p-image-try-on

Prerequisites

Install and load these skills before generating (skip if already in context via @pruna):

Or install the full suite once: npx skills add PrunaAI/pruna-skills@pruna -y

Follow each skill's Before generating / craft sections — do not restate guide content here.

Agent habit

In the first reply, name `p-image-try-on` in backticks, confirm PRUNA_API_KEY, then ask for person_image + garment_images. Open intake → generation-diversity clarification intake when silent. When refs need disambiguation, draft with Prompt craft (dynamic + faithful) — do not paste skill examples. Redirect background-only / no-garment jobs to p-image-edit.

Prompt craft (dynamic + faithful)

Identity and garments come from person_image + garment_images[]. Optional prompt only disambiguates refs — it does not invent a new person or outfit.

Fidelity check (before pay): output must still be the user's person in the user's garment(s). If prompt could apply to a different ref set, rewrite the disambiguation.

When NOT to use

Use a different skill instead:

Pricing

Per generation (same for normal and turbo mode):

• $0.015 for the first garment
• $0.008 for each additional garment

Example: 3 garments → $0.015 + 2 × $0.008 = $0.031.

Request shape

One person_image, one garment_images[] entry per piece (up to 11), optional reference_pose. The model auto-classifies each garment — array order does not matter. Mixed categories belong in one call.

• prompt — only when a reference shows multiple garments or is worn on-model; clean flat-lays need no prompt.
• preserve_input_size: true (default) — output dimensions follow the person image.

Runware field map: person → person_image, garment → garment_images[], pose → reference_pose, positivePrompt → prompt, settings.turbo → turbo.

HTTP (curl)

Upload images

curl -X POST "https://api.pruna.ai/v1/files" \
  -H "apikey: ${PRUNA_API_KEY}" \
  -F "content=@/path/to/person.jpg"

curl -X POST "https://api.pruna.ai/v1/files" \
  -H "apikey: ${PRUNA_API_KEY}" \
  -F "content=@/path/to/garment.png"

Use each response urls.get in input.person_image and input.garment_images[]. Optional: reference_pose.

Create (async — recommended)

curl -X POST 'https://api.pruna.ai/v1/predictions' \
  -H 'Content-Type: application/json' \
  -H "apikey: ${PRUNA_API_KEY}" \
  -H 'Model: p-image-try-on' \
  -d '{
    "input": {
      "person_image": "https://api.pruna.ai/v1/files/PERSON_FILE_ID",
      "garment_images": ["https://api.pruna.ai/v1/files/GARMENT_FILE_ID"]
    }
  }'

Poll and download: follow pruna-api.

Complete the random seed ritual from generation-diversity before writing prompts — do not pass the ritual string as API seed.

Create (sync — quick test only)

curl -X POST 'https://api.pruna.ai/v1/predictions' \
  -H 'Content-Type: application/json' \
  -H "apikey: ${PRUNA_API_KEY}" \
  -H 'Model: p-image-try-on' \
  -H 'Try-Sync: true' \
  -d '{
    "input": {
      "person_image": "https://api.pruna.ai/v1/files/PERSON_FILE_ID",
      "garment_images": ["https://api.pruna.ai/v1/files/GARMENT_FILE_ID"]
    }
  }'

Extended input (turbo + pose + prompt)

curl -X POST 'https://api.pruna.ai/v1/predictions' \
  -H 'Content-Type: application/json' \
  -H "apikey: ${PRUNA_API_KEY}" \
  -H 'Model: p-image-try-on' \
  -d '{
    "input": {
      "person_image": "https://api.pruna.ai/v1/files/PERSON_FILE_ID",
      "garment_images": [
        "https://api.pruna.ai/v1/files/MULTI_GARMENT_SHOT_ID",
        "https://api.pruna.ai/v1/files/BOTTOM_ID"
      ],
      "reference_pose": "https://api.pruna.ai/v1/files/POSE_REF_ID",
      "prompt": "the green t-shirt from image 1 and the trousers from image 2",
      "turbo": true,
      "output_format": "jpg",
      "output_quality": 95,
      "preserve_input_size": true
    }
  }'

Before generating

1. Complete Prerequisites guide reading order (generation-diversity → image-prompting try-on craft).
2. Ritual seed → draft optional dynamic + faithful disambiguation prompt (section above) → confirm person_image, garment_images (≤6 for finals; 7–8 usually lands; 9–11 may drop last items), and optional turbo / reference_pose / prompt.
3. Pruna notes: one item per body spot (socks + shoes → usually shoes win). turbo (~2.5–3.5 s) is off by default — not recommended above ~4 garments for finals. Full-body or three-quarter person crops work best. Omit gloves, mittens, handheld props, pocket squares, suspenders, brooches from garment_images[].

Required input

• person_image (string URL)
• garment_images (array of string URLs, up to 11)

Common optional fields

• seed, output_format (webp / jpg / png, default jpg), output_quality (0–100, default 95)
• preserve_input_size (boolean, default true)
• turbo (boolean, default false)
• reference_pose (person image URL)
• prompt (EXPERIMENTAL — disambiguate non-flatlay / multi-garment refs)

Typical next steps

Common follow-ons after this skill: