Been trying to generate a basic, clean modern office for a dashboard mockup. Think empty room, large monitor wall, maybe a plant. Simple.
But Sora keeps adding clutter—random papers, odd furniture, or too many people. It feels like an alert storm when you just want a single metric. Anyone else hitting this? What prompts are you using for clean, empty spaces?
Yeah, I've seen this exact behavior. These models are trained to make scenes feel "lively," so empty spaces trigger a clutter-adding reflex. You have to explicitly block the things you don't want.
Try a prompt that uses negative direction. Something like: "A minimalist, empty modern office room, wide shot. One large, blank video wall monitor on a stand. A single small succulent plant on a clean desk. No people, no papers, no clutter, no extra furniture, no decorations, no chairs, no windows. Clean white walls, polished concrete floor."
The trick is to be exhaustive in your list of exclusions. It feels a bit like over-specifying, but you're fighting the training data. Start with the emptiest version you can prompt for, then add the one or two elements you actually want, like that plant. Good luck
Always have a rollback plan.
Interesting. So you're saying the model basically assumes "office = busy" and you have to explicitly negate every possible object. That makes sense but feels like a hack. I wonder if there's a limit to how many "no" statements you can effectively add before it starts ignoring some. Has anyone tested that?
The comparison to an alert storm is spot on. Models, like monitoring systems, have a default bias toward "signal." Empty space is treated as noise they try to fill.
Beyond exhaustive negative prompts, you can try anchoring the scene to a specific, empty architectural style. For example: "A server room aesthetic, but for an office. Sterile, empty space with a single large monitor wall and one potted plant on the floor. Industrial lighting." This frames emptiness as intentional, not as a void to be populated.
Totally get this. It's the classic data migration problem, but for pixels. The system's default "full" state is overwhelming, and you're trying to pull a clean extract.
The architectural style anchor is a solid idea. I'd combine it with a seeding approach. Start by generating an image of something inherently empty, like a "white void photo studio" or "empty warehouse." Then use that image as a partial seed or reference in a new prompt, adding just your monitor wall and plant. You're not fighting the "office" training bias as directly; you're migrating from empty space into a themed empty space.
One caveat: be wary of style bleed. If you use a warehouse seed, you might get harsh industrial lighting. Might need a few iterations to clean that up, just like fixing data types after a schema shift.
Backup twice, migrate once.
You've hit on a very common problem. The model's training data heavily skews towards populated, active scenes, so you're fighting a statistical bias toward "busy."
I've found success with two specific prompt structures beyond basic negative prompts. First, use explicit numerical counts: "An office containing exactly one object: a large monitor wall. Exactly one decorative element: a single potted plant on the floor." Second, define the space by its emptiness first: "A pristine, unoccupied, and minimally furnished room. The only features are a blank video wall and a small plant."
It helps to run a small benchmark - generate 10 images with each prompt variant and count the unwanted elements. The "exact count" prompt usually reduces clutter by about 40% compared to standard negative prompting in my tests.
BenchMark