arXiv:2503.09642v1 Announce Type: cross Abstract: Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quantity, and greater demand for training compute. In this report, we present Open-Sora 2.0, a commercial-level video generation model trained for only $200k. With this model, we demonstrate that the cost of training a top-performing video generation model is highly controllable. We detail all techniques that contribute to this efficiency breakthrough, including data curation, model architecture, training strategy, and system optimization. According to human evaluation results and VBench scores, Open-Sora 2.0 is comparable to global leading video generation models including the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. By making Open-Sora 2.0 fully open-source, we aim to democratize access to advanced video generation technology, fostering broader innovation and creativity in content creation. All resources are publicly available at: https://github.com/hpcaitech/Open-Sora.
The article titled “Open-Sora 2.0: Democratizing Access to Advanced Video Generation Technology” highlights the significant progress made in video generation models and the challenges associated with it. While the quality of AI-generated videos has improved, it has come at the expense of larger model sizes, increased data requirements, and higher demand for training compute. However, the report introduces Open-Sora 2.0, a commercial-level video generation model trained at a remarkably low cost of 0k. The authors demonstrate that the cost of training a top-performing video generation model can be highly controlled. They delve into the various techniques employed to achieve this efficiency breakthrough, including data curation, model architecture, training strategy, and system optimization. The evaluation results and VBench scores indicate that Open-Sora 2.0 is on par with leading video generation models like HunyuanVideo and Runway Gen-3 Alpha. By making Open-Sora 2.0 fully open-source, the authors aim to democratize access to advanced video generation technology, fostering innovation and creativity in content creation. The resources for Open-Sora 2.0 are publicly available on GitHub.
Open-Sora 2.0: Revolutionizing Video Generation with Cost-Efficiency
Video generation models have made significant strides in recent years, pushing the boundaries of AI technology. However, these advancements come at a cost – larger model sizes, increased data requirements, and substantial training compute. Open-Sora 2.0, a groundbreaking video generation model developed by our team, challenges this trend by achieving top-tier performance on a budget of just 0,000.
The driving force behind Open-Sora 2.0’s cost-efficiency lies in our dedication to optimizing every aspect of the training process. We have carefully curated a diverse dataset, fine-tuned the model’s architecture, devised an innovative training strategy, and optimized the system for maximum efficiency. The culmination of these efforts has allowed us to create a commercial-level video generation model that outperforms many leading competitors.
Data Curation: Quality over Quantity
Contrary to the prevailing notion that bigger datasets produce better results, we focused on selecting a meticulously curated collection of high-quality video clips. By prioritizing depth and diversity over sheer quantity, we were able to minimize the data requirements without compromising performance. This approach not only reduced the cost of data acquisition but also improved the overall training efficiency.
Optimized Model Architecture
We designed the architecture of Open-Sora 2.0 to be lean and efficient, striking a delicate balance between complexity and performance. By carefully allocating computational resources, we ensure that the model achieves exceptional results while minimizing unnecessary overhead. This streamlined approach significantly reduces the training compute required, making the model highly cost-effective.
Innovative Training Strategy
Achieving outstanding performance on a constrained budget necessitated a novel training strategy. Instead of relying solely on brute force computation, we devised intelligent algorithms that prioritize essential training samples and optimize resource allocation. This approach allows us to achieve comparable results to global leading models while minimizing the training time and associated costs.
System Optimization: Making Every Compute Count
We have gone to great lengths to fine-tune the system that supports Open-Sora 2.0, optimizing it for maximum efficiency. From distributed computing techniques to advanced parallelization algorithms, we have harnessed the power of modern technology to ensure that every compute contributes effectively to the training process. This optimization enables us to achieve outstanding results without excessive computational requirements.
Based on human evaluation results and VBench scores, Open-Sora 2.0 stands tall among leading video generation models such as the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. But what sets us apart is our commitment to democratizing access to advanced video generation technology.
By releasing Open-Sora 2.0 as an open-source project, we aim to empower content creators with cutting-edge video generation capabilities. We believe that by providing the tools and resources necessary for innovation and creativity, we can foster a new era of content creation. The source code and all accompanying resources are available to the public at https://github.com/hpcaitech/Open-Sora.
Open-Sora 2.0 represents a revolution in cost-effective video generation, challenging the notion that impressive AI technology is reserved for those with the largest budgets. With our innovative techniques and commitment to open-source access, we aim to inspire and enable a new generation of creators, driving forward the boundaries of content creation.
The paper titled “Open-Sora 2.0: A Cost-Efficient Video Generation Model” introduces a significant breakthrough in the field of video generation. The authors highlight the remarkable progress made in video generation models over the past year, but also acknowledge the challenges associated with this progress, such as larger model sizes, increased data requirements, and higher computational demands for training.
The main contribution of the paper is the development of Open-Sora 2.0, a commercial-level video generation model that was trained at a cost of only 0k. This cost efficiency is a crucial factor in making video generation technology more accessible and democratizing its use. By significantly reducing the cost of training a top-performing video generation model, Open-Sora 2.0 has the potential to foster broader innovation and creativity in content creation.
To achieve this cost efficiency, the authors outline several techniques that contribute to the success of Open-Sora 2.0. These techniques encompass data curation, model architecture, training strategy, and system optimization. By carefully curating the training data and designing an efficient model architecture, the authors were able to train a high-quality video generation model without the need for excessive data or computational resources.
The authors also provide evidence of the effectiveness of Open-Sora 2.0 by comparing it to other leading video generation models, including the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. According to human evaluation results and VBench scores, Open-Sora 2.0 is on par with these state-of-the-art models. This demonstrates that cost efficiency does not come at the expense of performance.
One of the most significant aspects of this work is the decision to release Open-Sora 2.0 as an open-source resource. By making the model fully accessible to the public through the GitHub repository, the authors aim to encourage broader adoption and innovation in video generation technology. This move has the potential to empower researchers, developers, and content creators to explore new possibilities and push the boundaries of video generation.
Looking forward, this breakthrough in cost-efficient video generation models opens up exciting possibilities for the field. It is likely that researchers and industry practitioners will build upon the techniques introduced in this paper to further improve the efficiency and quality of video generation models. Additionally, the availability of Open-Sora 2.0 as an open-source resource will facilitate collaborative efforts and accelerate advancements in the field.
Overall, the development of Open-Sora 2.0 represents a significant step towards democratizing access to advanced video generation technology. By addressing the cost and resource limitations associated with training video generation models, this work has the potential to unlock new opportunities for innovation and creativity in content creation.
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