Image
AI Style Transfer on Martini
Lock the style, vary the subject. Style transfer in 2026 is mostly an emergent property of edit-aware models like Flux Kontext and Nano Banana 2 — drop a style reference image as a separate canvas anchor, wire it into every downstream node, and the look stays consistent while subjects change. Mood-board → style anchor → fan out across formats.
What this feature solves
Brand-consistent visual style is one of the hardest things to maintain at scale with AI image tools. The first generation looks great in your style; the second drifts; the third is cinematic-realistic when the brief was painterly-illustrative. Without a way to anchor the style across every downstream generation, creators end up curating a small percentage of usable outputs and discarding the rest. The look that defined the project drifts away from itself one generation at a time.
Tab-based AI tools force you to re-prompt the style description in every new session, which is unreliable. Style words like "cinematic," "painterly," "editorial" mean different things to different models, and the model's interpretation drifts even within a single session. The solution that actually works is a style reference image — but most tools only support one reference at a time, mixing subject and style references into a single slot. The result is a generation where neither the style nor the subject is fully respected.
And there is the modern reality of style-transfer tooling. Dedicated style-transfer models from the early 2020s have been replaced by edit-aware models that handle style as one of many references. Flux Kontext and Nano Banana 2 read a style image and a subject image as separate inputs and apply the style cleanly. But this means the workflow needs a tool that can manage multiple reference anchors with clear roles, which is exactly what tab-based generators cannot do.
Why Martini is different
Martini treats the style reference as a separate canvas anchor — different from the character or scene reference. Drop a mood-board image into a labeled image node ("campaign style"), drop a subject image into another labeled node ("product front"), and wire both into a Flux Kontext or Nano Banana 2 node. The model receives style as one input and subject as another, and the output respects both roles. Multi-anchor reference is the canvas advantage applied specifically to style-driven work.
Fan out across formats while the style holds. Wire the style anchor into multiple downstream nodes — square for in-feed, vertical for stories, horizontal for hero placements — and run them in parallel with different subject inputs. The style stays consistent across every aspect ratio and placement. For brand campaigns where the look has to be the same on a billboard and a stories ad, that consistency is the work itself, not an afterthought.
Save the style canvas as a template. Once the style anchor and the chain produce on-brand output, save the entire setup. Future campaigns drop in new subject references and re-run; the style is locked because the anchor and the model chain are baked into the template. The brand visual style becomes a reusable asset across every project rather than a one-time prompt configuration.
Common use cases
Lock a campaign visual style across many subjects
Drop one mood-board image as the style anchor, then run different products, characters, or scenes through the same style on the canvas.
Consistent illustrative or painterly look across a content series
A weekly content drop with a unified illustrative style — anchor the style once, swap the subject prompt per episode.
Brand mood-board to deliverable assets pipeline
The mood-board image is the canvas anchor; downstream assets inherit the look without manual style-prompting per piece.
Editorial style applied to a product line photography
Apply an editorial style reference to every SKU in a fashion or lifestyle line so the catalog reads as one editorial.
Stylized transformation of existing photography
Take real photos and transform them into a unified illustrated, painterly, or graphic look across a campaign.
Cross-format style consistency for a launch
Same look across square in-feed, vertical stories, horizontal hero, and out-of-home placements — anchored to one style reference.
Recommended model stack
flux-kontext
imageEdit-aware style application that respects subject identity while applying the style anchor.
nano-banana-2
imageMulti-reference handling — subject and style as separate inputs, output respects both.
midjourney
imageStylized output with strong creative range when style is described or referenced.
flux
imageHigh-fidelity style-driven output for hero campaign assets.
gpt-image-2
imageEdit-aware refinement applied to style-transferred outputs for cleaner final results.
How the workflow works in Martini
- 1
1. Build the mood board and pick the style anchor
Curate three to five style references that share a coherent look. Pick the strongest single image as the canvas anchor — labeled clearly.
- 2
2. Drop the style anchor as a labeled image node
Place the style image on the canvas. Label it "campaign style" or whatever the project naming convention requires.
- 3
3. Add subject anchors as separate image nodes
Each subject — product, character, scene — is its own labeled node on the canvas. The style and subject roles stay separate.
- 4
4. Wire both anchors into a Flux Kontext or Nano Banana 2 node
Connect style anchor → model node → subject anchor → model node. The model receives both as references with clear roles. Prompt for the desired output.
- 5
5. Fan out across formats and subjects
Duplicate the chain for different aspect ratios, different subjects, different placements. The style anchor stays consistent across all branches.
- 6
6. Save the style canvas as a template
Once the style is locked and the output is on-brand, save the entire canvas. Future campaigns swap the subject anchor and re-run; the style stays.
Example workflow
A boutique brand is launching a fragrance line with twelve SKUs and needs every product hero to share a unified painterly-editorial style across square, vertical, and horizontal placements. The team curates a mood board and picks one painterly fashion editorial as the style anchor. They drop it onto a canvas labeled "fragrance campaign style." Each of the twelve SKUs gets its own subject anchor node — the bottle, photographed on a neutral surface. They wire the style anchor and each subject anchor into Flux Kontext nodes for the square in-feed, Nano Banana 2 nodes for the vertical stories, and Flux nodes for the horizontal hero. Thirty-six total outputs, all sharing the painterly style, all keeping the bottle clearly recognizable. The team picks the strongest take per placement, upscales the heroes through the image-upscale tool, and exports the bundle as the campaign asset pack. The style canvas saves as the template for the next two seasonal drops.
Tips and common mistakes
Tips
- Style reference is a separate role from subject reference. Label both clearly on the canvas to keep them straight.
- Strong style anchors share a coherent look. A mood board that mixes three different styles produces drifty output.
- Flux Kontext and Nano Banana 2 are the best edit-aware models for style transfer in this lineup. Lead with them.
- Test the style on one subject first. If the output is off, refine the anchor before fanning out across the campaign.
- For brand work, save the style canvas as a template the moment the look lands. Future campaigns become a subject swap.
Common mistakes
- Mixing style and subject in a single reference slot. Multi-anchor with clear roles produces cleaner output.
- Relying on prompt-only style descriptions. Reference images beat prompts for consistency.
- Naming a famous artist by name. Avoid "Studio Ghibli style" or "Hayao Miyazaki" — keep style references generic and use original mood-board imagery.
- Picking a stylistically incoherent mood board. The anchor needs to share a single recognizable look.
- Forgetting that style is emergent in modern edit-aware models, not a separate dedicated mode. Lean on the reference, not on a "style transfer toggle."
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Comparisons
Frequently asked questions
Is there a dedicated style-transfer model on Martini?
Style transfer in 2026 is mostly an emergent property of edit-aware models — Flux Kontext, Nano Banana 2, GPT Image 2 — rather than a dedicated style-transfer mode. The wedge on Martini is multi-anchor reference handling: drop a style image and a subject image as separate canvas nodes, and the model applies one to the other.
Can I use any image as a style reference?
Yes, but pick one that has a clear, coherent look — a single editorial photograph, a single illustration, a single painting. Mood boards with mixed styles produce drifty output. Avoid copyrighted artist styles; use original mood-board imagery instead.
How is this different from prompt-driven style description?
Prompt-driven style ("cinematic painterly editorial") is interpreted differently across models and even across sessions. A style reference image is a stable input — every model that supports references treats the same anchor consistently. Reference images beat style prompts for repeat-generation work.
Will the subject stay recognizable?
Edit-aware models like Flux Kontext and Nano Banana 2 are designed to apply style while preserving subject identity. The control depends on the strength of both anchors — a strong subject reference and a strong style reference produce output that respects both roles.
Can I apply one style across many subjects?
Yes — that is the canvas pattern. Drop one style anchor, drop multiple subject anchors, fan out into multiple model nodes. The style stays consistent across every output. Save the canvas as a template for repeat use.
How do I avoid copying a real artist's style?
Do not name living or recently active artists in style references or prompts. Build original mood boards from generic visual cues — composition, palette, brushwork — rather than referencing a specific artist by name. The output stays creative without infringing on individual artists.
Build it on the canvas
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