LAMS-Edit: Latent and Attention Mixing with Schedulers for Improved Content Preservation in Diffusion-Based Image and Style Editing

• URL: http://arxiv.org/abs/2601.02987v1
• Date: Tue, 06 Jan 2026 12:57:05 GMT
• Title: LAMS-Edit: Latent and Attention Mixing with Schedulers for Improved Content Preservation in Diffusion-Based Image and Style Editing
• Abstract summary: We propose LAMS-Edit, leveraging intermediate states from the inversion process.<n>Latent representations and attention maps from both processes are combined at each step using weighted, controlled by a scheduler.<n>This technique integrates with Prompt-to-Prompt (P2P) to form LAMS-Edit--an framework that supports precise editing with region masks and enables style transfer via LoRA.
• Score: 12.144338545920649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-Image editing using diffusion models faces challenges in balancing content preservation with edit application and handling real-image editing. To address these, we propose LAMS-Edit, leveraging intermediate states from the inversion process--an essential step in real-image editing--during edited image generation. Specifically, latent representations and attention maps from both processes are combined at each step using weighted interpolation, controlled by a scheduler. This technique, Latent and Attention Mixing with Schedulers (LAMS), integrates with Prompt-to-Prompt (P2P) to form LAMS-Edit--an extensible framework that supports precise editing with region masks and enables style transfer via LoRA. Extensive experiments demonstrate that LAMS-Edit effectively balances content preservation and edit application.

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