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

• URL: http://arxiv.org/abs/2607.17972v1
• Date: Mon, 20 Jul 2026 14:10:53 GMT
• Title: DiFA: Inference-Time Forward-Process Alignment for Diffusion Models
• Abstract summary: Forward-Process Aligned Diffusion prediction (textbfDiFA) is a training-free framework that reframes inference-time data prediction refinement as a sequential state estimation problem.<n>Inspired by Kalman filtering, DiFA aggregates historical predictions according to structural consistency and noise-level compatibility.
• Score: 8.208222349301785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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