MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data

• URL: http://arxiv.org/abs/2406.18790v2
• Date: Wed, 11 Sep 2024 21:56:02 GMT
• Title: MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data
• Authors: William Berman, Alexander Peysakhovich,
• Abstract summary: We bootstrap a multimodal dataset by extracting semantically meaningful image crops corresponding to words in the 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.
• Score: 50.94623170336122
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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