  Diffusion models excel at producing high-quality images; however, scaling to
higher resolutions, such as 4K, often results in over-smoothed content,
structural distortions, and repetitive patterns. To this end, we introduce
ResMaster, a novel, training-free method that empowers resolution-limited
diffusion models to generate high-quality images beyond resolution
restrictions. Specifically, ResMaster leverages a low-resolution reference
image created by a pre-trained diffusion model to provide structural and
fine-grained guidance for crafting high-resolution images on a patch-by-patch
basis. To ensure a coherent global structure, ResMaster meticulously aligns the
low-frequency components of high-resolution patches with the low-resolution
reference at each denoising step. For fine-grained guidance, tailored image
prompts based on the low-resolution reference and enriched textual prompts
produced by a vision-language model are incorporated. This approach could
significantly mitigate local pattern distortions and improve detail refinement.
Extensive experiments validate that ResMaster sets a new benchmark for
high-resolution image generation and demonstrates promising efficiency. The
project page is https://shuweis.github.io/ResMaster .
