Resize a batch of images, convert them to WebP and remove metadata with one reusable Pipeline.
Preparing one image for the Web is quick. Repeating the same operations across 20, 50 or 100 files is not. A batch workflow lets you resize, convert and clean a whole set without processing every image separately.
The Bethemesh template chains: import multiple images → resize → convert to WebP → remove metadata → batch download.
A photo several thousand pixels wide is rarely necessary when it will be displayed in a much smaller area. Reducing dimensions first avoids carrying unnecessary pixels and can significantly reduce final file size.
Choose dimensions that match the real use: thumbnail, article illustration, product image or full-width visual. Avoid artificially enlarging small sources.
WebP is widely supported by modern browsers and often provides a good quality-to-size ratio for Web delivery. The Pipeline applies the conversion to the whole batch instead of making you repeat it file by file.
Keep your originals when you may need another format, a print asset or a different export later.
Images can contain metadata that is not needed for publication. Removing it creates cleaner delivery files and avoids retaining some information attached to the originals. Keep archival originals when that metadata matters to your workflow.
Select the files once, apply the same recipe and retrieve the outputs together. Compatible transformations run locally in the browser. Very large batches or high-resolution photos may hit your device’s memory limits, in which case splitting the job into smaller groups is sensible.
Review a few representative outputs for dimensions, sharpness, transparency where required and final size. Good Web optimization balances visual quality and weight rather than chasing the smallest possible file.
Modern browsers support it well, and it often provides efficient files for Web images.
No. Keep them if you may need another size, format or the original metadata.
Yes, provided their size and your device’s available memory allow it. Because processing is local, very large source images may be better handled in several batches.
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