title: Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent
    Diffusion Models
summary: In this paper, we present Diffusion-4K, a novel framework for direct
  ultra-high-resolution image synthesis using text-to-image diffusion models. The
  core advancements include: (1) Aesthetic-4K Benchmark: addressing the absence
  of a publicly available 4K image synthesis dataset, we construct Aesthetic-4K,
  a comprehensive benchmark for ultra-high-resolution image generation. We
  curated a high-quality 4K dataset with carefully selected images and captions
  generated by GPT-4o. Additionally, we introduce GLCM Score and Compression
  Ratio metrics to evaluate fine details, combined with holistic measures such as
  FID, Aesthetics and CLIPScore for a comprehensive assessment of
  ultra-high-resolution images. (2) Wavelet-based Fine-tuning: we propose a
  wavelet-based fine-tuning approach for direct training with photorealistic 4K
  images, applicable to various latent diffusion models, demonstrating its
  effectiveness in synthesizing highly detailed 4K images. Consequently,
  Diffusion-4K achieves impressive performance in high-quality image synthesis
  and text prompt adherence, especially when powered by modern large-scale
  diffusion models (e.g., SD3-2B and Flux-12B). Extensive experimental results
  from our benchmark demonstrate the superiority of Diffusion-4K in
  ultra-high-resolution image synthesis.
paper_authors._id: 67e217a272e17348c5b3f0a2
paper_authors.name: Jinjin Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T19:02:26.353Z
paper_authors._id: 63a95f207930fa8c7dd6634e
paper_authors.avatarUrl: /avatars/0dcf0e263300e3773e5da0b46ab4aa6f.svg
paper_authors.fullname: Jinjin Zhang
paper_authors.type: user
paper_authors.user: zhang0jhon
paper_authors._id: 67e217a272e17348c5b3f0a3
paper_authors.name: Qiuyu Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:24:20.054Z
paper_authors._id: 6708e399672d9dcd31575fbc
paper_authors.avatarUrl: /avatars/0f947f17b5426186aadaa4224571f47b.svg
paper_authors.fullname: qiuyuhuang
paper_authors.type: user
paper_authors.user: qiuyuhuang
paper_authors._id: 67e217a272e17348c5b3f0a4
paper_authors.name: Junjie Liu
paper_authors._id: 67e217a272e17348c5b3f0a5
paper_authors.name: Xiefan Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:24:02.736Z
paper_authors._id: 64905cd589f22918ecaca080
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/2S7I7uZL49CXbUN2T7p63.jpeg
paper_authors.fullname: Xiefan Guo
paper_authors.type: user
paper_authors.user: xiefan-guo
paper_authors._id: 67e217a272e17348c5b3f0a6
paper_authors.name: Di Huang
paper_title: Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent
    Diffusion Models
paper_summary: In this paper, we present Diffusion-4K, a novel framework for direct
  ultra-high-resolution image synthesis using text-to-image diffusion models. The
  core advancements include: (1) Aesthetic-4K Benchmark: addressing the absence
  of a publicly available 4K image synthesis dataset, we construct Aesthetic-4K,
  a comprehensive benchmark for ultra-high-resolution image generation. We
  curated a high-quality 4K dataset with carefully selected images and captions
  generated by GPT-4o. Additionally, we introduce GLCM Score and Compression
  Ratio metrics to evaluate fine details, combined with holistic measures such as
  FID, Aesthetics and CLIPScore for a comprehensive assessment of
  ultra-high-resolution images. (2) Wavelet-based Fine-tuning: we propose a
  wavelet-based fine-tuning approach for direct training with photorealistic 4K
  images, applicable to various latent diffusion models, demonstrating its
  effectiveness in synthesizing highly detailed 4K images. Consequently,
  Diffusion-4K achieves impressive performance in high-quality image synthesis
  and text prompt adherence, especially when powered by modern large-scale
  diffusion models (e.g., SD3-2B and Flux-12B). Extensive experimental results
  from our benchmark demonstrate the superiority of Diffusion-4K in
  ultra-high-resolution image synthesis.
paper_ai_summary: Diffusion-4K synthesizes ultra-high-resolution images using text-to-image diffusion models with wavelet-based fine-tuning and a new Aesthetic-4K benchmark for comprehensive evaluation.
paper_ai_keywords: Aesthetic-4K Benchmark wavelet-based fine-tuning latent diffusion models SD3-2B Flux-12B GLCM Score Compression Ratio FID Aesthetics CLIPScore