SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model
<|reference_start|>SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model: 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.<|reference_end|>
@article{li2024sned:,
    title={SNED: Superposition Network Architecture Search for Efficient Video
  Diffusion Model},
    author={Zhengang Li, Yan Kang, Yuchen Liu, Difan Liu, Tobias Hinz, Feng Liu,
  Yanzhi Wang},
    journal={arXiv preprint arXiv:2406.00195},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00195},
    primaryClass={cs.CV cs.AI}
}