IC-Custom: Diverse Image Customization via In-Context Learning

IC-Custom Team
Paper Code HF Demo HF Model

Introduction

IC-Custom introduces a novel approach to image customization through in-context learning, enabling diverse and flexible image generation scenarios.

Position-aware

Precise control over object placement with reference images and target backgrounds

Position-free

Generate new images while preserving reference object identity

IC-Custom Method Overview

Figure 1: IC-Custom methodology overview

Position-aware Image Customization

Hover over the images to see the generated results - Our model seamlessly integrates reference content into target scenes

Position-free Image Customization

🖱️ Hover over any image to see the generated result and text prompt - Our model creates images based on text prompts while maintaining reference identity

Reference Image Generated Image

"wandering through a lush jungle..."

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Reference Image Generated Image

"Strolling in the pond..."

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Reference Image Generated Image

"on the blanket..."

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Reference Image Generated Image

"...rests on a rustic wooden table..."

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Reference Image Generated Image

"The yellow alarm clock is perched on a snowy mountain peak at sunrise..."

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Reference Image Generated Image

"A cat is lying on some ancient books..."

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Reference Image

Reference Image

Generated Image

Generated Image

Text Prompt

BibTeX

@article {li2025ic,
title ={IC-Custom: Diverse Image Customization via In-Context Learning},
author ={Li, Yaowei and Li, Xiaoyu and Zhang, Zhaoyang and Bian, Yuxuan and Liu, Gan and Li, Xinyuan and Xu, Jiale and Hu, Wenbo and Liu, Yating and Li, Lingen and others},
journal ={arXiv preprint arXiv:2507.01926},
year ={2025}
}
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