
• Abstract summary: Flow matching has emerged as a promising framework for training generative models.<n>We introduce a self-corrected flow distillation method that integrates consistency models and adversarial training.<n>This work is a pioneer in achieving consistent generation quality in both few-step and one-step sampling.
• Score: 3.8959351616076745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 at https://github.com/VinAIResearch/SCFlow

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