Autonomous Video Editor

A complete AI-powered autonomous video editing system that combines reasoning, perception, and editing capabilities to automatically edit videos based on natural language prompts.

🚀 Features

Core Capabilities

• Autonomous Video Editing: Edit videos using natural language prompts
• Hybrid AI System: Combines CodeLLaMA (reasoning), CLIP (vision), and Whisper (audio)
• Advanced Effects: 15+ professional video effects with GPU acceleration
• Auto-Download: Automatic model and dataset downloading
• 5-Phase Training: Comprehensive training pipeline (pretraining → distillation → fine-tuning → RLHF → autonomous)
• Production Ready: Complete orchestration with validation and error handling

Video Effects

• Fade in/out, zoom effects, color grading
• Cinematic effects, vintage film look
• Cyberpunk aesthetic, dramatic shadows
• Vibrant colors, film grain, vignette
• Motion blur, lens flare, and more

Training Pipeline

1. Pretraining: Learn basic video understanding
2. Distillation: Compress knowledge from larger models
3. Fine-tuning: Task-specific adaptation with LoRA
4. RLHF: Human preference alignment
5. Autonomous: End-to-end autonomous editing

📋 Requirements

System Requirements

• Python 3.8+
• CUDA-capable GPU (recommended for training)
• 16GB+ RAM (32GB recommended for training)
• 50GB+ storage for models and datasets

Dependencies

# Core ML libraries
torch>=2.0.0
transformers>=4.30.0
opencv-python>=4.8.0
numpy>=1.24.0
pandas>=2.0.0

# Audio/Video processing
librosa>=0.10.0
moviepy>=1.0.3

# Training and optimization
accelerate>=0.20.0
deepspeed>=0.9.0
wandb>=0.15.0

# Optional enhancements
peft>=0.4.0  # For LoRA fine-tuning
bitsandbytes>=0.39.0  # For quantization

🛠 Installation

1. Clone the repository

git clone <repository-url>
cd auto_editor_prototype

2. Install dependencies

# Install core dependencies
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install transformers opencv-python numpy pandas librosa moviepy
pip install accelerate deepspeed wandb tqdm requests

# Optional: Install advanced features
pip install peft bitsandbytes  # For LoRA and quantization

3. Setup the application

python autonomous_video_editor.py --setup

This creates necessary directories and prepares the environment.

🚀 Quick Start

1. Download models and datasets

# Download AI models (CodeLLaMA, CLIP, Whisper)
python autonomous_video_editor.py --download-models

# Download training datasets (WebVid, AudioSet, etc.)
python autonomous_video_editor.py --download-data

2. Train the model (optional)

# Run full 5-phase training pipeline
python autonomous_video_editor.py --train

3. Edit a video

# Edit with natural language prompt
python autonomous_video_editor.py \
    --edit-video /path/to/your/video.mp4 \
    --prompt "Make this video more cinematic with dramatic lighting" \
    --output edited_video.mp4

4. Run demo

# See effects in action with sample videos
python autonomous_video_editor.py --demo

💻 Usage Examples

Basic Video Editing

from src.inference.autonomous_editor import quick_edit

# Simple editing with prompt
result = quick_edit(
    video_path="input.mp4",
    prompt="Add cinematic color grading and fade effects",
    output_path="output.mp4"
)

Advanced Usage

from autonomous_video_editor import AutonomousVideoEditorApp

# Initialize with custom config
app = AutonomousVideoEditorApp(config={
    'effects': {'quality': 'ultra', 'gpu_acceleration': True},
    'model': {'backbone': 'codellama/CodeLlama-7b-hf'}
})

app.setup()
app.load_model("path/to/trained/model.pt")

# Edit multiple videos
videos = ["video1.mp4", "video2.mp4", "video3.mp4"]
for video in videos:
    app.edit_video(
        video_path=video,
        prompt="Create a vintage film aesthetic with warm tones",
        output_path=f"edited_{video}"
    )

Batch Processing

from src.inference.autonomous_editor import batch_process_videos

# Process multiple videos with same style
batch_process_videos(
    video_paths=["video1.mp4", "video2.mp4"],
    prompt="Apply cyberpunk aesthetic with neon colors",
    output_dir="edited_videos/"
)

🎨 Available Effects

🔧 Configuration

Model Configuration

MODEL_CONFIG = {
    'backbone': 'microsoft/DialoGPT-small',  # Main reasoning model
    'vision_encoder': 'openai/clip-vit-base-patch32',  # Vision understanding
    'audio_encoder': 'openai/whisper-tiny',  # Audio processing
    'text_dim': 768,
    'vision_dim': 512,
    'audio_dim': 512,
    'fusion_dim': 1024,
    'hidden_dim': 2048
}

Training Configuration

TRAINING_CONFIG = {
    'batch_size': 4,
    'learning_rate': 1e-4,
    'num_epochs': 10,
    'bf16': True,  # Use bfloat16 for efficiency
    'gradient_accumulation_steps': 4,
    'phases': {
        'pretraining': {'enabled': True, 'epochs': 3},
        'distillation': {'enabled': True, 'epochs': 2},
        'fine_tuning': {'enabled': True, 'epochs': 3},
        'rlhf': {'enabled': True, 'epochs': 2},
        'autonomous': {'enabled': True, 'epochs': 2}
    }
}

Dataset Configuration

DATASET_CONFIG = {
    'auto_download': True,
    'webvid': {'enabled': True, 'samples': 10000},
    'audioset': {'enabled': True, 'samples': 5000},
    'activitynet': {'enabled': True, 'samples': 5000}
}

🏗 Architecture

Core Components

1. HybridVideoAI (src/core/hybrid_ai.py)

   • Main AI system combining reasoning, vision, and audio
   • Multimodal fusion and video understanding
   • Editing plan generation

2. AdvancedEffectGenerator (src/generation/effect_generator.py)

   • Professional video effects with GPU acceleration
   • Real-time processing capabilities
   • Extensible effect system

3. ModelDownloader (src/utils/model_downloader.py)

   • Automatic HuggingFace model downloading
   • Caching and fallback systems
   • Model validation and cleanup

4. DatasetAutoDownloader (src/utils/dataset_downloader.py)

   • Automatic dataset fetching and processing
   • Support for WebVid, AudioSet, ActivityNet, etc.
   • Progress tracking and resume capability

5. TrainingOrchestrator (src/training/training_orchestrator.py)

   • Complete 5-phase training pipeline
   • Automatic setup and validation
   • Comprehensive error handling

6. AutonomousVideoEditor (src/inference/autonomous_editor.py)

   • High-level editing interface
   • Natural language prompt processing
   • Video I/O and processing

Training Phases

graph LR
    A[Pretraining] --> B[Distillation]
    B --> C[Fine-tuning]
    C --> D[RLHF]
    D --> E[Autonomous]

1. Pretraining: Learn basic video understanding on large datasets
2. Distillation: Compress knowledge from larger teacher models
3. Fine-tuning: Task-specific adaptation using LoRA
4. RLHF: Align with human preferences for editing quality
5. Autonomous: End-to-end autonomous editing training

📊 Performance

Hardware Recommendations

Processing Speed

• Effect Application: ~30-60 FPS (1080p, RTX 3080)
• AI Inference: ~5-15 FPS (depending on model size)
• Training: ~2-8 hours per phase (RTX 4090, 10K samples)

🔍 Troubleshooting

Common Issues

1. CUDA Out of Memory

# Reduce batch size or use gradient accumulation
export CUDA_VISIBLE_DEVICES=0
python autonomous_video_editor.py --train --config small_config.json

2. Model Download Fails

# Clear cache and retry
rm -rf models/cache/*
python autonomous_video_editor.py --download-models

3. Import Errors

# Install missing dependencies
pip install -r requirements.txt

4. Video Processing Errors

# Check video format and codec
ffmpeg -i input.mp4  # Check video info

Performance Optimization

Enable GPU Acceleration:

config = {
    'effects': {'gpu_acceleration': True},
    'training': {'bf16': True, 'gradient_checkpointing': True}
}

Memory Optimization:

config = {
    'training': {
        'gradient_accumulation_steps': 8,
        'batch_size': 2,
        'dataloader_num_workers': 2
    }
}

🤝 Contributing

Development Setup

# Install development dependencies
pip install -e .
pip install black flake8 pytest

# Run tests
python -m pytest tests/

# Format code
black src/ tests/

Adding New Effects

# In src/generation/effect_generator.py
def apply_my_custom_effect(self, frame: np.ndarray, **kwargs) -> np.ndarray:
    # Implement your effect
    return processed_frame

Adding New Datasets

# In src/utils/dataset_downloader.py
def download_my_dataset(self):
    # Implement dataset download and processing
    pass

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• HuggingFace Transformers for model infrastructure
• OpenAI for CLIP and Whisper models
• Meta for CodeLLaMA
• OpenCV community for video processing
• All dataset creators and contributors

📞 Support

For issues and questions:

1. Check the troubleshooting section
2. Search existing GitHub issues
3. Create a new issue with detailed information

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Made with ❤️ for the video editing community

Key Capabilities:

• Understanding video like a human editor – narrative, emotions, rhythm, style
• Learning continuously – from new videos, edits, and user feedback
• Writing its own code – to generate transitions, effects, shaders, or editing algorithms
• Expanding knowledge – adapting to any editing genre: cinematic, AMVs, phonk, sports, comedy, documentaries
• Becoming an autonomous agent – capable of professional-level video creation

We're not just connecting tools like a conductor — we're fusing them into one hybrid AI model that learns the capabilities of Whisper, RT-DETR, CLIP, etc. inside a single unified brain.ous AI Video Editor 🌌 Vision This project is not just a video editor — it’s a self-thinking, self-learning, and self-improving AI system capable of: Understanding video like a human editor – narrative, emotions, rhythm, style. Learning continuously – from new videos, edits, and user feedback. Writing its own code – to generate transitions, effects, shaders, or even new editing algorithms. Expanding knowledge – adapting to any editing genre: cinematic, AMVs, phonk, sports, comedy, documentaries, etc. Becoming an autonomous agent – capable of professional-level video creation with minimal human input.

We’re not just connecting tools like a conductor (orchestrator) — we’re trying to fuse them into one hybrid AI model, so it doesn’t just use Whisper, YOLO, SAM, etc. separately, but actually learns their capabilities inside a single unified brain. That means: Fine-tuning / Multi-modal fusion → train an AI that natively understands video (vision + audio + text + editing logic). Knowledge distillation → compress the knowledge of many specialized models into one hybrid foundation model. Self-improving → the fused AI keeps training itself, not just switching between external tools.

🧠 Hybrid AI Stack

1. Reasoning & Core Brain CodeLLaMA 34B (fine-tuned) – For reasoning + code generation. GPT-NeoX – Large transformer backbone. Mixtral-8x7B / DeepSeek-MoE – Efficient Mixture-of-Experts reasoning + code-writing. Transformers Library – Model orchestration. AutoCoder Engine – Writes/refactors Python, GLSL, and FFmpeg scripts for new effects.
2. Perception & Analysis Layer RT-DETR – Real-time transformer-based object detection. SigLIP / EVA-CLIP – State-of-the-art vision-language understanding. HQ-SAM / MobileSAM – High-quality segmentation & masking. MediaPipe – Face & landmark analysis. VideoMAE v2 – Temporal transformer for scene activity & motion understanding.
3. Audio Intelligence Distil-Whisper + MMS – Scalable multilingual speech recognition & subtitles. Hybrid BeatNet – Transformer-based beat and tempo analysis. Demucs v4 – Music/speech source separation. CREPE – Pitch + vocal melody extraction. MusicLM Embeddings – Music understanding and genre context.
4. Video Editing Core FFmpeg – Rendering, video processing backbone. MoviePy – Timeline and composition. RAFT Optical Flow – Smooth motion interpolation. HDRNet (Google) – Automatic cinematic color correction. Diffusion Video Transformers (AnimateDiff v3 / DynamiCrafter) – High-quality AI-driven video edits.
5. Content Generation Stable Diffusion XL + ControlNet – Frame/overlay generation. AnimateDiff v3 + DynamiCrafter – Video generation from prompts with temporal consistency. DreamGaussian / 4D Gaussian Surfels – Experimental 3D/CGI elements.
6. Self-Learning Loop DPO (Direct Preference Optimization) – More stable training from feedback. VBench – Automatic multi-metric video quality scoring. RLHF – AI improves by comparing versions. Knowledge Expansion – Periodic re-training on new datasets (AMVs, movie edits, sports highlights, etc.). 📊 Capabilities Autonomous Planning – AI generates an editing plan from a text prompt. Beat-Synced Cuts – Perfect sync to music drops (phonk, AMV, reels). Code-Generated Effects – AI writes its own transitions, shaders, overlays. Narrative Understanding – Cuts and pacing based on emotion and storytelling. Genre Mastery – Learns new styles from data (TikTok edits, trailers, anime, films). Self-Improvement – Reviews its output and improves effects over time. Unified Understanding: One model that sees, hears, and understands video/audio in context. Autonomous Editing: Generates full edit timelines from a prompt. Code-Writing Power: Can produce new effect/transitions if not in its learned library. Self-Learning: Expands its editing knowledge continuously. Scalable Knowledge: Learns from millions of videos → masters all editing styles. can handle mutliple heavy timelines at a time can make a 10-20 sec reel with hundreads of transitions, effects, filters, audio and timelines at a time one upon other and more.

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🛠 Tools for Fusion Training PyTorch + Hugging Face Transformers – training core. DeepSpeed / FSDP – distributed training for huge multimodal models. LoRA / QLoRA / UniAdapter – parameter-efficient multi-modal fusion. Progressive Distillation – step-by-step compression of expert models. OpenCLIP / SigLIP – strong vision-language embeddings. TorchAudio + TorchVision + decord – video/audio pipeline. Weights & Biases / MLflow – experiment tracking. ⚡ Training Roadmap Phase 1 – Fusion Pretraining Train multimodal embedding space with WebVid-10M + AudioSet + text captions. Phase 2 – Distillation Distill knowledge from RT-DETR (objects), HQ-SAM (masks), Whisper-MMS (speech). Compress into hybrid core. Phase 3 – Editing Fine-Tuning Train on AMV, cinematic trailers, TikTok edit datasets. Align outputs with editing tokens (cut, transition, color-grade, sync). Phase 4 – Self-Improvement Add reinforcement loop: AI critiques and improves its edits. Expand code-writing ability (auto-generate FFmpeg/GLSL transitions). Phase 5 – Autonomous Hybrid AI Single fused model capable of understanding, editing, generating, and learning.

🔮 Future Directions Neural Rendering Fusion – add text-to-3D and CGI pipelines. Meta-Learning – AI studies film editing books, tutorials, essays. Global Trends Model – learns editing styles from viral TikToks and YouTube edits. Collaborative Agent – works with human editors interactively.

🔄 Final Fusion Order Summary Reasoning brain → fuse LLaMA, Mistral, GPT-NeoX into DeepSeek-MoE. Vision fusion → distill RT-DETR + HQ-SAM + VideoMAE into backbone. Audio fusion → distill Whisper + MMS + MusicLM embeddings. Editing core → fine-tune on editing tokens, distill RAFT + HDRNet. Content generation → distill SDXL + AnimateDiff + DreamGaussian. Self-learning → add RLHF/DPO + auto-retraining loop.