  The diffusion-based text-to-image model harbors immense potential in
transferring reference style. However, current encoder-based approaches
significantly impair the text controllability of text-to-image models while
transferring styles. In this paper, we introduce DEADiff to address this issue
using the following two strategies: 1) a mechanism to decouple the style and
semantics of reference images. The decoupled feature representations are first
extracted by Q-Formers which are instructed by different text descriptions.
Then they are injected into mutually exclusive subsets of cross-attention
layers for better disentanglement. 2) A non-reconstructive learning method. The
Q-Formers are trained using paired images rather than the identical target, in
which the reference image and the ground-truth image are with the same style or
semantics. We show that DEADiff attains the best visual stylization results and
optimal balance between the text controllability inherent in the text-to-image
model and style similarity to the reference image, as demonstrated both
quantitatively and qualitatively. Our project page is
https://tianhao-qi.github.io/DEADiff/.
