Flow Generator Matching

• URL: http://arxiv.org/abs/2410.19310v1
• Date: Fri, 25 Oct 2024 05:41:28 GMT
• Title: Flow Generator Matching
• Authors: Zemin Huang, Zhengyang Geng, Weijian Luo, Guo-jun Qi,
• Abstract summary: Flow Generator Matching (FGM) is designed to accelerate the sampling of flow-matching models into a one-step generation. On the CIFAR10 unconditional generation benchmark, our one-step FGM model achieves a new record Fr'echet Inception Distance (FID) score of 3.08. MM-DiT-FGM one-step text-to-image model demonstrates outstanding industry-level performance.
• Score: 35.371071097381346
• License:
• Abstract: In the realm of Artificial Intelligence Generated Content (AIGC), flow-matching models have emerged as a powerhouse, achieving success due to their robust theoretical underpinnings and solid ability for large-scale generative modeling. These models have demonstrated state-of-the-art performance, but their brilliance comes at a cost. The process of sampling from these models is notoriously demanding on computational resources, as it necessitates the use of multi-step numerical ordinary differential equations (ODEs). Against this backdrop, this paper presents a novel solution with theoretical guarantees in the form of Flow Generator Matching (FGM), an innovative approach designed to accelerate the sampling of flow-matching models into a one-step generation, while maintaining the original performance. On the CIFAR10 unconditional generation benchmark, our one-step FGM model achieves a new record Fr\'echet Inception Distance (FID) score of 3.08 among few-step flow-matching-based models, outperforming original 50-step flow-matching models. Furthermore, we use the FGM to distill the Stable Diffusion 3, a leading text-to-image flow-matching model based on the MM-DiT architecture. The resulting MM-DiT-FGM one-step text-to-image model demonstrates outstanding industry-level performance. When evaluated on the GenEval benchmark, MM-DiT-FGM has delivered remarkable generating qualities, rivaling other multi-step models in light of the efficiency of a single generation step.

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