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

Cartoondiff: Training-free Cartoon Image Generation with Diffusion Transformer Models