SparkFrame

Overview

SparkFrame was developed to address one of the most frustrating bottlenecks in AI-driven narrative generation: temporal character and visual style consistency across multi-panel storyboards. By chaining generative vision models with constrained latent embeddings and automated quality checks, SparkFrame guarantees that character traits, outfits, and aesthetic cues remain identical across multiple generated scenes.

Key Metrics & Performance

Architecture & Design

The system uses an agentic pipeline orchestration layer. User prompts pass through a Semantic Decomposition Agent that breaks scripts into scene-by-scene visual blueprints. Next, an Embedding Anchor Manager locks facial and stylistic embeddings before passing tokens into the diffusion model. An automated Vision Evaluator inspects generated keyframes against character reference latents, triggering micro-refinements if drift is detected.

Key Capabilities

• Latent character embedding anchoring across arbitrary scene transitions
• Automated storyboard script decomposition into multi-panel scene blueprints
• Zero-shot visual style transfer with strict style adherence
• FastAPI async inference pipeline with streaming frame preview

Usage & Integration

from sparkframe.pipeline import StoryEngine
from sparkframe.models import CharacterAnchor

# Initialize Storyboard Engine with Character Anchor
anchor = CharacterAnchor.from_reference("assets/character_ref.png")
engine = StoryEngine(model="gemini-nano-vision", anchor=anchor)

# Generate multi-frame consistent narrative
storyboard = engine.generate_sequence(
    script="The engineer steps into the server room, checks the glowing neural core, and logs the metrics.",
    num_panels=4,
    style="dark-editorial-cinematic"
)
storyboard.export("output/scene_01.pdf")

Tech Stack & Tools