API Reference

Complete API reference for OpenTryOn modules. Current package: v0.0.5.

Models are also available through the unified CLI, MCP server, and (for upstream media HTTP) OpenAPI / Postman snapshots:

CLI · MCP · OpenAPI & Postman

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.

`GoogleVTONAdapter`

Dedicated Vertex AI Virtual Try-On (virtual-try-on-001). Person + product images; not Gemini API / Nano Banana.

from tryon.api import GoogleVTONAdapter

adapter = GoogleVTONAdapter()  # GOOGLE_CLOUD_PROJECT + ADC

images = adapter.generate_and_decode(
    person="person.jpg",
    garment="sweater.jpg",
    number_of_images=1,
)

Parameters:

project (str, optional): GCP project. Defaults to GOOGLE_CLOUD_PROJECT
location (str, optional): Vertex location. Defaults to GOOGLE_CLOUD_LOCATION or global

Methods:

generate_and_decode(person, garment, ...) - Generate and decode to PIL Images

See Google Virtual Try-On for auth, CLI, and MCP.

`OutfitAnyonePlusAdapter`

Dedicated Alibaba DashScope try-on (aitryon-plus). Beijing-region key. Not Qwen-Image composition.

from tryon.api import OutfitAnyonePlusAdapter

adapter = OutfitAnyonePlusAdapter()  # DASHSCOPE_API_KEY (Beijing)

images = adapter.generate_and_decode(
    person="person.jpg",
    garment="top.jpeg",
)

Parameters:

api_key (str, optional): Defaults to DASHSCOPE_API_KEY
base_url (str, optional): Defaults to OUTFITANYONE_BASE_URL or China DashScope

Methods:

generate_and_decode(person, garment, top_garment=..., bottom_garment=..., restore_face=True, resolution=-1)

See OutfitAnyone-Plus for auth, CLI, and MCP.

`PhotoroomVTONAdapter`

Photoroom Image Editing API — shopper Virtual Try-On and catalog Virtual Model (POST /v2/edit).

from tryon.api import PhotoroomVTONAdapter

adapter = PhotoroomVTONAdapter()  # PHOTOROOM_API_KEY
worn = adapter.generate_and_decode(person="selfie.jpg", garment="dress.jpg")
catalog = adapter.generate_virtual_model(garment="flatlay.jpg", preset_model="avery")

Parameters:

api_key (str, optional): Defaults to PHOTOROOM_API_KEY
base_url (str, optional): Defaults to PHOTOROOM_BASE_URL or https://image-api.photoroom.com

Methods:

generate_and_decode(person, garment, mode="try-on", ...)
generate_virtual_model(garment, ...)

See Photoroom for auth, CLI, and MCP.

`LeffaAdapter` / `CatVTONAdapter`

Dedicated local virtual try-on (GPU, pip install opentryon[local]). Not composition I2I.

from tryon.models import LeffaAdapter, CatVTONAdapter

LeffaAdapter().generate_and_decode("person.jpg", "garment.jpg")
CatVTONAdapter().generate_and_decode("person.jpg", "garment.jpg")

See Leffa and CatVTON. CatVTON weights are CC BY-NC-SA 4.0.

`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.

`PImageTryOnAdapter`

Adapter for Pruna AI's P-Image-Try-On API -- multi-garment virtual try-on (up to 11 garment reference images in one call). Lives under tryon.api.vton and shares tryon.api.pruna.client.PrunaClient with the newer P-Image / P-Video adapters in tryon.api.pruna.

from tryon.api.vton import PImageTryOnAdapter

adapter = PImageTryOnAdapter(api_key="your_api_key")

images = adapter.generate_and_decode(
    person_image="person.jpg",
    garment_images=["top.jpg", "bottoms.jpg"],
)

Parameters:

api_key (str, optional): Pruna API key. Defaults to PRUNA_API_KEY environment variable

Methods:

generate(person_image, garment_images, ...) - Generate a virtual try-on result (returns a URL)
generate_and_decode(person_image, garment_images, ...) - Generate and decode to PIL Images

See Pruna AI Documentation for complete details (also covers PImageAdapter, PImageIdeogramAdapter, PImageEditAdapter, PImageUpscaleAdapter, PVideoAdapter, PVideo2ProAdapter, PVideoReplaceAdapter, PVideoAvatarAdapter, PVideoAnimateAdapter). Dedicated page: P-Image-Ideogram.

`FashnVTONAdapter`

Adapter for FASHN AI virtual try-on (tryon-max and tryon-v1.6). Lives under tryon.api.vton (use-case directory) rather than a dedicated tryon.api.fashn package.

from tryon.api.vton import FashnVTONAdapter

adapter = FashnVTONAdapter(api_key="your_api_key")

images = adapter.generate_and_decode(
    model_image="person.jpg",
    product_image="garment.jpg",
    model_name="tryon-max",
    resolution="2k",
)

Parameters:

api_key (str, optional): FASHN API key. Defaults to FASHN_API_KEY

Methods:

generate(model_image, product_image, model_name, ...) - Run try-on (returns URLs / data URIs)
generate_and_decode(model_image, product_image, model_name, ...) - Generate and decode to PIL Images

See FASHN AI Virtual Try-On 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.

`NanoBanana2LiteAdapter`

Adapter for Gemini 3.1 Flash-Lite Image (Nano Banana 2 Lite) -- Google's fastest and cheapest Gemini image tier (1K resolution only). Also exposes generate_virtual_tryon(), a lightweight try-on convenience method built on multi-image composition (not the highest-fidelity option -- see the note in Nano Banana API Documentation).

from tryon.api.nano_banana import NanoBanana2LiteAdapter

adapter = NanoBanana2LiteAdapter(api_key="your_api_key")

images = adapter.generate_text_to_image(
    prompt="A fashion model wearing a summer collection",
    aspect_ratio="16:9",
)

# Lightweight virtual try-on
images = adapter.generate_virtual_tryon(
    person="person.jpg",
    garment="jacket.jpg",
    garment_description="olive green bomber jacket",
)

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 (1K only)
generate_image_edit(image, prompt, aspect_ratio, ...) - Edit images with text prompts
generate_multi_image(images, prompt, aspect_ratio, ...) - Compose multiple images
generate_virtual_tryon(person, garment, garment_description, ...) - Lightweight virtual try-on
generate_batch(prompts, aspect_ratio, ...) - Batch generation

See Nano Banana API Documentation for complete details.

`Flux2ProAdapter`

Adapter for FLUX.2 [PRO] - high-quality image generation with standard controls.

from tryon.api import Flux2ProAdapter

adapter = Flux2ProAdapter(api_key="your_api_key")

images = adapter.generate_text_to_image(
    prompt="A professional fashion model wearing elegant evening wear",
    width=1024,
    height=1024,
    seed=42
)

Parameters:

api_key (str, optional): BFL API key. Defaults to BFL_API_KEY environment variable

Methods:

generate_text_to_image(prompt, width, height, seed, safety_tolerance, output_format, ...) - Generate images from text
generate_image_edit(prompt, input_image, width, height, seed, ...) - Edit images with text prompts
generate_multi_image(prompt, images, width, height, seed, ...) - Compose multiple images (up to 8)

See FLUX.2 API Documentation for complete details.

`Flux2FlexAdapter`

Adapter for FLUX.2 [FLEX] - flexible image generation with advanced controls.

from tryon.api import Flux2FlexAdapter

adapter = Flux2FlexAdapter(api_key="your_api_key")

images = adapter.generate_text_to_image(
    prompt="A stylish fashion model wearing elegant evening wear",
    width=1024,
    height=1024,
    guidance=7.5,  # Higher = more adherence to prompt (1.5-10)
    steps=50,  # More steps = higher quality
    prompt_upsampling=True,
    seed=42
)

Parameters:

api_key (str, optional): BFL API key. Defaults to BFL_API_KEY environment variable

Methods:

generate_text_to_image(prompt, width, height, seed, guidance, steps, prompt_upsampling, ...) - Generate images from text with advanced controls
generate_image_edit(prompt, input_image, width, height, seed, guidance, steps, ...) - Edit images with advanced controls
generate_multi_image(prompt, images, width, height, seed, guidance, steps, ...) - Compose multiple images with advanced controls

See FLUX.2 API Documentation for complete details.

`GPTImageAdapter`

OpenAI Images API. Constructor / --model gpt-image default is GPT-Image-1.5. ChatGPT Images 2.5 is --model gpt-image-2.5 (Flare) and --model gpt-image-2.5-sunburst. Same OPENAI_API_KEY.

from tryon.api.openAI.image_adapter import GPTImageAdapter

adapter = GPTImageAdapter(model_version="gpt-image-2.5")  # Flare
images = adapter.generate_text_to_image("editorial still, linen trench")

See GPT Image for sizes, quality (xhigh / max on 2.5), and edit / mask usage.

Video Generation API Adapters

Also available via the CLI/MCP registry:

Adapter	CLI model	Docs
`SeedanceAdapter`	`seedance`	Seedance & Seedream
`LumaRay32Adapter`	`luma-ray-3.2`	Luma Ray 3.2
`KlingVideoAdapter`	`kling-v3` / `kling-v3-omni` / `kling-v2-5-turbo`	Kling Video
`GrokImagineVideoAdapter`	`grok-imagine-video`	Grok Imagine
`LTXVideoAdapter`	`ltx-2.5-api`	LTX-2.5 API
`HailuoVideoAdapter`	`hailuo-2.3`	Hailuo
`MiniMaxH3Adapter`	`minimax-h3` / `minimax-h3-max`	MiniMax H3
`FalH3MaxAdapter`	`fal-h3-max` / `fal-h3-max-lipsync`	MiniMax H3 Max (Fal)
`PVideo2ProAdapter`	`p-video-2-pro`	Pruna AI
`WanVideoAdapter`	`wan-api` / `wan-3.0`	Wan
`RunwayVideoAdapter`	`runway-gen4.5`	Runway Gen-4.5
`Cosmos3VideoAdapter`	`cosmos3`	NVIDIA NIM
`SoraVideoAdapter` / `VeoAdapter` / `LumaAIVideoAdapter`	`sora` / `veo` / `luma-video`	existing pages

Local twins: LTX25Adapter (ltx-2.5), MiniMaxH3LocalAdapter (minimax-h3-local), Wan22Adapter (wan-2.2). Wan 3.0 is API-only.

Image counterparts: SeedreamAdapter (seedream), IdeogramAdapter (ideogram), PImageIdeogramAdapter (p-image-ideogram), GrokImagineImageAdapter (grok-imagine-image), MuseImageAdapter (muse-image). Muse Video has no API yet — see Muse Video.

`GeminiOmniAdapter`

Adapter for Gemini Omni Flash (gemini-omni-flash-preview) -- multimodal video generation and conversational editing via the Interactions API.

from tryon.api.omni import GeminiOmniAdapter

adapter = GeminiOmniAdapter(api_key="your_api_key")

video = adapter.generate_text_to_video(
    prompt="A fashion model walking a runway",
    aspect_ratio="9:16",
)

Parameters:

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

Methods:

generate_text_to_video(prompt, aspect_ratio, previous_interaction_id, ...) - Text-to-video (or edit turn)
generate_image_to_video(image, prompt, aspect_ratio, reference_images, ...) - Image-to-video
edit_video(prompt, previous_interaction_id, ...) - Conversational edit of a prior clip

See Gemini Omni Flash Documentation for complete details.

Multimodal Understanding API Adapters

`KimiUnderstandAdapter`

Adapter for Moonshot AI's Kimi K2.6, K2.7 Code, and K3 models -- general-purpose, natively multimodal image and video understanding (not limited to fashion).

from tryon.api import KimiUnderstandAdapter

adapter = KimiUnderstandAdapter()  # kimi-k2.6 by default

result = adapter.understand_image(
    "garment.jpg",
    prompt="Describe this outfit: color, pattern, style, fit, and material."
)
print(result["text"])

Parameters:

api_key (str, optional): Moonshot API key. Defaults to MOONSHOT_API_KEY environment variable
model (str, optional): "kimi-k2.6" (default), "kimi-k2.7-code", "kimi-k2.7-code-highspeed", "kimi-k3", or "kimi-k2.5"

Methods:

understand_image(image, prompt, ...) - Understand one or more images
understand_video(video, prompt, ...) - Understand video content
understand(image=None, video=None, prompt=...) - Single entry point accepting either/both
chat(messages, tools=None, ...) - Multi-turn/tool-calling escape hatch

See Kimi API Documentation for complete details, or the open-weight Kimi-VL local model for GPU-only deployment.

`GLMUnderstandAdapter`

Adapter for Zhipu's GLM-5.3-FlashX via Z.ai — general-purpose, natively multimodal image and video understanding (200 tok/s serving tier of GLM-5.3-Flash).

from tryon.api import GLMUnderstandAdapter

adapter = GLMUnderstandAdapter()  # glm-5.3-flashx by default

result = adapter.understand_image(
    "garment.jpg",
    prompt="Describe this outfit: color, pattern, style, fit, and material."
)
print(result["text"])

Parameters:

api_key (str, optional): Z.ai key. Defaults to ZAI_API_KEY environment variable
model (str, optional): "glm-5.3-flashx" (default)
base_url (str, optional): Defaults to ZAI_BASE_URL or the Z.ai default

Methods:

understand_image(image, prompt, ...) - Understand one or more images
understand_video(video, prompt, ...) - Understand video content
understand(image=None, video=None, prompt=...) - Single entry point accepting either/both

Thinking is always on (thinking.type only supports enabled); use reasoning_effort (low / high / max) to control depth.

See GLM-5.3-FlashX Documentation for complete details.

`QwenUnderstandAdapter`

Adapter for Alibaba DashScope Qwen3.8-Max and Qwen3.8-Omni-Flash — native multimodal understanding (text + image + video → text on Max; text + image + audio + video → text on Omni-Flash) with thinking / reasoning_effort. OpenTryOn exposes the understand path (plus chat() for multi-turn/tools on Max).

from tryon.api import QwenUnderstandAdapter

adapter = QwenUnderstandAdapter()  # qwen3.8-max by default

result = adapter.understand_image(
    "garment.jpg",
    prompt="Describe this outfit: color, pattern, style, fit, and material."
)
print(result["text"])

Parameters:

api_key (str, optional): Defaults to DASHSCOPE_API_KEY
model (str, optional): "qwen3.8-max" (default) or "qwen3.8-omni-flash"
base_url (str, optional): Defaults to QWEN_BASE_URL or the international DashScope compatible-mode URL

Methods:

understand_image(image, prompt, ...) - Understand one or more images
understand_video(video, prompt, ...) - Understand video content
understand(image=None, video=None, audio=None, prompt=...) - Single entry point; audio requires model="qwen3.8-omni-flash"
chat(messages, tools=None, ...) - Multi-turn/tool-calling escape hatch

Series capabilities (vendor): ~1M context on Max, long video, coding/agent strengths, structured output and built-in tools on DashScope. Local open counterpart: qwen3.8 (Qwen/Qwen3.8-27B).

See Qwen3.8-Max API Documentation for complete details, or the open-weight Qwen3.8 local model for GPU-only deployment.

`Hy4Adapter`

Tencent Hy4 preview (770B MoE LLM) via TokenHub or a local vLLM/SGLang OpenAI server. Same class for --model hy4-preview and hy4-preview-local.

from tryon.api import Hy4Adapter

adapter = Hy4Adapter()  # TOKENHUB_API_KEY
result = adapter.understand(prompt="Write a lookbook caption for a linen trench.")
print(result["text"])

See Hy4 TokenHub and Hy4 local.

`NemotronOmniUnderstandAdapter` / `Cosmos3ReasonerAdapter` / `Cosmos3VideoAdapter`

NVIDIA NIM Path A (NVIDIA_API_KEY). Omni and Reasoner are OpenAI-compatible chat understand models; Cosmos 3 Generator is T2V/I2V infer (b64_video).

from tryon.api.nvidia import NemotronOmniUnderstandAdapter, Cosmos3VideoAdapter

print(NemotronOmniUnderstandAdapter().understand(image="garment.jpg")["text"])
mp4 = Cosmos3VideoAdapter().generate_text_to_video("runway walk at dusk")

See NVIDIA NIM.

`QwenImageAdapter`

Adapter for Alibaba DashScope Qwen-Image 3.0 — text-to-image, image editing (1–3 refs), and person+garment virtual try-on. Same DASHSCOPE_API_KEY as QwenUnderstandAdapter.

from tryon.api import QwenImageAdapter

adapter = QwenImageAdapter()  # qwen-image-3.0-pro by default
images = adapter.generate_text_to_image("editorial lookbook, linen trench")
tryon = adapter.generate_virtual_tryon("person.jpg", "garment.jpg")

Parameters:

api_key (str, optional): Defaults to DASHSCOPE_API_KEY
model (str, optional): "qwen-image-3.0-pro" (default), "qwen-image-3.0", "qwen-image-2.0-pro", "qwen-image-2.0"
base_url (str, optional): Defaults to QWEN_IMAGE_BASE_URL or the international DashScope /api/v1 host

Methods:

generate_text_to_image(prompt, size=None, n=1, ...) — T2I
generate_image_edit(image, prompt, ...) — I2I (one image or a list of 1–3)
generate_multi_image(images, prompt, ...) — I2I composition
generate_virtual_tryon(person, garment, garment_description=None, ...) — VTON wrapper

CLI: opentryon generate|edit|vton --model qwen-image. Pair with understand --model qwen3.8-max to caption a garment first. Local twin: QwenImageLocalAdapter / --model qwen-image-local (local docs).

See Qwen-Image API Documentation for complete details.

Background Removal API

`BEN2BackgroundRemoverAdapter`

Adapter for BEN2 (Background Erase Network 2) - state-of-the-art background removal for fashion and product images.

from tryon.api.ben2 import BEN2BackgroundRemoverAdapter

adapter = BEN2BackgroundRemoverAdapter()

# Single image background removal
result = adapter.remove_background("model.jpg", refine=True)
result[0].save("model_no_bg.png")

# Batch processing
results = adapter.remove_background_batch(
    ["model1.jpg", "model2.jpg", "model3.jpg"],
    refine=True
)

Parameters:

weights_path (str, optional): Custom weights path. Auto-downloads from Hugging Face if not specified
device (str, optional): Device to use ("cuda" or "cpu"). Auto-detected if not specified

Methods:

remove_background(image, refine=False) - Remove background from single image
remove_background_batch(images, refine=False) - Remove background from multiple images
load_image(input_data) - Load image from path, URL, or BytesIO

Features:

Automatic weight download from Hugging Face
GPU acceleration with CUDA support
Foreground refinement for higher quality edges
Batch processing for multiple images
Supports file paths, URLs, BytesIO, and PIL Images

See BEN2 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.

Preprocessing API​

segment_garment​

extract_garment​

segment_human​

extract_garment (Single Image)​

TryOnDiffusion API​

Diffusion​

Virtual Try-On API Adapters​

SegmindVTONAdapter​

KlingAIVTONAdapter​

GoogleVTONAdapter​

OutfitAnyonePlusAdapter​

PhotoroomVTONAdapter​

LeffaAdapter / CatVTONAdapter​

AmazonNovaCanvasVTONAdapter​

PImageTryOnAdapter​

FashnVTONAdapter​

Image Generation API Adapters​

NanoBananaAdapter​

NanoBananaProAdapter​

NanoBanana2LiteAdapter​

Flux2ProAdapter​

Flux2FlexAdapter​

GPTImageAdapter​

Video Generation API Adapters​

GeminiOmniAdapter​

Multimodal Understanding API Adapters​

KimiUnderstandAdapter​

GLMUnderstandAdapter​

QwenUnderstandAdapter​

Hy4Adapter​

NemotronOmniUnderstandAdapter / Cosmos3ReasonerAdapter / Cosmos3VideoAdapter​

QwenImageAdapter​

Background Removal API​

BEN2BackgroundRemoverAdapter​

Utility Functions​

convert_to_jpg​

Preprocessing API

`segment_garment`

`extract_garment`

`segment_human`

`extract_garment` (Single Image)

TryOnDiffusion API

`Diffusion`

Virtual Try-On API Adapters

`SegmindVTONAdapter`

`KlingAIVTONAdapter`

`GoogleVTONAdapter`

`OutfitAnyonePlusAdapter`

`PhotoroomVTONAdapter`

`LeffaAdapter` / `CatVTONAdapter`

`AmazonNovaCanvasVTONAdapter`

`PImageTryOnAdapter`

`FashnVTONAdapter`

Image Generation API Adapters

`NanoBananaAdapter`

`NanoBananaProAdapter`

`NanoBanana2LiteAdapter`

`Flux2ProAdapter`

`Flux2FlexAdapter`

`GPTImageAdapter`

Video Generation API Adapters

`GeminiOmniAdapter`

Multimodal Understanding API Adapters

`KimiUnderstandAdapter`

`GLMUnderstandAdapter`

`QwenUnderstandAdapter`

`Hy4Adapter`

`NemotronOmniUnderstandAdapter` / `Cosmos3ReasonerAdapter` / `Cosmos3VideoAdapter`

`QwenImageAdapter`

Background Removal API

`BEN2BackgroundRemoverAdapter`

Utility Functions

`convert_to_jpg`