AI Models
One Garment, Many Body Types: Inclusive AI Modelling Done Right
How to show the same garment on several sizes, ages and skin tones with AI — accurately and respectfully.
AI ModelsQuick answer: Use the same garment reference and the same set, change only the model's body type, and describe real fit for each size (how length, volume and drape change). Review with people from those groups where possible, and never present AI models as real customer reviews.
Accuracy over tokenism
Showing diverse bodies only helps if the garment fits each body the way it really would. A size 46 model wearing a garment that fits like a size 36 misleads shoppers.
The workflow
- Fix garment, set, light and pose for the whole set.
- Choose the body types that match your size range and customers.
- Describe fit per size from your real fit notes.
- Generate one image per body type.
- Check fabric strain, length and drape match your size chart.
- Label size worn and height on each image.
Prompt to copy
Same garment, set and light as the reference. Model: [age, height, body type, skin tone], wearing size [X]. Show the real fit for this size: [length, how it sits at hips and bust]. Natural, confident pose, e-commerce full-body.
Common mistakes
- Same fit on every body.
- Only changing skin tone, not body shape.
- No size labels.
Where to do this in the Lab
- Creative Workspace — pay as you go, no subscription.
- Virtual Try-On — pay as you go, no subscription.
- AI Consistency workshop — learn how to keep the same face and the same product in every shot.
Related
- Plus-size AI models for inclusive fashion
- Age-inclusive AI fashion models 40+
- AI model diversity and ethical casting
FAQ
Can AI show one garment on many body types?
Yes, by keeping everything the same except the model.
Is it misleading?
Not if the fit is accurate for each size and labelled clearly.
Should I still use real models?
Many brands mix both; real fit photos remain valuable.
What body types should I show?
The range your customers actually wear.
Next step: learn the whole workflow in a recorded workshop (see all workshops) or save with a bundle.
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