stylegan | StyleGAN - Official TensorFlow Implementation | Machine Learning library
kandi X-RAY | stylegan Summary
kandi X-RAY | stylegan Summary
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 researchinquiries@nvidia.com For press and other inquiries, please contact Hector Marinez at hmarinez@nvidia.com.
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Top functions reviewed by kandi - BETA
- 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
stylegan Key Features
stylegan Examples and Code Snippets
Lines of Code : 215License : Non-SPDX (NOASSERTION)
Copyusage: encode_images.py [-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]
[--ba usage: encode_images.py [-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]
[--ba 
License : No LicenseCopypip install git+https://github.com/lukemelas/pytorch-pretrained-gans
pip install hydra-core==1.1.0dev5 pytorch_lightning albumentations tqdm retry kornia
config
├── data_gen
│ ├── generated.yaml # <- for generating data with 1 laten Community Discussions
Trending Discussions on stylegan
QUESTION
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,)):
ANSWER
Answered 2022-Feb-05 at 01:29As 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.
QUESTION
(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 -> https://github.com/adamian98/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 run.py 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)
install anaconda
use this .yml file -> https://github.com/leihuayi/pulse/blob/feature/docker/pulse.yml (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.
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 run.py 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 "run.py" file)
...ANSWER
Answered 2021-Jan-15 at 02:58based on new log evidence using this script simultaneously alongside the run.py file
QUESTION
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
...ANSWER
Answered 2020-Dec-27 at 02:28Simply do this:
QUESTION
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:
...ANSWER
Answered 2020-Sep-02 at 20:38Flask doesn't bind to 5000 by default (8000 is the default IIRC), so you need to pass it as an arg to app.run:
QUESTION
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
ANSWER
Answered 2020-Aug-18 at 14:34Lambda 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:
QUESTION
I downloaded stylegan code from https://github.com/NVlabs/stylegan and want to train it with my dataset. I am working on an ubuntu machine (Ubuntu 18.04.3 LTS) and
...ANSWER
Answered 2020-May-12 at 04:35Providing 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
QUESTION
Recently I have been playing around with StyleGAN and I have generated a dataset but I get the following when I try to run train.py.
...ANSWER
Answered 2020-Jan-08 at 21:50As 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.
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Install stylegan
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.
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