Image to editable layers
Turn one flat image into editable AI-reconstructed layers
AI Layer Splitter analyzes a finished JPG, PNG, or WebP and rebuilds selected visual elements as separate layers. Name the objects you need, optionally mark their regions, then preview the reconstructed composition before downloading the base image, transparent object layers, and placement metadata. It is built for practical edits such as changing a background, moving a product, hiding a logo, or reusing one element in a new layout.
- Selection
- 1-6 named targets with optional regions
- Resolution
- 1K, 1.5K, or 2K output
- Export
- PNG layers, ZIP, and manifest.json
Real decomposition test
A product photo separated into four reusable layers
In this 1K test, the model returned a completed red background plus three object layers for the shoe body, the white brand mark, and the laces. The four outputs were positioned with bounding-box and z-index data, then recomposed into the preview shown below. The end-to-end run took about 80 seconds; processing time varies with image complexity and provider load.
Source image
Recomposed from layers
Completed background
Main subject
Brand mark
Laces
What can you do with an AI image layer splitter?
Use the same decomposition workflow wherever a flattened image blocks a useful edit. The strongest cases start with a clear subject and a specific list of elements to isolate.
E-commerce product edits
Separate the product, packaging, label, shadow, and background so marketplace images can be resized or localized without rebuilding the whole scene.
Ad and poster localization
Extract a person, product, logo, or decorative object before replacing copy and rearranging the composition for another market or aspect ratio.
Game and creator assets
Isolate characters, props, foreground effects, and backgrounds for thumbnails, prototypes, motion tests, or lightweight parallax scenes.
Rapid creative variations
Move one object, hide another, swap the background, or reuse a transparent layer in a new social post without repeating the original generation.
How to split an image into layers
The workflow is target-driven, which keeps output count and credit usage predictable before the task starts.
- 1
Upload a clear source image
Choose a well-lit image with visible boundaries between the subject, accessories, text, and background.
- 2
Name and locate each element
Add up to six target names. Assign an optional region when a name could refer to more than one object.
- 3
Review, toggle, and export
Check the composite, hide individual layers, download any PNG, or export the complete ZIP with manifest data.
What the ZIP export contains
Every completed task keeps the visual outputs and the coordinates needed to rebuild the composition outside this editor.
- A completed base image with selected objects removed and hidden areas reconstructed.
- One transparent PNG for each requested object layer, including its own crop size.
- A manifest.json file with layer names, order, descriptions, URLs, canvas size, and placement boxes.
What AI layers can and cannot recover
A flattened image does not contain the original layer stack. When an object covers the background or another object, the model must imagine those hidden pixels. Fine hair, reflections, transparent materials, tiny text, and heavy overlap can produce soft edges or reconstructed detail.
Treat the result as an editable starting point, not a pixel-perfect PSD recovery. Review edges at full size, keep target names specific, and use the optional region selector for crowded scenes.
AI Layer Splitter FAQ
Practical answers about output format, image quality, processing, and credit calculation.
Start with the elements you actually need to edit
Upload one image, list the product parts or visual objects you want separated, and review the exact credit estimate before generation.
Need a fixed number of layers? Try Qwen LayeredView credit plans