# Sxela/WarpFusion

WarpFusion

Repository: https://github.com/Sxela/WarpFusion
Canonical: https://ross.abutalabs.com/products/warpfusion
Language: Batchfile
License: NOASSERTION
License Family: other
Topics: img2img, stablediffusion, text2video, vid2vid, video, video2video
Last push: 2025-04-27T22:11:38+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 18, release rhythm 8, longevity 100
- inputs: {"age_days": 1452, "days_push": 493, "days_rel": 696, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1016, forks 109 (observed 2026-08-28T04:03:14.500518+00:00)

## What it is
WarpFusion is a Stable Diffusion-based video-to-video style transfer tool distributed as a Jupyter/Colab notebook. It applies AI animation to source videos using optical-flow warping for temporal consistency.

## Use cases
- turn a video into an AI animation with stable diffusion
- vid2vid style transfer with temporal consistency
- apply text prompts to restyle video frames
- img2img video processing in Google Colab
- create AI-generated animation from footage
- video style transfer with optical flow warping

## When to choose
- you want to stylize an existing video with Stable Diffusion while keeping frame-to-frame consistency
- you want to run on Colab or a local GPU without complex setup
- you need fine control over masking, warping, and consistency settings

## When to avoid
- you need a production pipeline or API rather than a notebook workflow
- you have no GPU and don't want to pay for Colab Pro
- you want simple one-click video generation without tuning parameters

## Facets
- artifact type: application
- maturity: active
- function: video-processing, image-processing, stable-diffusion, machine-learning, gpu-computing
- domain: image-processing, artificial-intelligence, media
- platform: python, cross-platform
- tags: stable-diffusion, vid2vid, text2video, img2img, colab-notebook, ai-animation, video-style-transfer, video, gpu, web-server

## Member repositories
- Sxela/WarpFusion (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:14.500518+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 2026-08-30T07:11:17.037628+00:00, confidence not recorded.
  - readme: https://github.com/Sxela/WarpFusion (fetched 2026-08-28T04:03:14.500518+00:00, sha b119bda0daae)
- Data as of 2026-08-30T08:39:29.467469+00:00.