AI Virtual Clothing Try-On Development: End-to-End
Returns due to wrong size or fit account for 20-40% of orders. Virtual try-on lets customers see the product on themselves before purchase, reducing returns by 20-40% and increasing conversion by 10-25%. Over the years we have delivered more than 30 projects—from prototypes to production. We use SOTA models: IDM-VTON, human parsing with SegFormer, and FastAPI for seamless integration with your catalog.
How IDM-VTON Solves Realistic Try-On
IDM-VTON is the current SOTA for virtual try-on: it accurately warps fabric, preserves texture and lighting. Compared to VITON-HD and HR-VITON, it produces 15% fewer artifacts. We adapt the official implementation to your catalog, including fine-tuning on your products for even more precise overlay.
In practice, the customer uploads their photo, selects an item from the catalog—within 10-15 seconds they receive a realistic image in their pose. The system handles up to 1000 requests per day on a single GPU. We help set up the infrastructure: from GPU selection to load balancing. The key challenge is low latency with high quality. We optimize the model using TensorRT and ONNX Runtime, accelerating inference by 2-3x.
import torch
from diffusers import AutoPipelineForInpainting
from transformers import AutoProcessor, CLIPVisionModelWithProjection
import numpy as np
from PIL import Image
import io
class VirtualTryOnService:
def __init__(self):
# IDM-VTON is based on SDXL inpainting + specialized encoder
self.pipeline = self._load_idm_vton()
self.parsing_model = self._load_human_parsing() # SCHP / CIHP
self.pose_estimator = self._load_pose_estimator() # OpenPose / DWPose
def _load_idm_vton(self):
from idm_vton import TryOnPipeline
return TryOnPipeline.from_pretrained(
"yisol/IDM-VTON",
torch_dtype=torch.float16
).to("cuda")
def try_on(
self,
person_image: bytes,
garment_image: bytes,
garment_description: str = "",
seed: int = 42,
num_steps: int = 30
) -> bytes:
person_pil = Image.open(io.BytesIO(person_image)).convert("RGB")
garment_pil = Image.open(io.BytesIO(garment_image)).convert("RGB")
# Body parsing: define the try-on area
person_parse = self.parsing_model(person_pil)
pose_map = self.pose_estimator(person_pil)
result = self.pipeline(
human_img=person_pil,
garm_img=garment_pil,
garment_desc=garment_description,
mask_only=False,
seed=seed,
num_inference_steps=num_steps
).images[0]
buf = io.BytesIO()
result.save(buf, format="PNG")
return buf.getvalue()
Human Parsing: Precise Body Segmentation
We use SegFormer B2 trained on clothing (model mattmdjaga/segformer_b2_clothes). It identifies 19 classes: outerwear, trousers, dresses, accessories. The mask is created based on these labels, which is critical for correct overlay.
from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
import torch
class HumanBodyParser:
LABELS = {
0: "background", 1: "hat", 2: "hair", 4: "upper-clothes",
5: "skirt", 6: "pants", 7: "dress", 9: "belt",
10: "left-shoe", 11: "right-shoe", 13: "face",
14: "left-leg", 15: "right-leg", 16: "left-arm", 17: "right-arm",
18: "bag", 19: "scarf"
}
def __init__(self):
self.processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes")
self.model = SegformerForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
self.model.eval()
def get_clothing_mask(self, image: Image.Image, clothing_type: str = "upper") -> Image.Image:
inputs = self.processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = self.model(**inputs)
segmap = self.processor.post_process_semantic_segmentation(
outputs, target_sizes=[image.size[::-1]]
)[0]
clothing_ids = {
"upper": [4], # upper outerwear
"lower": [5, 6], # skirt, pants
"dress": [7], # dress
"full": [4, 5, 6, 7], # all clothing
}
target_ids = clothing_ids.get(clothing_type, [4])
mask = np.zeros(segmap.shape, dtype=np.uint8)
for label_id in target_ids:
mask[segmap.numpy() == label_id] = 255
return Image.fromarray(mask)
Catalog Preprocessing: Automated Description via GPT-4o
Catalog items are first background-removed (rembg), resized to a uniform 768x1024, and a text description is generated using GPT-4o Vision. This improves generation quality as IDM-VTON accepts garment description.
class GarmentCatalogProcessor:
"""Preprocess product images for virtual try-on"""
async def prepare_garment(self, garment_image: bytes) -> dict:
img = Image.open(io.BytesIO(garment_image)).convert("RGB")
# Remove background from garment
from rembg import remove
garment_no_bg = remove(garment_image)
# Standardize size
img_resized = Image.open(io.BytesIO(garment_no_bg)).resize((768, 1024))
# Generate garment description via GPT-4o Vision
description = await self.describe_garment(garment_image)
return {
"processed_image": img_resized,
"description": description,
"category": await self.classify_garment_type(garment_image)
}
async def describe_garment(self, garment_image: bytes) -> str:
client = AsyncOpenAI()
import base64
response = await client.chat.completions.create(
model="gpt-4o",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "Garment description for virtual try-on system (material, color, cut, details). One sentence, in English."},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64.b64encode(garment_image).decode()}"}}
]
}]
)
return response.choices[0].message.content
FastAPI Service: Easy Integration
from fastapi import FastAPI, File, UploadFile, Form
app = FastAPI()
tryon = VirtualTryOnService()
@app.post("/try-on")
async def virtual_try_on(
person: UploadFile = File(...),
garment: UploadFile = File(...),
garment_desc: str = Form("")
):
person_bytes = await person.read()
garment_bytes = await garment.read()
result = tryon.try_on(person_bytes, garment_bytes, garment_desc)
return Response(content=result, media_type="image/png")
Why Choose IDM-VTON
Comparison with alternatives: IDM-VTON wins on FID (12.5 vs 16.8 for VITON-HD) and LPIPS (0.18 vs 0.25). Thanks to the text description from GPT-4o, it better understands cut and material, giving +10% warping accuracy.
IDM-VTON: Improve Diffusion Model for Virtual Try-on — official publication by the authors.
Quality Metrics
| Metric | Description | Target |
|---|---|---|
| SSIM | Structural similarity with GT | > 0.80 |
| FID | Realism quality | < 15 |
| LPIPS | Perceptual similarity | < 0.20 |
| Warping accuracy | Fabric deformation precision | > 85% |
Model Performance Comparison
| Model | FID | LPIPS | Inference time (A100) |
|---|---|---|---|
| VITON-HD | 16.8 | 0.25 | 8 sec |
| HR-VITON | 14.2 | 0.22 | 12 sec |
| IDM-VTON | 12.5 | 0.18 | 15 sec |
Technical details of inference optimization
To achieve response times under 10 seconds, we use TensorRT or ONNX Runtime with FP16. Load testing shows that with batch size 1, latency p99 is 18 seconds on A100. With batch size 4, it's 35 seconds, but throughput increases.
What’s Included in the Work
We deliver end-to-end:
- API documentation (OpenAPI) and integration examples.
- Source code with comments, covered by tests.
- Training for your team on using the service.
- Support for 1 month after launch.
- Guarantee of stable operation under load up to 1000 requests/day.
A Concrete Case from Our Practice
For a mid-size fashion retailer, we implemented IDM-VTON fine-tuned on their 5000-item catalog. After optimizing with TensorRT, inference time dropped from 15s to 7s on a single RTX 4090. The result: SSIM 0.85, FID 11.8. The client reported a 25% increase in conversion and 35% reduction in returns within the first quarter.
How We Work: Project Phases
- Analysis — gather requirements, audit catalog, measure latency and throughput.
- Design — choose model, service architecture, MLOps plan.
- Development — implement API, integrate parsing and preprocessing, fine-tune on your collection.
- Testing — A/B tests with real users, measure metrics.
- Deployment — deploy on your GPU or cloud, monitoring.
Timelines
- MVP — from 3 weeks.
- Production service — 2-3 months.
Budget is determined after a thorough analysis of your catalog and requirements. We provide a project assessment in 1 day — leave us a request.
Our solutions are built on open-source code: IDM-VTON and libraries from Hugging Face. We always adapt to your brand's specifics.
With over 10 years of experience in production AI and 40+ projects delivered, we bring reliability and expertise to every partnership.