Diffusion transformers (DiTs) have emerged as a dominant architecture for text-to-image generation, yet their performance drops when generating at resolutions beyond their training range. Existing training-free approaches mitigate this by modifying inference-time attention behavior, often through Rotary Position Embeddings (RoPE) extrapolation combined with attention scaling. However, these strategies apply a uniform and content-agnostic scaling across RoPE components with distinct frequency characteristics, inducing a trade-off between preserving global structure and recovering fine detail. We introduce SEGA, a training-free method that dynamically scales attention across RoPE components according to the latent's spatial-frequency structure at each denoising step. This adaptive scaling improves both structural coherence and fine-detail fidelity. Experiments show that SEGA consistently improves high-resolution synthesis across multiple target resolutions, outperforming state-of-the-art training-free baselines.
@article{arxiv.2605.22668,
title = {SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers},
author = {Javad Rajabi and Kimia Shaban and Koorosh Roohi and David B. Lindell and Babak Taati},
journal= {arXiv preprint arXiv:2605.22668},
year = {2026}
}