DiFA introduces a training‑free, inference‑time framework that aligns the forward diffusion process with the denoising model, reframing data prediction refinement as sequential state estimation. By doing so, it addresses the statistical uncertainty inherent in diffusion generation that is ignored when treating the model as an exact numerical integrator. The approach improves alignment between forward and reverse processes without requiring additional training.