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

TL;DR

InstantBooth enables instant personalized text-to-image generation without test-time finetuning, using a learnable image encoder and adapter layers to preserve identity and details efficiently.

Contribution

It introduces a method that achieves fast, test-time finetuning-free personalization in text-to-image models using novel components trained solely on text-image pairs.

Findings

Generates competitive personalized images without finetuning.

Operates 100 times faster than existing methods.

Maintains high identity preservation and image fidelity.

Abstract

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