{"name":"FlashVideo: Efficient High-Resolution Video Generation with Flowing Fidelity","description":"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","url":"https://osrepos.com/repo/foundationvision-flashvideo","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/foundationvision-flashvideo","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/foundationvision-flashvideo.md","json":"https://osrepos.com/repo/foundationvision-flashvideo.json","topics":["diffusion-models","efficient-generative-model","generative-models","text-to-video","video-generation","Python","AI","Machine Learning"],"keywords":["diffusion-models","efficient-generative-model","generative-models","text-to-video","video-generation","Python","AI","Machine Learning"],"stars":null,"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.","content":"## Introduction\nFlashVideo, 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.\n\n## Installation\nTo get started with FlashVideo, follow these steps to set up your environment and download the necessary model checkpoints.\n\n### Environment Setup\nThis repository is tested with PyTorch 2.4.0+cu121 and Python 3.11.11. Install the required dependencies using pip:\n\nshell\npip install -r requirements.txt\n\n\n### Preparing the Checkpoints\nDownload the 3D VAE (identical to CogVideoX), Stage-I, and Stage-II weights. Navigate to the FlashVideo directory and use `huggingface-cli` to download them:\n\nshell\ncd FlashVideo\nmkdir -p ./checkpoints\nhuggingface-cli download --local-dir ./checkpoints FoundationVision/FlashVideo\n\n\nEnsure your checkpoints are organized as follows:\n\n??? 3d-vae.pt\n??? stage1.pt\n??? stage2.pt\n\n\n## Examples\nFlashVideo 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.\n\n### Jupyter Notebook\nYou can conveniently provide user prompts and generate videos using the provided Jupyter notebook:\n\npython\nflashvideo/demo.ipynb\n\nFor GPUs with less memory, consider increasing the spatial and temporal slice configuration in the VAE Decoder.\n\n### Inferring from a Text File\nFor generating videos with multiple GPUs or from a text file containing prompts, use the following script:\n\npython\nbash inf_270_1080p.sh\n\n\nExperience the quality of FlashVideo's output:\n\n

\n\"FlashVideo\n

\n\n## Why Use FlashVideo\nFlashVideo 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.\n\n## Links\n* **GitHub Repository:** FoundationVision/FlashVideo\n* **Project Page:** More visualizations and examples\n* **arXiv Paper:** FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution Video Generation","metrics":{"detailViews":3,"githubClicks":4},"dates":{"published":null,"modified":"2025-11-05T08:01:38.000Z"}}