Inv-Adapter: ID Customization Generation via Image Inversion and Lightweight Adapter

• URL: http://arxiv.org/abs/2406.02881v2
• Date: Thu, 6 Jun 2024 06:59:46 GMT
• Title: Inv-Adapter: ID Customization Generation via Image Inversion and Lightweight Adapter
• Authors: Peng Xing, Ning Wang, Jianbo Ouyang, Zechao Li,
• Abstract summary: 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. 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. 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.
• Score: 23.690420512911146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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