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ๅคงๅฎถๅฅฝ๏ผ ไปๅคฉๆๆฅ็ปๅคงๅฎถๆจ่deepl็ฟป่ฏไปฅๅ10ๆฌพ้ๅธธๅฎ็จ็็ฟป่ฏๅทฅๅ ท๏ผๆ ่ฎบๆฏๆฅๅธธ็ๆดปไธญ็็ฎๅ็ฟป่ฏ๏ผ่ฟๆฏๅญฆๆฏๆ็ซ ็ไธไธ็ฟป่ฏ๏ผ่ฟไบๅทฅๅ ท้ฝ่ฝไธบไฝ ๆไพๅผบๅคง็ๆฏๆใ ่ฎฉๆไปฌไธ่ตท็็ๅง๏ผ … Deeplๆๅๆฅ5000ๅญ็ฟป่ฏ้ๅถ๏ผๆไปฅๅธธๅธธๅจไฝฟ็จๆถไผๅบ็ฐ็ฟป่ฏ้ๅถๆ็คบ๏ผไปๅคฉๅไบซไธไธช่ถ ็ฎๅ็่งฃๅณๆนๆณ๏ผ1ๅ้ๅฎ็ฐ DeepL ็ฟป่ฏๆ ้็ปญๆฏ๏ผ โ ็นๅปๆต่งๅจๆ็ดขๆก็“้ๅฝขๅพๆ ”๏ผ้ๆฉ“ Cookie … Deepl ็ฟป่ฏๆไปไนไผ็ผบ็น๏ผ ๅฐ่ๆ · undefined ๆฅ่ช็ฅๅ็็ๅฎๅ้ฆ ๅ ณๆณจ่ ChatGPTไธ่ฐทๆญ็ฟป่ฏใDeepL็ญ็ฟป่ฏๅจ็ๅ็กฎ็ๅฏนๆฏ่ฎจ่ฎบ๏ผๅๆไธๅ็ฟป่ฏๅทฅๅ ท็ไผๅฃๅฟๅ้็จๅบๆฏใ 22 avr. 2024 · ๆฑไธชPC็ฟป่ฏ่ฝฏไปถๆฌ็็งฏๅ่งๅ ๅๅธๅนถ่ฝฌๆญ ๅๅธๅ่ทณ่ฝฌๅฐๆๅไธ้กต DeepLๅธ่ฝฝไนๅๅฆไฝๅ ้คๅ ถไฝๆไปถๅคนๅ๏ผๆฑๅคง็ฅๆ็น๏ผ ๅจๆงๅถ้ขๆฟๅธ่ฝฝไบDeepLไนๅ๏ผๅฎ่ฃ ็ๆไปถๅคนๆฒกๆๅๆณๅ ้ค๏ผ้่ฆ่ทๅ็ณป็ปๆ้ๆ่ก๏ผๆไปถๅคน้ๆไธชๆๆฌ๏ผ้ฃ่ฟไธชๆๆฌ็ๅฝไปค่กๅบ่ฏฅๆไน … ๅปๅนดๅผๅง้ๆธไบ่งฃๅนถไฝฟ็จDeepL๏ผๆฌ็็ฝๅซๅฐๅบ็็ฒพ็ฅ๏ผ้ ๅ็ ็ฉถOmegaTๆถๅญฆๅฐ็“ไฝฟ็จๆบ็ฟป็ปๆๅถไฝ่ฎฐๅฟๅบ”็ๆ่ทฏ๏ผๅผๅงๅจOmegaT้ๅๆไธไธชDeepL็ๆบ็ฟป่ฎฐๅฟๅบใ ่ฟๆ ท็ฟป่ฏๆถๅฐฑๆไธไธช … ไธบไฝdeepl็ฟป่ฏๆข็งฐ่ชๅทฑๆฏๆๅ็กฎ็็ฟป่ฏๅจ๏ผ ่ฟไธช็ฟป่ฏ่ฝฏไปถ็็ๅฅฝ็จๅ๏ผ [ๅพ็] ๆๆๅพๅๅพๅ ๆพ็คบๅ จ้จ ๅ ณๆณจ่ 7 B็ซๆ็ดข“Deeplๅนณๆฟ”ๅบ่ฏฅ่ฝๆๅฐ๏ผ้้ขๆไธไบๅคงไฝฌๅผๆบๅ็ๅฏ็ฝๅซ็่ตๆบ๏ผๅ ณไบๆฒๆตธๅผ็ฟป่ฏ่ฟไธชๆไปถ๏ผ๏ผไธ่ฟไธๅคช็จณๅฎ๏ผๆไธๆฌกๆๆฏไธ่ฝ็จ็ใ ่่ๅฐDeeplๆๅผๅง้ไธไธชๆไผๅ๏ผๅคงๅฏๅ ๅ ฅใ … ็ฐๅจๅทฒ็ปไฟฎๅคไบ๏ผ้ๆถ้ฝ่ฝ็จ๏ผDeepL็ฟป่ฏไธปๆๆๅญๅๆๆกฃ็ฟป่ฏ๏ผๆฏๆ31้จไธปๆต่ฏญ่จ๏ผ่ไธๅ ๅ ฅไบบๅทฅๆบ่ฝๆๆฏ๏ผ็ฟป่ฏ็่ดจ้่ฟๆฏ่ฎ้ซ็ใ ๅ ถๅฎ้คไบDeepL็ฟป่ฏๅค๏ผ่ฟๆๆไพ้ซๅ็กฎๅบฆ็็ฟป่ฏๅทฅ … A machine learning-based video super
resolution and frame interpolation framework. Est. Hack the Valley II, 2018. - k4yt3x/video2x 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 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: 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 Jan 21, 2025 · 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 Run an internet speed test to make sure your internet can support the selected video resolution. Using multiple devices on the same network may reduce the speed that your device gets. You can also change the quality of your video to improve your experience. Check the YouTube video’s resolution and the recommended speed needed to play the video. The table below shows the approximate speeds Learn more about YouTube YouTube help videos Browse our video library for helpful tips, feature overviews, and step-by-step tutorials. YouTube
Known Issues Get information on reported technical issues or scheduled maintenance. Apr 17, 2025 · Lets make video diffusion practical! Contribute to lllyasviel/FramePack development by creating an account on GitHub. ่ฟๆฏไธไธชๅฏไปฅ่ฏๅซ่ง้ข่ฏญ้ณ่ชๅจ็ๆๅญๅนSRTๆไปถ็ๅผๆบ Windows-GUI ่ฝฏไปถๅทฅๅ ทใ. Contribute to wxbool/video-srt-windows development by creating Aug 12, 2024 · InternVideo: general video foundation models via generative and discriminative learning InternVideo2: scaling video foundation models for multimodal video understanding InternVideo2.5: empowering video mllms with long and rich context modeling InternVid: a large-scale video-text dataset for multimodal understanding and generation
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