# williamyang1991/Rerender_A_Video

[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation

Repository: https://github.com/williamyang1991/Rerender_A_Video
Canonical: https://ross.abutalabs.com/products/rerender_a_video
Homepage: https://www.mmlab-ntu.com/project/rerender/
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: controlnet, diffusion, video-processing
Last push: 2024-03-09T12:34:21+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 86
- inputs: {"age_days": 1213, "days_push": 907, "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 2999, forks 196 (observed 2026-08-28T04:07:37.341672+00:00)

## What it is
The official PyTorch implementation of 'Rerender A Video', a SIGGRAPH Asia 2023 zero-shot text-guided video-to-video translation framework. It adapts text-to-image diffusion models to video using hierarchical cross-frame constraints and temporal-aware patch matching, without requiring training or fine-tuning.

## Use cases
- translate a video into a new style using a text prompt
- apply stable diffusion styles to video with temporal consistency
- restyle video frames without training a model
- use controlnet or lora to guide video translation
- convert source video to anime or artistic style
- research code for zero-shot video-to-video translation

## When to choose
- you want text-guided video restyling with temporal consistency
- you need a zero-shot approach with no training or fine-tuning
- you want to combine ControlNet or LoRA models with video translation
- you are reproducing or building on the SIGGRAPH Asia 2023 paper

## When to avoid
- you need real-time video processing on consumer hardware
- you want a polished end-user product rather than research code
- you need production-grade licensing clarity (license is non-standard)
- you need fast inference on CPU-only machines

## Facets
- artifact type: application
- maturity: stable
- function: video-processing, image-processing, machine-learning, deep-learning
- domain: computer-vision, artificial-intelligence, image-processing
- platform: python, cross-platform
- tags: stable-diffusion, diffusion-models, controlnet, lora, video-to-video, text-guided, zero-shot, temporal-consistency, pytorch, research-paper, video, gpu, linux

## Member repositories
- williamyang1991/Rerender_A_Video (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:37.341672+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 2026-08-30T07:30:36.113041+00:00, confidence not recorded.
  - readme: https://github.com/williamyang1991/Rerender_A_Video (fetched 2026-08-28T04:07:37.341672+00:00, sha 7773367b2717)
  - homepage: https://www.mmlab-ntu.com/project/rerender/ (fetched 2026-08-29T09:45:53.160392+00:00, sha 41859c765aee)
- Data as of 2026-08-30T08:39:29.467469+00:00.