Latent Space Super-Resolution for Higher-Resolution Image Generation
  with Diffusion Models
Authors: name: Jinho Jeong, name: Sangmin Han, name: Jinwoo Kim, name: Seon Joo Kim

In this paper, we propose LSRNA, a novel framework for higher-resolution
(exceeding 1K) image generation using diffusion models by leveraging
super-resolution directly in the latent space. Existing diffusion models
struggle with scaling beyond their training resolutions, often leading to
structural distortions or content repetition. Reference-based methods address
the issues by upsampling a low-resolution reference to guide higher-resolution
generation. However, they face significant challenges: upsampling in latent
space often causes manifold deviation, which degrades output quality. On the
other hand, upsampling in RGB space tends to produce overly smoothed outputs.
To overcome these limitations, LSRNA combines Latent space Super-Resolution
(LSR) for manifold alignment and Region-wise Noise Addition (RNA) to enhance
high-frequency details. Our extensive experiments demonstrate that integrating
LSRNA outperforms state-of-the-art reference-based methods across various
resolutions and metrics, while showing the critical role of latent space
upsampling in preserving detail and sharpness. The code is available at
https://github.com/3587jjh/LSRNA.