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.
This repo does not provide the code for training the networks.
To run the demo, simply execute predict.py.
The code was tested on Ubuntu 16.04 with an NVIDIA GTX 1080 using PyTorch v.0.2.0_4.
Example image:
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.