A new training method replaces MSE loss in diffusion models with an f-divergence-based transformation, creating a robust surrogate that improves performance under data contamination. The approach uses local divergence constructions under DDPM's Gaussian reverse-kernel, reducing the training objective to a one-dimensional function of denoising error, with bounded-influence divergences suppressing large errors and enhancing stability.