SegFit v1.3 – Virtual Try-On Model

What is SegFit v1.3?

SegFit v1.3 is an AI-powered virtual try-on model built for fashion retail and e-commerce applications. Leveraging advanced garment segmentation and photorealistic rendering, it fits any outfit image onto a model photo with natural draping, accurate boundaries, and smart automatic masking. Delivered as a serverless API, SegFit v1.3 streamlines digital workflows—no physical photoshoots required—so brands and creators can boost conversion rates with hyper-realistic product previews.

What's New in v1.3

Segfit v1.3 introduces a range of improvements designed to enhance both quality and performance:

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  Improved Auto Prompting
  More accurate and context-aware prompt generation for better outputs.

• •

  New Cloth Segmentation Model
  A significantly enhanced segmentation model for more precise clothing detection.

• •

  Enhanced Automasking
  Accurate masking for upper wear, lower wear, and full-body outfits, with intelligent hand and feet coverage based on the type of clothing.

• •

  Upgraded Inpainting Models
  Refined base and adapter models result in more realistic and consistent inpainted clothing.

• •

  Faster Generation Speed
  Optimized performance for quicker processing and output.

• •

  Fixes for Inpainting Artifacts
  Resolved issues such as black borders around inpainted areas for cleaner results.

This release focuses on delivering higher visual fidelity, improved automation, and a smoother user experience across workflows.

Key Features

• •Hyper-realistic Rendering: Generates high-fidelity images that preserve fabric texture, folds, and lighting.
• •Automatic Garment Masking: Separates clothing from background pixels, reducing manual editing by up to 90%.
• •Flexible Inputs:
  • outfit_image (required): Clear, front-facing picture of the garment.
  • model_image (required): Well-lit, focused photo of the model.
  • mask_image (optional): Custom mask for precise tryon.
• •Configurable Outputs:
  • image_format: PNG, JPEG, or WebP.
  • image_quality: Slider from 1–100, default 90 for optimal detail.
  • seed: Control randomness for reproducible results.

Best Use Cases

• •E-commerce Catalogs: Instantly generate garment previews on diverse body types.
• •Marketing Campaigns: Produce banner images and social media assets with minimal design overhead.
• •Virtual Fitting Rooms: Integrate real-time try-on experiences into retail websites and mobile apps.
• •Content Creation: Streamline lookbooks, blog visuals, and influencer marketing with automated styling.

Prompt Tips and Output Quality

1. •Start with high-resolution images (≥1024px) for both outfit_image and model_image.
2. •Use uniform, uncluttered backgrounds to simplify automatic masking.
3. •Tweak image_quality (default 90) to balance file size and fidelity—ideal for web-ready assets.

FAQs

How do I integrate SegFit v1.3 into my workflow?
Use our serverless API endpoint. Send multipart/form-data with outfit_image and model_image, plus optional parameters for masks and quality controls.

What image formats are supported?
SegFit v1.3 supports png, jpeg, and webp. PNG is recommended for lossless quality and precise alpha channel support.

Is background removal automatic?
Absolutely. Automatic garment masking detects clothing edges and separates them from any background, reducing manual editing time.

How do I reproduce the same result?
Set the seed parameter to a fixed integer (e.g., 777). Use -1 for a random seed on each request.

SegFit v1.3 – Quickstart & Best Practices

SegFit v1.3 is a serverless API that lets you virtually “try on” any garment with photorealistic draping, automatic masking and high-fidelity rendering. Use this guide to choose the right parameters for your use case, and get crisp, consistent results every time.

1. Getting Started

1. •Collect two high-resolution images (≥1024 px):
   • outfit_image: Front-facing, well-lit garment shot.
   • model_image: Portrait with neutral background and good lighting.
2. •Send a multipart/form-data POST to the SegFit endpoint, including these images plus any optional mask or config fields.
3. •Receive a ready-to-use PNG/JPEG/WebP with the try-on result.

2. Core Parameters

• •image_format (png/jpeg/webp):
  • PNG for lossless RGBA; WebP for web-optimized files; JPEG for photos.
• •image_quality (1–100, default 90):
  • 90 yields crisp edges with moderate file size. Lower to reduce bytes.
• •seed (integer or –1):
  • Fixed seed for reproducible outputs; –1 for random variations.

3. Advanced Controls (optional)

• •mask_image: Supply custom mask to isolate sleeves, collars or accessories.

4. Use-Case Parameter Sets

1. •E-commerce Catalogs
   • image_format: png
   • image_quality: 90
   • seed: 42
2. •Marketing Banners / Social Media
   • image_format: webp
   • image_quality: 80
   • seed: –1
3. •Virtual Fitting Rooms (real-time)
   • image_format: jpeg
   • image_quality: 75
   • seed: –1
4. •Influencer & Lookbook Content
   • image_format: png
   • image_quality: 95

5. Pro Tips

• •Shoot on neutral, uncluttered backgrounds for 80% fewer masking artifacts.
• •For consistent batch processing, lock your seed .
• •Reduce image_quality to 60–70 for mobile-optimized assets under 200 KB.

By tuning these parameters, SegFit v1.3 adapts to any retail or creative workflow—whether you need ultra-fast previews or studio-grade renders. Happy styling!

FAQs