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

2026-07-20

AI Summary

This work addresses a key limitation in existing diffusion model inference methods, which simplify the generative process as numerical integration while neglecting the statistical uncertainty inherent in denoising. To overcome this, the authors propose DiFA—a training-free inference framework that, for the first time, aligns the reverse process with the statistical structure of the forward diffusion by formulating data prediction as a sequential state estimation problem. DiFA employs a Kalman-filter-inspired temporal consistency mechanism to aggregate historical predictions through consensus and incorporates a bias-guidance strategy to preserve fine-grained details. Evaluated on CIFAR-10 and ImageNet, DiFA achieves substantial improvements across multiple metrics—including FID, Inception Score (IS), and FD-DINOv2—demonstrating markedly superior generation quality compared to current baselines.

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.

Problem

• diffusion models
• inference
• statistical uncertainty
• numerical integration
• generative fidelity

Innovation

• diffusion models
• inference-time alignment
• state estimation
• temporal consensus
• Kalman filtering