SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model

• URL: http://arxiv.org/abs/2406.00195v1
• Date: Fri, 31 May 2024 21:12:30 GMT
• Title: SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model
• Authors: Zhengang Li, Yan Kang, Yuchen Liu, Difan Liu, Tobias Hinz, Feng Liu, Yanzhi Wang,
• Abstract summary: This paper presents SNED, a superposition network architecture search method for efficient video diffusion model. Our method employs a supernet training paradigm that targets various model cost and resolution options. Our framework consistently produces comparable results across different model options with high efficiency.
• Score: 41.825824810180215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While AI-generated content has garnered significant attention, achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion models for video generation quality, the complex model architecture and substantial computational demands for both training and inference create a significant gap between these models and real-world applications. This paper presents SNED, a superposition network architecture search method for efficient video diffusion model. Our method employs a supernet training paradigm that targets various model cost and resolution options using a weight-sharing method. Moreover, we propose the supernet training sampling warm-up for fast training optimization. To showcase the flexibility of our method, we conduct experiments involving both pixel-space and latent-space video diffusion models. The results demonstrate that our framework consistently produces comparable results across different model options with high efficiency. According to the experiment for the pixel-space video diffusion model, we can achieve consistent video generation results simultaneously across 64 x 64 to 256 x 256 resolutions with a large range of model sizes from 640M to 1.6B number of parameters for pixel-space video diffusion models.

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