| # 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_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 | |
| --- | |
| ## 🔴 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. | |
| --- | |
| ## 🟠 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 | |
| --- | |
| ## 🟡 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](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](TODO.md). | |
| For recent changes, see [CHANGELOG.md](CHANGELOG.md). | |