Have you ever wondered how to transform plain face photos into mesmerizing artistic portrait drawings? With the advent of Generative Adversarial Networks (GANs), this creative transformation is at your fingertips! In this guide, we will walk you through the steps to utilize the APDrawingGAN framework, developed as part of a CVPR 2019 paper, to bring this idea to life using PyTorch.

What is APDrawingGAN?

APDrawingGAN stands for Artistic Portrait Drawing Generative Adversarial Network. This innovative model leverages hierarchical GANs to produce stunning artistic portraits from regular face images. Imagine a painter capturing essence and style, all powered by AI!

Getting Started with APDrawingGAN

Prerequisites

• Operating System: Linux or macOS
• Python Version: 2.7
• Hardware: CPU or NVIDIA GPU with CUDA/cuDNN

Installation Steps

1. Install PyTorch (version 0.4+) and torchvision. You can find them at http://pytorch.org.
2. Install additional dependencies such as visdom and dominate.
3. To install all the required packages, run the following command:

bash pip install -r requirements.txt

Quick Start: Testing a Pre-trained Model

1. Download a pre-trained model from the following link: Model1 and place it in checkpoints/formal_author.
2. To generate artistic portraits from images in your dataset, execute:

bash python test.py --dataroot dataset/data/test_single --name formal_author --model test --dataset_mode single --norm batch --use_local --which_epoch 300

3. The output will be saved in an HTML file at .results/formal_author/test_300/index.html.

Preparing Your Own Data

If you want to use your own photos, make sure to preprocess them following the instructions in the preprocessing steps. Once your data is prepared, apply the same test command replacing path_to_aligned_photos with your directory.

Training Your Model

1. Download the APDrawing dataset from here and copy the contents to the dataset folder.
2. For fast distance transformation and line detection, download the pre-training models from Model2.
3. Run the server for visualizing training:

python -m visdom.server

4. To train the model, use the command below. Make sure auxiliary models are in place:

bash python train.py --dataroot dataset/data --name formal --continue_train --use_local --discriminator_local --niter 300 --niter_decay 0 --save_epoch_freq 25

Troubleshooting

While running APDrawingGAN, you may encounter various issues. Here are some common troubleshooting tips:

• Error in File Paths: Ensure all paths are correctly specified especially for data input. Verify that your directories exist and contain the necessary files.
• Dependency Issues: Double-check that you have installed all dependencies listed in requirements.txt.
• GPU Utilization: If you are running on a CPU and experience slow performance, consider using a system with a compatible NVIDIA GPU for better results.

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

Explore Further

When you want to see how well your model is performing, navigate to http://localhost:8097 for training results and loss plots. For a digital showcase, access intermediate results at .checkpoints/formal/web/index.html.

Conclusion

Transforming face photos into artistic portraits through AI is not just an ambition, but a full-fledged reality with APDrawingGAN. So immerse yourself in this fascinating world of artificial intelligence and create visuals that captivate! 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.