MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data
<|reference_start|>MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data: We train a model to generate images from multimodal prompts of interleaved text and images such as "a <picture of a man> man and his <picture of a dog> dog in an <picture of a cartoon> animated style." We bootstrap a multimodal dataset by extracting semantically meaningful image crops corresponding to words in the image captions of synthetically generated and publicly available text-image data. Our model, MUMU, is composed of a vision-language model encoder with a diffusion decoder and is trained on a single 8xH100 GPU node. Despite being only trained on crops from the same image, MUMU learns to compose inputs from different images into a coherent output. For example, an input of a realistic person and a cartoon will output the same person in the cartoon style, and an input of a standing subject and a scooter will output the subject riding the scooter. As a result, our model generalizes to tasks such as style transfer and character consistency. Our results show the promise of using multimodal models as general purpose controllers for image generation.<|reference_end|>
@article{berman2024mumu:,
    title={MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data},
    author={William Berman, Alexander Peysakhovich},
    journal={arXiv preprint arXiv:2406.18790},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18790},
    primaryClass={cs.CV cs.AI}
}