ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
Authors: name: Rotem Shalev-Arkushin, name: Rinon Gal, name: Amit H. Bermano, name: Ohad Fried

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