Welcome to the intriguing world of Chinese calligraphy enhanced by cutting-edge technology! With an innovative approach using Conditional Adversarial Networks (CANs), you can now master the beautiful art of writing Chinese characters via the zi2zi application. This guide will walk you through the process, ensuring a user-friendly experience as you embark on this creative journey.

Introduction

The zi2zi tool lets you explore eastern Asian language typefaces using the GAN (Generative Adversarial Network) model, particularly an adaptation of the popular pix2pix model. To dive deeper into this fascinating topic, check out this blog post.

Understanding the Network Structure

The zi2zi application’s network design takes cues from the original pix2pix model but adds unique features for achieving an improved outcome.

Original Model

In essence, think of the original model as a skilled artist drawing characters from a book. The model uses two additional concepts, category loss and constant loss, from other successful models like AC-GAN and DTN. The artist learns not only from the images but also from subtle hints given about the type of character being drawn.

Updated Model with Label Shuffling

As the training progresses, the model’s performance tapers off, similar to an artist who cannot improve without challenges. Introducing label shuffling serves as a ‘gym’ for the model, where new and shuffled labels inspire it to tackle tasks beyond its comfort zone. By setting flip_labels=1 in the train.py script, you enable this beneficial aspect after the initial stages of training.

How to Use zi2zi

Step Zero: Download Fonts

• Gather an assortment of font styles that you would like to experiment with.

Requirements

• Python 2.7
• CUDA
• CuDNN
• Tensorflow = 1.0.1
• Pillow (PIL)
• Numpy = 1.12.1
• Scipy = 0.18.1
• Imageio

Data Preparation

To avoid IO bottleneck, you need to preprocess your data. Execute the following command:

sh python font2img.py --src_font=src.ttf --dst_font=tgt.otf --charset=CN --sample_count=1000 --sample_dir=dir --label=0 --filter=1 --shuffle=1

In this command, filter helps in sampling characters while discarding duplicates, while label associates the font with a category embedding. After generating images, run package.py to convert them into a binary format.

sh python package.py --dir=image_directories --save_dir=binary_save_directory --split_ratio=[0,1]

Experiment Layouts

Create a directory structure to keep data organized:

experiment/
            └── data/
                ├── train.obj
                └── val.obj

Training

To start training, execute:

sh python train.py --experiment_dir=experiment --experiment_id=0 --batch_size=16 --lr=0.001 --epoch=40 --sample_steps=50 --schedule=20 --L1_penalty=100 --Lconst_penalty=15

Troubleshooting Tips

• If your model’s loss does not decrease, check for any discrepancies in your dataset or consider increasing the number of training epochs.
• If you encounter errors during setup, ensure that all dependencies are correctly installed and compatible with your version of Python.
• For issues specific to your training process, re-evaluate your command syntax and make sure you are using the correct file paths.

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

Explore Further!

Once you’ve perfected your characters, consider using the pretrained model available here. This will help in accelerating your ongoing projects.

Conclusion

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.