InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning
This addresses the scalability and efficiency issue for users needing quick personalized image generation, though it is an incremental improvement over existing personalization techniques.
The paper tackles the problem of slow test-time finetuning in personalized text-to-image generation by proposing InstantBooth, which generates personalized images without finetuning, achieving competitive results and being 100 times faster than existing methods.
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.