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Adjust inference settings such as infer_steps, cfg_scale, and time_shift for optimal video qualityThe model employs two bilingual text encoders to process user prompts in both English and Chinese A DiT with 3D full attention is used to transform input noise into latent frames, with text embeddings and timesteps as conditioning factors The Video-DPO approach is applied to enhance visual

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Modify inference settings like infer_steps, cfg_scale, and time_shift to achieve the best video qualityIt utilizes a deep compression Variational Autoencoder (Video-VAE) designed for video generation tasks, achieving 16x16 spatial and 8x temporal compression ratios while maintaining exceptional video reconstruction quality The model supports both English and Chinese inputs through its b

Step-Video-T2V transforms your text prompts into high-quality videos, supporting multiple languagesGenerate videos up to 204 frames long with advanced compression to maintain high quality

Step-Video-T2V文本转视频Stepfun AI是一家知名的人工智能公司,最近推出了其最新的创新产品——Step-Video-T2V模型。这款先进的文本转视频生成模型旨在将文本描述转换为高质量视频,标志着人工智能驱动内容创作的一个重要里程碑。继2025-05-05成功发布的Step-Audio-Chat 130B后,Stepfun AI再次展现了其技术实力。本篇文章将深入探讨这一令人印象深刻的模型的特点和能力,探讨它如何颠覆我们生成和体验视频内容的方式。 1 理解文本转视频(T2V)技术文本转视频(T2V)技术代表了人工智能的重大进步,使得从文本描述自动生成视频内容成为可能。这项创新对内容创作、市场营

Stepfun开源Step-Video-T2V,SOTA 30B文本到视频模型,能可生成具有深度压缩视频VAE(16x16空间,8倍)的204帧的视频。 $新华传媒(SH)$评论郑重声明:用户在财富号/股吧/博客等社区发表的所有信息(包括但不限于文字、视频、音频、数据及图表)仅代表个人观点,与本网站立场无关,不对您构成任何建议,据此操作风险自担。请勿相信代客、免费荐股和炒股培训等宣传内容,远离非法证券活动。请勿添加发言用户的手机号码、公众号、微博、及等信息,谨防上当受骗! 帖子不见了!怎么办?退出表情郑重声明:用户在社区发表的所有资料、言论等仅代表个人观点

收藏0Step-Video-T2V 是由阶跃星辰与吉利汽车集团联合开源的一款先进文本生成视频(Text-to-Video)模型。这款模型在 2025 年 2 月 18 日正式发布,具备强大的多模态生成能力,支持中文和英文输入,适用于广告、影视制作、教育等多个场景。打开网站Step-Video-T2V是由阶跃星辰与吉利汽车集团联合开源的一款先进(Text-to-Video)模型。这款模型在 2025 年 2 月 18 日正式发布,具备强大的多模态生成能力,支持中文和英文输入,适用于广告、影视制作、教育等多个场景。文本生成视频其主要特点功能: 强大的模型参数:拥有 300 亿参数,能够生成最长 204 帧的视频,支持高分辨率和

深度压缩的变分自编码器(Video-VAE):Step-Video-TI2V 使用了深度压缩的变分自编码器(Video-VAE),实现了 16×16 的空间压缩和 8× 的时间压缩。显著降低了视频生成任务的计算复杂度,同时保持了优异的视频重建质量。Video-VAE 采用了双路径架构,能有效分离高低频信息,进一步优化视频生成的效果。基于扩散的 Transformer(DiT)架构:模型基于扩散的 Transformer(DiT)架构,包含 3D 全注意力机制。通过 Flow Matching 训练方法,将输入噪声逐步去噪为潜在帧,将文本嵌入和时间步作为条件因子。这种架构在生成具有强烈运动动态和高美学质量的视频方面表现出色。双语文本编