AI image tool

Virtual Makeup Try-On

Preview a makeup direction on a portrait before you recreate it. Choose a named look and intensity so the comparison is repeatable instead of relying on an unstructured prompt.

Token2Any is an independent browser-based Token2Any-style effect converter, online visual enhancer and AI image generator for creating visual references from uploaded game frames and character images. It keeps structure, pose and identity as reviewable constraints while changing style, lighting and materials, and also offers separate upscale and restoration workflows. It is not an official Token2Any runtime.

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One portrait · six looks · three intensities · about a minute · 1 credit

Virtual Makeup Try-On — Makeup previewFree reference caseVirtual Makeup Try-On — Portrait source
Illustrative preview: compare makeup colour and intensity while checking expression, face shape and skin texture.

What is Virtual Makeup Try-On?

Virtual Makeup Try-On is a browser-based virtual makeup workflow for people who already have a source image and need a controlled visual change. It uses a task-specific instruction rather than a blank prompt, so the requested output starts from the existing composition, subject or material. The page's before-and-after example is an illustrative reference; the result from your own file depends on its resolution, lighting, crop and visible detail.

How the workflow works

The workflow has three decisions. First, choose the largest original file instead of a screenshot or a second-generation social download. Next, confirm what is allowed to change and what must remain anchored: the task prompt is written for virtual makeup, while the page shows the applicable input and output limits before submission. Finally, compare the source and result at 100 percent and download only after the information-sensitive areas pass your review. This order matters because a plausible AI result can look polished while still changing a face, label, edge or background.

Input, output and source quality

Use a clear JPG, PNG or WebP with enough pixels for the details you care about. For this page, the published intake note is: One portrait · six looks · three intensities · about a minute · 1 credit. A clean input gives the model stronger evidence about edges, colour and texture. If the source is already heavily compressed, crop it tightly or find the original before spending a credit. Keep the source file beside the output; the service is an enhancement estimate, not a reversible edit history.

When to use this tool

  • Makeup moodboards
  • Creator content planning
  • Bridal look drafts
  • Colour and intensity comparisons

How it works

  1. Upload a portrait — Choose a clear, well-lit portrait where the eyes, brows and lips are visible. An optional makeup reference can guide the palette.
  2. Choose look and intensity — Pick everyday, Korean dewy, glam, bridal, latte or retro, then choose subtle, clearly visible or editorial intensity.
  3. Compare the details — Inspect the eyes, brows, lips, skin texture and expression against the original before saving the preview.

When this workflow fits

This workflow is a good fit for Makeup moodboards, Creator content planning, Bridal look drafts, Colour and intensity comparisons. It is especially useful when the subject is already recognizable and the job is to preview makeup on it while preserving the scene's main structure. It is less useful when the source has no recoverable detail, when the requested change is an exact measurement, or when the output will be used as an official record. In those cases, use the original capture, a design or editing application, or the receiving authority's own specification.

Limits and responsible use

Use a well-lit portrait with the eyes, brows and lips visible. The preview helps compare colour and intensity; it cannot predict exact products, skin response or how makeup will look in person. AI makeup previews are visual references, not cosmetic, medical or allergy advice. Confirm products and skin compatibility separately. The service cannot guarantee that a small character, logo, number, face or repeated pattern will remain unchanged. A larger output also does not create new factual evidence; it creates a more usable visual estimate.

Credits and delivery

Your allowance and the task cost are shown before a signed-in job starts. The public example is available for comparison, while processing a personal file uses the displayed credit amount. A failed generation on our side is handled by the account workflow according to the billing policy. Check the output target and the source dimensions first so a task is not spent on a size you cannot use. For batch or API work, compare the per-image cost with storage, retries and your own review time.

A practical quality review

Before publishing, compare the result with the original in a fixed order: overall composition, subject identity, long straight edges, small text or labels, repeated texture, colour and shadows. Zooming to 100 percent exposes halos and invented marks that a thumbnail hides. For portraits, check eyes, teeth, hairline and skin texture. For products or architecture, check logos, windows, perspective and material boundaries. For generated edits, check that the requested change is present without unrelated objects or background drift.

Frequently asked questions

Which makeup looks are available?

Read the answer

The studio includes soft everyday, Korean dewy, full glam evening, bridal, warm latte and retro looks, each with subtle, clearly visible and editorial intensity.

Can the result tell me which product to buy?

Read the answer

No. It is a visual reference for colour and placement. It does not identify a product, guarantee a shade match or provide allergy or skin-care advice.

Will the face stay the same?

Read the answer

The workflow asks the model to preserve face shape and expression, but generated details can drift. Review the eyes, lips, brows and skin texture before sharing.

Related tools

Portrait Retouch · Virtual Try-On · AI Headshot Enhancer

Check the source-linked GPU evidence →