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

Abstract:The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration. This perspective casts the model as an exact estimator, neglecting the inherent statistical uncertainty of the denoising process. In this work, we propose Forward-Process Aligned Diffusion prediction (\textbf{DiFA}), a training-free framework that reframes inference-time data prediction refinement as a sequential state estimation problem. Rather than reusing past outputs solely for numerical integration, DiFA treats iterative data predictions along the reverse trajectory as correlated observations to build a forward-aligned temporal consensus. Inspired by Kalman filtering, this consensus aggregates historical predictions according to structural consistency and noise-level compatibility. To counteract the over-smoothing tendency of temporal consensus, we introduce a deviation guidance mechanism to adaptively preserve 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.

Accepted to ICML 2026. 23 pages, 7 figures

Machine Learning (cs.LG)

arXiv:2607.17972 [cs.LG]

(or arXiv:2607.17972v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.17972 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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