  Recent surge in large-scale generative models has spurred the development of
vast fields in computer vision. In particular, text-to-image diffusion models
have garnered widespread adoption across diverse domain due to their potential
for high-fidelity image generation. Nonetheless, existing large-scale diffusion
models are confined to generate images of up to 1K resolution, which is far
from meeting the demands of contemporary commercial applications. Directly
sampling higher-resolution images often yields results marred by artifacts such
as object repetition and distorted shapes. Addressing the aforementioned issues
typically necessitates training or fine-tuning models on higher resolution
datasets. However, this undertaking poses a formidable challenge due to the
difficulty in collecting large-scale high-resolution contents and substantial
computational resources. While several preceding works have proposed
alternatives, they often fail to produce convincing results. In this work, we
probe the generative ability of diffusion models at higher resolution beyond
its original capability and propose a novel progressive approach that fully
utilizes generated low-resolution image to guide the generation of higher
resolution image. Our method obviates the need for additional training or
fine-tuning which significantly lowers the burden of computational costs.
Extensive experiments and results validate the efficiency and efficacy of our
method. Project page: https://yhyun225.github.io/DiffuseHigh/
