Edit-by-Example: Adaptive Exemplar-Based Image Editing

摘要

Recent advances in diffusion-based image editing models have demonstrated remarkable success. However, these models primarily rely on high-quality textual prompts to guide image manipulation, creating a significant barrier for non-expert users. In this demonstration, we present an exemplar-based image editing framework named Edit-by-Example, which eliminates the reliance on textual prompts, requires only a single pair of before-and-after images to encapsulate the desired editing effect that can readily be applied on the user-provided query image without any model fine-tuning. Technically, our framework comprises two components: an Adaptive Editing Policy Module (AEPM) and a Generation Module (GM). The AEPM jointly analyzes cross-image relationships in exemplar pairs and query image content to derive optimal editing directions, while GM executes these policies through an off-the-shelf image editor with optional semantic alignment verification. We introduce EEdBench, a comprehensive benchmark for exemplar-based image editing containing 1,500 test cases across 15 categories. Experiments demonstrate that our framework outperforms existing prompt-free methods in editing direction accuracy (S-Visual) and fidelity (FID).