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็ฅไน๏ผไธญๆไบ่็ฝ้ซ่ดจ้็้ฎ็ญ็คพๅบๅๅไฝ่ ่้็ๅๅๅ ๅฎนๅนณๅฐ๏ผไบ 2011 ๅนด 1 ๆๆญฃๅผไธ็บฟ๏ผไปฅใ่ฎฉไบบไปฌๆดๅฅฝ็ๅไบซ็ฅ่ฏใ็ป้ชๅ่ง่งฃ๏ผๆพๅฐ่ชๅทฑ็่งฃ็ญใไธบๅ็ไฝฟๅฝใ็ฅไนๅญๅ่ฎค็ใไธไธ … Feb 25, 2025 · 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: VisoMaster is a powerful yet easy-to-use tool for face swapping and editing in images and videos. It utilizes AI to produce natural-looking results with minimal effort, making it ideal for both casual users and professionals. LTX-Video is the first DiT-based video generation model that can generate high-quality videos in real-time. It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them. The model is trained on a large-scale dataset of diverse videos and can generate high-resolution videos with realistic and diverse content. The model supports image-to-video, keyframe-based 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. Feb 23, 2025 · Video-R1 significantly outperforms previous models across most benchmarks. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-R1-7B achieves a new state-of-the-art accuracy of 35.8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the About ๐ฌ ๅกๅกๅญๅนๅฉๆ | VideoCaptioner - ๅบไบ LLM ็ๆบ่ฝๅญๅนๅฉๆ - ่ง้ขๅญๅน็ๆใๆญๅฅใๆ กๆญฃใๅญๅน็ฟป่ฏๅ จๆต็จๅค็๏ผ - A powered tool for easy and efficient video subtitling. A machine learning-based video super resolution and frame interpolation
framework. Est. Hack the Valley II, 2018. - k4yt3x/video2x Feb 25, 2025 · Contribute to kijai/ComfyUI-WanVideoWrapper development by creating an account on GitHub. Introduced a novel taxonomy for Vid-LLMs based on video representation and LLM functionality. Added a Preliminary chapter, reclassifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the LLM Background section. ็ฅไน๏ผไธญๆไบ่็ฝ้ซ่ดจ้็้ฎ็ญ็คพๅบๅๅไฝ่ ่้็ๅๅๅ ๅฎนๅนณๅฐ๏ผไบ 2011 ๅนด 1 ๆๆญฃๅผไธ็บฟ๏ผไปฅใ่ฎฉไบบไปฌๆดๅฅฝ็ๅไบซ็ฅ่ฏใ็ป้ชๅ่ง่งฃ๏ผๆพๅฐ่ชๅทฑ็่งฃ็ญใไธบๅ็ไฝฟๅฝใ็ฅไนๅญๅ่ฎค็ใไธไธใๅๅ็็คพๅบๆฐๅดใ็ฌ็น็ไบงๅๆบๅถไปฅๅ็ปๆๅๅๆ่ทๅพ็ไผ่ดจๅ ๅฎน๏ผ่้ไบไธญๆไบ่็ฝ็งๆใๅไธใๅฝฑ่งใๆถ
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