MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation

• URL: http://arxiv.org/abs/2404.05674v1
• Date: Mon, 8 Apr 2024 16:55:49 GMT
• Title: MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
• Authors: Kunpeng Song, Yizhe Zhu, Bingchen Liu, Qing Yan, Ahmed Elgammal, Xiao Yang,
• Abstract summary: We present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. We train MoMA to serve a dual role as both a feature extractor and a generator. We introduce a novel self-attention shortcut method that efficiently transfers image features to an image diffusion model.
• Score: 22.69019130782004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. As foundational text-to-image models rapidly evolve, the demand for robust image-to-image translation grows. Addressing this need, MoMA specializes in subject-driven personalized image generation. Utilizing an open-source, Multimodal Large Language Model (MLLM), we train MoMA to serve a dual role as both a feature extractor and a generator. This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model. To better leverage the generated features, we further introduce a novel self-attention shortcut method that efficiently transfers image features to an image diffusion model, improving the resemblance of the target object in generated images. Remarkably, as a tuning-free plug-and-play module, our model requires only a single reference image and outperforms existing methods in generating images with high detail fidelity, enhanced identity-preservation and prompt faithfulness. Our work is open-source, thereby providing universal access to these advancements.

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