  The remarkable advancement in text-to-image generation models significantly
boosts the research in ID customization generation. However, existing
personalization methods cannot simultaneously satisfy high fidelity and
high-efficiency requirements. Their main bottleneck lies in the prompt image
encoder, which produces weak alignment signals with the text-to-image model and
significantly increased model size. Towards this end, we propose a lightweight
Inv-Adapter, which first extracts diffusion-domain representations of ID images
utilizing a pre-trained text-to-image model via DDIM image inversion, without
additional image encoder. Benefiting from the high alignment of the extracted
ID prompt features and the intermediate features of the text-to-image model, we
then embed them efficiently into the base text-to-image model by carefully
designing a lightweight attention adapter. We conduct extensive experiments to
assess ID fidelity, generation loyalty, speed, and training parameters, all of
which show that the proposed Inv-Adapter is highly competitive in ID
customization generation and model scale.
