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KNN-Diffusion: Image Generation via Large-Scale Retrieval

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arxiv 2204.02849 v2 pith:OQI4E6SG submitted 2022-04-06 cs.CV cs.AIcs.CLcs.GRcs.LG

Shelly Sheynin , Oron Ashual , Adam Polyak , Uriel Singer , Oran Gafni , Eliya Nachmani , Yaniv Taigman This is my paper · ORCID
classification cs.CVcs.AIcs.CLcs.GRcs.LG
keywords large-scaleresultsretrievaltext-to-imagedatadatasetsdiffusionefficient
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Recent text-to-image models have achieved impressive results. However, since they require large-scale datasets of text-image pairs, it is impractical to train them on new domains where data is scarce or not labeled. In this work, we propose using large-scale retrieval methods, in particular, efficient k-Nearest-Neighbors (kNN), which offers novel capabilities: (1) training a substantially small and efficient text-to-image diffusion model without any text, (2) generating out-of-distribution images by simply swapping the retrieval database at inference time, and (3) performing text-driven local semantic manipulations while preserving object identity. To demonstrate the robustness of our method, we apply our kNN approach on two state-of-the-art diffusion backbones, and show results on several different datasets. As evaluated by human studies and automatic metrics, our method achieves state-of-the-art results compared to existing approaches that train text-to-image generation models using images only (without paired text data)

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