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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsA two-image virtual try-on workflow sends one photo of a person and one photo of a garment to an image model, which returns a new picture of that person wearing the garment. It is a practical way to preview a holiday outfit before you commit to it, but the result is a style visualization, not a fit check. Useful results depend on three things: the quality of the two input photos, the file rules of the model you call, and realistic expectations about what the generated image can show.
The guidance below draws on Google’s published documentation for virtual try-on and on Google’s consumer try-on help page. It does not report benchmark results from any particular build, so treat the model names and limits as the documented starting point and confirm them before you rely on them.
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How the two-image workflow works
Google Cloud describes the core operation directly: you supply an image of a person and an image of a clothing product, and the service generates an image of that person wearing the product. The workflow has five stages.
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- Prepare the person image. Choose a clear, well-lit photo that follows the input guidance covered in the next section.
- Prepare the garment image. Use a clean photo of the clothing item itself. Google’s documentation describes this input only as a clothing-product image, so keep the garment fully visible and unobstructed.
- Send both images in one request to a model that supports virtual try-on. The Google Cloud documentation’s sample uses the
virtual-try-on-001model with a person image and a sweater product image, then saves the generated image. That is Google’s example, not a record of any particular project. - Receive the output. For
virtual-try-on-001, the output aspect ratio and resolution match the input, and up to four output images can be returned per prompt. - Review each output against the source photos before you use it for any decision about sizing or purchase.
Project setup and authentication are part of the Google Cloud path, and the documentation covers SDK and REST routes. Check the current SDK and authentication steps in Google’s documentation before writing any code, since those details change more often than the core workflow.
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Model names and documented limits
The virtual-try-on-001 model
Google Cloud’s model reference publishes these specifications for virtual-try-on-001, as of the 2026 version of the page:
| Setting | Documented value for virtual-try-on-001 |
|---|---|
| Maximum images per prompt | 2 |
| Maximum file size, inline and direct-console uploads | 7 MB per file |
| Maximum output images per prompt | 4 |
| Accepted formats | PNG and JPEG |
| Output aspect ratio and resolution | Same as the input |
| Billing | Standard PayGo is listed as supported |
| Regions | Listed in the model availability section of the reference; not restated here, so check it before you deploy |
The 7 MB ceiling applies to inline and direct-console uploads. Pricing and regional availability can change, so read the current model page rather than relying on any figure copied into a blog post, including this one.
Other models listed for virtual try-on
Google’s current documentation also lists the following models as supporting virtual try-on. Their individual limits are not restated in this article, so check each model’s own reference before you switch between them.
gemini-nano-banana-2.1gemini-3.1-flash-lite-imagegemini-3.1-flash-imagegemini-3-pro-image
Model names are volatile. Record which one you call, as described in the logging section below.
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Preparing the two photos
The person image
Google’s try-on guidance for shoppers gives the clearest preparation advice available. Its recommendations for the person photo are:
- Good, even lighting and an uncluttered background.
- A full-body photo, or a selfie, with no other people in the frame.
- Visible hands.
- Fitted clothing in the photo, because overly baggy clothing may distort the result.
These notes come from the consumer Google Shopping try-on help page. Google does not present them as a complete specification for the API, but they are the most concrete preparation guidance Google publishes for this kind of image. A steady phone stand, or a second person holding the camera, makes it easier to keep framing consistent across several outfits.
The garment image
Use one garment per image and keep the item flat, complete, and easy to identify. The garment’s visible details, such as a collar, a neckline, trim, or a pattern, are the details most likely to be altered in the output, so a sharp, well-lit product photo gives the model more to work with than a dim or cropped one.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchConsumer Shopping try-on versus a custom API workflow
Google has two separate try-on offerings, and the conditions for one do not transfer to the other. The Google Shopping try-on tool is a consumer feature. The Google Cloud virtual try-on model is a developer service. The table below compares the documented conditions for each.
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| Item | Google Shopping try-on (consumer) | Virtual try-on model (developer, virtual-try-on-001) |
|---|---|---|
| Eligible items | Tops, bottoms, and dresses. The help page says lingerie, bathing suits, and accessories are not supported. | Not stated in the model reference as a category restriction |
| User requirements | Logged in, at least 18 years old, with Search Services History and Personalized Recommendations turned on | Google Cloud project setup and authentication |
| Country availability | Limited to the countries listed on the help page | Regions listed in the model availability section |
| Image handling | An uploaded image can be changed or deleted | Two images per prompt; PNG or JPEG; 7 MB per inline or direct-console file |
| Ad placements | Unavailable for Shopping Ads. A missing “Try it on” control means the product is ineligible. | Not stated |
If you read a rule in the Shopping help page, such as the age requirement, assume it applies to that consumer feature only unless the developer documentation says otherwise.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the output can and cannot show
Google’s consumer help page describes the output this way: “This offers a helpful vibe check, giving you a general sense of how the item might complement your personal style as you explore different looks and discover new styles from countless retailers.” That framing is the right one for a holiday outfit workflow. The image helps you judge overall look and proportion at a glance. It does not measure the garment.
Google also warns that generated try-on images can contain mistakes in:
- Body shape.
- Personal features.
- Clothing details, such as construction, trim, or pattern.
Because of this, the output cannot establish:
- Whether the garment will fit or what size to buy.
- How the fabric will drape, stretch, or move.
- Exact fidelity to the product’s color, texture, or finish.
Do not present a generated image as a photograph of you wearing a purchased item. It is a visualization, and it should be labeled that way if you share it.
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Rights, consent, and permission
Google’s developer image-generation documentation says: “Make sure you have the necessary rights to any images you upload.” Use only photos of yourself, or of people who have agreed to the use, and only garment images you own or are licensed to use. The same documentation cautions against generating content that infringes others’ rights or that deceives, harasses, or harms. Those are the boundaries that matter for a holiday outfit project, especially if you plan to share outputs with friends, family, or customers.
A repeatable setup for a holiday outfit set
Consistency matters more than volume. Keep one reference person photo per setup, prepared under the same lighting and background, and run each garment through the same request pattern. That way, differences between outputs reflect the garment and not the photo conditions.
- Create one person reference image that meets the preparation notes above.
- Photograph each garment separately, with the item complete in frame.
- Run one garment at a time, and generate a small number of outputs first to check the result before scaling up.
- Compare each output with the garment photo and note any changes to details, proportions, or the person’s features.
- Keep the outputs for the outfits that look right, and discard the rest.
Keep a record of each run
Model names, limits, regions, and billing terms all change. Keep a short log for every run so you can explain later which settings produced which result:
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- Model name and the date you called it.
- Input file formats and sizes, checked against the limits in the table above.
- The number of output images requested.
- Any error message returned, with its exact text.
- Your notes on accuracy: body shape, facial or personal features, and garment details.
When a request fails or an output looks wrong, check the log first. Most failures trace back to one of three things: a file over the size limit or in an unsupported format, a person photo that is cluttered, dark, or shows baggy clothing, or a model name that is no longer current.
The workflow is straightforward once the inputs are right. The result is a useful preview of how an outfit comes together, provided you treat it as a visualization and not as a measurement.
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