- 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
- 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
- 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 - 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 - lllyasviel FramePack: Lets make video diffusion practical!
Lets make video diffusion practical! Contribute to lllyasviel FramePack development by creating an account on GitHub
- 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
- HunyuanVideo: A Systematic Framework For Large Video . . . - GitHub
HunyuanVideo introduces the Transformer design and employs a Full Attention mechanism for unified image and video generation Specifically, we use a "Dual-stream to Single-stream" hybrid model design for video generation In the dual-stream phase, video and text tokens are processed independently through multiple Transformer blocks, enabling each modality to learn its own appropriate
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