ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation

🤗 Upvotes: 12 | cs.CV, cs.GR

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

Title:
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation

Arxiv:
http://arxiv.org/abs/2502.09411v1

Abstract:
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

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