Spectral Profiling for Complementary Dual-Branch Capacity Allocation in One-Step Diffusion Super-Resolution

One-step diffusion super-resolution enables efficient generative restoration by replacing iterative sampling with a single denoising pass. A fixed U-Net exhibits heterogeneous spectral responses across layers, yet existing dual-LoRA methods typically retain uniform ranks across adapted layers. We propose Frequency-Response-Guided Diffusion Super-Resolution (FreqDiSR), which profiles a frozen U-Net by measuring the Fourier high-frequency response of each layer and converts the resulting profile into a static adaptation capacity map. Under a fixed total rank budget for the two branches at each profiled module, decreasing responses increase the capacity of the pixel branch, whereas increasing responses increase the capacity of the semantic branch, coupling the measured spectral ordering with their respective fidelity and perception objectives. Haar-based decoder modulation and low-frequency supervision extend the same separation of structure and detail to feature processing and training objectives without introducing additional denoising steps. Allocation controls with matched budgets favor the measured correspondence over inverse mappings, depth-based mappings, and permutations on perceptual metrics, while subset and degradation analyses show that the spectral profile is stable. On DRealSR, FreqDiSR improves PSNR by $0.62\mathrm{dB}$ and reduces LPIPS by $0.0140$ over PiSA-SR. Across three benchmarks, it achieves the lowest LPIPS and the lowest DISTS on RealSR and DRealSR.