Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing

TL;DR

This paper introduces Contrastive Denoising Score (CDS), a simple yet effective modification of Delta Denoising Score (DDS), leveraging contrastive learning and features from latent diffusion models to improve image editing fidelity and content preservation.

Contribution

The paper proposes a novel CDS method that incorporates CUT loss and LDM features for better structural preservation in image editing, enabling zero-shot translation and NeRF editing.

Findings

CDS achieves superior structural preservation in image editing.

The method enables zero-shot image-to-image translation.

Qualitative results demonstrate improved content control and fidelity.

Abstract

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/