---
license: other
tags:
  - image-colorization
  - ddcolor
  - comic-books
  - computer-vision
  - pytorch
datasets:
  - cenkbircanoglu/comic-books-classification
base_model: piddnad/ddcolor_modelscope
pipeline_tag: image-to-image
---

# DDColor — Comic Book Colorization

Fine-tuned weights of [DDColor](https://github.com/piddnad/DDColor) for automatic colorization of grayscale comic book pages and covers.  
Developed as part of the **Digital Image Processing (PID)** course at the **Universidad de Sevilla**.

---

## Model Description

DDColor is a state-of-the-art image colorization model presented at **ICCV 2023** ("DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders", Kang et al.). It uses an encoder–dual-decoder architecture: a **ConvNeXt backbone** extracts image features, a **pixel decoder** restores spatial resolution at multiple scales, and a **transformer-based color decoder** learns semantic-aware color queries via cross-attention. The two decoders are fused to produce vivid, photo-realistic colorized outputs in the LAB color space.

This checkpoint fine-tunes the base DDColor weights specifically on comic book imagery — covers and interior pages — enabling the model to handle the distinctive flat shading, high-contrast line art, and stylized aesthetics typical of comics.

---

## Intended Uses

- Automatic colorization of black-and-white comic pages and covers
- Restoration or re-colorization of vintage comics
- Research and education in digital image processing

### Out-of-scope Uses

- General natural photo colorization (use the original DDColor weights for that)
- High-resolution medical or satellite imagery

---

## Training Details

| Field | Value |
|---|---|
| **Base model** | DDColor (piddnad/ddcolor_modelscope) |
| **Framework** | PyTorch + BasicSR |
| **Input size** | 256 × 256 (L channel) |
| **Output** | ab channels → combined LAB → RGB |
| **Dataset** | [Comic Books Images](https://www.kaggle.com/datasets/cenkbircanoglu/comic-books-classification) (Kaggle) |
| **Total images** | 52,156 RGB images across 86 classes |
| **Training images** | ~41,725 (80% split per class) |
| **Test images** | ~10,431 (20% split per class) |
| **Original resolution** | 1988 × 3056 px |
| **Resized resolution** | 288 × 432 px (via OpenCV) |
| **Total iterations** | 200,000 (warmup: 1,000) |
| **Batch size** | 1 per GPU |
| **Optimizer (G)** | AdamW — lr 1e-4, weight decay 0.01, betas (0.9, 0.99) |
| **Optimizer (D)** | Adam — lr 1e-4, betas (0.9, 0.99) |
| **LR scheduler** | MultiStepLR — milestones at 50k / 75k / 100k / 125k / 150k iters, γ 0.5 |
| **Training input size** | 128 × 128 (gt_size) |
| **Validation input size** | 256 × 256 (gt_size) |
| **Hardware** | 1× NVIDIA GeForce RTX 4060 |

### Dataset

The model was fine-tuned on the **Comic Books Images** dataset published on Kaggle by Cenk Bircanoglu. It contains **52,156 RGB images** spanning **86 comic book classes** (covers and interior pages). The original images at 1988 × 3056 px were downscaled to **288 × 432 px** using OpenCV before training. The dataset was split per class with an **80/20 train/test ratio**, yielding ~41,725 training images and ~10,431 test images. Images were converted to grayscale (L channel) at training time; the original color images served as ground truth for the ab channels.

### Loss Functions

| Loss | Weight |
|---|---|
| L1 Pixel Loss | 0.1 |
| Perceptual Loss (VGG16-BN, layers conv1–conv5) | 5.0 |
| GAN Loss (vanilla) | 1.0 |
| Colorfulness Loss | 0.5 |

A **color enhancement factor of 1.2** was applied during training to boost output vibrancy.

---

### Requirements

```bash
pip install torch torchvision
pip install modelscope
pip install basicsr
```

### Inference

```python
import cv2
from modelscope.outputs import OutputKeys
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

colorizer = pipeline(
    Tasks.image_colorization,
    model="AlejandroParody/PID_Proyect" 
)

result = colorizer("path/to/grayscale_comic_page.jpg")
cv2.imwrite("colorized_output.png", result[OutputKeys.OUTPUT_IMG])
```

Or load the weights manually with PyTorch:

```python
import torch
from ddcolor.ddcolor_arch import DDColor  # from the original DDColor repo

model = DDColor(...)  # use the same config as base model
model.load_state_dict(torch.load("pytorch_model.pt", map_location="cpu"))
model.eval()
```

---

## Limitations

- Performance may degrade on images with very dense text or complex panel layouts not well represented in the training data.
- The model inherits DDColor's fixed input resolution of 256 × 256; output is then upsampled to the original image dimensions.
- Color choices are learned from the training distribution — unusual or highly stylized comic styles may yield inconsistent results.

---

## Citation

If you use this work, please cite the original DDColor paper:

```bibtex
@inproceedings{kang2023ddcolor,
  title     = {DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders},
  author    = {Kang, Xiaoyang and Yang, Tao and Ouyang, Wenqi and Ren, Peiran and Li, Lingzhi and Xie, Xuansong},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2023}
}
```

---

## Authors

Developed by students of the **Universidad de Sevilla**:

- [@grnln](https://github.com/grnln)
- [@AlejandroParody](https://github.com/AlejandroParody)
- [@josmorlop10](https://github.com/josmorlop10)

---

## Acknowledgements

This project was developed for the **Procesamiento de Imágenes Digitales (PID)** course at the **Universidad de Sevilla**.  
Base model and training code: [piddnad/DDColor](https://github.com/piddnad/DDColor).  
Training dataset: [Comic Books Images](https://www.kaggle.com/datasets/cenkbircanoglu/comic-books-classification) by Cenk Bircanoglu on Kaggle.