rembg is a developer-oriented tool for removing image backgrounds with machine-learning models. You can use it from the command line, embed it in Python, run its HTTP server, or deploy it with Docker. It supports CPU use as well as optional GPU runtimes, and its model choices have different tasks and licensing terms. The official project documents these interfaces and requirements in its README.
What rembg does
rembg processes an image to separate foreground content from its background and produce a background-removed result. The project describes this as background removal; it does not promise perfect edges or reliable results for every image. The repository does not provide a comparative accuracy or speed benchmark that establishes how it performs against other tools.
It is aimed at developers and technical users: besides a command-line interface (CLI), the project documents a Python library, an HTTP server, and Docker usage.
Choose how to run rembg
| Interface | Useful for | Key considerations |
|---|---|---|
| CLI | Removing the background from an individual file or processing a folder | Quick to invoke from a terminal or script; command examples follow below. |
| Python library | Calling background removal from a Python application | Integrates the tool into a Python workflow; the project documents library usage in its README. |
| HTTP server | Making rembg available through an HTTP service | The CLI includes a server subcommand; plan deployment and runtime requirements for your environment. |
| Docker | Running rembg in a containerized workflow | The project documents Docker usage; container deployment does not remove model, runtime, or licensing considerations. |
Install rembg
The current project README specifies Python 3.11 or later, but below 3.14. CPU use is documented, so a graphics card is not required. GPU acceleration is optional and depends on installing and configuring a compatible runtime for your hardware.
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CPU installation
The README documents a CPU installation extra and a separate CLI extra. For command-line use, install the CPU and CLI extras in an environment using a supported Python version:
pip install "rembg[cpu,cli]"
For an application that uses the library rather than the CLI, follow the project README’s library installation instructions and use the CPU runtime path where appropriate.
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Optional GPU acceleration
- NVIDIA: The project documents a GPU extra, but acceleration depends on compatible ONNX Runtime, CUDA, and cuDNN setup. Installing the extra alone does not guarantee that a machine’s driver and runtime configuration is compatible.
- AMD: The README describes a separate ROCm runtime path. Use the installation instructions for the applicable ROCm environment rather than assuming the NVIDIA setup applies.
If GPU setup is unnecessary or incompatible with your system, the documented CPU path remains an option.
Remove a background from an image
Process one file
- Install rembg with the CLI extra in a supported Python environment.
- Run the project’s individual-image command, replacing the paths with your input and desired output paths:
rembg i path/to/input.png path/to/output.png - Inspect the resulting image in your intended workflow. Background removal can leave edge artifacts, so check the output rather than assuming the result is suitable without review.
Process a folder
Use the CLI’s folder-processing subcommand, p, when you need to process a directory of images. Consult the current README for its exact arguments and output-path behavior; those details can depend on how you want the files organized.
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Other CLI commands
The CLI also documents subcommands for running an HTTP server (s), handling RGB24 binary pixel streams (b), downloading models (d), and migrating model files from the legacy ~/.u2net directory (m). Check the README for the current options and syntax before using these less common workflows.
Select a model for the task
rembg offers several local ONNX models, with descriptions covering general-purpose background removal as well as tasks such as human or clothing segmentation and anime imagery. Its list also includes lightweight options, SAM, multiple BiRefNet variants, and BRIA RMBG. The repository’s model descriptions and trade-offs are the appropriate starting point for choosing; the available documentation does not establish one model as universally best for a particular image type.
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Local models are downloaded on first use and stored under ~/.rembg/models/. Account for that download and storage location when setting up a machine, container, or deployment that may have limited disk space or restricted network access.
Local models versus the withoutbg backend
The withoutbg option is different from the local ONNX models: the README describes it as a cloud API backend and says images are sent to withoutBG’s servers. It also gives a maximum upload size of 20 MB. Do not treat this option as local processing; the repository does not establish additional claims about image retention, security, or privacy.
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Check both software and model licenses
The rembg project identifies its software as MIT-licensed, but that license does not automatically grant commercial-use rights for every model weight. The README specifically warns that BRIA RMBG-2.0 requires a paid agreement for commercial use.
Before using a model in a commercial product or workflow, check that model’s own license and terms. Treat the software license and the model-weight license as separate questions.
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