  Despite the impressive results of arbitrary image-guided style transfer
methods, text-driven image stylization has recently been proposed for
transferring a natural image into a stylized one according to textual
descriptions of the target style provided by the user. Unlike the previous
image-to-image transfer approaches, text-guided stylization progress provides
users with a more precise and intuitive way to express the desired style.
However, the huge discrepancy between cross-modal inputs/outputs makes it
challenging to conduct text-driven image stylization in a typical feed-forward
CNN pipeline. In this paper, we present DiffStyler, a dual diffusion processing
architecture to control the balance between the content and style of the
diffused results. The cross-modal style information can be easily integrated as
guidance during the diffusion process step-by-step. Furthermore, we propose a
content image-based learnable noise on which the reverse denoising process is
based, enabling the stylization results to better preserve the structure
information of the content image. We validate the proposed DiffStyler beyond
the baseline methods through extensive qualitative and quantitative
experiments. Code is available at
\url{https://github.com/haha-lisa/Diffstyler}.
