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Recently, diffusion models have been used successfully to fit distributionsfor cross-modal data translation and multimodal data generation. However, thesemethods rely on extensive scaling, overlooking the inefficiency andinterference between modalities. We develop Partially Shared U-Net (PS-U-Net)architecture which is an efficient multimodal diffusion model that allows textand image inputs to pass through dedicated layers and skip-connections forpreserving modality-specific fine-grained details. Inspired by imageinpainting, we also propose a new efficient multimodal sampling method thatintroduces new scenarios for conditional generation while only requiring asimple joint distribution to be learned. Our empirical exploration of theMS-COCO dataset demonstrates that our method generates multimodal text andimage data with higher quality compared to existing multimodal diffusion modelswhile having a comparable size, faster training, faster multimodal sampling,and more flexible generation.

Efficient Multimodal Diffusion Models Using Joint Data Infilling with Partially Shared U-Net