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Recent advances in personalized image generation allow a pre-trainedtext-to-image model to learn a new concept from a set of images. However,existing personalization approaches usually require heavy test-time finetuningfor each concept, which is time-consuming and difficult to scale. We proposeInstantBooth, a novel approach built upon pre-trained text-to-image models thatenables instant text-guided image personalization without any test-timefinetuning. We achieve this with several major components. First, we learn thegeneral concept of the input images by converting them to a textual token witha learnable image encoder. Second, to keep the fine details of the identity, welearn rich visual feature representation by introducing a few adapter layers tothe pre-trained model. We train our components only on text-image pairs withoutusing paired images of the same concept. Compared to test-time finetuning-basedmethods like DreamBooth and Textual-Inversion, our model can generatecompetitive results on unseen concepts concerning language-image alignment,image fidelity, and identity preservation while being 100 times faster.

InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning