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
claimed_verified
2025-04-18T09:40:37.750Z
66b5f733f0c16f37f307f35e
/avatars/29a97e10b4d65aa23d7eae238f809499.svg
JinHo Jeong
user
3587jjh
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, a diffusion model framework, improves high-resolution image generation by combining latent space super-resolution and region-wise noise addition to address common issues in reference-based methods.
diffusion models latent space super-resolution region-wise noise addition high-resolution image generation manifold deviation manifold alignment