Towards Generalized and Training-Free Text-Guided Semantic Manipulation

• URL: http://arxiv.org/abs/2504.17269v2
• Date: Tue, 01 Jul 2025 14:10:46 GMT
• Title: Towards Generalized and Training-Free Text-Guided Semantic Manipulation
• Authors: Yu Hong, Xiao Cai, Pengpeng Zeng, Shuai Zhang, Jingkuan Song, Lianli Gao, Heng Tao Shen,
• Abstract summary: Text-guided semantic manipulation refers to semantically editing an image generated from a source prompt to match a target prompt.<n>We propose a novel $textitGTF$ for text-guided semantic manipulation, which has the following attractive capabilities.<n>Our experiments demonstrate the efficacy of our approach, highlighting its potential to advance the state-of-the-art in semantics manipulation.
• Score: 123.80467566483038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-guided semantic manipulation refers to semantically editing an image generated from a source prompt to match a target prompt, enabling the desired semantic changes (e.g., addition, removal, and style transfer) while preserving irrelevant contents. With the powerful generative capabilities of the diffusion model, the task has shown the potential to generate high-fidelity visual content. Nevertheless, existing methods either typically require time-consuming fine-tuning (inefficient), fail to accomplish multiple semantic manipulations (poorly extensible), and/or lack support for different modality tasks (limited generalizability). Upon further investigation, we find that the geometric properties of noises in the diffusion model are strongly correlated with the semantic changes. Motivated by this, we propose a novel $\textit{GTF}$ for text-guided semantic manipulation, which has the following attractive capabilities: 1) $\textbf{Generalized}$: our $\textit{GTF}$ supports multiple semantic manipulations (e.g., addition, removal, and style transfer) and can be seamlessly integrated into all diffusion-based methods (i.e., Plug-and-play) across different modalities (i.e., modality-agnostic); and 2) $\textbf{Training-free}$: $\textit{GTF}$ produces high-fidelity results via simply controlling the geometric relationship between noises without tuning or optimization. Our extensive experiments demonstrate the efficacy of our approach, highlighting its potential to advance the state-of-the-art in semantics manipulation.

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