WAVEALIGN: Wavelet-Based Alignment for Training-Free High-Resolution Image Generation

High-resolution image generation aims to extend pretrained generator beyond their native training resolution while preserving coherent global structure and enabling finer details to emerge. Existing progressive methods transfer information across resolutions, yet parent and child stages differ in spatial scale and sampling phase, leading to mismatches in initialization, reference selection, and structural guidance. To tackle this problem, we formulate progressive high-resolution generation as a cross-resolution correspondence problem involving stochastic, temporal, and structural coherence. Based on this perspective, we introduce , a training-free framework that establishes these correspondences in a shared orthonormal wavelet representation. Specifically, couples sampled noise and constructs a mixed clean anchor for coherent initialization, derives scale-time conjugacy for phase-matched parent references, and selectively corrects shared coarse structures while progressively releasing guidance for high-resolution detail synthesis. Experiments on FLUX.1-dev demonstrate effective progressive $1\mathrm{K}\to 2\mathrm{K}\to 4\mathrm{K}$ generation, with achieving the lowest FID, KID, pFID, and pKID at $4\mathrm{K}$ among the evaluated FLUX-based pipelines.