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API Reference

Complete API reference for OpenTryOn modules.

Preprocessing API​

segment_garment​

Segment garments from images using U2Net model.

from tryon.preprocessing import segment_garment

segment_garment(
    inputs_dir: str,
    outputs_dir: str,
    cls: str = "all"
)

Parameters:

  • inputs_dir (str): Directory containing input garment images
  • outputs_dir (str): Directory to save segmented masks
  • cls (str): Garment class. Options: "upper", "lower", "dress", "all"

Returns: None (saves masks to output directory)

extract_garment​

Extract garments from images and prepare for virtual try-on.

from tryon.preprocessing import extract_garment

extract_garment(
    inputs_dir: str,
    outputs_dir: str,
    cls: str = "all",
    resize_to_width: Optional[int] = None
)

Parameters:

  • inputs_dir (str): Directory containing input garment images
  • outputs_dir (str): Directory to save extracted garments
  • cls (str): Garment class
  • resize_to_width (int, optional): Resize output width

Returns: None (saves extracted garments)

segment_human​

Segment human subjects from images.

from tryon.preprocessing import segment_human

segment_human(
    image_path: str,
    output_dir: str
)

Parameters:

  • image_path (str): Path to input human image
  • output_dir (str): Directory to save segmented mask

Returns: None (saves mask as PNG)

extract_garment (Single Image)​

Extract garment from a single PIL Image object.

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

garments = extract_garment(
    image: Image.Image,
    cls: str = "all",
    resize_to_width: Optional[int] = None,
    net: Optional[torch.nn.Module] = None,
    device: Optional[torch.device] = None
)

Parameters:

  • image (PIL.Image): Input image object
  • cls (str): Garment class
  • resize_to_width (int, optional): Resize output width
  • net (torch.nn.Module, optional): Pre-loaded U2Net model
  • device (torch.device, optional): Device to run inference on

Returns: Dict[str, PIL.Image] - Dictionary mapping garment class names to PIL Image objects

TryOnDiffusion API​

Diffusion​

Main diffusion model class.

from tryondiffusion.diffusion import Diffusion

diffusion = Diffusion(
    device: torch.device,
    pose_embed_dim: int,
    time_steps: int = 256,
    beta_start: float = 1e-4,
    beta_end: float = 0.02,
    unet_dim: int = 64,
    noise_input_channel: int = 3,
    beta_ema: float = 0.995
)

Methods:

  • sample(use_ema: bool, conditional_inputs: tuple) -> torch.Tensor
  • fit(args) - Start training
  • prepare(args) - Prepare data and optimizer

See TryOnDiffusion Documentation for details.

Virtual Try-On API Adapters​

SegmindVTONAdapter​

Adapter for Segmind Try-On Diffusion API for virtual try-on generation.

from tryon.api import SegmindVTONAdapter

adapter = SegmindVTONAdapter(api_key="your_api_key")

images = adapter.generate_and_decode(
    model_image="person.jpg",
    cloth_image="garment.jpg",
    category="Upper body"
)

Parameters:

  • api_key (str, optional): Segmind API key. Defaults to SEGMIND_API_KEY environment variable

Methods:

  • generate(model_image, cloth_image, category, ...) - Generate virtual try-on images
  • generate_and_decode(model_image, cloth_image, category, ...) - Generate and decode to PIL Images

See Segmind API Documentation for complete details.

KlingAIVTONAdapter​

Adapter for Kling AI Kolors Virtual Try-On API with asynchronous processing.

from tryon.api import KlingAIVTONAdapter

adapter = KlingAIVTONAdapter(api_key="your_api_key", secret_key="your_secret_key")

images = adapter.generate_and_decode(
    source_image="person.jpg",
    reference_image="garment.jpg",
    model="kolors-virtual-try-on-v1-5"
)

Parameters:

  • api_key (str, optional): Kling AI API key. Defaults to KLING_AI_API_KEY environment variable
  • secret_key (str, optional): Kling AI secret key. Defaults to KLING_AI_SECRET_KEY environment variable
  • base_url (str, optional): Base URL for API. Defaults to KLING_AI_BASE_URL or Singapore endpoint

Methods:

  • generate(source_image, reference_image, model, ...) - Generate virtual try-on images (returns URLs)
  • generate_and_decode(source_image, reference_image, model, ...) - Generate and decode to PIL Images
  • query_task_status(task_id) - Query task status
  • poll_task_until_complete(task_id, ...) - Poll task until completion

See Kling AI API Documentation for complete details.

AmazonNovaCanvasVTONAdapter​

Adapter for Amazon Nova Canvas Virtual Try-On through AWS Bedrock.

from tryon.api import AmazonNovaCanvasVTONAdapter

adapter = AmazonNovaCanvasVTONAdapter(region="us-east-1")

images = adapter.generate_and_decode(
    source_image="person.jpg",
    reference_image="garment.jpg",
    mask_type="GARMENT",
    garment_class="UPPER_BODY"
)

Parameters:

  • region (str, optional): AWS region. Defaults to AMAZON_NOVA_REGION or 'us-east-1'

Methods:

  • generate(source_image, reference_image, mask_type, garment_class, ...) - Generate virtual try-on images
  • generate_and_decode(source_image, reference_image, mask_type, garment_class, ...) - Generate and decode to PIL Images

See Nova Canvas API Documentation for complete details.

Image Generation API Adapters​

NanoBananaAdapter​

Adapter for Gemini 2.5 Flash Image (Nano Banana) - fast and efficient image generation.

from tryon.api.nano_banana import NanoBananaAdapter

adapter = NanoBananaAdapter(api_key="your_api_key")

images = adapter.generate_text_to_image(
    prompt="A nano banana dish in a fancy restaurant",
    aspect_ratio="16:9"
)

Parameters:

  • api_key (str, optional): Google Gemini API key. Defaults to GEMINI_API_KEY environment variable

Methods:

  • generate_text_to_image(prompt, aspect_ratio, ...) - Generate images from text
  • generate_image_edit(image, prompt, aspect_ratio, ...) - Edit images with text prompts
  • generate_multi_image(images, prompt, aspect_ratio, ...) - Compose multiple images
  • generate_batch(prompts, aspect_ratio, ...) - Batch generation

See Nano Banana API Documentation for complete details.

NanoBananaProAdapter​

Adapter for Gemini 3 Pro Image Preview (Nano Banana Pro) - advanced image generation with 4K support.

from tryon.api.nano_banana import NanoBananaProAdapter

adapter = NanoBananaProAdapter(api_key="your_api_key")

images = adapter.generate_text_to_image(
    prompt="A professional nano banana dish",
    resolution="4K",
    aspect_ratio="16:9",
    use_search_grounding=True
)

Parameters:

  • api_key (str, optional): Google Gemini API key. Defaults to GEMINI_API_KEY environment variable

Methods:

  • generate_text_to_image(prompt, resolution, aspect_ratio, use_search_grounding, ...) - Generate images from text
  • generate_image_edit(image, prompt, resolution, aspect_ratio, ...) - Edit images with text prompts
  • generate_multi_image(images, prompt, resolution, aspect_ratio, ...) - Compose multiple images
  • generate_batch(prompts, resolution, aspect_ratio, ...) - Batch generation

See Nano Banana API Documentation for complete details.

Utility Functions​

convert_to_jpg​

Convert image to JPG format.

from tryon.preprocessing import convert_to_jpg

convert_to_jpg(
    image_path: str,
    output_dir: str,
    size: Optional[tuple] = None
)

Parameters:

  • image_path (str): Path to input image
  • output_dir (str): Directory to save converted JPG
  • size (tuple, optional): Desired output size (width, height)

For complete API documentation, see individual module documentation.