Training-Free Disentangled Text-Guided Image Editing via Sparse Latent Constraints

• URL: http://arxiv.org/abs/2512.21637v1
• Date: Thu, 25 Dec 2025 11:38:10 GMT
• Title: Training-Free Disentangled Text-Guided Image Editing via Sparse Latent Constraints
• Authors: Mutiara Shabrina, Nova Kurnia Putri, Jefri Satria Ferdiansyah, Sabita Khansa Dewi, Novanto Yudistira,
• Abstract summary: Text-driven image manipulation often suffers from attribute entanglement, where modifying a target attribute unintentionally alters other semantic properties such as identity or appearance.<n>The Predict, Prevent, and Evaluate framework addresses this issue by leveraging pre-trained vision-language models for disentangled editing.<n> Experimental results demonstrate that the proposed approach enforces more focused and controlled edits, effectively reducing unintended changes in non-target attributes while preserving facial identity.
• Score: 2.4140502941897544
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Text-driven image manipulation often suffers from attribute entanglement, where modifying a target attribute (e.g., adding bangs) unintentionally alters other semantic properties such as identity or appearance. The Predict, Prevent, and Evaluate (PPE) framework addresses this issue by leveraging pre-trained vision-language models for disentangled editing. In this work, we analyze the PPE framework, focusing on its architectural components, including BERT-based attribute prediction and StyleGAN2-based image generation on the CelebA-HQ dataset. Through empirical analysis, we identify a limitation in the original regularization strategy, where latent updates remain dense and prone to semantic leakage. To mitigate this issue, we introduce a sparsity-based constraint using L1 regularization on latent space manipulation. Experimental results demonstrate that the proposed approach enforces more focused and controlled edits, effectively reducing unintended changes in non-target attributes while preserving facial identity.

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