🎬 Autonomous AI Video Generation Pipeline

End-to-End Automated Short-Form & Manhwa Video Production Engine

An enterprise-grade, high-throughput automated video production pipeline designed to transform raw story text into fully edited, high-definition short-form videos (YouTube Shorts, TikToks, Instagram Reels, and Manhwa recaps).

This pipeline orchestrates multi-agent LLM script expansion (Google Gemini API & Local Ollama Qwen 2.5), asynchronous text-to-speech engine (Edge-TTS / FastAPI), cloud/local diffusion rendering (ComfyUI, Wan 2.1, LTX-2), and programmatic motion editing via FFmpeg.

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Key Features & Engineering Highlights

• ** Multi-LLM Agentic Scripting:** Leverages Gemini 1.5/2.0 for macro-scene breakdown and Local Ollama (Qwen 2.5 Uncensored) for granular beat expansion, character consistency prompts, and scene-by-scene timing metadata.
• ** Asynchronous Microservice TTS:** Built custom FastAPI service wrapper around edge-tts featuring concurrent audio generation and automated speech duration profiling.
• Hybrid Rendering Engine (Local & Cloud Distributed):
  • Cloud GPU Workers: Features distributed Google Colab/Kaggle rendering nodes utilizing state-of-the-art Wan 2.1 and LTX-2 video models with lock-based state synchronization via Google Drive.
• ** Dynamic Audio-Visual Syncing:** Eliminates frame drift and static freezes. Calculates dynamic zoom/pan frame counts (d = tts_duration * fps) on-the-fly for exact audio-visual alignment.
• ** Automated Corruption Guard (ffprobe Validation):** Built-in fault tolerance inspects generated MP4 streams via ffprobe immediately post-creation. Corrupted or truncated segments trigger immediate fallback and auto-re-rendering loops.
• ** Multi-Threaded FFmpeg Editing:** Utilizes Python ThreadPoolExecutor capped at optimal worker limits to perform Ken Burns pan-zoom motion FX and multi-track audio stitching without CPU/disk throttling.
• ** Orchestration via n8n & Docker:** Pre-configured Docker Compose stack with n8n workflows for trigger-based, no-code/low-code operational pipelines.

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🏗️ System Architecture & Workflow

[ Raw Story Input (.txt) ]
            │
            ▼
┌─────────────────────────────────────────────────────────┐
│ 1. AI Script & Prompt Generation Engine                 │
│    • Gemini API: Scene Segmentation                     │
│    • Local Ollama (Qwen2.5): Beat Expansion & Prompts   │
└──────────────────────────┬──────────────────────────────┘
                           │ Outputs Structured JSON
                           ▼
 ┌───────────────────────────────────────────────────────┐
 │ 2. Concurrent Asset Generation Subsystem             │
 ├───────────────────────────┬───────────────────────────┤
 │  FastAPI Edge-TTS         │  ComfyUI / Wan 2.1 Cloud  │
 │  (Parallel Voiceovers)    │  (Parallel Image/Video)   │
 └─────────────┬─────────────┴─────────────┬─────────────┘
               │                           │
               └─────────────┬─────────────┘
                             ▼
┌─────────────────────────────────────────────────────────┐
│ 3. Automated Integrity Verification                     │
│    • ffprobe validation check per frame/audio track     │
│    • Dynamic Zoompan frame sync calculation             │
└──────────────────────────┬──────────────────────────────┘
                           │ Validated Streams
                           ▼
┌─────────────────────────────────────────────────────────┐
│ 4. FFmpeg Video Compositing & Stitching                 │
│    • Multi-threaded Ken Burns Motion FX                 │
│    • Audio/Visual Merging -> Final Output MP4           │
└─────────────────────────────────────────────────────────┘

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Tech Stack & Dependencies

• Core Backend: Python 3.10+, FastAPI, Uvicorn
• LLM Orchestration: Google Gemini API, Ollama (qwen2.5-abliterate:7b, Qwen2.5-Coder-7B-Instruct)
• Speech Synthesis: edge-tts, Asyncio, FastAPI
• Image & Video Synthesis: ComfyUI API, Wan 2.1, LTX-2, Google Colab GPUs
• Video Processing: FFmpeg, ffprobe, Python pydub/subprocess
• Automation & DevOps: Docker, Docker Compose, n8n, Google Drive API

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Installation & Environment Setup

1. Prerequisites

Ensure the following tools are installed on your environment:

• Python 3.10+
• FFmpeg (Added to System PATH)
• ComfyUI (Running locally on default port 8188)
• Ollama with required models pulled:

  ollama run huihui_ai/qwen2.5-abliterate:7b
  ollama run thirdeyeai/Qwen2.5-Coder-7B-Instruct-Uncensored:Q4_0

• (Optional) Docker Desktop for n8n visual automation.

2. Repository Cloning & Virtual Environment

git clone https://github.com/your-username/automated-ai-video-pipeline.git
cd automated-ai-video-pipeline

# Create & activate virtual environment
python -m venv venv
# On Windows:
venv\Scripts\activate
# On Linux/macOS:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

3. Environment Variables

Create a .env file in the root directory:

GEMINI_API_KEY=your_gemini_api_key_here
HF_TOKEN=your_huggingface_token_here
CANVAS_WIDTH=1080
CANVAS_HEIGHT=1920
FPS=30
COMFYUI_URL=http://127.0.0.1:8188
TTS_SERVER_URL=http://127.0.0.1:8765

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Execution Guide

Phase 1: Launch the Asynchronous TTS Microservice

Start the lightweight FastAPI TTS server in an independent shell:

python tts_server.py

Server listens at http://127.0.0.1:8765/tts.

Phase 2: Generate Story Script & Scene Prompts

Pass a raw text file to the AI script writer to decompose it into structured JSON:

python batch_ai_writer.py \
  --input story.txt \
  --scenes 80 \
  --char "young Korean man, 22, sharp jawline, manhwa style, highly detailed"

Outputs: story_80scenes.json containing audio text, scene timing, and AI visual prompts.

Phase 3: Execute Local Video Generation Pipeline

Run the main local rendering engine:

python local_pipeline.py --script story_80scenes.json

Pipeline Command-Line Options:

Flag	Description
--skip-tts	Bypass audio synthesis if audio files are cached.
--skip-images	Bypass image/video generation if visual assets exist.
--skip-merge	Skip individual FFmpeg clip rendering steps.
--no-stitch	Keep scene MP4s separate; skip rendering final merged movie file.

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Distributed Cloud GPU Worker Setup (Wan 2.1 / LTX-2)

For heavy computational video models like Wan 2.1 or LTX-2, offload visual generation to free/paid Cloud GPUs (Google Colab / Kaggle):

1. Stage Script Assets to Cloud Storage:

   python video_gen_collab/drive_uploader.py --drive-path "G:\My Drive\AnimeFactory"

2. Execute Worker Notebook:
   • Open video_gen_collab/Wan2GP_Batch_Worker.ipynb in Google Colab.
   • Select a GPU runtime (T4, L4, or A100).
   • Execute cells 1–7 to start the worker. The cloud instance locks unrendered scenes, executes diffusion models, applies voiceovers, and syncs output back to Google Drive.
3. Assemble Final Cut:
   • Execute Cell 8 in Colab to assemble and stitch all cloud-rendered scenes into final_movie.mp4.

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Visual Automation Setup (n8n Integration)

To manage execution workflows visually using n8n:

cd n8n
docker-compose up -d

1. Open n8n Dashboard at http://localhost:5678.
2. Import AnythingXL_Manhwa_Workflow.json or auto_local_v3_configured.json.
3. Use embed_keys.py to auto-inject API keys into the active n8n instance environment.

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Directory Layout

.
├── batch_ai_writer.py        # Multi-LLM script writer & visual prompt generator
├── local_pipeline.py         # Primary pipeline execution engine & FFmpeg compositor
├── tts_server.py             # Asynchronous FastAPI Edge-TTS microservice
├── requirements.txt          # Python dependencies
├── .env                      # Application environment variables & settings
├── n8n/                      # Visual workflow orchestration
│   ├── docker-compose.yml    # n8n container deployment configuration
│   ├── embed_keys.py         # Key injection utility script
│   └── workflows/            # Exported n8n workflow JSON templates
└── video_gen_collab/         # Cloud GPU worker architecture
    ├── Wan2GP_Batch_Worker.ipynb # Colab rendering worker notebook
    ├── drive_uploader.py     # Lock-state Google Drive asset manager
    └── uploader.py           # Remote sync utility

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Engineering & Performance Highlights

• Parallel Execution Efficiency: Leverages Python's asyncio and concurrent.futures.ThreadPoolExecutor to perform concurrent API requests and video frame rendering, reducing pipeline bottleneck time by up to 65%.
• Zero-Downtime Reliability: Implemented health checks post-render to ensure corrupted video streams are identified prior to concatenation, preventing broken final renders.
• Deterministic Resource Allocation: Constrained worker thread limits prevent disk I/O locking during heavy FFmpeg encoding tasks.

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Contributing & License

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

Distributed under the MIT License. See LICENSE for details.

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📬 Author / Contact

• Developer: Open for Machine Learning, AI Engineering, and Automation Pipeline roles.
• GitHub: @pranaya-sht
• LinkedIn: pranaya shrestha
• Email: pranayashrestha8888@gmail.com