Copyright © 2019, NVIDIA Corporation. This repository contains the official TensorFlow implementation of the following paper: For business inquiries, please contact [email protected] For press and other inquiries, please contact Hector Marinez at [email protected] ★★★ NEW: StyleGAN2 is available at http… It generates a batch of random images and feeds them directly to the Inception-v3 network without having to convert the data to numpy arrays in between. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. One or more high-end NVIDIA GPUs, NVIDIA drivers, CUDA 10.0 toolkit and cuDNN 7.5. The following table lists the available metrics along with their expected runtimes and random variation: Note that some of the metrics cache dataset-specific data on the disk, and they will take somewhat longer when run for the first time. Preface Nvidia Research labs. The remaining keyword arguments are optional and can be used to further modify the operation (see below). Abstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. To reproduce the results reported in the paper, you need an NVIDIA GPU with at least 16 GB of DRAM. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. We have verified that the results match the paper when training with 1, 2, 4, or 8 GPUs. For business inquiries, please contact [email protected] Create custom datasets by placing all training images under a single directory. Work fast with our official CLI. This artificial intelligence art project utilizes Style Generative Adversarial Network (StyleGAN) , which is developed by NVIDIA researches. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila, Paper: http://arxiv.org/abs/1912.04958 Learn more. The dlatents array stores a separate copy of the same w vector for each layer of the synthesis network to facilitate style mixing. Code . TensorFlow 2.x is not supported. In a paper published last week, NVIDIA researchers come up with a way to generate photos that look like they were clicked with a camera. arxiv, 2019. High-quality images to be used in articles, blog posts, etc. To convert the data to multi-resolution TFRecords, run: Custom. The semantic segmentation feature is powered by PyTorch deeplabv2 under MIT licesne. Abstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. download the GitHub extension for Visual Studio. StyleGAN2 - Official TensorFlow Implementation. A minimal example of using a pre-trained StyleGAN generator is given in pretrained_example.py. If nothing happens, download the GitHub extension for Visual Studio and try again. The results are placed in results//*.png. For press and other inquiries, please contact Hector Marinez at [email protected], ★★★ NEW: StyleGAN2-ADA-PyTorch is now available; see the full list of versions here ★★★. The following table lists typical training times using NVIDIA DGX-1 with 8 Tesla V100 GPUs: Training curves for FFHQ config F (StyleGAN2) compared to original StyleGAN using 8 GPUs: After training, the resulting networks can be used the same way as the official pre-trained networks: To reproduce the numbers for config F in Tables 1 and 3, run: For other configurations, see the StyleGAN2 Google Drive folder. NVIDIA demos a style-based generative adversarial network that can generate extremely realistic images; has ML community enthralled. The training and evaluation scripts operate on datasets stored as multi-resolution TFRecords. The NVIDIA paper proposes an alternative generator architecture for GAN that draws insights from style transfer techniques. The average w needed to manually perform the truncation trick can be looked up using Gs.get_var('dlatent_avg'). Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA) The mapping network as developed in the original NVIDIA Style GAN paper has 8 fully connected layers of 512X1 dimension. StyleGAN is a novel generative adversarial network introduced by Nvidia researchers in December 2018, and made source available in February 2019. To obtain other datasets, including LSUN, please consult their corresponding project pages. eye-color). Both Linux and Windows are supported, but we strongly recommend Linux for performance and compatibility reasons. Similar to Gs, the sub-networks are represented as independent instances of dnnlib.tflib.Network: The above code is from generate_figures.py. Please see the file listing for remaining networks. By default, the scripts expect to find the datasets at datasets//-.tfrecords. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. You signed in with another tab or window. JYZ is supported by the Facebook Graduate Fellowship, and TP is supported by the Samsung Scholarship. NVIDIA PyTorch GAN library with distributed and mixed precision support - lookcat/imaginaire We recommend Anaconda3 with numpy 1.14.3 or newer. Example videos produced using our generator. randomize_noise determines whether to use re-randomize the noise inputs for each generated image (True, default) or whether to use specific noise values for the entire minibatch (False). See run_generator.py and pretrained_networks.py for examples. Runtime performance can be fine-tuned via structure='fixed' and dtype='float16'. This is done via using generative adversarial … In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. There is a separate *.tfrecords file for each resolution, and if the dataset contains labels, they are stored in a separate file as well. We recommend installing Visual Studio Community Edition and adding into PATH using "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvars64.bat". PyTorch . When executed, the script downloads a pre-trained StyleGAN generator from Google Drive and uses it to generate an image: A more advanced example is given in generate_figures.py. By default, the script will evaluate the Fréchet Inception Distance (fid50k) for the pre-trained FFHQ generator and write the results into a newly created directory under results. Individual segments of the result video as high-quality MP4. FFHQ. source The mapping network is used because the traditional GAN is unable to control the features and styles to a great extent. You can change the location with --result-dir. Use Git or checkout with SVN using the web URL. Generated using Flickr-Faces-HQ dataset at 1024×1024. I was unable to find a styleGAN specific forum to post this in, and styleGAN is an Nvidia project, is anyone aware of such a forum? 0. Note that the metrics are evaluated using a different random seed each time, so the results will vary between runs. “Work” means the Software and any additions to or derivative works of the Software that are made available under this License. For example, --result-dir=~/my-stylegan2-results. Definitions “Licensor” means any person or entity that distributes its Work. The basis of the model was established by a research paper published by Tero Karras, Samuli Laine, and Timo Aila, all researchers at NVIDIA. vgg16_zhang_perceptual.pkl is further derived from the pre-trained LPIPS weights by Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. You can import the networks in your own Python code using pickle.load(). In order for pickle.load() to work, you will need to have the dnnlib source directory in your PYTHONPATH and a tf.Session set as default. Please note that we have used 8 GPUs in all of our experiments. Training with fewer GPUs may not produce identical results – if you wish to compare against our technique, we strongly recommend using the same number of GPUs. Finally, our model allows users to easily control the style and content of synthesis results as well as create multi-modal results. Linux is recommended for performance and compatibility reasons. You can use, redistribute, and adapt the material for non-commercial purposes, as long as you give appropriate credit by citing our paper and indicating any changes that you've made. "Semantic Image Synthesis with Spatially-Adaptive Normalization", in CVPR, 2019. This new project called StyleGAN2, presented at CVPR 2020, uses transfer learning to generate a seemingly infinite numbers of portraits in an infinite variety of painting styles. Learn a style-guided image translation model that can generate translations in unseen domains. Abstract: Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. Binary classifier trained to detect a single attribute of CelebA-HQ. Note that truncation is always disabled when using the sub-networks directly. The NVIDIA GauGAN beta is based on NVIDIA's CVPR 2019 paper on Semantic Image Synthesis with Spatially-Adaptive Normalization or SPADE. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. truncation_psi=0.5. To obtain the CelebA-HQ dataset (datasets/celebahq), please refer to the Progressive GAN repository. We recommend TensorFlow 1.14, which we used for all experiments in the paper, but TensorFlow 1.15 is also supported on Linux. High-quality version of the result video. Auxiliary networks for the quality and disentanglement metrics. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. Over the years, NVIDIA researchers have contributed several breakthroughs to GANs. StyleGAN2 is Nvidia’s open-source GAN that consists of two cooperating networks, a generator for creating synthetic images and a discriminator that learns what realistic photos should look like based on the training data set. This makes it possible to reliably detect if an image is generated by a particular network. To download the Flickr-Faces-HQ dataset as multi-resolution TFRecords, run: LSUN. We recommend NVIDIA DGX-1 with 8 Tesla V100 GPUs. head shape) to the finer details (eg. Visualizing generator and discriminator. The output is a batch of images, whose format is dictated by the output_transform argument. Below, you can either reference them directly using the syntax gdrive:networks/.pkl, or download them manually and reference by filename. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. Generative Adversarial Neural Networks (GANs) are a type of neural network that can generate random “fake” images based on a training set of real images. Citation. This work is made available under the Nvidia Source Code License-NC. Picture: These people are not real – they were produced by our generator that allows control over different aspects of the image. The exact details of the generator are defined in training/networks_stylegan.py (see G_style, G_mapping, and G_synthesis). In par… Each dataset is represented by a directory containing the same image data in several resolutions to enable efficient streaming. StyleGAN - Official TensorFlow Implementation. Abstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. AI will soon massively empower architects in their day-to-day practice. For license information regarding the FFHQ dataset, please refer to the Flickr-Faces-HQ repository. On Windows you need to use TensorFlow 1.14, as the standard 1.15 installation does not include necessary C++ headers. This potential is around the corner and my work provides a proof of concept. Over time, as it receives feedback from the discriminator, it learns to synthesize more “realistic” images. Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. Material related to our paper is available via the following links: Additional material can be found on Google Drive: All material, excluding the Flickr-Faces-HQ dataset, is made available under Creative Commons BY-NC 4.0 license by NVIDIA Corporation. 7. To test that your NVCC installation is working correctly, run: On Windows, the compilation requires Microsoft Visual Studio to be in PATH. To convert the images to multi-resolution TFRecords, run: To find the matching latent vectors for a set of images, run: To reproduce the training runs for config F in Tables 1 and 3, run: For other configurations, see python run_training.py --help. To view a copy of this license, visit https://nvlabs.github.io/stylegan2/license.html. ECCV 2020 The network was originally shared under Apache 2.0 license on the TensorFlow Models repository. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. Each dataset consists of multiple *.tfrecords files stored under a common directory, e.g., ~/datasets/ffhq/ffhq-r*.tfrecords. Liu et. Look up Gs.components.mapping and Gs.components.synthesis to access individual sub-networks of the generator. GAN-Generated masterplan. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. al. Use Gs.get_output_for() to incorporate the generator as a part of a larger TensorFlow expression: The above code is from metrics/frechet_inception_distance.py. Add training configs for FFHQ at lower resolutions. Generated using LSUN Car dataset at 512×384. StyleGAN2 relies on custom TensorFlow ops that are compiled on the fly using NVCC. Perfect. StyleGAN trained with CelebA-HQ dataset at 1024×1024. “Work” means the Software and any additions to or derivative works of the Software that are made available under this License. The script reproduces the figures from our paper in order to illustrate style mixing, noise inputs, and truncation: The pre-trained networks are stored as standard pickle files on Google Drive: The above code downloads the file and unpickles it to yield 3 instances of dnnlib.tflib.Network. Pre-trained networks as pickled instances of. In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images. ICCV 2019: COCO-FUNIT: Improve FUNIT with a content-conditioned style encoding scheme for style code computation. Very Deep Convolutional Networks for Large-Scale Visual Recognition. TensorFlow 1.10.0 or newer with GPU support. NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator Augmentation (ADA) 1. Finally, we introduce a new, highly varied and high-quality dataset of human faces. Learn more. StyleGAN was originally an open-source project by NVIDIA to create a generative model that could output high-resolution human faces. When using the mapping network directly, you can specify dlatent_broadcast=None to disable the automatic duplication of dlatents over the layers of the synthesis network. Datasets are stored as multi-resolution TFRecords, similar to the original StyleGAN. Generated using LSUN Bedroom dataset at 256×256. 3044. The second argument is reserved for class labels (not used by StyleGAN). The StyleGAN paper, “A Style-Based Architecture for GANs”, was published by NVIDIA in 2018. The directory can be changed by editing config.py: To obtain the FFHQ dataset (datasets/ffhq), please refer to the Flickr-Faces-HQ repository. GANs trained to produce human faces have received much media attention since the release of NVIDIA StyleGAN in 2018. We thank Ming-Yu Liu for an early review, Timo Viitanen for his help with code release, and Tero Kuosmanen for compute infrastructure. Use Git or checkout with SVN using the web URL. The paper proposed a new generator architecture for GAN that allows them to control different levels of details of the generated samples from the coarse details (eg. Expected evaluation time and results for the pre-trained FFHQ generator using one Tesla V100 GPU: Please note that the exact results may vary from run to run due to the non-deterministic nature of TensorFlow. Forty years since PAC-MAN first hit arcades in Japan, the retro classic has been reimagined, courtesy of artificial intelligence (AI). By. If nothing happens, download GitHub Desktop and try again. The session can initialized by calling dnnlib.tflib.init_tf(). In the bottom-up composition phase, given a pair of music and dance, the team leverages the MM-GAN to learn how to organize the dance units conditioned on the given music. StyleGAN trained with LSUN Bedroom dataset at 256×256. The results are written to a newly created directory. We recommend Anaconda3 with numpy 1.14.3 or newer. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. One of the key elements in the original StyleGAN architecture was the adaptive instance normalization layer in the building blocks of the GAN synthesis network. Generator. Nvidia released StyleGAN on Github Published on February 6, 2019 February 6, 2019 • 3 Likes • 0 Comments. All rights reserved. It can be disabled by setting truncation_psi=1 or is_validation=True, and the image quality can be further improved at the cost of variation by setting e.g. StyleGAN trained with LSUN Car dataset at 512×384. NVIDIA's StyleGAN2 StyleGAN2 is NVIDIA's most recent GAN development, and as you'll see from the video, using so-called transfer learning it has managed to generate a seemingly infinite number of portraits depicting different human faces in an infinite variety of painting styles. The GAN-based model performs so well that most people can’t distinguish the … Video: https://youtu.be/c-NJtV9Jvp0. NVIDIA driver 391.35 or newer, CUDA toolkit 9.0 or newer, cuDNN 7.3.1 or newer. The network was originally shared under Creative Commons BY 4.0 license on the Very Deep Convolutional Networks for Large-Scale Visual Recognition project page. We thank Jaakko Lehtinen, David Luebke, and Tuomas Kynkäänniemi for in-depth discussions and helpful comments; Janne Hellsten, Tero Kuosmanen, and Pekka Jänis for compute infrastructure and help with the code release. StyleGAN2. This work was supported in part by NSF SMA-1514512, NSF IIS-1633310, a Google Research Award, Intel Corp, and hardware donations from NVIDIA. The specific values can be accessed via the tf.Variable instances that are found using [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]. Work fast with our official CLI. 6. If nothing happens, download Xcode and try again. Paper. Picture: These people are not real – they were produced by our generator that allows control over different aspects of the image. Fréchet Inception Distance using 50,000 images. Researchers observed that several changes to the main StyleGAN block are possible and they defined a revised architecture (shown in the diagram below). It first transforms a batch of latent vectors into the intermediate W space using the mapping network and then turns these vectors into a batch of images using the synthesis network. StyleGAN trained with LSUN Cat dataset at 256×256. Abstract: Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. One or more high-end NVIDIA GPUs with at least 11GB of DRAM. vgg16.pkl and vgg16_zhang_perceptual.pkl are derived from the pre-trained VGG-16 network by Karen Simonyan and Andrew Zisserman. “Software” means the original work of authorship made available under this License. The system can learn and separate different aspects of … “Software” means the original work of authorship made available under this License. 64-bit Python 3.6 installation. Definitions “Licensor” means any person or entity that distributes its Work. The former disables support for progressive growing, which is not needed for a fully-trained generator, and the latter performs all computation using half-precision floating point arithmetic. Analyzing and Improving the Image Quality of StyleGAN Example images produced using our generator. The basic components of every GAN are two neural networks - a generator that synthesizes new samples from scratch, and a discriminator that takes samples from both the training data and the generator’s output and predicts if they are “real” or “fake”.The generator input is a random vector (noise) and therefore its initial output is also noise. There are three ways to use the pre-trained generator: Use Gs.run() for immediate-mode operation where the inputs and outputs are numpy arrays: The first argument is a batch of latent vectors of shape [num, 512]. The work builds on the team’s previously published StyleGAN project. Perceptual Path Length for path endpoints in. inception_v3_features.pkl and inception_v3_softmax.pkl are derived from the pre-trained Inception-v3 network by Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Both Linux and Windows are supported. For business inquiries, please contact [email protected] As an additional contribution, we construct a higher-quality version of the CelebA dataset. 3 min read. 100,000 generated images for different amounts of truncation. The weights were originally shared under BSD 2-Clause "Simplified" License on the PerceptualSimilarity repository. The training may take several days (or weeks) to complete, depending on the configuration. Note that training FFHQ at 1024×1024 resolution requires GPU(s) with at least 16 GB of memory. 64-bit Python 3.6 installation. To generate images, you will typically want to use Gs – the other two networks are provided for completeness. StyleGAN2 has trained various AI models such as GauGAN – an AI painting app, GameGAN – a game engine mimicker, and GANimal – a pet photo transformer. Generated using LSUN Cat dataset at 256×256. Expected training times for the default configuration using Tesla V100 GPUs: The quality and disentanglement metrics used in our paper can be evaluated using run_metrics.py. If nothing happens, download GitHub Desktop and try again. https://arxiv.org/abs/1812.04948. Analyzing and Improving the Image Quality of StyleGAN Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila Paper: http://arxiv.org/abs/1912.04958 Video: https://youtu.be/c-NJtV9Jvp0 Abstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. Saito et. StyleGAN trained with Flickr-Faces-HQ dataset at 1024×1024. For this to work, you need to include the dnnlib source directory in PYTHONPATH and create a default TensorFlow session by calling dnnlib.tflib.init_tf(). The following keyword arguments can be specified to modify the behavior when calling run() and get_output_for(): truncation_psi and truncation_cutoff control the truncation trick that that is performed by default when using Gs (ψ=0.7, cutoff=8).
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