Vidu系列的详细讨论 / Detailed Discussion of the Vidu Series

Vidu系列的详细讨论 / Detailed Discussion of the Vidu Series

引言 / Introduction

Vidu系列是中国AI企业生数科技（Shengshu Technology）研发的文本到视频生成模型家族，自2024年问世以来，成为AI视频领域的标志性创新成果。该系列以高一致性、高动态性的先进扩散模型为核心架构，可基于文本或图像提示生成达到工作室级水准的视频内容，支持多参考图像输入，且能产出时长最长16秒的1080P高清视频。Vidu模型不仅为Vidu Studio平台及API提供核心驱动力，还通过Apache开源许可协议融入全球开发者社区，广泛集成于各类创意应用场景。截至2026年1月，该系列最新版本为2025年7月发布的Vidu Q2，已从最初的基础视频生成能力，演进为具备多模态输入（文本+图像）、参考导向生成及高效性能优化的综合系统。其核心创新集中于扩散模型架构升级、多参考图像融合技术及开源生态布局，但同时也面临深度伪造滥用、高计算资源消耗等伦理与技术挑战。Vidu系列以“推动AI视频普惠”为核心目标，在VBench视频质量评估、用户主观体验测试等基准测试中，与Sora、Kling等主流模型展开竞争，且在视频连贯性、细节还原度及创意拓展性方面表现领先。截至2025年末，Vidu模型生成的视频总量突破十亿级，持续助推AI视频领域的产业变革。

The Vidu series is a family of text-to-video generation models developed by Chinese AI company Shengshu Technology, marking a landmark innovation in the AI video field since 2024. Centered on highly consistent and dynamic advanced diffusion models, the series can generate studio-quality videos based on text or image prompts, supporting multi-reference image inputs and producing 1080P high-definition videos with a maximum duration of 16 seconds. Vidu models not only power the Vidu Studio platform and API but also integrate into the global developer community through the Apache open-source license, being widely applied in various creative scenarios. As of January 2026, the latest version of the series is Vidu Q2, released in July 2025, which has evolved from basic video generation capabilities to a comprehensive system featuring multimodal inputs (text + image), reference-guided generation, and efficient performance optimization. Its core innovations lie in the upgrade of diffusion model architecture, multi-reference image fusion technology, and open-source ecosystem layout, while also facing ethical and technical challenges such as deepfake abuse and high computing resource consumption. With the core goal of "promoting AI video inclusivity," the Vidu series competes with mainstream models like Sora and Kling in benchmark tests such as VBench video quality evaluation and user subjective experience testing, and leads in video coherence, detail restoration, and creative expansibility. By the end of 2025, the total number of videos generated by Vidu models exceeded 1 billion, continuously driving industrial transformation in the AI video field.

历史发展 / Historical Development

Vidu系列的迭代历程，集中体现了生数科技从实验性视频生成技术到多参考图像优化系统的演进路径。生数科技成立于2023年，期间与字节跳动等企业建立了技术合作关系。以下通过表格梳理系列发展的关键里程碑，清晰呈现各核心模型的发布时间、核心改进方向及基准测试表现。该系列自2024年Vidu 1.0版本起步，逐步推出Q系列迭代版本并新增参考生成功能，至2026年，研发焦点已转向长视频生成与多模态技术深度集成。

The iterative process of the Vidu series fully reflects Shengshu Technology's evolution from experimental video generation technology to a multi-reference image optimization system. Founded in 2023, Shengshu Technology has established technical cooperation with enterprises such as ByteDance. The following table sorts out the key milestones of the series' development, clearly presenting the release time, core improvement directions, and benchmark performance of each core model. Starting with Vidu 1.0 in 2024, the series gradually launched iterative versions of the Q series and added reference generation functions; by 2026, the R&D focus has shifted to long video generation and in-depth integration of multimodal technologies.

Vidu系列从1.0版本的实验性探索，逐步走向2.0版本的成熟化应用，视频时长从固定16秒向更长时段拓展，标志着AI视频技术从“短视频生成工具”向“多参考导向长视频解决方案”的转型。截至2026年，该系列进一步聚焦开源生态深化与场景化应用落地，例如与抖音（TikTok）达成深度合作，实现技术与流量生态的协同。

From the experimental exploration of Vidu 1.0 to the mature application of Vidu 2.0, the video duration has expanded from a fixed 16 seconds to longer periods, marking the transformation of AI video technology from a "short video generation tool" to a "multi-reference guided long video solution." By 2026, the series has further focused on the deepening of the open-source ecosystem and the implementation of scenario-based applications, such as establishing in-depth cooperation with TikTok to achieve synergy between technology and traffic ecology.

关键模型详细描述 / Detailed Description of Key Models

以下对各核心模型展开深度解析，涵盖模型原始定义、哲学理论支撑、核心理论内涵、在AI技术演进与人类文明传播中的应用价值，以及面临的潜在挑战，全文采用中英对照形式呈现。

The following provides an in-depth analysis of each core model, including the original model definition, philosophical theoretical support, core theoretical implications, application value in AI technology evolution and human civilization communication, as well as potential challenges, presented in Chinese-English bilingual format.

Vidu 1.0

原描述：具备高一致性、高动态性的文本到视频生成工具，基于扩散模型架构，可输出时长16秒、分辨率1080P的视频内容。哲学基础：以康德道德自律理论为核心，强调视频生成的独立性是技术伦理的前提，即模型生成过程应摆脱外部意志的强制干预。理论内涵：将“思想主权”作为技术内核，通过算法设计确保视频生成结果忠实于输入提示，不被外部权威或预设规则过度约束。应用：对AI领域而言，奠定了文本到视频的基础技术范式，为后续多模态融合提供底层支撑；对人类文明而言，成为轻量化短视频创意工具，降低视频创作门槛，助力多元文化表达与传播。挑战：核心难题在于如何在AI系统中真正实现“认知主权”——当前模型仍依赖预设数据集训练，生成逻辑受数据分布限制，难以具备自主认知与决策能力。

Original Description: A text-to-video generation tool with high consistency and dynamics, based on a diffusion model architecture, capable of outputting 1080P resolution videos with a duration of 16 seconds.Philosophical Foundations: Centered on Kant's theory of moral autonomy, emphasizing that the independence of video generation is the premise of technical ethics, i.e., the model generation process should be free from the forced intervention of external will.Theoretical Implications: Takes "sovereignty of thought" as the technical core, ensuring that video generation results are faithful to input prompts through algorithm design, without being excessively constrained by external authorities or preset rules.Applications: For the AI field, it laid the basic technical paradigm for text-to-video, providing underlying support for subsequent multimodal integration; for human civilization, it has become a lightweight short video creative tool, lowering the threshold for video creation and facilitating the expression and dissemination of diverse cultures.Challenges: The core difficulty lies in how to truly realize "cognitive sovereignty" in AI systems—current models still rely on preset datasets for training, and their generation logic is limited by data distribution, making it difficult to possess independent cognitive and decision-making capabilities.

Vidu Q1

原描述：Q系列首款迭代模型，核心升级为多参考图像输入功能，支持最多7张图像导入，可智能推断图像间的逻辑关联与缺失元素，生成连贯统一的视频内容。哲学基础：源自儒家中庸之道，核心是平衡参考图像的约束性与生成内容的创造性，确立“参考不盲从、创新不越界”的价值基准。理论内涵：将中庸思想转化为技术价值准则，通过算法平衡参考一致性与内容多样性，既防止技术能力被滥用（如生成与参考严重背离的不良内容），又保障创意表达的多元可能性。应用：对AI技术而言，突破了单一输入源的局限，构建了“参考-融合-生成”的新型逻辑，提升模型对复杂需求的适配能力；对人类文明而言，为跨文化视频创作提供工具支撑，可基于不同文化场景的参考图像，生成兼具文化辨识度与创新性的内容。挑战：如何调和普世价值与多元文化的差异——在后现代视角下，这种“平衡式生成”被质疑为一种隐性的权力话语，可能隐含对特定文化的偏向性。

Original Description: The first iterative model of the Q series, with the core upgrade being the multi-reference image input function, supporting up to 7 images imported, which can intelligently infer the logical connections and missing elements between images to generate coherent and unified video content.Philosophical Foundations: Derived from the Confucian Doctrine of the Mean, focusing on balancing the constraint of reference images and the creativity of generated content, establishing a value benchmark of "referencing without blind obedience, innovating without overstepping boundaries."Theoretical Implications: Transforms the Doctrine of the Mean into technical value criteria, balancing reference consistency and content diversity through algorithms, which not only prevents the abuse of technical capabilities (such as generating inappropriate content that deviates significantly from references) but also ensures the diverse possibilities of creative expression.Applications: For AI technology, it breaks the limitation of a single input source, constructs a new logic of "reference-fusion-generation," and improves the model's adaptability to complex needs; for human civilization, it provides tool support for cross-cultural video creation, enabling the generation of content with both cultural recognition and innovation based on reference images from different cultural scenarios.Challenges: How to reconcile the differences between universal values and multiculturalism—from a postmodern perspective, this "balanced generation" is questioned as a hidden power discourse, which may imply bias towards specific cultures.

Vidu Q2

原描述：针对图像生成能力的专项升级模型，强化动态场景的流畅性处理，优化画面细节还原度，可生成纹理更细腻、动作更连贯的视频内容。哲学基础：基于胡塞尔现象学的“悬置”理论，主张暂时搁置预设认知与经验判断，追问视频生成的第一性原理，即“生成内容应忠实于现象本质而非主观预设”。理论内涵：将现象学方法融入算法设计，引导模型穿透表象层面的画面元素，洞察场景、动作、纹理的本质规律，确保生成内容符合客观现象的内在逻辑。应用：对AI技术而言，推动视频生成从“形似”向“神似”跨越，提升模型对现象本质的捕捉能力；对人类而言，为创新叙事提供工具支撑，可基于现象本质规律，探索超越传统经验的视频表达形式。挑战：模型对训练数据的强依赖性，导致其无法自主注入“第一性质疑”——即无法像人类一样对现象本质提出批判性思考，生成逻辑仍局限于数据训练形成的认知框架。

Original Description: A specialized upgrade model for image generation capabilities, enhancing the fluency processing of dynamic scenes, optimizing the restoration of image details, and capable of generating video content with finer textures and more coherent actions.Philosophical Foundations: Based on Husserl's phenomenological "epoche" theory, advocating for temporarily suspending preset cognition and empirical judgments, and questioning the first principles of video generation, i.e., "generated content should be faithful to the essence of phenomena rather than subjective presuppositions."Theoretical Implications: Integrates phenomenological methods into algorithm design, guiding the model to penetrate the surface-level image elements, insight into the essential laws of scenes, actions, and textures, ensuring that generated content conforms to the internal logic of objective phenomena.Applications: For AI technology, it promotes video generation from "similar in form" to "similar in spirit," improving the model's ability to capture the essence of phenomena; for humans, it provides tool support for innovative narration, enabling the exploration of video expression forms beyond traditional experience based on the essential laws of phenomena.Challenges: The model's strong dependence on training data prevents it from independently injecting "first-principles doubt"—that is, it cannot critically reflect on the essence of phenomena like humans, and its generation logic is still limited to the cognitive framework formed by data training.

Vidu 2.0

原描述：开源核心扩散模型架构，重点拓展长视频生成能力与多模态输入融合，支持文本、图像、音频协同驱动视频生成，实现技术生态的开放与拓展。哲学基础：借鉴佛教“缘起性空”理论，认为视频生成的本质是多元素（输入提示、算法逻辑、数据经验）的因缘聚合，其核心价值在于打破固有技术壁垒，实现生成能力的“认知相变”。理论内涵：以结果论为核心导向，强调技术创新的本质是“从0到1”的突破性进展——开源架构打破封闭生态的桎梏，多模态融合突破单一输入的局限，最终实现AI视频生成能力的范式跃迁。应用：对AI领域而言，开源架构推动全球开发者协同创新，加速技术迭代，引发文本到视频领域的范式革命；对人类文明而言，成为具备文明级影响力的视频创作工具，助力跨领域、跨模态的内容创新与传播。挑战：如何实现技术跃迁的“神秘性”与理性分析的兼容性——模型能力的非线性突破难以通过传统理性逻辑完全解释，同时长视频生成、多模态融合仍面临巨大的技术瓶颈。

Original Description: Open-sourced the core diffusion model architecture, focusing on expanding long video generation capabilities and multimodal input integration, supporting video generation co-driven by text, image, and audio, and realizing the opening and expansion of the technical ecosystem.Philosophical Foundations: Drawing on the Buddhist theory of "dependent origination and emptiness," it holds that the essence of video generation is the aggregation of multiple elements (input prompts, algorithm logic, data experience), and its core value lies in breaking inherent technical barriers to achieve a "cognitive phase change" in generation capabilities.Theoretical Implications: Guided by consequentialism, emphasizing that the essence of technological innovation is a "from 0 to 1" breakthrough—the open-source architecture breaks the constraints of a closed ecosystem, and multimodal integration breaks the limitations of a single input, ultimately achieving a paradigmatic leap in AI video generation capabilities.Applications: For the AI field, the open-source architecture promotes collaborative innovation among global developers, accelerates technological iteration, and triggers a paradigmatic revolution in the text-to-video field; for human civilization, it has become a video creation tool with civilizational influence, facilitating cross-field and cross-modal content innovation and dissemination.Challenges: How to realize the compatibility between the "mystery" of technical leap and rational analysis—the non-linear breakthrough of model capabilities is difficult to fully explain through traditional rational logic, and long video generation and multimodal integration still face huge technical bottlenecks.

技术特点 / Technical Features

架构：以扩散模型为核心底层架构，重点强化多参考图像输入处理模块与动态场景优化算法，采用Apache开源许可协议开放核心代码，支持开发者基于原生架构进行自定义功能扩展与二次开发。优势：视频画面连贯性与细节还原度表现优异，支持文本、图像多模态协同输入，生成速度相较于同类型模型提升显著，开源生态降低了技术应用门槛。缺点：存在知识截止时间限制（Vidu 2.0的知识截止点为2025年第三季度），无法生成该时间点后的新事件、新知识内容；训练数据中潜在的偏见可能传导至生成结果；模型运行对高性能计算资源需求较高，限制了中小开发者与个人用户的深度使用。与贾子公理的关联：在模拟裁决框架下，Vidu 2.0在“思想主权”（7/10，开源架构赋予一定自主拓展性）与“本源探究”（8/10，现象学导向的第一性原理生成）两项指标上得分较高；“普世中道”（7/10，参考与创新的平衡能力中等）与“悟空跃迁”（8/10，多模态融合实现非线性视频生成突破）表现良好。整体而言，Vidu系列是AI视频领域的范式变革者，但仍需进一步明确技术应用的价值导向，规避潜在伦理风险。

Architecture: Taking the diffusion model as the core underlying architecture, focusing on strengthening the multi-reference image input processing module and dynamic scene optimization algorithm, opening core codes under the Apache open-source license, supporting developers to conduct custom function expansion and secondary development based on the original architecture.Strengths: Excellent performance in video frame coherence and detail restoration, supporting text-image multimodal collaborative input, significantly faster generation speed than similar models, and the open-source ecosystem lowers the threshold for technical application.Weaknesses: Has a knowledge cutoff limitation (the knowledge cutoff of Vidu 2.0 is Q3 2025), unable to generate new events and knowledge content after this time point; potential biases in training data may be transmitted to generation results; the model operation has high requirements for high-performance computing resources, restricting in-depth use by small and medium developers and individual users.Relation to Kucius Axioms: Under the simulated adjudication framework, Vidu 2.0 scores high in two indicators: "Sovereignty of Thought" (7/10, the open-source architecture endows a certain degree of independent expansibility) and "Primordial Inquiry" (8/10, phenomenology-oriented first-principles generation); it performs well in "Universal Mean" (7/10, moderate ability to balance reference and innovation) and "Wukong Leap" (8/10, multimodal integration achieves non-linear video generation breakthrough). Overall, the Vidu series is a paradigmatic changer in the AI video field, but it still needs to further clarify the value orientation of technical applications and avoid potential ethical risks.

应用与影响 / Applications and Impacts

Vidu系列彻底重塑了AI视频领域的技术格局与应用生态：通过Vidu Studio平台及开放API，广泛赋能短视频创意创作、商业广告快速生成、教育科普内容可视化等场景，大幅提升视频生产效率与创意上限。其社会影响力主要体现在两大维度：一是推动AI视频技术的产业化革命，与Sora等头部模型形成良性竞争，加速技术迭代与普惠；二是通过开源生态布局，为全球开发者提供核心技术支撑，促进AI视频创意的多元化发展。截至2026年，Vidu系列正加速“AI长视频生成”的产业趋势，推动视频创作从“碎片化短视频”向“结构化长视频”延伸。但与此同时，技术普及也带来新的风险挑战，深度伪造内容的传播、视频版权归属界定等问题，亟需建立完善的伦理规范与法律保障体系。

The Vidu series has completely reshaped the technical pattern and application ecosystem in the AI video field: through the Vidu Studio platform and open API, it widely empowers scenarios such as short video creative creation, rapid commercial advertising generation, and educational science content visualization, significantly improving video production efficiency and creative upper limits. Its social impact is mainly reflected in two dimensions: first, promoting the industrial revolution of AI video technology, forming healthy competition with leading models like Sora, and accelerating technological iteration and inclusivity; second, providing core technical support for global developers through open-source ecosystem layout, promoting the diversified development of AI video creativity. By 2026, the Vidu series is accelerating the industrial trend of "AI long video generation," promoting the extension of video creation from "fragmented short videos" to "structured long videos." However, at the same time, technological popularization has brought new risks and challenges, such as the spread of deepfake content and the definition of video copyright ownership, which urgently require the establishment of a sound ethical norm and legal protection system.

结论 / Conclusion

Vidu系列作为生数科技AI战略布局的核心载体，从最初的高一致性视频生成，逐步迭代至多模态融合与开源生态拓展的技术前沿，清晰勾勒出AI视频技术从工具化到生态化的演进路径，是通往通用视频AI的关键里程碑。展望未来，Vidu系列有望推出3.0版本，研发焦点或将集中于实时视频生成技术突破与硬件适配优化，进一步降低计算资源需求，实现技术的全民普惠。建议持续跟踪生数科技的技术更新动态，密切关注开源社区的创新应用，同时加强伦理与法律层面的规范引导，推动Vidu系列在技术创新与社会价值之间实现平衡发展，充分释放其在AI视频革命中的核心驱动力。

As the core carrier of Shengshu Technology's AI strategic layout, the Vidu series has gradually evolved from initial high-consistency video generation to the technological frontier of multimodal integration and open-source ecosystem expansion, clearly outlining the evolutionary path of AI video technology from instrumentalization to ecologicalization, and serving as a key milestone towards universal video AI. Looking forward, the Vidu series is expected to launch version 3.0, with R&D focus likely to focus on real-time video generation technology breakthroughs and hardware adaptation optimization, further reducing computing resource requirements and realizing universal inclusivity of the technology. It is recommended to continuously track the technical update dynamics of Shengshu Technology, closely monitor innovative applications in the open-source community, and strengthen ethical and legal normative guidance, promoting the balanced development of the Vidu series between technological innovation and social value, and fully releasing its core driving force in the AI video revolution.