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muapi-ugc-lifestyle-try-on

muapi-ugc-lifestyle-try-on

github.com/samuraigptMarketing & growth25660 installsUpdated 4w ago
Repository

About muapi-ugc-lifestyle-try-on

muapi-ugc-lifestyle-try-on is an image-generation workflow for marketing content. It creates a product try-on or usage image from a reference, then produces a lifestyle variation, with inputs for the model, setting, and platform.

Key features

  1. 01Generate try-on or usage images from product references
  2. 02Use separate paths for clothing and other products
  3. 03Create a lifestyle-context variation
  4. 04Support aspect ratios for social platforms

Use cases

  1. 01Create UGC-style product images for social media
  2. 02Show clothing or accessories being worn
  3. 03Place a product image in a lifestyle setting

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/samuraigpt/generative-media-skills --skill muapi-ugc-lifestyle-try-on

After installing, type/muapi-ugc-lifestyle-try-onin Claude Code to confirm it loaded.

Details

From the project README

UGC Lifestyle Try-On

Generate UGC-style (User Generated Content) lifestyle photos of a person wearing or using your product — authentic, relatable, social-media-native imagery.

Inputs

NameTypeRequiredDefaultDescription
product_nametextyes—The product to feature (e.g. "white oversized hoodie", "blue light blocking glasses", "leather crossbody bag").
product_imageimage_urlyes—Product image or photo URL to use as reference for the try-on.
model_descriptiontextnowoman, 25-30 years old, natural look, diverseDescription of the model (e.g. "man, athletic build, 20s", "woman, curvy, 30s, warm skin tone").
settingtextnocasual lifestyle, natural lightingScene and mood (e.g. "urban street style", "cozy home morning routine", "gym workout", "coffee shop").
platformtextnoinstagramTarget platform — "instagram", "tiktok", "pinterest", "amazon".

Steps

Submit the plan with TWO steps:

Step 1 — Outfit/Product Try-On
  1. Try-on generation — muapi image edit (model=ai-dress-change) if product is wearable clothing, otherwise muapi image edit (model=flux-kontext-pro-i2i):
    • For clothing/wearables with ai-dress-change:
      • Product: {{product_image}}
      • Prompt: {{model_description}} wearing the product naturally in a {{setting}} environment. Authentic UGC-style photo, candid pose, natural expression.
    • For accessories/non-clothing with flux-kontext-pro-i2i:
      • Prompt: {{model_description}} using/wearing {{product_name}} in a {{setting}}. The product from the reference image is clearly visible and featured. Natural UGC-style lifestyle photography, authentic candid feel.
    • Aspect ratio: 4:5 for Instagram, 9:16 for TikTok, 2:3 for Pinterest
Step 2 — UGC Lifestyle Variant
  1. Lifestyle context shot — muapi image edit (model=gpt4o-edit) using the output from Step 1:
    • Prompt: Make this look like authentic UGC content — add realistic environment context for {{setting}}, adjust lighting to feel natural and unposed, subtle film grain, candid photography style. Keep product {{product_name}} clearly visible and well-lit.

After generation:

  • Present both the try-on and lifestyle variant
  • Offer to generate a 3-image carousel set with different poses/settings
  • Suggest adding a short UGC-style video with kling-v3.0-pro-image-to-video

Notes

  • UGC performs best when it looks "accidental" — avoid overly polished or symmetrical compositions.
  • For TikTok/Reels, suggest animating the best static shot into a video.
  • For Amazon, refer back to the amazon-product-listing skill for white-background variants.

Trigger Keywords

ugc, try on, lifestyle photo, model wearing, outfit photo, wear product, user generated, ugc content, lifestyle try on


Notes for the Executing Agent

  • This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
  • For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
  • Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.

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