HoltSeer: Stable Feature Forecasting under Sparse Anchors for Accelerated Diffusion Sampling

Feature caching offers training-free acceleration for diffusion models by skipping selected network evaluations. Cache-then-reuse methods substitute cached features, whereas cache-then-forecast methods predict their temporal evolution. At high acceleration ratios, computed features become sparse, making forecasts over the intervening timesteps increasingly sensitive to local modeling errors. The challenge is to represent local feature evolution accurately with a lightweight predictor under this limited computation budget. We propose **HoltSeer**, a training-free cache-then-forecast method that models local diffusion feature evolution through a smoothed level–trend state inspired by Holt's exponential smoothing. The level component summarizes recent cached features, the trend component tracks local directional changes, and a trend-scaling coefficient controls extrapolation. We implement this predictor through a bounded sequence of recursive forecasts with periodic full network evaluations; predicted features enter the same state update as computed features. Experiments on FLUX.1-dev, Qwen-Image, and Wan2.1 show improved reference-based fidelity over the evaluated caching baselines at large cache intervals while retaining competitive latency. Our code will be released upon acceptance.