The Technical Context
I completely understand the frustration when “it redraws all the details.” This isn't a whim of the model but a fundamental problem of chained edits. With each step, I'm asking the system not just to draw a picture, but to maintain identity, style, materials, and small forms while changing exactly one part. For AI implementation in design, this is one of the most frustrating pitfalls.
I usually see three reasons for this failure. First, the model doesn't store your style as a fixed state; it reassembles the scene probabilistically every time. Second, a text prompt is too weak an anchor for fine details without proper reference-image conditioning. Third, each new edit accumulates drift, and after 3-5 iterations, the face, fabric, light, and geometry are “almost the same,” but actually different.
I’ve experimented with various pipelines, and the effective foundation looks boring but honest. You need a fixed style block that you copy without changes between iterations: palette, light type, material, lens feel, mood. Plus, not just one reference, but several, preferably with different angles and no background clutter.
If the task is more complex than “change the button color,” I almost always use the previous result as a visual anchor and explicitly state what should not be touched. Sometimes, without a LoRA or at least a custom adaptation layer, there's no point in wrestling with prompts. And this is where many give up, expecting magic from a single text input field.
What This Means for Business and Automation
If you have a stream of banners, product cards, characters, or interior variations, the cost of error quickly shifts from aesthetics to team time. A designer ends up fixing what the AI was supposed to accelerate. As a result, automation with AI turns into manual retouching with an extra step.
Those who build a pipeline, rather than just praying to the model, win. Fixed references, a prompt template, rules for unchangeable zones, sometimes a fine-tune for a specific style—only then can you scale. Those who go into production with a “we'll just tweak the prompt” mentality lose.
At Nahornyi AI Lab, we tackle these exact bottlenecks in practice: determining where AI integration with a proper reference chain is sufficient, and where a separate layer for your visual language is needed. If your AI constantly breaks layouts and consumes your team's hours, let's look at the whole process and build an AI solution development plan so that edits finally become predictable.