- GitHub - k4yt3x video2x: A machine learning-based video super . . .
A machine learning-based video super resolution and frame interpolation framework Est Hack the Valley II, 2018 - k4yt3x video2x
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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
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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:
- 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
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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
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GitHub is where people build software More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects
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Official repository for LTX-Video Contribute to Lightricks LTX-Video development by creating an account on GitHub
- GitHub - THUDM CogVideo: text and image to video generation: CogVideoX . . .
VideoTuna: VideoTuna is the first repo that integrates multiple AI video generation models for text-to-video, image-to-video, text-to-image generation ConsisID: An identity-preserving text-to-video generation model, bases on CogVideoX-5B, which keep the face consistent in the generated video by frequency decomposition
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