Enlarge small web images
Create a larger derivative when a low-resolution asset must fill more screen space.
Choose image
JPG, PNG or WebP
The ONNX super-resolution model runs in your browser. Images are processed tile by tile and are not uploaded to Zavilo for inference.
About AI Image Upscaler
AI Image Upscaler uses a compact Real-ESRGAN ONNX model and processes the image in small overlapping tiles. This keeps inference memory bounded compared with running a large full-image transformer model.
Common use cases
Improve presentation assets
Upscale modest-size graphics or photos before using them in slides or layout mockups.
Prepare a larger working copy
Generate a 2× image before additional non-forensic editing or design work.
How it works
1
The source image is decoded and divided into manageable regions when necessary.
2
A browser ONNX super-resolution model predicts a higher-resolution representation of those pixels.
3
The enhanced regions are combined into a new enlarged image for comparison and download.
Privacy & file handling
Super-resolution runs on the device
The supported upscaling model executes in the browser, so the original image does not need to be uploaded to Zavilo for this operation.
Limitations and important notes
- Upscaling can synthesize plausible texture but cannot recover authoritative detail that was never captured.
- Severe blur, compression damage or noise can also be enlarged or transformed into artifacts.
- AI-upscaled output should not be treated as forensic evidence or a reliable source for identifying small details.
AI upscale vs ordinary resize
| Feature | Primary goal | What changes |
|---|---|---|
| AI Image Upscaler | Improve perceived detail while enlarging | Model-generated high-resolution pixels |
| Resize Image | Change dimensions predictably | Interpolation scales existing image information |
How to use AI Image Upscaler
- 1
Choose a JPG, PNG or WebP image.
- 2
Click AI Upscale 2×.
- 3
Wait while the browser processes the image tile by tile.
- 4
Review the final dimensions and image detail.
- 5
Download the result as PNG.
Tips for better results
- The model estimates plausible fine detail rather than recovering information that was never present.
- Using a higher-quality original file can help produce a better conversion result.
- Tile processing is slower than one large GPU pass but uses much less peak memory.
Frequently asked questions
Why does Zavilo process the image in tiles?
Small fixed-size tiles keep temporary AI tensors bounded, avoiding the multi-gigabyte memory spikes that can occur when a large image is processed in one inference pass.
Which file formats can I use?
Check the file picker and tool description for the supported formats. Available formats can vary by tool.
Why is the output 2× when the model is a 4× model?
Each small tile is inferred at 4× and then downsampled to 2× while stitching. This keeps a useful 2× output size and limits the final canvas memory requirement.