Computer Science > Computer Vision and Pattern Recognition
[Submitted on 3 Dec 2024 (v1), last revised 17 Jan 2025 (this version, v4)]
Title:HunyuanVideo: A Systematic Framework For Large Video Generative Models
View PDF HTML (experimental)Abstract:Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at this https URL.
Submission history
From: Zijian Zhang [view email][v1] Tue, 3 Dec 2024 23:52:37 UTC (44,386 KB)
[v2] Fri, 6 Dec 2024 17:02:10 UTC (44,386 KB)
[v3] Thu, 2 Jan 2025 09:13:42 UTC (48,072 KB)
[v4] Fri, 17 Jan 2025 10:16:18 UTC (48,072 KB)
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