Work/AI Film Pipeline
Case Study
AI Film PipelineIn productionZeramental

AI Film Pipeline

End-to-end character production pipeline for Zeramental — a dark fantasy AI series. Concept brief to finished shot sequence, fully automated. Character identity consistent across every frame.

NB2
Primary image model
Kling 3
Video generation
FAL
LoRA training
Automated
Brief to CDN
Sin — full body, blackout aesthetic
Jack — wide shot, chrome spikes
Kezia — mid shot, amber eyes
Multi-character scene — OTS blocking
The project

Zeramental — dark fantasy AI production.

Zeramental is a dark fantasy AI character series — WoW-style aesthetic, original characters, and a pipeline built to produce consistent cinematic output at scale.

The problem with AI character production at scale is identity drift. Run a character prompt 50 times and you get 50 variations. The pipeline here solves that: per-character LoRA models, dual-reference injection, and the SAME anchor mechanic enforce identity across every generation.

The output is film-grade. The process is automated. Brief goes in, CDN assets come out.

Character roster

Sin — protagonist hero frame

Sin

Protagonist

Full-body blackout with pale veining and crackle. Face clear. Pale icy grey eyes. Chrome bracers and stiletto blade. Recurring male lead.

Jack — villain wide shot

Jack

Villain

Massive male figure. Black eyes. Chrome shoulder spikes. Primary antagonist. Shot in wide and mid compositions.

Kezia — character frame

Kezia

Secondary villain

Female villain. Amber eyes. Blackout arms. Present in multi-character scenes with Jack and Sin.

Pipeline breakdown

Every stage from brief to delivered asset.

01

Character brief

Physical attributes + visual identity written in photography language. No CGI vocabulary. Brief fed into the pipeline as structured parameters.

02

20-image reference build

Single-subject atmospheric shots across lighting conditions and angles. CSFD threshold check before training begins. 20 images is the floor — below this, LoRA underfit.

03

FAL LoRA training

Per-character model. $2.40–$8 per run. Three param schemes mapped. Training run checked against trigger word and config before production use.

04

Dual-reference injection

For multi-character scenes, both trained character models are injected as dual references. Standard single-ref fails when two trained characters share a generation.

05

Image generation — NB2 + GPT Image 2

NB2 (thinking_budget 1024) as primary. SAME anchor mechanic as mandatory closing line on every prompt. Photography-language vocabulary enforced throughout.

06

Video — Kling 3.0 + Seedance 2.0

Kling 3.0 for lip-sync and motion control. Seedance 2.0 for narrative multi-shot sequences. 180° rule, static camera priority, shot duration 6–9s target 8s.

07

QC + CDN delivery

CSFD threshold identity check. Duration and resolution verification. Auto-reject and re-queue on failure. R2 CDN upload on pass, URL registered in Supabase.

n8n film pipeline — workflow graph
LoRA training set — 20-image reference bundle
Zeramental — video frame sequence
Visual output

Dark fantasy aesthetic. Consistent across every run.

Zeramental — atmospheric environment
Sin — close-up, veining detail
Zeramental — action shot
Zeramental — video frame 1
Jack — villain wide shot
Multi-character — battle composition

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