Practical overview
When it fits
Use it when you have an image dataset and a CUDA-equipped GPU and want to train and sample a StyleGAN2 model locally rather than use a hosted generation service.
Who it is for
- Machine-learning practitioners who want to train StyleGAN2 on their own image collections.
- Developers and researchers experimenting with generative image models through a command-line workflow.
Documented uses
- Train an image generator from a local dataset.
- Save and organize model checkpoints and generated samples.
- Generate images or latent-space interpolation videos from a trained checkpoint.
- Experiment with training controls such as network capacity, augmentation, and multi-GPU execution.
Requirements & limits
The repository is public and lists an MIT license. The documented installation is through pip; the README requires a GPU and CUDA.
- A GPU and CUDA are prerequisites in the documented installation instructions.
- Higher network capacity can improve generation results but consumes more memory.
- The README documents multi-GPU training through a command-line flag.
- Differentiable augmentation options are available for low-data training experiments.
- The README cautions that training may diverge and that longer training may be needed at higher resolutions.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Train a generator from a local image directory
Install the package and start command-line training on an image folder.
Before you start
- A machine with a GPU and CUDA installed.
- An image directory accessible on that machine.
- Python package installation with pip.
Steps
Install the package with the command below.
pip install stylegan2_pytorchRun
stylegan2_pytorch --data /path/to/images, replacing the path with the image directory.Find sample images under
results/defaultand periodically saved models undermodels/default.
Expected outcome
A training run using the supplied images, with sample outputs and model checkpoints in the default directories.
Guide limitations
- The instructions require a GPU and CUDA.
- Increasing network capacity uses more memory.
- Training duration may need adjustment for image resolution and divergence.
Generate samples from a trained checkpoint
Use the command line to generate images after training.
Before you start
- The package installed in the environment.
- A trained model checkpoint accessible to the command-line tool.
Steps
Run
stylegan2_pytorch --generateto generate images from the latest checkpoint.Optionally use
--load-from {checkpoint number}to select an earlier checkpoint.Optionally adjust sample truncation with
--trunc-psi, or use--generate-interpolationto create a latent-space interpolation video.
Expected outcome
Generated images from a checkpoint; interpolation mode produces a video, and `--save-frames` saves its individual frames.
Guide limitations
- Lower truncation values trade sample variety for improved appearance, according to the README.
- The guide presumes training checkpoints are already available.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- lucidrains/stylegan2-pytorch · Official homepage title · retained in its source language