Layer generation and layer separation solve different problems.
Layer separation begins with pixels that already exist and tries to divide them. Layer generation begins with a creative brief and produces the visual parts as layers. The right choice depends on whether you are recovering control over an old composite or creating a new editable composition.
Layer separation
This can be useful when the only available source is a JPEG, PNG, screenshot, or other flattened asset. The system identifies visible regions and may reconstruct pixels that were hidden behind foreground objects.
Layer generation
This is LayerAxon's core layered workflow. The requested layer structure is part of the creation process, so the system generates the visual layers and assembles them rather than first making one flat image and cutting it up.
Why the starting point matters
A flat image stores the appearance of the final composite. It does not retain a complete record of the decisions that produced it. When a subject covers a background, the pixels behind the subject are absent. When text is rasterized, the original font, wording structure, and type settings may be unavailable. When effects are merged, their separate parameters are gone.
Layer separation therefore performs inference. It can identify subjects, edges, text regions, and backgrounds, then create a useful approximation of editable parts. Its success is judged by how well those inferred parts represent the visible source and how convincingly missing areas are reconstructed.
Layer generation has a different contract. It is not trying to discover a lost source document. It is creating a new image whose structure is planned before the final file is assembled. Its success is judged by the quality of the generated composition, the usefulness of its independently created parts, and how well those parts support the intended edit.
The workflows side by side
Starting point
- Layer separation
- An existing flat image
- Layer generation
- A prompt or reference-guided brief
Primary operation
- Layer separation
- Segment, extract, or reconstruct visible parts
- Layer generation
- Generate the requested visual layers independently
Relationship to input
- Layer separation
- Tries to preserve or infer the visible source
- Layer generation
- Creates a new layered interpretation
Hidden content
- Layer separation
- May need inpainting where objects overlap
- Layer generation
- Layers are authored for the new composition
Best fit
- Layer separation
- Editing an existing flattened asset
- Layer generation
- Creating a new visual with planned editability
Choose from the job, not the label
Choose separation when the existing image is the subject.
Examples include isolating elements from a supplied campaign visual, rebuilding a missing source file as closely as practical, or extracting a subject and background for a limited revision. The visible composite is the authority.
Choose generation when the new composition is the subject.
Examples include creating concept art with movable depth planes, building a product scene whose object and shadow must stay separate, or generating campaign artwork intended for later variants. The brief and intended editability are the authority.
Use a reference with generation when direction matters more than reconstruction.
A reference can guide composition, placement, palette, or atmosphere while the system creates a new layer structure. That is different from claiming to recover the reference's original layers. Read the reference-image workflow for that distinction.
Questions about the two approaches
Is layer generation always better than layer separation?
No. Separation is the appropriate choice when an existing flat image is the source that must be decomposed. Generation is appropriate when the goal is a new composition whose parts should be created independently.
Can layer separation recover an original PSD exactly?
A flattened image does not retain every original layer, mask, hidden pixel, font setting, or object relationship. Separation can estimate useful parts, but it cannot recover information that the flat composite no longer contains.
What does LayerAxon generate independently?
The Layered AI workflow uses the requested layer plan to generate visual layers as separate elements and then assemble them into an editable layered output.
Creating something new?
Start with a text brief or a reference-guided layer plan.