  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.
