ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation

• URL: http://arxiv.org/abs/2502.09411v1
• Date: Thu, 13 Feb 2025 15:36:12 GMT
• Title: ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
• Authors: Rotem Shalev-Arkushin, Rinon Gal, Amit H. Bermano, Ohad Fried,
• Abstract summary: Diffusion models struggle to generate rare or unseen concepts. We propose ImageRAG, a method that dynamically retrieves relevant images based on a given text prompt. Our approach is highly adaptable and can be applied across different model types.
• Score: 25.39019070750831
• License:
• 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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