Giant Wisteria
Your room, untouched, and overgrown with cascading wisteria.
Works best with a clear background or scene photo.
What it does
Giant Wisteria fills your photographed interior with massive hanging wisteria blooms, while the original room stays photographically intact beneath the flowers.
When to use it
- Transform a living room or bedroom photo into a surreal cascading-flower space
- Create dreamy, atmospheric interior content for social media and mood boards
- Visualize a botanical installation or flower-styling concept inside a real room
- Turn cafe, studio or event-space photos into eye-catching floral scenes
- Make striking seasonal or wedding-inspired imagery from ordinary interiors
- Give hallways, stairwells and reading nooks a luxurious overgrown look
When not to use it
- Portraits or photos where a person is the main subject
- Outdoor landscapes or scenes without an enclosed interior
- Swapping the wisteria for a different flower or plant species
- Redesigning, restyling or rearranging the room itself
How it works
Giant Wisteria reads your uploaded interior photo as a fixed scene and keeps every structural element -- architecture, furniture, perspective, lighting and shadows -- exactly as captured. It then introduces enormous wisteria into that untouched space, growing flower clusters from the ceiling, over furniture and toward the floor so they overlap the existing objects naturally. The blooms inherit the room's original exposure, color temperature and shadows, so the finished image looks like a real photograph of a room that simply happens to be full of flowers.
Updates
- v1May 19, 2026
Initial release
Specifications
- Approach
- Introduces giant wisteria into your photo while leaving the original room untouched.
- Stylization
- Adds botanically realistic flowers, vines and stems with no change to room materials or geometry.
- What stays
- Architecture, layout, furniture, windows, lighting, perspective, shadows and color grading.
- What changes
- Enormous cascading wisteria appears, hanging from the ceiling and draping over the space.
- Ideal source
- A clear, well-lit photo of an enclosed interior room.
- Maximum input
- Up to 25 MP per image
- Supported formats
- JPEG, PNG, HEIC
- Output format
- PNG -- preserves the fine petal detail and soft shadows of the cascading wisteria.
- Pricing
- 2 credits per image
Frequently asked questions
Does my original room stay the same?
Yes. Giant Wisteria treats your photo as a fixed environment, so the architecture, furniture, windows, perspective and lighting are all preserved. Only the wisteria flowers are added on top of the room you photographed.
Will it redesign or restyle my interior?
No. The model never redraws the room, moves furniture or changes materials. It introduces flowers into the existing space, so the result is the same room you uploaded, just overgrown with wisteria.
Can it add a different flower instead of wisteria?
No. This model is built specifically for cascading wisteria in soft lavender, violet, lilac and white tones. For other flowers or plants, look for a model dedicated to them.
What kind of photo works best?
A clear, well-lit photo of an enclosed interior, a room with visible walls, ceiling and furniture. Sharp, evenly lit shots give the flowers the most natural surfaces to grow from and the most believable shadows.
Will my photo be cropped or reframed?
No. Giant Wisteria keeps your original framing, perspective and camera angle. The output matches the composition you uploaded, with the wisteria added inside the same frame.
Can I use the result commercially?
Yes -- generated images on upsa are yours to use for personal and commercial purposes. We recommend you keep the consent and rights to the source photo as well, since the output preserves the identity in it.
How long are my photos stored?
Source uploads and generated results are stored for 48 and then automatically deleted from upsa's storage. We don't train any model on your photos.
AI results vary
Each generation uses a different random seed, so your result may not exactly match the example shown. If the first try doesn't fit what you had in mind, run it again — output differs noticeably between runs.
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