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

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

2. 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

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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.