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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,50 +1,50 @@ | ||
| Usage: dataset_tool.py [OPTIONS] | ||
| Convert an image dataset into a dataset archive usable with StyleGAN2 ADA | ||
| PyTorch. | ||
| The input dataset format is guessed from the --source argument: | ||
| --source *_lmdb/ - Load LSUN dataset | ||
| --source cifar-10-python.tar.gz - Load CIFAR-10 dataset | ||
| --source path/ - Recursively load all images from path/ | ||
| --source dataset.zip - Recursively load all images from dataset.zip | ||
| The output dataset format can be either an image folder or a zip archive. | ||
| Specifying the output format and path: | ||
| --dest /path/to/dir - Save output files under /path/to/dir | ||
| --dest /path/to/dataset.zip - Save output files into /path/to/dataset.zip archive | ||
| Images within the dataset archive will be stored as uncompressed PNG. | ||
| Image scale/crop and resolution requirements: | ||
| Output images must be square-shaped and they must all have the same power- | ||
| of-two dimensions. | ||
| To scale arbitrary input image size to a specific width and height, use | ||
| the --width and --height options. Output resolution will be either the | ||
| original input resolution (if --width/--height was not specified) or the | ||
| one specified with --width/height. | ||
| Use the --transform=center-crop or --transform=center-crop-wide options to | ||
| apply a center crop transform on the input image. These options should be | ||
| used with the --width and --height options. For example: | ||
| python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \ | ||
| --transform=center-crop-wide --width 512 --height=384 | ||
| Options: | ||
| --source PATH Directory or archive name for input dataset | ||
| [required] | ||
| --dest PATH Output directory or archive name for output | ||
| dataset [required] | ||
| --max-images INTEGER Output only up to `max-images` images | ||
| --resize-filter [box|lanczos] Filter to use when resizing images for | ||
| output resolution [default: lanczos] | ||
| --transform [center-crop|center-crop-wide] | ||
| Input crop/resize mode | ||
| --width INTEGER Output width | ||
| --height INTEGER Output height | ||
| --help Show this message and exit. | ||
| Usage: dataset_tool.py [OPTIONS] | ||
|
|
||
| Convert an image dataset into a dataset archive usable with StyleGAN2 ADA | ||
| PyTorch. | ||
|
|
||
| The input dataset format is guessed from the --source argument: | ||
|
|
||
| --source *_lmdb/ - Load LSUN dataset | ||
| --source cifar-10-python.tar.gz - Load CIFAR-10 dataset | ||
| --source path/ - Recursively load all images from path/ | ||
| --source dataset.zip - Recursively load all images from dataset.zip | ||
|
|
||
| The output dataset format can be either an image folder or a zip archive. | ||
| Specifying the output format and path: | ||
|
|
||
| --dest /path/to/dir - Save output files under /path/to/dir | ||
| --dest /path/to/dataset.zip - Save output files into /path/to/dataset.zip archive | ||
|
|
||
| Images within the dataset archive will be stored as uncompressed PNG. | ||
|
|
||
| Image scale/crop and resolution requirements: | ||
|
|
||
| Output images must be square-shaped and they must all have the same power- | ||
| of-two dimensions. | ||
|
|
||
| To scale arbitrary input image size to a specific width and height, use | ||
| the --width and --height options. Output resolution will be either the | ||
| original input resolution (if --width/--height was not specified) or the | ||
| one specified with --width/height. | ||
|
|
||
| Use the --transform=center-crop or --transform=center-crop-wide options to | ||
| apply a center crop transform on the input image. These options should be | ||
| used with the --width and --height options. For example: | ||
|
|
||
| python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \ | ||
| --transform=center-crop-wide --width 512 --height=384 | ||
|
|
||
| Options: | ||
| --source PATH Directory or archive name for input dataset | ||
| [required] | ||
| --dest PATH Output directory or archive name for output | ||
| dataset [required] | ||
| --max-images INTEGER Output only up to `max-images` images | ||
| --resize-filter [box|lanczos] Filter to use when resizing images for | ||
| output resolution [default: lanczos] | ||
| --transform [center-crop|center-crop-wide] | ||
| Input crop/resize mode | ||
| --width INTEGER Output width | ||
| --height INTEGER Output height | ||
| --help Show this message and exit. |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,69 +1,69 @@ | ||
| Usage: train.py [OPTIONS] | ||
| Train a GAN using the techniques described in the paper "Training | ||
| Generative Adversarial Networks with Limited Data". | ||
| Examples: | ||
| # Train with custom images using 1 GPU. | ||
| python train.py --outdir=~/training-runs --data=~/my-image-folder | ||
| # Train class-conditional CIFAR-10 using 2 GPUs. | ||
| python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \ | ||
| --gpus=2 --cfg=cifar --cond=1 | ||
| # Transfer learn MetFaces from FFHQ using 4 GPUs. | ||
| python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \ | ||
| --gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10 | ||
| # Reproduce original StyleGAN2 config F. | ||
| python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \ | ||
| --gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug | ||
| Base configs (--cfg): | ||
| auto Automatically select reasonable defaults based on resolution | ||
| and GPU count. Good starting point for new datasets. | ||
| stylegan2 Reproduce results for StyleGAN2 config F at 1024x1024. | ||
| paper256 Reproduce results for FFHQ and LSUN Cat at 256x256. | ||
| paper512 Reproduce results for BreCaHAD and AFHQ at 512x512. | ||
| paper1024 Reproduce results for MetFaces at 1024x1024. | ||
| cifar Reproduce results for CIFAR-10 at 32x32. | ||
| Transfer learning source networks (--resume): | ||
| ffhq256 FFHQ trained at 256x256 resolution. | ||
| ffhq512 FFHQ trained at 512x512 resolution. | ||
| ffhq1024 FFHQ trained at 1024x1024 resolution. | ||
| celebahq256 CelebA-HQ trained at 256x256 resolution. | ||
| lsundog256 LSUN Dog trained at 256x256 resolution. | ||
| <PATH or URL> Custom network pickle. | ||
| Options: | ||
| --outdir DIR Where to save the results [required] | ||
| --gpus INT Number of GPUs to use [default: 1] | ||
| --snap INT Snapshot interval [default: 50 ticks] | ||
| --metrics LIST Comma-separated list or "none" [default: | ||
| fid50k_full] | ||
| --seed INT Random seed [default: 0] | ||
| -n, --dry-run Print training options and exit | ||
| --data PATH Training data (directory or zip) [required] | ||
| --cond BOOL Train conditional model based on dataset | ||
| labels [default: false] | ||
| --subset INT Train with only N images [default: all] | ||
| --mirror BOOL Enable dataset x-flips [default: false] | ||
| --cfg [auto|stylegan2|paper256|paper512|paper1024|cifar] | ||
| Base config [default: auto] | ||
| --gamma FLOAT Override R1 gamma | ||
| --kimg INT Override training duration | ||
| --batch INT Override batch size | ||
| --aug [noaug|ada|fixed] Augmentation mode [default: ada] | ||
| --p FLOAT Augmentation probability for --aug=fixed | ||
| --target FLOAT ADA target value for --aug=ada | ||
| --augpipe [blit|geom|color|filter|noise|cutout|bg|bgc|bgcf|bgcfn|bgcfnc] | ||
| Augmentation pipeline [default: bgc] | ||
| --resume PKL Resume training [default: noresume] | ||
| --freezed INT Freeze-D [default: 0 layers] | ||
| --fp32 BOOL Disable mixed-precision training | ||
| --nhwc BOOL Use NHWC memory format with FP16 | ||
| --nobench BOOL Disable cuDNN benchmarking | ||
| --workers INT Override number of DataLoader workers | ||
| --help Show this message and exit. | ||
| Usage: train.py [OPTIONS] | ||
|
|
||
| Train a GAN using the techniques described in the paper "Training | ||
| Generative Adversarial Networks with Limited Data". | ||
|
|
||
| Examples: | ||
|
|
||
| # Train with custom images using 1 GPU. | ||
| python train.py --outdir=~/training-runs --data=~/my-image-folder | ||
|
|
||
| # Train class-conditional CIFAR-10 using 2 GPUs. | ||
| python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \ | ||
| --gpus=2 --cfg=cifar --cond=1 | ||
|
|
||
| # Transfer learn MetFaces from FFHQ using 4 GPUs. | ||
| python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \ | ||
| --gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10 | ||
|
|
||
| # Reproduce original StyleGAN2 config F. | ||
| python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \ | ||
| --gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug | ||
|
|
||
| Base configs (--cfg): | ||
| auto Automatically select reasonable defaults based on resolution | ||
| and GPU count. Good starting point for new datasets. | ||
| stylegan2 Reproduce results for StyleGAN2 config F at 1024x1024. | ||
| paper256 Reproduce results for FFHQ and LSUN Cat at 256x256. | ||
| paper512 Reproduce results for BreCaHAD and AFHQ at 512x512. | ||
| paper1024 Reproduce results for MetFaces at 1024x1024. | ||
| cifar Reproduce results for CIFAR-10 at 32x32. | ||
|
|
||
| Transfer learning source networks (--resume): | ||
| ffhq256 FFHQ trained at 256x256 resolution. | ||
| ffhq512 FFHQ trained at 512x512 resolution. | ||
| ffhq1024 FFHQ trained at 1024x1024 resolution. | ||
| celebahq256 CelebA-HQ trained at 256x256 resolution. | ||
| lsundog256 LSUN Dog trained at 256x256 resolution. | ||
| <PATH or URL> Custom network pickle. | ||
|
|
||
| Options: | ||
| --outdir DIR Where to save the results [required] | ||
| --gpus INT Number of GPUs to use [default: 1] | ||
| --snap INT Snapshot interval [default: 50 ticks] | ||
| --metrics LIST Comma-separated list or "none" [default: | ||
| fid50k_full] | ||
| --seed INT Random seed [default: 0] | ||
| -n, --dry-run Print training options and exit | ||
| --data PATH Training data (directory or zip) [required] | ||
| --cond BOOL Train conditional model based on dataset | ||
| labels [default: false] | ||
| --subset INT Train with only N images [default: all] | ||
| --mirror BOOL Enable dataset x-flips [default: false] | ||
| --cfg [auto|stylegan2|paper256|paper512|paper1024|cifar] | ||
| Base config [default: auto] | ||
| --gamma FLOAT Override R1 gamma | ||
| --kimg INT Override training duration | ||
| --batch INT Override batch size | ||
| --aug [noaug|ada|fixed] Augmentation mode [default: ada] | ||
| --p FLOAT Augmentation probability for --aug=fixed | ||
| --target FLOAT ADA target value for --aug=ada | ||
| --augpipe [blit|geom|color|filter|noise|cutout|bg|bgc|bgcf|bgcfn|bgcfnc] | ||
| Augmentation pipeline [default: bgc] | ||
| --resume PKL Resume training [default: noresume] | ||
| --freezed INT Freeze-D [default: 0 layers] | ||
| --fp32 BOOL Disable mixed-precision training | ||
| --nhwc BOOL Use NHWC memory format with FP16 | ||
| --nobench BOOL Disable cuDNN benchmarking | ||
| --workers INT Override number of DataLoader workers | ||
| --help Show this message and exit. |
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