TweezeEdit: Consistent and Efficient Image Editing with Path Regularization

TL;DR

TweezeEdit is a novel image editing framework that enhances semantic preservation and efficiency by regularizing the denoising path, enabling real-time edits with minimal steps and improved target alignment.

Contribution

It introduces a tuning- and inversion-free approach that regularizes the entire denoising path, significantly improving semantic retention and editing efficiency over existing methods.

Findings

Outperforms existing methods in semantic preservation and target alignment.

Requires only 12 steps (1.6 seconds) per edit for real-time application.

Effectively maintains source image semantics during editing.

Abstract

Large-scale pre-trained diffusion models empower users to edit images through text guidance. However, existing methods often over-align with target prompts while inadequately preserving source image semantics. Such approaches generate target images explicitly or implicitly from the inversion noise of the source images, termed the inversion anchors. We identify this strategy as suboptimal for semantic preservation and inefficient due to elongated editing paths. We propose TweezeEdit, a tuning- and inversion-free framework for consistent and efficient image editing. Our method addresses these limitations by regularizing the entire denoising path rather than relying solely on the inversion anchors, ensuring source semantic retention and shortening editing paths. Guided by gradient-driven regularization, we efficiently inject target prompt semantics along a direct path using a consistency model. Extensive experiments demonstrate TweezeEdit's superior performance in semantic preservation and target alignment, outperforming existing methods. Remarkably, it requires only 12 steps (1.6 seconds per edit), underscoring its potential for real-time applications.