  We present a novel algorithm for text-driven image-to-image translation based
on a pretrained text-to-image diffusion model. Our method aims to generate a
target image by selectively editing the regions of interest in a source image,
defined by a modifying text, while preserving the remaining parts. In contrast
to existing techniques that solely rely on a target prompt, we introduce a new
score function that additionally considers both the source image and the source
text prompt, tailored to address specific translation tasks. To this end, we
derive the conditional score function in a principled manner, decomposing it
into the standard score and a guiding term for target image generation. For the
gradient computation of the guiding term, we assume a Gaussian distribution of
the posterior distribution and estimate its mean and variance to adjust the
gradient without additional training. In addition, to improve the quality of
the conditional score guidance, we incorporate a simple yet effective mixup
technique, which combines two cross-attention maps derived from the source and
target latents. This strategy is effective for promoting a desirable fusion of
the invariant parts in the source image and the edited regions aligned with the
target prompt, leading to high-fidelity target image generation. Through
comprehensive experiments, we demonstrate that our approach achieves
outstanding image-to-image translation performance on various tasks.
