AI Image Vectorizer
Convert raster images into scalable vector graphics using AI
Smart Vectorization with AI
Modern AI-powered vectorization doesn’t just trace outlines, it interprets images. It can recognize subtle edge transitions, simplify noisy backgrounds, and intelligently reduce color complexity while maintaining visual clarity. This means your images are not just vectorized – they are optimized for performance, clarity, and design flexibility.
With AI Image Vectorizer, converting your images is fast, intuitive, and effortless. Adjust vectorization settings manually, or let AI automatically enhance edges, balance color palettes, and minimize file size – all without the need to sign up or install anything.
How Does the Image Vectorizer Work?
- Upload Your Image – Start by uploading any supported raster image format, such as PNG, JPG, GIF, TIFF, BMP, or ICO.
- Quantize the colors (optional) – Reduce the number of colors in your image to simplify the tracing process.
- Vectorize the image – Tune tracing accuracy, smoothing, and stroke width, then click the “Vectorize” button.
- Apply AI Settings – Describe what you want, like “smooth edges, under 50 KB”, and press “Apply via AI”. Let AI fine-tune the result automatically.
- Download the result – Save the Quantized PNG or the final SVG.
Modes & Parameters
Fine-tune your images during vectorization to effectively manage detail levels. This provides you with added creative flexibility in your design processes:
| Area | Control | Range | What it does (under the hood) |
|---|---|---|---|
| Quantize | colors | 1 – 255 | Palette size for RgbQuant (fewer colors -> smaller SVG, more -> better color fidelity). |
| method | 1 or 2 | 1 = uniform 1-D histogram, 2 = adaptive 2-D histogram. | |
| minHueCols | ≥ 0 (step 100) | Minimum distinct hues to keep when down-sampling. | |
| scale | 0.1 – 5 | Pre-scales the bitmap before quantization & tracing. | |
| grayscale | on/off | Converts to gray before quantizing (faster, tiny output). | |
| Vectorize | threshold | 0 – 200 | Error tolerance for Bézier-curve fitting (fitCurve). Higher = fewer path segments. |
| severity (= extent) | 0 – 10 | Smoothing radius in pixels (smooth / moving-average). | |
| line-width | 0 – 50 | Stroke width applied after scaling × 10. | |
| trace paths | on/off | Adds a mid-point marker (marker-mid) so you can inspect individual path segments. |
How to Formulate a Request or Question for the AI Image Vectorizer?
- Be specific – “Sharpen the image lines and reduce final SVG size.”
- Mention use-case – “I want a monochrome look with minimal file size.”
- Use one request at a time for the fastest and most accurate tuning.
By following these guidelines, you’ll receive precise, relevant suggestions to perfect your vectorization process.