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Toolkiya's AI tools are unusual in that the models actually run in your browser. Most online AI tools — image upscalers, background removers, photo enhancers — upload your image to a server, run inference there, and return the result. Toolkiya runs the same families of models locally using ONNX Runtime Web and WebGPU, so your images never leave your device.

The trade-off is honest: open-source models do not match the absolute state-of-the-art proprietary ones from companies like Topaz or Adobe. For a portrait with very fine hair against a busy background, Remove.bg's commercial model still produces cleaner edges. For most inputs, the gap is small or invisible, and the cost difference (free vs $0.20–$1 per high-res output) makes browser-native the right default for bulk work.

Models are cached after first download, so subsequent runs are fast. WebGPU acceleration (where supported) gives 5–10× speedup over CPU-only inference, comparable to a small GPU server. On a recent laptop, a 1080p image can be background-removed in under two seconds.

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Frequently Asked Questions

Where does the AI inference run?

Inside your browser, on your device. Models are downloaded once (typically 30–200 MB depending on the tool) and cached. WebGPU acceleration is used when your browser supports it; CPU fallback otherwise.

Are these the same models as Remove.bg or Topaz?

No. Toolkiya uses open-source models (RemBG, Real-ESRGAN, GFPGAN, etc.) that are competitive but not as polished as the leading proprietary models. For most inputs the quality difference is invisible; for hard inputs it can be noticeable.

Do my images get uploaded?

No. AI inference happens locally. Your images stay on your device, which matters for personal photos, family content, or any commercial image you don't want to expose to a third-party service's logs.

How long does inference take?

Typically 1–10 seconds depending on the tool and your device. WebGPU-capable browsers (Chrome, Edge, recent Safari) are 5–10× faster than CPU-only fallback.

Why is the first run slow?

The model has to download (one-time, 30–200 MB) and warm up the WebGPU pipeline. After that, repeat runs use the cached model and are much faster.

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