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- 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 - Lightricks LTX-Video: Official repository for LTX-Video
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
- 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 - visomaster VisoMaster: Powerful Easy-to-Use Video Face . . .
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
- GitHub - lllyasviel FramePack: Lets make video diffusion practical!
Lets make video diffusion practical! Contribute to lllyasviel FramePack development by creating an account on GitHub
- Wan: Open and Advanced Large-Scale Video Generative Models
Wan: Open and Advanced Large-Scale Video Generative Models We are excited to introduce Wan2 2, a major upgrade to our foundational video models With Wan2 2, we have focused on incorporating the following innovations: 👍 Effective MoE Architecture: Wan2 2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models By separating the denoising process cross timesteps with
- GitHub - hkchengrex MMAudio: [CVPR 2025] MMAudio: Taming Multimodal . . .
MMAudio generates synchronized audio given video and or text inputs Our key innovation is multimodal joint training which allows training on a wide range of audio-visual and audio-text datasets
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