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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 consistent depth accuracy
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
Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub Our Video-R1-7B obtain strong performance on several video reasoning benchmarks For example, Video-R1-7B attains a 35 8% accuracy on video spatial reasoning benchmark VSI-bench, surpassing the commercial proprietary model GPT-4o
GitHub - Kosinkadink ComfyUI-VideoHelperSuite: Nodes related to video . . . Load Video Converts a video file into a series of images video: The video file to be loaded force_rate: Discards or duplicates frames as needed to hit a target frame rate Disabled by setting to 0 This can be used to quickly match a suggested frame rate like the 8 fps of AnimateDiff force_size: Allows for quick resizing to a number of
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: 👍 SOTA Performance: Wan2 1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks 👍
GitHub - thu-ml TurboDiffusion: TurboDiffusion: 100–200× Acceleration . . . This repository provides the official implementation of TurboDiffusion, a video generation acceleration framework that can speed up end-to-end diffusion generation by 100 ∼ 200 × on a single RTX 5090, while maintaining video quality TurboDiffusion primarily uses SageAttention, SLA (Sparse-Linear Attention) for attention acceleration, and rCM for timestep distillation Paper: TurboDiffusion