# DeOldify Roadmap

This document outlines the development roadmap for DeOldify (Modernized), organized by priority and timeline. The project has successfully completed a major modernization effort and is now focused on expanding hardware support and exploring new deployment options.

## 🎯 Project Vision

Make DeOldify accessible and performant across modern hardware platforms (NVIDIA, Intel, AMD) while maintaining the cutting-edge colorization quality that made it popular. Enable deployment in diverse environments from local machines to cloud infrastructure.

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## ✅ Recently Completed (v2.0 - November 2025)

### Core Modernization
- ✅ **PyTorch 2.5+ Migration**: Removed dependency on obsolete FastAI 1.x library
- ✅ **CUDA 12.x Support**: Full support for modern NVIDIA GPUs
- ✅ **Intel GPU Support**: Arc and Data Center GPU support via Intel Extension for PyTorch (IPEX)
- ✅ **Unified Device Management**: Automatic detection and fallback (Intel → NVIDIA → CPU)
- ✅ **Compatibility Layer**: Created `deoldify.fastai_compat` for seamless PyTorch integration

### Infrastructure & Tooling
- ✅ **Model Migration**: All weights migrated to GitHub Releases with SHA256 verification
- ✅ **Verification Scripts**: `verify_models.py` and `verify_refactor.py` for validation
- ✅ **GitHub Community Standards**: Code of Conduct, Contributing Guidelines, Security Policy
- ✅ **CI/CD**: Unit tests and automated workflows
- ✅ **Browser Implementation**: Local ONNX-based colorization in browser

### Documentation
- ✅ **Setup Guides**: Comprehensive guides for NVIDIA and Intel GPUs
- ✅ **Hardware Guide**: Benchmarks and requirements
- ✅ **Deployment Guide**: Local serving instructions
- ✅ **Modernized Notebooks**: Updated Colab notebooks with file upload widgets

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## 🔴 High Priority (Q1 2025)

### Intel NPU Support
**Goal**: Enable Neural Processing Unit acceleration for Intel Core Ultra processors

- [ ] **Research & Investigation**
  - Investigate OpenVINO toolkit integration
  - Evaluate Intel Extension for PyTorch NPU capabilities
  - Benchmark NPU performance vs GPU/CPU inference
  
- [ ] **Implementation**
  - Add NPU device detection to `deoldify.device`
  - Implement NPU-specific optimizations
  - Update fallback chain: Intel NPU → Intel GPU → NVIDIA GPU → CPU
  
- [ ] **Documentation & Testing**
  - Create `docs/intel_npu_setup.md`
  - Add NPU tests to CI/CD pipeline
  - Update hardware requirements guide

**Expected Impact**: Enable efficient inference on laptops and mobile workstations without discrete GPUs.

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## 🟠 Medium Priority (Q2 2025)

### AMD GPU Support
**Goal**: Support Radeon GPUs via ROCm

- [ ] Add ROCm device detection
- [ ] Create `environment_amd.yml` for ROCm environments
- [ ] Test on RDNA 2/3 architecture
- [ ] Document AMD setup process

### Performance Optimizations
**Goal**: Improve inference speed and memory efficiency

- [ ] **Quantization**: INT8/FP16 inference modes
- [ ] **Dynamic Batching**: Process multiple images efficiently
- [ ] **Model Pruning**: Reduce model size without quality loss
- [ ] **ONNX Runtime**: Evaluate ONNX Runtime for cross-platform inference

### Enhanced Browser Implementation
**Goal**: Improve browser-based colorization UX

- [ ] Add WebGPU support for hardware acceleration
- [ ] Implement progressive rendering for large images
- [ ] Add batch processing capabilities
- [ ] Create comparison slider UI

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## 🟡 Low Priority (Q3-Q4 2025)

### Cloud Deployment
**Goal**: Simplify cloud deployment for production use

- [ ] **Google Cloud Platform**
  - Vertex AI deployment scripts
  - Container images for Cloud Run
  - Example Terraform configurations
  
- [ ] **AWS**
  - SageMaker deployment templates
  - Lambda@Edge for serverless inference
  
- [ ] **Azure**
  - Azure ML deployment guide
  - Container Apps examples

### Model Improvements
**Goal**: Enhance colorization quality and capabilities

- [ ] **Fine-Tuning Tools**
  - Scripts for domain-specific fine-tuning
  - Transfer learning examples
  - Custom dataset preparation guides
  - **Custom Training Guides**: Documentation for fine-tuning NoGAN on domain-specific footage (e.g., anime, old film)

- [ ] **Post-Processing Tools**
  - Advanced deflicker integration (FFmpeg)
  - Temporal smoothing helpers
  - Comparison tools for different render factors
  
- [ ] **New Model Variants**
  - Lightweight mobile-optimized model
  - Ultra-high-resolution model (8K+)
  - Real-time video colorization model

### API & Integration
**Goal**: Make DeOldify easier to integrate into other applications

- [ ] **REST API**
  - FastAPI/Flask-based serving
  - Docker containers with API
  - OpenAPI/Swagger documentation
  
- [ ] **Python Package**
  - Publish to PyPI
  - Simplified installation (`pip install deoldify`)
  - High-level API for common use cases

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## 🔵 Future Exploration (2026+)

### Advanced Features
- **Temporal Coherence**: Improved video stability with optical flow
- **User Guidance**: Interactive colorization with color hints
- **Style Transfer**: Multiple artistic colorization styles
- **4K/8K Support**: Native ultra-high-resolution processing

### Research Directions
- **Diffusion Models**: Explore stable diffusion for colorization
- **Transformer Architectures**: Evaluate Vision Transformers (ViT)
- **Few-Shot Learning**: Colorize with minimal reference images
- **Historical Accuracy**: Training on verified historical color photos

### Community Features
- **Model Zoo**: User-contributed fine-tuned models
- **Plugin System**: Extensible architecture for custom filters
- **Web Service**: Official hosted API (potential paid tier)

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## 📊 Success Metrics

We measure progress through:

- **Hardware Coverage**: Percentage of modern GPUs supported (Target: 90%+)
- **Inference Speed**: FPS for 1080p video colorization (Target: 30+ FPS on modern GPU)
- **Model Quality**: User satisfaction and comparison to commercial solutions
- **Adoption**: GitHub stars, PyPI downloads, community contributions
- **Documentation**: Completeness and clarity based on user feedback

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## 🤝 How to Contribute

We welcome contributions aligned with this roadmap! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

**High-Impact Areas**:
- Testing on different hardware configurations
- Documentation improvements and translations
- Performance benchmarking and optimization
- Bug reports with reproducible examples

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## 📝 Roadmap Updates

This roadmap is reviewed and updated quarterly. Last updated: **December 2025**

For detailed technical tasks, see [TODO.md](TODO.md).  
For recent changes, see [CHANGELOG.md](CHANGELOG.md).