Image cartoonization has attracted significant interest in the field of image generation. However, most of the existing image cartoonization techniques require re-training models using images of cartoon style. In this paper, we present CartoonDiff, a novel training-free sampling approach which generates image cartoonization using diffusion transformer models. Specifically, we decompose the reverse process of diffusion models into the semantic generation phase and the detail generation phase. Furthermore, we implement the image cartoonization process by normalizing high-frequency signal of the noisy image in specific denoising steps. CartoonDiff doesn't require any additional reference images, complex model designs, or the tedious adjustment of multiple parameters. Extensive experimental results show the powerful ability of our CartoonDiff. The project page is available at: https://cartoondiff.github.io/

翻译：图像卡通化在图像生成领域引起了广泛兴趣。然而，现有的大多数图像卡通化技术需要使用卡通风格图像重新训练模型。本文提出了一种新颖的免训练采样方法CartoonDiff，该方法利用扩散变换模型生成图像卡通化效果。具体而言，我们将扩散模型的反向过程解耦为语义生成阶段和细节生成阶段。此外，通过在特定去噪步骤中对含噪图像的高频信号进行归一化处理，实现了图像卡通化过程。CartoonDiff无需任何额外参考图像、复杂模型设计或繁琐的多参数调整。大量实验结果表明了我们提出的CartoonDiff方法具有强大能力。项目页面可访问：https://cartoondiff.github.io/