A Practical Workflow for Turning Flat Images into Editable Layers

Working with a finished PNG or JPEG can be frustrating when the original design file is unavailable. You may want to move a subject, remove a decoration, or reuse text, but the image is still one flattened surface.

A useful workflow is to treat the image as a set of visual components and reconstruct those components as transparent layers. Magic Layers is an AI image tool built around that idea: it separates a finished PNG or JPEG into a base image and individual transparent PNG layers, with a name, description, and position for each result.

A simple layer-separation workflow

1. Start with the best source image available. A larger, cleaner image gives the separation model more visual information. Avoid adding compression artifacts if you can export the source again.
2. Decide what you actually need to separate. A target of two to fifteen images is supported, and an optional description can call out the elements that matter most—for example, the subject, headline, background, or decorative shapes.
3. Review the generated layers as raster assets. Each output is a transparent PNG, so it can be moved and composited in Photoshop, Figma, Canva, a presentation, or a game engine. The original positions are retained so the layers can be aligned with the base image.
4. Check edges and small details. Hair, shadows, text, and partially occluded objects can be difficult to recover perfectly. Treat the result as an editable starting point and clean up masks or pixels where the project requires precision.
5. Keep the source and outputs organized. The source image and generated layers are stored privately until they are deleted, which makes it easier to return to a separation later.

What this workflow does—and does not do

Layer separation recovers useful raster components from a finished image; it does not recreate a Photoshop, Figma, or other project file. Results can vary by image, especially when several elements share similar colors or overlap heavily.

Pricing is usage-based: each output image costs 2 Credits, with monthly plans and one-time Credit Packs available. That model can work well when you only need to rescue a few assets from an archive or adapt an existing visual for a new format.

The main benefit is speed. Instead of redrawing every visible component from scratch, you get named, positioned layers that can be inspected and refined in the tool you already use.