This paper presents β-CFG, a dynamic guidance method for text-to-image diffusion models. Unlike standard CFG, which uses a fixed guidance scale, β-CFG adapts guidance strength over time using a β-distribution. This improves image quality, keeps sampling closer to the data manifold, and achieves better FID while maintaining prompt alignment. - View it on GitHub