alias-free-torch 0.0.6
alias free torch
Requires Python: >=3
Keywords: alias,torch,pytorch,filter

# Alias-Free-Torch

Simple *torch* module implementation of [Alias-Free GAN](https://nvlabs.github.io/alias-free-gan/).

This repository including
- Alias-Free GAN style lowpass sinc filter @[filter.py](/src/alias_free_torch/filter.py)

- Alias-Free GAN style up/downsample @[resample.py](/src/alias_free_torch/resample.py)

- Alias-Free activation @[act.py](/src/alias_free_torch/act.py)

- and test codes @[./test](/src/alias_free_torch/test)

**Note: Since this repository is unofficial, filter and upsample could be different with [official implementation](https://github.com/NVlabs/stylegan3).**

**Still working! If you notice some error or typo, please open new issue!**
**v0.0.8 is TESTED**

**UPDATE: You can download alias-free-torch from [pip](https://pypi.org/project/alias-free-torch/)** 
```shell
python -m pip install alias-free-torch
```


## 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

For custom torch users, `pip` will not check torch version.

## TODO
- [x] 2d sinc filter
- [x] 2d resample
- [x] devide 1d and 2d modules
- [x] pip packaging
- [x] rewrite upsample
- [x] Upsample pad size issue
- [x] \(Upsample\) support calculation for \[B,C,T/(H,W)\] (now only supports \[B,T/(H,W)\] or \[B,1,T/(H,W)\])
- [x] set filter as register buffer
- [x] \(Downsample & Filter\) support calculation for \[B,C,T/(H,W)\] (now only supports \[B,T/(H,W)\] or \[B,1,T/(H,W)\])
- [ ] provide loadable ckpt for lower version of torch
- [ ] documentation

## Test results 1d
| Filter sine              | Filter noise                  |
| --------------------- | --------------------------- |
| ![filtersin](asset/filtersin.png) | ![filternoise](asset/filternoise.png) |

| upsample              | downsample                  |
| --------------------- | --------------------------- |
| ![up2](asset/up2.png) | ![down10](asset/down10.png) |
| ![up256](asset/up256.png) | ![down100](asset/down100.png) |

## Test results 2d
| Filter L1 norm sine   | Filter noise                  |
| --------------------- | --------------------------- |
| ![filter2dsin](asset/filter2dsin.png) | ![filter2dnoise](asset/filter2dnoise.png) |

| upsample              | downsample                  |
| --------------------- | --------------------------- |
| ![up2d2](asset/up2d2.png) | ![downsample2d2](asset/downsample2d2.png) |
| ![up2d8](asset/up2d8.png) | ![downsample2d4](asset/downsample2d4.png) |

## References

- [Alias-Free GAN](https://nvlabs-fi-cdn.nvidia.com/alias-free-gan/alias-free-gan-paper.pdf)
- [adefossez](https://github.com/adefossez)/[julius](https://github.com/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](https://github.com/mindslab-ai).

Thanks to teammates at MINDsLab Inc.
- [Hyoungkyu Song](https://github.com/deepkyu)
- [Kang-wook Kim](https://github.com/wookladin)
- [Dongho Choi](https://github.com/dhchoi99)
- [Sang Hoon Woo](https://github.com/tonyswoo)