RealUHR: Harnessing Patch-Cascade Flows for Photorealistic Ultra-High-Resolution Synthesis
Yongsheng Yu, Haitian Zheng, Zhe Lin, Connelly Barnes, Yuqian Zhou, Zhifei Zhang, Jiebo Luo
Abstract
Ultra-high-resolution (UHR) text-to-image synthesis faces significant hurdles, including immense computational costs and a scarcity of training data. To address these, we introduce RealUHR, an efficient and scalable framework for generating photorealistic 4K images. At its core, RealUHR employs a Patch-Cascade Flow Matching pipeline that ensures global coherence without costly patch fusion by initiating generation from a semantically meaningful structure. This enables highly efficient, few-step inference for independent patches. Our key contribution is Guidance-Consistent Adaptation (GCA), a novel two-stage strategy to resolve the fundamental objective mismatch in guidance-distilled models. GCA allows powerful backbones like FLUX to be effectively adapted for patch-aware UHR synthesis. The framework
BibTeX
@inproceedings{aaai2026_realuhrharnessin,
title = {RealUHR: Harnessing Patch-Cascade Flows for Photorealistic Ultra-High-Resolution Synthesis},
author = {Yongsheng Yu and Haitian Zheng and Zhe Lin and Connelly Barnes and Yuqian Zhou and Zhifei Zhang and Jiebo Luo},
booktitle = {AAAI 2026},
year = {2026}
}