#FACER CREATOR PULSE LICENSE#
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. Vgg16.pkl and vgg16_zhang_perceptual.pkl are derived from the pre-trained VGG-16 network by Karen Simonyan and Andrew Zisserman. The network was originally shared under Apache 2.0 license on the TensorFlow Models repository. 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. 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.įor license information regarding the FFHQ dataset, please refer to the Flickr-Faces-HQ repository. Please see the file listing for remaining networks.Īll material, excluding the Flickr-Faces-HQ dataset, is made available under Creative Commons BY-NC 4.0 license by NVIDIA Corporation.
Standard LPIPS metric to estimate perceptual similarity.īinary classifier trained to detect a single attribute of CelebA-HQ. Standard Inception-v3 classifier that outputs a raw feature vector. StyleGAN trained with LSUN Cat dataset at 256×256.Īuxiliary networks for the quality and disentanglement metrics.
StyleGAN trained with LSUN Car dataset at 512×384. StyleGAN trained with LSUN Bedroom dataset at 256×256. StyleGAN trained with CelebA-HQ dataset at 1024×1024. StyleGAN trained with Flickr-Faces-HQ dataset at 1024×1024. Pre-trained networks as pickled instances of. Raw data for the Flickr-Faces-HQ dataset. Individual segments of the result video as high-quality MP4. Generated using LSUN Cat dataset at 256×256.Įxample videos produced using our generator. Generated using LSUN Car dataset at 512×384.
Generated using LSUN Bedroom dataset at 256×256. Generated using Flickr-Faces-HQ dataset at 1024×1024. High-quality images to be used in articles, blog posts, etc.ġ00,000 generated images for different amounts of truncation. High-quality version of the result video.Įxample images produced using our generator. Material related to our paper is available via the following links:Īdditional material can be found on Google Drive: Path
#FACER CREATOR PULSE FULL#
★★★ NEW: StyleGAN2-ADA-PyTorch is now available see the full list of versions here ★★★ Resources Finally, we introduce a new, highly varied and high-quality dataset of human faces.įor business inquiries, please visit our website and submit the form: NVIDIA Research Licensing
#FACER CREATOR PULSE GENERATOR#
To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. 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. 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. Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA)Ībstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. This repository contains the official TensorFlow implementation of the following paper:Ī Style-Based Generator Architecture for Generative Adversarial Networks Picture: These people are not real – they were produced by our generator that allows control over different aspects of the image. StyleGAN - Official TensorFlow Implementation
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