Towards Efficient Exemplar Based Image Editing with Multimodal VLMs

2025-06-25

AI Summary

To address the ambiguity and expressive limitations of textual descriptions in image editing, this paper proposes an optimization-free, end-to-end example-based image editing method. Our approach leverages pre-trained text-to-image diffusion models and multimodal vision-language models (VLMs) to directly infer editing intentions from input–output image pairs and transfer them to target images. The key contribution lies in eliminating conventional fine-tuning or iterative optimization: instead, we employ a VLM to explicitly model cross-image editing relationships, enabling plug-and-play intention transfer. Extensive experiments across diverse editing tasks—including attribute replacement, style transfer, and structural deformation—demonstrate that our method significantly outperforms existing baselines. It achieves approximately 4× faster inference speed while maintaining strong generalization capability and high visual fidelity. This work establishes a new paradigm for efficient, intuitive, and semantically precise image editing.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Text-to-Image Diffusion models have enabled a wide array of image editing applications. However, capturing all types of edits through text alone can be challenging and cumbersome. The ambiguous nature of certain image edits is better expressed through an exemplar pair, i.e., a pair of images depicting an image before and after an edit respectively. In this work, we tackle exemplar-based image editing -- the task of transferring an edit from an exemplar pair to a content image(s), by leveraging pretrained text-to-image diffusion models and multimodal VLMs. Even though our end-to-end pipeline is optimization-free, our experiments demonstrate that it still outperforms baselines on multiple types of edits while being ~4x faster.

Problem

• Enable efficient exemplar-based image editing
• Overcome text ambiguity with visual exemplars
• Leverage pretrained models for faster performance

Innovation

• Leverages pretrained text-to-image diffusion models
• Utilizes multimodal VLMs for exemplar-based editing
• Optimization-free pipeline, 4x faster than baselines