The moon was right. The colors were right. Even the tiny event details were right. The headline was the one thing that needed to change: NIGHT BLOOM had to become AFTER DARK.
This is a controlled poster example from our EditableImage text-editing workflow, not a client campaign. Regenerating the entire frame would throw away the parts we wanted to keep. So the useful question wasn't “Can AI write the new words?” It was: Can we change those words without quietly changing everything around them?
| Before: NIGHT BLOOM | After: AFTER DARK |
|---|---|
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Only the headline was meant to change. The moon, vines, gradient, and small event details were meant to stay put.
The test I care about is outside the edit
A side-by-side comparison can miss subtle drift. A model might make the moon slightly smaller, move a leaf, or rewrite the event time while fixing the headline. Those are failures when the surrounding design is the part you wanted to preserve.
For this test, we marked the headline, generated replacement lettering, and composited only the marked result onto the original image. We then compared the two versions pixel by pixel. The image below is an annotated change map, not the finished poster:
Orange marks the old “NIGHT BLOOM” lettering; blue marks the new “AFTER DARK” lettering. The two phrases overlap because they occupy the same part of the poster, so this map is not meant to be read as a headline. The dimmed background is shown only for reference.
On the web-compressed examples above, the meaningful differences occupy under 4% of the frame, all in the headline area. The comparison ignores faint compression noise (up to 22/255 here). The downloadable editor result is a lossless PNG; outside the selected lines, it retains the original pixels. The 4% figure describes this poster, not a promise for every image.
That distinction matters. “Looks similar” is subjective. “Nothing outside the marked area changed” is a property you can check.
A practical check for your own image
You don't need a special tool to apply the same reasoning:
- Keep the original file. Compare against that file, not a screenshot of it.
- Align dimensions. If the output was resized or cropped, a pixel diff will light up everywhere and tell you little.
- Mark the allowed region. Decide before editing which pixels may change. For this poster, it's the headline; for a product screenshot, it might be one button label.
- Look outside that region. Any substantial change there deserves an explanation. If you're comparing JPEG or WebP copies, first allow for compression noise; use lossless exports when you can.
- Zoom in at 100%. The diff can tell you where pixels changed, but not whether the new lettering has a clean edge or a believable font.
The same test is especially useful for an approved UI screenshot with an outdated button label or a campaign image with last month's date. The design around the words is not disposable just because the words are wrong.
Where the result can still disappoint
Keeping the original outside the marked lines creates a trade-off at the boundary. On a smooth gradient like this poster, the seam is hard to notice. On a photograph, grain, or a busy pattern, the newly inferred background behind old letters may not join perfectly with the original. A full-image redraw can hide that seam, but it can also shift details elsewhere. Neither mode is universally better; compare both at full resolution.
This also doesn't recover an original font file. The new lettering is drawn to resemble the surrounding type; an exact font match isn't guaranteed. If the lettering is the artwork—a hand-painted logo, for example—I would rebuild it manually rather than expect a text edit to preserve its character.
My takeaway from this poster isn't that AI can “fix any image.” It's a narrower, more useful rule: when one part of an image is wrong, define the permitted change and inspect everything outside it. That turns a plausible-looking edit into one you can actually evaluate.
The full poster case and its limitations are on our site. If you want to try the same bounded edit on your own asset, here's the text-editing workflow.
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