Tag: tutorial

Picking a first, or new programming language to learn can be a challenge. With many emerging technologies such as Machine Learning, Blockchains, Automation, IOT, and more it can be difficult to find a language to start with. This list is a compilation of languages to add to any developer’s skill set whether you are just beginning your programming journey or a seasoned programming veteran. Take a look below for a list of The Top 10 Programming Languages to Learn in 2022.

1. Python

Python is one of the easiest to learn languages from this list. The syntax is very beginner friendly. Python is a good language to learn as it provides high paying career opportunities. Another reason to learn Python is its heavy use in the emerging machine learning sector. Python makes programming machine learning applications simple with its syntax and the support of many libraries. The above reasons and many more make Python a great beginner programming language. The average Python developer salary is $73k-$120k per year.

2. C#

C# is another high paying, popular, and robust programming language to learn in 2022. One of the biggest selling features of C# is its robust and powerful development tool set. Using Visual Studio you are able to do debugging, testing, development, version control, performance analysis, and much more all from a single development IDE. The final reason to use C# is its popularity with developers. As of 2020 C# remained the most highly adopted language in the programming job sector. The average C# developer salary is $93k-$140k per year.

3. PHP

PHP is one of the most widely used programming languages on the internet. As of 2021 70% of all websites used PHP. Some examples of popular websites that use PHP Microsoft.com, Etsy.com, slack.com. these are just a few of the millions of sites that use PHP. Therefor with the use of the many powerful frameworks available to PHP such as SQL allows you to develop robust web applications. PHP will not be going anywhere anytime soon. Despite the language losing some of its popularity overtime it is still king of back end web development. The average PHP developer salary is $93k-$110k per year.

4. JavaScript

JavaScript is the go to language for front end web scripting. Almost every modern website uses JavaScript in one way or another. JavaScript like Python is a highly abstract language. This means that it is easier for the average person to read and write. JavaScript has some of the highest available employment opportunities in the web development field. It is a fast growing language with a large supportive community of like minded developers. The average JavaScript developer salary is $100k-$120k per year.

5. C

The C Programming Language is one of the more difficult options for a beginner to learn but there are many benefits. C unlike many other high-level languages requires the developer to implement a lot of the methods and API’s themselves. Examples of these would be stacks, queues, arrays, etc. You will learn a lot more about how variables, arrays etc are stored in memory than many of the higher-level languages. You will also learn how to handle the many corner cases that come up in C. The Final reason to learn C is its efficiency. It is one of the fastest languages to use for development in terms of performance.The average C developer salary is $90k-$135k per year.

6. LUA

LUA is another great language to start with as a beginner. Its syntax is very straightforward and easy to read, similar to Python (although there are some differences between the two). The language was designed to be a flexible embedded scripting language, where your code is interpreted and run within the LUA interpreter. It is used a lot in game development, mods, and addons – such as (but not limited to) Garry’s Mod, Computers within the ComputerCraft mod for Minecraft, Roblox, and Factorio. It can also be a popular choice for enterprise, showing up in the codebases of a lot of known brands like Affirm, Venmo, and Shopify. The average LUA developer’s salary is $80k-110k per year.

7. C++

Another highly versatile language to learn is C++. It was created in 1979 by the Stroustrup group at Bell Labs. C++ is sometimes known as “C with classes” as it is essentially the same besides the addition of classes and a more modern compiler. It also supports four different types of memory management. C++ uses a three year release schedule and the compiler is maintained by many vendors such as Microsoft, Oracle, and IBM. This language is ideal for cross platform applications as well as video game development. It is a more advanced language than Python or LUA but the potential is limitless for what you can achieve. As of 2022 C++ was the 4th most popular programming language. The Average C++ developer’s salary is $54k-75k per year.

8. Java

The next programming language on the list is Java. The language was first released by Sun Microsystems in May of 1995. All of Java’s core components such as its compilers and virtual machines were all developed and released by Sun Microsystems. Some of the popular applications written using Java are Minecraft, Angry Birds, and Skype to name a few. One of the best reasons to use Java is its cross platform compatibility. Many operating systems are able to run Java applications such as Windows, Mac, Android, and Linux. Another reason to use Java is its simple syntax. The Java framework is made up of four core components, methods, objects, classes, and instance variables. This makes it easy to learn compared to the C or C++ programming languages. The average Java developer’s salary is $92k-100k per year.

9. Rust

Rust is rapidly gaining popularity with its usage in the web3/blockchain industry. Similar to C++ Rust provides advanced features such as safe concurrency, memory safety, and type interfaces. It was originally developed in 2006 by Graydon Hoare while he was doing research for Mozilla. The first stable version was released in 2014. Since the public release many companies have adopted Rust including Discord, Dropbox, and Microsoft to name a few. Rust aims to be a high performance language with similar speeds to C and C++. They were able to achieve this without sacrificing memory safety. Rust is more efficient and performance compared to other memory safe languages since it does not use a Garbage  collector. Instead memory and other system resources are controlled through resource acquisition at the initial convention. This provides optional access to reference counting. One of the main reasons to learn Rust is that it can run on embedded devices. It also integrates seamlessly with other languages. Another benefit to learning rust is the high salary. The average Rust developer’s salary is 175K-200K.

10. Swift

The Swift language is mainly used to develop iPhone, iPad, Apple Watch, and OS X applications. It was originally developed and released by Apple and the open source community in 2014. Swift was created by Apple mainly to phase out its use of the Objective-C runtime library. This allowed seamless integration with the rest of Apple’s software suite. Swift is a good language to learn as it employs modern programming theory concepts and provides a simple to use syntax. Swift does not use pointers in contrast to Objective-C. Another reason to learn Swift is that it constantly improves. New features are added by Apple every few months. Furthermore its debugging is simple and painless thanks to the Swift interactive tool called Playground. This allows you to troubleshoot your code in a script-like environment. The average Swift developers salary is 45K-95K per year.

Related Resources

View more programming articles and tutorials HERE.

View The 5 Best Linux Distributions to Install in 2022.

If you want to learn how to generate images with neural networks click HERE.

View our other Machine Learning Tutorials.

Learn more cool things in Linux with our Linux Tutorials.

View all of our available online tools and converters at Formatswap.com.

In this tutorial you will learn how to transfer the style of one image onto the content of another. This program uses deep learning with python. The project uses an algorithm based on a convolutional neural network. Style any image using machine learning image processing. We will be using Ubuntu 20.04 for this tutorial. Almost any Linux distribution should work.

The project is based off of a PyTorch implementation of the A Neural Algorithm of Artistic Style article. It was published by Leon A. Gatys, Alexander S, Ecker, and Matthias Bethge. It is based on the Neural-Style code by Justin Johnson’s. Here is an example of The Scream painting’s style being transferred onto a picture of a New York City:

Dependencies Installation

This project requires you to install the following

Required Dependencies:

• PyTorch

Optional dependencies:

• For CUDA backend:
  • CUDA 7.5 or above
• For cuDNN backend:
  • cuDNN v6 or above
• For ROCm backend:
  • ROCm 2.1 or above
• For MKL backend:
  • MKL 2019 or above
• For OpenMP backend:
  • OpenMP 5.0 or above

Setup

Navigate to the directory you would like to download neural-style-pt project to. Then git clone the repository.

git clone https://github.com/ProGamerGov/neural-style-pt.git

Download Model

Next you will cd into the cloned directory and download the VGG model files.

cd neural-style-pt/
python models/download_models.py

This will download multiple model files. If you are running on a lighter system use the option -model_file models/nin_imagenet.pth If you have a strong system with a powerful gpu use the option -model_file models/vgg19-d01eb7cb.pth The second option will provide drastically better results at the expense of more strain to the GPU. If you have issues with VGG19 or VGG18 revert back to nin_imagenet.pth.

Creating Deep Learning Style Transfer Images

In this example we will be using the cuDNN with NIN Model. Feel free to use the model of your choice. See the bottom of the tutorial for speed comparisons between the different models. Run the following command replacing -style_image with the path to the style image you want to use. You must all add the path of your content image to -content_image. Feel free to change the -image_size option to increase the resolution of your output machine learning image.

python neural_style.py -style_image examples/inputs/picasso_selfport1907.jpg -content_image examples/inputs/brad_pitt.jpg -output_image profile.png -model_file models/nin_imagenet.pth -gpu 0 -backend cudnn -num_iterations 1000 -seed 123 -content_layers relu0,relu3,relu7,relu12 -style_layers relu0,relu3,relu7,relu12 -content_weight 10 -style_weight 500 -image_size 512 -optimizer adam

Content Image

Style Image

Deep Learning Output Image

Deep Learning Image Options

• -image_size: Maximum side length (in pixels) of the generated deep learning image. Default is 512.
• -style_blend_weights: The weight for blending the style of multiple style images, as a comma-separated list, such as -style_blend_weights 3,7. By default all style images are equally weighted.
• -gpu: Zero-indexed ID of the GPU to use; for CPU mode set -gpu to c.

Advanced Optimization Options

• -content_weight: How much to weight the content reconstruction term. Default is 5e0.
• -style_weight: How much to weight the style reconstruction term. Default is 1e2.
• -tv_weight: Weight of total-variation (TV) regularization; this helps to smooth the image. Default is 1e-3. Set to 0 to disable TV regularization.
• -num_iterations: Default is 1000.
• -init: Method for generating the generated image; one of random or image. Default is random which uses a noise initialization as in the paper; image initializes with the content image.
• -init_image: Replaces the initialization image with a user specified image.
• -optimizer: The optimization algorithm to use; either lbfgs or adam; default is lbfgs. L-BFGS tends to give better results, but uses more memory. Switching to ADAM will reduce memory usage; when using ADAM you will probably need to play with other parameters to get good results, especially the style weight, content weight, and learning rate.
• -learning_rate: Learning rate to use with the ADAM optimizer. Default is 1e1.
• -normalize_gradients: If this flag is present, style and content gradients from each layer will be L1 normalized.

Output and Layer Options

• -output_image: Name of the output image. Default is out.png.
• -print_iter: Print progress every print_iter iterations. Set to 0 to disable printing.
• -save_iter: Save the image every save_iter iterations. Set to 0 to disable saving intermediate results.
• -content_layers: Comma-separated list of layer names to use for content reconstruction. Default is relu4_2.
• -style_layers: Comma-separated list of layer names to use for style reconstruction. Default is relu1_1,relu2_1,relu3_1,relu4_1,relu5_1.

Other Deep Learning Options

• -style_scale: Scale at which to extract features from the style image. Default is 1.0.
• -original_colors: If you set this to 1, then the output image will keep the colors of the content image.
• -model_file: Path to the .pth file for the VGG Caffe model. Default is the original VGG-19 model; you can also try the original VGG-16 model.
• -pooling: The type of pooling layers to use; one of max or avg. Default is max. The VGG-19 models uses max pooling layers, but the paper mentions that replacing these layers with average pooling layers can improve the results. I haven’t been able to get good results using average pooling, but the option is here.
• -seed: An integer value that you can specify for repeatable results. By default this value is random for each run.
• -multidevice_strategy: A comma-separated list of layer indices at which to split the network when using multiple devices. See the Multi GPU Section for more details.
• -backend: nn, cudnn, openmp, or mkl. Default is nn. mkl requires Intel’s MKL backend.
• -cudnn_autotune: When using the cuDNN backend, pass this flag to use the built-in cuDNN autotuner to select the best convolution algorithms for your architecture. This will make the first iteration a bit slower and can take a bit more memory, but may significantly speed up the cuDNN backend.

GTX 1080 Benchmark Speeds

    -backend nn -optimizer lbfgs: 56 seconds
    -backend nn -optimizer adam: 38 seconds
    -backend cudnn -optimizer lbfgs: 40 seconds
    -backend cudnn -optimizer adam: 40 seconds
    -backend cudnn -cudnn_autotune -optimizer lbfgs: 23 seconds
    -backend cudnn -cudnn_autotune -optimizer adam: 24 seconds

FAQ and Issues

Problem #1:

When running the program you run out of memory.

Solution #1

Try reducing the image size: -image_size 256 (or lower). Note that different image sizes will likely require non-default values for -style_weight and -content_weight for optimal results. If you are running on a GPU, you can also try running with -backend cudnn to reduce memory usage.

Problem #2:

The -backend cudnn performs slower than the default backend.

Solution #2:

Add the flag -cudnn_autotune; This will instead use the built-in cuDNN autotuner to select the best convolution algorithm which will result in much better performance.

Problem #3:

You receive this error message.

Missing key(s) in state_dict: "classifier.0.bias", "classifier.0.weight", "classifier.3.bias", "classifier.3.weight". Unexpected key(s) in state_dict: "classifier.1.weight", "classifier.1.bias", "classifier.4.weight", "classifier.4.bias".

Solution #3:

Due to a mix up with layer locations, older models require an update to be compatible with newer versions of PyTorch. The included donwload_models.py script will perform these updates after downloading the models.

Problem #4:

The image generated is solid gray.

Solution #4:

This is a bug that sometimes occurs in cuda. You need to reduce or increase the size of the image by at least 1px.

Related Resources

View more image examples on the Computer Dreams Twitter.

If you want to learn how to generate images with neural networks click HERE.

View our other Machine Learning Tutorials.

Click here to learn How to Install MySQL on Ubuntu 22.04 LTS.

Learn more cool things in Linux with our Linux Tutorials.

View all of our available online tools at Formatswap.com.

Questions

Feel free to leave a comment below if you have any further questions. Thank you for reading the tutorial.

In this tutorial you will learn how to generate images from text prompts using Python, VQGAN, and neural networks. You can create some very interesting machine learning generated artwork using this software. The possibility are limitless when it comes to the different types of images you can generate. This article will be using Ubuntu 22.04 for the operating system. However any Ubuntu based version of Linux should work. Keep in mind you will want to make sure to have a graphics card with at least 6GB of VRAM. If not you will have to generate lower resolution images. If creating machine learning generated artwork sounds interesting to you continue reading this tutorial below.

Step 1: Install Required Dependencies

The first thing to do is install Anaconda. You can find the latest version and instructions HERE. This tutorial has been tested on Ubuntu 16.04, Ubuntu 18.04, Ubuntu 20.04, and Ubuntu 22.04.

Step 2: Create the Conda Environment

conda create --name vqgan python=3.9
conda activate vqgan

Next you will set up the Conda environment. This is where you will run VQGAN from.

Step 3: Install Pytorch in the new environment:

Note: This installs the CUDA version of Pytorch for Nvidia graphics cards. If you are using an AMD graphics card, read our AMD section at the bottom of the article.

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

Step 4: Install other required Python packages:

pip install ftfy regex tqdm omegaconf pytorch-lightning IPython kornia imageio imageio-ffmpeg einops torch_optimizer

Step 5: Clone the required VQGAN repositories:

git clone 'https://github.com/nerdyrodent/VQGAN-CLIP'
cd VQGAN-CLIP
git clone 'https://github.com/openai/CLIP'
git clone 'https://github.com/CompVis/taming-transformers'
pip install taming-transformers && pip install CLIP
pip install setuptools==59.5.0

Now you need to clone the git repository’s. After cloning install taming-transformers and CLIP using pip. Finally install setuptools==59.5.0. This is required as the latest version of setuptools will not work with VQGAN.

Step 6: Download at least 1 VQGAN pretrained model

mkdir checkpoints

curl -L -o checkpoints/vqgan_imagenet_f16_16384.yaml -C - 'https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1' #ImageNet 16384
curl -L -o checkpoints/vqgan_imagenet_f16_16384.ckpt -C - 'https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fckpts%2Flast.ckpt&dl=1' #ImageNet 16384

Choose at least one of the above pretrained models, and download it using the curl command. I recommend downloading both of the models so that you can try each to see which works best.

Pretrained Model Information

Visit https://github.com/CompVis/taming-transformers#overview-of-pretrained-models to learn more about VQGAN pre-trained models, including download links. The model .yaml and .ckpt files need to be the checkpoints directory.

Generating VQGAN image from basic text prompt

You have now finished installing VQGAN. You are ready to begin generating images. To generate an image from text, specify your text prompt as shown in the example below.

python generate.py -p "A illustration of a pineapple in a fruit bowl"

Generating VQGAN image from multiple prompts

You are also able to generate images from split text prompts. In the below example you can see I am using four different descriptors to generate the image.

python generate.py -p "A painting of a pineapple in a fruit bowl | psychedelic | surreal:0.5 | weird:0.25"

Additionally you can use an input image as one of your split prompt inputs. This will use the supplied image as a sample during the generation process.

python generate.py -p "A picture of a bathroom with a portrait of Van Gogh" -ip "samples/VanGogh.jpg | samples/Bedroom.png"

Generating VQGAN “story mode” images

Story mode allows you to create a story from multiple text prompts using the carrot symbol. This will generate you a .mp4 video file. For example:

python generate.py -p "A painting of a apple|photo:-1 ^ a painting of a banana ^ a painting of a grape ^ a painting of a watermelon ^ a photograph of strawberry" -cpe 1500 -zvid -i 6000 -zse 10 -vl 20 -zsc 1.005 -opt Adagrad -lr 0.15 -se 6000

VQGAN Feedback loop animation’s

You are able to generate multiple images into a video. The script makes slight changes to each image creating a warping effect. The “150” at the end of the line is the number of frames. “blackhole.png” is your output filename. This will produce a .mp4 file named “video.mp4” by default. Example below:

./zoom.sh "A painting of a green firetruck spinning through a black hole" blackhole.png 150

The ImageMagick package is requred to generate mp4 animated loop videos. If you don’t have it installed, you can install it using the below command.

sudo apt install imagemagick

Generating multiple random images

You can also supply multiple phrases to be used at random to generate multiple images. There is a random list of words in the “./zoom.sh” file. Edit this file if you want to change the words used in the random phrase generator. Here is an example of the random images put into a collage.

chmod +x ./zoom.sh
./zoom.sh

• #1 ‘A pencil art sketch of a criticizing pickle and a menu in the style of strange colors and Futurism’
• #2 ‘A painting of a wild hotel and a flower in the style of Constructionist and Edgar Degas’
• #3 ‘A spray painting of a awaiting computer and a bedroom in the style of Edgar Degas and Art Nouveau’
• #4 ‘A photograph of a benefiting AR-15 and a pickle in the style of Modern art and Edgar Degas’
• #5 ‘A sculpture of a undertaking computer and a figurine in the style of Pop Art and Picasso’
• #6 ‘A painting of a tree on a dresser in the style of Surreal Art and Claude Monet’
• #7 ‘A pencil art sketch of a touching statue and a AR-15 in the style of Surreal Art and Claude Monet’
• #8 ‘A pencil art sketch of a adding table and a fish in the style of Surreal Art and Art Nouveau’
• #9 ‘An illustration of a raining lamp and a spanner in the style of Pop Art and Michelangelo Caravaggio’

Advanced settings

There are many advanced flags you can use in the VQGAN program. To view all of the options from the terminal use the -h flag.

python generate.py -h

usage: generate.py [-h] [-p PROMPTS] [-ip IMAGE_PROMPTS] [-i MAX_ITERATIONS] [-se DISPLAY_FREQ]
[-s SIZE SIZE] [-ii INIT_IMAGE] [-in INIT_NOISE] [-iw INIT_WEIGHT] [-m CLIP_MODEL]
[-conf VQGAN_CONFIG] [-ckpt VQGAN_CHECKPOINT] [-nps [NOISE_PROMPT_SEEDS ...]]
[-npw [NOISE_PROMPT_WEIGHTS ...]] [-lr STEP_SIZE] [-cuts CUTN] [-cutp CUT_POW] [-sd SEED]
[-opt {Adam,AdamW,Adagrad,Adamax,DiffGrad,AdamP,RAdam,RMSprop}] [-o OUTPUT] [-vid] [-zvid]
[-zs ZOOM_START] [-zse ZOOM_FREQUENCY] [-zsc ZOOM_SCALE] [-cpe PROMPT_FREQUENCY]
[-vl VIDEO_LENGTH] [-ofps OUTPUT_VIDEO_FPS] [-ifps INPUT_VIDEO_FPS] [-d]
[-aug {Ji,Sh,Gn,Pe,Ro,Af,Et,Ts,Cr,Er,Re} [{Ji,Sh,Gn,Pe,Ro,Af,Et,Ts,Cr,Er,Re} ...]]
[-cd CUDA_DEVICE]

optional arguments:
  -h, --help            show this help message and exit
  -p PROMPTS, --prompts PROMPTS
                        Text prompts
  -ip IMAGE_PROMPTS, --image_prompts IMAGE_PROMPTS
                        Image prompts / target image
  -i MAX_ITERATIONS, --iterations MAX_ITERATIONS
                        Number of iterations
  -se DISPLAY_FREQ, --save_every DISPLAY_FREQ
                        Save image iterations
  -s SIZE SIZE, --size SIZE SIZE
                        Image size (width height) (default: [512, 512])
  -ii INIT_IMAGE, --init_image INIT_IMAGE
                        Initial image
  -in INIT_NOISE, --init_noise INIT_NOISE
                        Initial noise image (pixels or gradient)
  -iw INIT_WEIGHT, --init_weight INIT_WEIGHT
                        Initial weight
  -m CLIP_MODEL, --clip_model CLIP_MODEL
                        CLIP model (e.g. ViT-B/32, ViT-B/16)
  -conf VQGAN_CONFIG, --vqgan_config VQGAN_CONFIG
                        VQGAN config
  -ckpt VQGAN_CHECKPOINT, --vqgan_checkpoint VQGAN_CHECKPOINT
                        VQGAN checkpoint
  -nps [NOISE_PROMPT_SEEDS ...], --noise_prompt_seeds [NOISE_PROMPT_SEEDS ...]
                        Noise prompt seeds
  -npw [NOISE_PROMPT_WEIGHTS ...], --noise_prompt_weights [NOISE_PROMPT_WEIGHTS ...]
                        Noise prompt weights
  -lr STEP_SIZE, --learning_rate STEP_SIZE
                        Learning rate
  -cuts CUTN, --num_cuts CUTN
                        Number of cuts
  -cutp CUT_POW, --cut_power CUT_POW
                        Cut power
  -sd SEED, --seed SEED
                        Seed
  -opt, --optimiser {Adam,AdamW,Adagrad,Adamax,DiffGrad,AdamP,RAdam,RMSprop}
                        Optimiser
  -o OUTPUT, --output OUTPUT
                        Output file
  -vid, --video         Create video frames?
  -zvid, --zoom_video   Create zoom video?
  -zs ZOOM_START, --zoom_start ZOOM_START
                        Zoom start iteration
  -zse ZOOM_FREQUENCY, --zoom_save_every ZOOM_FREQUENCY
                        Save zoom image iterations
  -zsc ZOOM_SCALE, --zoom_scale ZOOM_SCALE
                        Zoom scale
  -cpe PROMPT_FREQUENCY, --change_prompt_every PROMPT_FREQUENCY
                        Prompt change frequency
  -vl VIDEO_LENGTH, --video_length VIDEO_LENGTH
                        Video length in seconds
  -ofps OUTPUT_VIDEO_FPS, --output_video_fps OUTPUT_VIDEO_FPS
                        Create an interpolated video (Nvidia GPU only) with this fps (min 10. best set to 30 or 60)
  -ifps INPUT_VIDEO_FPS, --input_video_fps INPUT_VIDEO_FPS
                        When creating an interpolated video, use this as the input fps to interpolate from (>0 & <ofps)
  -d, --deterministic   Enable cudnn.deterministic?
  -aug, --augments {Ji,Sh,Gn,Pe,Ro,Af,Et,Ts,Cr,Er,Re} [{Ji,Sh,Gn,Pe,Ro,Af,Et,Ts,Cr,Er,Re} ...]
                        Enabled augments
  -cd CUDA_DEVICE, --cuda_device CUDA_DEVICE
                        Cuda device to use

AMD GPU Instructions

If you have an AMD graphics card you are able to use ROCm instead of CUDA. You can check if your card supports TensorFlow here: https://github.com/RadeonOpenCompute/ROCm#supported-gpus

Install ROCm accordng to the instructions and don’t forget to add the user to the video group as detailed in the link: https://docs.amd.com/bundle/ROCm-Installation-Guide-v5.1/page/How_to_Install_ROCm.html

The usage and set up instructions above are the same, except for the line where you install Pytorch. Instead of pip install torch==1.9.0+cu111 ..., use the one or two lines which are displayed here (select Pip -> Python-> ROCm): https://pytorch.org/get-started/locally/

Troubleshooting

RuntimeError: CUDA out of memory

For example:

RuntimeError: CUDA out of memory. Tried to allocate 150.00 MiB (GPU 0; 23.70 GiB total capacity; 21.31 GiB already allocated; 78.56 MiB free; 21.70 GiB reserved in total by PyTorch)

Your request doesn’t fit into your GPU’s VRAM. Reduce the image size of the image by editing “generate.py”.

What is the best optimizing agent?

The Adam agent is usually a good general purpose agent to use. If you would like more information see the Pytorch Optimizer and Optim articles.

Can I download and use all pre-trained models simultaneously?

Yes, all you need to do is set everything to true in the download_models.sh file.

Errors during video generation

Try installing ffmpeg:

sudo apt install ffmpeg

If you are running VQGAN with Ananconda try:

conda install -c conda-forge ffmpeg

Related resources

View more image examples on the FormatSwap Twitter.

Check our our The Best Mechanical Gaming Keyboards to Purchase in 2023 article.

Read our Deep Learning Image Style Transfer Tutorial Using Neural Style Pt.

View our other Machine Learning Tutorials.

Learn How to Create a Mapped Network Drive in Windows 10.

Click here to learn How to Install MySQL on Ubuntu 22.04 LTS.

View all of our available online tools at Formatswap.com.

Questions

Feel free to leave a comment below if you have any further questions. Thank you for reading the tutorial.