Rembg-Fuse

🎬 Automatic Background Remover for DaVinci Resolve Fusion

Free and open-source plugin that integrates the power of rembg into Fusion workflows for seamless background removal.

✨ Features

• 🔍 AI-powered background removal using U-2-Net
• 🎞️ Designed for DaVinci Resolve Fusion workflows
• 🛠️ Lightweight, script-based implementation (Python + Fuse)
• 🧩 Easy to integrate, works with both Free and Studio version
• 🆓 100% Free and Open Source

⬇️ Download

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⚙️ How to Install

1. First install the python3 from www.python.org. Version Requirement: python: >=3.11, <3.14
2. Download/Clone the Rembg-Fuse Repo
3. Paste the Rembg folder in the fuse plugin directory of Resolve. Know How
4. Follow the Rembg setup, either using rembg_manager.py or install manually.
5. Open the Fusion page in DaVinci Resolve
6. Search for the Rembg plugin in the node menu (Shift+Spacebar)
7. Connect the rembg node with any footage
8. Select the model and let the plugin do its work
9. The output will be displayed through the media out (if connected)

⮞ Automatic Setup for beginners [Rembg_Manager]

An easy-to-use Python application has been developed to simplify the Rembg setup. Just open the rembg_manager.py file—either directly in Python or via the Fuse interface — and follow the installation steps.

Or Setup Manually

If you encounter any error or prefer to manually install Rembg and its models, follow the steps below:

• Install rembg using pip/pip3 command

pip install rembg

For CUDA support, use rembg[gpu]
For AMD/ROCM support, use rembg[rocm]

• Download the models using this script commands:

import rembg

rembg.new_session("model_name") # replace model name with the actual model name

• Also write the model_name in the models.txt file (newline)
  For fixing issues, check this page: wiki

📦 Available Models

Model Name	Description	Estimated Download Size
u2net	Standard U2-Net model for high-quality general segmentation	168 MB
u2netp	Lightweight U2-Net for faster, lower-resource inference	4 MB
u2net_human_seg	U2-Net model specialized for human segmentation	168 MB
u2net_cloth_seg	U2-Net model specialized for clothing segmentation	168 MB
isnet-general-use	ISNet model for general-purpose image segmentation	170 MB
isnet-anime	ISNet model optimized for anime-style image segmentation	168 MB
silueta	Silueta model for silhouette and background removal	43 MB
sam	Segment Anything Model (SAM) ViT decoder for versatile segmentation	400 MB
birefnet-general	BiRefNet model for high-quality general segmentation	928 MB
birefnet-general-lite	Lightweight BiRefNet for general segmentation	214 MB
birefnet-portrait	BiRefNet model tailored for portrait segmentation	928 MB
ben2-base	BEN2 base model for efficient background removal	213 MB

For more details, visit this repo: https://github.com/danielgatis/rembg

🪄 Video Demo

🌱 Overview

Fuse Version	0.3
Script Version	0.2
Setup Version	1.1
DaVinci Resolve Requirement	Free or Studio : 18+
License	MIT
Copyright	2026
Author	Akash Bora

🐞 Debugging

To view plugin logs and troubleshoot issues, open the console through Fusion page ⮞ Workspace ⮞ Console. Make sure not to check the Disable Logging option in the fuse.
📙 Here is full troubleshooting guide you can follow: wiki

🚧 Planned Improvements

• Streamline Python Script Execution on Windows. Eliminate the disruptive console popup by implementing a cleaner method to trigger the processing script. Maybe consider using comp:DoAction() or comp:Execute() for a more integrated Fusion workflow.

• Optimize Model Reloading and improve overall performance during repeated operations.

• Refactor Image I/O Handling, replace the use of the Clip() method with direct image data passing. Maybe consider the GetPixel() method.

• Add a python path parameter for fixing python version conflicts.

Whether you're fixing bugs, suggesting enhancements, or adding new features—your input is valued. Feel free to fork, improve, and submit pull requests to help evolve this tool.

Get more Resolve plugins at www.akascape.com 👈

Thank You