Challenges in training AI for fashion: Realism vs. hallucinations

AI is shaking things up in fashion. From designing custom pieces to creating virtual fitting rooms, it’s changing how we think about creativity and commerce. But here’s the thing: building AI that truly “gets” fashion isn’t easy. It’s not just about feeding it a ton of data and calling it a day. To get it right, you need precision, a keen eye for detail, and a real understanding of how the fashion world works.

Let me explain. Unlike other industries, fashion is all about the little things—how fabric drapes, the way light plays on textures, or how a seam sits just so. If AI messes those up, the result can feel off, even uncanny. Worse, it can erode trust in the technology.

As an engineer, I see this as both a challenge and an opportunity. Getting AI to create realistic, high-quality fashion images that can pass as authentic? That’s no small feat. But solving problems like these is what keeps us innovating. The real question we have to ask ourselves is: how do we make sure our AI is as good as the human eye when it comes to understanding and recreating fashion?

The Challenge With Small Details

Imagine zooming in on a high-resolution image of a beautiful dress, only to see the lace turn into a blurry, pixelated mess. Or a zipper that looks like it’s melting into the fabric. 

It’s frustrating, right? And in fashion, where every detail matters, these distortions can be a dealbreaker. Whether it’s a catalog image or a virtual try-on tool, the expectation is crystal-clear realism—anything less stands out.

One of the trickiest parts of training AI for fashion is solving this exact problem - getting the smallest of details exactly right without any human involvement or repeating attempts. The root of the problem lies in how AI learns. It’s fantastic at recognizing patterns but struggles when those patterns get complex or intricate. Lace, for instance, isn’t just a repeating motif; it’s delicate and nuanced. 

Teaching an AI to replicate that level of detail without cutting corners is as intricate as the fabric. But it’s not impossible. By refining how we train these models and using smarter techniques to fill in the gaps, we’re getting closer to AI that can handle high-res fashion imagery like a pro. 

The goal? No more blurry seams or distorted zippers—just crisp, accurate images that look as good as the real thing.

Precision: The Heart of Fashion AI

Precision in fashion AI isn’t just a goal—it’s a necessity. Misplaced stitches or awkward folds aren’t minor issues; they’re glaring errors that can make even the best designs look off. As engineers, it’s our job to teach the AI to recognize and recreate the kind of subtle details that bring a garment to life. But how do we do that?

It starts with the data. We use thousands of high-quality images—think close-ups of seams, fabric textures under different lighting, and draping on different body types. This helps the AI understand the difference between, say, a crisp pleat and a soft fold. But raw data isn’t enough. We also developed new algorithms to focus on these intricate elements, training them to spot what’s “right” versus what feels unnatural.

Then there’s the testing phase, which is where things get really interesting. We don’t just train the AI and hope for the best—we stress-test it. We throw challenging scenarios at it: complex lace patterns, tricky fabrics like velvet or satin, or designs with lots of layers. If the AI doesn’t get it right, we iterate on the model’s architecture and go again. It’s a cycle of constant refinement.

In the end, it’s not just about avoiding mistakes. It’s about making sure every image the AI produces could pass as something a designer or photographer created. That’s the level of precision we’re after—and getting there takes a mix of creativity, technical know-how, and a lot of patience.

Tackling Bias in Training Data

One of the biggest challenges in training fashion AI is making sure it’s fair and accurate across the board. Why? Because the data you use to train the AI shapes how it “sees” the world. If the dataset leans too heavily on one type of clothing, body shape, or style, the AI ends up biased—and that’s a big problem in an industry as diverse as fashion.

So, how do we fix it? First, it starts with building better datasets. We carefully curate images that represent a wide range of clothing types, fabrics, and styles. But it’s not just about the clothes. We also focus on diversity in models—different body types, skin tones, and even poses. The goal is to train the AI to understand and reflect the real variety you’d see in a fashion catalog or on a runway.

Next, we dive into the data itself. AI doesn’t know what it doesn’t see, so we have to make sure the datasets are balanced. That means if we’re training it to generate summer dresses, we don’t just include flowy fabrics for tall models; we make sure to show structured cuts, petite fits, and everything in between. It’s about covering all the bases.

But building the right dataset is only half the battle. We also use tools to audit the AI’s output. For example, if we notice that the AI tends to over-represent certain styles or consistently struggles with certain fabrics, we adjust the training process. This might mean adding more examples of that fabric or tweaking how the AI learns to interpret texture and color.

Finally, we collaborate with experts in fashion to spot gaps we might miss. Stylists, designers, and even photographers can tell us when the AI feels “off” in a way that’s hard to quantify. Their insights help us push the boundaries of what our AI can do.

The result? An AI that doesn’t just work for one segment of fashion but one that’s inclusive, versatile, and ready to handle the industry’s incredible variety. That’s the kind of accuracy we’re aiming for, and it all starts with better, smarter data.

Balancing Realism and Hallucination

One of the most fascinating—and frustrating—things about AI is its tendency to hallucinate. In fashion, this can mean adding details that don’t exist or creating elements that look just a little too perfect to be real. 

A blouse with buttons that magically float or a hand with more than 5 fingers? That’s AI hallucination in action. It’s creative, sure, but in fashion, it’s also a problem. So how do we keep AI grounded in reality without stifling its ability to create? The key lies in finding the right balance.

First, we teach the AI where to draw the line. During training, we give it clear examples of what’s real—actual photos of garments—and what’s not. Then we use techniques like regularization, which essentially acts as a set of guardrails, keeping the AI from straying too far into the realm of fantasy.

Next, we layer in human feedback. This is where the collaboration between engineers and fashion professionals comes into play. Designers and stylists review the AI’s work, pointing out when a hemline doesn’t fall quite right or a texture looks overly smooth. Their insights help us refine the model, making sure it stays true to the realities of fabric, fit, and form.

We also use something called hybrid modeling. This means combining AI’s generative capabilities with rule-based systems that reinforce what’s possible. For example, if the AI generates a dress, the rule-based system checks that the proportions, seams, and textures align with how an actual dress would look and behave.

Lastly, we run quality assurance at every step. We simulate real-world scenarios to see how the AI performs. Can it create a believable leather jacket with subtle creases? Does it handle layered outfits without blending pieces together? If not, we adjust and retrain until it can.

Balancing realism and hallucination isn’t about shutting down AI’s creativity—it’s about directing it. When done right, the AI doesn’t just generate images; it becomes a tool that enhances the creative process while staying firmly rooted in the real world.

A Call for Collaboration

It’s not just about coding algorithms or feeding the system data—it’s about weaving together technology, creativity, and a deep understanding of the industry’s needs. And while we’ve made incredible progress, the challenges we face, from precision to inclusivity, can’t be solved in a vacuum.

This is where collaboration comes in. Deep learning researchers, designers, and industry professionals each bring something unique to the table. Engineers can push the boundaries of what the technology can do, while practitioners ground it in the practicalities of what their industries require. And let’s not overlook the venture capitalists who fund this innovation—they play a crucial role in enabling the time, resources, and experimentation needed to tackle these problems.

Our direct relationship with hundreds of thousands of fashion businesses and thousands of customers—makes this collaboration even more dynamic. These relationships provide us with candid feedback that helps us build AI solutions that are not just innovative but also practical and aligned with real-world needs. It’s this continuous exchange that ensures we’re delivering tools that truly help our users solve their challenges, improve efficiencies, and unlock new possibilities.

By working together, we can create AI that isn’t just a tool but a partner—one that delivers realism, respects diversity, and meets the exacting standards of its users. The potential here is enormous: faster workflows, more accessible product photo solutions for fashion brands, and entirely new ways to overcome the standard headaches, planning, creating and finalizing fashion photoshoots have.