HunyuanVideo: A Systematic Framework For Large Video Generative Models (2412.03603v2)

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 https://github.com/Tencent/HunyuanVideo.

Overview of "HunyuanVideo: A Systematic Framework For Large Video Generative Models"

The paper, "HunyuanVideo: A Systematic Framework For Large Video Generative Models," presents HunyuanVideo, an open-source video generation model that aims to bridge the persistent gap between open-source and closed-source video generative models. Despite the advancements within the domain of image generative models, video generation has remained relatively underexplored, primarily due to the lack of robust publicly accessible models. This paper highlights HunyuanVideo's capacity to deliver high-quality video generation that benchmarks closely with, or even surpasses, existing closed-source frameworks. Several components make up the framework, encompassing data collection, model architecture design, model training techniques, and a progressive fine-tuning strategy.

Architectural Design and Implementation

HunyuanVideo integrates cutting-edge architectural strategies, such as the Transformer-based diffusion model, text-video alignment methodologies, and high-capacity model scaling. It employs a hierarchical data filtering pipeline, categorizing data into stringent quality spasms to bolster pre-training efficacy. This approach ensures the model is trained on a curated high-quality dataset, pivotal for extracting major experiential insights from both images and video sequences.

The model architecture itself represents an innovative combination of Causal 3D Variational AutoEncoders and scalable Transformers, designed to take full advantage of the increased data available in video formats. One of the standout features of the model is its 13-billion parameter size, accommodating exhaustive fine-tuning and showcasing exceptional detail in generated video content. HunyuanVideo enhances the generative process with support for scalable prompt interjection and reference-guided diffusion models.

Experimental Results and Evaluation

On the experimental front, HunyuanVideo claims noteworthy performance metrics against both proprietary and competitive models within the video generation landscape. Through a series of qualitative assessments, it outperformed state-of-the-art models like Runway Gen-3 and Luma 1.6, as well as top-performing Chinese video models. In particular, the paper underscores HunyuanVideo's excellence in maintaining visual quality, motion fidelity, and text-video alignment—a frequently challenging aspect in video generation models.

Strong empirical results are cited from user studies involving comprehensive prompt benchmarks, demonstrating HunyuanVideo's proficiency in generating high-motion dynamics, complex concept generalizations, and semantically coherent scene transitions. The consistent integration of auxiliary tasks such as video-to-audio generation and image-to-video conversion further supports domain expansion for multimedia applications.

Implications and Future Directions

The implications of HunyuanVideo’s release are substantial for both academic researchers and innovators within the creative industry. An open-source model of this scale and capacity invites ongoing community-driven improvements, allowing researchers to dive deeper into specific components or propose novel techniques leveraging its framework. Moreover, the model’s compatibility with diverse forms of generative tasks, including video modifications and avatar animations, offers practical utility and invites expanded use cases across different sectors.

For future trajectories in AI developments, HunyuanVideo's strategy of integrating progressive data scaling, translator-style attention mechanisms, and specialized prompt preprocessing suggests promising routes for advancing video generative models. The evolution of such models could significantly augment the applications of synthetic video content creation, impacting industries ranging from entertainment to education.

Overall, this paper provides a compelling narrative on the strategic design and application of an advanced video generative model, making it a valuable contribution to the field of video machine learning. The synthesis of policy, infrastructure, and robust experimental validation lays a foundational structure for future open-source video generative efforts.