# ChenyangQiQi/FateZero

[ICCV 2023 Oral] "FateZero: Fusing Attentions for Zero-shot Text-based Video Editing"

Repository: https://github.com/ChenyangQiQi/FateZero
Canonical: https://ross.abutalabs.com/products/fatezero
Homepage: http://fate-zero-edit.github.io/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: stable-diffusion, text-driven-editing, video-editing, image-editing, video-style-transfer
Last push: 2023-08-14T00:25:26+00:00

## Health v2 (maintenance only)
Score: 21/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 90
- inputs: {"age_days": 1266, "days_push": 1116, "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 1163, forks 109 (observed 2026-08-28T04:03:49.749697+00:00)

## What it is
FateZero is a zero-shot text-based video editing framework built on pretrained Stable Diffusion models, introduced in an ICCV 2023 Oral paper. It fuses attention maps captured during DDIM inversion to edit real-world videos from text prompts without per-prompt training or user-provided masks.

## Use cases
- edit a real video with a text prompt without training
- apply Van Gogh or anime style to an existing video
- swap objects in a video like cat to lion
- change video attributes while preserving motion and structure
- zero-shot video style transfer with stable diffusion
- edit video frames consistently using diffusion models

## When to choose
- you want text-driven edits on an existing video without training a model
- you need temporal consistency in diffusion-based video editing
- you want a research reference implementation for attention-fusion editing

## When to avoid
- you need fast, production-grade video editing with low compute
- you want a polished end-user application rather than research code
- you need support for the latest diffusion models or active maintenance

## Facets
- artifact type: library
- maturity: maintenance
- function: video-processing, image-processing, machine-learning, deep-learning
- domain: image-processing, artificial-intelligence, computer-vision
- platform: python, cross-platform
- tags: stable-diffusion, diffusion-models, zero-shot-editing, video-editing, text-driven-editing, style-transfer, research-paper, iccv-2023, video, gpu

## Member repositories
- ChenyangQiQi/FateZero (main) score 21

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:49.749697+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 2026-08-30T06:31:32.120325+00:00, confidence not recorded.
  - readme: https://github.com/ChenyangQiQi/FateZero (fetched 2026-08-28T04:03:49.749697+00:00, sha 52657f311851)
  - homepage: http://fate-zero-edit.github.io/ (fetched 2026-08-29T12:35:42.491403+00:00, sha 33eba3c39d7b)
- Data as of 2026-08-30T08:39:29.467469+00:00.