This is an inference sample written in PyTorch of the original Theano/Lasagne code.

I recreated the network as described in the paper of Karras et al. Since some layers seemed to be missing in PyTorch, these were implemented as well. The network and the layers can be found in model.py.

For the demo, a 100-celeb-hq-1024x1024-ours snapshot was used, which was made publicly available by the authors. Since I couldn't find any model converter between Theano/Lasagne and PyTorch, I used a quick and dirty script to transfer the weights between the models (transfer_weights.py).

This repo does not provide the code for training the networks.

To run the demo, simply execute predict.py. You can specify other weights with the --weights flag.

Example image:

To try the latent space interpolation, use latent_interp.py. All output images will be saved in ./interp.

The following flags are defined:

• --weights - path to pretrained PyTorch state dict
• --output - Directory for storing interpolated images
• --batch_size - batch size for DataLoader
• --num_workers - number of workers for DataLoader
• --nb_latents - number of frames to generate
• --filter - gaussian filter length for interpolating latent space
• --seed - random seed for numpy and PyTorch
• --cuda - use GPU

Example interpolation:

The pretrained lasagne weights can be transferred to a PyTorch state dict using transfer_weights.py.

To transfer other snapshots from the paper (other than CelebA), you have to modify the model architecture accordingly and use the corresponding weights.

The code was tested on Ubuntu 16.04 with an NVIDIA GTX 1080 using PyTorch v.0.2.0_4.

transfer_weights.py needs Theano and Lasagne to load the pretrained weights.

A single forward pass took approx. 0.031 seconds.

• Original code (Theano/Lasagne implementation)

• Paper (research.nvidia.com)

This code is a modified form of the original code under the CC BY-NC license with the following copyright notice:

# Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

According the Section 3, I hereby identify Tero Karras et al. and NVIDIA as the original authors of the material.