Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent
  Diffusion Models
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.
67e217a272e17348c5b3f0a2
Jinjin Zhang
claimed_verified
2025-03-25T19:02:26.353Z
63a95f207930fa8c7dd6634e
/avatars/0dcf0e263300e3773e5da0b46ab4aa6f.svg
Jinjin Zhang
user
zhang0jhon
67e217a272e17348c5b3f0a3
Qiuyu Huang
admin_assigned
2025-03-25T09:24:20.054Z
6708e399672d9dcd31575fbc
/avatars/0f947f17b5426186aadaa4224571f47b.svg
qiuyuhuang
user
qiuyuhuang
67e217a272e17348c5b3f0a4
Junjie Liu
67e217a272e17348c5b3f0a5
Xiefan Guo
admin_assigned
2025-03-25T09:24:02.736Z
64905cd589f22918ecaca080
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/2S7I7uZL49CXbUN2T7p63.jpeg
Xiefan Guo
user
xiefan-guo
67e217a272e17348c5b3f0a6
Di Huang
Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent
  Diffusion Models
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.
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.
Aesthetic-4K Benchmark wavelet-based fine-tuning latent diffusion models SD3-2B Flux-12B GLCM Score Compression Ratio FID Aesthetics CLIPScore