Interactive Memory Demo

# AI Video Generation Pipeline — System Architecture

## Pipeline Overview
A fully automated, asynchronous pipeline that takes text prompts or scripts as input and orchestrates multiple AI models to produce a finished, stylized video with voiceovers, background music, and synchronized subtitles.

## Core Stages

### 1. Script & Prompt Processing
- **Input Gateway** — API endpoint receiving user requests (theme, tone, target length).
- **LLM Orchestrator** — Uses GPT-4o to expand the initial prompt into a detailed scene-by-scene script.
- **Prompt Engineering** — Generates specific image and video generation prompts for each scene (e.g., specifying camera angles, lighting, and style).

### 2. Asset Generation (Parallel Execution)
- **Audio Generation**
  - **Voiceover** — ElevenLabs API generates high-quality TTS from the script.
  - **Music/SFX** — Suno or custom AudioLDM models generate background tracks matching the scene's mood.
- **Visual Generation**
  - **Base Images** — Midjourney or Stable Diffusion XL creates keyframes for each scene.
  - **Video Synthesis** — Runway Gen-2 or Sora animates the keyframes based on the motion prompts.

### 3. Assembly & Synchronization
- **Timeline Alignment** — A custom Python service (using MoviePy) aligns video clips with the audio track.
- **Subtitle Generation** — Whisper API transcribes the generated voiceover to create precise timestamped SRT files.
- **Compositing** — FFmpeg overlays subtitles, applies transitions (e.g., crossfades), and mixes audio tracks (ducking music during voiceovers).

### 4. Quality Assurance & Delivery
- **Automated QA** — A lightweight vision model checks for visual artifacts or black frames.
- **Rendering** — Final output is encoded in H.264 (MP4) at 1080p or 4K.
- **Distribution** — Uploaded to S3, with CDN links returned via webhook or WebSocket to the user dashboard.

## Infrastructure & Scaling
- **Job Queue** — Redis + Celery manages the asynchronous generation tasks.
- **GPU Compute** —

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