Most AI photo editors are optimized for "make this look different." A clothing swap has the opposite requirement: the person has to look the same. If the face, hairline, body shape, hands, or background drift, the result stops being useful. It looks like a different person wearing the clothes.
We built Outfia around that constraint. You upload a photo of yourself and up to three garment photos, tag each item as a top, bottom, dress, jacket, or full outfit, and the model redraws only the clothing. The face, pose, hands, lighting, and background stay in place. A run takes about 20 seconds.
This post is about the engineering and product decisions behind that, not a launch announcement.
The constraint is the product
A normal image-to-image prompt gives the model too much freedom. "Put this jacket on the person" can change the person's face, the camera angle, the room, or all three. The output may look good in isolation, but it fails the actual job: helping someone decide whether a garment suits them.
So the prompt is built around a preserve list, not just a change request. The important parts are:
- face, hairline, skin tone, and expression
- body shape, pose, and hands
- background, lighting, and camera angle
- the original image's crop and framing
The clothing is the only variable. That sounds simple, but it changes how every other decision is made.
Garment parsing matters more than prompt wording
The first version treated every clothing image the same. It worked for a full outfit and failed for a top: the model would replace trousers that were not part of the request, or blend a jacket into a shirt.
Tagging the garment fixes most of that. A top only changes the upper body. A bottom only changes the lower body. A dress replaces the full outfit. A jacket is layered over what is already there. A full outfit can include several pieces.
The second improvement was accepting multiple garments in one run. Real outfits are combinations, not single items. Limiting a run to one piece forced people to chain generations, and each extra generation increased the chance of identity drift. Combining up to three pieces in one pass keeps the result more coherent.
Model choice is a trade-off, not a ranking
Outfia uses two model families:
- Google Nano Banana 2 for Standard and HD 4K runs
- OpenAI GPT Image for Pro runs
Both can edit an image, but they fail differently. One may preserve the face more reliably while shifting a fine print. The other may handle fabric texture better while changing the pose slightly. The right choice depends on the input photo and the garment.
That is why the product exposes the model choice instead of hiding it behind a single "best" setting. For a quick preview, Standard is usually enough. For a product shot or a final image, HD or Pro is worth the extra credits.
Evaluation has to be visual
You cannot evaluate a clothing swap with a text metric. A result can have a high similarity score and still look wrong because the collar sits strangely or the hands changed shape.
Our evaluation process is intentionally manual:
- Use a range of body types, skin tones, poses, and lighting conditions.
- Include flat lays, hanger shots, product photos, and screenshots from shops.
- Compare the original and result side by side, not just the result on its own.
- Check the face, hands, hairline, background, and garment details separately.
- Treat a failed run as a product bug, not a user error.
The before/after slider in the product exists for the same reason. It makes the failure modes visible instead of hiding them behind a polished single image.
The failure modes we see most often
- Complex prints and fine text can shift slightly. A logo may be approximated rather than reproduced.
- Very loose clothing can change the perceived body shape even when the model tries to preserve it.
- Low-light or blurry input photos give the model less to hold on to.
- Arms close to the body make sleeves harder to separate from the torso.
- Full-body photos work better than tight crops when the request includes trousers or shoes.
We do not claim these are solved. They are documented because the tool is a visual preview, not a technical specification for manufacturing.
Privacy is part of the product
Clothing photos are often personal. Outfia deletes uploads and results after 24 hours and does not use them for model training. Free results carry a small watermark and are capped at 2048 px; paid runs can download full-resolution images without a watermark.
Failed runs refund their credits automatically on every plan and credit pack. That matters when a model makes a mistake: the user should not pay for a result that did not work.
What we would improve next
The next improvements are less about "more AI" and more about control:
- better garment masks for unusual silhouettes
- clearer handling of shoes and accessories
- a faster way to compare multiple garments in one session
- more transparent feedback when an input photo is likely to fail
The core lesson is simple: a clothing swap is only useful if the person survives the edit. Everything else is secondary.
If you want to try the current version, Outfia is free to start at https://www.outfia.io. Upload a photo, add a garment, and check the before/after slider. The failure cases are as informative as the good ones.
