MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models

• URL: http://arxiv.org/abs/2510.17519v2
• Date: Wed, 22 Oct 2025 10:01:01 GMT
• Title: MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models
• Authors: Yongshun Zhang, Zhongyi Fan, Yonghang Zhang, Zhangzikang Li, Weifeng Chen, Zhongwei Feng, Chaoyue Wang, Peng Hou, Anxiang Zeng,
• Abstract summary: Training large-scale video generation models remains challenging and resource-intensive.<n>We present a training framework that optimize four pillars: data processing, model architecture, training strategy, and infrastructure.<n>We open-source the complete stack, including model weights, Megatron-Core-based large-scale training code, and inference pipelines for video generation and enhancement.
• Score: 23.09416541835573
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, large-scale generative models for visual content (\textit{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.

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