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ComfyDeploy: How SAMURAI Nodes for ComfyUI works in ComfyUI?

What is SAMURAI Nodes for ComfyUI?

ComfyUI nodes for video object segmentation using [a/SAMURAI](https://github.com/yangchris11/samurai) model.

How to install it in ComfyDeploy?

Head over to the machine page

  1. Click on the "Create a new machine" button
  2. Select the Edit build steps
  3. Add a new step -> Custom Node
  4. Search for SAMURAI Nodes for ComfyUI and select it
  5. Close the build step dialig and then click on the "Save" button to rebuild the machine

SAMURAI Nodes for ComfyUI

ComfyUI nodes for video object segmentation using SAMURAI model.

Installation

Note: It is recommended to use Conda environment for installation and running the nodes. Make sure to use the same Conda environment for both ComfyUI and SAMURAI installation! It is highly recommended to use the console version of ComfyUI

Requirements

  • NVIDIA GPU with CUDA support
  • Python 3.10 or higher
  • ComfyUI
  • Conda (recommended) or pip

  1. Follow the SAMURAI installation guide to install the base model

  2. Clone this repository into your ComfyUI custom nodes directory:

cd ComfyUI/custom_nodes

git clone https://github.com/takemetosiberia/ComfyUI-SAMURAI--SAM2-.git samurai_nodes

  1. Copy the SAMURAI installation folder into ComfyUI/custom_nodes/samurai_nodes/

  2. Download model weights as described in SAMURAI guide

Project Structure

After installation, your directory structure should look like this:

ComfyUI/
└── custom_nodes/
    └── samurai_nodes/
        ├── samurai/     # SAMURAI model installation
        ├── init.py      # Module initialization
        ├── samurai_node.py
        └── utils.py

Additional Dependencies

Most dependencies are included with SAMURAI installation. Additional required packages:

pip install hydra-core omegaconf loguru

Usage

The workflow consists of three main nodes:

SAMURAI Box Input

Allows selecting a region of interest (box) in the first frame of a video sequence.

  • Input: video frames
  • Output: box coordinates and start frame number

SAMURAI Points Input

Enables point-based object selection in the first frame.

  • Input: video frames
  • Output: point coordinates, labels, and start frame number

SAMURAI Refine

Performs video object segmentation using selected area.

  • Input: video frames, box/points from input nodes
  • Output: segmentation masks

Example Workflow

  1. Connect Load Video to SAMURAI Box/Points Input
  2. Draw box or place points around object of interest
  3. Connect to SAMURAI Refine
  4. Convert masks to images and save/combine as needed

For more examples and details, see SAMURAI documentation.

Troubleshooting

If you encounter any issues:

  1. Make sure you're using the correct Conda environment
  2. Verify that all dependencies are installed in your Conda environment
  3. Check if SAMURAI models is properly installed in the samurai/sam2/checkpoints directory

For CUDA-related issues, ensure your Conda environment has the correct PyTorch version with CUDA support.