ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912886190768128 |
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| author | Shalev-Arkushin, Rotem Gal, Rinon Bermano, Amit H. Fried, Ohad |
| author_facet | Shalev-Arkushin, Rotem Gal, Rinon Bermano, Amit H. Fried, Ohad |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09411 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation Shalev-Arkushin, Rotem Gal, Rinon Bermano, Amit H. Fried, Ohad Computer Vision and Pattern Recognition Graphics 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 |
| title | ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2502.09411 |