  Classifier-free guidance is an effective sampling technique in diffusion
models that has been widely adopted. The main idea is to extrapolate the model
in the direction of text guidance and away from null-text guidance. In this
paper, we demonstrate that null-text guidance in diffusion models is secretly a
cartoon-style creator, i.e., the generated images can be efficiently
transformed into cartoons by simply perturbing the null-text guidance.
Specifically, we proposed two disturbance methods, i.e., Rollback disturbance
(Back-D) and Image disturbance (Image-D), to construct misalignment between the
noisy images used for predicting null-text guidance and text guidance
(subsequently referred to as \textbf{null-text noisy image} and \textbf{text
noisy image} respectively) in the sampling process. Back-D achieves
cartoonization by altering the noise level of null-text noisy image via
replacing $x_t$ with $x_{t+\Delta t}$. Image-D, alternatively, produces
high-fidelity, diverse cartoons by defining $x_t$ as a clean input image, which
further improves the incorporation of finer image details. Through
comprehensive experiments, we delved into the principle of noise disturbing for
null-text and uncovered that the efficacy of disturbance depends on the
correlation between the null-text noisy image and the source image. Moreover,
our proposed techniques, which can generate cartoon images and cartoonize
specific ones, are training-free and easily integrated as a plug-and-play
component in any classifier-free guided diffusion model. Project page is
available at \url{https://nulltextforcartoon.github.io/}.

Computer Vision and Pattern Recognition;Artificial Intelligence