# NUS-HPC-AI-Lab/VideoSys

VideoSys: An easy and efficient system for video generation

Repository: https://github.com/NUS-HPC-AI-Lab/VideoSys
Canonical: https://ross.abutalabs.com/products/videosys
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-08-27T07:47:32+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 39, release rhythm 40, longevity 66
- inputs: {"age_days": 928, "days_push": 371, "days_rel": 699, "gap_med": 19, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2022, forks 129 (observed 2026-08-28T04:06:06.439194+00:00)

## What it is
VideoSys is an open-source Python library providing easy and efficient infrastructure for video generation, supporting training, inference, serving, and compression of diffusion-based video models. It integrates models like Open-Sora, CogVideoX, Latte, and Vchitect-2.0, and includes acceleration techniques such as Pyramid Attention Broadcast (PAB) and Dynamic/Data-Centric Sequence Parallelism.

## Use cases
- generate videos with open-source diffusion models
- accelerate DiT-based video generation to real-time
- train video generation models with sequence parallelism
- run CogVideoX or Open-Sora inference efficiently
- reduce memory usage for video model training
- serve and compress video generation models

## When to choose
- you want a unified, high-performance pipeline for open-source video generation models
- you need GPU acceleration techniques like PAB or sequence parallelism for video diffusion models
- you want to train or fine-tune DiT-based video models on multi-GPU setups

## When to avoid
- you need simple image generation rather than video
- you want a plug-and-play consumer app with no GPU infrastructure
- you need commercial closed-model video generation APIs

## Facets
- artifact type: library
- maturity: active
- function: video-processing, machine-learning, deep-learning, llm-training, gpu-computing
- domain: machine-learning, deep-learning, gpu-computing, artificial-intelligence
- platform: python
- tags: video-generation, diffusion-transformers, model-inference, distributed-training, pytorch, open-sora, cogvideox, latte, sequence-parallelism, attention-broadcast, video, gpu, linux, docker

## Member repositories
- NUS-HPC-AI-Lab/VideoSys (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:06.439194+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:00:04.312166+00:00, confidence not recorded.
  - readme: https://github.com/NUS-HPC-AI-Lab/VideoSys (fetched 2026-08-28T04:06:06.439194+00:00, sha 9a8947929a4a)
- Data as of 2026-08-30T08:39:29.467469+00:00.