title: Latent Space Super-Resolution for Higher-Resolution Image Generation
    with Diffusion Models
summary: 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.
paper_authors._id: 67e367ee4363e3c4bbbaca3a
paper_authors.name: Jinho Jeong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:40:37.750Z
paper_authors._id: 66b5f733f0c16f37f307f35e
paper_authors.avatarUrl: /avatars/29a97e10b4d65aa23d7eae238f809499.svg
paper_authors.fullname: JinHo Jeong
paper_authors.type: user
paper_authors.user: 3587jjh
paper_authors._id: 67e367ee4363e3c4bbbaca3b
paper_authors.name: Sangmin Han
paper_authors._id: 67e367ee4363e3c4bbbaca3c
paper_authors.name: Jinwoo Kim
paper_authors._id: 67e367ee4363e3c4bbbaca3d
paper_authors.name: Seon Joo Kim
paper_title: Latent Space Super-Resolution for Higher-Resolution Image Generation
    with Diffusion Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: diffusion models latent space super-resolution region-wise noise addition high-resolution image generation manifold deviation manifold alignment