RectifiedHR：通过能量校正实现高效高分辨率图像生成

RectifiedHR: Enable Efficient High-Resolution Image Generation via Energy Rectification

March 4, 2025

作者: Zhen Yang, Guibao Shen, Liang Hou, Mushui Liu, Luozhou Wang, Xin Tao, Pengfei Wan, Di Zhang, Ying-Cong Chen

cs.AI

摘要

扩散模型在各类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时，其性能明显下降。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，要么受制于复杂的操作。本文提出RectifiedHR，一种无需训练的高效简洁的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，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，我们首次观察到在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。为解决这一问题，我们提出了能量校正策略，通过修改无分类器引导的超参数有效提升了生成性能。我们的方法完全无需训练，且实现逻辑简单。通过与多种基线方法的广泛对比，RectifiedHR展现了卓越的有效性和效率。

English

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.