RectifiedHR: Enable Efficient High-Resolution Image Generation via
  Energy Rectification
Authors: name: Zhen Yang, name: Guibao Shen, name: Liang Hou, name: Mushui Liu, name: Luozhou Wang, name: Xin Tao, name: Pengfei Wan, name: Di Zhang, name: Ying-Cong Chen

Diffusion models have achieved remarkable advances in various image
generation tasks. However, their performance notably declines when generating
images at resolutions higher than those used during the training period.
Despite the existence of numerous methods for producing high-resolution images,
they either suffer from inefficiency or are hindered by complex operations. In
this paper, we propose RectifiedHR, an efficient and straightforward solution
for training-free high-resolution image generation. Specifically, we introduce
the noise refresh strategy, which theoretically only requires a few lines of
code to unlock the model's high-resolution generation ability and improve
efficiency. Additionally, we first observe the phenomenon of energy decay that
may cause image blurriness during the high-resolution image generation process.
To address this issue, we propose an Energy Rectification strategy, where
modifying the hyperparameters of the classifier-free guidance effectively
improves the generation performance. Our method is entirely training-free and
boasts a simple implementation logic. Through extensive comparisons with
numerous baseline methods, our RectifiedHR demonstrates superior effectiveness
and efficiency.