I Built an AI Tool That Turns Flat Images into Editable Layers
A flat image can look complete, but it is often difficult to edit.
If you want to move a product, remove a shadow, replace a background, edit text, or reuse part of a composition, you usually have to recreate the image structure manually in Photoshop.
That was the problem we wanted to solve with Image2Layer.
What Image2Layer does
Image2Layer uses AI to analyze a flat JPG, PNG, or WebP image and separate it into meaningful editable layers.
Depending on the image, the result may include:
- Main subjects
- Foreground objects
- Product elements
- Text and logos
- Shadows
- Decorative elements
- Background areas
- Reconstructed regions behind removed objects
Each layer keeps its transparency, so it can be moved, hidden, edited, or recomposed independently.
Export formats
After processing an image, you can export:
- Transparent PNG layers
- A ZIP package containing the layers
- A layered PSD file for Photoshop and other creative workflows
The goal is not to recreate the original source file perfectly. Instead, Image2Layer provides a useful editable starting point from an image that would otherwise be difficult to work with.
Who is it useful for?
Some possible workflows include:
- Retouching product photos
- Creating new e-commerce compositions
- Reusing poster and marketing assets
- Preparing images for animation
- Separating characters and objects from illustrations
- Rebuilding backgrounds
- Moving from an AI-generated image to an editable design
How it works
- Upload a JPG, PNG, or WebP image.
- Choose the layer detail level.
- Add separation notes if certain elements are important.
- Generate the layers.
- Review and download the result as PNG, ZIP, or PSD.
You can try it here:
What we are still improving
AI separation is not equally easy for every image. Fine details, transparent objects, reflections, complex typography, and heavily overlapping elements can still be challenging.
We are continuing to improve:
- Layer boundaries
- Background reconstruction
- Text and logo separation
- PSD export quality
- Control over the desired layer structure
If you work with image editing, design, or creative automation, I would be interested to hear what kind of images you would want to split into layers.
Top comments (1)
Hello Jesus Lopez, I am Kane Lim from Hong Kong. I have over 10 years of development experience. I am writing this because your post was interesting.
Image2Layer is solving a surprisingly difficult inverse graphics problem. Recovering useful layers from a flattened raster is much more than segmentation because the model has to infer object boundaries, depth relationships, occlusion masks, and missing pixels that were never present in the source.
One approach I would consider is a hierarchical decomposition pipeline. First use vision transformers for semantic segmentation and instance detection, then perform depth ordering and alpha matting before running an inpainting model specifically on occluded regions. For typography, an OCR plus vector reconstruction stage could preserve text as editable geometry instead of treating it as pixels.
I would also introduce confidence scores per layer. Low confidence regions could be flagged for manual refinement rather than silently producing incorrect masks. For PSD generation, maintaining layer metadata such as bounding boxes, alpha channels, semantic labels, z ordering, and provenance would make the output much more useful for downstream creative automation.
The really interesting direction would be turning this into an editable scene graph rather than simply exporting separated images. That could enable programmatic transformations, animation, relighting, and intelligent recomposition.
I would like to get to know you better and discuss about your post. Would you please contact me? t_g_@kanelim1997