xainflow Image Decomposer
Decompose any composite image into individual layers and elements. Takes a screenshot, banner, ad, mockup, or game asset and creates a workflow that extracts each element in isolation.
Phase 1: Image Analysis
Analyze the provided image using vision. Identify and list all distinct visual elements with their categories:
| Category | Aspect Ratio | Remove BG | Description |
|---|---|---|---|
| Background | Original ratio | No | Scenes, landscapes, ambient backgrounds, effects, patterns, textures |
| Character | 1:1 | Yes | People, mascots, avatars, animals |
| Icon | 1:1 | Yes | Small symbols, emojis, badges |
| Text/Title | 1:1 | Yes | Headings, stylized typography |
| UI/CTA | 1:1 | Yes | Buttons, timers, progress bars, badges |
| Logo/Brand | 1:1 | Yes | Logotypes, watermarks, brand marks |
| Product | 1:1 | Yes | Packshots, physical objects, merchandise |
| Decorative | 1:1 | Yes | Frames, borders, ornaments, sparkles, ribbons |
Present the detected elements as a numbered list:
Detected elements:
1. Pirate character (Character) - 1:1 + RemoveBG
2. Ship deck and ocean background (Background) - 16:9
3. Title "Plunder the Loot" with skull decorations (Text/Title) - 1:1 + RemoveBG
4. Reward icons: gold coins, cash bills (Icon) - 1:1 + RemoveBG
5. Ship wheel with target board (Decorative) - 1:1 + RemoveBG
6. CTA button "Default" (UI/CTA) - 1:1 + RemoveBG
Phase 2: User Confirmation
Let the user review and adjust the element list:
- Remove elements: "remove #6"
- Add elements: "add the corner skulls as decorative"
- Edit categories: "#5 is an icon, not decorative"
- Merge elements: "merge #4 and #5"
- Confirm: "ok" / "looks good"
Phase 3: Build Workflow
Use xainflow_create_workflow to create a workflow named "Decompose - [image name]".
Workflow Structure
Build the workflow with these nodes:
Asset node (original image):
Position: (0, 0)
Type: asset
Data: { selectedAsset: { file_url: "[IMAGE_URL]" }, output: "[IMAGE_URL]" }
For each element, create an imageGenerator node:
Position: (400, element_index * 200)
Type: imageGenerator
Model: gpt-image-1-5 (or user's choice — models with high reference support work best)
Ratio: category ratio from table above
Extraction prompt for each element:
Extract only the [element description] from this reference image.
Generate it isolated on a clean white background.
Maintain the exact same style, colors, proportions, and details as in the original.
Do not add, remove, or modify any details.
Output only the [element description], nothing else.
For elements that need background removal, add a removeBackground node:
Position: (800, element_index * 200)
Type: removeBackground
Edges
- Asset → each imageGenerator (asset-input)
- imageGenerator → removeBackground (where applicable)
Layout
Nodes organized in 3 columns:
Col 1 (x=0) Col 2 (x=400) Col 3 (x=800)
[Asset: Original] -> [Gen: Background]
-> [Gen: Character] -> [RemoveBG: Character]
-> [Gen: Title] -> [RemoveBG: Title]
-> [Gen: Icons] -> [RemoveBG: Icons]
-> [Gen: Decorative] -> [RemoveBG: Decorative]
-> [Gen: CTA] -> [RemoveBG: CTA]
Vertical spacing: ~200px between nodes.
Recommended Models
Models with high reference support for faithful extraction:
- GPT Image 1.5 (16 references) — highest fidelity
- Nano Banana Pro (14 references) — good balance
- SeeDream 4.5 (10 references) — alternative
Before Executing
Show a summary with:
- Number of elements to extract
- Which elements get background removal
- Model being used
- Estimated credits: ~[N elements] x [model cost] + [M removals] x 2 cr
Ask for confirmation.
Edge Cases
- Very complex images (20+ elements): Group similar items (e.g., "all reward icons" as one extraction)
- Abstract/artistic images: Propose broad categories, let user refine
- Text-heavy images: Each text block can be separate or grouped