AI cutouts for product photography
Separate products from an inconsistent setting while retaining control around handles, narrow gaps, and irregular outlines.
Use local AI to isolate a product, person, logo, or object, then improve the mask with positive and negative points. Your image stays on your device from selection to PNG export.


or drop up to five files
JPG, PNG or WebP · 20 MB eachYour images are processed locally and are not uploaded to our application server.
The tool runs a SAM-HQ ViT-Tiny encoder and decoder with ONNX Runtime Web. The encoder analyzes the selected image once and stores an embedding in the current browser session. A point on the subject guides the first mask. Each later Keep or Remove point reuses the embedding and runs the smaller decoder again, so you can refine the cutout without repeating the full image analysis. The interface reports model loading, encoding, and mask progress instead of hiding the processing state.

Separate products from an inconsistent setting while retaining control around handles, narrow gaps, and irregular outlines.
Create headshots and profile assets, then guide the model around clothing, hair, glasses, and accessories where needed.
Prepare reusable objects for moodboards, interface mockups, presentations, thumbnails, and social graphics.
Choose Auto, Product, Portrait, or Logo, then add a supported file from your device.
Inspect the transparent preview. Add Keep points to the subject or Remove points to unwanted areas.
Download the completed cutout at its original width and height with a transparent alpha channel.
The tool uses a SAM-HQ ViT-Tiny encoder and decoder exported to ONNX and executed with ONNX Runtime Web in your browser.
No. The model files download to the browser, and inference happens on your device. The selected photo, mask, points, and output PNG are not sent to our application server.
Segmentation can be difficult when edges are blurred, colors are similar, objects overlap, or parts of the subject are transparent. Keep and Remove points provide direct guidance for that specific image.
The first session loads about 55 MB of compressed model and WebAssembly runtime files. These are program assets used for local inference, not copies of your image.