Rectified Flow

Rectified flow is a novel approach to generative modeling and data translation that leverages ordinary differential equations (ODEs) to efficiently transport probability distributions. Current research focuses on refining training methods for rectified flow models, developing efficient algorithms like Schrödinger Bridge Flow, and applying the framework to diverse tasks such as image restoration, enhancement, and synthesis, as well as audio and text generation. This approach offers significant advantages in terms of speed and efficiency compared to traditional methods, particularly for high-dimensional data, making it a promising tool for various applications across multiple scientific domains.