StyleGAN2 for practice

This version of famous StyleGAN2 is intended mostly for fellow artists, who rarely look at scientific metrics, but rather need a working creative tool. At least, this is what I use daily myself. Tested on Tensorflow 1.14, requires pyturbojpeg for JPG support. Sequence-to-video conversions require FFMPEG. For more explicit details refer to the original implementations.

Notes about StyleGAN2-ada:

1. ADA version on Tensorflow has shown smoother and faster convergence on the rich enough datasets, but sometimes resulted in lower output variety (comparing to Diff Augmentation approach). It has also failed in my tests on few-shot datasets (50~100 images), while Diff Aug succeeded there. So meanwhile i personally prefer this repo with Diff Augmentation training.
2. The latest PyTorch-based StyleGAN2-ada flavour is claimed to be up to 30% faster, works with flat folder datasets, way easier to tweak/debug than TF-based one. It's also the only option for modern GPUs (GeForce 30xx, etc.). On my tests/datasets it was regularly failing to learn the variety of macro features though, so I have rarely used it in production. Anyway, here is such repo, adapted to the features below (custom generation, non-square RGBA data, etc.).

Features

• inference (image generation) in arbitrary resolution (finally with proper padding on both TF and Torch)
• multi-latent inference with split-frame or masked blending
• non-square aspect ratio support (auto-picked from dataset; resolution must be divisible by 2**n, such as 512x256, 1280x768, etc.)
• various conversion options (changing resolution/aspect, adding alpha channel, etc.) for pretrained models (for further finetuning)
• transparency (alpha channel) support (auto-picked from dataset)
• models mixing (SWA) and layers-blending (from Justin Pinkney)
• freezing lower D layers for better finetuning on similar data (from Freeze the Discriminator)

Few operation formats ::

• Windows batch-files, described below (if you're on Windows with powerful GPU)
• local Jupyter notebook (for non-Windows platforms)
• Colab notebook (max ease of use, requires Google drive)

also, from Data-Efficient GANs ::

• differential augmentation for fast training on small datasets (~100 images)
• support of custom CUDA-compiled TF ops and (slower) Python-based reference ops

also, from Aydao ::

• funky "digression" technique (added inside the network for ~6x speed-up)
• cropping square models to non-square aspect ratio (experimental)

also, from Peter Baylies and skyflynil ::

• non-progressive configs (E,F) with single-scale datasets
• raw JPG support in TFRecords dataset (auto-picked from source images)
• conditional support (labels)
• vertical mirroring augmentation

Presumed file structure

Training

• Put your images in data as subfolder. Ensure they all have the same color channels (monochrome, RGB or RGBA).
  If needed, crop square fragments from source video or directory with images (feasible method, if you work with patterns or shapes, rather than compostions):

 multicrop.bat source 512 256 

This will cut every source image (or video frame) into 512x512px fragments, overlapped with 256px shift by X and Y. Result will be in directory source-sub, rename it as you wish. Non-square dataset should be prepared separately.

• Make TFRecords dataset from directory data/mydata:

 prepare_dataset.bat mydata

This will create file mydata-512x512.tfr in data directory (if your dataset resolution is 512x512). Images without alpha channel will be stored directly as JPG (dramatically reducing file size). For conditional model split the data by subfolders (mydata/1, mydata/2, ..) and add --labels option.

• Train StyleGAN2 on prepared dataset:

 train.bat mydata --kimg 1000

This will run training process, according to the settings in src/train.py (check and explore those!!). If there's no TFRecords file from the previous step, it will be created at this point. Results (models and samples) are saved under train directory, similar to original Nvidia approach.
Batch size is auto-calculated for GPU with 16gb RAM; you may set it lower explicitly with --batch_size X, if you face OOM. Another downgrading option in such case is to set less capable network with --config E.

Please note: we save both compact models (containing only Gs network for inference) as <dataset>-...pkl (e.g. mydata-512-0360.pkl), and full models (containing G/D/Gs networks for further training) as snapshot-...pkl. The naming is for convenience only, it does not affect the operations anymore (as the arguments are stored inside the models).

For small datasets (100x images instead of 10000x) one should add --d_aug option to use Differential Augmentation for more effective training. Training duration is defined by --kimg X argument (amount of thousands of samples processed). Reasonable value for training from scratch is 5000, while for finetuning in --d_aug mode 1000 may be sufficient.
Add --cond if you want to train conditional model on the dataset with labels.

• Resume training on mydata dataset from the last saved model at train/000-mydata-512-f directory:

 train_resume.bat mydata 000-mydata-512-f --kimg 1000

• Uptrain (finetune) trained model ffhq-512.pkl on new data:

 train_resume.bat newdata ffhq-512.pkl --kimg 1000

--d_aug would greatly enhance training here. There's also --freezeD option, supposedly enhancing finetuning on similar data.

Generation

Results (frame sequences and videos) are saved by default under _out directory.

• Test the model in its native resolution:

 gen.bat ffhq-1024.pkl

• Generate custom animation between random latent points (in z space):

 gen.bat ffhq-1024 1920-1080 100-20

This will load ffhq-1024.pkl from models directory and make a 1920x1080 px looped video of 100 frames, with interpolation step of 20 frames between keypoints. Please note: omitting .pkl extension would load custom network, effectively enabling arbitrary resolution, multi-latent blending, etc. Using filename with extension will load original network from PKL (useful to test foreign downloaded models). There are --cubic and --gauss options for animation smoothing, and few --scale_type choices. Add --save_lat option to save all traversed dlatent w points as Numpy array in *.npy file (useful for further curating). Set --seed X value to produce repeatable results.

• Generate more various imagery:

 gen.bat ffhq-1024 3072-1024 100-20 -n 3-1

This will produce animated composition of 3 independent frames, blended together horizontally (like the image in the repo header). Argument --splitfine X controls boundary fineness (0 = smoothest). Instead of simple frame splitting, one can load external mask(s) from b/w image file (or folder with file sequence):

 gen.bat ffhq-1024 1024-1024 100-20 --latmask _in/mask.jpg

Arguments --digress X would add some animated funky displacements with X strength (by tweaking initial const layer params). Arguments --trunc X controls truncation psi parameter, as usual.

NB: Windows batch-files support only 9 command arguments; if you need more options, you have to edit batch-file itself.

• Project external images onto StyleGAN2 model dlatent points (in w space):

 project.bat ffhq-1024.pkl photo

The result (found dlatent points as Numpy arrays in *.npy files, and video/still previews) will be saved to _out/proj directory.

• Generate smooth animation between saved dlatent points (in w space):

 play_dlatents.bat ffhq-1024 dlats 25 1920-1080

This will load saved dlatent points from _in/dlats and produce a smooth looped animation between them (with resolution 1920x1080 and interpolation step of 25 frames). dlats may be a file or a directory with *.npy or *.npz files. To select only few frames from a sequence somename.npy, create text file with comma-delimited frame numbers and save it as somename.txt in the same directory (check examples for FFHQ model). You can also "style" the result: setting --style_dlat blonde458.npy will load dlatent from blonde458.npy and apply it to higher layers, producing some visual similarity. --cubic smoothing and --digress X displacements are also applicable here.

• Generate animation from saved point and feature directions (say, aging/smiling/etc for faces model) in dlatent w space:

 play_vectors.bat ffhq-1024.pkl blonde458.npy vectors_ffhq

This will load base dlatent point from _in/blonde458.npy and move it along direction vectors from _in/vectors_ffhq, one by one. Result is saved as looped video.

Tweaking models

• Strip G/D networks from a full model, leaving only Gs for inference:

 model_convert.bat snapshot-1024.pkl 

Resulting file is saved with -Gs suffix. It's recommended to add -r option to reconstruct the network, saving necessary arguments with it. Useful for foreign downloaded models.

• Add or remove layers (from a trained model) to adjust its resolution for further finetuning:

 model_convert.bat snapshot-256.pkl --res 512

This will produce new model with 512px resolution, populating weights on the layers up to 256px from the source snapshot (the rest will be initialized randomly). It also can decrease resolution (say, make 512 from 1024). Note that this effectively changes number of layers in the model.

This option works with complete (G/D/Gs) models only, since it's purposed for transfer-learning (resulting model will contain either partially random weights, or wrong ToRGB params).

• Crop or pad layers of a trained model to adjust its aspect ratio:

 model_convert.bat snapshot-1024.pkl --res 1280-768

This produces working non-square model. In case of basic aspect conversion (like 4x4 => 5x3), complete models (G/D/Gs) will be trainable for further finetuning.
These functions are experimental, with some voluntary logic, so use with care.

• Add alpha channel to a trained model for further finetuning:

 model_convert.bat snapshot-1024.pkl --alpha

All above (adding/cropping/padding layers + alpha channel) can be done in one shot:

 model_convert.bat snapshot-256.pkl --res 1280-768 --alpha

• Add X labels to a trained model for further finetuning as conditional:

 model_convert.bat snapshot-1024.pkl --labels X

• Combine lower layers from one model with higher layers from another:

 models_blend.bat model1.pkl model2.pkl <res> <level>

<res> is resolution, at which the models are switched (usually 16/32/64); <level> is 0 or 1.
For inference (generation) this method works properly only for models from one "family", i.e. uptrained (finetuned) from the same original model. For further training may be useful in other cases too (not tested yet!).

• Mix few models by stochastic averaging all weights:

 models_mix.bat models_dir

This would work properly only for models from one "family", i.e. uptrained (finetuned) from the same original model.

• Convert sg2-1024.pt model, created in Rosinality or StyleGAN2-NADA repo to Nvidia PKL format, by copying the weights on top of the base sg2-1024.pkl model of the same resolution:

 python src/model_pt2pkl.py --model_pt sg2-1024.pt --model_pkl sg2-1024.pkl

Credits

StyleGAN2: Copyright © 2019, NVIDIA Corporation. All rights reserved. Made available under the Nvidia Source Code License-NC Original paper: http://arxiv.org/abs/1912.04958

Differentiable Augmentation for Data-Efficient GAN Training: https://arxiv.org/abs/2006.10738

Other contributions: follow the links in the descriptions.