DiFA improves diffusion inference by aligning predictions with forward statistics
cn-scut · hf · 2026-07-21
What DiFA is
DiFA is a training-free inference-time framework for diffusion models that treats reverse-process prediction as a sequential state-estimation problem rather than plain numerical integration.
Key idea
- Standard samplers often ignore the statistical uncertainty in denoising.
- DiFA reuses iterative predictions as correlated observations and forms a forward-aligned temporal consensus.
- The consensus is inspired by Kalman filtering and combines historical predictions based on structural consistency and noise-level compatibility.
- A deviation-guidance mechanism is added to prevent over-smoothing and preserve details.
Results
On CIFAR-10 and ImageNet, DiFA improves metrics including FID, IS, and FD-DINOv2, showing that aligning inference with the forward statistical structure can improve generation quality.
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