UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale
  High-Quality Dataset
Authors: name: Chen Zhao, name: En Ci, name: Yunzhe Xu, name: Tiehan Fan, name: Shanyan Guan, name: Yanhao Ge, name: Jian Yang, name: Ying Tai
Organization: Nanjing University

Ultra-high-resolution (UHR) text-to-image (T2I) generation has seen notable
progress. However, two key challenges remain : 1) the absence of a large-scale
high-quality UHR T2I dataset, and (2) the neglect of tailored training
strategies for fine-grained detail synthesis in UHR scenarios. To tackle the
first challenge, we introduce UltraHR-100K, a high-quality dataset of
100K UHR images with rich captions, offering diverse content and strong visual
fidelity. Each image exceeds 3K resolution and is rigorously curated based on
detail richness, content complexity, and aesthetic quality. To tackle the
second challenge, we propose a frequency-aware post-training method that
enhances fine-detail generation in T2I diffusion models. Specifically, we
design (i) Detail-Oriented Timestep Sampling (DOTS) to focus learning
on detail-critical denoising steps, and (ii) Soft-Weighting Frequency
Regularization (SWFR), which leverages Discrete Fourier Transform (DFT) to
softly constrain frequency components, encouraging high-frequency detail
preservation. Extensive experiments on our proposed UltraHR-eval4K benchmarks
demonstrate that our approach significantly improves the fine-grained detail
quality and overall fidelity of UHR image generation. The code is available at
https://github.com/NJU-PCALab/UltraHR-100k{here}.