  In this work, we investigate the capability of generating images from
pre-trained diffusion models at much higher resolutions than the training image
sizes. In addition, the generated images should have arbitrary image aspect
ratios. When generating images directly at a higher resolution, 1024 x 1024,
with the pre-trained Stable Diffusion using training images of resolution 512 x
512, we observe persistent problems of object repetition and unreasonable
object structures. Existing works for higher-resolution generation, such as
attention-based and joint-diffusion approaches, cannot well address these
issues. As a new perspective, we examine the structural components of the U-Net
in diffusion models and identify the crucial cause as the limited perception
field of convolutional kernels. Based on this key observation, we propose a
simple yet effective re-dilation that can dynamically adjust the convolutional
perception field during inference. We further propose the dispersed convolution
and noise-damped classifier-free guidance, which can enable
ultra-high-resolution image generation (e.g., 4096 x 4096). Notably, our
approach does not require any training or optimization. Extensive experiments
demonstrate that our approach can address the repetition issue well and achieve
state-of-the-art performance on higher-resolution image synthesis, especially
in texture details. Our work also suggests that a pre-trained diffusion model
trained on low-resolution images can be directly used for high-resolution
visual generation without further tuning, which may provide insights for future
research on ultra-high-resolution image and video synthesis.
