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

2026-06-25 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

ResilPhase is a novel acceleration framework designed to mitigate the significant inference latency of powerful diffusion models. It addresses the quality degradation observed in existing "cache-then-forecast" schemes, which suffer from discrete extrapolation on misaligned and unstable representations, causing accumulated spatial errors and noisy derivative amplification. ResilPhase reformulates accelerated inference as stable macro-trajectory extrapolation within ordinary differential equation (ODE) space. It aligns forecasting with the model's Global Drift (GD), an end-to-end state evolution, to resolve feature inconsistency and memory overhead. To counter the "derivative fallacy" of noisy higher-order temporal derivatives, the framework employs a derivative-free barycentric Lagrange extrapolator. Additionally, a bounded Phase Mapping regularizes the extrapolation domain, effectively suppressing oscillatory error growth. Experiments on FLUX.1-dev and HunyuanVideo demonstrate ResilPhase's ability to achieve state-of-the-art fidelity even under aggressive acceleration ratios.

Key takeaway

For Machine Learning Engineers optimizing diffusion model inference, ResilPhase offers a robust solution to achieve aggressive acceleration without compromising output quality. If your current "cache-then-forecast" methods suffer from fidelity degradation, you should investigate adopting ResilPhase's approach of stable macro-trajectory extrapolation. This framework, by aligning forecasting with Global Drift and employing derivative-free methods, can significantly reduce latency for models like FLUX.1-dev and HunyuanVideo, enhancing deployment efficiency for high-performance applications.

Key insights

ResilPhase accelerates diffusion models by reformulating inference as stable, derivative-free macro-trajectory extrapolation in ODE space, improving fidelity.

Principles

• Align forecasting with end-to-end state evolution.
• Bypass derivative instability with derivative-free methods.
• Regularize extrapolation domains to suppress errors.

Method

Reformulate accelerated inference as stable macro-trajectory extrapolation in ODE space. Align forecasting with Global Drift (GD). Employ a derivative-free barycentric Lagrange extrapolator and a bounded Phase Mapping to regularize the extrapolation domain.

In practice

• Accelerate DiT-based diffusion models.
• Improve fidelity at high acceleration ratios.
• Reduce memory overhead in forecasting.

Topics

• Diffusion Models
• Inference Acceleration
• Macro-Trajectory Extrapolation
• Global Drift
• Phase Mapping
• FLUX.1-dev

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.