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---

license: mit
tags:
- image-colorization
- gan
- computer-vision
- pytorch
- onnx
library_name: pytorch
---


# DeOldify Model Weights

This repository contains pretrained weights for **DeOldify**, a deep learning model for colorizing and restoring old black and white images and videos.

**Original Repository**: [thookham/DeOldify](https://github.com/thookham/DeOldify)  
**Original Author**: Jason Antic ([jantic/DeOldify](https://github.com/jantic/DeOldify))

## Model Overview

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.

### Three Specialized Models

1. **Artistic** - Highest quality with vibrant colors and interesting details  
   - Best for: General images, historical photos  
   - Backbone: ResNet34 U-Net  
   - Training: 5 NoGAN cycles, 32% ImageNet

2. **Stable** - Best for portraits and landscapes, reduced artifacts  
   - Best for: Faces, nature scenes  
   - Backbone: ResNet101 U-Net  
   - Training: 3 NoGAN cycles, 7% ImageNet

3. **Video** - Optimized for smooth, flicker-free video  
   - Best for: Video colorization, consistency  
   - Backbone: ResNet101 U-Net  
   - Training: Initial cycle only, 2.2% ImageNet

## Available Files

### ONNX Models (Browser/Inference)

| File | Size | Description |
|------|------|-------------|
| `deoldify-art.onnx` | 243 MB | Artistic model in ONNX format for browser use |
| `deoldify-quant.onnx` | 61 MB | Quantized artistic model (75% smaller, slightly lower quality) |

### PyTorch Weights (Training & Inference)

**Generator Weights** (Main):
- `ColorizeArtistic_gen.pth` (243 MB)
- `ColorizeStable_gen.pth` (834 MB)
- `ColorizeVideo_gen.pth` (834 MB)

**Critic Weights** (Main):
- `ColorizeArtistic_crit.pth` (361 MB)
- `ColorizeStable_crit.pth` (361 MB)
- `ColorizeVideo_crit.pth` (361 MB)

**PretrainOnly Weights** (For continued training):
- `ColorizeArtistic_PretrainOnly_gen.pth` (729 MB)
- `ColorizeArtistic_PretrainOnly_crit.pth` (1.05 GB)
- `ColorizeStable_PretrainOnly_crit.pth` (1.05 GB)
- `ColorizeVideo_PretrainOnly_crit.pth` (1.05 GB)

> **Note**: Stable and Video PretrainOnly generators are split files hosted on [GitHub Releases](https://github.com/thookham/DeOldify/releases/tag/v2.0-models).

## Usage

### Browser (ONNX)

```html

<!DOCTYPE html>

<html>

<head>

  <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>

</head>

<body>

  <script>

    async function colorize() {

      // Load model from Hugging Face

      const session = await ort.InferenceSession.create(

        "https://huggingface.co/thookham/DeOldify/resolve/main/deoldify-art.onnx"

      );

      

      // Run inference (see full example in GitHub repo)

      // ...

    }

  </script>

</body>

</html>

```

### PyTorch (Python)

```python

from huggingface_hub import hf_hub_download

import torch



# Download model weights

model_path = hf_hub_download(

    repo_id="thookham/DeOldify",

    filename="ColorizeArtistic_gen.pth"

)



# Load weights (requires deoldify package installed)

# See GitHub repository for full usage examples

```

### Installation

```bash

# Clone the main repository

git clone https://github.com/thookham/DeOldify

cd DeOldify



# Install dependencies

pip install -r requirements.txt



# Download a model

from huggingface_hub import hf_hub_download

model = hf_hub_download(repo_id="thookham/DeOldify", filename="ColorizeStable_gen.pth")

```

## Technical Details

### Architecture
- **Generator**: U-Net with ResNet34/101 backbone, spectral normalization, self-attention layers
- **Critic**: PatchGAN discriminator
- **Loss**: Perceptual loss (VGG16) + GAN loss

### NoGAN Training
A novel training approach that combines:
1. Generator pretraining with feature loss
2. Critic pretraining on generated images
3. Short GAN training (30-60 minutes) at inflection point
4. Optional cycle repeats for more colorful results

This eliminates typical GAN artifacts while maintaining realistic colorization.

### Training Data
- Dataset: ImageNet subsets (1-32% depending on model)
- Resolution: 192px during training
- Augmentation: Gaussian noise for video stability

## Model Card

### Model Details
- **Developed by**: Jason Antic (original), Travis Hookham (modernization)
- **Model type**: Conditional GAN for image-to-image translation
- **Language(s)**: N/A (computer vision)
- **License**: MIT
- **Parent Model**: Based on FastAI U-Net and Self-Attention GAN papers

### Intended Use
**Primary Use**: Colorizing black and white photographs and videos  
**Out-of-Scope**: Real-time processing, guaranteed historical accuracy

### Limitations
- Colors may not be historically accurate  
- Performance degrades on very low quality/damaged images
- Artistic model may require render_factor tuning

- Video model trades some color vibrancy for consistency



## Related Models & Resources



### Similar Colorization Models on Hugging Face



**GAN-based Colorization:**

- [Hammad712/GAN-Colorization-Model](https://huggingface.co/Hammad712/GAN-Colorization-Model) - GAN model for grayscale to color transformation

- [jessicanono/filparty_colorization](https://huggingface.co/jessicanono/filparty_colorization) - ResNet-based model for historical photos



**Stable Diffusion-based:**

- [rsortino/ColorizeNet](https://huggingface.co/rsortino/ColorizeNet) - ControlNet adaptation of SD 2.1 for colorization

- [AlekseyCalvin/ColorizeTruer_KontextFluxVar6_BySAP](https://huggingface.co/AlekseyCalvin/ColorizeTruer_KontextFluxVar6_BySAP) - Advanced Flux-based colorization



**Interactive Demos (Spaces):**

- [aryadytm/Photo-Colorization](https://huggingface.co/spaces/aryadytm/Photo-Colorization)

- [Shashank009/Black-And-White-Image-Colorization](https://huggingface.co/spaces/Shashank009/Black-And-White-Image-Colorization)

- [CA611/Image-Colorization](https://huggingface.co/spaces/CA611/Image-Colorization)



### Why Choose DeOldify?



DeOldify stands out for:

- **NoGAN Training**: Unique approach eliminating typical GAN artifacts

- **Specialized Models**: Three purpose-built models (Artistic, Stable, Video)

- **Video Support**: Flicker-free temporal consistency

- **Proven Track Record**: Powers MyHeritage InColor and widely adopted

- **ONNX Support**: Browser-ready models for offline use



## Citation



If you use these models, please cite:



```bibtex

@misc{deoldify,

  author = {Antic, Jason},

  title = {DeOldify},

  year = {2019},

  publisher = {GitHub},

  url = {https://github.com/jantic/DeOldify}

}

```



## Links



- **GitHub Repository**: https://github.com/thookham/DeOldify

- **Original DeOldify**: https://github.com/jantic/DeOldify

- **MyHeritage InColor** (Commercial version): https://www.myheritage.com/incolor

- **Demo (Browser)**: See browser/ folder in GitHub repo



## License



MIT License. See [LICENSE](https://github.com/thookham/DeOldify/blob/master/LICENSE) file.