Latent Space Super-Resolution for Higher-Resolution Image Generation
  with Diffusion Models
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.
67e367ee4363e3c4bbbaca3a
Jinho Jeong
67e367ee4363e3c4bbbaca3b
Sangmin Han
67e367ee4363e3c4bbbaca3c
Jinwoo Kim
67e367ee4363e3c4bbbaca3d
Seon Joo Kim
Latent Space Super-Resolution for Higher-Resolution Image Generation
  with Diffusion Models
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.
LSRNA diffusion models latent space super-resolution structural distortions content repetition reference-based methods manifold deviation RGB space manifold alignment Region-wise Noise Addition (RNA) high-frequency details