- 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:
- Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub
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
- 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
- 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
- GitHub - Lightricks LTX-Video: Official repository for LTX-Video
Official repository for LTX-Video Contribute to Lightricks LTX-Video development by creating an account on GitHub
- GitHub - veo-3 veo-3
Veo 3 is Google DeepMind’s latest AI-powered video generation model, introduced at Google I O 2025 It enables users to create high-quality, 1080p videos from simple text or image prompts, integrating realistic audio elements such as dialogue, sound effects, and ambient noise
- 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 - stepfun-ai Step-Video-T2V
Step-Video-T2V exhibits robust performance in inference settings, consistently generating high-fidelity and dynamic videos However, our experiments reveal that variations in inference hyperparameters can have a substantial effect on the trade-off between video fidelity and dynamics
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