ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation

Rotem Shalev-Arkushin, Rinon Gal, Amit H. Bermano, Ohad Fried

ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation: 22 upvotes on Hugging Face Daily Papers, #8 of 23 papers on 2025-02-17. Day-by-day upvote history.

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

Paper page on Hugging Face · arXiv

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