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.
✅ 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_compatfor seamless PyTorch integration
Infrastructure & Tooling
- ✅ Model Migration: All weights migrated to GitHub Releases with SHA256 verification
- ✅ Verification Scripts:
verify_models.pyandverify_refactor.pyfor 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
🔴 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
- Add NPU device detection to
Documentation & Testing
- Create
docs/intel_npu_setup.md - Add NPU tests to CI/CD pipeline
- Update hardware requirements guide
- Create
Expected Impact: Enable efficient inference on laptops and mobile workstations without discrete GPUs.
🟠 Medium Priority (Q2 2025)
AMD GPU Support
Goal: Support Radeon GPUs via ROCm
- Add ROCm device detection
- Create
environment_amd.ymlfor 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
🟡 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
🔵 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)
📊 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
🤝 How to Contribute
We welcome contributions aligned with this roadmap! See 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
📝 Roadmap Updates
This roadmap is reviewed and updated quarterly. Last updated: December 2025
For detailed technical tasks, see TODO.md.
For recent changes, see CHANGELOG.md.