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taki0112
UGATIT
Python2020–2021
taki0112 / UGATIT

UGATIT

Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)

5Distinct people
5Mentions
0Explicit recommenders
6.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month5 dated mentions

Observed mentions: 2020-04-16 22:25:12 UTC to 2021-05-07 05:26:07 UTC.

01232020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 commentsApr 20202020-05-01 00:00:00 UTC to 2020-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-06-01 00:00:00 UTC to 2020-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-07-01 00:00:00 UTC to 2020-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-08-01 00:00:00 UTC to 2020-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-09-01 00:00:00 UTC to 2020-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-10-01 00:00:00 UTC to 2020-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 20202020-11-01 00:00:00 UTC to 2020-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-12-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2021-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-02-01 00:00:00 UTC to 2021-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-03-01 00:00:00 UTC to 2021-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-04-01 00:00:00 UTC to 2021-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-05-01 00:00:00 UTC to 2021-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 2021

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC303
May 20202020-05-01 00:00:00 UTC2020-06-01 00:00:00 UTC101
Jun 20202020-06-01 00:00:00 UTC2020-07-01 00:00:00 UTC000
Jul 20202020-07-01 00:00:00 UTC2020-08-01 00:00:00 UTC000
Aug 20202020-08-01 00:00:00 UTC2020-09-01 00:00:00 UTC000
Sep 20202020-09-01 00:00:00 UTC2020-10-01 00:00:00 UTC000
Oct 20202020-10-01 00:00:00 UTC2020-11-01 00:00:00 UTC000
Nov 20202020-11-01 00:00:00 UTC2020-12-01 00:00:00 UTC000
Dec 20202020-12-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
Jan 20212021-01-01 00:00:00 UTC2021-02-01 00:00:00 UTC000
Feb 20212021-02-01 00:00:00 UTC2021-03-01 00:00:00 UTC000
Mar 20212021-03-01 00:00:00 UTC2021-04-01 00:00:00 UTC000
Apr 20212021-04-01 00:00:00 UTC2021-05-01 00:00:00 UTC000
May 20212021-05-01 00:00:00 UTC2021-06-01 00:00:00 UTC101

What Hacker News says

5 sources
The following instructions are for Linux or macOS. It may work on Windows too, but I'm not very experienced with Windows. No special hardware is required (you can run it on a CPU; no GPU needed). Install pyenv[0] and pyenv-virtualenv[1]. Clone the repo and set up an environment: git clone https://github.com/taki0112/UGATIT cd UGATIT pyenv install 3.6.10 # [2] pyenv virtualenv 3.6.10 UGATIT pyenv local UGATIT pyenv activate pip install opencv-python==4.2.0.34 tensorflow==1.14.0 Download the pretrained weights from https://github.com/taki0112/UGATIT/issues/50#issuecomment-53.... Extract them: tar xf ugatit100.tar.xz mkdir checkpoint mv UGATIT_selfie2anime* checkpoint mkdir -p dataset/selfie2anime/{train,test}{A,B} Crop your images in a 1:1 aspect ratio so that they contain only the head. Place them in the dataset/se
Read the rest
lfie2anime/testA/ directory. Run the program: python main.py --dataset selfie2anime --phase test Open results/*/index.html in your browser to see the results. [0] https://github.com/pyenv/pyenv#installation [1] https://github.com/pyenv/pyenv-virtualenv#installation [2] Other versions may work, but this is the version mentioned in https://github.com/taki0112/UGATIT#requirements
In Selfie 2 Waifu

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