Updated September 2026
Most "AI stencil generator" pages are vague about what actually happens to your photo. This one isn't — the entire pipeline is four short JavaScript functions that run in your browser tab, and there's no mystery model behind it. Here's exactly what happens between the moment you drop a photo and the moment a stencil appears.
Your image is drawn onto an off-screen <canvas>, and every pixel's red, green and blue values are combined into a single brightness value using the standard luminance weighting (0.299R + 0.587G + 0.114B — the same formula broadcast TV used for black-and-white compatibility). Color information is discarded at this point; a tattoo stencil is a line drawing, and lines come from brightness edges, not hue.
Before edges are found, the grayscale image is optionally softened with a box blur — each pixel is averaged with its neighbors in a square window sized by the "Smoothing" slider (0 to 4). A photo straight from a phone camera has sensor noise: tiny brightness flickers that a raw edge detector would read as thousands of stray dots. Blurring first removes that noise so the real outlines survive cleanly. At 0, no blur is applied — useful for already-clean line art or logos where you don't want to lose sharp detail.
This is the core of the tool. For every pixel, two small 3×3 convolution kernels (the Sobel operator) estimate how sharply brightness is changing horizontally (Gx) and vertically (Gy) around that point. The two values combine into a gradient magnitude — √(Gx² + Gy²) — a single number representing "how much of an edge is here." Any pixel whose magnitude exceeds the "Line detail" threshold you set (8 to 120) is marked as a line; everything below it becomes background. Set the threshold low and faint, subtle transitions get drawn in; set it high and only the strongest, highest-contrast outlines survive.
A raw Sobel result often produces one-pixel-wide lines — too thin to survive printing, thermal transfer, and the tattoo needle actually following them. The "Line thickness" control runs a dilation pass: any background pixel touching a line pixel becomes a line pixel too, repeated for as many iterations as you choose (0–4). Each pass fattens every line by roughly one pixel in every direction.
The final binary result (line / not-line) is painted back as pure black-on-white — or white-on-black if you check "Invert," useful for certain transfer methods that expect the reverse. That canvas is exactly what downloads as your PNG.
Because it's a classical edge detector and not a generative AI model, the output is deterministic and literal — it traces exactly what's in your photo's brightness contrast, nothing invented, nothing smoothed into "art" that wasn't there. That's a strength for turning a real photo or existing drawing into a traceable stencil, and a limit if you were hoping for a stylized reinterpretation: a soft, low-contrast photo produces a soft, sparse stencil no matter how you adjust the sliders, because there's no real edge for the algorithm to find. See choosing a good source photo for how to work with that limitation rather than fight it.
All five steps above run using the browser's built-in Canvas API — getImageData, pixel-array math, putImageData — entirely client-side. Your photo is loaded from disk into memory via URL.createObjectURL, never attached to a network request. You can verify this yourself: open your browser's DevTools, go to the Network tab, and run a conversion — no request containing image data will appear, because none is sent. This isn't a policy promise, it's a consequence of how the code is written; there is no server-side image endpoint in this tool at all.
Is this AI? No. It's a fixed, deterministic image-processing algorithm (Sobel edge detection plus blur and dilation) — the same category of technique used in classic computer-vision tools for decades, not a trained model.
Why does my stencil look different from a hand-drawn tattoo design? Because it traces contrast literally rather than reinterpreting the subject artistically — see the "what this means in practice" section above.
Does the algorithm ever change? The pipeline is stable; if we ever change it, this page will be updated to match, since the whole point of this page is that it describes what the code actually does.