Maintainer: ali-vilab

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Last updated 5/27/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
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Paper LinkNo paper link provided

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Model Overview

The MS-Vid2Vid-XL model aims to improve the spatiotemporal continuity and resolution of video generation. It serves as the second stage of the I2VGen-XL model to generate 720P videos. The model can also be used for various tasks such as text-to-video synthesis and high-quality video transfer. MS-Vid2Vid-XL utilizes the same underlying video latent diffusion model (VLDM) and spatiotemporal UNet (ST-UNet) as the first stage of I2VGen-XL, which is designed based on the VideoComposer project.

Model Inputs and Outputs


  • Video Path: The input video path to be processed.
  • Text: The text description to guide the video generation.


  • Output Video: The generated high-resolution video.


MS-Vid2Vid-XL can generate high-definition (720P) and widescreen (16:9 aspect ratio) videos with improved spatiotemporal continuity and texture compared to existing open-source video generation models. The model has been trained on a large dataset of high-quality videos and images, allowing it to produce videos with good semantic consistency, temporal stability, and realistic textures.

What Can I Use It For?

The MS-Vid2Vid-XL model can be used for a variety of applications, such as:

  • Text-to-Video Synthesis: Generate videos based on text descriptions.
  • High-Quality Video Transfer: Enhance the resolution and quality of existing low-resolution videos.
  • Video Generation for Media and Entertainment: Create high-quality video content for films, TV shows, and other media.

Things to Try

While the MS-Vid2Vid-XL model can generate high-quality 720P videos, it may have some limitations. The model can sometimes produce blurry results when the target is far away, and the computation time for generating a single video is over 2 minutes due to the large latent space size. To address these issues, users can try providing more detailed text descriptions to guide the model's generation process.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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The MS-Image2Video (I2VGen-XL) project aims to address the task of generating high-definition video from input images. This model, developed by DAMO Academy, consists of two stages. The first stage ensures semantic consistency at low resolutions, while the second stage uses a Video Latent Diffusion Model (VLDM) to denoise, improve resolution, and enhance temporal and spatial consistency. The model is based on the publicly available VideoComposer work, inheriting design concepts such as the core UNet architecture. With a total of around 3.7 billion parameters, I2VGen-XL demonstrates significant advantages over existing video generation models in terms of quality, texture, semantics, and temporal continuity. Similar models include the i2vgen-xl and text-to-video-ms-1.7b projects, also developed by the ali-vilab team. Model inputs and outputs Inputs Single input image: The model takes a single image as the conditioning frame for video generation. Outputs Video frames: The model outputs a sequence of video frames, typically at 720P (1280x720) resolution, that are visually consistent with the input image and exhibit temporal continuity. Capabilities The I2VGen-XL model is capable of generating high-quality, widescreen videos directly from input images. The model ensures semantic consistency and significantly improves upon the resolution, texture, and temporal continuity of the output compared to existing video generation models. What can I use it for? The I2VGen-XL model can be used for a variety of applications, such as: Content Creation**: Generating visually appealing video content for entertainment, marketing, or educational purposes based on input images. Visual Effects**: Extending static images into dynamic video sequences for use in film, television, or other multimedia productions. Automated Video Generation**: Developing tools or services that can automatically create videos from user-provided images. Things to try One interesting aspect of the I2VGen-XL model is its two-stage architecture, where the first stage focuses on semantic consistency and the second stage enhances the video quality. You could experiment with the model by generating videos with different input images, observing how the model handles different types of content and scene compositions. Additionally, you could explore the model's ability to maintain temporal continuity and coherence, as this is a key advantage highlighted by the maintainers. Try generating videos with varied camera movements, object interactions, or lighting conditions to assess the model's robustness.

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