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-# 🎨 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!

+---
+license: apache-2.0
+title: DDColor
+sdk: gradio
+emoji: 😻
+colorFrom: pink
+colorTo: gray
+---