UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks
<|reference_start|>UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks: 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 to 6K) within a single model, while maintaining computational efficiency. UltraPixel leverages semantics-rich representations of lower-resolution images in the later denoising stage to guide the whole generation of highly detailed high-resolution images, significantly reducing complexity. Furthermore, 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.<|reference_end|>
@article{ren2024ultrapixel:,
    title={UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks},
    author={Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo,
  Long Peng, Fenglong Song, Lei Zhu},
    journal={arXiv preprint arXiv:2407.02158},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02158},
    primaryClass={cs.CV}
}