Input
JPEG, PNG, or WebP room photo plus room and style selections.
The model receives a room, a direction, and strict boundaries.
virtualstagingai.dev sends the uploaded room image, selected room type, selected style, and a geometry-preserving instruction to a model through 302.AI. The instruction asks for scaled furniture and decor while preserving the architecture and view.
The response is a generated image, not a measured design plan. The model infers depth and scale from pixels, so photo quality and room visibility strongly affect the result.
Generation
A server-side request through the configured 302.AI image endpoint.
Decision
Human comparison, rejection or regeneration, disclosure, and export.
Better constraints beat longer decoration prompts.
The most important instructions are about what must not change: camera position, perspective, room dimensions, openings, structural features, floor, and the view outside. Style language should then guide a restrained furniture palette and room function.
- Choose the actual room type, not the room you wish it were.
- Use a style compatible with the building and likely audience.
- Regenerate when the model distorts permanent features.
- Avoid using generated images to conceal damage or condition.
Fast generation does not remove editorial responsibility.
Inspect the source and result side by side at full size. Check geometry first, then furniture scale, shadows, reflections, blocked doors, impossible clearances, duplicated objects, and visual artifacts.
A good workflow assigns a reviewer and stores only approved images in the publish folder. Teams should also record which images are virtually staged so disclosure is not lost during handoff.
virtualstagingai.dev is an MVP with an intentionally narrow job.
The current app provides private account sessions, queued 302.AI generation, authenticated image history and downloads, a durable credit ledger, and Creem subscription checkout.
That boundary is reflected across the FAQ, pricing, privacy, and legal policies so the marketing site does not promise capabilities that are still on the roadmap.
Questions, answered.
Is AI virtual staging automatic?
Generation is automated, but publishing should not be. A person needs to compare the output with the source and reject any material change or visual error.
Which AI model does virtualstagingai.dev use?
virtualstagingai.dev uses 302.AI as a configurable gateway. The exact image model and endpoint are environment settings so the provider can change without rewriting the studio.
Does virtualstagingai.dev train on uploaded images?
The virtualstagingai.dev application does not contain a training pipeline. In production mode, the configured 302.AI service receives the image for generation; provider handling and retention must be confirmed against the final provider terms.
Next step
Stage a roomTry the focused AI staging flow.
Upload, choose a direction, generate, and compare the result with the source room.