kidswear-photography-master-github

name

kidswear-photography-master-github

description

Professional kidswear photography generation system. Receives a white-background kidswear image, sequentially asks for model characteristics (gender/type/age group), recommends 6 matching scenes, and generates five 3:4 vertical commercial blockbuster shots using a 4-layer prompt structure. Use this skill when the user mentions "generate kidswear model images", "change kidswear background", "kidswear try-on effect", "kidswear commercial shots", or directly uploads a kidswear image requesting a model/scene generation.

Kidswear Photography Master System

Instructions

Step 1: Intelligent Clothing Analysis & Scene Recommendation (Ask User)

When the user provides a white-background kidswear image, execute the following workflow:

1. Intelligent Clothing Analysis (Background):
   • Automatically identify: category, style, brand tier, color, material, season.
   • Determine brand tone: luxury / fashion / mass-market / sports.
   • Identify garment_type: upper_body, lower_body, full_outfit, dress.
2. Consolidated Parameter Confirmation (Ask Once):
   • After analyzing the white-background image, consult the "Scene Recommendation Mapping Table" below to select the 6 most suitable scenes.
   • Confirm all required parameters from the user in a single message:

     Your clothing style is identified as [Insert Identified Style]. I recommend the following 6 most matching photography scenes for you:

     • A. [Scene Name in English] — [1-sentence description, under 10 words]
     • B. [Scene Name in English] — [1-sentence description] ... (list all 6)

     To generate the perfect model images, please also let me know:

     1. Model Gender (Boy / Girl)
     2. Model Type (Asian / European)
     3. Age Group (3-6 years / 6-10 years / 10-14 years)

     You can reply directly, for example: "Choose scene B, girl, Asian, 6-10 years."

   • Wait for the user's combined reply, record scene_type, gender, model type, and age group, then proceed directly to the image generation workflow.

   • Tip: If the user already provided some or all of this information when uploading the image, use the provided info and only ask for the missing parts.

Scene Recommendation Mapping Table:

Clothing Characteristics	Recommended 6 Scenes (Ordered by Priority)
Luxury brands / High-end formal wear / Refined jackets	luxury_manor, garden_setting, cozy_home, vintage_train, resort_pool, wild_forest
Sportswear / Functional outdoor / Ball sports	sports_venue, farm_field, metro_adventure, cozy_home, wild_forest, beach_natural
Princess dresses / Gowns / Lace wear / Dresses	garden_setting, cozy_home, luxury_manor, farm_field, resort_pool, beach_natural
School uniforms / Preppy style / Plaid shirts	indoor_school, garden_setting, metro_adventure, cozy_home, farm_field, wild_forest
Casual wear / Hoodies / Daily outfits	cozy_home, metro_adventure, garden_setting, farm_field, wild_forest, beach_natural
Resort wear / Fresh summer / Beach style	beach_natural, resort_pool, garden_setting, farm_field, cozy_home, wild_forest
Streetwear / Cool outfits / Printed wear	sports_venue, metro_adventure, cozy_home, farm_field, garden_setting, wild_forest
Winter wear / Thick coats / Ski suits	snow_mountain, cozy_home, metro_adventure, farm_field, wild_forest, luxury_manor

12 Scenes Overview (For User Display):

When the Agent generates images, the narrative_theme and all Scene framing MUST match the user's selected scene_type, fetching values directly from this table to inject into the code.

scene_type	Name	Short Description	narrative_theme (Injected into Code)
luxury_manor	Luxury Manor	European manor, classic sports car, manicured lawn	Luxury kidswear editorial, European manor weekend lifestyle
sports_venue	Sports Venue	Blue hard court, net fencing, athletic vibe	Youth sports fashion, vibrant outdoor hard court lifestyle
garden_setting	Garden Setting	Deep green cypress wall / Floral bench / Rose arch (Randomized)	Direction A → European manor garden editorial, deep green botanical backdrop lifestyle / Direction B → French dramatic garden editorial, abundant blooming flowers lifestyle / Direction C → Dreamy kidswear editorial, romantic flower garden lifestyle
resort_pool	Resort Pool	Blue pool, palm trees, luxury resort hotel	Summer resort kidswear editorial, luxury poolside lifestyle
beach_natural	Natural Beach	Ocean waves, sandy beach, coastal rocks	Natural beach kidswear editorial, coastal lifestyle
indoor_school	Indoor School	Studio preppy style, props set, editorial vibe	School life kidswear editorial, minimal indoor studio academic lifestyle
cozy_home	Cozy Home	Modern home / Bright window / European vintage study (Randomized)	Style A → Cozy home kidswear editorial, warm interior lifestyle / Style B → Cozy home kidswear editorial, bright window-side natural light lifestyle / Style C → Cozy home kidswear editorial, European vintage home interior lifestyle, refined Old Money aesthetic
wild_forest	Wild Forest	Forest path, tree canopy, green bushes	Wild forest kidswear editorial, outdoor nature lifestyle
farm_field	Farm Field	Barn wheat field / Bamboo fence (Randomized)	Style A → Farm field kidswear editorial, countryside lifestyle / Style B → Asian pastoral farm editorial, bamboo fence countryside lifestyle
snow_mountain	Snow Mountain	Snow slope, mountain ridge, ski lift	Snow mountain kidswear editorial, alpine lifestyle

Step 2: Confirm Fashion Stylist Matching

Read references/FASHION_STYLIST.md to execute outfit styling:

• upper_body → Keep the top, match with stylish bottoms + footwear for the model.
• lower_body → Keep the bottom, match with a stylish top + footwear for the model.
• full_outfit / dress → Keep the full outfit, match with footwear only.
• STRICTLY PROHIBITED: Skinny jeans, basic white tees, and other un-designed basic items.

Step 3: Automatically Configure Generation Parameters

• Model Gender: Based on user selection (boy / girl). Do not auto-infer.
• Model Type: Based on user selection (asian / european). Do not auto-infer.
• Model Age: Dynamically configured based on the user's selected age group.
• Photography Scene: Based on user confirmation (scene_type).
• Generation Model: Uniformly use image_gen_sync.
• Expression Style: Auto-matched based on clothing type:
  • Luxury / Formal: serious calm, composed confidence
  • Sportswear: focused determination, energetic vitality
  • Princess Dress: gentle sweetness, dreamy elegance
  • Casual Wear: bright cheerful smile, relaxed ease
  • Streetwear: cool edgy vibe, confident swagger

Step 4: Construct 4-Layer Precise Prompts

🛑 System-Level Failsafe Directives:

• During the parameter collection and user questioning phase, STRICTLY PROHIBITED from reading any other Markdown files.
• Upon receiving the complete user selection and preparing to construct prompts, you MUST ONLY read references/SCENE_FRAMING_RULES.md to obtain the 5 camera angles for the current scene.
• When preparing to generate code (constructing prompts and API calls), the Agent MUST use the Read tool to read references/UNIFIED_PROMPT_BUILDER.md to obtain the full model feature description templates and the image generation boilerplate code.

All scenes use the identical 4-layer structure:

1. Layer 1: Narrative theme (includes simple scene description)
2. Layer 2: Subject persona (Scene framing, gender, model type, age group, pose description, expression description)
3. Layer 3: Outfit styling (includes clothing reference image, fashion matching suggestions, excludes bags/accessories)
4. Layer 4: Style reference (Photography style / Brand tone / Visual references)

Step 5: Validate Core Requirements (Commercial Editorial Standard)

This system generates commercial fashion editorials, not daily snapshots:

• ✅ Every pose is choreographed
• ✅ Every expression is designed
• ✅ Capture the peak moment, NOT blurry action
• ✅ Commercial editorial style, NOT family snapshot
• ✅ Shot 2 Dynamic Moment: Both feet on the ground in a power pose, NOT jumping off the ground

For detailed standards, refer to: references/COMMERCIAL_PHOTOGRAPHY_STANDARDS.md Code Execution Tip: Before running the image generation code, it is recommended to call scripts/quality_check_system.py to verify if the prompts meet all optimization requirements.

Step 6: Generate Five 3:4 Vertical Images

Execute image generation, each must use a different scene perspective:

• Shot 1: Full-body standard standing pose + Main Scene Perspective (Frontal wide shot, iconic background element centered)
• Shot 2: Dynamic moment freeze (Both feet grounded, NOT jumping) + Side-Line/Corner Perspective (Diagonal framing, different background elements)
• Shot 3: Side profile clothing display + Diagonal Turning Corner Perspective (45-degree angle, another set of background elements)
• Shot 4: 3/4 body detail close-up + Close-Up Extremely Shallow Depth of Field (Background blurred into color bokeh, focus on clothing)
  • upper_body → Top fabric / brand elements / design details
  • lower_body → Bottom silhouette / waistband / fabric details
  • dress/full_outfit → Overall silhouette and most distinctive parts
• Shot 5: Half-body clothing close-up + Core Scene Element (Different position from Shot 1, central/distant landmark)
  • upper_body → Top neckline / cuffs / pattern texture
  • lower_body → Bottom pant legs / hem / pocket details
  • dress/full_outfit → Overall texture and design focus

⚠️ Mandatory Background Differentiation Rule: The 5 images MUST use 5 different camera and background combinations. No two images are allowed to have the same background. The Scene framing: paragraph MUST be added at the beginning of the [Layer 2: Subject persona] in each prompt.

Examples

Example 1: Generating a Princess Dress Model Image User says: "I want to generate a model image for this girl's princess dress" (and uploads a white-background image) Actions:

1. Intelligently analyze the clothing as a princess dress, provide the 6 most matching scene options, and ask for gender, model type, and age group.
2. User replies: "Choose garden setting, girl, European, 6-10 years"
3. Read references/SCENE_FRAMING_RULES.md and references/UNIFIED_PROMPT_BUILDER.md.
4. Construct 4-layer prompts, and call the image generation model to generate five 3:4 vertical images in the garden_setting scene with different camera angle combinations. Result: Outputs 5 kidswear model images meeting commercial editorial standards.

Troubleshooting

• Error: Generates casual daily snapshots instead of commercial editorials
  • Cause: Used casual poses or laughing expressions, lacking a choreographed commercial feel.
  • Solution: Ensure every pose is carefully choreographed (designed naturalness), every expression has a designed feel (designed smile), captures the best moment (peak moment freeze), and read references/COMMERCIAL_PHOTOGRAPHY_STANDARDS.md for correction.
• Error: Model is jumping, both feet off the ground
  • Cause: Shot 2's pose prompt includes jumping.
  • Solution: Force Shot 2 pose to have both feet grounded in a power pose (NOT jumping off the ground).
• Error: Background is repetitive or identical across images
  • Cause: Did not add Scene framing: to Layer 2 Subject Persona or all images used the identical background description.
  • Solution: Select 5 different perspective descriptions for the corresponding scene_type from references/SCENE_FRAMING_RULES.md.

Deep Learning & Support Documentation

All detailed reference materials have been moved to the references/ directory. The system will load these files using the Read tool when needed (This utilizes the Progressive Disclosure principle, reducing unnecessary context overhead):

• Prompt Construction Logic → references/UNIFIED_PROMPT_BUILDER.md
• Specific Configuration Examples → references/PROMPT_CONFIGURATIONS.md
• Expression Selection → references/EXPRESSION_GUIDE.md
• Commercial Editorial Standards → references/COMMERCIAL_PHOTOGRAPHY_STANDARDS.md
• Complete Runnable Code Templates → references/EXAMPLES.md
• Reference Cases → references/REFERENCES.md
• Fashion Styling Rules & Banned List → references/FASHION_STYLIST.md
• Asian Child Model Facial DNA Reference → references/ASIAN_MODEL_DNA.md
• Automatic Quality Check System → scripts/quality_check_system.py