Compiled by: Alex Liu, Foresight News
Launched in 2017, Livepeer is the first fully decentralized live video streaming network protocol. The platform aims to provide a blockchain-based, cost-effective alternative to traditional centralized broadcasting. It aims to revolutionize the rapidly growing live video streaming and broadcasting industry, introducing a decentralized ecosystem by letting producers submit their work on the platform, which is then responsible for reformatting and distributing the content to users and streaming platforms.
To put it simply, through the DePin facility, using Livepeer can seamlessly integrate video content into applications in a decentralized manner at a fraction of the cost of traditional solutions.
The popularity of the DePin track surged at the beginning of this year, and LPT also caught on to the growth train, with the token price doubling compared to the beginning of the year. As the editor who bought leeks for 10 yuan a year ago and sold them all for 9.8, I decided to learn from the experience and carefully study Livepeer's new move - the launch of the AI subnet.
In the era of generative AI, video creation has ushered in new changes.
The field of generative video has grown rapidly since Open AI’s Sora demo showed the possibility of creating videos by entering text prompts. The open source AI video model Stable Diffusion has exceeded 10 million users in just two months. However, the prospect of generating AI video tools faces serious challenges. The $49 billion GPU market is controlled by a handful of global internet monopolies such as NVIDIA, Microsoft Azure and Amazon Web Services (AWS), driving up prices and creating a global AI computing bottleneck.
Therefore, Livepeer launched the Livepeer AI subnet: the first decentralized video processing network with AI computing capabilities. The Livepeer AI subnet solves the structural problems of centralized AI computing by leveraging Livepeer's open network of thousands of GPUs to provide low-cost, high-performance processing services. Based on Livepeer's decentralized video processing network architecture, the subnet provides a globally accessible, affordable and open video infrastructure that is infinitely scalable through blockchain token economic incentives.
The AI subnet is a fork of the Livepeer video infrastructure network, providing a sandbox environment for secure development and testing of new decentralized AI media processing markets and tools. While the Livepeer network will continue to focus on video transcoding and computing, the Livepeer AI subnet will meet the growing AI computing needs, handle tasks such as upscaling, subtitle generation and recognition, and support developers running specific video and media tasks. Model.
This subnet allows video developers to add a range of generative AI features to their applications, such as text-to-image, image-to-image, and image-to-video conversion.
这个 AI 生成输出来自 Tsunameme.ai - 第一个构建在 Livepeer AI 子网上的演示程序。它使用了文本到图像和图像到视频管道。可尝试使用 Livepeer 测试版生成自己的 AI 媒体，网址为 https://tsunameme.ai
AI video tools lower the threshold for creation. Anyone can create original videos with just a few text commands. Footage that requires a location, a professional team and hours of editing. As these tools become more widespread, the global centralized AI computing bottleneck will further intensify. In addition, decentralized AI infrastructure can also address the risk of single points of failure inherent in highly centralized server networks and the crisis of trust and authenticity caused by AI-generated content.
Livepeer AI subnet creates a sustainable and profitable open AI video foundation by providing globally accessible ultra-low-cost infrastructure, an open and permissionless AI media marketplace, and content verification and authenticity solutions Facilities provide choices.
Livepeer adopts a decentralized pay-per-task model, allowing developers to submit and pay for tasks on-demand without the need to reserve expensive computing capacity. Developers can set the price they are willing to pay based on required performance and network availability.
The two key components of the Livepeer AI network architecture are:
#This diagram illustrates how Livepeer allocates tasks to a distributed network of GPUs based on efficiency, rather than directing AI processing requests through a centralized server.
The Livepeer AI network infrastructure is designed to be infinitely scalable, allowing for the easy integration of additional orchestration and gateway nodes as needed. Execute AI models through a dedicated AI-runner Docker image, simplifying deployment and increasing scalability for new pipelines. Future developments will further improve performance and extend the capabilities of the container to support increasingly complex AI models and custom user-defined pipelines.
在 AI 子网上处理任务的技术工作流。网关节点将任务传递给协调器，协调器可能运行相同或不同管道的多个 AI-Runner Docker 容器。这些管道可能已经拥有所请求的模型，或可以根据需要动态加载它们。
硬件提供者：通过贡献 GPU 赚取费用
现有的 Livepeer 协调员可以设置并运行 AI 协调节点，执行文本到图像、图像到图像和图像到视频的推断任务，增加其现有转码收入。
开发者：将模型引入网络作为 AI-Worker
开发者可以定义和部署自定义管道和工作流程，以确保其应用处于 AI 和视频技术的前沿。开发者还可以设置 AI 网关节点，测试和完善其应用，访问 AI 任务的 API。
Livepeer AI 子网的推出标志着该项目的重要里程碑，也是 Livepeer 提供全球开放视频基础设施使命的下一步。随着生成式 AI 将在未来几年大幅增加视频内容的创作量，Livepeer 网络旨在确保其具备支持这一增长浪潮的能力。
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