Image
AI Product Photography on Martini
Your SKU hero, in every scene, on one canvas. Drop the product reference, brand colors, and scene reference as separate canvas anchors and fan into Imagen 4, Nano Banana 2, Flux Kontext, and Seedream for hero shots, lifestyle stills, and catalog cuts. Sister page to ai-product-video-generator — this one is images only.
What this feature solves
Real product photography is expensive and slow. A single SKU shoot — studio time, photographer, stylist, set design, post-production — runs into thousands of dollars and days of turnaround. For a brand with hundreds of SKUs, seasonal drops, and constant new placements, the photography budget consumes most of the marketing spend before campaigns even reach paid placement. AI product photography promises to compress the cost, but most AI tools deliver outputs that look generic, drift away from brand color, or fail to match the actual product detail close enough to ship.
The deeper problem is consistency at scale. A single great AI product image is fine. A campaign of forty placements where every image needs the same product, same brand color script, same visual style — that requires the AI to anchor the product reference across dozens of generations, lifestyle scenes, and aspect ratios without drifting. Tab-based tools force re-uploading the product reference into every new session, and the output drifts because the chain has no shared memory.
And there is the integration with the rest of the catalog and ad workflow. The hero image is one of many assets a campaign needs — lifestyle stills, packaging mockups, app store screenshots, social-feed cuts, ad-creative variants. Generic AI image tools produce one-off outputs that the team then has to organize, tag, and integrate into a brand asset pack manually. Without a workflow that spans hero through to delivery, the AI is just another disconnected tool in the marketing stack.
Why Martini is different
Martini's canvas treats product photography as a multi-anchor workflow. Drop the product reference (the SKU's official photograph) as one labeled image node, the brand color script as another, the scene or lifestyle reference as a third. Wire all three into Imagen 4, Nano Banana 2, Flux Kontext, or Seedream nodes for the hero, lifestyle, and catalog cuts. The model receives every reference role distinctly, and the output respects the product detail, brand palette, and scene context simultaneously.
Multi-model fanout for hero shots, single-model for the catalog sweep. Run hero placements across multiple engines in parallel — Imagen 4 for photoreal, Nano Banana 2 for product detail, Flux Kontext for compositing into specific scenes, Seedream for stylistic range — and pick the winner per placement. For the catalog sweep across many SKUs, lock the model and reference setup as a template and run hundreds of variations through the same chain. The expensive iteration happens on the heroes; the catalog scales on the template.
Downstream chaining closes the loop. Once the product images land, chain into the image-upscale tool for delivery resolution, into the background-removal tool for cutout variants, into the ai-product-video-generator workflow for motion versions of the same product. The canvas integrates the entire product visual workflow rather than treating each step as a separate tab. Brand asset packs export from the same canvas where the work happens.
Common use cases
Hero product shot for a launch campaign
Generate the hero across Imagen 4, Nano Banana 2, and Flux Kontext, pick the winner, upscale, and export for billboard and digital placements.
Lifestyle scene library for an SKU
Drop the product reference, fan out across kitchen, bathroom, beach, vanity, and travel scenes. One canvas, one product, many lifestyle placements.
Catalog photography for a multi-SKU collection
Lock the model and reference setup as a template, run every SKU through the same chain. The catalog reads as one cohesive shoot.
App store screenshots and product UI shots
Apply the brand style and product reference to UI mockup generations for app store placements and product launch decks.
Brand-consistent ad creative variants
Generate ad creative variants for A/B testing while the product, brand colors, and style stay locked across every variant.
Packaging and 3D product mockup
Combine the product reference with packaging mockup prompts to render packaging visuals before physical production.
Recommended model stack
nano-banana-2
imageStrongest reference adherence for product detail across new scenes and contexts.
flux-kontext
imageEdit-aware composition for placing the product into new scenes while preserving its details.
imagen-4
imagePhotoreal output with strong tonal range for premium hero placements.
gpt-image-2
imageEdit-aware refinement for compositional fixes and small product detail adjustments.
seedream
imageStylistic range for editorial and lifestyle product photography variants.
flux
imageHigh-fidelity creative output for stylized hero placements and editorial work.
How the workflow works in Martini
- 1
1. Drop the product reference and brand color script
Upload the official product photograph as one image node and the brand color script (hex values, brand palette, brand assets) as another. Label both clearly.
- 2
2. Add the scene or lifestyle reference if applicable
For lifestyle placements, drop a reference scene image (kitchen, bathroom, beach) as a third anchor. The model receives it as scene context.
- 3
3. Wire the anchors into a Flux Kontext or Nano Banana 2 node
The model node receives product, brand color, and scene as distinct inputs. Prompt the placement type — hero close, lifestyle wide, catalog product-on-white.
- 4
4. Fan out for hero placements
Duplicate the chain across Imagen 4, Nano Banana 2, Flux Kontext, and Seedream. Pick the winner per placement type.
- 5
5. Lock the template for catalog sweep
Once the hero chain works, save the canvas as a template. Run every other SKU through the same chain by swapping the product anchor.
- 6
6. Upscale and export to brand asset pack
Chain chosen masters through the image-upscale tool node, then export the bundle as the campaign asset pack ready for production.
Example workflow
A consumer beauty brand is launching twelve new lipstick SKUs and needs hero, lifestyle, and catalog imagery for each. The team builds a Martini canvas with the brand color script (deep magenta, warm gold, soft cream), a hero composition reference (a fashion-editorial mood board), and the official product photograph of SKU one. They wire the anchors into four image model nodes — Imagen 4 for the hero, Nano Banana 2 for a vanity-table lifestyle, Flux Kontext for a magazine-style flat lay, Seedream for a stylized editorial. After picking winners for SKU one, they save the canvas as a template. The other eleven SKUs run through the template by swapping only the product anchor — color and style stay locked. Hero, lifestyle, and catalog imagery for all twelve SKUs lands on a single canvas. The team upscales the heroes through the image-upscale tool node and exports the brand asset pack. Total time: a single afternoon for what would have been a week of studio booking.
Tips and common mistakes
Tips
- Anchor the brand color script as a separate node. Brand consistency across the campaign depends on a stable color reference.
- Use Imagen 4 and Nano Banana 2 as primary engines for hero work. Imagen for photoreal, Nano Banana for product detail.
- Lock the canvas as a template after the first SKU works. Catalog sweeps scale on template reuse.
- For lifestyle scenes, a separate scene reference produces more believable placement than scene-prompts alone.
- Frame as augment, not replace. AI product photography is excellent for catalog and lifestyle volume; top-tier ad campaigns still benefit from a real shoot.
Common mistakes
- Mixing product and scene into a single reference. Multi-anchor with distinct roles produces sharper product detail.
- Skipping the brand color anchor. Color drift across a multi-SKU campaign undermines the cohesion the brand needs.
- Running every SKU through fan-out. Use fan-out for heroes; use the template for catalog volume to control cost.
- Treating AI as a full replacement for top-tier shoot work. Frame as augmentation — top-line ad photography still benefits from real production.
- Forgetting to upscale before export. Catalog and ecommerce delivery typically needs 4K masters or higher.
Related how-to guides
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Related docs
Related reading
Comparisons
Frequently asked questions
Can AI replace a real product photo shoot?
For catalog volume, lifestyle variants, and rapid iteration, AI product photography is highly effective and dramatically faster than studio booking. For top-tier ad campaign hero shots, a real shoot still wins on shadow, material, and skin detail. Frame AI as augmentation: catalog and lifestyle on AI, hero ad on traditional, with overlap depending on the brand.
Which model is best for product photography?
No single model wins everything. Nano Banana 2 leads on product detail and reference adherence. Imagen 4 wins on photoreal hero shots. Flux Kontext is best for compositing the product into specific scenes. Seedream offers stylistic range for editorial cuts. The canvas lets you pick per placement.
Will the product detail stay accurate?
With a strong product reference (the official photograph) anchored on the canvas, edit-aware models reproduce product detail well. For tightly brand-controlled SKUs, review every output against the source reference before locking. Watch for label, packaging copy, and material highlight accuracy.
How do I keep brand colors consistent?
Drop the brand color script as a separate canvas anchor. The model receives it as a brand-color reference, and the chain holds the palette across multiple generations. Pair with template reuse so color stays consistent across the entire campaign.
Can I scale this across hundreds of SKUs?
Yes — that is the canvas template advantage. Build the chain on one SKU, lock it as a template, swap the product anchor for each subsequent SKU. Run the template on every SKU in batch. The catalog scales on template reuse rather than per-SKU rebuild.
How does this fit into the rest of my product workflow?
Chain the product photography output into background removal for cutouts, into image-upscale for delivery resolution, into ai-product-video-generator for motion versions, into the brand asset pack for export. The product visual workflow stays on one canvas rather than spanning multiple tabs.
Build it on the canvas
Open Martini and wire this workflow up in minutes. Free to start — no card required.