We have witnessed the unprecedented success of diffusion-based video generation over the past year. Recently proposed models from the community have wielded the power to generate cinematic and high-resolution videos with smooth motions from arbitrary input prompts. However, as a supertask of image generation, video generation models require more computation and are thus hosted mostly on cloud servers, limiting broader adoption among content creators. In this work, we propose a comprehensive acceleration framework to bring the power of the large-scale video diffusion model to the hands of edge users. From the network architecture scope, we initialize from a compact image backbone and search out the design and arrangement of temporal layers to maximize hardware efficiency. In addition, we propose a dedicated adversarial fine-tuning algorithm for our efficient model and reduce the denoising steps to 4. Our model, with only 0.6B parameters, can generate a 5-second video on an iPhone 16 PM within 5 seconds. Compared to server-side models that take minutes on powerful GPUs to generate a single video, we accelerate the generation by magnitudes while delivering on-par quality.
@article{arxiv.2412.10494,
title = {SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device},
author = {Yushu Wu and Zhixing Zhang and Yanyu Li and Yanwu Xu and Anil Kag and Yang Sui and Huseyin Coskun and Ke Ma and Aleksei Lebedev and Ju Hu and Dimitris Metaxas and Yanzhi Wang and Sergey Tulyakov and Jian Ren},
journal= {arXiv preprint arXiv:2412.10494},
year = {2025}
}