AI Image Generators: What are they and how do they create?

Have you ever wondered what your face would look like if you were of another ethnicity, gender or age? Or what a landscape of another planet, a work of art of a different style or an animal that never existed would look like? These are some of the questions that can be answered by image generators, which are computer programs capable of producing images from texts, sounds, other images or any other form of information.

In this article, we will explain what image generators are, how they work, what their applications and benefits are, and what are the challenges and limitations they face. We will also show some examples of impressive image generators, that can create realistic, creative and even scary images.

What are image generators?

Image generators are computer programs that can create images from different types of information, such as texts, sounds and other images. For example, an image generator can receive a textual description like “a gray cat with green eyes” and produce a corresponding image. Or it can receive a sound of a music and generate an image that represents its rhythm, melody or emotion. Or it can receive an image of a person and generate another image that shows how they would look like if they were older, younger or even of another gender.

Image generators are a form of artificial intelligence, which is the area of computer science that studies how to create machines that can perform tasks that normally require human intelligence, such as recognizing objects, understanding natural language or playing chess, for example. Image generators are a specific type of artificial intelligence that focuses on computer vision, which is the subarea that studies how to make machines able to see, understand and manipulate images.

How do image generators work?

There are different ways to create image generators, but one of the most popular and advanced is using neural networks, which are computational models inspired by the functioning of the human brain. Neural networks are composed of units called neurons, which receive, process and transmit information. The neurons are connected to each other by synapses, which are the weights that determine the strength of the connection.

A neural network can have several layers of neurons, where the first layer receives the input (for example, a text, a sound or an image), and the last layer produces the output (for example, an image). The intermediate layers are called hidden layers, and are responsible for extracting and combining relevant features from the input.

A neural network needs to be trained to learn how to generate images from a given input. The training consists of providing the neural network with several examples of pairs of input and desired output, and adjusting the weights of the synapses according to the error between the output produced and the output expected. The goal is to minimize the error and make the neural network able to generate images that are as similar as possible to the expected outputs.

A special type of neural network that is widely used to generate images is called GAN, which stands for Generative Adversarial Network. A GAN is composed of two neural networks that compete with each other: one called generator, which tries to generate images from an input, and another called discriminator, which tries to distinguish between real and generated images.

The generator receives an input (for example, a text, a sound or an image) and produces an image. The discriminator receives the image generated by the generator and a real image, and tries to classify them as real or fake. The goal of the generator is to fool the discriminator, making it classify the generated images as real. The goal of the discriminator is to unmask the generator, making it classify the generated images as fake. The GAN is trained in such a way that the generator and the discriminator improve together, until the generator can generate images that are indistinguishable from the real ones.

What are the applications and benefits of image generators?

Image generators have various applications and benefits in various domains and sectors, such as art, entertainment, education, health and security. Some examples and possibilities are:

• Art: Image generators can be used to create original works of art, such as paintings, drawings, sculptures, etc. They can also be used to imitate the style of famous artists, such as Van Gogh, Picasso, Monet and others. Or even to create collages, montages and animations.
• Entertainment: Image generators can be used to create characters, scenarios and special effects for movies, games and comics. They can also be used to create memes, avatars, filters, stickers and much more for social networks and apps.
• Education: Image generators can be used to create illustrations, diagrams or maps for books, magazines and websites. They can also be used to create simulations, experiments and educational games to teach concepts and skills.
• Health: Image generators can be used to create medical images, such as x-rays, tomographies and resonances. They can also be used to create anatomical, surgical or dental models for diagnosis, treatment and training.
• Security: Image generators can be used to create security images, such as cameras, sensors and scanners. They can also be used to create identification images, such as documents, cards, faces and fingerprints for verification, authentication and recognition.

The benefits of image generators are many, such as:

• Creativity: Image generators can create images that have never been seen before, that can inspire new ideas, solutions and products.
• Quality: Image generators can create images that are realistic, detailed and consistent, that can improve the appearance, the accuracy and the reliability.
• Efficiency: Image generators can create images that are fast, cheap and easy, that can save time, money and resources.
• Diversity: Image generators can create images that are varied, personalized and adaptable, that can meet different needs, preferences and contexts.

What are the problems and limitations of image generators?

Image generators also have some problems and limitations that should be considered, such as:

• Ethics: Image generators can create images that are false, misleading, offensive or illegal, that can violate rights, norms and laws. For example, image generators can create images of people that do not exist, that can be used for frauds, extortions and harassments. Or they can create images of people that exist, but without their consent, that can be used to invade their privacy, defame their reputation or violate their image.
• Quality: Image generators can create images that are unreal, distorted or inconsistent, that can compromise the appearance, the accuracy and the reliability. For example, image generators can create images that have artifacts, noises or defects, that can reduce their visual quality. Or they can create images that have errors, contradictions or inconsistencies, that can affect their logic or semantics.
• Efficiency: Image generators can create images that are slow, expensive or difficult, that can consume a lot of time, money and resources. For example, image generators can require a lot of computational power, memory and storage to train and run the neural networks. Or they can require a lot of data, knowledge and supervision to provide the inputs and evaluate the outputs.

What are some examples of impressive image generators?

There are several examples of impressive image generators, that can create realistic, creative and even scary images. Below, we present only some of them.

DALL-E

Imagine a tool capable of transforming text into surreal and unique images: Welcome to the world of DALL-E. This powerful image generator, based on artificial intelligence, makes the fusion between words and visuals a reality and is part of the revolutionary moment we live in.

With an overwhelming ability to interpret even the most complex descriptions, DALL-E impresses by its skill in generating works that range from anthropomorphic animals playing instruments to science fiction scenes that would rival those seen in Hollywood movies.

Using an advanced deep learning technology, the model can create variations of existing images or conceive completely from scratch, opening a new horizon for content creators and marketing professionals in search of authenticity and originality.

Midjourney

Midjourney is the tool that is redefining collaborative creativity. With an approach focused on the visual journey that a user wants to undertake, this AI is a master in offering hyper-realistic or artistic images, depending on the input it receives.

Its differential lies in the refinement capacity and the richness of details, offering results that are true journeys for the eyes. In addition, Midjourney shines by allowing quick iterations, creating a bridge between the first ideas and high-quality final results.

Stable Diffusion

Stable Diffusion is a digital disruptor that democratizes image generation by AI. Designed to work effectively even on less powerful hardware, this tool is known for its accessibility and its commitment to ethics in design.

Employing a diffusion model that learns from a vast set of data, Stable Diffusion can conjure up anything you can describe, from detailed portraits to ethereal landscapes.

Leonardo AI

Leonardo AI emerges as the genie of the lamp in the modern era for artists and designers. The tool, although it may not have the same fame as some of its contemporaries, does not fail to enchant with its ability to detail and improve visuals generated from text.

In addition to a name that honors one of the greatest artists of all time, Leonardo AI holds a technology that blends classical art with computational art, providing high-quality images that can serve as initial sketches or finished masterpieces.

Its artificial intelligence is an expert in capturing the essence of its users’ vision, becoming an essential tool for those who seek to enhance the aesthetics of their projects with efficiency.

Adobe Firefly

Last but not least, we have Adobe Firefly, whose innovative glow promises to illuminate the future of image generators. Integrated into the Adobe ecosystem, Firefly does more than just create images; it facilitates their inclusion in existing creative workflows.

With an intuitive interface and a strong focus on usability, Adobe Firefly is accessible to both professionals and hobbyists. Powered by Adobe’s gigantic database, it is a great ally in content creation, offering a wide range of styles, from vector illustrations to photorealistic textures.

Conclusion

Image generators are computer programs that can create images from different types of information, such as texts, sounds or other images, for example. Image generators are a form of artificial intelligence that uses neural networks, especially GANs, to learn how to generate realistic images, having various applications and benefits in various domains and sectors, such as art, entertainment, education, health and security.

Image generators also have some problems and limitations that should be considered, such as ethics, quality and efficiency. There are several examples of impressive image generators, that can create creative, inspiring and even scary images.

Here’s the spoiler: we’ll bring a lot of content and tips to generate amazing images here on the blog. Stay tuned!