Flow Matching Guide and Code
This is an incremental resource for researchers and practitioners to understand and apply Flow Matching in generative modeling.
The paper provides a comprehensive review and implementation of Flow Matching, a generative modeling framework that has achieved state-of-the-art performance in domains like image, video, audio, speech, and biological structures, by offering a guide and PyTorch package with examples.
Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and biological structures. This guide offers a comprehensive and self-contained review of FM, covering its mathematical foundations, design choices, and extensions. By also providing a PyTorch package featuring relevant examples (e.g., image and text generation), this work aims to serve as a resource for both novice and experienced researchers interested in understanding, applying and further developing FM.