AI Art Generators: Which Ones to Choose

Art generated by artificial intelligence (AI) is a form of creative expression that uses algorithms and machine learning models to produce images, sounds, texts or other artistic works. There are different types of AI art generators, each with its own features, advantages and disadvantages. Let’s see them in detail.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a class of machine learning models that consist of two neural networks competing with each other: a generative network that tries to create realistic works from random noise, and a discriminative network that tries to distinguish the generated works from the real ones. GANs are able to produce high-quality and diverse images, often inspired by famous artistic styles or specific themes.

Advantages of GANs

• GANs are very powerful and flexible, as they can generate original and different works every time, without needing specific input data.
• GANs can imitate or reinterpret existing artistic styles, such as cubism, impressionism or surrealism, or create new styles based on combinations or transformations of existing ones.
• GANs can generate works based on keywords, textual descriptions or reference images, allowing the user to have some control over the final result.

Disadvantages of GANs

• GANs require a lot of computational resources and time to be trained and optimized, as they have to learn from large amounts of data and constantly refine their generative and discriminative abilities.
• GANs can produce artificial, inconsistent or unrealistic works, as they can make mistakes or distortions in the generation of images, especially if the training data are limited or noisy.
• GANs can raise ethical or legal issues regarding the intellectual property and the responsibility of the generated works, as they can violate the copyrights or the privacy of the original sources, or create offensive or deceptive works.

Neural Style Transfer (NST)

Neural Style Transfer (NST) is a machine learning technique that consists of transferring the style of an image (for example a painting) to the content of another image (for example a photograph), creating a new image that combines both. The NST is based on a convolutional neural network that extracts the visual features of the two images and fuses them together according to a weight parameter that determines the degree of influence of the style on the content.

Advantages of NST

• NST is a simple and intuitive technique, as it requires only two input images (style and content) and a weight parameter to generate the output.
• NST is a versatile and customizable technique, as it allows the user to freely choose the style and content of the images to combine, obtaining different results depending on the preferences.
• NST is a fun and educational technique, as it can stimulate the creativity and curiosity of the user, as well as make them more familiar with artistic styles and painting techniques.

Disadvantages of NST

• NST is a limited and repetitive technique, as it can generate only images based on the transfer of style between two existing images, without creating anything new or original.
• NST is a technique dependent on the quality of the input images, as it can generate unsatisfactory or inconsistent results if the style or content images are blurred, noisy or unsuitable.
• NST is a technique that requires some degree of experimentation and adjustment, as the weight parameter that determines the balance between style and content can vary depending on the chosen images and personal taste.

Text-to-Image Synthesis (T2I)

Text-to-Image Synthesis (T2I) is a machine learning technique that consists of generating images from textual descriptions, trying to visually render the meaning and details of the words. The T2I is based on a generative neural network that receives as input a sequence of words and produces as output a matrix of pixels that represents the corresponding image.

Advantages of T2I

• T2I is an innovative and challenging technique, as it requires to integrate two different modes of communication (text and image) and to translate natural language into visual language.
• T2I is an expressive and creative technique, as it allows the user to generate images based on their own ideas, fantasies or emotions, without limits of form or content.
• T2I is a useful and applicable technique, as it can facilitate the creation of visual content for various purposes, such as illustration, advertising, entertainment or education.

Disadvantages of T2I

• T2I is a complex and imperfect technique, as it requires to handle the variability and ambiguity of natural language and to generate coherent and realistic images from words.
• T2I is a limited and dependent on text technique, as it can generate only images based on textual descriptions existing or provided by the user, without being able to modify or enrich the initial text.
• T2I is a potentially dangerous or harmful technique, as it can generate false or manipulated images from deceptive or malicious texts, creating confusion or misinformation.

Conclusions

Art generated by artificial intelligence is an increasingly widespread and fascinating reality, that offers new possibilities of expression and communication. However, it is not without problems and risks, that require attention and responsibility from users and developers. Moreover, it cannot replace or equal human art, which remains unique and unrepeatable in its essence and value.