DiFA: Inference-Time Forward-Process Alignment for Diffusion Models

The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration. This perspective casts the model as an accurate estimator, neglecting the inherent statistical uncertainty of the denoising process. In this work, we propose Forward-Process Aligned Diffusion prediction (DiFA), a training-free framework that reframes inference-time clean-signal prediction refinement within diffusion sampling as a sequential state estimation problem. Instead of discarding historical predictions, DiFA treats the inference trajectory as a sequence of correlated observations with varying uncertainties. Inspired by Kalman estimation, DiFA builds a logSNR-aware temporal consensus to align historical clean predictions. Crucially, to counteract the over-smoothing typically associated with temporal consensus, we introduce a deviation guidance mechanism that adaptively preserves residual details. Empirically, DiFA yields significant improvements on CIFAR-10 and ImageNet across the evaluated metrics, including FID, IS, and FD-DINOv2, demonstrating that aligning inference with the forward statistical structure substantially improves generative fidelity. Code is available at https://github.com/ShiguiLi/DiFA.