In previous publications, we considered what artificial intelligence and computer vision are. Now we offer to learn more about how AI draws images . We will also talk about the best free AI image generators.
Artificial intelligence has made a breakthrough in many areas of human activity, the field of drawing is no exception. Now it is not so important to be able to use a brush or pencils, the main thing is to clearly select words , and the rest of the work is already done by neural networks .
The introduction of AI technologies allows companies to reduce errors , increase efficiency and set up a continuous process . Importantly, with AI, companies do not need to enter and edit all the data manually , as the information is updated automatically . This and more can be provided by KLONA. Thanks to many years of experience , KLONA will be able to determine exactly what needs to be optimized in your business and offer innovative solutions to the problem.
How does AI work with images? More on generating images later, but to get a clearer idea of how neural networks recognize objects, we suggest playing a game from Google.
Content
- More about types of neural networks
- What are Convolutional Neural Networks?
- How do convolutional network algorithms recognize pictures?
- How are convolutional neural networks arranged?
- What is an artificial intelligence image generator?
- How do neural networks generate images?
- The best AI generators for drawing pictures
- Disadvantages of using neural networks when generating images
- What’s next? The future of drawing AI
More about types of neural networks
Neural networks train the computer to perform tasks that are already natural for humans. There are different types of neural networks in deep learning: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Artificial Neural Networks (ANN) and others. So, it was convolutional neural networks (CNNs) that made a significant contribution to the field of computer vision and image analysis.
What are Convolutional Neural Networks?
While artificial neural networks are used to process tabular and textual information, convolutional neural network algorithms are more efficient in dealing with visual data , since they process data in a less linear way .
Convolutional Neural Network ( CNN ) is a technology that has one or more convolutional layers and is used for image processing , data classification and segmentation.
The technology algorithm processes the data in a grid and then extracts important features. One of the huge benefits of using a CNN is that you don’t have to do a lot of image pre-processing . The main goal of a Convolutional Neural Network is to transform the data into forms that are easier to process without losing features that are important in figuring out what the data is.
How do convolutional network algorithms recognize pictures?
Well, we found out what type of neural networks are best suited when working with images. At a naive level, the concept of recognition explained data science professor Matthew Stewart using swans as an example .
Suppose we want to create a neural network model capable of recognizing swans in images. The swan has certain characteristics that are used to determine whether a swan is in the photo or not, for example, its long neck , its white color , and others.
Let’s also take into account non-standard cases , then more complex parameters come into play.
Could the situation get worse? Definitely yes.
But the world is diverse, according to such characteristics, one can confuse a swan with some other bird. For more accurate identification of objects, convolutional neural networks are used.
How are convolutional neural networks arranged?
As we have already indicated, convolutional neural networks can consist of one or more layers. In total there are 3 main types of layers :
- Convolutional layer
- unifying layer
- Fully connected (FC) layer
- Convolutional layer
The first layer of convolutional networks is the convolutional layer. This type is used to extract feature maps from the input image while maintaining relationships between pixels. The early layer deals with basic elements like colors and borders.
- unifying layer
The pooling layer is one of the building blocks of convolutional neural networks. This is the pooling level that retains the most important information while reducing the number of parameters . In particular, this layer works effectively with large images. Spatial merging can be performed with different maximum, average, and total values.
- Fully Connected Layer
The last layer of a convolutional neural network is a fully connected layer. This layer takes the output of the previous layers, ” flattens ” it, and turns it into a single vector. Neural networks are a set of dependent non-linear functions. As usual, adding a fully connected layer is an inexpensive way to explore non-linear combinations of these features.
See also: Creating architectural models: from the house to the trees
What is an artificial intelligence image generator?
A neural network art generator is software that uses AI to create pictures from text input. Images vary in style, usually the functionality allows you to create art in any style . This includes anime, fantasy, isometric, cinematic, modern, renaissance and others.
How do neural networks generate images?
AI draws pictures in the process of machine learning, when the machine “learns” some information and uses it to create a new image .
To create new images, we use an architecture called Generative Adversarial Networks (GANs). The main components of a GAN are a generator and a discriminator , which are built on convolutional neural networks.
The first network, the generator , generates new data. The second network, the discriminator , is used to measure the quality of the generated images. The network guesses whether it is a real image obtained from a dataset or a fake image generated by a generator. The discriminator evaluates the quality of the generator results on a scale from 0 to 1. If the score is too low , the generator corrects the data and resubmits it for validation.
An important component is the CLIP model, which, among the set of captions, finds the most accurate description for the image. For this, there is a submodule that evaluates the similarity between an image and a text description.
The final picture is created by combining CLIP and GAN . GAN generates an image, CLIP finds the similarity between the text description and the image, and so on until we get the maximum similarity.
The best AI generators for drawing pictures
Numerous AI image generators use artificial intelligence algorithms to convert text into graphics . These AI tools can be a great way to quickly visualize your thoughts or ideas in a couple of seconds.
Which AI Image Generator Should You Try? We have prepared the top 6 available editors and examples of images generated by them. For a better assessment of which platform suits you, we have prepared all the pictures under the general description : “Beautiful misty mountain landscape with pink flowers ”.
night cafe
- NightCafe is one of the biggest names in the world of artificial intelligence generators. It is known for having more algorithms and options than other generators, but it is also very easy for beginners to learn . NightCafe is based on a credit system but has a generous free tier.
How does the editor work ?
- The first way is a neural-style transfer. With this art creation method, you need to upload a photo and then select an image “style” to have the AI recreate the photo.
- The second way is to generate images from text to image . With this method, all you have to do is enter your digital art idea, follow the prompts, and let NightCafe’s powerful AI create the artwork for you.
Images.ai
Images.ai is intentionally designed to be simple, no confusing commands – just enter a search term and an impressive piece of art will be created.
wombo dream
If you want to try your hand at AI image generation, then WOMBO Dream is a good option for beginners . You can upload your own image or photo as the basis for the graphic, or just use your own phrase as a clue.
Artbreeder
Artbreeder offers a wide range of stock photos for editing. Users also have the option to upload their own base photos to create artwork. Users change the values of various settings and image properties. Take, for example, portraits. Users have the ability to change their facial expression, hair color , eye color, and gender among other things. The AI app is free, but there are also paid options.
Deep Dream Generator
Google founded Deep Dream Generator in 2009 as a computer vision program, and it was designed to find and improve image patterns based on existing image data that is processed by a computer. Over time, some artists began to use it to create visual effects in a surreal and abstract style. Some artists use the Deep Dream Generator and its tools to create artwork instead of using traditional painting methods .
With Deep Dream Generator you can explore the idea of deep neural networks . After studying the art style and drawing, the image turns into an abstract work of art with different layers and features.
Craiyon
The advantage is that the service is absolutely free. The good thing is that since you have an unlimited number of hints, you can keep tweaking the hints until you get exactly what you want. The site is also so easy to use, which makes this AI generator a strong contender.
Disadvantages of using neural networks when generating images
But not everything is so perfect, in many ways AI drawings fall short of human creations. There are a number of reasons for this:
- To obtain satisfactory results, a large number of examples , up to several thousand, is required. The complexity of manually specifying formulas is replaced by searching for a large amount of data and processing them to a similar structure.
- Images are difficult to control : standard implementations do not allow, for example, changing some parts of an image without changing other parts. To do this, it is necessary to seriously change the architecture of the network.
- AI generators use images without the permission of the artists. Image generating programs can simply take this data and work on it without notifying the original owners . Some people end up noticing artwork that looks like their own but that they never created.
What’s next? The future of drawing AI
When it comes to creating images, you cannot say that neural networks are superior to humans. But still ahead : we are smoothly moving on from the development of the algorithm to the study of its capabilities .
We should perceive neural networks not as competitors, but as helpers. The same goes for creating illustrations. Neural networks become a kaleidoscope of data . The artist, like a child, looks at the images and chooses the best of them, like a critic. As a result, the neural network allows you to look into the surreal world , the journey through which is as interesting as its images.
What exactly AI cannot generate on its own is a high-quality 3D model . It is better to entrust such work to real specialists . KLONA provides its services in industrial design.
In addition to designing a new 3D model, the company will also help you refine and eliminate errors in an already finished one, build a 3D model from a photograph , drawing or sample. And that’s not all services, find out more here .
