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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
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
【EMNLP 2024 】Video-LLaVA: Learning United Visual . . . - GitHub [2024 09 25] 🔥🔥🔥 Our Video-LLaVA has been accepted at EMNLP 2024! We earn the meta score of 4 [2024 07 27] 🔥🔥🔥 A fine-tuned Video-LLaVA focuses on theme exploration, narrative analysis, and character dynamics
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
FastVideo is a unified framework for accelerated video generation. It features a clean, consistent API that works across popular video models, making it easier for developers to author new models and incorporate system- or kernel-level optimizations With FastVideo's optimizations, you can achieve more than 3x inference improvement compared to other systems
Video-T1: Test-Time Scaling for Video Generation - GitHub Pipeline of Test-Time Scaling for Video Generation Top: Random Linear Search for TTS video generation is to randomly sample Gaussian noises, prompt the video generator to generate a sequence of video clips through step-by-step denoising in a linear manner, and select the highest score from the test verifiers
GitHub - lllyasviel FramePack: Lets make video diffusion practical! FramePack is a next-frame (next-frame-section) prediction neural network structure that generates videos progressively FramePack compresses input contexts to a constant length so that the generation workload is invariant to video length FramePack can process a very large number of frames with 13B