ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
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
67b33c3f8904ba09caa986fb
Rotem Shalev-Arkushin
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2025-02-18T09:32:25.103Z
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Rotem Shalev-Arkushin
user
Rotemsha
67b33c3f8904ba09caa986fc
Rinon Gal
67b33c3f8904ba09caa986fd
Amit H. Bermano
67b33c3f8904ba09caa986fe
Ohad Fried
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
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
ImageRAG improves the generation of rare concepts by integrating dynamic image retrieval into existing image generation models without requiring specialized training.
diffusion models Retrieval-Augmented Generation RAG image generation text prompt image conditioning models