# bytedance/Bernini

Bernini is a unified framework for video generation and editing that combines an MLLM-based semantic planner with a DiT-based renderer.

Repository: https://github.com/bytedance/Bernini
Canonical: https://ross.abutalabs.com/products/bernini
Homepage: https://bernini-ai.github.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: video-editing, video-generation, image-editing, image-generation
Last push: 2026-08-13T12:11:19+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 6
- inputs: {"age_days": 96, "days_push": 20, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1287, forks 100 (observed 2026-08-28T04:04:14.989089+00:00)

## What it is
Bernini is a unified framework for video generation and editing that combines an MLLM-based semantic planner with a DiT-based renderer performing flow-matching denoising in VAE latent space. It includes open-sourced inference and training code plus model weights (1.3B and 14B renderer variants) released on HuggingFace.

## Use cases
- generate videos from text prompts
- edit videos with natural language instructions
- remove watermarks or subtitles from videos
- apply style transfer to videos
- generate videos guided by reference images
- insert an image or video into an existing video
- train a custom video diffusion renderer

## When to choose
- you need a unified open-source model for both video generation and instruction-based video editing
- you want reference-guided video generation with multiple input images
- you need DiT-based video diffusion weights compatible with the Diffusers ecosystem
- you want to reproduce or extend research on latent semantic planning for video diffusion

## When to avoid
- you need lightweight CPU-only video processing without GPU hardware
- you only need simple video cutting or transcoding rather than generative editing
- you require a production-ready commercial-grade API with support guarantees
- your tasks are complex human generation where the smaller 1.3B model lags behind

## Facets
- artifact type: library
- maturity: active
- function: video-processing, image-processing, machine-learning, deep-learning, llm-inference
- domain: image-processing, artificial-intelligence, deep-learning, large-language-models
- platform: python
- tags: video-generation, video-editing, diffusion-transformer, mllm, text-to-video, flow-matching, diffusers, bytedance, video, gpu, linux

## Member repositories
- bytedance/Bernini (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:14.989089+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 2026-08-30T04:55:51.114549+00:00, confidence not recorded.
  - readme: https://github.com/bytedance/Bernini (fetched 2026-08-28T04:04:14.989089+00:00, sha 229c99a80df8)
  - homepage: https://bernini-ai.github.io/ (fetched 2026-08-29T12:11:59.032738+00:00, sha 2ff71dad357c)
- Data as of 2026-08-30T08:39:29.467469+00:00.