- Wan: Open and Advanced Large-Scale Video Generative Models
👍 Multiple Tasks: Wan2 1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation 👍 Visual Text Generation: Wan2 1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications
- stepfun-ai Step-Video-T2V - GitHub
We present Step-Video-T2V, a state-of-the-art (SoTA) text-to-video pre-trained model with 30 billion parameters and the capability to generate videos up to 204 frames To enhance both training and inference efficiency, we propose a deep compression VAE for videos, achieving 16x16 spatial and 8x temporal compression ratios
- HunyuanVideo: A Systematic Framework For Large Video . . . - GitHub
We present HunyuanVideo, a novel open-source video foundation model that exhibits performance in video generation that is comparable to, if not superior to, leading closed-source models In order to train HunyuanVideo model, we adopt several key technologies for model learning, including data
- 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
- 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
- Lightricks LTX-Video: Official repository for LTX-Video - GitHub
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 It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them
- HunyuanCustom: A Multimodal-Driven Architecture for Customized Video . . .
Customized video generation aims to produce videos featuring specific subjects under flexible user-defined conditions, yet existing methods often struggle with identity consistency and limited input modalities In this paper, we propose HunyuanCustom, a multi-modal customized video generation
- Lightricks ComfyUI-LTXVideo: LTX-Video Support for ComfyUI - GitHub
Sequence Conditioning – Allows motion interpolation from a given frame sequence, enabling video extension from the beginning, end, or middle of the original video Prompt Enhancer – A new node that helps generate prompts optimized for the best model performance See the Example Workflows section for more details
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