  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.
