Très Tilda × Vision

How a Luxury Fashion Label Went from iPhone Fitting Photos to a Full eCommerce Collection in 31 Days

The Economics of Fashion Photography

A luxury fashion brand needs imagery that matches the quality of its product. The problem is that producing that imagery the traditional way costs more than most brands want to spend before they know whether a collection will sell. Studio rental, photographer day rates, models, stylists, lighting crew, post-production: the expenses stack up before a single image is finalized. The math has never been friendly.

Vision by Lucid Modules was built on a different premise. What if the production quality could stay high while the logistics shrank to almost nothing?

For the Très Tilda project, the answer came with a number attached.

The Client

Très Tilda is a luxury fashion label from Florence. Its creative director Olga, who is based in Kuwait, came to Vision with a clear brief and a tight timeline: a full eCommerce shoot covering 18 looks, multiple angles per garment, packshots, accessory styling, everything ready to go live. The images had to look polished, had to be consistent, and had to be done without the overhead of a traditional production. The background imagery was to be shot in Kuwait, where Olga lives.

There was also a practical constraint that made the traditional route even less viable. As Olga noted, there is a significant shortage of professional model talent in Kuwait. Hiring models internationally would have meant additional costs for flights, accommodation, and scheduling coordination on top of an already expensive production pipeline. Virtual AI models eliminated that entire layer of logistics.

By the Numbers

Olga contacted Vision on February 16, 2026. All 60+ final images were live on the Très Tilda eCommerce store by March 19, 2026. Thirty-one days from first contact to production.

That timeline included a week spent developing the Apparel Photography module itself, which did not exist before this project. The tool was built to serve the brief, not the other way around.

The total cost of the project, including AI generation and post-processing, came to approximately $1,000. A comparable traditional production for 18 looks and 60+ final images—with international model booking, studio time, a full crew, and post-production—would conservatively run between $15,000 and $30,000, and take six to ten weeks.

The Workflow Vision Made Possible

The Apparel Studio module at the core of Vision changed the production equation. Instead of building around a studio and hoping the logistics would come together, the workflow started with source images: Olga photographed each garment during first fittings, shot on an iPhone. No professional photographer was involved. These reference images captured the shape, drape, and proportions of each piece as worn on a human body. From there, Vision generated virtual models in the correct poses and placed the apparel onto them, rendering final composites against styled backgrounds.

This distinction matters. Early in the project, the team attempted to work from photographs of garments hanging on hangers. The results were poor: the AI model tried to interpret the shape of the garment from a flat, lifeless silhouette and produced inaccurate forms. Luxury fashion has its own rules. Fabric needs to sit on a body before its true shape becomes legible. Once that lesson was established, every primary source image involved a person wearing the garment, which gave the AI the reference it needed to render convincingly. The hanger photos were not discarded entirely. They proved useful as supplementary references when a fitting photo was taken from too far away and lacked the detail needed to render fine elements of the garment accurately.

For Très Tilda, the team developed multi-view actor generation to maintain visual consistency across shots. Each virtual model was rendered from the front, left profile, right profile, and back, ensuring that a customer browsing the collection would see the same character across every look. A silhouette-on-background technique was developed to keep scale consistent from image to image, something that had been a persistent problem with earlier AI generation approaches.

The result was a repeatable system. Not a one-off solution patched together for a single project, but a documented workflow with a reusable prompt library covering apparel composites, mannequin extractions, packshots, and scarf draping on mannequins. Each technique was built to be handed off.

The Scarf Problem

Très Tilda’s collection included scarves by a featured artist. These presented a unique challenge: the scarves needed to be shown worn in multiple ways—as a head covering, as a top, and as a midi skirt—but the team only had flat lay images of each scarf design and reference photos of one example scarf being worn in each style.

The workflow had to bridge that gap. First, the AI was used to drape each scarf design onto a virtual mannequin, matching the correct pattern and fabric behavior to each wearing style. These intermediate renders served as references: they established how a specific scarf design would look when worn as a head wrap versus tied as a top versus draped as a skirt. Once those references existed, they could be used as source material for the final composite images, placing the correctly draped scarf onto the virtual model against the styled background.

This was a two-stage generation process. Getting the drape right required understanding both the flat pattern of the scarf and the physics of how the fabric would fall in each configuration. It was one of the more technically demanding parts of the project, and the technique is now part of the reusable workflow.

What Went Wrong, and How It Was Solved

No production is without problems. This one had several, and each one led to a refinement that became part of the permanent workflow.

Lighting Inconsistency in Source Images

Because all source images were shot on an iPhone during fittings rather than in a controlled studio environment, lighting varied from image to image. White and ivory fabrics appeared yellowish in certain photos, and the color shifts carried through into the AI-generated composites. Some images required color correction in pre-processing. Others were too far off to salvage, and Olga had to reshoot those garments with better lighting to preserve the correct colors and shapes. The lesson was clear: even with AI handling the final output, the quality of the source material still matters.

Scale and Proportion

Getting AI to understand spatial scale across different backgrounds proved difficult. Garments would render at inconsistent sizes from image to image. The solution was the silhouette-on-background technique: a black silhouette overlay was placed on the background image to give the AI a clear reference for the proportions of the figure against the scene. For the Très Tilda shoot, which used an infinite pool against a seascape, this anchored every composite to a consistent human scale.

Detail Interpretation Errors

The AI occasionally misinterpreted fine details on garments. In one case, an embossed logo on a piece was rendered as engraved—a subtle but important distinction for a luxury brand. These issues required careful review at every stage and manual correction in post-processing.

Prompt Engineering for Unique Designs

Because Olga’s designs are distinctive and do not conform to standard garment templates, the team had to invest in precise prompt engineering. Generic descriptions produced generic results. The prompts needed specificity: that a dress featured a lavalière neckline, that a skirt was midi-length, that a particular silhouette had a defined waistline. Each garment required its own descriptive language to be rendered accurately.

Source Image Pre-Processing

Before any generation could begin, the source images required careful preparation. Backgrounds had to be removed. Faces of the reference models had to be blurred. The prompts had to explicitly instruct the AI to use only the garment from the green-screen source and not carry over features—such as hairstyle or skin tone—from the person wearing it. Without these steps, the AI would blend characteristics from the reference wearer into the virtual model, producing inconsistent and unusable results.

Each of these problems was solved. More importantly, each solution was documented and folded into the workflow, so it would not need to be solved again on the next project.

The Team

Three people ran the project. Olga handled creative direction and approval, keeping the visual identity of Très Tilda consistent across every image. Matt managed AI generation and quality control, running each output through a review process before it moved forward. Dolly handled Photoshop post-processing, finishing each image to the standard the brand required.

Post-processing was not optional. It was essential to matching the brand’s feel. Beyond general retouching, the team had to manually add or correct brand logos—for instance, jacket button logos that were unclear or missing in the source photographs but were required on the final composites. AI generation got the image most of the way there. The remaining work was craft.

Vision served as the connective layer between the team members. The platform held the workflow together, made outputs reviewable, and stored prompt history per generated image so that any variation could be traced back to its source. When something needed to be adjusted, the team could see exactly what had changed and why.

What Was Delivered

The project produced 18 completed looks and more than 60 final images. Every garment in the brief was covered. Every required angle was rendered. The prompt library built during the project is now a reusable asset that speeds up every subsequent engagement.

The White Glove service tier, which Très Tilda was among the first clients to use, was formalized and launched. The tier exists because projects like this one proved the workflow could deliver at the quality level luxury brands require.

The Result

The collection launched. The images are live.

The client’s response to the work: “You have aesthetic sense and a creative soul.”

That kind of feedback does not come from pushing a button. It comes from a process that encountered real problems, solved them carefully, and never treated the brand as an afterthought.

What This Means

Fashion brands no longer have to choose between quality and cost. That tradeoff was always a product of the logistics, not the craft. When the logistics change, the tradeoff changes with them.

Vision does not replace the people who understand fashion. It gives them a better set of tools. The taste still has to come from somewhere. The judgment, the eye, the decision about what looks right—those remain human. What Vision removes is the friction between the idea and the image.

For Très Tilda, the workflow turned a production that would have required international model booking, multi-day studio shoots, and weeks of turnaround into a thirty-one-day process run by three people for approximately one thousand dollars. The Apparel Photography module that made it possible did not exist before this project started. It does now.