@inproceedings{
ren2024ultrapixel,
title={UltraPixel: Advancing Ultra High-Resolution Image Synthesis to New Peaks},
author={Jingjing Ren and Wenbo Li and Haoyu Chen and Renjing Pei and Bin Shao and Yong Guo and Long Peng and Fenglong Song and Lei Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=voJCpdlw53}
}
Ultra-high-resolution image generation poses great challenges, such as increased semantic planning complexity and detail synthesis difficulties, alongside substantial training resource demands. We present UltraPixel, a novel architecture utilizing cascade diffusion models to generate high-quality images at multiple resolutions (\textit{e.g.}, 1K, 2K, and 4K) within a single model, while maintaining computational efficiency. UltraPixel leverages semantics-rich representations of lower-resolution images in a later denoising stage to guide the whole generation of highly detailed high-resolution images, significantly reducing complexity. Specifically, we introduce implicit neural representations for continuous upsampling and scale-aware normalization layers adaptable to various resolutions. Notably, both low- and high-resolution processes are performed in the most compact space, sharing the majority of parameters with less than 3$\%$ additional parameters for high-resolution outputs, largely enhancing training and inference efficiency. Our model achieves fast training with reduced data requirements, producing photo-realistic high-resolution images and demonstrating state-of-the-art performance in extensive experiments.
UltraPixel: Advancing Ultra High-Resolution Image Synthesis to New Peaks
Jingjing Ren Wenbo Li Haoyu Chen Renjing Pei Bin Shao Yong Guo Long Peng Fenglong Song Lei Zhu