Simple StyleGAN2 for PyTorch
Introduction
StyleGAN2 is a state-of-the-art generative adversarial network (GAN) particularly famous for generating visually realistic images. The "Simple StyleGAN2 for PyTorch" project is a streamlined implementation of StyleGAN2 using PyTorch. It is designed to be user-friendly by allowing users to train models directly from the command line without needing to write code.
The project showcases its application by generating non-existent images of flowers, hands, cities, and celebrities.
Installation
To get started with stylegan2-pytorch, ensure you have a machine equipped with a GPU and CUDA. Then, you can install the package via pip with:
$ pip install stylegan2_pytorch
For Windows users, these steps are recommended:
$ conda install pytorch torchvision -c python
$ pip install stylegan2_pytorch
Basic Usage
Training a StyleGAN2 model is straightforward. Simply point it towards the image dataset you wish to use:
$ stylegan2_pytorch --data /path/to/images
Images produced during training will be saved in results/default, and models will be periodically saved in models/default.
Advanced Usage
Custom Project Naming
You can personalize your project naming for better organization:
$ stylegan2_pytorch --data /path/to/images --name my-project-name
Custom Storage Locations
Specify where to store results and models:
$ stylegan2_pytorch --data /path/to/images --name my-project-name --results-dir /path/to/results/dir --models-dir /path/to/models/dir
Increasing Network Capacity
Improve generation quality by increasing the network capacity, which uses more memory:
$ stylegan2_pytorch --data /path/to/images --network-capacity 256
Resuming and Restarting Training
The training process automatically resumes from the last checkpoint. To start with fresh settings, use the new flag:
$ stylegan2_pytorch --new --data /path/to/images --name my-project-name --image-size 512 --batch-size 1 --gradient-accumulate-every 16 --network-capacity 10
Generating Images and Interpolations
Once trained, generate images or create latent space interpolations to visualize transitions between random points:
$ stylegan2_pytorch --generate
$ stylegan2_pytorch --generate-interpolation --interpolation-num-steps 100
$ stylegan2_pytorch --generate-interpolation --save-frames
Multi-GPU Training
Leverage multiple GPUs for efficient training by utilizing the --multi-gpus flag. Batch sizes are evenly distributed across the GPUs:
$ stylegan2_pytorch --data ./data --multi-gpus --batch-size 32 --gradient-accumulate-every 1
Working with Limited Data
With recent advances, only 1-2k images might be needed for good results using differentiable augmentation:
$ stylegan2_pytorch --data ./data --aug-prob 0.25
$ stylegan2_pytorch --data ./data --aug-prob 0.25 --aug-types [translation,cutout,color]
When to Stop Training
It's recommended to train until model performance diverges. Default steps are set for 128x128 images, but higher resolutions require more steps:
$ stylegan2_pytorch --data ./data --image-size 512 --num-train-steps 1000000
Enhancing with Attention
Introduce self-attention layers for improved results. More attention often leads to better outcomes:
$ stylegan2_pytorch --data ./data --attn-layers 1
$ stylegan2_pytorch --data ./data --attn-layers [1,2]
Memory Considerations
For optimal training, particularly for high-resolution images, more GPU memory is beneficial. Adjust batch size and network capacity if limited:
$ stylegan2_pytorch --data /path/to/data --batch-size 3 --gradient-accumulate-every 5 --network-capacity 16
Deploying on AWS
The project can be deployed on AWS, using GPU-backed EC2 instances. Steps include data archiving, instance provision, AWS CLI installation, and training script execution.
Research Features
FID Scores
Calculate Frechet Inception Distance (FID) to evaluate image generation quality:
$ pip install pytorch-fid
$ stylegan2_pytorch --data ./data --calculate-fid-every 5000
Model Loader for Programming
Programmatically generate images with the ModelLoader class in PyTorch.
Experimental Features
The project offers a series of experimental features like Top-k Training, Feature Quantization, and various loss regularizations to enhance training efficacy and stability.
Conclusion
This "Simple StyleGAN2 for PyTorch" project offers an accessible entryway into advanced GANs for both novices and experienced developers, providing a rich set of options and configurations for customizing model training and deployment on different infrastructures.