Cartoondiff: Training-free Cartoon Image Generation with Diffusion
  Transformer Models
Feihong He Gang Li Lingyu Si Leilei Yan Shimeng Hou Hongwei Dong Fanzhang Li
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/