ali-vilab/VACE

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3934 stars · 280 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

VACE is the official implementation of an all-in-one video creation and editing model from Tongyi Lab, built on Wan2.1 diffusion models. It supports reference-to-video (R2V), video-to-video (V2V), and masked video-to-video (MV2V) tasks that can be freely composed.

Use cases

• generate videos from reference images and text prompts
• edit existing videos with text instructions
• inpaint or replace objects in videos using masks
• animate a still image into a video
• extend or outpaint video frames
• swap or move objects within a video

When to choose

• you need a single unified model for multiple video generation and editing tasks
• you want to run state-of-the-art open-source video editing on your own GPU
• you are doing research on controllable video diffusion models

When to avoid

• you need fast real-time video editing without a high-end GPU
• you want a polished end-user GUI application rather than Python code and model weights
• your use case is simple trimming or cutting that a standard editor handles

Facets

library · maturity active

video-processing machine-learning deep-learning image-processing artificial-intelligence deep-learning media python cross-platform video-generation video-editing diffusion-model text-to-video wan21 iccv-2025 research video gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ali-vilab/VACE")