UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale
  High-Quality Dataset
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}.
68fd1ade6cdff8b857f46e34
Chen Zhao
claimed_verified
2025-10-29T12:50:34.860Z
671b3b9e8750e04558d1d884
/avatars/db96dd71f46e274d2eeb6edf15c4881c.svg
chen zhao
zhihefang
user
zhihefang
68fd1ade6cdff8b857f46e35
En Ci
68fd1ade6cdff8b857f46e36
Yunzhe Xu
68fd1ade6cdff8b857f46e37
Tiehan Fan
68fd1ade6cdff8b857f46e38
Shanyan Guan
68fd1ade6cdff8b857f46e39
Yanhao Ge
68fd1ade6cdff8b857f46e3a
Jian Yang
68fd1ade6cdff8b857f46e3b
Ying Tai
claimed_verified
2025-10-29T12:50:30.685Z
65734004769f3ee9bde1af10
/avatars/d6310ed861972fd691687d8f47413f33.svg
Ying Tai
yingtai
user
yingtai
UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale
  High-Quality Dataset
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}.
A new dataset and frequency-aware post-training method improve fine-grained detail synthesis in ultra-high-resolution text-to-image diffusion models.
UltraHR-100K frequency-aware post-training Detail-Oriented Timestep Sampling (DOTS) Soft-Weighting Frequency Regularization (SWFR) Discrete Fourier Transform (DFT) UltraHR-eval4K