P image try on
Agent skills and plugins to give your agents access to Pruna API and generation workflows.
npx -y skills add PrunaAI/pruna-skills --skill p-image-try-onAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
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Use when someone wants virtual try-on — dress a person in clothes from reference photos for fashion or ecommerce.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
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Prerequisites
Install and load these skills before generating (skip if already in context via @pruna):
| Skill | Description | Install |
|---|---|---|
generation-diversity | Use when writing any generative prompt — ritual seed, explicit structure, scenario axes, and quality gates before paid API calls. | npx skills add PrunaAI/pruna-skills@generation-diversity -y |
image-prompting | Use when crafting still-image prompts for any generative model — composition, identity sheets, edits, try-on, and photoreal personas. | npx skills add PrunaAI/pruna-skills@image-prompting -y |
pruna-api | Use before any Pruna or Replicate HTTP call — credentials, upload/poll/download, parallel batches, and agent safety. | npx skills add PrunaAI/pruna-skills@pruna-api -y |
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. 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.
| Do | Don't |
|---|---|
Lock person_image and every garment_images[] URL first; omit prompt on clean flat-lays | Describe a new scene, model, or garment the user did not supply |
When refs are ambiguous: the green t-shirt from image 1 and the trousers from image 2 (image-prompting try-on craft) | Mood-only prompts (fashion editorial vibe) or copy this skill's extended example when refs differ |
| Ritual seed before drafting disambiguation wording; vary phrasing when multiple valid mappings exist | Use prompt for background swaps — redirect to p-image-edit |
Show prompt (if needed) before POST when refs are ambiguous | Silent try-on that changes pose, face, or garments beyond the brief |
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:
| Skill | Description | Install |
|---|---|---|
p-image | Use when someone wants a fast AI image — product shots, hero visuals, mood boards, or draft photos from a text prompt. | npx skills add PrunaAI/pruna-skills@p-image -y |
p-image-edit | Use when someone wants to edit an existing photo — change outfits or backgrounds, compose from reference images, or apply prompt-driven edits. | npx skills add PrunaAI/pruna-skills@p-image-edit -y |
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
- Complete Prerequisites guide reading order (
generation-diversity→image-promptingtry-on craft). - Ritual seed → draft optional dynamic + faithful disambiguation
prompt(section above) → confirmperson_image,garment_images(≤6 for finals; 7–8 usually lands; 9–11 may drop last items), and optionalturbo/reference_pose/prompt. - 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 fromgarment_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, defaultjpg),output_quality(0–100, default 95)preserve_input_size(boolean, defaulttrue)turbo(boolean, defaultfalse)reference_pose(person image URL)prompt(EXPERIMENTAL — disambiguate non-flatlay / multi-garment refs)
Typical next steps
Common follow-ons after this skill:
| Skill | Description | Install |
|---|---|---|
p-image-upscale | Use when someone wants to upscale or sharpen an existing image for print, large crops, or higher-quality delivery. | npx skills add PrunaAI/pruna-skills@p-image-upscale -y |
p-video | Use when someone wants one short video clip from text or images — B-roll, start/end frame animation, or a quick motion shot. Not for full multi-scene films or lip-synced hosts. | npx skills add PrunaAI/pruna-skills@p-video -y |
p-video-avatar | Use when someone wants a person on camera speaking a script — lip-synced host, spokesperson, or narrated avatar from a portrait photo. | npx skills add PrunaAI/pruna-skills@p-video-avatar -y |