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Visomaster

Visomaster is an open-source application for AI-assisted face swapping and editing in images and videos.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Visomaster — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a face-swap edit needs per-face masking, expression refinement or frame-specific video adjustments in one desktop workflow.

Who it is for

  • Creators making image or video edits involving face swaps.
  • Editors who need to handle multiple faces and tune masks or expressions.

Documented uses

  • Swap faces in images and video.
  • Select multiple faces and adjust masks for facial parts or occlusions.
  • Restore original expressions on swapped faces.
  • Adjust facial expressions, poses and selected color values with the LivePortrait-based editor.
  • Preview processed video and set adjustments at individual frames.
Before you choose

Requirements & limits

The repository is public and specifies the GPL-3.0 license. The README describes an automatic installer for Windows users with an Nvidia GPU and a separate manual Nvidia setup.

  • The manual installation path requires Git, Miniconda, Python 3.10.13, CUDA 12.4.1, cuDNN, dependencies, models and asset files.
  • The README lists both image and video editing, but the automatic installer is specifically described for Windows users with an Nvidia GPU.
  • The project’s usage guidance places responsibility on users to obtain permission for likenesses and avoid deceptive or harmful uses.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install VisoMaster manually with the Nvidia setup

Set up the application using the repository’s manual Nvidia installation instructions.

Before you start

  • Git
  • Miniconda
  • Nvidia GPU setup using the specified CUDA and cuDNN packages

Steps

  1. Clone the VisoMaster repository and change into its directory.

  2. Create and activate a Conda environment with Python 3.10.13.

  3. Install CUDA runtime 12.4.1, cuDNN, scikit-image and the dependencies listed in requirements_cu124.txt.

  4. Run python download_models.py, then place the downloaded asset files in the dependencies folder.

  5. Start the application by opening Start.bat.

Expected outcome

The README’s manual setup sequence ends by launching VisoMaster from the prepared environment.

Guide limitations

  • These manual instructions are labeled for Nvidia and use CUDA 12.4.1.
  • The README says to check GPU drivers if CUDA issues occur and to verify model locations if models are missing.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
visomaster/VisoMaster · Official homepage title · retained in its source language
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