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AI Character Consistency
You built a character — face, wardrobe, vibe, name — and you need her to be the same person in every image and every video clip across the campaign. Martini gives you a reference-driven canvas where the character image plugs into every downstream node, image and video, so your AI influencer or spokesperson stops shape-shifting between generations.
What this feature solves
Character drift kills AI content the moment you need a series. The first generation looks great; the second has a different jawline; the third aged her by ten years and changed her hair. For an AI influencer, brand spokesperson, or recurring character in a narrative, that drift is unusable. The audience expects the same person every time, and the model expects to invent a new one every time.
Tab-based tools force a manual workaround — you re-upload the reference into each new session, regenerate ten times until the face approximately matches, and curate the closest hits by hand. That loop falls apart at scale. A campaign needs sixty images and ten clips of the same person across different settings, outfits, and emotions, and there is no way to grind that out one tab at a time.
The deeper problem is cross-modal continuity. The character that looks right in your hero photo has to look right in the video clip, in the spokesperson cut, in the lip-synced dialogue. Image and video models often treat references differently, and without a way to lock the identity across both, your character is one person in stills and a different person in motion.
Why Martini is different
Martini treats the character reference as a first-class node. Drop your hero portrait once, then wire it into every image node and every video node downstream. The same anchor feeds Nano Banana 2 for new poses, Flux Kontext for outfit changes, Vidu and Kling for video shots — and because all of them see the same source, the subject stays identical across modalities. No re-uploading, no per-tab curation.
Model chaining bridges image to video. Generate a polished character still with strong reference adherence on Nano Banana 2, then chain that exact still into a Kling 3 or Vidu video node for motion. The video model anchors to the still, which anchored to your original reference — so the chain preserves identity all the way from concept to final clip. This is impossible inside a single-model tool.
The canvas makes the character reusable across every project. Save your character node setup as a template. Next campaign, drop in a new outfit prompt, swap the location, and re-run — the identity stays locked because the reference and the model chain are baked into the template. AI influencers and recurring characters become real assets, not one-shot generations.
Common use cases
Build and scale an AI influencer
Lock one persona across hundreds of social posts, multiple wardrobes, and recurring video content without identity drift.
Brand spokesperson across image and video
Keep the spokesperson identical from the hero photo to the explainer video to the talking-head dialogue.
Recurring character in an episodic series
Run the same protagonist across episodes, locations, and emotions for a serialized narrative or branded show.
Product line modeled by one AI talent
Photograph an entire fashion or beauty line on the same AI model so the catalog reads as one campaign.
Outfit and pose variations from one identity
Generate dozens of outfit and pose changes using Flux Kontext while keeping the face and identity locked.
Pre-vis casting before live-action shoot
Lock a stand-in character on the canvas so the team can review wardrobe, location, and shot blocking before booking talent.
Recommended model stack
nano-banana-2
imageStrongest reference adherence for character identity across new scenes.
flux-kontext
imageOutfit, pose, and scene changes with the original face preserved.
vidu
videoReference-driven video that holds the subject across clips.
kling-o3
videoCharacter-aware motion for spokesperson and dialogue cuts.
kling-3
videoCinematic camera language with strong character anchoring.
How the workflow works in Martini
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1. Generate or upload the canonical character reference
Start with one strong, well-lit portrait of the character — generated on Nano Banana 2 or uploaded from your library. This is the anchor every downstream node will see.
- 2
2. Drop the reference as an image node on the canvas
The image node is the single source of truth. Label it clearly so you can wire it into every downstream node without confusion.
- 3
3. Fan into image nodes for poses, outfits, and scenes
Wire the reference into multiple Nano Banana 2 or Flux Kontext nodes, each with a different prompt — new outfit, new location, new emotion. The face and identity stay locked.
- 4
4. Chain into video nodes for motion
Take the strongest character stills and feed them into Vidu, Kling 3, or Kling O3 video nodes. The video model inherits the locked identity from the still, preserving the character into motion.
- 5
5. Add lip-sync or dialogue if needed
For talking-head and spokesperson use, chain the video output into a lip-sync node and add ElevenLabs voice. The character speaks in your scripted voice without losing identity.
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6. Save the canvas as a character template
Once the chain works, save the entire setup as a template. Next campaign, swap prompts and re-run — the character stays consistent because the reference and chain are preserved.
Example workflow
An e-commerce brand is building an AI spokesperson named Mia for a 12-week content series. They generate a canonical portrait of Mia on Nano Banana 2, then drop that image as the anchor node on a canvas. Twelve Flux Kontext nodes branch off — each prompted for a different outfit and location. The strongest stills feed into four Vidu video nodes for short product spots, and one Kling 3 node for a hero spokesperson cut. Lip-sync wires ElevenLabs voice into the spokesperson clip so Mia delivers the brand line on camera. Mia's face is identical across all twelve weeks of content, every outfit holds, and the canvas saves as the master template for future campaigns. The brand stops paying for re-shoots and starts shipping consistent content weekly.
Tips and common mistakes
Tips
- Invest in one canonical reference. The quality of every downstream generation traces back to the strength of the source portrait.
- Use Nano Banana 2 for face-locked stills and Flux Kontext for outfit and scene changes — they complement each other.
- For video, chain the strongest still into the video node rather than feeding the original reference. The chain preserves identity better through motion.
- Save the canvas as a template the moment the character looks right across image and video. Reuse beats re-creation.
- Test new prompts on a single node before fanning out. One bad seed can cost you a dozen failed generations.
Common mistakes
- Using a low-quality or stylized reference. The downstream chain inherits every flaw — start with a clean, sharp, well-lit portrait.
- Trying to lock identity with prompt-only descriptions. Reference images beat prompts every time for visual consistency.
- Mixing multiple character references in one chain. The model averages them and you lose the original identity.
- Skipping the still-to-video chain and feeding the raw reference into video models directly. Image-locked stills hand off to video far better.
- Re-uploading the reference into each new node instead of wiring one anchor everywhere. Wire once, fan out — that is the canvas advantage.
Related how-to guides
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Related docs
Related reading
Comparisons
Frequently asked questions
Which model gives the most consistent character?
For face-locked stills, Nano Banana 2 leads. For outfit and pose changes that keep the face, Flux Kontext is strongest. For video, Vidu and Kling 3 hold the subject well when fed a high-quality character still as the reference.
Can I keep the character consistent in video?
Yes. The trick is to lock the character with image generations first, then chain the strongest still into the video node. Feeding the original reference straight into a video model usually works less well than the chained approach because the video model anchors more reliably to a high-fidelity still than to a portrait it has to interpret.
How do I handle outfit changes without breaking identity?
Use Flux Kontext with the canonical reference and a prompt that describes the new outfit. Flux Kontext is built for scene and outfit edits while preserving the face — far more reliable than re-prompting from scratch.
How is this different from OpenArt character mode?
OpenArt's character mode is a guided UI inside one tool. Martini lets you build a character chain across multiple best-in-class models — Nano Banana 2, Flux Kontext, Vidu, Kling — on one canvas, then save the chain as a reusable template. The character becomes a real asset across image and video, not just a session inside a single tool.
Do I need to retrain a model on my character?
No. Reference-based generation skips the training step entirely. Drop your portrait, wire it into the chain, and run. Training a custom LoRA or fine-tune is still possible for advanced use cases, but the reference-driven canvas covers the majority of consistency needs out of the box.
Can I use a real person as the character reference?
You can if you have rights and consent — common for spokespersons, executives, or contracted talent. For unlicensed celebrities or public figures, do not. Reference-based identity locking is powerful and the responsibility for usage rights sits with you.
Build it on the canvas
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