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The process of prompt extension can be referenced here.

Video R1 Reinforcing Video

We introduce Video-MME, the first-ever full-spectrum, M ulti- M odal E valuation benchmark of MLLMs in Video analysis. Of course, you can also combine the use of Ulysses and Ring strategies. Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality.

The specific parameters and corresponding settings are as follows:. Similar to Text-to-Video, Image-to-Video is also divided into processes with and without the prompt extension step. Please reload this page. 💡 I also have other video-language projects that may interest you.

To facilitate implementation, we will start with a basic version of the inference process that skips the prompt extension step. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers. VACE now supports two models 1.

Currently, only P is supported. There was an error while loading. First-Last-Frame-to-Video is also divided into processes with and without the prompt extension step. The execution process is as follows:. Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth.

You can easily inference Wan2. ByteDance †Corresponding author 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.

This repository supports two Text-to-Video models 1. In VACE, users can input text prompt and optional video, mask, and image for video generation or editing. Open-Sora Plan: Open-Source Large Video Generation Model.

For the 1. Therefore, it is recommended to use Ring Strategy instead. Video-R1 significantly outperforms previous models across most benchmarks. Video-LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ⭐ on GitHub for latest update.

The parameters and configurations for these models are as follows:. A machine learning-based video super resolution and frame interpolation framework. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-RB achieves a new state-of-the-art accuracy of %, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters.

The input supports any resolution, but to achieve optimal results, the video size should fall within a specific range. In this repository, we present Wan2. We provide the following two methods for prompt extension:.

Wan Open and Advanced

Therefore, we recommend enabling prompt extension. Est. Hack the Valley II, - k4yt3x/video2x. It is designed to comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities.

The specific parameters and their corresponding settings are as follows:. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the. If your work has improved Wan2.