Image
AI Interior Design on Martini
Interior designer presenting three room concepts to a client by Friday — same actual living room photograph, three different style directions: Scandinavian, Japandi, mid-century. Drop the client's empty-room photo as the anchor, fan style references across Flux Kontext (edit-mode), Flux, Nano Banana 2, Imagen 4, and Midjourney. The output is a client preview and mood board — never a fabrication-ready spec.
What this feature solves
Interior designers and homeowners hit the same iteration problem from opposite sides. The designer wants to present three credible room directions to a client by Friday — same actual living room, three different style takes (Scandinavian, Japandi, mid-century) — and a verbal description of each is not enough; the client wants to see the room. The homeowner wants to explore renovation options for their kitchen or living room before committing $40,000 to a contractor and needs a credible preview of how warmer cabinetry, different lighting, or a bigger island would feel in the actual space.
Tab-based AI tools force the workflow into a one-room-at-a-time loop. The designer uploads the client's photo into one tool, prompts a Scandinavian look, downloads the result, opens another session, re-uploads the same photo, prompts Japandi, repeats. By the third style, the room has drifted (different couch position, different window light, different proportions) and the comparison the client expected is impossible. Without an upstream anchor that locks the actual room photo, the workflow breaks the moment style variation is the goal.
And there is the spec-vs-concept gap. AI interior design output is excellent for client mood boards, renovation previews, and inspiration — but it is not a fabrication-ready spec. Material accuracy (real fabric, real wood grain, real stone), lighting accuracy (actual fixtures, actual sunlight angles), and dimension accuracy (will the new couch actually fit?) all drift in AI output. A workflow that promises buildable specs is shipping false promises; the honest framing is concept visualization, with material samples and dimension verification handled separately by the designer or contractor.
Why Martini is different
Martini treats the actual room photo as the upstream anchor. Drop the client's empty living room photo as a labeled image node — the actual space. Drop one or more style references (Scandinavian mood, Japandi mood, mid-century mood) as separate anchors. Wire the room photo and the style anchor into Flux Kontext, which is edit-aware and respects the source room while applying the style. The original room geometry, window placement, and architectural detail stay locked; the materials, furniture, and color script transform per style direction. Three credible directions for the same space land in one canvas.
Multi-model fanout for the hero presentation, single-model for the variant set. For the client meeting hero — the room shot the designer leads with — fan across Flux Kontext (edit-aware room transformation), Flux (high-fidelity rendering), Nano Banana 2 (detail fidelity for furniture and material), Imagen 4 (atmospheric photoreal), and Midjourney (editorial composition). Pick the strongest direction. For the variant set across rooms (kitchen, primary bedroom, dining), lock the winning model and run each room through the same chain. The presentation pack lands as a coherent client deliverable rather than disconnected one-off images.
Downstream chaining handles the deliverable layer. Once the room concepts land, chain into the image-upscale tool for the client-presentation master, into background-removal for furniture-cutout PNGs the designer drops into the proposal deck, and into ai-image-to-video for a short walkthrough animation of the proposed direction. The same canvas produces every client deliverable from one approved concept set. Save the canvas as a project template; future clients with similar room types inherit the chain.
Common use cases
Three-direction client preview for a Friday meeting
Designer presents three style directions for the same room — Scandinavian, Japandi, mid-century — anchored to the client's actual photograph.
Empty-room to fully-furnished concept
Homeowner uploads an empty living room and explores furnishing options — three couch styles, two lighting schemes, two rug treatments — for renovation direction.
Renovation before-and-after for a kitchen remodel
Existing kitchen photo anchored; AI generates the renovated direction — new cabinetry, island, lighting — for the contractor proposal preview.
AirBnB or short-term rental refresh visualization
Host visualizes refresh options for a property listing — same room, two style refresh directions — to test which lifts booking conversion.
Multi-room concept pack for a full-home design engagement
Designer produces concepts for living, primary bed, kitchen, and dining — all anchored to the client home and one style direction — as a coherent project deck.
Furniture placement preview for a stager or seller
Stager visualizes furnished versions of the empty rooms in a listing — the buyer sees the home alive rather than vacant.
Recommended model stack
flux-kontext
imageEdit-aware room transformation that preserves the actual room geometry while applying a style direction.
flux
imageHigh-fidelity rendering for the polished client-presentation hero direction.
nano-banana-2
imageDetail fidelity for furniture, fabric, and material rendering in close-up room shots.
imagen-4
imageAtmospheric photoreal lighting for golden-hour and evening interior renderings.
midjourney
imageEditorial composition and dramatic mood for hero interior shots.
How the workflow works in Martini
- 1
1. Drop the actual room photo as the upstream anchor
The client's living room, kitchen, or bedroom photograph — labeled clearly on the canvas. Every concept direction inherits the actual room geometry from this anchor.
- 2
2. Anchor the style references as separate nodes
For three-direction work, drop one Scandinavian mood reference, one Japandi mood reference, one mid-century mood reference. Each style stays in its own labeled node.
- 3
3. Wire room + style into Flux Kontext for edit-mode rendering
Flux Kontext is edit-aware — it respects the actual room and applies the style direction without inventing a different space. Run each style direction in parallel.
- 4
4. Fan the hero direction across additional models
For the client-meeting hero shot, fan the strongest direction across Flux, Nano Banana 2, Imagen 4, and Midjourney for additional angles, lighting moods, and editorial polish.
- 5
5. Refine through GPT Image 2 if a detail drifts
For the showcase concept — the kitchen island shot, the living room hero — pipe through GPT Image 2 for edit-aware detail cleanup.
- 6
6. Chain into image-upscale and background-removal
Wire approved room concepts into image-upscale for client-presentation masters and into background-removal for furniture-cutout PNGs the designer drops into proposal slides.
- 7
7. Label deliverables as concept visualization, not fabrication spec
Every export ships with explicit framing — concept visualization for client preview, final materials and dimensions to be selected from samples and verified by the contractor.
Example workflow
Inés is an interior designer presenting three room concepts to a coastal-home client by Friday. The client wants to see Scandinavian, Japandi, and mid-century directions for the actual living room. Inés opens a workspace canvas and drops the client's living room photo as the upstream anchor — large windows facing the ocean, original wood floors, double-height ceiling. She drops three style references — one Scandinavian (light woods, cream fabric, simple silhouettes), one Japandi (warm woods, low-profile furniture, neutral palette with deep accents), one mid-century (walnut, mustard, sculptural seating). Each style wires with the room anchor into Flux Kontext for the edit-aware transformation. Three direction renderings land — the same windows, the same floors, the same ceiling, three different style universes. For the meeting hero, Inés fans the Japandi direction across Flux, Nano Banana 2, and Imagen 4 for additional angles (afternoon light, evening warm-glow, sofa close-up). The presentation pack lands on the canvas. She chains the heroes through image-upscale and background-removal for the client deck. The Friday meeting opens with three credible directions — the client picks Japandi within ten minutes. Inés frames the deliverables explicitly: concept visualization, materials to be sampled and confirmed before fabrication.
Tips and common mistakes
Tips
- Use Flux Kontext for the edit-aware room transformation. It respects the actual room geometry while applying the style direction; other models invent a different space.
- Anchor the actual room photo once. Three-direction work, multi-room work, before-and-after work — all pull from the same locked room anchor.
- Save each style direction as a separate canvas anchor. Mixing styles into one prompt produces drifty output; multi-anchor with clear roles is the wedge.
- For renovation conversations, label every deliverable as concept visualization. Material samples and dimensions get verified separately.
- Save the canvas as a project template. Future clients with similar room types or style preferences inherit the chain; only the room photo and style references swap.
Common mistakes
- Treating AI interior output as a fabrication-ready spec. Material accuracy, lighting accuracy, and dimension accuracy can all drift; verify with samples and measurements before construction.
- Listing specific furniture brand names in prompts unless intentional. Output may inadvertently mirror trademarked product designs from IKEA, West Elm, or others; build with generic descriptions instead.
- Skipping the actual-room anchor. AI generation from a description alone invents a different room every time; the client sees beautiful images that have no relationship to their actual space.
- Promising the AI rendering as the budget reference. Material costs, labor costs, and fabrication costs are not encoded in the rendering; budgets get built from real material samples and contractor quotes.
- Letting the room geometry drift across style directions. Without Flux Kontext or another edit-aware model, the comparison is impossible because each direction shows a different room.
Related how-to guides
Related models and tools
Tool
AI Image Upscaling
Upscale images and keyframes before final video generation on Martini.
Tool
AI Background Removal
Remove backgrounds from images for assets and compositing on Martini.
Provider
Google's Veo video, Imagen image, and Nano Banana model workflows on Martini.
Provider
ByteDance
ByteDance's Seedance video and Seedream image model families on Martini.
Provider
OpenAI
OpenAI's GPT Image and Sora video model workflows available on Martini.
Related features
AI Architecture Rendering — Building and Space Visualization
Generate architectural renderings, exterior visualizations, and concept art on Martini.
AI Product Photography — Studio-Quality Product Images on Martini
Generate studio-quality product photos for e-commerce on Martini's canvas.
AI Style Transfer — Apply Artistic Styles to Images on Martini
Transfer artistic styles between images using AI on Martini.
AI Mockup Generator — Product, Device, and Brand Mockups
Generate product, device, and brand mockups for marketing on Martini's canvas.
AI Character Consistency Across Images and Video
Keep a subject consistent across image and video generations on Martini using reference workflows.
AI Character Reference — Reference-Image Workflows on Martini
Use reference images to guide AI model outputs on Martini's canvas.
AI Photo Restoration — Restore Old Photos on Martini
Restore old, damaged, or low-quality photos with AI on Martini's canvas.
AI Headshot Generator — Professional Headshots in Minutes
Generate professional headshots for LinkedIn, resumes, and team pages on Martini's canvas.
AI Thumbnail Generator — YouTube and Social Thumbnails
Generate scroll-stopping thumbnails for YouTube, podcasts, and social on Martini.
AI Logo Generator — Brand Marks and Wordmarks on Martini
Generate logo concepts, brand marks, and wordmarks on Martini's canvas.
AI Emoji Generator — Custom Emoji on Martini
Generate custom emoji and stickers for Slack, Discord, and brand on Martini.
AI Sticker Generator — Telegram, WhatsApp, Discord Packs
Generate sticker packs for Telegram, WhatsApp, Discord, and iMessage on Martini.
AI Comic Strip Generator — Multi-Panel Comics on Martini
Generate multi-panel comic strips with consistent characters on Martini's canvas.
AI Presentation Slides — Pitch Decks and Slide Visuals
Generate slide visuals, pitch deck imagery, and presentation graphics on Martini.
AI Icon Generator — App and UI Icons on Martini
Generate app icons, UI icons, and brand icon sets on Martini's canvas.
AI Character Design — Game and Story Characters on Martini
Design original characters for games, stories, and animations on Martini's canvas.
AI Game Asset Generator — Sprites, Concept Art, Backgrounds
Generate game-ready assets, sprites, concept art, and backgrounds on Martini.
Related docs
Related reading
Comparisons
Frequently asked questions
Can I use this to plan an actual renovation?
Use the AI rendering for inspiration, mood boards, and client previews — not as a fabrication-ready spec. Material accuracy (real fabric, real wood grain, real stone) and dimension accuracy (will the actual couch fit?) drift in AI output. For renovation planning, the AI is a concept-direction tool; material samples, exact dimensions, and contractor specs get handled separately.
How do I keep my actual room geometry while applying a style?
Use Flux Kontext as the model. It is edit-aware — it respects the actual room photo and applies the style direction without inventing a different space. Other models tend to generate a generic room in the requested style rather than transforming the room you uploaded. Anchor the room photo once; let Flux Kontext do the style work.
How is this different from AI architecture rendering?
Architecture rendering is exterior — buildings, sites, landscapes. Interior design is inside — rooms, decor, furnishing. Architecture is closer to developer pre-viz and engineering pre-construction marketing; interior is closer to mood-board client preview and renovation visualization. Both share the multi-anchor canvas wedge but the deliverables and disclosure framing differ.
Can AI design furnish an empty room for me?
Yes. Drop the empty room photo as the anchor and prompt the model to furnish in a chosen style. Flux Kontext respects the empty room geometry; the output adds furniture, lighting, art, and decor in the style direction. For real-estate staging conversations, this is excellent for visualizing the home alive before showing the property.
Will the AI output show real furniture brands accurately?
Avoid naming specific furniture brands in prompts unless intentional. AI image models trained on furniture imagery may inadvertently mirror trademarked product designs. Use generic style descriptions ("low-profile linen sofa," "walnut sculptural armchair") rather than brand names. For a final spec, source the actual furniture from real catalogs.
Which model is best for interior design?
Flux Kontext is the wedge model for the edit-aware "transform my actual room" workflow. Flux delivers high-fidelity hero rendering for client presentations. Nano Banana 2 holds detail fidelity for fabric, wood grain, and close-up material work. Imagen 4 brings atmospheric photoreal lighting for evening interior shots. Midjourney offers editorial composition for hero direction.
Build it on the canvas
Open Martini and wire this workflow up in minutes. Free to start — no card required.