Mobile Video Diffusion
Authors: name: Haitam Ben Yahia, name: Denis Korzhenkov, name: Ioannis Lelekas, name: Amir Ghodrati, name: Amirhossein Habibian

Video diffusion models have achieved impressive realism and controllability
but are limited by high computational demands, restricting their use on mobile
devices. This paper introduces the first mobile-optimized video diffusion
model. Starting from a spatio-temporal UNet from Stable Video Diffusion (SVD),
we reduce memory and computational cost by reducing the frame resolution,
incorporating multi-scale temporal representations, and introducing two novel
pruning schema to reduce the number of channels and temporal blocks.
Furthermore, we employ adversarial finetuning to reduce the denoising to a
single step. Our model, coined as MobileVD, is 523x more efficient (1817.2 vs.
4.34 TFLOPs) with a slight quality drop (FVD 149 vs. 171), generating latents
for a 14x512x256 px clip in 1.7 seconds on a Xiaomi-14 Pro. Our results are
available at https://qualcomm-ai-research.github.io/mobile-video-diffusion/