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

• URL: http://arxiv.org/abs/2311.16488v1
• Date: Tue, 28 Nov 2023 04:34:44 GMT
• Title: Efficient Multimodal Diffusion Models Using Joint Data Infilling with Partially Shared U-Net
• Authors: Zizhao Hu, Shaochong Jia, Mohammad Rostami
• Abstract summary: Partially Shared U-Net (PS-U-Net) is an efficient multimodal diffusion model that allows text and image inputs to pass through dedicated layers and skip-connections for preserving modality-specific fine-grained details. Inspired by image inpainting, we also propose a new efficient multimodal sampling method that introduces new scenarios for conditional generation while only requiring a simple joint distribution to be learned. Our empirical exploration of the MS-COCO dataset demonstrates that our method generates multimodal text and image data with higher quality compared to existing multimodal diffusion models.
• Score: 20.437172251393257
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, diffusion models have been used successfully to fit distributions for cross-modal data translation and multimodal data generation. However, these methods rely on extensive scaling, overlooking the inefficiency and interference between modalities. We develop Partially Shared U-Net (PS-U-Net) architecture which is an efficient multimodal diffusion model that allows text and image inputs to pass through dedicated layers and skip-connections for preserving modality-specific fine-grained details. Inspired by image inpainting, we also propose a new efficient multimodal sampling method that introduces new scenarios for conditional generation while only requiring a simple joint distribution to be learned. Our empirical exploration of the MS-COCO dataset demonstrates that our method generates multimodal text and image data with higher quality compared to existing multimodal diffusion models while having a comparable size, faster training, faster multimodal sampling, and more flexible generation.

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