MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation
  Models
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}.
68f7743ea7b9b96b2c13ed5b
Yongshun Zhang
68f7743ea7b9b96b2c13ed5c
Zhongyi Fan
68f7743ea7b9b96b2c13ed5d
Yonghang Zhang
68f7743ea7b9b96b2c13ed5e
Zhangzikang Li
68f7743ea7b9b96b2c13ed5f
Weifeng Chen
68f7743ea7b9b96b2c13ed60
Zhongwei Feng
68f7743ea7b9b96b2c13ed61
Chaoyue Wang
68f7743ea7b9b96b2c13ed62
Peng Hou
68f7743ea7b9b96b2c13ed63
Anxiang Zeng
MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation
  Models
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}.
A training framework for large-scale video generation models optimizes data processing, model architecture, training strategy, and infrastructure, resulting in a model that matches state-of-the-art performance and is open-sourced with Megatron-Core-based training code.
generative models video generation cross-modal text-video alignment spatiotemporal dependencies data processing model architecture training strategy infrastructure video compression parameter scaling curriculum-based pretraining alignment-focused post-training Megatron-Core multi-node scaling