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https://github.com/piddnad/DDColor
[ICCV 2023] DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders
https://github.com/piddnad/DDColor
computer-vision image-colorization pytorch
Last synced: 22 days ago
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[ICCV 2023] DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders
- Host: GitHub
- URL: https://github.com/piddnad/DDColor
- Owner: piddnad
- License: apache-2.0
- Created: 2022-12-23T08:36:07.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-08-26T05:25:50.000Z (25 days ago)
- Last Synced: 2024-08-26T06:46:09.043Z (25 days ago)
- Topics: computer-vision, image-colorization, pytorch
- Language: Python
- Homepage:
- Size: 13 MB
- Stars: 1,001
- Watchers: 13
- Forks: 112
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🎨 DDColor
Official PyTorch implementation of ICCV 2023 Paper "DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders".
[](https://arxiv.org/abs/2212.11613)
[](https://huggingface.co/piddnad/DDColor-models)
[](https://www.modelscope.cn/models/damo/cv_ddcolor_image-colorization/summary)
[](https://replicate.com/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.
🎲 It can even colorize/recolor landscapes from anime games, transforming your animated scenery into a realistic real-life style! (Image source: Genshin Impact)
## 🔥 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 [](https://www.modelscope.cn/models/damo/cv_ddcolor_image-colorization/summary) and Replicate at [](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.
## Installation
### Requirements
- Python >= 3.7
- PyTorch >= 1.7
### Installation with conda (recommended)
```
conda create -n ddcolor python=3.9
conda activate ddcolor
pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
python3 setup.py develop # install basicsr
```
## Quick Start
### Inference with Modelscope library
1. Install modelscope:
```
pip install modelscope
```
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!