stylegan | StyleGAN - Official TensorFlow Implementation | Machine Learning library

 by   NVlabs Python Version: Current License: Non-SPDX

kandi X-RAY | stylegan Summary

kandi X-RAY | stylegan Summary

stylegan is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. stylegan has no bugs, it has no vulnerabilities and it has medium support. However stylegan build file is not available and it has a Non-SPDX License. You can download it from GitHub.

This repository contains the official TensorFlow implementation of the following paper:. A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA) Abstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. 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. 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. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces. For business inquiries, please contact For press and other inquiries, please contact Hector Marinez at

            kandi-support Support

              stylegan has a medium active ecosystem.
              It has 13465 star(s) with 3112 fork(s). There are 455 watchers for this library.
              It had no major release in the last 6 months.
              stylegan has no issues reported. There are 11 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of stylegan is current.

            kandi-Quality Quality

              stylegan has 0 bugs and 0 code smells.

            kandi-Security Security

              stylegan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              stylegan code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              stylegan has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              stylegan releases are not available. You will need to build from source code and install.
              stylegan has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              stylegan saves you 1724 person hours of effort in developing the same functionality from scratch.
              It has 3818 lines of code, 294 functions and 29 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed stylegan and discovered the below as its top functions. This is intended to give you an instant insight into stylegan implemented functionality, and help decide if they suit your requirements.
            • Start training loop
            • Applies the gradients to each device
            • Resets the optimizer state
            • Returns the loss scaling variable
            • GPG paper
            • 2d convolution layer
            • Apply bias to x
            • Create a weight variable
            • Run the model
            • Simple logistic regression
            • Create a TFRecord from images
            • Process multiple items in parallel
            • Create one - hot image for training
            • Calculate the G_hing coefficient
            • Loads CIFAR - 10 images
            • Create MNIST dataset
            • Submit a new run
            • Execute command line tool
            • Performs the paper
            • Evaluate the graph
            • Evaluate the TensorFlow graph
            • Attempt to download a given URL
            • Dummy DAG
            • Create LSUN dataset
            • Creates an LSUN dataset
            • Evaluate the model
            Get all kandi verified functions for this library.

            stylegan Key Features

            No Key Features are available at this moment for stylegan.

            stylegan Examples and Code Snippets

            StyleGAN — Encoder for Official TensorFlow Implementation
            Jupyter Notebookdot img1Lines of Code : 215dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            usage: [-h] [--data_dir DATA_DIR] [--mask_dir MASK_DIR]
                                    [--load_last LOAD_LAST] [--dlatent_avg DLATENT_AVG]
                                    [--model_url MODEL_URL] [--model_res MODEL_RES]
            StyleGAN — Encoder for Official TensorFlow Implementation
            Jupyter Notebookdot img2Lines of Code : 215dot img2License : Non-SPDX (NOASSERTION)
            copy iconCopy
            usage: [-h] [--data_dir DATA_DIR] [--mask_dir MASK_DIR]
                                    [--load_last LOAD_LAST] [--dlatent_avg DLATENT_AVG]
                                    [--model_url MODEL_URL] [--model_res MODEL_RES]
            How to run
            Pythondot img3Lines of Code : 106dot img3no licencesLicense : No License
            copy iconCopy
            pip install git+
            pip install hydra-core==1.1.0dev5 pytorch_lightning albumentations tqdm retry kornia
            ├── data_gen
            │   ├── generated.yaml  # <- for generating data with 1 laten  

            Community Discussions


            How does Mapping Network in StyleGAN work?
            Asked 2022-Feb-05 at 01:29

            I am learning StyleGAN architecture and I got confused about the purpose of the Mapping Network. In the original paper it says:

            Our mapping network consists of 8 fully-connected layers, and the dimensionality of all input and output activations— including z and w — is 512.

            And there is no information about this network being trained in any way.

            Like, wouldn’t it just generate some nonsense values?

            I've tried creating a network like that (but with a smaller shape (16,)):



            Answered 2022-Feb-05 at 01:29

            As I understand the mapping network is not trained separately. It it part of generator network and adjusts weights based on gradients just like other parts of the network.

            In their stylegan generator code implementation it written the Generator is composed of two sub networks one mapping and another synthesis. In stylegan3 generator source it is much easier to see. The output of mapping is passed to synthesis network which generates image.



            PULSE on github (link provided) RuntimeError: CUDA out of memory.... preventing the program "" from executing
            Asked 2021-Jan-15 at 02:58

            (As a student I am kind of new to this but did quite a bit of research and I got pretty far, I'm super into learning something new through this!)

            This issue is for the project pulse ->

            the readme if you scroll down a bit on the page, gives a much better explanation than I could. It will also give a direct "correct" path to judge my actions against and make solving the problem a lot easier.

            Objective: run program using the file

            Issue: I got a "RuntimeError: CUDA out of memory" despite having a compatible gpu and enough vram

            Knowledge: when it comes to coding i just started a few days ago and have a dozen hours with anaconda now, comfterable creating environments.

            What I did was... (the list below is a summary and the specific details are after it)

            1. install anaconda

            2. use this .yml file -> (it changes dependencies to work for windows which is why I needed to grab a different one than the one supplied on the master github page) to create a new environment and install the required packages. It worked fantastically! I only got an error trying to install dlib, it didn't seem compatible with A LOT of the packages and my python version.

            3. I installed the cuda toolkit 10.2 , cmake 3.17.2, and tried to install dlib into the environment directly. the errors spat out in a blaze of glory. The dlib package seems to be only needed for a different .py file and not though so I think it may be unrelated to this error

            logs are below and I explain my process in more detail

            START DETAILS AND LOGS: from here until the "DETAILS 2" section should be enough information to solve, the rest past there is in case

            error log for runing out of memory--> (after executing the "" file)



            Answered 2021-Jan-15 at 02:58

            based on new log evidence using this script simultaneously alongside the file



            Averaging an Array of Tensors in Python with Numpy
            Asked 2020-Dec-27 at 02:28

            I'm doing a project with StyleGans and I actually don't really know Python very well or Numpy

            I have an array of vector



            Answered 2020-Dec-27 at 02:28


            Python package run in Docker results in ERR_EMPTY_RESPONSE
            Asked 2020-Sep-02 at 20:45

            I have created a python package which is a Flask application. I want to run that application in a Docker container. This is my Dockerfile:



            Answered 2020-Sep-02 at 20:38

            Flask doesn't bind to 5000 by default (8000 is the default IIRC), so you need to pass it as an arg to



            WHat does Lambda do in this code (python keras)?
            Asked 2020-Aug-18 at 14:34
            def AdaIN(x):
                #Normalize x[0] (image representation)
                mean = K.mean(x[0], axis = [1, 2], keepdims = True)
                std = K.std(x[0], axis = [1, 2], keepdims = True) + 1e-7
                y = (x[0] - mean) / std
                #Reshape scale and bias parameters
                pool_shape = [-1, 1, 1, y.shape[-1]]
                scale = K.reshape(x[1], pool_shape)
                bias = K.reshape(x[2], pool_shape)#Multiply by x[1] (GAMMA) and add x[2] (BETA)
                return y * scale + bias
            def g_block(input_tensor, latent_vector, filters):
                gamma = Dense(filters, bias_initializer = 'ones')(latent_vector)
                beta = Dense(filters)(latent_vector)
                out = UpSampling2D()(input_tensor)
                out = Conv2D(filters, 3, padding = 'same')(out)
                out = Lambda(AdaIN)([out, gamma, beta])
                out = Activation('relu')(out)
                return out


            Answered 2020-Aug-18 at 14:34

            Lambda layers in keras are used to call custom functions inside the model. In g_block Lambda calls AdaIN function and passes out, gamma, beta as arguments inside a list. And AdaIN function receives these 3 tensors encapsulated within a single list as x. And also those tensors are accessed inside AdaIN function by indexing list x(x[0], x[1], x[2]).

            Here's pytorch equivalent:



            CUDNN_STATUS_NOT_INITIALIZED error while training StyleGan
            Asked 2020-May-12 at 04:35

            I downloaded stylegan code from and want to train it with my dataset. I am working on an ubuntu machine (Ubuntu 18.04.3 LTS) and



            Answered 2020-May-12 at 04:35

            Providing the solution here (Answer Section), even though it is present in the Comment Section, for the benefit of the community.

            First need to remove all cuDNN files



            I keep getting an Assertion Error with StyleGAN
            Asked 2020-Jan-08 at 22:27

            Recently I have been playing around with StyleGAN and I have generated a dataset but I get the following when I try to run



            Answered 2020-Jan-08 at 21:50

            As answered by @Chrispresso in the comments of this question, the directory that I was referencing in the following line was invalid and had to set it to a valid directory.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install stylegan

            You can download it from GitHub.
            You can use stylegan like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            gh repo clone NVlabs/stylegan

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