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Wan: Open and Advanced Large-Scale Video Generative Models Visit their webpage for more details LightX2V, a lightweight and efficient video generation framework that integrates Wan2 1 and Wan2 2, supporting multiple engineering acceleration techniques for fast inference LightX2V-HuggingFace, offers a variety of Wan-based step-distillation models, quantized models, and lightweight VAE models
Wan: Open and Advanced Large-Scale Video Generative Models Wan: Open and Advanced Large-Scale Video Generative Models In this repository, we present Wan2 1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation Wan2 1 offers these key features: 👍 SOTA Performance: Wan2 1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks 👍
Troubleshoot YouTube video errors - Google Help Check the YouTube video’s resolution and the recommended speed needed to play the video The table below shows the approximate speeds recommended to play each video resolution
GitHub - yt-dlp yt-dlp: A feature-rich command-line audio video . . . yt-dlp is a feature-rich command-line audio video downloader with support for thousands of sites The project is a fork of youtube-dl based on the now inactive youtube-dlc INSTALLATION Detailed instructions Release Files Update Dependencies Compile USAGE AND OPTIONS General Options Network Options Geo-restriction Video Selection Download Options Filesystem Options Thumbnail Options Internet
GitHub - Lightricks LTX-Video: Official repository for LTX-Video LTX-Video is the first DiT-based video generation model that contains all core capabilities of modern video generation in one model: synchronized audio and video, high fidelity, multiple performance modes, production-ready outputs, API access, and open access It can generate up to 50 FPS videos at native 4K resolution with synchronized audio in one pass The model is trained on a large-scale
Pixelle-Video —— AI 全自动短视频引擎 - GitHub Pixelle-Video 采用模块化设计，整个视频生成流程清晰简洁： 从输入文本到最终视频输出，整个流程简洁清晰： 文案生成 → 配图规划 → 逐帧处理 → 视频合成 每个环节都支持灵活定制，可选择不同的 AI 模型、音频引擎、视觉风格等，满足个性化创作需求。
DepthAnything Video-Depth-Anything - GitHub This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth accuracy