Abstract
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.
Abstract (translated)
扩散模型在各种图像生成任务中取得了显著的进展。然而，当它们用于生成高于训练期间所用分辨率的高分辨率图像时，其性能会明显下降。尽管存在许多可以产生高质量高分辨率图像的方法，但这些方法要么效率低下，要么受到复杂操作的限制。在这篇论文中，我们提出了RectifiedHR，这是一种无需训练即可生成高分辨率图像的有效且简单的解决方案。具体来说，我们引入了噪声刷新策略，理论上仅需修改几行代码就能解锁模型的高分辨率生成能力，并提高其效率。此外，我们首次观察到了在高分辨率图像生成过程中可能出现的能量衰减现象，这可能导致图像模糊。为了解决这个问题，我们提出了能量校正策略，在该策略中，通过调整无分类器引导（classifier-free guidance）中的超参数可以有效提升生成性能。我们的方法完全不需要重新训练，并且拥有简单明了的实现逻辑。通过与众多基线方法进行广泛的比较，RectifiedHR展示了其在有效性及效率上的优越性。
URL
https://arxiv.org/abs/2503.02537
