Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent
  Diffusion Models
Authors: name: Jinjin Zhang, name: Qiuyu Huang, name: Junjie Liu, name: Xiefan Guo, name: Di Huang

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.