Free Lunch in Diffusion Inversion: Tensorial Classifier-Free Guidance at Its Origin

Inversion errors can compromise source preservation before semantic editing begins. We propose POLARIS, a lightweight, training-free guidance plugin for improving the source trajectory in diffusion-based editing. Building on reconstruction-residual control in diffusion inversion, POLARIS specializes the local inverse-consistency objective to the classifier-free guidance (CFG) coefficients. With adjacent network predictions held fixed, the deterministic DDIM cycle residual is a timestep-dependent scalar multiple of the adjacent guided-noise discrepancy. This yields a coordinate-wise least-squares update using existing conditional and unconditional predictions, without gradient-based optimization or an auxiliary network. Structured-guidance ablations and measured cycle residuals support the local analysis, while comparisons with both high-CFG and null-branch fixed-scale controls demonstrate improved reconstruction fidelity. Paired evaluations further show better background preservation when POLARIS is integrated with existing image, audio, and video editors. A separate transport-based extension improves background fidelity on Wan2.1 video editing without additional model evaluations. Across the measured image backbones, peak memory overhead is 2.0–3.2%. These results support adaptive guidance as a practical component for source preservation; improved inversion alone does not guarantee target-semantic editing.