Revolutionizing Virtual Try-On: 50x Faster, 98% Lower Compute Costs

In the fast-paced world of e-commerce, speed is everything. Studies show that over 53% of users abandon a website or app if it takes more than three seconds to load or respond. For Virtual Try-On (VTON) applications, where customers expect real-time interaction, latency can make or break the user experience. Traditional diffusion-based models, while delivering impressive realism, often take 10–20 seconds per inference. This delay disrupts the seamless flow users expect, leading to frustration, abandoned carts, and missed sales opportunities. Imagine waiting 15 seconds just to see how a T-shirt looks on you virtually—most users won’t stick around for that. At SensePro AI, we’ve tackled this challenge head-on. Our mission is to make high-quality virtual try-on as instantaneous and responsive as flipping through photos on your phone. With our optimized VTON engine, we’ve achieved a groundbreaking 50x speedup and reduced compute costs by 98% per inference. Here’s how we did it, why it matters, and how it can transform e-commerce at scale.

We modified diffusion model to generate the VTON results in only two steps

The Problem with Traditional Virtual Try-On

Diffusion models, the backbone of many state-of-the-art VTON solutions, excel at generating photorealistic images. However, their computational demands create significant barriers to real-world deployment. The core issue lies in their high inference latency—often requiring 50 or more steps to produce a single image, which translates to 10–20 seconds of wait time per try-on. This not only frustrates users but also drives up GPU usage, making costs unsustainable for large-scale applications. For e-commerce platforms serving millions of users, these delays and expenses represent a critical bottleneck, hindering both user engagement and business scalability.

Our model works fast while keep the quality the output results

SensePro’s Solution: Speed and Scalability Without Compromise

At SensePro, we’ve reimagined the VTON pipeline to prioritize real-time performance, cost-efficiency, and uncompromised visual quality. Our approach focuses on streamlining the underlying technology to eliminate latency and reduce resource demands. We’ve achieved this through several key innovations. We had deep model architecture tweaking, multi-level deep training with different loss types, and finally applied quantization. These strategies significantly reduced computational overhead while maintaining output fidelity. Underpinning these advancements is our proprietary in-house dataset—a comprehensive, meticulously annotated collection of diverse images. This dataset enables our models to train more effectively, ensuring high-quality outputs even with fewer processing steps.

Benchmarks on an Nvidia L4 (similar results on RTX4000A), a mid-range GPU, demonstrate the impact: latency drops from 10–20 seconds (with classifier-free-guidance) to as low as 0.23 seconds per inference, throughput increases from less than 0.1 images per second to over 4, and the cost per inference plummets by over 98%—from $0.005–$0.01 to as little as $0.00007. This isn’t just an incremental improvement; it’s a complete paradigm shift in how VTON can be deployed and scaled for real-world use.

Performance Benchmarks (on Nvidia L4 - or RTX 4000A)

Metric	Baseline (50 Steps)	SensePro (4 Steps)	SensePro (2 Steps)
Latency	10–20 sec	0.34 sec	0.23 sec
Speedup	1x	29–58x	43–87x
Throughput (img/s)	0.05–0.1	2.94	4.35
Inference Cost	$0.005–$0.01	$0.0001	$0.00007

Rapid Model Training for Fast Fashion Cycles

In the dynamic world of fast fashion, staying ahead means adapting quickly to new trends and inventory. SensePro’s optimized training pipeline is built to keep pace with this rapid turnover. We enable full fine-tuning on new garments in under 48 hours, ensuring that your VTON system can showcase the latest arrivals without delay. Using just 4 NVIDIA RTX 6000 GPUs, a training session costs between $150 and $300, making it both efficient and affordable. Flexibility is also at the core of our approach. With SensePro, there’s no vendor lock-in—you can train models on-premises or in your preferred cloud environment, giving you full control over the process. Moreover, our system supports zero-downtime deployment, allowing you to update models for new garments while continuing to serve live traffic seamlessly. This means your platform remains operational and up-to-date, even during peak shopping seasons, ensuring a consistent and cutting-edge user experience.

Fine-tune on new garments in <48 hours

Metric	SensePro
Full Fine-Tuning Time	<48 hours
GPU Resources	4 x RTX A6000
Cost/Training Session	$150–$300

The Real-World Impact: Transforming E-Commerce at Scale

To understand the significance of these advancements, let’s consider a practical scenario. Imagine you’re a senior engineer at a major e-commerce platform, serving 50 million users monthly. If each user performs an average of 10 virtual try-ons, that’s 500 million inferences per month. With a traditional diffusion-based model taking 20 seconds per try-on and costing $0.01 per inference, your monthly bill would be a staggering $5 million. Switch to SensePro’s 2-step model, with a latency of 0.23 seconds and a cost of $0.00007 per inference, and that expense drops to just $35,000—a savings of nearly $5 million per month, or close to $60 million annually. Beyond the cost savings, the near-instant try-on experience keeps users engaged, reduces bounce rates, and drives higher conversion rates. This is the kind of innovation that reshapes how businesses operate at scale.

Monthly Cost Comparison

Model	Cost/Inference	Number of users monthly	Monthly Total
Baseline VTON	$0.01	50M x 10 try-ons	$5,000,000
SensePro (2 Steps)	$0.00007	50M x 10 try-ons	$35,000

Seamless Integration for Developers and Retailers

SensePro is designed with ease of use in mind. Our API-first architecture ensures that developers and product teams can integrate advanced VTON capabilities into any platform quickly and without disruption. There’s no need to overhaul your backend or invest in new infrastructure—SensePro operates entirely in the cloud via a simple REST API. It works with any standard RGB image, meaning compatibility with any webcam or smartphone camera, without requiring specialized hardware like depth sensors or AR glasses. For fashion retailers, virtual fitting room providers, and online marketplaces, we offer a fully managed SaaS solution that delivers scalability, uptime, and performance out of the box, allowing you to focus on your users while we handle the AI infrastructure.

Ready to Experience the Future of Virtual Try-On?

We’re excited to offer a zero-friction pilot program for e-commerce partners. This includes a proof-of-concept for one or two product categories (such as T-shirts or jackets), seamless API-based integration with no infrastructure changes, and support in evaluating latency, cost savings, and user engagement impact. At SensePro, we believe that if trying on clothes virtually isn’t instant, it simply doesn’t work. That’s why we’ve made it instant—and beautiful. Join us in revolutionizing the e-commerce experience. Let’s make virtual try-on a game-changer for your business.