DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing

TL;DR

This paper introduces Dual-Conditional Inversion (DCI), a new method for diffusion model inversion that jointly uses source prompts and reference images to improve reconstruction accuracy and editing flexibility.

Contribution

DCI formulates a dual-condition fixed-point optimization for inversion, enhancing the balance between semantic alignment and structural consistency in diffusion-based image editing.

Findings

Achieves state-of-the-art performance in multiple editing tasks.

Significantly improves reconstruction quality and editing precision.

Demonstrates robustness and generalizability in inversion tasks.

Abstract

Diffusion models have achieved remarkable success in image generation and editing tasks. Inversion within these models aims to recover the latent noise representation for a real or generated image, enabling reconstruction, editing, and other downstream tasks. However, to date, most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility. This limitation arises from the difficulty of maintaining both semantic alignment and structural consistency during the inversion process. In this work, we introduce Dual-Conditional Inversion (DCI), a novel framework that jointly conditions on the source prompt and reference image to guide the inversion process. Specifically, DCI formulates the inversion process as a dual-condition fixed-point optimization problem, minimizing both the latent noise gap and the reconstruction error under the joint guidance. This design anchors the inversion trajectory in both semantic and visual space, leading to more accurate and editable latent representations. Our novel setup brings new understanding to the inversion process. Extensive experiments demonstrate that DCI achieves state-of-the-art performance across multiple editing tasks, significantly improving both reconstruction quality and editing precision. Furthermore, we also demonstrate that our method achieves strong results in reconstruction tasks, implying a degree of robustness and generalizability approaching the ultimate goal of the inversion process.