AI SHORT-FILM STUDIO · iPad + iPhone · BYOK
Same character.
Every scene.
Across every AI model.
By scene three, your character usually looks like their own cousin. Sceneable locks your lead’s look at the image layer — then renders every shot with the AI models you already pay for. The face holds.
The problem isn’t generating. It’s holding.
By scene three, they look like their own cousin.
Eye color shifts, freckles vanish, the jawline slides at second four. Multi-model AI video has one fatal flaw: drift.
17 tabs open to finish 3 minutes of film.
Image gen here, image-to-video there, character reference somewhere else. Every shot is a tab-shuffle.
You finish a short — then start the next from scratch.
The lead resets. No memory between projects. Last week’s character is gone.
Image-first. Lock once. Render with anything.
- 01
Sketch the shot cheaply
Generate a cut image — the image layer first, cheap to try, cheap to retry. Approve the one that holds.
- 02
Lock your lead
The approved image becomes the anchor. A character bible carries the look forward — shot 10 is more consistent than shot 2, not less.
- 03
Render with your stack
Commit the approved image to motion with your own key — Seedance, Veo, Kling, Hailuo. Different models, same character.
- 04
Finish + share
Stitch the timeline into a single MP4. Drop a Sceneable card on Reels / Shorts / Stories that links back.
Why a single-model tool can’t copy this
Single-model tools — Runway, Kling, Higgsfield — lock you to their model and hide the image layer. Sceneable sits above your stack: it locks consistency at the image layer, then dispatches to whatever model you choose. Cross-model consistency is something a single-model provider structurally can’t ship.
You bring the keys. We never charge for inference — and the Spend Estimator shows you what each generation costs, money we don’t even take.
Why I built it
“I kept seeing the same workflow on X for five weeks straight: GPT Image 2 to build the storyboard, Seedance 2.0 to animate it. Creators like Heather Cooper (@HBCoop_) had the cleanest version. The technique works — but it’s manual, scattered across four tabs, and falls apart by scene six. So I built Sceneable: that workflow as one focused tool. One anchor frame, N shots, your own keys at both layers, character stays the same across every scene.”
- Your API keys live in your device’s Keychain only — never sent to our servers.
- No subscription to your AI spend. BYOK at both layers; you pay the vendor.
- Works on iPad + iPhone. Made by an indie.
Questions
What is Sceneable?
Sceneable is an iOS (iPad + iPhone) AI short-film studio focused on one thing — keeping a character’s look consistent across every shot and every AI model. It uses an image-first workflow: you approve a cut image, which locks the character, then render motion with your own image-to-video provider.
Which AI models does Sceneable support?
It is BYOK (bring your own key). Image: GPT Image 2, Flux, Nano Banana. Video (image-to-video): Seedance 2.0, Kling, Hailuo, Veo. You pay the model vendors directly; Sceneable never charges for inference and shows a per-generation cost estimate.
How is this different from Runway, Kling, or Higgsfield?
Those are single-model tools — they lock you to their own model and hide the image layer where consistency is actually controlled. Sceneable sits above your stack and locks consistency at the image layer, so the same character renders identically whether you use Seedance, Veo, or Kling.
Why image-first instead of text-to-video?
Because the reference image does more work than the prompt. Locking the character in an approved still — then animating that still — is the dominant 2026 “anchor frame” workflow. It is cheaper to iterate on a still than to re-roll video, and it is what keeps the face from drifting.
Does it work for the GPT Image 2 + Seedance 2.0 workflow?
Yes — that specific workflow is exactly what Sceneable was built to productize, including 9-panel sprite-sheet storyboards sliced into a shot sequence.