PrunaAI 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. 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.
| 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 explicitly wants the fastest, cheapest photo generation — mood boards, bulk panels, or quick iterations — not when controlled photoreal or in-image text is needed. | 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 a simple short clip from text or images — quick B-roll, drafts, or start/end frame animation. Not when the brief needs the highest quality or tight lip-sync. | 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 |