Diffusion models enable high-quality and diverse visual content synthesis. However, they struggle to generate rare or unseen concepts. To address this challenge, we explore the usage of Retrieval-Augmented Generation (RAG) with image generation models. We propose ImageRAG, a method that dynamically retrieves relevant images based on a given text prompt, and uses them as context to guide the generation process. Prior approaches that used retrieved images to improve generation, trained models specifically for retrieval-based generation. In contrast, ImageRAG leverages the capabilities of existing image conditioning models, and does not require RAG-specific training. Our approach is highly adaptable and can be applied across different model types, showing significant improvement in generating rare and fine-grained concepts using different base models. Our project page is available at: https://rotem-shalev.github.io/ImageRAG
@article{arxiv.2502.09411,
title = {ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation},
author = {Rotem Shalev-Arkushin and Rinon Gal and Amit H. Bermano and Ohad Fried},
journal= {arXiv preprint arXiv:2502.09411},
year = {2026}
}