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---
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license: mit
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tags:
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- image-colorization
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- gan
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- computer-vision
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- pytorch
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- onnx
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library_name: pytorch
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---
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# DeOldify Model Weights
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This repository contains pretrained weights for **DeOldify**, a deep learning model for colorizing and restoring old black and white images and videos.
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**Original Repository**: [thookham/DeOldify](https://github.com/thookham/DeOldify)
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**Original Author**: Jason Antic ([jantic/DeOldify](https://github.com/jantic/DeOldify))
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## Model Overview
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DeOldify uses a Self-Attention Generative Adversarial Network (SAGAN) with a novel **NoGAN** training approach to achieve stable, high-quality colorization without the typical GAN artifacts.
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### Three Specialized Models
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1. **Artistic** - Highest quality with vibrant colors and interesting details
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- Best for: General images, historical photos
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- Backbone: ResNet34 U-Net
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- Training: 5 NoGAN cycles, 32% ImageNet
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2. **Stable** - Best for portraits and landscapes, reduced artifacts
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- Best for: Faces, nature scenes
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- Backbone: ResNet101 U-Net
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- Training: 3 NoGAN cycles, 7% ImageNet
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3. **Video** - Optimized for smooth, flicker-free video
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- Best for: Video colorization, consistency
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- Backbone: ResNet101 U-Net
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- Training: Initial cycle only, 2.2% ImageNet
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## Available Files
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### ONNX Models (Browser/Inference)
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| File | Size | Description |
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|------|------|-------------|
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| `deoldify-art.onnx` | 243 MB | Artistic model in ONNX format for browser use |
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| `deoldify-quant.onnx` | 61 MB | Quantized artistic model (75% smaller, slightly lower quality) |
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### PyTorch Weights (Training & Inference)
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**Generator Weights** (Main):
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- `ColorizeArtistic_gen.pth` (243 MB)
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- `ColorizeStable_gen.pth` (834 MB)
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- `ColorizeVideo_gen.pth` (834 MB)
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**Critic Weights** (Main):
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- `ColorizeArtistic_crit.pth` (361 MB)
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- `ColorizeStable_crit.pth` (361 MB)
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- `ColorizeVideo_crit.pth` (361 MB)
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**PretrainOnly Weights** (For continued training):
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- `ColorizeArtistic_PretrainOnly_gen.pth` (729 MB)
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- `ColorizeArtistic_PretrainOnly_crit.pth` (1.05 GB)
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- `ColorizeStable_PretrainOnly_crit.pth` (1.05 GB)
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- `ColorizeVideo_PretrainOnly_crit.pth` (1.05 GB)
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> **Note**: Stable and Video PretrainOnly generators are split files hosted on [GitHub Releases](https://github.com/thookham/DeOldify/releases/tag/v2.0-models).
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## Usage
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### Browser (ONNX)
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```html
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<!DOCTYPE html>
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<html>
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<head>
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<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>
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</head>
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<body>
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<script>
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async function colorize() {
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// Load model from Hugging Face
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const session = await ort.InferenceSession.create(
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"https://huggingface.co/thookham/DeOldify/resolve/main/deoldify-art.onnx"
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);
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// Run inference (see full example in GitHub repo)
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// ...
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}
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</script>
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</body>
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</html>
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```
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### PyTorch (Python)
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```python
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from huggingface_hub import hf_hub_download
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import torch
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# Download model weights
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model_path = hf_hub_download(
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repo_id="thookham/DeOldify",
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filename="ColorizeArtistic_gen.pth"
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)
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# Load weights (requires deoldify package installed)
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# See GitHub repository for full usage examples
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```
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### Installation
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```bash
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# Clone the main repository
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git clone https://github.com/thookham/DeOldify
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cd DeOldify
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# Install dependencies
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pip install -r requirements.txt
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# Download a model
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from huggingface_hub import hf_hub_download
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model = hf_hub_download(repo_id="thookham/DeOldify", filename="ColorizeStable_gen.pth")
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```
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## Technical Details
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### Architecture
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- **Generator**: U-Net with ResNet34/101 backbone, spectral normalization, self-attention layers
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- **Critic**: PatchGAN discriminator
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- **Loss**: Perceptual loss (VGG16) + GAN loss
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### NoGAN Training
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A novel training approach that combines:
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1. Generator pretraining with feature loss
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2. Critic pretraining on generated images
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3. Short GAN training (30-60 minutes) at inflection point
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4. Optional cycle repeats for more colorful results
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This eliminates typical GAN artifacts while maintaining realistic colorization.
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### Training Data
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- Dataset: ImageNet subsets (1-32% depending on model)
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- Resolution: 192px during training
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- Augmentation: Gaussian noise for video stability
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## Model Card
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### Model Details
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- **Developed by**: Jason Antic (original), Travis Hookham (modernization)
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- **Model type**: Conditional GAN for image-to-image translation
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- **Language(s)**: N/A (computer vision)
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- **License**: MIT
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- **Parent Model**: Based on FastAI U-Net and Self-Attention GAN papers
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### Intended Use
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**Primary Use**: Colorizing black and white photographs and videos
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**Out-of-Scope**: Real-time processing, guaranteed historical accuracy
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### Limitations
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- Colors may not be historically accurate
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- Performance degrades on very low quality/damaged images
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- Artistic model may require render_factor tuning
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- Video model trades some color vibrancy for consistency
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## Related Models & Resources
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### Similar Colorization Models on Hugging Face
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**GAN-based Colorization:**
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- [Hammad712/GAN-Colorization-Model](https://huggingface.co/Hammad712/GAN-Colorization-Model) - GAN model for grayscale to color transformation
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- [jessicanono/filparty_colorization](https://huggingface.co/jessicanono/filparty_colorization) - ResNet-based model for historical photos
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**Stable Diffusion-based:**
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- [rsortino/ColorizeNet](https://huggingface.co/rsortino/ColorizeNet) - ControlNet adaptation of SD 2.1 for colorization
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- [AlekseyCalvin/ColorizeTruer_KontextFluxVar6_BySAP](https://huggingface.co/AlekseyCalvin/ColorizeTruer_KontextFluxVar6_BySAP) - Advanced Flux-based colorization
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**Interactive Demos (Spaces):**
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- [aryadytm/Photo-Colorization](https://huggingface.co/spaces/aryadytm/Photo-Colorization)
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- [Shashank009/Black-And-White-Image-Colorization](https://huggingface.co/spaces/Shashank009/Black-And-White-Image-Colorization)
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- [CA611/Image-Colorization](https://huggingface.co/spaces/CA611/Image-Colorization)
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### Why Choose DeOldify?
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DeOldify stands out for:
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- **NoGAN Training**: Unique approach eliminating typical GAN artifacts
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- **Specialized Models**: Three purpose-built models (Artistic, Stable, Video)
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- **Video Support**: Flicker-free temporal consistency
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- **Proven Track Record**: Powers MyHeritage InColor and widely adopted
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- **ONNX Support**: Browser-ready models for offline use
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## Citation
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If you use these models, please cite:
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```bibtex
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@misc{deoldify,
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author = {Antic, Jason},
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title = {DeOldify},
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year = {2019},
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publisher = {GitHub},
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url = {https://github.com/jantic/DeOldify}
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}
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```
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## Links
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- **GitHub Repository**: https://github.com/thookham/DeOldify
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- **Original DeOldify**: https://github.com/jantic/DeOldify
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- **MyHeritage InColor** (Commercial version): https://www.myheritage.com/incolor
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- **Demo (Browser)**: See browser/ folder in GitHub repo
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## License
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MIT License. See [LICENSE](https://github.com/thookham/DeOldify/blob/master/LICENSE) file.
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