Welcome to our comprehensive guide on how to use DDColor, the innovative PyTorch implementation that brings life to historical black and white photos. Released at the ICCV 2023, DDColor utilizes cutting-edge dual decoders to enable photo-realistic image colorization.

What is DDColor?

DDColor is designed to add vibrant colors to images, even transforming anime game landscapes into realistic scenes. It boasts an impressive ability to maintain the integrity and quality of the original images, offering users an easy way to revive old photographs or recreate their favorite animated environments.

Installation Requirements

Your first step to unlocking DDColor is ensuring you have the correct setup:

• Python: 3.7
• PyTorch: 1.7 or higher

Installation Steps

Here’s how you can install DDColor using conda (we highly recommend this method):

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

Once you have DDColor installed, you’re ready to start using it for image colorization. Below are a few straightforward methods.

1. Inference with the ModelScope Library

First, ensure you have the ModelScope library installed:

pip install modelscope

Then use the following code:

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])

This code will automatically download the DDColor model for you.

2. Inference from Local Script

For running the inference from your local script:

from modelscope.hub.snapshot_download import snapshot_download

model_dir = snapshot_download('damo/cv/ddcolor_image-colorization', cache_dir='.')
print(f'Model assets saved to {model_dir}')

The model can also be pulled directly from Hugging Face.

3. Inference with Hugging Face

To load the model from Hugging Face, you can use the following code snippets:

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')

For detailed instructions, you can reference the inference/colorization_pipeline_hf.py to initiate the model reliably.

Troubleshooting

If you encounter any issues while using DDColor, here are some helpful troubleshooting tips:

• Dependency Issues: Make sure that all dependencies are correctly installed and that the PyTorch version aligns with the specifications.
• Model Not Found: If the model cannot be downloaded, check your internet connection or verify that the model paths are correct.
• Runtime Errors: Review your code syntax and ensure that you are referencing the correct libraries as shown in the examples.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

The Analogy: Think of it Like Painting

Picture this: you have an artist’s palette of colors representing learnable color tokens. The multi-scale visual features act like your references for how to apply those colors effectively. The dual decoders can be compared to having two skilled artists who use different techniques to blend and layer colors seamlessly into your artwork. Together, they transform a black-and-white canvas into a vibrant, lifelike masterpiece.

Conclusion

By following this guide, you’re equipped to revitalize old photos and create stunning colorizations with DDColor. Whether for personal or professional projects, this tool can help bring images back to life!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.