Introduction
The virtual try-on workflow enables fashion brands and technology teams to replace traditional 3D fitting with a faster, more scalable, and more cost-effective AI pipeline. By leveraging optimized APIs, teams can deliver high-quality try-on images in seconds while maintaining stability under high load.
Why Replace Traditional 3D Try-On?
Key Challenges with 3D Try-On
- High development cost and long production cycles
- Requires specialized 3D modelling skills
- Often slow to render under high traffic
Benefits of AI-Based Virtual Try-On
- Official-grade image quality without 3D rendering complexity
- Consistent 10-second generation times
- Straightforward integration into existing product pages or fitting apps
- Cost-effective at scale
Workflow Overview
The process moves from user upload, through AI generation, to a completed virtual try-on image.
Step 1: User Upload
- Accepts photo via web or mobile interface
- Automatic validation (resolution, face detection, clothing visibility)
- Optional background removal and image normalization
Step 2: API Request to AI Model
Our AI pipeline connects seamlessly with Nano Banana model equivalents:
- gemini-2.5-flash-image for standard try-on outputs
- gemini-3-pro-image-preview for advanced pro styling options
Step 3: Image Generation
- 10-second average generation time
- Stable outputs even under high concurrent requests
- Return format in base64 for direct embedding
Step 4: Image Delivery
- Serve via CDN for global users
- Embed into UX instantly with minimal delay
API Integration Example
Basic image generation request:
curl --location --request POST 'https://wisdom-gate.juheapi.com/v1/chat/completions' \
--header 'Authorization: sk-hUdyZM5408OFCjHhdvvsQOtVO4X6FKut8X4azpMBcK7ZXbyx' \
--header 'Content-Type: application/json' \
--header 'Accept: */*' \
--data-raw '{
"model": "gemini-2.5-flash-image",
"messages": [{"role": "user","content": [{"text": "generate a high-quality image.","type": "text"},
{"image_url": {"url": "https://blog-images.juhedata.cloud/9105_output_1794ff4b.jpeg"},"type": "image_url/base64"}]}
],
"stream": false
}'
Pricing Advantage
Standard Model Pricing
- Official Nano Banana: 0.039 USD/image
- Our pipeline: 0.02 USD/image
- Savings: ~48% per image
Pro Model Pricing
- Official Nano Banana Pro: 0.134 USD/image
- Our Pro pipeline: 0.068 USD/image
- Savings: ~49% per image
Impact at Scale
For 100,000 images:
- Official cost: $3,900
- Our cost: $2,000
- Savings: $1,900
UX Considerations
Key Principles
- Minimal friction from photo upload to result display
- Inline progress indicators (10-second feedback)
- Optional "Compare" mode for before/after
Integration Time
- Most dev teams complete integration in under 10 minutes
Extensions Beyond Fashion
While fashion is the primary use case, this workflow adapts well to:
- Eyewear try-on
- Cosmetic previews (lipstick, hair color)
- Furniture placement in rooms
Video Generation Option (Sora AI)
For brands needing video-based previews:
Step 1: Make Video
curl -X POST "https://wisdom-gate.juheapi.com/v1/videos" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: multipart/form-data" \
-F model="sora-2" \
-F prompt="A serene lake surrounded by mountains at sunset" \
-F seconds="15"
Step 2: Check Progress
curl -X GET "https://wisdom-gate.juheapi.com/v1/videos/{task_id}" \
-H "Authorization: Bearer YOUR_API_KEY"
Pricing:
- Official Sora AI video: 1.0–1.5 USD/video
- Our pipeline: 0.12 USD/video
Architecture Diagram
Logical Flow
- User Upload → Client
- Preprocessing → Background removal, normalization
- Model Request → API call, choose model type
- Image Generation → AI rendering (~10s)
- Delivery → Base64 to client, optional CDN cache
Drop-In Replacement
- Directly replace existing Nano Banana endpoints with ours
- Maintain same data structures and response formats
Performance Considerations
- High concurrency stability due to optimized queue
- Edge-cached CDN delivery
- API latency measured consistently under load
Security and Compliance
- HTTPS only
- Token-based authentication
- No PII stored after generation completes
Conclusion
By replacing costly, slow 3D try-on models with an optimized AI-based virtual try-on workflow, teams achieve better performance, faster integration, and significant cost savings. With ready-to-use endpoints and proven stability under load, this pipeline is a practical choice for any technical team aiming to scale virtual fitting solutions.