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Diffusion Models for Multi-Task Generative Modeling

Changyou Chen · Han Ding · Bunyamin Sisman · Yi Xu · Ouye Xie · Benjamin Yao · son tran · Belinda Zeng

Halle B #74
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Thu 9 May 7:30 a.m. PDT — 9:30 a.m. PDT


Diffusion-based generative modeling has been achieving state-of-the-art results on various generation tasks. Most diffusion models, however, are limited to a single-generation modeling. Can we generalize diffusion models with the ability of multi-modal generative training for more generalizable modeling? In this paper, we propose a principled way to define a diffusion model by constructing a unified multi-modal diffusion model in a common {\em diffusion space}. We define the forward diffusion process to be driven by an information aggregation from multiple types of task-data, {\it e.g.}, images for a generation task and labels for a classification task. In the reverse process, we enforce information sharing by parameterizing a shared backbone denoising network with additional modality-specific decoder heads. Such a structure can simultaneously learn to generate different types of multi-modal data with a multi-task loss, which is derived from a new multi-modal variational lower bound that generalizes the standard diffusion model. We propose several multi-modal generation settings to verify our framework, including image transition, masked-image training, joint image-label and joint image-representation generative modeling. Extensive experimental results on ImageNet indicate the effectiveness of our framework for various multi-modal generative modeling, which we believe is an important research direction worthy of more future explorations.

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