stylegan2 | StyleGAN2 - Official TensorFlow Implementation | Machine Learning library
kandi X-RAY | stylegan2 Summary
kandi X-RAY | stylegan2 Summary
Analyzing and Improving the Image Quality of StyleGAN Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila. Paper: Video: Abstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. 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. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network. 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. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality. 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
- Run training loop
- Creates a tf summary
- Create tensorflow variable
- Generate a G_syntrical graph
- Blur a 2D image
- Apply noise to x
- Apply bias to x
- Generate a style mixing map
- Logistic regularization
- Growth operator
- Evaluate the residuals
- Upsample the input tensor
- Evaluate reals
- Create LSUN dataset with lmdb
- Compares two datasets
- Downsamples x
- Upper function for 2d
- Upsample x
- Setup snapshot image grid
- Run the model
- Basic 2D dataset
- Fuse the fused bias function
- Execute the command line parser
- Grouper G - style style
- Evaluate the graph
stylegan2 Key Features
stylegan2 Examples and Code Snippets
Lines of Code : 50
License : No License
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│ README.md -------------------------------- Description of the projects.
│ latent.sh -------------------------------- The script to find latent feature of a given image.
│ run.sh ----------------------------------- The script to train the %tensorflow_version 1.x
!nvidia-smi
from google.colab import drive
from pathlib import Path
content_path = Path('/').absolute() / 'content'
drive_path = content_path / 'drive'
drive.mount(str(drive_path))
stylegan2_repo_url = 'https://github.com/ transforms:
- layer: 4
transform: "translate"
params: [0.25, 0.0]
features: "all"
feature-param:
- layer: 5
transform: "ablate"
params: []
features: "cluster"
feature-param: 2
- layer: 12
transform: "binary-thresh"
params: [0.5]
features: Community Discussions
Trending Discussions on stylegan2
QUESTION
I am trying to run the training of stylegan2-pytorch on a remote system. The remote system has gcc (9.3.0) installed on it. I'm using conda env that has the following installed (cudatoolkit=10.2, torch=1.5.0+, and ninja=1.8.2, gcc_linux-64=7.5.0). I encounter the following error:
...ANSWER
Answered 2021-Dec-12 at 16:12Just to share, not sure it will help you. However it shows that in standard conditions it is possible to use the conda gcc as described in the documentation instead of the system gcc.
QUESTION
I want to use mogrify to set the background colour of a large number of images to be whatever colour is at a specific pixel, to make them square.
The vast majority have a single colour in the image background, or are photos in front of a single colour (so with only slight variations from shadows, etc.).
(The specific purpose in this case is to make the images all the same size and square for StyleGAN2-ADA training, so I want to avoid big "letterbox" rectangles where possible as it would be seen by the discriminator as relevant to the image, where a more faded-in background that approximately matches would be more likely to be ignored. Specifically, I have thousands of pictures of single dolls and action figures from various sources, some of which are "trimmed out" to have a truly solid colour background, others of which are against solid colour tables/walls/etc, for instance, from eBay images and such.)
Since they do not all have the same colour in the image background (the colour in the image, as opposed to the 'background colour' setting as per ImageMagick's jargon), I need to sample a pixel and set the background, but I can't figure out how. I tried things based on methods used to set the whole image to one colour, to no avail.
I have tried:
...ANSWER
Answered 2021-Nov-04 at 16:22EDITED TO ADD: While testing various commands I came across a way to modify your original command to make it work on ImageMagick versions as far back as IMv6.8.
QUESTION
I am trying to produce an image using StyleGan2.
Executing this cell:
...ANSWER
Answered 2021-Oct-03 at 11:47I have found:
The issue is the input dimension of the image must be at least [3, 1024, 1024] mine was [3, 600, 600]
QUESTION
I try to install a package (https://github.com/lucidrains/stylegan2-pytorch) on my AWS EC2 instance. I have installed Python 3.7 and is trying to install the package through that by running:
...ANSWER
Answered 2021-Sep-18 at 21:19By using the --verbose-flag I saw:
QUESTION
I'm trying to run Pytorch on a laptop that I have. It's an older model but it does have an Nvidia graphics card. I realize it is probably not going to be sufficient for real machine learning but I am trying to do it so I can learn the process of getting CUDA installed.
I have followed the steps on the installation guide for Ubuntu 18.04 (my specific distribution is Xubuntu).
My graphics card is a GeForce 845M, verified by lspci | grep nvidia:
ANSWER
Answered 2020-Oct-31 at 21:26PyTorch doesn't use the system's CUDA library. When you install PyTorch using the precompiled binaries using either pip or conda it is shipped with a copy of the specified version of the CUDA library which is installed locally. In fact, you don't even need to install CUDA on your system to use PyTorch with CUDA support.
There are two scenarios which could have caused your issue.
You installed the CPU only version of PyTorch. In this case PyTorch wasn't compiled with CUDA support so it didn't support CUDA.
You installed the CUDA 10.2 version of PyTorch. In this case the problem is that your graphics card currently uses the 418.87 drivers, which only support up to CUDA 10.1. The two potential fixes in this case would be to either install updated drivers (version >= 440.33 according to Table 2) or to install a version of PyTorch compiled against CUDA 10.1.
To determine the appropriate command to use when installing PyTorch you can use the handy widget in the "Quick start locally" section at pytorch.org. Just select the appropriate operating system, package manager, and CUDA version then run the recommended command.
In your case one solution was to use
QUESTION
I'm using StyleGANv2 (https://github.com/moono/stylegan2-tf-2.x) with original weights. My TF version is 2.2.0. The default training process and inference work fine.
But I'm trying to do learning steps with respect to input:
...ANSWER
Answered 2020-Oct-30 at 10:02It works now. Thanks to @klapeyron. I updated tf.Variable values with values from input_tensor:
QUESTION
I'm using the AI Platform notebook and I want to install cuda because the Tensorflow can't use GPU.
...ANSWER
Answered 2020-Oct-08 at 02:53Which linux distro are you on? Like Kali has default password as root or toor or kali depending on version
QUESTION
I'm trying to run stylegan2 on Google Colab but with all the files on my Drive and avoiding using !git clone from the github of stylegan2. Here is my code on the specific cell:
...ANSWER
Answered 2020-Jul-03 at 14:44You have to enable the GPU first in the Notebook settings.
You can easily do it by clicking on Edit > Notebook settings and selecting GPU as hardware accelerator.
That should be it.
QUESTION
ANSWER
Answered 2020-Mar-04 at 20:52Thanks to @talonmies, the solution is as follows: Firstly, c++ package doesn't come by default with a clean new installation of VS, you need to select the package. Secondly, it seems that now the path for cl.exe is here
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Install stylegan2
You can use stylegan2 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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