The Consistency Problem in AI Fashion Production
AI image generation has made it possible to produce fashion imagery at speed, but producing a coherent set of images across an entire collection is a different challenge. Surajnarayanan S, founder of 8M Studio, an AI production studio for fashion, has seen where these projects break down and what actually fixes them.
When Direction Drifts After Generation Starts
Asked when a collection's image production has gone sideways because results did not hold together shot to shot, Surajnarayanan points to campaign work rather than catalogue. "Catalogue is easier to control: the poses are set and the garment reference is fixed," he says. "Campaign is where the creative calls come in, and that's where things tend to drift."
The pattern is familiar: a mood board and direction get signed off, the full set is generated, and then once everyone sees the images together, the direction changes. "That's where AI images have a limit people don't expect," he explains. "You can't keep editing the same image. After three or four rounds it starts to burn: detail goes soft, artefacts and hallucinations creep in. By the time the image is where everyone wants it, the quality is gone, and it has to be regenerated from scratch."
So the root cause is often not the AI itself. "It's a direction that keeps moving after generation has started," he says. The studio's response is to put weight on the mood board up front: "We lock what works and what doesn't with the brand before the first image is made, so the set holds together from the start."
The Input Problem the Usual Advice Leaves Out
Much of the advice on keeping AI fashion images consistent focuses on the generation side — better prompts, better models, better tools. Surajnarayanan argues that misses the input. "AI fashion imagery is garbage in, garbage out," he says. "For catalogue especially, the output can only be as good as the product photos you start from."
Those source photos need to be shot properly, in a studio or as clean flat lays, with true colour and even light. Crucially, they need to be shot the same way across the collection. "If every garment is photographed differently, no prompt will make the set hold together," he says.
Even with strong inputs, garment fidelity remains the recurring issue. "Colour, print, fit, trims. Matching the real product is about 90% of a catalogue job, and brands want it close to perfect," he says. "That takes a lot of iteration and real technical knowledge. You rarely get there in three or four generations."
He also flags a shift on the model side. Brands are moving away from random AI faces toward digital twins of real models who have agreed to it. "A generated face can end up looking like a real person, and that's a legal risk brands don't want to carry," he says. "A consented twin removes that risk and gives you the same face on every image in the collection."
Why a Few Good Images Is Not the Same as a System
For brands trying AI product images across a whole collection for the first time, the early mistake is assuming a handful of good images means the problem is solved. "A handful of images is easy. A whole collection is a system problem," Surajnarayanan says.
The failure mode is consistent: a few images don't come out as planned, the team keeps editing them, and every edit shifts the pose or something else until those images no longer match the rest of the set. "Fixing images one by one doesn't scale," he says.
What works instead is a repeatable system — people who understand both AI image and video, proper QA with a person checking every image, and a clear picture of where images fail and why. "Once you know that, you fix the process, not just the one image," he says.
Practical Takeaways
- Lock the creative direction before generating the first image; a moving brief is a common cause of inconsistency.
- Treat source product photography as part of the production system: consistent lighting, true colour, and uniform shooting across the collection.
- Expect garment fidelity to require iteration; matching colour, print, fit, and trims is the bulk of catalogue work.
- Consider consented digital twins of real models to reduce legal risk and keep a consistent face across a set.
- Build a repeatable process with human QA rather than fixing images individually.
For brands moving from a few test images to full-collection production, the lesson from 8M Studio is that consistency is less about finding the perfect prompt and more about controlling inputs, locking direction, and treating the whole collection as a system.