In this study, we delve into the generation of high-resolution images from
pre-trained diffusion models, addressing persistent challenges, such as
repetitive patterns and structural distortions, that emerge when models are
applied beyond their trained resolutions. To address this issue, we introduce
an innovative, training-free approach FouriScale from the perspective of
frequency domain analysis. We replace the original convolutional layers in
pre-trained diffusion models by incorporating a dilation technique along with a
low-pass operation, intending to achieve structural consistency and scale
consistency across resolutions, respectively. Further enhanced by a
padding-then-crop strategy, our method can flexibly handle text-to-image
generation of various aspect ratios. By using the FouriScale as guidance, our
method successfully balances the structural integrity and fidelity of generated
images, achieving an astonishing capacity of arbitrary-size, high-resolution,
and high-quality generation. With its simplicity and compatibility, our method
can provide valuable insights for future explorations into the synthesis of
ultra-high-resolution images. The code will be released at
this https URL

从高分辨率图像生成、降低重复模式和结构变形等问题出发，本研究通过引入频域分析的无需训练的创新方法 FouriScale 来解决这些挑战。通过替换预训练扩散模型中的原始卷积层，并结合扩张技术和低通操作，以实现结构一致性与比例一致性。通过填充和裁剪策略对其进行进一步增强，我们的方法能够灵活地处理各种纵横比的文本图像生成。通过 FourScale 的指导，我们的方法成功地平衡了生成图像的结构完整性和保真度，实现了任意大小、高分辨率、高质量的生成，并为未来超高分辨率图像合成的探索提供了有价值的见解。