Upload a photo of a model and a photo of the garment, and get the two combined into one believable shot, no photo studio, no live model booking, no reshoot for every colorway. The face, pose and body stay the same while the outfit changes.
A fashion brand does not really sell fabric, it sells the idea of wearing it, and that idea has always needed a model, a studio and a full day of shooting to prove. AI garment try-on tools skip most of that. Keep one photo of a person, hand the model your product shots, and see the dressed result in seconds, without booking a shoot for every colorway, size or scene you need to show.
A single garment in three colorways and two crops already means six photo variants, and a small catalog turns that into hundreds. Booking a studio, a model and a stylist for every one of those variants is not something most sellers or small brands can do, so a lot of product pages ship with a flat lay instead of a photo anyone can picture themselves wearing.
Virtual try-on closes that gap without pretending to be a full production. You supply a model photo and the garment photos, and the model handles the part that used to require a fitting room: draping the fabric on a body, matching the pose, keeping the lighting believable. Picasso IA runs this alongside 488 other models, so a scene change, an upscale or a background swap is the same Toolkit session rather than a second piece of software. New accounts include free credits, enough to test whether a garment reads well on a given model before committing to a full shoot list.
Not every fashion image needs the same input. A catalog thumbnail and an editorial banner ask different things of the same garment photo, and matching the shot type to what you actually feed the model saves a lot of wasted generations.
| Shot type | What you provide | Best for |
|---|---|---|
| Studio catalog swap | A flat garment photo plus a plain-background model photo | Clean product listings, size-chart pages |
| Editorial full body | Garment photos plus a posed, styled model photo | Lookbooks, homepage banners |
| Lifestyle outdoor scene | Garment photos plus a model photo shot outdoors | Social ads, seasonal campaigns |
| Close-up detail shot | A cropped garment photo of the fabric, trim or hardware | Texture callouts, premium fabric claims |
| Full outfit combination | Top, bottom and accessory photos run together | Capsule collections, styled bundles |
Run the same garment through two or three of these before deciding which one goes live. A blazer that reads as generic on plain white can look considerably more expensive against an outdoor lifestyle scene, and a technical jacket photographed only indoors never tells the story the same jacket does in motion.
This is the workflow that gets you from a flat product photo to a dressed model shot, using prunaai/p-image-try-on as the core step. It assumes a garment photo and a model photo to start from.
The scene, the pose and the lighting are all fair game to adjust after the fact. The garment is not. A shopper who receives the real item has to recognize it from the photo, so the color, the print, the seams and the proportions need to survive every version you generate.
The try-on step is built for exactly this: face, pose and body shape stay intact while only the clothing changes, and the output keeps your original photo's resolution rather than resampling it down. You can combine up to six garment images in one run, mixing a top, bottom and accessory in a single pass, and an experimental prompt field lets you point at one specific piece when a garment photo shows more than one item, such as picking out the blue jacket from a lookbook shot.
Pro tip: photograph every garment the same way, flat, centered, even light, no shadows crossing the fabric. The model reads that reference literally, so a garment photo taken at an angle or under warm indoor light comes out with a color shift that a shopper will notice against the real product.
For a small apparel brand, the real saving is not one photo, it is the multiplication. A blazer in four colorways used to mean four separate fittings; now it means one model photo and four garment photos run through the same session. A seller adding a new size range can preview it on a range of body types before a single unit ships, instead of guessing from a size chart alone.
Marketing teams get a faster path from product sample to campaign asset: a lookbook that would have taken a studio day becomes an afternoon of generating variations and picking the strongest ones, then finishing the scene with the same AI product photography techniques used for objects that are not worn. Teams already relying on background removal tools for listing photos can compare that workflow against a broader catalog in the Photoroom comparison.
None of this replaces a real photoshoot when the brand needs one; it replaces the shoots that were never going to happen anyway. The honest limits:
None of these is a reason to skip the tool for a first pass. They are the reasons a final quality check still belongs to a human before anything ships to a live listing.
Picasso IA gives new accounts free credits, and a single try-on generation is one of the cheaper things to spend them on compared with video or 3D work. That is enough to test whether a garment reads well on a given model photo before deciding whether to build a full catalog around the workflow. Paid plans and current limits are listed on the pricing page, since numbers here would go stale.
The core model is p-image-try-on, built specifically to place garments onto a person photo while keeping the original face, pose and body shape intact. It accepts up to six garment images per run, supports a turbo mode for simpler outfits, and returns the result at your original photo's resolution rather than a resized version.
Yes. The model photo does not have to be a real person; a consistent AI character built with a reference-image workflow works the same way as a photographed model, and it means the same face can wear an entire season's catalog without booking anyone. Keep the reference image and reuse it across every garment for a consistent look.
Up to six garment images in a single run, which is enough to mix a top, a bottom and one or two accessories into one full outfit shot. The model also supports up to eleven in less common cases, though results get less predictable as the count climbs, so most workflows stay closer to the recommended six.
Yes, through the optional reference pose input. Supply a photo showing the stance you want, and the model repositions the person to match it before the garments are placed. This is an experimental feature and can fail on some generations, so it is worth generating a couple of variants rather than relying on one attempt.
The try-on step preserves whatever body shape is in your source model photo, so the honest way to show a size range is to start from model photos across that range. The tool will not invent a body type that is not represented in what you upload, which is worth planning for if the catalog needs to show genuine size diversity.
Generally yes, since the garment photo is the direct reference for color and pattern, but close inspection matters. Dense prints, metallic threads and fine embroidery can shift slightly, so always zoom into the result and compare it against the original garment photo before publishing, the same check you would run on any product photo.
Yes, background removal runs as a separate step after the try-on, using a model built for that job rather than trying to do everything in one pass. Splitting the two keeps each result cleaner: the try-on model focuses on the garment and the person, and the background tool focuses on a clean cutout.
Generations are paid in credits, and the exact cost depends on the model and settings used, so a specific number here would be wrong within a month. Image generations sit toward the cheaper end of the catalog compared with video or 3D work, and new accounts start with free credits to test the workflow before spending anything; current plan pricing lives on the pricing page.
Generated fashion photos are commonly used for listings, ads and lookbooks once they pass a quality check, the same review any product photo gets before it ships. Verify the garment details against the source photo, follow the licensing terms of any model photo you did not shoot yourself, and disclose AI generation where the platform or region requires it.
A season's worth of catalog photos does not have to start with a studio booking. Open the Picasso IA toolkit and see how the next garment looks on a real model in the next few minutes.
