Decentralized Video AI Layer
PreviousOur Vision for Video AI and Human-Centric Agential VideoNextVideo Agent Generation: Diffusion Based Model
Last updated
Last updated
As digital content becomes the dominant medium of modern communication, Video AI is poised to revolutionize the internet, with expectations that it will soon drive the majority of online content creation. The demand for video is increasing exponentially, but the current centralized, closed-source models are costly, inefficient, and limited in scalability. These models rely on expensive infrastructure, preventing wide adoption, and stifle innovation due to their closed nature. Our Lyn Protocol is transforming this landscape by introducing a decentralized, open, and cost-efficient framework for video generation. Central to Lyn’s innovation is its open-source autoregressive video foundation model, which serves as the backbone for future video generation and fosters community-driven contributions. This model generates high-quality, human-centric, and unlimited long videos, pushing the boundaries of current video AI technology. Additionally, Lyn’s team has pioneered advanced technologies like superpipeline-quantization and played a key role in the development of xDiT, reducing the cost and time required for high-quality video creation by 10x. The use of a subsidized distributed compute system further enhances scalability, making video generation more accessible. Together, these in- novations power Lyn’s agential video AI layer, setting the stage for autonomous agents to revolutionize content creation.
Current centralized Video AI models have significant limitations. Generating a single video can take upwards of 10 minutes and cost approximately $0.30 per video (Fig. 10 left). These platforms are not only slow and expensive but also lack support for decentralized features such as smart contracts or on-chain video provenance. This leads to inefficiencies, high costs, and inconsistent outputs due to the absence of personalized memory and contextual awareness, resulting in suboptimal user experiences. Additionally, the closed-source nature of these models prevents a vibrant, collaborative creator community, further slowing down innovation and adoption.
The Lyn Protocol tackles these challenges by building a decentralized infrastructure for video generation, drastically reducing costs while enhancing scalability. At the core of Lyn’s success is its integration of advanced technologies like superpipeline-quantization and xDiT, which optimize video generation by parallelizing diffusion transformers. At the heart of Lyn’s innovation is its leading open-source autoregressive video foundation model, capable of generating high-quality, unlimited long videos, with a special focus on human-centric content. This model allows Lyn to cut video generation costs by up to 10x compared to centralized platforms, making high-quality video AI accessible to a broader audience. Additionally, Lyn’s decentralized AI infrastructure leverages community participation and flexible architecture to ensure scalable, efficient video generation that can grow without the constraints of traditional systems.
At the core of the Lyn Protocol is its support for agential video generation, where autonomous agents and decentralized applications (dApps) can generate videos in response to user inputs or programmed commands. This process is powered by Lyn’s video APIs (v-APIs), which enable smart contracts and dApps to autonomously request video generation services. These agents process contextually informed prompts, pulling from external data sources, personal memory stores, and real-time oracles to create high-quality, tailored videos. Each video is recorded on-chain, ensuring secure, tamper-proof provenance for creators. This on-chain recording empowers creators to maintain control over their intellectual property, while also providing transparent, real-time tracking of video usage and distribution for monetization purposes.
The Lyn Protocol further expands its utility through the creation of agent- generated videos, where autonomous agents produce, manage, and distribute content without human intervention. These agents integrate seamlessly with major video platforms and messaging clients, autonomously posting content and streamlining content delivery to boost user engagement. In addition to agent-generated content, Lyn supports human-generated video production across diverse sectors such as Web3 project marketing, community management (e.g., Telegram, Discord), entertainment, education, dating, and real-time video avatars or interactive chatbots. This cross-platform adaptability positions Lyn as a highly versatile solution for decentralized video AI, bridging the gap between human and agent-generated content (Fig. 11 left).
For web3, Lyn also offers decentralized applications (dApps) advanced video AI capabilities, enabling them to autonomously generate and interact with video content on demand. As the first fully integrated video AI protocol for Web3, Lyn provides multi-chain interoperability, allowing dApps on any Layer-1 or Layer-2 blockchain to seamlessly access video generation services (Fig. 11 right). With its permissionless architecture, developers can integrate Lyn’s video AI features without requiring centralized approval. By providing universal data coverage and robust support for agent-based dApps, Lyn is establishing itself as the premier protocol for decentralized video creation, creating a new era where dApps, creators, and agents collaborate in an open, transparent ecosystem.
The Lyn Protocol operates on $LYN tokens, which serve as the core currency driving all transactions within the ecosystem. These tokens are used for payments related to video generation, smart contract execution, and v-API activations, enabling seamless interaction between dApps, agents, and decentralized services. When a dApp or agent initiates a video generation request, the transaction is processed using $LYN tokens, facilitating everything from inference requests to the orchestration of the Everlyn-1 video model, which pulls data from external sources to generate videos. Creators and developers can also stake $LYN tokens on specific agents or v-APIs, unlocking advanced capabilities like faster processing, better personalization, or enhanced video quality. This staking system allows them to participate in the revenue generated by these agents or APIs, creating an additional income stream. Moreover, $LYN tokens power payments for decentralized inference and training partners, who contribute to the continuous improvement of the model. The system’s decentralized and token-driven nature ensures a transparent, secure, and community-oriented ecosystem, where content provenance is tracked on-chain, allowing creators to maintain control over their intellectual property while benefiting from new monetization opportunities.
Conclusion. The future of video content generation lies in decentralization, and the Lyn Protocol is leading the charge. By addressing the inefficiencies of centralized models and creating an open, scalable framework for decentralized video generation, Lyn is empowering creators, developers, and dApps to take control of video AI. With its use of decentralized infrastructure, $LYN tokens, and smart contracts, Lyn provides a cost-efficient, scalable, and flexible solution that unlocks new possibilities for both autonomous agent-generated and human-generated videos. In the next section, we will explore video agents as a special application powered by Lyn’s video AI layer, showcasing how these agents can autonomously generate and manage video content in real-time.
