ScriptEnhancer Agent in ViMax: Function, Implementation, and Pipeline Integration

The ScriptEnhancer agent refines outline-level scripts into production-ready screenplays by adding sensory details, enforcing continuity, and polishing dialogue while strictly preserving scene structure.

The HKUDS/ViMax repository implements a multi-agent pipeline for automated video generation. The ScriptEnhancer agent serves as the critical bridge between narrative planning and visual asset creation, transforming sparse script outlines into richly detailed screenplays that downstream components can process effectively.

Core Functions of the ScriptEnhancer Agent

The ScriptEnhancer operates as a dedicated refinement layer within the ViMax generation pipeline. Positioned immediately after the Script Planner and before the Storyboard Artist, it receives raw, outline-style scripts and applies four specific enhancement strategies defined in agents/script_enhancer.py.

Adding Concrete Sensory Details

The agent injects specific environmental descriptors including lighting conditions, textures, ambient sounds, weather patterns, and time-of-day indicators. These details provide essential visual cues for subsequent image and video generation models, ensuring the final assets match the intended atmosphere.

Enforcing Narrative Continuity

It validates consistency across character attributes (names, ages, relationships) and location details, ensuring the script maintains logical coherence from scene to scene. This prevents visual contradictions that would confuse downstream generation agents.

Polishing Dialogue and Action Lines

The agent rewrites speech to sound natural and conversational while keeping it concise. It refines action descriptions to be vivid yet precise, ensuring the Storyboard Artist and Image/Video Generators receive clear direction for visual composition.

Preserving Structural Integrity

Unlike generative agents that might add or remove content, the ScriptEnhancer strictly maintains the existing scene structure and plot points. It only enhances what is already present, ensuring the narrative arc planned by the Script Planner remains intact.

Technical Implementation

The implementation wraps any LangChain-compatible ChatModel interface with a carefully engineered system prompt and response validation layer.

The enhance_script Method

The core method enhance_script is asynchronous and handles the communication with the underlying LLM. According to the source code in agents/script_enhancer.py, this method:

• Accepts a raw script string as input
• Applies retry logic with up to three attempts on failure
• Returns the polished script as a plain string
• Includes structured logging for pipeline visibility

Response Schema and Validation

A Pydantic schema extracts the enhanced script from the LLM response. This ensures type-safe output that integrates cleanly with downstream agents, preventing malformed scripts from propagating to the storyboard generation phase.

Pipeline Position and Integration

In the ViMax architecture, agents execute sequentially to transform a concept into final video assets. The ScriptEnhancer sits squarely in the middle of this flow:

Script Planner → Script Enhancer → Storyboard Artist → Image/Video Generators → Render Backend

Wiring in script2video_pipeline.py

The orchestration file pipelines/script2video_pipeline.py instantiates the ScriptEnhancer class and invokes enhance_script on the output from the Script Planner. This ensures the Storyboard Artist (defined in agents/storyboard_artist.py) receives a detail-rich, continuity-validated script rather than a sparse outline that would produce inconsistent visual assets.

Code Examples

Direct Usage of ScriptEnhancer

from agents.script_enhancer import ScriptEnhancer

# Initialise the enhancer (replace with your own endpoint / key)

enhancer = ScriptEnhancer(
    chat_model="gpt-4o-mini",
    base_url="https://api.openai.com/v1",
    api_key="YOUR_API_KEY",
)

planned = """
INT. COFFEE SHOP – DAY

JULIE (28, witty) sits at a table, scrolling on her phone.
"""

# Run the enhancement (async context required)

enhanced_script = await enhancer.enhance_script(planned)
print(enhanced_script)

Integration Within the ViMax Pipeline

# Inside script2video_pipeline.py

from agents.script_planner import ScriptPlanner
from agents.script_enhancer import ScriptEnhancer

planner = ScriptPlanner(...)
enhancer = ScriptEnhancer(...)

async def generate_video(concept: str):
    raw_script = await planner.plan_script(concept)
    polished_script = await enhancer.enhance_script(raw_script)
    # ... pass `polished_script` to later agents

Summary

• The ScriptEnhancer agent sits between script planning and storyboard generation in the HKUDS/ViMax pipeline
• It adds sensory details, enforces continuity, and polishes dialogue without changing scene structure or plot points
• Implemented in agents/script_enhancer.py with the async enhance_script method
• Uses a Pydantic schema to validate LLM outputs and includes retry logic for reliability
• Integrated via pipelines/script2video_pipeline.py to ensure downstream agents receive production-ready scripts

Frequently Asked Questions

What is the difference between the Script Planner and ScriptEnhancer in ViMax?

The Script Planner generates the initial narrative outline including scene breakdowns and plot points according to agents/script_planner.py. The ScriptEnhancer then refines this outline into a detailed screenplay by adding sensory descriptions and polishing dialogue, without altering the underlying structure or story beats.

Does the ScriptEnhancer agent modify the plot or add new scenes?

No. According to the implementation constraints in agents/script_enhancer.py, the agent is explicitly designed to preserve the existing scene structure. It only enhances existing content by adding descriptive details and improving narrative flow, never introducing new scenes or removing existing ones.

Which LLM models work with the ScriptEnhancer?

The ScriptEnhancer accepts any chat model conforming to the LangChain ChatModel interface. The implementation is model-agnostic, allowing integration with OpenAI GPT models, local LLMs, or other compatible providers via configurable base_url and api_key parameters passed during initialization.

How does the ScriptEnhancer handle API failures or invalid responses?

The enhance_script method includes retry logic that attempts the operation up to three times on failure. It also uses a Pydantic schema to validate and extract the enhanced script from the LLM response, ensuring only properly formatted content passes to the agents/storyboard_artist.py and subsequent generation stages.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}