UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks

• URL: http://arxiv.org/abs/2407.02158v2
• Date: Thu, 4 Jul 2024 08:11:19 GMT
• Title: UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks
• Authors: Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, Lei Zhu,
• Abstract summary: We present UltraPixel, a novel architecture utilizing cascade diffusion models to generate high-quality images at multiple resolutions. We use semantics-rich representations of lower-resolution images in the later denoising stage to guide the whole generation of highly detailed high-resolution images. Our model achieves fast training with reduced data requirements, producing photo-realistic high-resolution images.
• Score: 36.61645124563195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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