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Configuration

Learn how to configure OpenTryOn for your specific needs. OpenTryOn supports three main categories: Preprocessing, API Integrations, and Datasets.

Environment Variables​

OpenTryOn uses environment variables for configuration. Create a .env file in your project root:

Preprocessing (Required for Local Preprocessing)​

# U2Net Model Checkpoints (Required for garment/human segmentation)
U2NET_CLOTH_SEG_CHECKPOINT_PATH=path/to/cloth_segm.pth
U2NET_SEGM_CHECKPOINT_PATH=path/to/u2net.pth

# Optional: GPU Configuration
CUDA_VISIBLE_DEVICES=0

# Optional: Logging
LOG_LEVEL=INFO

API Integrations (Optional - Only configure APIs you plan to use)​

# Segmind Try-On Diffusion API
SEGMIND_API_KEY=your_segmind_api_key

# Kling AI Virtual Try-On API
KLING_AI_API_KEY=your_kling_api_key
KLING_AI_SECRET_KEY=your_kling_secret_key
KLING_AI_BASE_URL=https://api-singapore.klingai.com  # Optional

# Amazon Nova Canvas (AWS Bedrock)
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AMAZON_NOVA_REGION=us-east-1  # Options: us-east-1, ap-northeast-1, eu-west-1
AMAZON_NOVA_MODEL_ID=amazon.nova-canvas-v1:0  # Optional

# Google Gemini (Nano Banana Image Generation)
GEMINI_API_KEY=your_gemini_api_key

Datasets (Optional - Only if using HuggingFace datasets)​

# HuggingFace datasets cache (for Subjects200K)
# Defaults to ~/.cache/huggingface/datasets if not set
HF_DATASETS_CACHE=path/to/cache

Note: You only need to configure the APIs and features you plan to use. For example:

  • Preprocessing only: Only U2Net checkpoints required
  • API integrations only: Only API keys required (no local models needed)
  • Datasets only: No configuration needed (automatic download/caching)

Loading Environment Variables​

Always load environment variables before using OpenTryOn:

from dotenv import load_dotenv
load_dotenv()

# Now import and use OpenTryOn modules
from tryon.preprocessing import segment_garment
from tryon.api import SegmindVTONAdapter
from tryon.datasets import FashionMNIST

Getting API Keys​

Segmind Try-On Diffusion​

  1. Sign up at Segmind API Portal
  2. Obtain your API key from the dashboard
  3. Add to .env: SEGMIND_API_KEY=your_key

Kling AI Virtual Try-On​

  1. Sign up at Kling AI Developer Portal
  2. Obtain API key (access key) and secret key
  3. Add to .env: 
    KLING_AI_API_KEY=your_api_key
    KLING_AI_SECRET_KEY=your_secret_key

Amazon Nova Canvas​

  1. Set up AWS account with Bedrock access
  2. Enable Nova Canvas in AWS Bedrock console (Model access section)
  3. Configure AWS credentials (via .env or AWS CLI): 
    AWS_ACCESS_KEY_ID=your_access_key
    AWS_SECRET_ACCESS_KEY=your_secret_key
    AMAZON_NOVA_REGION=us-east-1

Google Gemini (Nano Banana)​

  1. Sign up at Google AI Studio
  2. Obtain API key from API Keys page
  3. Add to .env: GEMINI_API_KEY=your_key

Configuration Options​

GPU Configuration​

Specify which GPU to use:

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # Use first GPU

Model Checkpoint Paths​

Set custom checkpoint paths:

import os
os.environ["U2NET_CLOTH_SEG_CHECKPOINT_PATH"] = "/custom/path/cloth_segm.pth"
os.environ["U2NET_SEGM_CHECKPOINT_PATH"] = "/custom/path/u2net.pth"

Logging Configuration​

Configure logging level:

import logging
logging.basicConfig(level=logging.INFO)

Default Settings​

OpenTryOn uses sensible defaults:

  • Image Size: Automatically resized based on model requirements
  • Batch Size: 1 (can be adjusted for batch processing)
  • Device: Auto-detects CUDA if available, falls back to CPU
  • Normalization: Images normalized to [-1, 1] range

Custom Configuration​

You can override defaults when calling functions:

from tryon.preprocessing.extract_garment_new import extract_garment
from PIL import Image
import torch

# Use specific device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load model once for efficiency
net = load_cloth_segm_model(device, os.environ.get("U2NET_CLOTH_SEGM_CHECKPOINT_PATH"))

# Use pre-loaded model
image = Image.open("garment.jpg")
garments = extract_garment(
    image=image,
    cls="upper",
    resize_to_width=400,
    net=net,  # Reuse model
    device=device
)

Quick Configuration Examples​

Preprocessing Only​

U2NET_CLOTH_SEG_CHECKPOINT_PATH=./models/cloth_segm.pth
U2NET_SEGM_CHECKPOINT_PATH=./models/u2net.pth

API Integrations Only (No Local Models)​

SEGMIND_API_KEY=your_segmind_key
GEMINI_API_KEY=your_gemini_key

Full Setup (Preprocessing + APIs + Datasets)​

# Preprocessing
U2NET_CLOTH_SEG_CHECKPOINT_PATH=./models/cloth_segm.pth
U2NET_SEGM_CHECKPOINT_PATH=./models/u2net.pth

# APIs
SEGMIND_API_KEY=your_segmind_key
KLING_AI_API_KEY=your_kling_key
KLING_AI_SECRET_KEY=your_kling_secret
GEMINI_API_KEY=your_gemini_key
AWS_ACCESS_KEY_ID=your_aws_key
AWS_SECRET_ACCESS_KEY=your_aws_secret
AMAZON_NOVA_REGION=us-east-1

Best Practices​

  1. Always use .env file: Never commit API keys or paths to version control
  2. Load environment variables first: Before importing any OpenTryOn modules
  3. Use absolute paths: For checkpoint paths to avoid issues
  4. Check GPU availability: Verify CUDA before running intensive operations
  5. Only configure what you need: Don't add API keys for services you won't use
  6. Keep .env in .gitignore: Protect your credentials

Next Steps​