Rembg is a tool to remove images background
|
PhotoRoom Remove Background API
https://photoroom.com/api
Fast and accurate background remover API |
If your system is compatible, run: ```bash pip install "rembg[gpu]" # for library pip install "rembg[gpu,cli]" # for library + cli ``` > **Note:** NVIDIA GPUs may require `onnxruntime-gpu`, CUDA, and `cudnn-devel`. See [#668](https://github.com/danielgatis/rembg/issues/668#issuecomment-2689830314) for details. If `rembg[gpu]` doesn't work and you can't install CUDA or `cudnn-devel`, use `rembg[cpu]` with `onnxruntime` instead. ### GPU support (AMD/ROCm) ROCm support requires the `onnxruntime-rocm` package. Install it by following [AMD's documentation](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/install-onnx.html). Once `onnxruntime-rocm` is installed and working, install rembg with ROCm support: ```bash pip install "rembg[rocm]" # for library pip install "rembg[rocm,cli]" # for library + cli ``` ## Usage as a CLI After installation, you can use rembg by typing `rembg` in your terminal. The `rembg` command has these subcommands: - `i` - single files - `p` - folders (batch processing) - `s` - HTTP server - `b` - RGB24 pixel binary stream - `d` - download models ahead of time - `m` - migrate models from the legacy `~/.u2net` directory You can get help about the main command using: ```shell rembg --help ``` You can also get help for any subcommand: ```shell rembg --help ``` ### rembg `i` Used for processing single files. **Remove background from a remote image:** ```shell curl -s http://input.png | rembg i > output.png ``` **Remove background from a local file:** ```shell rembg i path/to/input.png path/to/output.png ``` **Omit the output path** (writes `.out.png` next to the input): ```shell rembg i path/to/input.png # → path/to/input.out.png ``` If `stdout` is redirected (e.g. `rembg i input.png > out.png`), the output is written to `stdout` instead. **Specify a model:** ```shell rembg i -m u2netp path/to/input.png path/to/output.png ``` **Return only the mask:** ```shell rembg i -om path/to/input.png path/to/output.png ``` **Apply alpha matting:** ```shell rembg i -a path/to/input.png path/to/output.png ``` **Remove color fringing from soft edges:** ```shell rembg i -dc path/to/input.png path/to/output.png ``` See [Color decontamination](#color-decontamination) for what this does. **Pass extra parameters (SAM example):** ```shell rembg i -m sam -x '{ "sam_prompt": [{"type": "point", "data": [724, 740], "label": 1}] }' examples/plants-1.jpg examples/plants-1.out.png ``` **Pass extra parameters (custom model):** ```shell rembg i -m u2net_custom -x '{"model_path": "~/.u2net/u2net.onnx"}' path/to/input.png path/to/output.png ``` **Use the withoutBG cloud API:** [Get 50 free credits with signup](https://withoutbg.com/signup?ref=rembg). [Sample results](https://withoutbg.com/pro-model/results?ref=rembg). ```shell export WITHOUTBG_API_KEY=sk_... rembg i -m withoutbg path/to/input.png path/to/output.png ``` Or pass the key via extras: ```shell rembg i -m withoutbg -x '{"api_key":"sk_..."}' path/to/input.png path/to/output.png ``` ### rembg `p` Used for batch processing entire folders. **Process all images in a folder:** ```shell rembg p path/to/input path/to/output ``` **Watch mode (process new/changed files automatically):** ```shell rembg p -w path/to/input path/to/output ``` ### rembg `s` Used to start an HTTP server. ```shell rembg s --host 0.0.0.0 --port 7000 --log_level info ``` For complete API documentation, visit: `http://localhost:7000/api` **Disable the Gradio UI (reduces idle CPU usage):** ```shell rembg s --no-ui ``` **Remove background from an image URL:** ```shell curl -s "http://localhost:7000/api/remove?url=http://input.png" -o output.png ``` **Remove background from an uploaded image:** ```shell curl -s -F file=@/path/to/input.jpg "http://localhost:7000/api/remove" -o output.png ``` ### rembg `b` Process a sequence of RGB24 images from stdin. This is intended to be used with programs like FFmpeg that output RGB24 pixel data to stdout. ```shell rembg b -o ``` **Arguments:** | Argument | Description | |----------|-------------| | `width` | Width of input image(s) | | `height` | Height of input image(s) | | `output_specifier` | Printf-style specifier for output filenames (e.g., `output-%03u.png` produces `output-000.png`, `output-001.png`, etc.). Omit to write to stdout. | **Example with FFmpeg:** ```shell ffmpeg -i input.mp4 -ss 10 -an -f rawvideo -pix_fmt rgb24 pipe:1 | rembg b 1280 720 -o folder/output-%03u.png ``` > **Note:** The width and height must match FFmpeg's output dimensions. The flags `-an -f rawvideo -pix_fmt rgb24 pipe:1` are required for FFmpeg compatibility. ## Usage as a Library **Input and output as bytes:** ```python from rembg import remove with open('input.png', 'rb') as i: with open('output.png', 'wb') as o: input = i.read() output = remove(input) o.write(output) ``` **Input and output as a PIL image:** ```python from rembg import remove from PIL import Image input = Image.open('input.png') output = remove(input) output.save('output.png') ``` **Input and output as a NumPy array:** ```python from rembg import remove import cv2 input = cv2.imread('input.png') output = remove(input) cv2.imwrite('output.png', output) ``` **Force output as bytes:** ```python from rembg import remove with open('input.png', 'rb') as i: with open('output.png', 'wb') as o: input = i.read() output = remove(input, force_return_bytes=True) o.write(output) ``` **Batch processing with session reuse (recommended for performance):** ```python from pathlib import Path from rembg import remove, new_session session = new_session() for file in Path('path/to/folder').glob('*.png'): input_path = str(file) output_path = str(file.parent / (file.stem + ".out.png")) with open(input_path, 'rb') as i: with open(output_path, 'wb') as o: input = i.read() output = remove(input, session=session) o.write(output) ``` **withoutBG cloud API:** [Get 50 free credits with signup](https://withoutbg.com/signup?ref=rembg). [Sample results](https://withoutbg.com/pro-model/results?ref=rembg). ```python from rembg import remove, new_session session = new_session("withoutbg", api_key="sk_...") # or set WITHOUTBG_API_KEY and omit api_key= with open('input.png', 'rb') as i: with open('output.png', 'wb') as o: output = remove(i.read(), session=session) o.write(output) ``` For more examples, see the [examples](USAGE.md) page. ## Choosing an edge mode Rembg has three ways to turn a mask into a cutout. They differ only in how they treat the *soft* pixels along an edge — hair, fur, fabric, motion blur. | Mode | Flag | Fixes edge color | Refines the mask | Cost | | --- | --- | --- | --- | --- | | Naive | *(default)* | No | No | Free | | Decontaminate | `-dc` | Yes | No | Negligible | | Alpha matting | `-a` | Yes | Yes | Slow | | ViTMatte | `-vm` | Yes | Yes | Slow, extra download | They are alternatives, not layers — `-a` already decontaminates internally, so passing both changes nothing. Pick one: **Use the default (naive)** when the subject has hard edges — products, cars, logos, screenshots — or when the background was already close in color to the subject. There is nothing to correct, so the extra work buys nothing. **Use `-dc`** when the cutout has a colored halo: the subject was shot against a strongly colored background (green grass, blue sky, a painted wall) and that color survives as a rim around hair or fine detail. This is the common case, and it is cheap enough to leave on for a whole batch. **Use `-a`** when the *shape* of the mask is wrong, not just its color — the model cut through strands of hair, or left a hard stair-stepped edge where the subject is genuinely soft. It re-estimates coverage with a closed-form solver, and it is much slower than `-dc`. That solver can fail to converge on some images; rembg falls back to a decontaminated cutout when that happens. **Use `-vm`** for the same problem as `-a`, when you want the fine detail back. ViTMatte predicts the alpha with a network instead of solving for it, so it recovers more of the wispy strands `-a` tends to clip, and it cannot fail to converge. It costs an extra ~110 MB download on first use and runs slower than `-a`. Pick a different checkpoint with `-x '{"vitmatte_model": "base-distinctions-646"}'`: | Checkpoint | Download | Notes | | --- | --- | --- | | `small-distinctions-646` | ~110 MB | The default. Best quality per byte. | | `small-composition-1k` | ~110 MB | Trained on synthetic composites. | | `base-distinctions-646` | ~380 MB | Slightly more detail, ~2.5x the runtime. | | `base-composition-1k` | ~380 MB | Larger, synthetic training set. | ### Which model to pair it with The newer models (`bria-rmbg`, `birefnet-*`, `isnet-general-use`) already produce a soft, well-shaped alpha, so their masks rarely need `-a` — reach for `-dc` first and only try `-a` if the edge shape itself is wrong. `bria-rmbg` is the default, so this is the advice that applies unless you pass `-m`. The older models (`u2net`, `u2netp`, `silueta`) tend to produce firmer, blockier edges. They benefit most from `-a` on hair-heavy portraits, and are also where its solver is most likely to struggle. For portraits specifically, `birefnet-portrait` with `-dc` is a good starting point, and `-vm` when the hair detail matters more than the runtime. If you are batch-processing and cannot inspect each result, prefer `-dc` over `-a`: it is faster and cannot fail. > Note: `-ppm` (post-process mask) thresholds the mask into a fully binary one, > which leaves no partially transparent pixels at all. Combining it with `-dc` > is pointless — there is nothing left to correct. Use one or the other. ## Color decontamination On a soft edge — hair, fur, motion blur — a pixel is not purely foreground or purely background. The camera captured a blend of the two: ``` captured = alpha * foreground + (1 - alpha) * background ``` Making that pixel semi-transparent does not undo the blend, so the background color stays mixed into it and shows up as a colored halo around fine detail. A subject shot against green grass keeps a green rim; against a blue wall, a blue one. This is the same operation Photoshop calls *Decontaminate Colors* and Nuke calls *decontamination*. Rembg can estimate the true foreground color and write that instead. This is opt-in: ```python from rembg im