MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
<|reference_start|>MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation: 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.<|reference_end|>
@article{song2024moma:,
    title={MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation},
    author={Kunpeng Song, Yizhe Zhu, Bingchen Liu, Qing Yan, Ahmed Elgammal, Xiao
  Yang},
    journal={arXiv preprint arXiv:2404.05674},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05674},
    primaryClass={cs.CV}
}