  Diffusion models (DM) have become fundamental components of generative
models, excelling across various domains such as image creation, audio
generation, and complex data interpolation. Signal-to-Noise diffusion models
constitute a diverse family covering most state-of-the-art diffusion models.
While there have been several attempts to study Signal-to-Noise (S2N) diffusion
models from various perspectives, there remains a need for a comprehensive
study connecting different viewpoints and exploring new perspectives. In this
study, we offer a comprehensive perspective on noise schedulers, examining
their role through the lens of the signal-to-noise ratio (SNR) and its
connections to information theory. Building upon this framework, we have
developed a generalized backward equation to enhance the performance of the
inference process.
