Use Cases
Show a client how the space will look in minutes.
Short answer
The strongest method for AI interior visualisation is taking a photo of the actual space as reference and using Stable Diffusion with ControlNet to change the style while preserving the room's geometry. Rooms generated from scratch look good but do not match the real space's dimensions and window positions — a visual presented to a client has to be buildable.
In interior design, the real problem AI solves is explanation. A plan and a material board are enough for the designer; the client cannot decide until they can see the space, and until they decide the process stretches.
The traditional solution is a 3D render: accurate but expensive and slow. Rendering one room takes days, and when the client asks "what if the walls were darker?" the process starts again.
AI compresses that loop to minutes. But doing it correctly has a condition: the generated image must preserve the real space's geometry. Otherwise you have shown the client something that will not happen.
Shoot from a corner of the room, as wide as possible. This photo becomes the depth and edge reference for ControlNet; the space's real geometry comes from it.
Use ControlNet's depth or edge mode and describe the new style in the prompt. Walls, windows and ceiling height stay in place while materials, colours and furniture change.
Make sure every element in the generated image can actually be sourced. Showing a client furniture that does not exist is the most expensive mistake a presentation can make.
Generate a room from a description and the model produces a beautiful but imaginary space: a window where there is none, a five-metre ceiling where the real one is three, the door on a different wall.
In a client presentation that is a serious problem. The image is approved, the job is signed, and at the build stage everyone meets the fact that there is no window there. The loss of trust costs more than the time saved.
The solution is ControlNet. You supply a photograph of the room as a depth map or edge outline; the model keeps walls, windows and ceiling height where they are and changes only surfaces, colours and furniture.
This is the feature that separates Stable Diffusion from the other tools in interior work, and it alone justifies the steep learning curve for this profession.
AI images can be good enough to be indistinguishable from real photographs, and that creates a responsibility in the client relationship.
Say clearly that what you are presenting is a design proposal, not a photograph. That is both an ethical requirement and a practical protection: if the client expects the image literally and the build differs, the liability comes back to you.
The second point is product reality. The furniture, fittings and materials in the image have to be sourceable. AI generates a sofa that does not exist and the client wants exactly that sofa; this is the problem interior designers hit most often.
A practical precaution: present the visualisation alongside a material board built from real product photographs. Let the image convey the atmosphere and the board show what will actually be bought.
Photograph the room and use it as a depth or edge reference in Stable Diffusion's ControlNet. The model keeps walls, windows and ceiling height in place and changes only materials, colours and furniture. Generating from a description will not achieve this.
You can, but say clearly what it is: a design proposal, not a render that will be built literally. Also make sure every piece of furniture and material in the image can actually be sourced — a client wanting a product AI invented is the most common problem in this field.
If you need geometry preservation, Stable Diffusion with ControlNet is the only real option and it requires setup. If you only want style and atmosphere ideas, Midjourney or Flux are far easier. To change a specific part of an existing photograph, Adobe Firefly's Generative Fill inside Photoshop is the most practical route.
Prompts are written in English because most tools give markedly better results with English prompts.
the same room in warm minimalist style, oak flooring, off-white lime plaster walls, linen curtains, one large arched mirror, soft afternoon daylight from the left window, natural materials, no clutter, architectural photography
Works well in these tools: stable-diffusion, flux, midjourney
Note: Used with ControlNet, this prompt changes only surfaces and furniture without disturbing the room's geometry.
interior visualisation of an empty apartment living room, three metre ceiling, one large window on the left wall, matte white walls, light oak floor, no furniture, even natural daylight, wide angle architectural render, neutral colours
Works well in these tools: stable-diffusion, flux, midjourney
Note: Empty-space images give you a base for testing different furniture scenarios.
Open source; installed locally it is free, uncensored and completely controllable.
The strongest open-weight family for photorealistic people and for text rendered inside images.
The subscription generator that delivers the most consistent artistic and aesthetic results.
Trained on licensed data; for teams that cannot take legal risk on commercial work.