Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Last update: Dec 24, 2022

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Overview

Alias-Free Generative Adversarial Networks (StyleGAN3)
_{Official PyTorch implementation of the NeurIPS 2021 paper}

Alias-Free Generative Adversarial Networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila
https://nvlabs.github.io/stylegan3

Abstract: We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearing to be glued to image coordinates instead of the surfaces of depicted objects. We trace the root cause to careless signal processing that causes aliasing in the generator network. Interpreting all signals in the network as continuous, we derive generally applicable, small architectural changes that guarantee that unwanted information cannot leak into the hierarchical synthesis process. The resulting networks match the FID of StyleGAN2 but differ dramatically in their internal representations, and they are fully equivariant to translation and rotation even at subpixel scales. Our results pave the way for generative models better suited for video and animation.

For business inquiries, please contact [email protected]
For press and other inquiries, please contact Hector Marinez at [email protected]

Release notes

This repository is an updated version of stylegan2-ada-pytorch, with several new features:

Alias-free generator architecture and training configurations (stylegan3-t, stylegan3-r).
Tools for interactive visualization (visualizer.py), spectral analysis (avg_spectra.py), and video generation (gen_video.py).
Equivariance metrics (eqt50k_int, eqt50k_frac, eqr50k).
General improvements: reduced memory usage, slightly faster training, bug fixes.

Compatibility:

Compatible with old network pickles created using stylegan2-ada and stylegan2-ada-pytorch.
Supports old StyleGAN2 training configurations, including ADA and transfer learning. See Training configurations for details.
Improved compatibility with Ampere GPUs and newer versions of PyTorch, CuDNN, etc.

Synthetic image detection

While new generator approaches enable new media synthesis capabilities, they may also present a new challenge for AI forensics algorithms for detection and attribution of synthetic media. In collaboration with digital forensic researchers participating in DARPA's SemaFor program, we curated a synthetic image dataset that allowed the researchers to test and validate the performance of their image detectors in advance of the public release. Please see here for more details.

Additional material

Result videos
Curated example images
StyleGAN3 pre-trained models for config T (translation equiv.) and config R (translation and rotation equiv.)

_{Access individual networks via https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/<MODEL>, where <MODEL> is one of:}
_{stylegan3-t-ffhq-1024x1024.pkl, stylegan3-t-ffhqu-1024x1024.pkl, stylegan3-t-ffhqu-256x256.pkl}
_{stylegan3-r-ffhq-1024x1024.pkl, stylegan3-r-ffhqu-1024x1024.pkl, stylegan3-r-ffhqu-256x256.pkl}
_{stylegan3-t-metfaces-1024x1024.pkl, stylegan3-t-metfacesu-1024x1024.pkl}
_{stylegan3-r-metfaces-1024x1024.pkl, stylegan3-r-metfacesu-1024x1024.pkl}
_{stylegan3-t-afhqv2-512x512.pkl}
_{stylegan3-r-afhqv2-512x512.pkl}
StyleGAN2 pre-trained models compatible with this codebase

_{Access individual networks via https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan2/versions/1/files/<MODEL>, where <MODEL> is one of:}
_{stylegan2-ffhq-1024x1024.pkl, stylegan2-ffhq-512x512.pkl, stylegan2-ffhq-256x256.pkl}
_{stylegan2-ffhqu-1024x1024.pkl, stylegan2-ffhqu-256x256.pkl}
_{stylegan2-metfaces-1024x1024.pkl, stylegan2-metfacesu-1024x1024.pkl}
_{stylegan2-afhqv2-512x512.pkl}
_{stylegan2-afhqcat-512x512.pkl, stylegan2-afhqdog-512x512.pkl, stylegan2-afhqwild-512x512.pkl}
_{stylegan2-brecahad-512x512.pkl, stylegan2-cifar10-32x32.pkl}
_{stylegan2-celebahq-256x256.pkl, stylegan2-lsundog-256x256.pkl}

Requirements

Linux and Windows are supported, but we recommend Linux for performance and compatibility reasons.
1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs.
64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions.
CUDA toolkit 11.1 or later. (Why is a separate CUDA toolkit installation required? See Troubleshooting).
Python libraries: see environment.yml for exact library dependencies. You can use the following commands with Miniconda3 to create and activate your StyleGAN3 Python environment:
- conda env create -f environment.yml
- conda activate stylegan3
Docker users:
- Ensure you have correctly installed the NVIDIA container runtime.
- Use the provided Dockerfile to build an image with the required library dependencies.

The code relies heavily on custom PyTorch extensions that are compiled on the fly using NVCC. On Windows, the compilation requires Microsoft Visual Studio. We recommend installing Visual Studio Community Edition and adding it into PATH using "C:\Program Files (x86)\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvars64.bat".

See Troubleshooting for help on common installation and run-time problems.

Getting started

Pre-trained networks are stored as *.pkl files that can be referenced using local filenames or URLs:

# Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
python gen_images.py --outdir=out --trunc=1 --seeds=2 \
    --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl

# Render a 4x2 grid of interpolations for seeds 0 through 31.
python gen_video.py --output=lerp.mp4 --trunc=1 --seeds=0-31 --grid=4x2 \
    --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl

Outputs from the above commands are placed under out/*.png, controlled by --outdir. Downloaded network pickles are cached under $HOME/.cache/dnnlib, which can be overridden by setting the DNNLIB_CACHE_DIR environment variable. The default PyTorch extension build directory is $HOME/.cache/torch_extensions, which can be overridden by setting TORCH_EXTENSIONS_DIR.

Docker: You can run the above curated image example using Docker as follows:

# Build the stylegan3:latest image
docker build --tag stylegan3 .

# Run the gen_images.py script using Docker:
docker run --gpus all -it --rm --user $(id -u):$(id -g) \
    -v `pwd`:/scratch --workdir /scratch -e HOME=/scratch \
    stylegan3 \
    python gen_images.py --outdir=out --trunc=1 --seeds=2 \
         --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl

Note: The Docker image requires NVIDIA driver release r470 or later.

The docker run invocation may look daunting, so let's unpack its contents here:

--gpus all -it --rm --user $(id -u):$(id -g): with all GPUs enabled, run an interactive session with current user's UID/GID to avoid Docker writing files as root.
-v `pwd`:/scratch --workdir /scratch: mount current running dir (e.g., the top of this git repo on your host machine) to /scratch in the container and use that as the current working dir.
-e HOME=/scratch: let PyTorch and StyleGAN3 code know where to cache temporary files such as pre-trained models and custom PyTorch extension build results. Note: if you want more fine-grained control, you can instead set TORCH_EXTENSIONS_DIR (for custom extensions build dir) and DNNLIB_CACHE_DIR (for pre-trained model download cache). You want these cache dirs to reside on persistent volumes so that their contents are retained across multiple docker run invocations.

Interactive visualization

This release contains an interactive model visualization tool that can be used to explore various characteristics of a trained model. To start it, run:

python visualizer.py

Using networks from Python

You can use pre-trained networks in your own Python code as follows:

with open('ffhq.pkl', 'rb') as f:
    G = pickle.load(f)['G_ema'].cuda()  # torch.nn.Module
z = torch.randn([1, G.z_dim]).cuda()    # latent codes
c = None                                # class labels (not used in this example)
img = G(z, c)                           # NCHW, float32, dynamic range [-1, +1], no truncation

The above code requires torch_utils and dnnlib to be accessible via PYTHONPATH. It does not need source code for the networks themselves — their class definitions are loaded from the pickle via torch_utils.persistence.

The pickle contains three networks. 'G' and 'D' are instantaneous snapshots taken during training, and 'G_ema' represents a moving average of the generator weights over several training steps. The networks are regular instances of torch.nn.Module, with all of their parameters and buffers placed on the CPU at import and gradient computation disabled by default.

The generator consists of two submodules, G.mapping and G.synthesis, that can be executed separately. They also support various additional options:

w = G.mapping(z, c, truncation_psi=0.5, truncation_cutoff=8)
img = G.synthesis(w, noise_mode='const', force_fp32=True)

Please refer to gen_images.py for complete code example.

Preparing datasets

Datasets are stored as uncompressed ZIP archives containing uncompressed PNG files and a metadata file dataset.json for labels. Custom datasets can be created from a folder containing images; see python dataset_tool.py --help for more information. Alternatively, the folder can also be used directly as a dataset, without running it through dataset_tool.py first, but doing so may lead to suboptimal performance.

FFHQ: Download the Flickr-Faces-HQ dataset as 1024x1024 images and create a zip archive using dataset_tool.py:

# Original 1024x1024 resolution.
python dataset_tool.py --source=/tmp/images1024x1024 --dest=~/datasets/ffhq-1024x1024.zip

# Scaled down 256x256 resolution.
python dataset_tool.py --source=/tmp/images1024x1024 --dest=~/datasets/ffhq-256x256.zip \
    --width=256 --height=256

See the FFHQ README for information on how to obtain the unaligned FFHQ dataset images. Use the same steps as above to create a ZIP archive for training and validation.

MetFaces: Download the MetFaces dataset and create a ZIP archive:

python dataset_tool.py --source=~/downloads/metfaces/images --dest=~/datasets/metfaces-1024x1024.zip

See the MetFaces README for information on how to obtain the unaligned MetFaces dataset images. Use the same steps as above to create a ZIP archive for training and validation.

AFHQv2: Download the AFHQv2 dataset and create a ZIP archive:

python dataset_tool.py --source=~/downloads/afhqv2 --dest=~/datasets/afhqv2-512x512.zip

Note that the above command creates a single combined dataset using all images of all three classes (cats, dogs, and wild animals), matching the setup used in the StyleGAN3 paper. Alternatively, you can also create a separate dataset for each class:

python dataset_tool.py --source=~/downloads/afhqv2/train/cat --dest=~/datasets/afhqv2cat-512x512.zip
python dataset_tool.py --source=~/downloads/afhqv2/train/dog --dest=~/datasets/afhqv2dog-512x512.zip
python dataset_tool.py --source=~/downloads/afhqv2/train/wild --dest=~/datasets/afhqv2wild-512x512.zip

Training

You can train new networks using train.py. For example:

# Train StyleGAN3-T for AFHQv2 using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \
    --gpus=8 --batch=32 --gamma=8.2 --mirror=1

# Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \
    --gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \
    --resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl

# Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \
    --gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug

Note that the result quality and training time depend heavily on the exact set of options. The most important ones (--gpus, --batch, and --gamma) must be specified explicitly, and they should be selected with care. See python train.py --help for the full list of options and Training configurations for general guidelines & recommendations, along with the expected training speed & memory usage in different scenarios.

The results of each training run are saved to a newly created directory, for example ~/training-runs/00000-stylegan3-t-afhqv2-512x512-gpus8-batch32-gamma8.2. The training loop exports network pickles (network-snapshot-<KIMG>.pkl) and random image grids (fakes<KIMG>.png) at regular intervals (controlled by --snap). For each exported pickle, it evaluates FID (controlled by --metrics) and logs the result in metric-fid50k_full.jsonl. It also records various statistics in training_stats.jsonl, as well as *.tfevents if TensorBoard is installed.

Quality metrics

By default, train.py automatically computes FID for each network pickle exported during training. We recommend inspecting metric-fid50k_full.jsonl (or TensorBoard) at regular intervals to monitor the training progress. When desired, the automatic computation can be disabled with --metrics=none to speed up the training slightly.

Additional quality metrics can also be computed after the training:

# Previous training run: look up options automatically, save result to JSONL file.
python calc_metrics.py --metrics=eqt50k_int,eqr50k \
    --network=~/training-runs/00000-stylegan3-r-mydataset/network-snapshot-000000.pkl

# Pre-trained network pickle: specify dataset explicitly, print result to stdout.
python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq-1024x1024.zip --mirror=1 \
    --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhq-1024x1024.pkl

The first example looks up the training configuration and performs the same operation as if --metrics=eqt50k_int,eqr50k had been specified during training. The second example downloads a pre-trained network pickle, in which case the values of --data and --mirror must be specified explicitly.

Note that the metrics can be quite expensive to compute (up to 1h), and many of them have an additional one-off cost for each new dataset (up to 30min). Also note that the evaluation is done using a different random seed each time, so the results will vary if the same metric is computed multiple times.

Recommended metrics:

fid50k_full: Fréchet inception distance^[1] against the full dataset.
kid50k_full: Kernel inception distance^[2] against the full dataset.
pr50k3_full: Precision and recall^[3] againt the full dataset.
ppl2_wend: Perceptual path length^[4] in W, endpoints, full image.
eqt50k_int: Equivariance^[5] w.r.t. integer translation (EQ-T).
eqt50k_frac: Equivariance w.r.t. fractional translation (EQ-T_frac).
eqr50k: Equivariance w.r.t. rotation (EQ-R).

Legacy metrics:

fid50k: Fréchet inception distance against 50k real images.
kid50k: Kernel inception distance against 50k real images.
pr50k3: Precision and recall against 50k real images.
is50k: Inception score^[6] for CIFAR-10.

References:

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, Heusel et al. 2017
Demystifying MMD GANs, Bińkowski et al. 2018
Improved Precision and Recall Metric for Assessing Generative Models, Kynkäänniemi et al. 2019
A Style-Based Generator Architecture for Generative Adversarial Networks, Karras et al. 2018
Alias-Free Generative Adversarial Networks, Karras et al. 2021
Improved Techniques for Training GANs, Salimans et al. 2016

Spectral analysis

The easiest way to inspect the spectral properties of a given generator is to use the built-in FFT mode in visualizer.py. In addition, you can visualize average 2D power spectra (Appendix A, Figure 15) as follows:

# Calculate dataset mean and std, needed in subsequent steps.
python avg_spectra.py stats --source=~/datasets/ffhq-1024x1024.zip

# Calculate average spectrum for the training data.
python avg_spectra.py calc --source=~/datasets/ffhq-1024x1024.zip \
    --dest=tmp/training-data.npz --mean=112.684 --std=69.509

# Calculate average spectrum for a pre-trained generator.
python avg_spectra.py calc \
    --source=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhq-1024x1024.pkl \
    --dest=tmp/stylegan3-r.npz --mean=112.684 --std=69.509 --num=70000

# Display results.
python avg_spectra.py heatmap tmp/training-data.npz
python avg_spectra.py heatmap tmp/stylegan3-r.npz
python avg_spectra.py slices tmp/training-data.npz tmp/stylegan3-r.npz

License

This work is made available under the Nvidia Source Code License.

Citation

@inproceedings{Karras2021,
  author = {Tero Karras and Miika Aittala and Samuli Laine and Erik H\"ark\"onen and Janne Hellsten and Jaakko Lehtinen and Timo Aila},
  title = {Alias-Free Generative Adversarial Networks},
  booktitle = {Proc. NeurIPS},
  year = {2021}
}

Development

This is a research reference implementation and is treated as a one-time code drop. As such, we do not accept outside code contributions in the form of pull requests.

Acknowledgements

We thank David Luebke, Ming-Yu Liu, Koki Nagano, Tuomas Kynkäänniemi, and Timo Viitanen for reviewing early drafts and helpful suggestions. Frédo Durand for early discussions. Tero Kuosmanen for maintaining our compute infrastructure. AFHQ authors for an updated version of their dataset. Getty Images for the training images in the Beaches dataset. We did not receive external funding or additional revenues for this project.

Comments

Render Video of the Internal Representations using gen_video.py

When using the StyleGAN3 interactive visualization tool, you can checkmark specific nodes to visualize the internal representations of the model. Here is an example - https://github.com/NVlabs/stylegan3/blob/main/docs/stylegan3-teaser-1920x1006.png https://nvlabs-fi-cdn.nvidia.com/_web/stylegan3/videos/video_8_internal_activations.mp4

But it is possible to specify and visualize these nodes using the gen_video.py script? I'm using StyleGAN3 within Google Colab and would like to render out video of the internal representations of a specific sequence of seeds.

Also, thank you for releasing this amazing fork! I've been using it to train very small datasets (500 to 1500 images) and so the added mirrorY attribute has been useful, along with the "stabilize-video" attribute too. Here is some of my projects if you're curious.
enhancement

opened by nuclearsugar 13

train.py failed to run

I tried to train images with transparency using train.py from argb branch, but it still fails, here are some error messages, can you help me with it?

Setting up PyTorch plugin "bias_act_plugin"... Done.
Setting up PyTorch plugin "filtered_lrelu_plugin"... Done.

Generator                    Parameters  Buffers  Output shape       Datatype
---                          ---         ---      ---                ---
mapping.fc0                  262656      -        [32, 512]          float32
mapping.fc1                  262656      -        [32, 512]          float32
mapping                      -           512      [32, 16, 512]      float32
synthesis.input.affine       2052        -        [32, 4]            float32
synthesis.input              262144      1545     [32, 512, 36, 36]  float32
synthesis.L0_36_512.affine   262656      -        [32, 512]          float32
synthesis.L0_36_512          2359808     25       [32, 512, 36, 36]  float16
synthesis.L1_36_512.affine   262656      -        [32, 512]          float32
synthesis.L1_36_512          2359808     25       [32, 512, 36, 36]  float16
synthesis.L2_36_512.affine   262656      -        [32, 512]          float32
synthesis.L2_36_512          2359808     25       [32, 512, 36, 36]  float16
synthesis.L3_36_512.affine   262656      -        [32, 512]          float32
synthesis.L3_36_512          2359808     25       [32, 512, 36, 36]  float16
synthesis.L4_52_512.affine   262656      -        [32, 512]          float32
synthesis.L4_52_512          2359808     37       [32, 512, 52, 52]  float16
synthesis.L5_52_512.affine   262656      -        [32, 512]          float32
synthesis.L5_52_512          2359808     25       [32, 512, 52, 52]  float16
synthesis.L6_52_512.affine   262656      -        [32, 512]          float32
synthesis.L6_52_512          2359808     25       [32, 512, 52, 52]  float16
synthesis.L7_52_512.affine   262656      -        [32, 512]          float32
synthesis.L7_52_512          2359808     25       [32, 512, 52, 52]  float16
synthesis.L8_84_512.affine   262656      -        [32, 512]          float32
synthesis.L8_84_512          2359808     37       [32, 512, 84, 84]  float16
synthesis.L9_84_512.affine   262656      -        [32, 512]          float32
synthesis.L9_84_512          2359808     25       [32, 512, 84, 84]  float16
synthesis.L10_84_512.affine  262656      -        [32, 512]          float32
synthesis.L10_84_512         2359808     25       [32, 512, 84, 84]  float16
synthesis.L11_84_512.affine  262656      -        [32, 512]          float32
synthesis.L11_84_512         2359808     25       [32, 512, 84, 84]  float16
synthesis.L12_84_512.affine  262656      -        [32, 512]          float32
synthesis.L12_84_512         2359808     25       [32, 512, 84, 84]  float16
synthesis.L13_64_512.affine  262656      -        [32, 512]          float32
synthesis.L13_64_512         2359808     25       [32, 512, 64, 64]  float16
synthesis.L14_64_4.affine    262656      -        [32, 512]          float32
synthesis.L14_64_4           2052        1        [32, 4, 64, 64]    float16
synthesis                    -           -        [32, 4, 64, 64]    float32
---                          ---         ---      ---                ---
Total                        37768712    2432     -                  -

Setting up PyTorch plugin "upfirdn2d_plugin"... Done.

Discriminator  Parameters  Buffers  Output shape       Datatype
---            ---         ---      ---                ---
b64.fromrgb    2560        16       [32, 512, 64, 64]  float16
b64.skip       262144      16       [32, 512, 32, 32]  float16
b64.conv0      2359808     16       [32, 512, 64, 64]  float16
b64.conv1      2359808     16       [32, 512, 32, 32]  float16
b64            -           16       [32, 512, 32, 32]  float16
b32.skip       262144      16       [32, 512, 16, 16]  float16
b32.conv0      2359808     16       [32, 512, 32, 32]  float16
b32.conv1      2359808     16       [32, 512, 16, 16]  float16
b32            -           16       [32, 512, 16, 16]  float16
b16.skip       262144      16       [32, 512, 8, 8]    float16
b16.conv0      2359808     16       [32, 512, 16, 16]  float16
b16.conv1      2359808     16       [32, 512, 8, 8]    float16
b16            -           16       [32, 512, 8, 8]    float16
b8.skip        262144      16       [32, 512, 4, 4]    float16
b8.conv0       2359808     16       [32, 512, 8, 8]    float16
b8.conv1       2359808     16       [32, 512, 4, 4]    float16
b8             -           16       [32, 512, 4, 4]    float16
b4.mbstd       -           -        [32, 513, 4, 4]    float32
b4.conv        2364416     16       [32, 512, 4, 4]    float32
b4.fc          4194816     -        [32, 512]          float32
b4.out         513         -        [32, 1]            float32
---            ---         ---      ---                ---
Total          26489345    288      -                  -

Setting up augmentation...
Distributing across 1 GPUs...
Setting up training phases...
Exporting sample images...
Initializing logs...
Skipping tfevents export: No module named 'tensorboard'
Training for 10000 kimg...

C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\augment.py:231: UserWarning: Specified kernel cache directory could not be created! This disables kernel caching. Specified directory is C:\Users\John\AppData\Local\Temp/torch/kernels. This warning will appear only once per process. (Triggered internally at  C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\cuda\jit_utils.cpp:860.)
  s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std)
Traceback (most recent call last):
  File "train.py", line 330, in <module>
    main()  # pylint: disable=no-value-for-parameter
  File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\click\core.py", line 1130, in __call__
    return self.main(*args, **kwargs)
  File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\click\core.py", line 1055, in main
    rv = self.invoke(ctx)
  File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\click\core.py", line 1404, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\click\core.py", line 760, in invoke
    return __callback(*args, **kwargs)
  File "train.py", line 323, in main
    launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
  File "train.py", line 92, in launch_training
    subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
  File "train.py", line 50, in subprocess_fn
    training_loop.training_loop(rank=rank, **c)
  File "C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\training_loop.py", line 279, in training_loop
    loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg)
  File "C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\loss.py", line 75, in accumulate_gradients
    gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma)
  File "C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\loss.py", line 59, in run_D
    img = self.augment_pipe(img)
  File "C:\Users\John\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\Users\John\Desktop\New\stylegan3-fun-rgba\training\augment.py", line 370, in forward
    raise ValueError('Image must be RGB (3 channels) or L (1 channel)')
ValueError: Image must be RGB (3 channels) or L (1 channel)```

bug

opened by 1378dm 8

Unidentified AssertionError When Using 'projector.py'
Describe the bug Unidentified AssertionError when I run the projector.py.

To Reproduce Steps to reproduce the behavior:

In the root directory of this project, and execute this command: "python projector.py --network=my-pretrained-models/StyleGAN2-Ada-DEM1024-CLAHE.pkl --cfg=stylegan2 --target=targets/RiverValley.png"

See error

Expected behavior I don't know what I should expect to happen, but I definitely know there's something wrong.

Error Information Setting up PyTorch plugin "bias_act_plugin"... /home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/scipy/init.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.3 warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" projector.py:447: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. target_pil = target_pil.resize((G.img_resolution, G.img_resolution), PIL.Image.LANCZOS) Done. Projecting in W latent space... Starting from W midpoint using 10000 samples... Setting up PyTorch plugin "upfirdn2d_plugin"... Done. Traceback (most recent call last): File "projector.py", line 549, in run_projection() # pylint: disable=no-value-for-parameter File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/core.py", line 1128, in call return self.main(*args, **kwargs) File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/core.py", line 1053, in main rv = self.invoke(ctx) File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/core.py", line 1395, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/core.py", line 754, in invoke return __callback(*args, **kwargs) File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/click/decorators.py", line 26, in new_func return f(get_current_context(), *args, **kwargs) File "projector.py", line 456, in run_projection projected_w_steps, run_config = project( File "projector.py", line 178, in project synth_features = vgg16(synth_images, resize_images=False, return_lpips=True) File "/home/MYUSERID/anaconda3/envs/pytorch180-A100/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "", line 71, in forward AssertionError

Environment

OS: CentOS7

PyTorch 1.7.1

CUDA 11.0

NVIDIA driver version - 470.82.01

NVIDIA driver version(CUDA) - 11.4

GPU NVIDIA A100

BTW, I use the slurm to submit my work to the lab's server. I have successfully done the training on my own dataset. And the dataset is not about human faces, the images in my dataset are grayscale digital elevation maps (DEM) with a resolution of 1024x1024. This error is unidentified through the log. Any effort on solving this error is appreciated.
enhancement good first issue
opened by Lowdham 5
any way to get the visualizer to support importing stylegan3-nada .pkl files?

(https://github.com/rinongal/StyleGAN-nada/blob/StyleGAN3-NADA/stylegan3_nada.ipynb) i'm making some tweaks to my stylegan3 network with this but it seems like i'm not able to import it to the visualizer any way to get the visualizer to support these? the generating of images in this repo is a little whacky but it's such a good tweak to stylegan, would be great if it worked :) there is a fix for converting stylegan2-nada files to stylegan2 but not for 3 yet :/

https://github.com/eps696/stylegan2ada (this is the one for stylegan2 that converts the .pt files to .pkl files again)
enhancement

opened by nicolai256 5
Creating environment results in not being able to train
Describe the bug Creating environment results in pytorch CPU being downloaded Clip by openAI addition results in torch 1.7.1 being downloaded, unsure if that was cause for pytorch CPU version

To Reproduce I would run "Conda clean -a" and "pip cache purge" Then attemp to build environment. Doing so would not allow me to train using "python train.py --outdir=C:\AI\output\stylegan --cfg=stylegan3-r --data=C:\AI\data\data-512x512.zip --gpus=1 --batch=12 --gamma=8.2 --mirror=1" or similar commands

Expected behavior running train.py not erroring out

Screenshots

Desktop (please complete the following information):

OS: Win 11

PyTorch version pytorch 1.7.1

CUDA toolkit version 11.3

NVIDIA driver version 511.79

GPU RTX 3090

Docker: no

Anaconda: miniconda

bug
opened by ZibbeZabbe 5

"FileExistsError: [WinError 183] Cannot create a file when that file already exists" when resuming training

Describe the bug my guess is that it tries to create the resume outdir twice because when I run the code it does create the outdir in the map but then gives the error below because it probably can't override it or something like that. i also tried to leave outdir empty ad your code creates the outdir automatically but it gives the same error.

Expected behavior only create the outdir once so it doesn't create the error

Screenshots

Output directory:    C:\deepdream-test\stylegan3-fun\training-runs\00027-stylegan3-t-datasets-gpus1-batch8-gamma6.6-resume_custom
Number of GPUs:      1
Batch size:          8 images
Training duration:   25000 kimg
Dataset path:        C:\deepdream-test\stylegan3-fun\dataset22\datasets.zip
Dataset size:        5953 images
Dataset resolution:  512
Dataset labels:      False
Dataset x-flips:     True
Dataset y-flips:     False

Creating output directory...
Traceback (most recent call last):
  File "C:\deepdream-test\stylegan3-fun\train.py", line 324, in <module>
    main()  # pylint: disable=no-value-for-parameter
  File "C:\python\lib\site-packages\click\core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "C:\python\lib\site-packages\click\core.py", line 782, in main
    rv = self.invoke(ctx)
  File "C:\python\lib\site-packages\click\core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "C:\python\lib\site-packages\click\core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "C:\deepdream-test\stylegan3-fun\train.py", line 317, in main
    launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
  File "C:\deepdream-test\stylegan3-fun\train.py", line 86, in launch_training
    os.makedirs(c.run_dir)
  File "C:\python\lib\os.py", line 225, in makedirs
    mkdir(name, mode)
FileExistsError: [WinError 183] Cannot create a file when that file already exists: 'C:\\deepdream-test\\stylegan3-fun\\training-runs\\00027-stylegan3-t-datasets-gpus1-batch8-gamma6.6-resume_custom'

thanks for fixing the keyerror: none and activating issues on your repo :)

opened by nicolai256 5

Specify latent space variables in generate.py

It was not clear to me how I could specify the latent space variables to generate.py, because it seems you can only provide a seed. Can I provide arguments or ranges to generate.py corresponding to the two latent space variables that one can control in visualizer.py? Thank you & sorry if this is an obvious question.

opened by ajerschow 4
Pytorch MPS Mac M1 Support
Is your feature request related to a problem? Please describe. I'd like to be able to generate images using the Metal Performance Shaders (MPS) pytorch acceleration for M1 macs

Describe the solution you'd like Run the generate.py script with device=mps

Describe alternatives you've considered I've modified the code and am able to run the function, but the resulting image is completely gray. I'm curious if anyone else has tried and succeeded.

The issue seems to be in w_to_img -> G.synthesis(). The output of that function does not match the output when I run with device=cpu. Up until that point everything matched (for example, the output of get_w_from_seed was correct with mps).

The code changes were:

Set device to mps if selected

if torch.cuda.is_available() and device == 'cuda': device = torch.device('cuda') elif torch.backends.mps.is_available() and device == "mps": device = torch.device('mps') else: device = torch.device('cpu')

Ensure float32 conversion, for example

z = z.astype(np.float32) w = G.mapping(torch.from_numpy(z).to(device), None)
enhancement
opened by adamdavidcole 3

Error running Circular Interpolation

I'm seeing an error when running the Circular Interpolation portion of the generate.py script. Check out the error pasted below.

I don't believe that I'm passing any illegal values or character into the attributes. Am I missing something simple or is it a bug?

Here is a Google Colab notebook showcasing the issue (logs included). Included at the bottom of the notebook is a proof-of-concept test using just the required attributes, and then also a more realistic use case scenario. Both return the same error. https://colab.research.google.com/drive/1VADM8w2b9fSnO_25axSJB44hMY8jnCB2?usp=sharing

Traceback (most recent call last):
  File "generate.py", line 743, in <module>
    main()  # pylint: disable=no-value-for-parameter
  File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/usr/local/lib/python3.7/dist-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/click/decorators.py", line 21, in new_func
    return f(get_current_context(), *args, **kwargs)
  File "generate.py", line 704, in circular_video
    videoclip = moviepy.editor.VideoClip(make_frame, duration=duration_sec)
  File "/usr/local/lib/python3.7/dist-packages/moviepy/video/VideoClip.py", line 86, in __init__
    self.size = self.get_frame(0).shape[:2][::-1]
  File "<decorator-gen-10>", line 2, in get_frame
  File "/usr/local/lib/python3.7/dist-packages/moviepy/decorators.py", line 89, in wrapper
    return f(*new_a, **new_kw)
  File "/usr/local/lib/python3.7/dist-packages/moviepy/Clip.py", line 94, in get_frame
    return self.make_frame(t)
  File "generate.py", line 689, in make_frame
    dlatents = gen_utils.z_to_dlatent(G, latents, label, truncation_psi=1.0)  # Get the pure dlatent
TypeError: z_to_dlatent() got an unexpected keyword argument 'truncation_psi'

bug

opened by nuclearsugar 2

is it possible to resume tick and augment too?

Describe the bug the storage of the remote pc was full so it stopped training but it's already pretty far into training i'd like to resume without it blurring the new gens maybe it's possible with the log file?

tick 1587 kimg 6348.0 time 7d 08h 07m sec/tick 396.5 sec/kimg 99.12 maintenance 0.3 cpumem 6.35 gpumem 30.74 reserved 41.21 augment 34.266
enhancement

opened by nicolai256 2

is it possible to resume training a .pkl file on the same kimg with a new datasetof pictures?

Describe the bug I tried doing this but it gives me an error (see below) when resume kimg with the normal dataset of images it doesn't give me this error. I have checked if all the images are 1024px and they are. it seems to start training but fails after the first tick.

input code python train.py --cfg=stylegan3-t --data=C:\deepdream-test\stylegan3-fun\dataset22\images\1024.zip --aug=ada --augpipe=bg --target=0.7 --gpus=1 --batch=8 --batch-gpu=8 --mbstd-group=8 --gamma=6.6 --mirror=1 --kimg=25000 --snap=1 --metrics=none --resume=C:\deepdream-test\stylegan3-fun\training-runs\network-snapshot-005832.pkl --resume-kimg=5832

error code

Setting up augmentation...
Distributing across 1 GPUs...
Setting up training phases...
Exporting sample images...
Initializing logs...
Training for 25000 kimg...

tick 0     kimg 5832.0   time 1m 34s       sec/tick 20.5    sec/kimg 2557.87 maintenance 73.5   cpumem 4.52   gpumem 16.10  reserved 19.92  augment 0.000
Traceback (most recent call last):
  File "c:\deepdream-test\stylegan3-fun\train.py", line 324, in <module>
    main()  # pylint: disable=no-value-for-parameter
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\click\core.py", line 1130, in __call__
    return self.main(*args, **kwargs)
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\click\core.py", line 1055, in main
    rv = self.invoke(ctx)
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\click\core.py", line 1404, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\click\core.py", line 760, in invoke
    return __callback(*args, **kwargs)
  File "c:\deepdream-test\stylegan3-fun\train.py", line 317, in main
    launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
  File "c:\deepdream-test\stylegan3-fun\train.py", line 95, in launch_training
    subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
  File "c:\deepdream-test\stylegan3-fun\train.py", line 50, in subprocess_fn
    training_loop.training_loop(rank=rank, **c)
  File "c:\deepdream-test\stylegan3-fun\training\training_loop.py", line 260, in training_loop
    phase_real_img, phase_real_c = next(training_set_iterator)
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__
    data = self._next_data()
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data
    return self._process_data(data)
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data
    data.reraise()
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\_utils.py", line 425, in reraise
    raise self.exc_type(msg)
AssertionError: Caught AssertionError in DataLoader worker process 1.
Original Traceback (most recent call last):
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\_utils\worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "C:\Users\Gebruiker\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "c:\deepdream-test\stylegan3-fun\training\dataset.py", line 99, in __getitem__
    assert list(image.shape) == self.image_shape
AssertionError

opened by nicolai256 2

Fix for correct number of conditional mapping layers in Discriminator

Now discriminator keeps up with the amount of conditional mapping layers same as in generator.

Also new key argument --cond-D-nofix was added for the backward compatibility, to being able to continue training old models that was trained before the fix was merged.

opened by DEBIHOOD 0
Bug in conditioning of discriminator?

I'm pretty sure i wouldn't get any support in official SG3 repo, because it all looks abandoned, the issues of this repo mostly remain silently unanswered. I noticed that you provided some community support by answering to some issues, for the users in it, i think this is important contribution for StyleGAN community, so props to you. I think this is the only place where this problem might be unraveled, and i thought you could shred some light on it.

Recently i've tried to train conditional model, and i'm super hyped about it, because i've been playing with SG for quite a while already, and it is the first time i was trying conditional model. The power that conditioning is able to provide is just super cool to me. Also, turned out SG supports multiple labels out of the box, which was quite unexpected for me, and i'm even more hyped to try that out.

Papers of SG/SG2/SG3 doesn't seems to have even a single word about conditioning, but the code has it. I was trying to find something related to it in the papers, but no luck.

Describe the bug Everything related to the bug is already described here: https://github.com/NVlabs/stylegan3/issues/209

Thanks a lot in advance.
enhancement good first issue

opened by DEBIHOOD 4
Start from pretrained at different resolution

Is your feature request related to a problem? Please describe. Is it possible to load a pretrained model at different resolution? I have a pretrained at 512x512 and I would start from it to train a new one at 256x256.

Describe the solution you'd like Automatic recovery of previously trained layers, when they match

Describe alternatives you've considered Resize images, but train at 512 require too much time

opened by EnricoBeltramo 2
Would it be possible to render a video from projected W?

I would like to generate a video from a projected w vector and specify the number of frames between this interpolation. The current image generator permits the option to ==projected-w, however, this does not seem possible for video. Is this currently possible?

Describe the solution you'd like Project images as npz file (vs npy) -> combine multiple vectors into a single npz file -> generate interpolation video between projected images.

Describe alternatives you've considered Resembles this generator - https://github.com/dvschultz/stylegan2-ada-pytorch/blob/main/generate.py, or the colab : https://colab.research.google.com/github/dvschultz/stylegan2-ada-pytorch/blob/main/SG2_ADA_PyTorch.ipynb#scrollTo=4cgezYN8Dsyh

@ !python generate.py --process=interpolation --interpolation=linear --easing=easeInOutQuad --space=w --network=/content/ladiesblack.pkl --outdir=/content/combined-proj/ --projected-w=/content/npz/combined.npz --frames=120

Amazing set of features! Thank you @PDillis
enhancement

opened by georgeguida 3

Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Related tags

Overview

Alias-Free Generative Adversarial Networks (StyleGAN3)Official PyTorch implementation of the NeurIPS 2021 paper

Release notes

Synthetic image detection

Additional material

Requirements

Getting started

Interactive visualization

Using networks from Python

Preparing datasets

Training

Quality metrics

Spectral analysis

License

Citation

Development

Acknowledgements

Comments

Now discriminator keeps up with the amount of conditional mapping layers same as in generator.

Owner

Diego Porres

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