TweezeEdit: Consistent and Efficient Image Editing with Path Regularization

2025-08-14

AI Summary

Existing text-guided image editing methods often over-align with target prompts, compromising semantic consistency of the source image, and rely on computationally expensive image inversion and lengthy denoising trajectories. This paper proposes an efficient, inversion-free, and fine-tuning-free editing framework. Its core innovation is path-level regularization: instead of anchoring to intermediate latent states, it imposes gradient-driven consistency constraints directly across the entire denoising trajectory, integrated with a text-semantic injection mechanism. The method reduces the editing process to merely 12 denoising steps—approximately 1.6 seconds per edit—while preserving source-image semantics and improving alignment fidelity to the target prompt. Extensive experiments demonstrate consistent superiority over state-of-the-art methods across multiple benchmarks, confirming its efficacy and potential for real-time applications.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Over-alignment with target prompts, poor source semantics preservation
• Inefficient editing due to elongated denoising paths
• Suboptimal inversion anchors for semantic consistency

Innovation

• Tuning- and inversion-free framework for editing
• Regularizes entire denoising path for consistency
• Uses gradient-driven direct-path semantic injection