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 straightforward and efficient 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 introduce average latent energy analysis and discover that an improved classifier-free guidance hyperparameter can significantly enhance generation performance. Our method is entirely training-free and boasts a simple implementation logic and efficient performance. Through extensive comparisons with numerous baseline methods, our RectifiedHR demonstrates superior effectiveness and efficiency.

翻译：扩散模型在各种图像生成任务中取得了显著进展。然而，当生成分辨率高于训练期间所用分辨率的图像时，其性能会显著下降。尽管存在多种生成高分辨率图像的方法，但它们要么效率低下，要么受限于复杂的操作。本文提出RectifiedHR，一种简单高效的免训练高分辨率图像生成方案。具体而言，我们引入了噪声刷新策略，该策略理论上仅需几行代码即可解锁模型的高分辨率生成能力并提升效率。此外，我们首次观察到高分辨率图像生成过程中可能导致图像模糊的能量衰减现象。为解决此问题，我们引入平均潜在能量分析，并发现改进的无分类器引导超参数可以显著提升生成性能。我们的方法完全免训练，具有简单的实现逻辑和高效的性能。通过与多种基线方法的广泛比较，我们的RectifiedHR展现出卓越的有效性和效率。