MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation
  Models
Authors: name: Yongshun Zhang, name: Zhongyi Fan, name: Yonghang Zhang, name: Zhangzikang Li, name: Weifeng Chen, name: Zhongwei Feng, name: Chaoyue Wang, name: Peng Hou, name: Anxiang Zeng
Organization: shopee-llm-mug team

In recent years, large-scale generative models for visual content
(e.g., images, videos, and 3D objects/scenes) have made remarkable
progress. However, training large-scale video generation models remains
particularly challenging and resource-intensive due to cross-modal text-video
alignment, the long sequences involved, and the complex spatiotemporal
dependencies. To address these challenges, we present a training framework that
optimizes four pillars: (i) data processing, (ii) model architecture, (iii)
training strategy, and (iv) infrastructure for large-scale video generation
models. These optimizations delivered significant efficiency gains and
performance improvements across all stages of data preprocessing, video
compression, parameter scaling, curriculum-based pretraining, and
alignment-focused post-training. Our resulting model, MUG-V 10B, matches recent
state-of-the-art video generators overall and, on e-commerce-oriented video
generation tasks, surpasses leading open-source baselines in human evaluations.
More importantly, we open-source the complete stack, including model weights,
Megatron-Core-based large-scale training code, and inference pipelines for
video generation and enhancement. To our knowledge, this is the first public
release of large-scale video generation training code that exploits
Megatron-Core to achieve high training efficiency and near-linear multi-node
scaling, details are available in
https://github.com/Shopee-MUG/MUG-V{our webpage}.