Simple torch module implementation of Alias-Free GAN.
This repository including
-
Alias-Free GAN style lowpass sinc filter @filter.py
-
Alias-Free GAN style up/downsample @resample.py
-
Alias-Free activation @act.py
-
and test codes @./test
Note: Since this repository is unofficial, filter and upsample could be different with official implementation.
Note: 2d lowpass filter is applying sinc instead of jinc (first order Bessel function of the first kind) in paper
- 2d sinc filter
- 2d resample
- devide 1d and 2d modules
- pip packaging
Filter sine | Filter noise |
---|---|
![]() |
![]() |
upsample | downsample |
---|---|
![]() |
![]() |
![]() |
![]() |
Filter L1 norm sine | Filter noise |
---|---|
![]() |
![]() |
upsample | downsample |
---|---|
![]() |
![]() |
![]() |
![]() |
Activation |
---|
![]() |
- Alias-Free GAN
- adefossez/julius
- A. V. Oppenheim and R. W. Schafer. Discrete-Time Signal Processing. Pearson, International Edition, 3rd edition, 2010
This work is done at MINDsLab Inc.
Thanks to teammates at MINDsLab Inc.