MDSGen: Fast and Efficient Masked Diffusion Temporal-Aware Transformers for Open-Domain Sound Generation

• URL: http://arxiv.org/abs/2410.02130v2
• Date: Thu, 13 Feb 2025 08:24:37 GMT
• Title: MDSGen: Fast and Efficient Masked Diffusion Temporal-Aware Transformers for Open-Domain Sound Generation
• Authors: Trung X. Pham, Tri Ton, Chang D. Yoo,
• Abstract summary: We introduce MDSGen, a novel framework for vision-guided open-domain sound generation. MDSGen employs denoising masked diffusion transformers, facilitating efficient generation without reliance on pre-trained diffusion models. Evaluated on the benchmark VGGSound dataset, our smallest model (5M parameters) achieves $97.9$% alignment accuracy. Our larger model (131M parameters) reaches nearly $99$% accuracy while requiring $6.5times$ fewer parameters.
• Score: 21.242398582282522
• License:
• Abstract: We introduce MDSGen, a novel framework for vision-guided open-domain sound generation optimized for model parameter size, memory consumption, and inference speed. This framework incorporates two key innovations: (1) a redundant video feature removal module that filters out unnecessary visual information, and (2) a temporal-aware masking strategy that leverages temporal context for enhanced audio generation accuracy. In contrast to existing resource-heavy Unet-based models, \texttt{MDSGen} employs denoising masked diffusion transformers, facilitating efficient generation without reliance on pre-trained diffusion models. Evaluated on the benchmark VGGSound dataset, our smallest model (5M parameters) achieves $97.9$% alignment accuracy, using $172\times$ fewer parameters, $371$% less memory, and offering $36\times$ faster inference than the current 860M-parameter state-of-the-art model ($93.9$% accuracy). The larger model (131M parameters) reaches nearly $99$% accuracy while requiring $6.5\times$ fewer parameters. These results highlight the scalability and effectiveness of our approach. The code is available at https://bit.ly/mdsgen.

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