PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset

Text-to-Image (T2I) models have recently seen notable progress around 1K and 2K resolution. With
the extreme desire for better visual experience and the rapid development of imaging technology,
the demand for Ultra-High-Resolution (UHR) image generation has grown significantly. However,
UHR image generation poses great challenges due to the scarcity and complexity of high-resolution
content. In this paper, we first introduce PixVerve-95K, a high-quality, open-source UHR T2I dataset
curated with a carefully designed data pipeline, which contains 95K images across diverse scenarios
(each image has a minimum pixel-count of 100M) and seven-dimensional annotations. Based on our
large-scale image-text dataset, we take a pioneering step to extend various T2I foundation models to
native 100MP generation with three training schemes. Finally, leveraging both conventional metrics
and multimodal large language model-based assessments, our proposed PixVerve-Bench benchmark
establishes a comprehensive evaluation protocol for UHR images encompassing visual quality and
semantic alignment. Extensive experimental results on our benchmark and the constructive exploration
of training strategies collaboratively provide valuable insights for future breakthroughs.