# wilson1yan/VideoGPT

Repository: https://github.com/wilson1yan/VideoGPT
Canonical: https://ross.abutalabs.com/products/videogpt
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-09-18T03:33:55+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1985, "days_push": 714, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1081, forks 129 (observed 2026-08-28T04:03:30.821714+00:00)

## What it is
VideoGPT is a PyTorch implementation of a video generation model that combines a VQ-VAE with 3D convolutions and axial attention with a GPT-style autoregressive transformer over discrete latents. It serves as a minimalistic, reproducible reference for transformer-based video generation research.

## Use cases
- generate videos with a transformer model
- train a VQ-VAE on video data
- reproduce the VideoGPT paper results
- experiment with autoregressive video generation
- learn how VQ-VAE and GPT combine for video synthesis

## When to choose
- you need a simple reference implementation of likelihood-based video generation
- you want to train or fine-tune a VQ-VAE plus transformer on your own video dataset
- you are doing research on discrete latent video models

## When to avoid
- you need production-ready, actively maintained video generation
- you want large-scale text-to-video generation
- you lack a GPU or cannot manage older CUDA/PyTorch dependencies

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, video-processing, transformers
- domain: deep-learning, computer-vision, machine-learning
- platform: python
- tags: vq-vae, video-generation, gpt, autoregressive-model, research-code, video, gpu

## Member repositories
- wilson1yan/VideoGPT (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:30.821714+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 2026-08-30T06:51:24.751859+00:00, confidence not recorded.
  - readme: https://github.com/wilson1yan/VideoGPT (fetched 2026-08-28T04:03:30.821714+00:00, sha 04599be83d56)
- Data as of 2026-08-30T08:39:29.467469+00:00.