RectifiedFlow is a unified PyTorch library for implementing Rectified Flow models, a type of generative model that learns ordinary differential equations to transform data between distributions. The library provides a modular framework for training various architectures (MLP, UNet, DiT, LightningDiT) on different datasets, with support for multiple sampling algorithms and interpolation schemes. Key features include a symbolic solver for affine interpolation equations, utilities for converting between different interpolation schemes while preserving rectified flow properties, and a flexible sampler base class for implementing custom integration methods. The codebase demonstrates strong software engineering practices with clear separation of components, comprehensive documentation through examples, and active maintenance.