  The recent success of text-to-image generation diffusion models has also
revolutionized semantic image editing, enabling the manipulation of images
based on query/target texts. Despite these advancements, a significant
challenge lies in the potential introduction of contextual prior bias in
pre-trained models during image editing, e.g., making unexpected modifications
to inappropriate regions. To address this issue, we present a novel approach
called Dual-Cycle Diffusion, which generates an unbiased mask to guide image
editing. The proposed model incorporates a Bias Elimination Cycle that consists
of both a forward path and an inverted path, each featuring a Structural
Consistency Cycle to ensure the preservation of image content during the
editing process. The forward path utilizes the pre-trained model to produce the
edited image, while the inverted path converts the result back to the source
image. The unbiased mask is generated by comparing differences between the
processed source image and the edited image to ensure that both conform to the
same distribution. Our experiments demonstrate the effectiveness of the
proposed method, as it significantly improves the D-CLIP score from 0.272 to
0.283. The code will be available at
https://github.com/JohnDreamer/DualCycleDiffsion.
