# A Generative-AI Video Production Pipeline
Author: Alex Nikulin (Александр Никулин) — https://www.alexnix.com
Original: https://www.alexnix.com/en/articles/generative-ai-production-pipeline-paper · Published: 2026-04-20 · Updated: 2026-10-03
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---
> A reference architecture: 6 stages from brief to video, 3 consistency mechanisms, and a dependency graph.
Tags: production-pipeline, video, generative-ai, publication, architecture
- Production video on generative models rests on a 6-stage pipeline, not on the model
- Consistency comes from 3 mechanisms (inheritance, reference types, lock layers), not training
- Scenes render in parallel; shots within a scene render strictly in sequence
## The problem
Most writing about generative video treats generation as a single step: a prompt goes in, a clip comes out. Production doesn't work that way. A thirty-shot spot isn't thirty independent generations — it's a dependency graph: shots in the same scene have to hold together, characters and locations have to stay recognizable from shot to shot, the style has to survive the full runtime, and someone has to write all those prompts so that each stage's output is usable by the next.
This paper describes a pipeline built and run on productions across 2024–2026 as a reference architecture. The thesis: production-quality generative video comes from a constraint-propagation pipeline, not from the model. The models inside it are interchangeable: swap the video model or the image model and the pipeline keeps working, because the pipeline is what holds the project together.
## Method
The pipeline breaks down into six stages; each adds one class of information and can be refined independently, without re-running the earlier ones.
setup → scenario → storyboard → moodboard → stills → video.
```mermaid
flowchart LR
S[Setup:
inputs] --> SC[Scenario:
shot list]
SC --> SB[Storyboard:
composition]
SC --> MB[Moodboard:
style, color, light]
SB --> ST[Stills:
render]
MB --> ST
ST --> V[Video:
motion]
```
Setup. The minimum description of the project: title, aspect ratio, runtime estimate, technical notes, knowledge-base files (briefs, scripts, reference documents). Nothing is generated yet — this is the staging area for inputs.
Scenario. A brief or script becomes a structured shot list. A model acting as production director outputs a list of shots, each with a name of the form "scene number + letter" (1A, 1B, 2A), a [seven-element description](https://www.alexnix.com/en/articles/seven-element-shot-description) (subject and action, location, framing, angle, movement, lens, lighting), a type (fully generative, composite, live action), a duration, and an optional paired-keyframe flag for stitching transitions. The naming scheme is load-bearing: shots that share a scene number form a series that has to stay visually consistent, and the scheme passes unchanged through every downstream stage. More in [the brief → scenario pipeline](https://www.alexnix.com/en/articles/brief-to-scenario-pipeline).
Storyboard. A fast, cheap visual lock on composition and camera language. The model outputs a per-shot prompt with a fixed hand-drawn sketch style prefix (black ink on white paper, red accents, hatching). Two properties make the stage load-bearing: one prefix across all projects turns the sketches into a neutral composition vocabulary that later stages read for framing, not style; and earlier sketches in a series become references for later ones, with the model deliberately choosing different angles while holding the style. See [the storyboard system](https://www.alexnix.com/en/articles/storyboard-generation-system).
Moodboard — in parallel with storyboard. A two-tier reference system: the project moodboard holds shared references, the scene moodboard holds overrides. Every reference has a typed role (location, character, style, prop, color, camera) that later stages use to decide which reference answers which question. Each scene gets one composite 16:9 moodboard frame that collapses many references into a single image. That way style, color, and light reach the stills stage as one visual input rather than a dozen raw images that would over-constrain generation.
Stills. A photoreal render per shot. The stage consumes six sources: the shot description text, the sketch (composition only), previous renders in the same series (continuity), the composite moodboard (style, color, light), moodboard captions (environment context), and the shot type. The system prompt deliberately forbids describing style, color, and light in text: those dimensions arrive through images. The text describes only subject, location, and camera — see [deliberate omission](https://www.alexnix.com/en/articles/deliberate-omission-paper). This inverts the [A.O.C.](https://www.alexnix.com/en/articles/aoc-framework-paper) approach for single and campaign shots, where text specifies all three axes: a production shot has dense visual references, a campaign shot often has one or two, and attention is allocated across whatever anchors are available.
```mermaid
flowchart LR
T[Text: subject,
location, camera] --> S[Stills:
shot render]
E[Sketch:
composition] --> S
P[Previous renders
in series] --> S
M[Moodboard composite:
style, color, light] --> S
C[Moodboard
captions] --> S
K[Shot type] --> S
```
Linear inheritance within a series replaces training a custom character model. Inside a series (1A → 1B → 1C), each render attaches the previous shot's image as a reference. The character's look established in 1A carries forward without training. The mechanism is described in detail in [the sequential consistency architecture](https://www.alexnix.com/en/articles/sequential-consistency-prompt-architecture-paper).
Video. Motion generation. The model outputs a one- or two-sentence prompt: camera movement and subject action, with no aesthetic adjectives and no scene description, because the scene is already rendered. The key capability is first/last-frame stitching: for a pair of shots flagged at the scenario stage, one render is fed in as the first frame and the other as the last, and the model generates only the motion between them. The result is a seamless transition with no dissolve and no cut. This is possible only because the pipeline is frame-first: motion is the last decision, not the first.
```mermaid
flowchart LR
A[Render A:
first frame] --> V[Video model:
motion only]
B[Render B:
last frame] --> V
V --> T[Seamless
transition]
```
Three consistency mechanisms. Thirty shots give a character's wardrobe thirty chances to drift, the lighting thirty chances to shift, the packaging text thirty chances to warp. The pipeline holds this with three interlocking mechanisms. The first is inheritance within a series: linear render order, with each shot inheriting the previous shot's render. The second is a reference typology across shots within a project: every reference has a declared role, and the model is instructed to state right in the prompt what each attached image contributes; silent use of references is forbidden. The third is prompt-level [lock layers](https://www.alexnix.com/en/articles/lock-layer-pattern-paper) for campaign shots: an IDENTITY LOCK or PRODUCT LOCK block with a subject descriptor (face, body, hair; or form, material, orientation, text) that stays unchanged across the whole batch and survives generation variance.
```mermaid
flowchart TD
K[Consistency
across 30 shots] --> I[Inheritance
within a series]
K --> R[Reference
typology]
K --> L[Lock layers
in the prompt]
I --> I1[Shot inherits the
previous render]
R --> R1[Every image's
role is declared]
L --> L1[IDENTITY or
PRODUCT LOCK]
```
The dependency graph. Between stages the pipeline is linear, but inside the later stages the shot graph becomes a forest of linear chains: there's no dependency between scenes, so they render in parallel; within a scene the order is strict, and a shot's render is blocked until the previous shots in that scene are done. Two gating conditions enforce this (the previous shots in the series have a sketch; the previous shots in the series have a render). The shape of the graph is what makes a large project tractable: work parallelizes across scenes, while consistency is guaranteed within each one.
```mermaid
flowchart TB
subgraph s1[Scene 1]
direction LR
A1[1A] -->|render| B1[1B]
B1 -->|render| C1[1C]
end
subgraph s2[Scene 2, in parallel]
direction LR
A2[2A] -->|render| B2[2B]
B2 -->|render| C2[2C]
end
s1 ~~~ s2
```
The orchestration layer. Every stage except moodboard compositing is an LLM call with a narrow contract: its own role, its own temperature (0.4 for brief analysis, 0.3 for parsing and edits), a strict output format. Two properties of the layer are load-bearing. System-prompt discipline: each role has a narrow mandate — the stills prompt explicitly forbids style, color, and light; the video prompt forbids aesthetics. A broad mandate lets the model contaminate the next stage with concerns that should have stayed in this one (see [the two-stage architect pattern](https://www.alexnix.com/en/articles/two-stage-architect-pattern-paper)). And cheap edits: every generative stage has a single-shot refinement mode that reuses the existing prompt as its baseline. If an edit cost as much as a fresh generation, producers would either commit too early or burn the budget; a cheap edit keeps them in the director's chair. The same stage charters also run as agent skills across different model providers (Happy Horse for video, MiniMax for speech, and others): the contract stays the same while the model behind it changes, and first/last-frame stitching remains a capability of the pipeline and its prompt architecture rather than of any particular model.
What the pipeline deliberately doesn't do. It doesn't train custom character models as the primary consistency mechanism: training is available but reserved for long-lived personas across projects; for a single production, inheritance and typology are cheaper and fast enough. It doesn't expose a single "generate the whole project" button: the stage-by-stage progression is the interface, and the producer stays in the loop at every stage. It doesn't try to author the narrative: parsing restructures, it doesn't invent; the brief is the author, the pipeline is the crew. It doesn't fight model quirks with fallback logic: generation failures surface to the producer as an invitation to iterate, not as silent retries.
**Update, September 2026.** A retrospective on 2025–2026 advertising projects shows where the line for training falls. One of the projects was made before image models could hold a character from a reference, and there training was the only workable path: LoRAs were trained for the main characters on a dataset of 12 frames, each frame with its own text description and a shared trigger. Scenes on Flux were assembled in two steps: first the environment, then the characters were placed into the frame by inpainting through those LoRAs, so they matched the scene's light and perspective instead of sitting on top of it. Once a model can hold a look from a reference, inheritance within a series and the reference typology take over that work, and training remains for long-lived personas.
What turned out to be load-bearing, roughly in order of how much its absence hurts: the shot-naming scheme, which every consistency mechanism depends on; the fixed storyboard style; minimalism in stills prompts, which saves renders from over-constraint; cheap edits, which make the pipeline useful rather than theoretical; first/last-frame stitching as the most visible result on screen; and predictable effort per stage, which is why producers trust the system.
## Where it breaks
- Cross-scene consistency for recurring characters rests on moodboard references and user discipline. The pipeline guarantees the series, not the whole project; a lightweight cross-scene anchor ("scene 3 features the same character as scene 1") would close the gap without training, but it doesn't exist yet.
- The forest of chains doesn't capture genuine cross-scene dependencies, such as a matched cut from the last shot of scene 1 to the first shot of scene 2. Today that's handled by stitching only at adjacent indices; a general DAG would be more honest.
- Shot type is set by hand at the brief stage, and scoring of seven-element descriptions remains ad hoc. Both call for an automatic heuristic or rubric.
- There's no consistency metric: whether 1A and 1B "feel like one scene" is decided by eye. Possible approaches (CLIP similarity, face-embedding distance, scene-graph comparison) haven't been tested.
- It's unknown whether the six-stage shape carries over to audio-led productions or interactive formats.
## Summary
- Production-quality generative video is a constraint-propagation pipeline; the models inside it are interchangeable components.
- Six stages, each adding one class of information and refined independently; composition, rendering, and motion are separated.
- Consistency comes from inheritance within a series, the reference typology, and lock layers — not from model training.
- The smallest set of commitments that reliably yields consistent, directable output: stage decomposition, series consistency, minimalism in stills prompts, and the "frame first, motion second" order.
---
© Alex Nikulin (Александр Никулин). Original: https://www.alexnix.com/en/articles/generative-ai-production-pipeline-paper. Quote only with credit to the author and a link to the original.