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Yes—Affinity can now split a flattened PNG into editable pixel layers. Its September 2026 Scene Decomposition feature (shown in the interface as Decompose Layer) analyzes a single composite image, separates likely foreground and background elements, and attempts to reconstruct pixels hidden behind them. The result can rescue an old campaign graphic, illustration, or photo composite when the original layered document is missing.
The catch is access: Scene Decomposition is part of Canva AI Studio, which Affinity says is unlocked with a Canva Premium plan. Most of the wider Affinity release is free, but this AI workflow is not available without that subscription.
What Scene Decomposition actually does
A flattened PNG contains only a final pixel composite. Text, objects, backgrounds, masks, and effects may all be baked into the same layer. Scene Decomposition works backward from that composite. It identifies visually distinct elements, generates separate pixel layers, and fills in an approximation of areas that were covered by foreground content.
Affinity Enablement Lead Benjamin Viney described the goal this way: “Scene Decomposition brings that flattened image back to life.” That is a useful description of the benefit, but not a promise that the lost source file can be recreated exactly.
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What you receive
- Separate, editable pixel layers for detected elements.
- Reconstructed content where an object or text previously hid part of the background.
- A starting point for renaming, masking, repainting, rearranging, or replacing elements.
What it cannot know with certainty
- The original vector paths or live text objects.
- The exact masks, blend modes, adjustment layers, or layer boundaries used in the source document.
- Pixels that were never visible and therefore cannot be inferred reliably.
Think of the output as an editable approximation, not a forensic recovery of the original Affinity or Photoshop file.
Do you need Canva Premium?
Yes. Canva AI Studio features in Affinity, including Scene Decomposition, require a Canva Premium plan to unlock. Affinity’s September 2026 update presents the broader release as containing more than 60 new features and improvements, but the AI Studio requirement is a separate access condition. Check the plan attached to the Canva account you use with Affinity before planning a production workflow.
Affinity says its AI approach is intended to automate tedious work without removing creative control: “Affinity uses AI where it earns its place: speeding up tedious tasks and tackling time-consuming fixes, while keeping the creative decisions in your hands.”
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How to turn a flat image into editable layers
- Switch modes. Open Affinity’s Studio Switcher and change to Canva AI mode. The Decompose Layer control is exposed through this AI workspace.
- Open the source image. Load the flattened PNG or other raster composite you need to revise. Keep an untouched copy of the original so you can compare the generated result.
- Run the command. Choose Decompose Layer. Affinity processes the image and creates separate pixel layers based on its interpretation of the scene.
- Inspect every layer. Toggle visibility, zoom into text and fine edges, and look for holes, halos, duplicated details, or background textures that do not line up.
- Refine the reconstruction. Rename layers, add or edit masks, repaint missing areas, and use Affinity’s normal pixel and adjustment tools to correct errors.
- Save an editable working file. Preserve the generated layers in an Affinity document, then export a flattened PNG, JPEG, or other format only when you need a delivery copy.
How accurate is the result?
There is no published official accuracy score, success rate, or processing-time benchmark for Scene Decomposition. A secondary walkthrough reported a processing time of approximately two minutes and described sensible separations for headings, text fragments, objects, and backgrounds, while noting that some generated layers were stronger than others. That is an individual observation, not a vendor benchmark, and results will vary with image complexity, resolution, contrast, and overlap.
| Image characteristic | Likely benefit | What to check manually |
|---|---|---|
| High-contrast objects on a simple background | Cleaner initial separation | Edge halos and shadows |
| Large text or headings | Useful placement as separate pixel content | Letter edges, missing counters, and whether text remains raster rather than editable type |
| Busy photography or overlapping subjects | Potentially useful rough masks | Hair, transparent materials, reflections, and object boundaries |
| Hidden background behind foreground objects | An inferred fill can reduce manual cloning | Repeated textures, incorrect perspective, and invented details |
For production work, budget time for cleanup. The feature can eliminate much of the initial cutting, masking, and repainting, but it does not remove the need for an artist to approve the pixels.
Do the detected-object numbers measure Scene Decomposition accuracy?
No. Affinity’s current help content cites 80 everyday object categories and eight scene-region categories for separate machine-learning selection features. Those figures describe what those selection tools can detect; they are not a Scene Decomposition accuracy rating or a guarantee that a flattened image will be split into a particular number of layers.
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Privacy and local-file handling
Affinity’s September 2026 update states: “Your work in Affinity isn’t used to train AI features, and Affinity can’t access your files – they’re stored locally on your device.” This is Affinity’s stated handling of files in the product. Organizations with additional compliance requirements should still verify their own account, network, and device policies before processing confidential artwork.
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When the feature is a good fit
- You have only a flattened export of an older design and need to change its composition.
- You need a rough separation quickly before doing focused retouching.
- You are rebuilding a social, campaign, or presentation asset whose original layered file is unavailable.
- You can accept rasterized text and inferred background pixels.
When to use a different approach
- The deliverable requires true editable type, vectors, or precisely matched blend modes.
- Pixel-perfect restoration is required for legal, archival, or brand-critical reasons.
- The image contains intricate hair, transparent objects, reflections, or repeated architectural patterns that are difficult to infer.
- Your team cannot use a Canva Premium account to unlock Canva AI Studio.
Practical production checklist
- Archive the original flattened file before running decomposition.
- Work at the image’s native resolution; avoid judging fine edges from a small preview.
- Rename generated layers immediately so later revisions remain understandable.
- Compare the reconstructed background against the original composite at 100% zoom.
- Rasterize expectations: separated lettering is pixel content unless you recreate it as live type.
- Have a human approve every visible edge before exporting a final asset.
The Bottom Line
Affinity’s Scene Decomposition can turn a flattened PNG into a genuinely workable layered starting point, especially when manual masking and background reconstruction would otherwise take hours. It does not recreate the original source document, its output needs inspection, and Canva Premium is required to access the Canva AI Studio workflow.
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