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StyleGAN2 pretrained models

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For use with the official StyleGAN3 implementation:



Use Case




Latest Version



October 15, 2021


4.79 GB

Model Overview

StyleGAN2 pretrained models for FFHQ (aligned & unaligned), AFHQv2, CelebA-HQ, BreCaHAD, CIFAR-10, LSUN dogs, and MetFaces (aligned & unaligned) datasets.

We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearing to be glued to image coordinates instead of the surfaces of depicted objects. We trace the root cause to careless signal processing that causes aliasing in the generator network. Interpreting all signals in the network as continuous, we derive generally applicable, small architectural changes that guarantee that unwanted information cannot leak into the hierarchical synthesis process. The resulting networks match the FID of StyleGAN2 but differ dramatically in their internal representations, and they are fully equivariant to translation and rotation even at subpixel scales. Our results pave the way for generative models better suited for video and animation

Model Architecture

You can see the details of this model on this link: and the related paper can be find here:


You can train new networks using This release contains an interactive model visualization tool that can be used to explore various characteristics of a trained model. To start it, run:



StyleGAN2 pretrained models for these datasets: FFHQ (aligned & unaligned), AFHQv2, CelebA-HQ, BreCaHAD, CIFAR-10, LSUN dogs, and MetFaces (aligned & unaligned) datasets.


the result quality and training time depend heavily on the exact set of options. The most important ones (--gpus, --batch, and --gamma) must be specified explicitly, and they should be selected with care. See python --help for the full list of options and Training configurations for general guidelines & recommendations, along with the expected training speed & memory usage in different scenarios.

The results of each training run are saved to a newly created directory, for example ~/training-runs/00000-stylegan3-t-afhqv2-512x512-gpus8-batch32-gamma8.2. The training loop exports network pickles (network-snapshot-.pkl) and random image grids (fakes.png) at regular intervals (controlled by --snap). For each exported pickle, it evaluates FID (controlled by --metrics) and logs the result in metric-fid50k_full.jsonl. It also records various statistics in training_stats.jsonl, as well as *.tfevents if TensorBoard is installed.

How to Use this Model


  • Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons.
  • 1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs.
  • 64-bit Python 3.8 and PyTorch 1.9.0 (or later). See for PyTorch install instructions.
  • CUDA toolkit 11.1 or later.
  • GCC 7 or later (Linux) or Visual Studio (Windows) compilers. Recommended GCC version depends on CUDA version.


Datasets are stored as uncompressed ZIP archives containing uncompressed PNG files and a metadata file dataset.json for labels.


you can check the output of the model in the paper at this address:



Copyright (C) 2021, NVIDIA Corporation & affiliates. All rights reserved.

This work is made available under the Nvidia Source Code License.