# Real estate
We can use image generation feature to transform empty or bare room photos into fully furnished and designed spaces. Whether you're showcasing a property, visualizing design options, or enhancing listings, this feature can automatically add furniture, decor, and other design elements to an empty room, creating a realistic, styled environment.
Let's change this image
### Image edit based
```json
{
"url": "https://s3.eu-central-1.amazonaws.com/deep-image.ai/api-examples/lost-places-597166_1280.jpg",
"background": {
"generate": {
"description": "A loft style furnishings",
"adapter_type": "control",
"model_type": "flux2-klein9b"
}
}
}
```
Other model types examples:
#### gemini-3-pro-image-preview
#### qwen
#### see-dream-4.5
### Controlnet based
```json
{
"url": "https://s3.eu-central-1.amazonaws.com/deep-image.ai/api-examples/lost-places-597166_1280.jpg",
"background": {
"generate": {
"description": "A loft style furnishings",
"adapter_type": "control",
"controlnet_conditioning_scale": 0.75
}
}
}
```
into:
Parameter "**adapter\_type**" is an algorithm type. Value "**control**" generates images based on given image and the edges extracted from the given image while value "**control2**" generates based only on extracted edges. Let's visualise those differences:
Having that image:
Edges extracted from that image (this is done under the hood during processing):
Result (the same prompt and other parameters) for adapter\_type = "**control**" (based on image and edges), image is mostly preserved, there are just minimal changes.
Using adapter\_type = "control2" it uses only edges of the given image:
Yet another example.
Description: "house at winter", adapter\_type="control2" (just edges).
.png?alt=media&token=82edd7ad-16b4-40dc-9754-a111ccbcecfe)
