title: ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
summary: 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_authors._id: 67b33c3f8904ba09caa986fb
paper_authors.name: Rotem Shalev-Arkushin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:32:25.103Z
paper_authors._id: 63999742ab8f65687a062763
paper_authors.avatarUrl: /avatars/813bd7f2ec125e209a2e10e29d9386db.svg
paper_authors.fullname: Rotem Shalev-Arkushin
paper_authors.type: user
paper_authors.user: Rotemsha
paper_authors._id: 67b33c3f8904ba09caa986fc
paper_authors.name: Rinon Gal
paper_authors._id: 67b33c3f8904ba09caa986fd
paper_authors.name: Amit H. Bermano
paper_authors._id: 67b33c3f8904ba09caa986fe
paper_authors.name: Ohad Fried
paper_title: ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
paper_summary: 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_ai_summary: ImageRAG improves the generation of rare concepts by integrating dynamic image retrieval into existing image generation models without requiring specialized training.
paper_ai_keywords: diffusion models Retrieval-Augmented Generation RAG image generation text prompt image conditioning models