  With the remarkable advent of text-to-image diffusion models, image editing
methods have become more diverse and continue to evolve. A promising recent
approach in this realm is Delta Denoising Score (DDS) - an image editing
technique based on Score Distillation Sampling (SDS) framework that leverages
the rich generative prior of text-to-image diffusion models. However, relying
solely on the difference between scoring functions is insufficient for
preserving specific structural elements from the original image, a crucial
aspect of image editing. To address this, here we present an embarrassingly
simple yet very powerful modification of DDS, called Contrastive Denoising
Score (CDS), for latent diffusion models (LDM). Inspired by the similarities
and differences between DDS and the contrastive learning for unpaired
image-to-image translation(CUT), we introduce a straightforward approach using
CUT loss within the DDS framework. Rather than employing auxiliary networks as
in the original CUT approach, we leverage the intermediate features of LDM,
specifically those from the self-attention layers, which possesses rich spatial
information. Our approach enables zero-shot image-to-image translation and
neural radiance field (NeRF) editing, achieving structural correspondence
between the input and output while maintaining content controllability.
Qualitative results and comparisons demonstrates the effectiveness of our
proposed method. Project page: https://hyelinnam.github.io/CDS/
