August 30, 2026AIExplore

How to Use Figma AI for Image Cleanup and Vectorization

Remove backgrounds, expand crops, isolate objects, boost resolution, and vectorize static images into editable layers inside Figma Design.

Figma AI

Figma AI image cleanup tools remove backgrounds, expand crops, isolate or erase objects, boost resolution, and vectorize static images into editable layers. Use them after the subject is right. Official agent tables also list convert static images to vector layers. Overview: /explore/figma-ai.

This guide covers choosing one cleanup action at a time. For generation see /blog/how-to-use-figma-ai-to-make-and-edit-images.

When cleanup and vectorization is the right job

Use cleanup when composition is acceptable but edges, crop, or resolution fail. Use vectorize for symbols, lettering, and simple illustrations that must become editable vectors. Do not vectorize expecting photographic fidelity.

Step by step workflow

1. Pick the single problem

Background, clipped edge, soft resolution, or need for vectors. Solve one problem per pass.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Problem card Issue: busy background Tool: Remove background Next: inspect edges at 100 percent. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Problem card Issue: busy background Tool: Remove background Next: inspect edges at 100 percent" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Inputs:
Use the actual source material required for this task, together with the intended audience, destination, format, and quality requirements. Specify relevant files, text, URLs, structured data, reference assets, dimensions, language, tone, timing, model or generation settings, credentials, field mappings, or integration values when they apply. Keep secrets out of the example and use the product's supported credential or configuration mechanism.

Workflow:
1. Start with one representative input that contains the important characteristics of the real workload.
2. Configure figma-ai specifically for the requested task and make the important settings explicit.
3. Run the first pass and inspect the result against the objective.
4. Correct only the settings or source material responsible for a failed requirement instead of changing everything at once.
5. Validate the intermediate result before passing it to the next step.
6. When the result meets the requirements, save the successful configuration so the same process can be repeated consistently.
7. If this is a multi-step workflow, verify each handoff and keep a clear fallback or review path for failures.

Requirements:
The example must use supported functionality only. Do not invent product features, pricing, limits, integrations, model names, API behavior, or unavailable settings. Preserve important source data and formatting. Validate required fields before processing, handle empty or invalid input explicitly, prevent accidental duplicate processing where relevant, and stop or route the item for review when a required step fails. For credentials, use the supported secure configuration rather than placing secrets directly in the content.

Expected output:
The final result should directly satisfy the "Problem card Issue: busy background Tool: Remove background Next: inspect edges at 100 percent" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

2. Run the tool from Actions or agent

Select the image layer, apply the tool, and keep the rest of the layout stable.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Action Remove background Keep soft contact shadow if possible Export intent: light card surface. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Action Remove background Keep soft contact shadow if possible Export intent: light card surface" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Action Remove background Keep soft contact shadow if possible Export intent: light card surface" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

3. Inspect at export zoom

Check fringing, perspective after Expand, and path quality after Vectorize before handoff.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: QA [ ] Edges clean [ ] No unwanted erase [ ] Resolution enough for destination [ ] Vectors editable if required. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "QA [ ] Edges clean [ ] No unwanted erase [ ] Resolution enough for destination [ ] Vectors editable if required" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "QA [ ] Edges clean [ ] No unwanted erase [ ] Resolution enough for destination [ ] Vectors editable if required" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Practical cleanup prompts

Remove background product

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Remove background on this product photo. Keep soft contact shadow if possible.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Remove background on this product photo. Keep soft contact shadow if possible." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Remove background on this product photo. Keep soft contact shadow if possible." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Remove background portrait

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Remove background. Keep hair edges clean. Output for a circular avatar mask.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Remove background. Keep hair edges clean. Output for a circular avatar mask." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Remove background. Keep hair edges clean. Output for a circular avatar mask." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Expand left

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Expand left to continue the desk surface. Match wood grain and window light. No new props.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Expand left to continue the desk surface. Match wood grain and window light. No new props." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Expand left to continue the desk surface. Match wood grain and window light. No new props." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Expand hero

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Expand top and bottom slightly for 9:16 story crop. Keep subject centered. No new people.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Expand top and bottom slightly for 9:16 story crop. Keep subject centered. No new people." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Expand top and bottom slightly for 9:16 story crop. Keep subject centered. No new people." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Isolate object

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Isolate the bottle only. Erase the price tag. Keep bottle label text as is.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Isolate the bottle only. Erase the price tag. Keep bottle label text as is." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Isolate the bottle only. Erase the price tag. Keep bottle label text as is." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Boost resolution

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Boost resolution for this logo raster before placing on a large poster frame.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Boost resolution for this logo raster before placing on a large poster frame." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Boost resolution for this logo raster before placing on a large poster frame." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Vectorize symbol

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Vectorize this hand drawn symbol into clean two color editable vectors.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Vectorize this hand drawn symbol into clean two color editable vectors." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Vectorize this hand drawn symbol into clean two color editable vectors." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Vectorize lettering

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Vectorize this lettering scan. Prefer smooth paths suitable for a logo lockup exploration.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Vectorize this lettering scan. Prefer smooth paths suitable for a logo lockup exploration." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Vectorize this lettering scan. Prefer smooth paths suitable for a logo lockup exploration." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Vectorize icon set

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Vectorize these three UI glyphs. Keep consistent stroke weight feel.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Vectorize these three UI glyphs. Keep consistent stroke weight feel." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Vectorize these three UI glyphs. Keep consistent stroke weight feel." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Erase cable

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Erase the cable across the desk. Reconstruct wood texture. Keep laptop unchanged.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Erase the cable across the desk. Reconstruct wood texture. Keep laptop unchanged." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Erase the cable across the desk. Reconstruct wood texture. Keep laptop unchanged." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

More copyable examples

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Expand map header Expand sides of this map texture. No readable street names. Match dusk grade.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Expand map header Expand sides of this map texture. No readable street names. Match dusk grade." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Expand map header Expand sides of this map texture. No readable street names. Match dusk grade." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Remove sticker Remove the sticker on the packaging. Reconstruct package material and highlight.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Remove sticker Remove the sticker on the packaging. Reconstruct package material and highlight." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Remove sticker Remove the sticker on the packaging. Reconstruct package material and highlight." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Boost UI capture Boost resolution on this soft UI screenshot crop before using it in a case study.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Boost UI capture Boost resolution on this soft UI screenshot crop before using it in a case study." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Boost UI capture Boost resolution on this soft UI screenshot crop before using it in a case study." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Vectorize stamp Vectorize this ink stamp mark for a two ink poster element.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Vectorize stamp Vectorize this ink stamp mark for a two ink poster element." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Vectorize stamp Vectorize this ink stamp mark for a two ink poster element." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Isolate food Isolate the bowl from the table photo for a menu card. Keep appetizing shadow lightly.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Isolate food Isolate the bowl from the table photo for a menu card. Keep appetizing shadow lightly." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Isolate food Isolate the bowl from the table photo for a menu card. Keep appetizing shadow lightly." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Expand product bleed Expand all sides slightly for print bleed on seamless white. Keep bottle scale.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Expand product bleed Expand all sides slightly for print bleed on seamless white. Keep bottle scale." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Expand product bleed Expand all sides slightly for print bleed on seamless white. Keep bottle scale." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Cleanup then vector Remove background, then vectorize the silhouette for a marketing icon.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Cleanup then vector Remove background, then vectorize the silhouette for a marketing icon." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Cleanup then vector Remove background, then vectorize the silhouette for a marketing icon." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Scenario:
A realistic figma-ai workflow is being prepared for Use Figma AI for First Draft Explorations using figma-ai. The starting requirement is: Agent chain Ask the agent: remove background on selected image, then boost resolution if still soft.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Agent chain Ask the agent: remove background on selected image, then boost resolution if still soft." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Agent chain Ask the agent: remove background on selected image, then boost resolution if still soft." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Tips

One cleanup goal per pass
Inspect at 100 percent zoom
Expand with continuation language
Vectorize for marks, not photos
Generate a better subject before endless cleanup
Watch credits on repeated boosts

Verification notes

Cleanup and vectorization capabilities appear in Use AI tools in Figma Design and the agent supported features table. Confirm tool names in your Actions menu. When plan access, seat rules, or credit behavior differ from memory, open help.figma.com and your AI balance. Limits change, so do not promise client timelines from outdated screenshots.

Keep a short session log beside the file: tool used, selection, prompt, and the one change you will try next. That habit turns four variants or parallel agent threads into learning instead of noise.

When Actions are missing, check whether an admin disabled Figma AI for the workspace before rewriting prompts. Access problems look like prompt problems until you confirm settings.

Treat the first AI pass as a sketch. Compare against your spacing grid, type styles, and accessibility targets before inviting stakeholders.

If you collaborate, store winning prompts on a shared page next to the library link. Reuse the skeleton and change only product nouns or channel constraints.

Separate exploration files from production libraries. Explore freely in drafts, then rebuild keepers with real components before handoff.

Close each session by naming frames clearly and discarding dead ends. A clean page saves time when someone asks for the pricing riff from Tuesday.

Prefer concrete product language over vague vibes alone. Screen type, primary action, and one constraint outperform a pile of adjectives.

Confirm AI credit balance and reset date before large image or agent batches. Shared credits power many Figma AI surfaces.

Inspect edges at one hundred percent zoom after Remove background. Soft fringing shows up late in export.

Vectorize for icons and marks, not for photographic realism. Photos need different cleanup paths.

Expand one clipped edge at a time when perspective matters. Multi side expands invent more accidents.

Cleanup is subtractive craft. Name the defect, pick the tool, and refuse to stack five operations before evaluating the first.

Remove background shines on product hero cutouts and fails when the subject needs grounded shadow storytelling. Decide which look the layout requires.

Expand prompts should describe continuation of light and surface. Asking for a new story at the border creates collage seams.

Vectorize is for marks you will edit. If you need photographic detail, keep raster and boost resolution instead.

Boost resolution cannot invent missing optical truth forever. Start from a sharper source when the asset is critical.

Isolate and erase are surgical. Zoom first, paint the problem, and protect nearby edges you must keep.

Credit spend climbs when teams repeatedly boost and re vectorize the same weak generation. Fix the subject earlier in Make image.

Export checks belong in the same session. Cleanup that looks fine at 25 percent zoom often fails at one hundred percent.

For icon pipelines, vectorize once, then refine paths manually. Repeated vectorize passes rarely improve geometry as much as a designer pass.

Background removal on hair and fur needs edge patience. If fringing persists, regenerate with a cleaner source instead of looping erase.

Print bleeds want Expand with quiet continuation, not new subjects. Borders should disappear into the art, not introduce a second story.

Keep a before layer when experimenting. Cleanup is hard to reverse psychologically even when undo exists.

Choose export format before cleanup. Transparent PNG needs different background decisions than a flat JPEG card image.

Vectorize lettering only when you will edit paths. If the mark is final and complex, a clean raster with boosted resolution may be safer.

Expand and Remove background both change silhouettes. Re check layout collision with nearby text after either tool.

For multi object scenes, isolate the hero object first. Global erase passes remove context you may still need.

Teach the team a default order: generate or place, remove background if needed, expand for crop, boost if soft, vectorize only for marks.

Archive rejected cleanup attempts on a hidden page if legal review may ask how an asset was produced.

If cleanup takes longer than regenerating a simpler subject, restart with Make image. Persistence is not always craft.

Vector outputs should be reviewed in outline mode. Hidden overlapping paths cause export pain later.

Cleanup quality is a team standard. Define when Remove background is required for marketplace cards so files do not mix cutouts and studio plates randomly.

For vector marks destined for code icons, simplify paths after Vectorize before export. Engineers prefer predictable geometry.

End cleanup by viewing the asset on both light and dark surfaces if your product supports themes. Fringing shows differently on each.

Common mistakes

Vectorizing photos expecting realism
Expanding every side when one edge is clipped
Removing backgrounds that need natural shadows
Skipping edge QA before export
Cleaning a wrong subject instead of regenerating
Chaining five tools before checking the first result

Related Figma AI articles: /blog/how-to-use-figma-ai-to-make-and-edit-images, /blog/how-to-use-figma-agent-for-conversational-design, and /blog/how-to-use-figma-ai-to-find-assets-and-designs. Return to /explore/figma-ai for the broader guide set.