# Picsart-AI-Research/Text2Video-Zero

[ICCV 2023 Oral] Text-to-Image Diffusion Models are Zero-Shot Video Generators

Repository: https://github.com/Picsart-AI-Research/Text2Video-Zero
Canonical: https://ross.abutalabs.com/products/text2video-zero
Homepage: https://text2video-zero.github.io/
Language: Python
License: NOASSERTION
License Family: other
Topics: video-editing, video-generation
Last push: 2023-05-06T22:35:28+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 90
- inputs: {"age_days": 1261, "days_push": 1215, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4245, forks 388 (observed 2026-08-28T04:08:40.361543+00:00)

## What it is
Official implementation of Text2Video-Zero, a zero-shot text-to-video generation method that adapts text-to-image diffusion models like Stable Diffusion to video without any training. It supports text-to-video synthesis, pose/edge/depth-conditioned generation, and instruction-guided video editing (Video Instruct-Pix2Pix).

## Use cases
- generate videos from a text prompt
- zero-shot text-to-video generation with stable diffusion
- edit videos with text instructions
- generate videos conditioned on pose or edge maps
- create temporally consistent videos without training a video model
- run text-to-video on a consumer GPU with limited VRAM

## When to choose
- you want text-to-video generation without training on video datasets
- you want to reuse existing Stable Diffusion or DreamBooth models for video
- you need instruction-guided video editing
- you have a GPU with ~7-12 GB VRAM and want low-cost video generation

## When to avoid
- you need production-grade, actively maintained software
- you require the highest-quality modern text-to-video output
- you have no GPU available
- you need a permissive license - the license is non-standard

## Facets
- artifact type: library
- maturity: maintenance
- function: video-processing, machine-learning, deep-learning, image-processing
- domain: deep-learning, artificial-intelligence, computer-vision
- platform: python
- tags: text-to-video, stable-diffusion, diffusion-models, zero-shot, video-editing, instruct-pix2pix, research-code, iccv-2023, video, gpu, linux

## Member repositories
- Picsart-AI-Research/Text2Video-Zero (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:40.361543+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 2026-08-29T18:22:12.700611+00:00, confidence not recorded.
  - readme: https://github.com/Picsart-AI-Research/Text2Video-Zero (fetched 2026-08-28T04:08:40.361543+00:00, sha a0275fe9b3ce)
  - homepage: https://text2video-zero.github.io/ (fetched 2026-08-29T09:11:54.989790+00:00, sha 6274d6137390)
- Data as of 2026-08-30T08:39:29.467469+00:00.