basicsr.egg-info/PKG-INFO

Metadata-Version: 2.1
Name: basicsr
Version: 1.3.4.6
Summary: Open Source Image and Video Super-Resolution Toolbox
Home-page: https://github.com/xinntao/BasicSR
Author: Xintao Wang
Author-email: xintao.wang@outlook.com
License: Apache License 2.0
Keywords: computer vision,restoration,super resolution
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Requires-Dist: dlib==19.24.2
Requires-Dist: lmdb==1.4.1
Requires-Dist: numpy==1.24.3
Requires-Dist: opencv_python==4.7.0.72
Requires-Dist: Pillow==10.1.0
Requires-Dist: PyYAML==6.0.1
Requires-Dist: Requests==2.31.0
Requires-Dist: scipy==1.9.1
Requires-Dist: timm==0.9.2
Requires-Dist: torch==2.2.0
Requires-Dist: torchvision==0.17.0
Requires-Dist: tqdm==4.65.0
Requires-Dist: wandb==0.15.5
Requires-Dist: scikit-image==0.22.0
Requires-Dist: tensorboard==2.15.1

# 🎨 DDColor

Official PyTorch implementation of ICCV 2023 Paper "DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders".


[![arXiv](https://img.shields.io/badge/arXiv-2212.11613-b31b1b.svg)](https://arxiv.org/abs/2212.11613)
[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FF8000)](https://huggingface.co/piddnad/DDColor-models)
[![ModelScope demo](https://img.shields.io/badge/%F0%9F%91%BE%20ModelScope-Demo-8A2BE2)](https://www.modelscope.cn/models/damo/cv_ddcolor_image-colorization/summary)
[![Replicate](https://replicate.com/piddnad/ddcolor/badge)](https://replicate.com/piddnad/ddcolor)
![visitors](https://visitor-badge.laobi.icu/badge?page_id=piddnad/DDColor)


> Xiaoyang Kang, Tao Yang, Wenqi Ouyang, Peiran Ren, Lingzhi Li, Xuansong Xie
>
> *DAMO Academy, Alibaba Group*

🪄 DDColor can provide vivid and natural colorization for historical black and white old photos.

<p align="center">
  <img src="assets/teaser.png" width="100%">
</p>

🎲 It can even colorize/recolor landscapes from anime games, transforming your animated scenery into a realistic real-life style! (Image source: Genshin Impact)

<p align="center">
  <img src="assets/anime_landscapes.png" width="100%">
</p>


## 🔥 News

* [2024-01-28] Support inferencing via Hugging Face! Thanks @[Niels](https://github.com/NielsRogge) for the suggestion and example code and @[Skwara](https://github.com/Skwarson96) for fixing bug.

* [2024-01-18] Add Replicate demo and API! Thanks @[Chenxi](https://github.com/chenxwh).

* [2023-12-13] Release the DDColor-tiny pre-trained model!

* [2023-09-07] Add the Model Zoo and release three pretrained models!

* [2023-05-15] Code release for training and inference!

* [2023-05-05] The online demo is available!

## Online Demo

We provide online demos through ModelScope at [![ModelScope demo](https://img.shields.io/badge/%F0%9F%91%BE%20ModelScope-Demo-8A2BE2)](https://www.modelscope.cn/models/damo/cv_ddcolor_image-colorization/summary) and Replicate at [![Replicate](https://replicate.com/piddnad/ddcolor/badge)](https://replicate.com/piddnad/ddcolor) .

Feel free to try them out!

## Methods

*In short:* DDColor uses multi-scale visual features to optimize **learnable color tokens** (i.e. color queries) and achieves state-of-the-art performance on automatic image colorization.

<p align="center">
  <img src="assets/network_arch.jpg" width="100%">
</p>


## Installation

### Requirements

- Python >= 3.7
- PyTorch >= 1.7

### Install with conda (Recommend)

```
conda create -n ddcolor python=3.8
conda activate ddcolor
pip install -r requirements.txt

python3 setup.py develop  # install basicsr
```

## Quick Start

### Inference with Modelscope library

1. Install modelscope:

```
pip install "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
```

2. Run the following codes:

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

img_colorization = pipeline(Tasks.image_colorization, model='damo/cv_ddcolor_image-colorization')
result = img_colorization('https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/audrey_hepburn.jpg')
cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
```

It will automatically download the DDColor models.

You can find the model file `pytorch_model.pt` in the local path ~/.cache/modelscope/hub/damo.

### Inference from local script

1. Download the pretrained model file by simply running:

```
from modelscope.hub.snapshot_download import snapshot_download

model_dir = snapshot_download('damo/cv_ddcolor_image-colorization', cache_dir='./modelscope')
print('model assets saved to %s'%model_dir)
```

then the weights will be `modelscope/damo/cv_ddcolor_image-colorization/pytorch_model.pt`.

Or, download the model from [Hugging Face](https://huggingface.co/piddnad/DDColor-models).

2. Run 
```
sh scripts/inference.sh
```

### Inference with Hugging Face

Now we can load model via Huggingface Hub like this:

```
from inference.colorization_pipeline_hf import DDColorHF

ddcolor_paper_tiny = DDColorHF.from_pretrained("piddnad/ddcolor_paper_tiny")
ddcolor_paper      = DDColorHF.from_pretrained("piddnad/ddcolor_paper")
ddcolor_modelscope = DDColorHF.from_pretrained("piddnad/ddcolor_modelscope")
ddcolor_artistic   = DDColorHF.from_pretrained("piddnad/ddcolor_artistic")
```

Check `inference/colorization_pipeline_hf.py` for the details of the inference, or directly perform model inference by simply running:

```
python inference/colorization_pipeline_hf.py --model_name ddcolor_modelscope --input ./assets/test_images
# model_name: [ddcolor_paper | ddcolor_modelscope | ddcolor_artistic | ddcolor_paper_tiny]
```

### Gradio Demo

1. Install the gradio and other required libraries

```python
!pip install gradio gradio_imageslider timm -q
```

2. Run the demo

```python
python gradio_app.py
```

## Model Zoo

We provide several different versions of pretrained models, please check out [Model Zoo](MODEL_ZOO.md).


## Train

1. Dataset preparation: download [ImageNet](https://www.image-net.org/) dataset, or prepare any custom dataset of your own. Use the following script to get the dataset list file:

```
python data_list/get_meta_file.py
```

2. Download pretrained weights for [ConvNeXt](https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth) and [InceptionV3](https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth) and put it into `pretrain` folder.

3. Specify 'meta_info_file' and other options in `options/train/train_ddcolor.yml`.

4. Run

```
sh scripts/train.sh
```

## Citation

If our work is helpful for your research, please consider citing:

```
@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},
  pages={328--338},
  year={2023}
}
```

## Acknowledgments
We thank the authors of BasicSR for the awesome training pipeline.

> Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR: Open Source Image and Video Restoration Toolbox. https://github.com/xinntao/BasicSR, 2020.

Some codes are adapted from [ColorFormer](https://github.com/jixiaozhong/ColorFormer), [BigColor](https://github.com/KIMGEONUNG/BigColor), [ConvNeXt](https://github.com/facebookresearch/ConvNeXt), [Mask2Former](https://github.com/facebookresearch/Mask2Former), and [DETR](https://github.com/facebookresearch/detr). Thanks for their excellent work!