# FlashVideo: Efficient High-Resolution Video Generation with Flowing Fidelity

This repository profile is provided by osrepos.com, an open source repository discovery platform.

Source: osrepos.com
Repository profile: https://osrepos.com/repo/foundationvision-flashvideo
Generated for open source discovery and AI-assisted research.

FlashVideo is an innovative GitHub repository that introduces a novel approach for efficient high-resolution video generation. It leverages a two-stage diffusion model to produce detailed videos, scaling from 270p to 1080p. This project focuses on maintaining fidelity to detail while significantly improving the efficiency of the video generation process.

GitHub: https://github.com/FoundationVision/FlashVideo
OSRepos URL: https://osrepos.com/repo/foundationvision-flashvideo

## Summary

FlashVideo is an innovative GitHub repository that introduces a novel approach for efficient high-resolution video generation. It leverages a two-stage diffusion model to produce detailed videos, scaling from 270p to 1080p. This project focuses on maintaining fidelity to detail while significantly improving the efficiency of the video generation process.

## Topics

- diffusion-models
- efficient-generative-model
- generative-models
- text-to-video
- video-generation
- Python
- AI
- Machine Learning

## Repository Information

Last analyzed by OSRepos: Wed Nov 05 2025 08:01:38 GMT+0000 (Western European Standard Time)
Detail views: 3
GitHub clicks: 4

## Safety Notice

OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything.

## Content

## Introduction
FlashVideo, from FoundationVision, presents a cutting-edge solution for efficient high-resolution video generation. This project, titled "Flowing Fidelity to Detail for Efficient High-Resolution Video Generation," utilizes advanced diffusion models to create detailed videos, starting from text prompts. It employs a unique two-stage process, first generating 270p videos and then enhancing them to stunning 1080p resolution, all while prioritizing computational efficiency.

## Installation
To get started with FlashVideo, follow these steps to set up your environment and download the necessary model checkpoints.

### Environment Setup
This repository is tested with PyTorch 2.4.0+cu121 and Python 3.11.11. Install the required dependencies using pip:

shell
pip install -r requirements.txt


### Preparing the Checkpoints
Download the 3D VAE (identical to CogVideoX), Stage-I, and Stage-II weights. Navigate to the FlashVideo directory and use `huggingface-cli` to download them:

shell
cd FlashVideo
mkdir -p ./checkpoints
huggingface-cli download --local-dir ./checkpoints  FoundationVision/FlashVideo


Ensure your checkpoints are organized as follows:

??? 3d-vae.pt
??? stage1.pt
??? stage2.pt


## Examples
FlashVideo offers flexible ways to generate videos from text prompts. It's important to note that both Stage-I and Stage-II models are trained with long, comprehensive prompts for best results.

### Jupyter Notebook
You can conveniently provide user prompts and generate videos using the provided Jupyter notebook:

python
flashvideo/demo.ipynb

For GPUs with less memory, consider increasing the spatial and temporal slice configuration in the VAE Decoder.

### Inferring from a Text File
For generating videos with multiple GPUs or from a text file containing prompts, use the following script:

python
bash inf_270_1080p.sh


Experience the quality of FlashVideo's output:

<p align="center">
<img src="https://github.com/FoundationVision/flashvideo-page/blob/main/static/images/output.gif" alt="FlashVideo Generated Example" width="100%">
</p>

## Why Use FlashVideo
FlashVideo stands out for its ability to generate high-resolution videos efficiently, maintaining exceptional fidelity to detail. Its two-stage generation process allows for flexible scaling from lower to higher resolutions, making it suitable for various applications. The project is built on robust diffusion models and provides clear instructions for setup and usage, making it accessible for researchers and developers in the generative AI space.

## Links
*   **GitHub Repository:** <a href="https://github.com/FoundationVision/FlashVideo" target="_blank">FoundationVision/FlashVideo</a>
*   **Project Page:** <a href="https://jshilong.github.io/flashvideo-page/" target="_blank">More visualizations and examples</a>
*   **arXiv Paper:** <a href="https://arxiv.org/abs/2502.05179" target="_blank">FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution Video Generation</a>