Spaces:
Running on Zero
Running on Zero
| title: BiRefNet Background Remover | |
| emoji: ✂️ | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.45.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Remove image backgrounds with BiRefNet. | |
| # BiRefNet Background Remover | |
| A small Gradio app that creates transparent PNG cut-outs with | |
| [BiRefNet-General](https://github.com/ZhengPeng7/BiRefNet), through | |
| [rembg](https://github.com/danielgatis/rembg). It replaces the previous | |
| RMBG-2.0/Transformers implementation. | |
| ## Run locally | |
| Requires Python 3.10 or newer. | |
| ```bash | |
| python -m venv .venv | |
| # Windows PowerShell | |
| .venv\Scripts\Activate.ps1 | |
| pip install -r requirements.txt | |
| python app.py | |
| ``` | |
| Open the local URL printed by Gradio. The first removal downloads the | |
| `birefnet-general` ONNX weights and caches them; later requests reuse the | |
| same in-process session. To choose a persistent cache location, set | |
| `REMBG_HOME` before starting the app. | |
| ## Deploy to Hugging Face Spaces | |
| 1. Create a new **Gradio** Space, preferably with Python 3.10+. | |
| 2. Push these files to the root of the Space: `app.py`, | |
| `remove_background.py`, `requirements.txt`, `README.md`, and `LICENSE`. | |
| 3. Wait for the build to finish. No secrets or manual model download are | |
| required. The model downloads automatically on the first inference. | |
| 4. For quicker or more reliable cold starts, use a persistent-storage upgrade | |
| and set `REMBG_HOME=/data/rembg` in the Space variables. This is optional; | |
| the default cache also works but is recreated after a free Space restarts. | |
| The app queues one request at a time to keep memory usage safe. It supports | |
| Hugging Face ZeroGPU and requests a GPU only while removing a background; a | |
| CPU Space also works, albeit more slowly. | |
| ## Licensing and attribution | |
| This repository's code is MIT licensed. BiRefNet's upstream repository is | |
| MIT licensed, and `rembg` is MIT licensed. The selected `birefnet-general` | |
| model is the BiRefNet general-use ONNX model distributed by `rembg`; unlike | |
| the prior BRIA RMBG-2.0 model, it is intended here to support commercial use. | |
| Keep this attribution and independently review upstream model and dataset | |
| terms for your specific deployment. | |
| Links: [BiRefNet](https://github.com/ZhengPeng7/BiRefNet) · | |
| [rembg model list](https://github.com/danielgatis/rembg#available-models) | |