Alias-Free-Torch
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
Requirements
Due to torch.kaiser_window and torch.i0 are implemeted after 1.7.0, our repository need torch>=1.7.0.
- Pytorch>=1.7.0
TODO
- [x] 2d sinc filter
- [x] 2d resample
- [x] devide 1d and 2d modules
- [ ] pip packaging
Test results 1d
| Filter sine | Filter noise |
|---|---|
 |
 |
| upsample | downsample |
|---|---|
 |
 |
 |
 |
Test results 2d
| Filter L1 norm sine | Filter noise |
|---|---|
 |
 |
| upsample | downsample |
|---|---|
 |
 |
 |
 |
| Activation |
|---|
 |
References
- Alias-Free GAN
- adefossez/julius
- A. V. Oppenheim and R. W. Schafer. Discrete-Time Signal Processing. Pearson, International Edition, 3rd edition, 2010
Acknowledgement
This work is done at MINDsLab Inc.
Thanks to teammates at MINDsLab Inc.
