ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration

The adoption of powerful diffusion models is hindered bytheir significant inference latency. Recent “cache-then-forecast” schemesalleviate this issue by accelerating DiTs using derivative-based polynomi-als, but they suffer from severe quality degradation at high accelerationratios. Our analysis reveals its root cause: the discrete extrapolation per-formed on representations that are misaligned with the continuous diffu-sion trajectory and are numerically unstable. Thus, accelerated DiTs suf-fer from accumulated spatial errors, noisy derivative amplification, andhigh-order instability. We therefore reformulate accelerated inference asstable macro-trajectory extrapolation in ordinary differential equation(ODE) space. Instead of predicting intermediate features, we align fore-casting with the model’s Global Drift (GD), i.e., the end-to-end stateevolution, thereby eliminating feature inconsistency and memory over-head. However, even this smooth macro-trajectory remains vulnerableto the derivative fallacy: its higher-order temporal derivatives are intrin-sically noisy. Thus, we introduce a derivative-free barycentric Lagrangeextrapolator to effectively bypass derivative instability and approxima-tion error. We further propose a bounded Phase Mapping that regu-larizes the extrapolation domain, suppressing oscillatory error growth.These elements collectively constitute ResilPhase, a noise-resilient ac-celeration framework. Experiments on FLUX.1-dev and HunyuanVideodemonstrate state-of-the-art fidelity under aggressive acceleration ratios.Code is publicly available at https://github.com/zqc214/ResilPhase.