# lucidrains/video-diffusion-pytorch

Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch

Repository: https://github.com/lucidrains/video-diffusion-pytorch
Canonical: https://ross.abutalabs.com/products/video-diffusion-pytorch
Language: Python
License: MIT
License Family: permissive
Topics: artificial-intelligence, deep-learning, ddpm, video-generation, text-to-video
Last push: 2024-05-03T16:58:59+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1609, "days_push": 852, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1383, forks 139 (observed 2026-08-28T04:04:34.630067+00:00)

## What it is
A Pytorch implementation of the Video Diffusion Models paper by Jonathan Ho, extending denoising diffusion probabilistic models (DDPMs) to video generation using a space-time factored 3D U-net. It supports unconditional and text-conditioned (BERT embeddings) video synthesis for research experimentation.

## Use cases
- generate short videos from text descriptions
- train a diffusion model on video datasets
- reproduce the Video Diffusion Models paper
- experiment with space-time factored U-nets for video synthesis
- generate moving MNIST-style video samples
- build a research prototype for text-to-video generation

## When to choose
- you want a lightweight, readable Pytorch implementation of video diffusion for research
- you need a starting codebase to extend with newer text-to-video techniques
- you want to train small-scale video diffusion models on custom datasets

## When to avoid
- you need a production-ready text-to-video system with high-quality output
- you want a pretrained model that generates realistic videos out of the box
- you need large-scale, well-supported training infrastructure or long video generation

## Facets
- artifact type: library
- maturity: experimental
- function: deep-learning, machine-learning, video-processing
- domain: deep-learning, artificial-intelligence
- platform: python
- tags: diffusion-models, ddpm, text-to-video, video-generation, pytorch, research-code, unet, video, gpu

## Member repositories
- lucidrains/video-diffusion-pytorch (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:34.630067+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 2026-08-30T04:40:03.163040+00:00, confidence not recorded.
  - readme: https://github.com/lucidrains/video-diffusion-pytorch (fetched 2026-08-28T04:04:34.630067+00:00, sha c499627a3e60)
  - registry_pypi: https://pypi.org/pypi/video-diffusion-pytorch/json (fetched 2026-08-29T11:55:49.397063+00:00, sha d574d1faf84c)
- Data as of 2026-08-30T08:39:29.467469+00:00.