Wanx 2.1, developed by Alibaba Cloud, is the latest iteration of the multimodal large model Tongyi Wanxiang. It can generate high-quality images and videos from text input. With a score of 84.7% on the VBench leaderboard, it excels in handling complex movements, enhancing pixel quality and adhering to physical rules. It supports both Chinese and English text effects. It can generate a 1-minute 1080p video in 15 seconds. Available for free on its Chinese website and through Model Studio, it will be open-sourced in Q2 2025 with the training dataset and toolkit.
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WanX-AI.net's answer
The WanX 2.1 AI Video Generator leads the industry with its VBench-topping 84.7% score, combining three breakthrough technologies: 1) Bilingual text-to-video with Chinese/English text effects 2) Physics-compliant motion generation using VAE+DiT architecture 3) Ultra-fast 1080p video synthesis at 4x previous speeds. Unlike other models, it maintains anatomical accuracy in complex sports movements while supporting both WanX AI Text to Video and Image to Video workflows.
WanX-AI.net's answer
The WanX 2.1 AI Video Generator outperforms Sora in VBench metrics for spatial relationships (92.1 vs 88.3) and motion accuracy (89.4 vs 84.7). Key advantages include bilingual support, 100+ built-in styles, and faster generation speeds. Unlike cloud-only competitors, WanX will offer both SaaS and self-hosted solutions post open-sourcing.
WanX-AI.net's answer
he primary audience of Wanx AI includes individual developers who are eager to explore the potential of AI in visual content creation and leverage its capabilities to build innovative applications. Corporate users, especially those in industries such as advertising, short video production, content creation platforms, and e-commerce, are also significant users. They seek to utilize Wanx AI to generate high-quality visual content, such as product promotional animations and engaging marketing materials. Additionally, professionals in the education and cultural heritage preservation sectors can benefit from it to create immersive teaching videos and digitally restore historical footage.
WanX-AI.net's answer
Wanx AI, specifically Wanx 2.1, is the latest iteration of Alibaba Cloud's multimodal large model Tongyi Wanxiang (Wanx), which first debuted in July 2023. Alibaba Cloud, as the digital technology and intelligence backbone of Alibaba Group, has been actively expanding its AI portfolio. The development of Wanx AI aims to push the boundaries of AI-driven visual content creation. The research team made significant technological progress in handling complex movements, enhancing pixel quality, and adhering to physical rules, which led to Wanx 2.1's outstanding performance on benchmarks like VBench. It represents a continuous effort to bridge the gap between advanced AI technology and creative industries.
WanX-AI.net's answer
Currently, among the potential biggest customers of Wanx AI are large advertising agencies that require high-quality and creative visual content for their clients' campaigns. Major short video platforms can also be significant customers, as they need to generate a large amount of engaging video materials quickly. E-commerce companies are likely to be big users as well, using Wanx AI to create personalized product promotional animations. In the education sector, large educational institutions and e-learning platforms may utilize it to develop immersive teaching videos. Cultural heritage organizations involved in digitizing historical footage could also be important customers. However, as Wanx AI is still evolving and becoming more widely accessible, the customer base is expected to expand further.
WanX-AI.net's answer
The primary technologies used in building Wanx AI include a proprietary Variational Autoencoder (VAE) and Denoising Diffusion Transformer (DiT) framework. These technologies enable the model to strengthen temporal and spatial relationships, achieving higher visual realism when dealing with complex motion and physical rules. A full space-time attention mechanism is employed to accurately mimic the complex dynamics of the real world. Innovative approaches for accelerating the training process with ultra-long context integration are also crucial, ensuring seamless integration of text instructions into video generation. Additionally, multimodal fusion technology is used to achieve precise analysis of complex semantics.
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