One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN

Published on 22 October 2020
Updated on 14 February 2026
2 min read
research

This page contains several models for our paper “One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN” (Preprint Here).

We provide models for a range of reconstruction experiments, denoising experiments, and experiments with our different training strategies.

Reconstruction Experiments

Here, we provide models for our reconstruction experiments shown in Table 1. We provide Models for our experiments on the FFHQ dataset and also the LSUN Church Dataset. Besides the models you will also find a link to the page where we logged the train run, giving you access to all log information and used hyperparameters. You can download the model by clicking the respective link in the attachment section.

FFHQ Experiments

LSUN Church Experiments

Denoising Experiments

Here, we provide only our best models trained for color and black and white denoising.

Different Training Strategies

We provide the models, we used to create the interpolation results, shown in Figure 13 of our paper. On the one hand a model trained using the two-network strategy (denoted as two-stem in our code) and a model trained using the learning rate strategy.

If you are interested in any other models, feel free to open an issue on Github and ask us!