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
Original Author: Jason Antic (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

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

Usage

Browser (ONNX)

<!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)

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

# 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:

Generator pretraining with feature loss
Critic pretraining on generated images
Short GAN training (30-60 minutes) at inflection point
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 - GAN model for grayscale to color transformation
jessicanono/filparty_colorization - ResNet-based model for historical photos

Stable Diffusion-based:

rsortino/ColorizeNet - ControlNet adaptation of SD 2.1 for colorization
AlekseyCalvin/ColorizeTruer_KontextFluxVar6_BySAP - Advanced Flux-based colorization

Interactive Demos (Spaces):

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:

@misc{deoldify,
  author = {Antic, Jason},
  title = {DeOldify},
  year = {2019},
  publisher = {GitHub},
  url = {https://github.com/jantic/DeOldify}
}

License

MIT License. See LICENSE file.

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DeOldify