DARTCache: Dual-Stage Temporal Reuse Planning for Efficient Inversion-Based Editing

Inversion-based image editing requires repeated model evaluations during both inversion and denoising, making inference computationally expensive. Training-free caching can reduce this cost, but applying caching policies independently to the two stages leaves a shared-budget planning problem unresolved: where to refresh predictions within each stage and how to allocate computation between stages. We propose **DARTCache**, a training-free framework that jointly plans these two decisions using a single trajectory-based surrogate objective. We introduce Temporal Variation Exposure (TVE), which quantifies cumulative prediction variation between refresh locations. A quadratic TVE objective promotes exposure-balanced refresh placement and guides stage-wise budget allocation. **DARTCache** estimates temporal variation profiles from full-computation trajectories on a small calibration set and constructs fixed refresh schedules without candidate-level replay or online schedule search. On RF-Inversion and OT-RF, **DARTCache** achieves end-to-end speedups of 2.80$\times$ and 2.67$\times$, with full-image LPIPS of 0.107 and 0.137 relative to the full-inference outputs, yielding favorable efficiency–fidelity trade-offs against evaluated acceleration baselines. Further experiments on U-Net-based MasaCtrl and EDICT support the applicability of the planning framework beyond FLUX-based editing pipelines.