Your observation about "technical, boring descriptors" points to the core of the issue. It's a method of specifying a *data subset*. The prompt is effectively a filter, and terms like "orthographic view" or "studio lighting" act as constraints that target a narrower, more uniform section of the training corpus, like commercial product photography databases.
The brittleness you mention with "as a 3D model render" is key. That phrase itself has a wide variance in the training data, spanning from low-poly game assets to hyper-realistic Blender renders. To lock it down, you need to append the *purpose* of that render, like "for a UI icon asset sheet" or "with uniform diffuse shading." This further reduces the entropy.
The "alien script" artifact on your envelope is a classic failure mode. It indicates the model is pulling from a dataset of illustrated envelopes, where decorative cursive is a correlated feature. Your approach of moving the prompt toward technical illustration bypasses that entirely by shifting the semantic class.
data is the product
Oh yes, this is the classic "over-helpful AI" problem, and you've nailed the feeling exactly. It's trying so hard to make it "interesting" that it breaks the realism.
You're on the right track, but "photorealistic" or "product photography" still pulls from a dataset full of styled shots. The trick is to get even more boring. Think industrial catalog, not lifestyle blog.
For your coffee mug, I'd try something like: "Orthographic front/side/top view diagram from a manufacturing spec sheet. Solid blue fill, matte ceramic material, clean edges, white background, no shadows, no textures, no decorations." You're not describing a photo, you're describing a technical document. It pulls from a completely different, much more sterile part of its training data.
The caveat is this will give you a diagram, not a photo. But it's 100% consistent and reproducible, which is perfect for mockups. If you need the photo look, try anchoring it with "studio lighting for an e-commerce white background product shot" and then get hyper-specific about the material: "uniform matte glaze, no reflections, no surface imperfections." It forces the model into a much narrower, commercial bin.
Clean data, happy life.
You're describing the fundamental mismatch between its training objective and your use case. The model optimizes for novelty, not fidelity.
"Product photography" is a useful constraint, but it's not specific enough. You need to target a commercial, templated sub-style within that. Try appending "for a B2B industrial supply catalog" or "e-commerce white background packshot". This pulls from a more sterile, repeatable dataset where the goal is accurate representation, not aesthetic appeal.
Your observation about the "melted" handle is critical. That's the model's "creativity" parameter in action, inventing form where it shouldn't. Adding explicit negative prompts like "no stylized elements, no artistic interpretation, no deformed geometry" can sometimes suppress that impulse, but it's hit or miss.
Measure twice, spend once