Wasserzeichen und Objekte entfernen ohne etwas hochzuladen

Eraser is a free AI watermark remover and object remover that runs entirely in your browser. You paint over the thing you want gone — a logo burned into a corner, a date stamp, a stranger in the background, a cable crossing a product shot — and the tool reconstructs what was behind it. Two engines are available: a zero-download Fast Marching inpainter for simple backgrounds, and a LaMa neural inpainter for everything else. Nothing is uploaded, there is no sign-up, and there is no per-image fee.

It is built for the jobs where an online removal service is the wrong answer: cleaning a client's photos you are not allowed to put on someone else's server, stripping watermarks from images you own, removing an exif date stamp from a personal archive, or clearing background clutter from a product photo before it goes into a catalogue. The image goes into your browser's memory and never leaves it.

How LaMa fills a hole

The AI engine is LaMa (Large Mask Inpainting), a Fourier-convolution network with roughly 51 million parameters, exported to ONNX and run with ONNX Runtime Web. LaMa was trained specifically for large-mask inpainting: rather than cloning nearby pixels, it learns the structure of the scene — the continuation of a wall texture, the geometry of a floor, the rhythm of a repeated pattern — and synthesises a plausible fill. That is what lets it remove an object that spans a large part of the frame without smearing.

The model receives two tensors: the RGB image and a single-channel mask where painted pixels are marked. It returns a complete image, and the tool blends only the masked region back into the original, so untouched pixels are bit-for-bit identical to what you imported. Edges are feathered with a Gaussian falloff so the seam is invisible even on smooth gradients like sky or skin.

Two engines: fast vs AI

Fast mode uses OpenCV's classic Fast Marching and Telea inpainting algorithms, reimplemented in TypeScript with a binary heap. They propagate pixel values inward from the boundary of the hole along an approximate geodesic front. There is no model download and the result is instant, which makes it ideal for watermarks sitting on flat or lightly textured areas — a semi-transparent logo over a gradient, a timestamp over sky.

AI mode downloads LaMa (~210 MB) once and caches it in IndexedDB, so every later run is offline-capable. It is the right choice for structured scenes: removing a person from a street, deleting a fence from a field, erasing text over bricks. If the two produce a similar result on your image, prefer Fast — it costs nothing and finishes in milliseconds.

Masking tools and precision

Four selection tools are provided. Brush is freehand painting with an adjustable radius; Box drags a rectangle, which is the fastest way to cover a corner watermark; Lasso traces an arbitrary outline for irregular objects; Erase Mask removes painted pixels you got wrong. Undo and redo cover every stroke, and the mask is drawn as a translucent overlay so you always see exactly what will be filled.

Mask quality decides output quality more than anything else. Paint a little beyond the object — a few pixels of margin on all sides — so the network has context on every side of the hole. Painting too tightly leaves a halo; painting far too widely makes the model invent more of the scene than necessary. For large subjects, erase in passes: remove the main body, then clean up the residue.

Resolution, memory and why we crop

Running a 51M-parameter model at full resolution on a 24-megapixel photo would need many gigabytes of memory. Instead the tool computes the bounding box of your mask, expands it with padding, and runs inference only on that region — a technique called region-of-interest inference. The rest of the image is never touched by the model. The ROI is then composited back with feathered edges.

The ROI is further scaled so its long edge is 1024 px on desktop and 512 px on phones and low-core machines, then padded to a multiple of 8 for the graph. Your export is unaffected: only the patch is downscaled for inference and upscaled back into the full-resolution original, so a 6000 × 4000 photo exports at 6000 × 4000.

WebGPU and the WASM fallback

Inference runs on WebGPU when the browser exposes it, and falls back to WebAssembly (CPU) when it does not. WebGPU is typically several times faster; the fallback still works, just slower. Some drivers report WebGPU support and then fail at the first real dispatch, so the tool catches those errors at runtime, disables the GPU for the session and retries on WASM automatically rather than leaving you with a stuck progress bar.

Source images are decoded with the browser's own decode pipeline and clamped to a 4096 px long edge, which is comfortably above what the model can usefully process and keeps memory predictable on mobile Safari.

Limits & requirements

Inpainting is reconstruction, not recovery. What was behind a watermark is genuinely gone, and the model guesses it from surrounding context. Large removals in highly structured scenes — a face half-occluded by a logo, dense text, fine regular patterns like chain-link fencing — can produce plausible-looking but wrong detail. Try Fast mode, shrink the mask, or work in smaller passes when that happens.

AI mode needs a one-time ~210 MB download and a device with enough free memory for a 1024 px fp32 graph; very old phones should stay on Fast mode. Nothing is queued on a server, so there is no rate limit and no queue — but there is also no cloud GPU, so a large fill can take a few seconds on CPU.

Privacy

Your image never leaves your device. Decoding, masking, inference and export all happen in the page. There is no upload endpoint, no account, no analytics on your image, and no server-side log of what you removed. The only network request is the one-time model download from a model CDN, and once LaMa is cached in IndexedDB the tool works fully offline.