Aquarius: A Family of Industry-Level Video Generation Models for Marketing Scenarios

• URL: http://arxiv.org/abs/2505.10584v1
• Date: Wed, 14 May 2025 13:39:53 GMT
• Title: Aquarius: A Family of Industry-Level Video Generation Models for Marketing Scenarios
• Authors: Huafeng Shi, Jianzhong Liang, Rongchang Xie, Xian Wu, Cheng Chen, Chang Liu,
• Abstract summary: This report introduces Aquarius, a family of industry-level video generation models for marketing scenarios.<n>Aquarius demonstrates exceptional performance in high-fidelity, multi-aspect-ratio, and long-duration video synthesis.<n>We are about to open-source the entire data processing framework named "Aquarius-Datapipe"
• Score: 30.314363181535118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This report introduces Aquarius, a family of industry-level video generation models for marketing scenarios designed for thousands-xPU clusters and models with hundreds of billions of parameters. Leveraging efficient engineering architecture and algorithmic innovation, Aquarius demonstrates exceptional performance in high-fidelity, multi-aspect-ratio, and long-duration video synthesis. By disclosing the framework's design details, we aim to demystify industrial-scale video generation systems and catalyze advancements in the generative video community. The Aquarius framework consists of five components: Distributed Graph and Video Data Processing Pipeline: Manages tens of thousands of CPUs and thousands of xPUs via automated task distribution, enabling efficient video data processing. Additionally, we are about to open-source the entire data processing framework named "Aquarius-Datapipe". Model Architectures for Different Scales: Include a Single-DiT architecture for 2B models and a Multimodal-DiT architecture for 13.4B models, supporting multi-aspect ratios, multi-resolution, and multi-duration video generation. High-Performance infrastructure designed for video generation model training: Incorporating hybrid parallelism and fine-grained memory optimization strategies, this infrastructure achieves 36% MFU at large scale. Multi-xPU Parallel Inference Acceleration: Utilizes diffusion cache and attention optimization to achieve a 2.35x inference speedup. Multiple marketing-scenarios applications: Including image-to-video, text-to-video (avatar), video inpainting and video personalization, among others. More downstream applications and multi-dimensional evaluation metrics will be added in the upcoming version updates.

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