Image generation using neural networks:
how modern algorithms work

In just a few years, neural network image generation has evolved from a lab experiment into a mass-market tool. Users enter a text query, select a style, and the model produces a realistic illustration, art, or design concept in seconds. The apparent simplicity of the interface conceals complex mathematical models, vast amounts of data, and multi-stage training. To use such technologies wisely, it’s important to understand the types of algorithms underlying image generation and how the entire "from text to pixel" process works.

Basic principles of image generation using neural networks

Modern image generation algorithms rely on the idea of training on large datasets: millions of images with captions allow the model to capture statistical patterns between text and visual objects. The neural network doesn’t "remember" individual images, but learns to numerically encode shapes, colors, textures, compositions, and relationships between objects.

The process can be simplified into a few steps. First, the text query is converted into a vector representation using a language model: each word and phrase becomes a set of numbers reflecting their meaning. Then, the generative part takes over, creating an image in the latent feature space based on this text description. Finally, the result is converted into a familiar raster image at a specified resolution.

Almost all modern architectures employ attention mechanisms, allowing the model to "look" at different parts of the text and different areas of the image with varying degrees of importance. This helps to more accurately convey relationships such as "a red car against a backdrop of mountains" or "an oil painting-style portrait."

Examples of effects: https://avalava.ai/categories/visual-effects

Basic classes of models for image generation

In recent years, several key approaches to image generation have emerged. The most common are generative adversarial networks (GANs), diffusion models, and models based on autoencoders and transformers.

GANs consist of two networks: a generator and a discriminator. The generator creates images from random noise, while the discriminator attempts to distinguish the generated images from real examples from the training set. During training, the two networks "compete," and the generator gradually learns to produce increasingly realistic images. This approach has demonstrated high quality, but is difficult to train and sensitive to settings.

Diffusion models work differently. They learn by inverting the process: first, noise is added to the image step by step, destroying its structure, and then the model learns to gradually remove the noise and restore the original image. At the generation stage, the opposite occurs: based on a text description, the model starts with a nearly completely noisy representation and gradually "clarifies" it until the final image is obtained. The diffusion approach is often used in popular services today due to its high stability and quality.

A separate area of research is latent space models . In these models, images are first compressed into a compact representation (latent code) using an autoencoder. Generation occurs in this compressed space, significantly speeding up computations and reducing resource requirements. The result is then decoded back into a high-resolution image.

Briefly, the types of models can be represented as follows:

• GAN — realistic images through adversarial training of a generator and discriminator.
• Diffusion models - step-by-step noise removal and gradual "clarification" of the image.
• Latent models with autoencoders - work in a compressed feature space to speed up generation.

How text is transformed into an image: the steps of the algorithm

Multimodal models that combine linguistic and visual representations play a key role in generating images based on text queries. They are trained on text-image pairs and can evaluate the correspondence between the description and the image.

The process in general looks like this:

• The user formulates a request: style, objects, composition, additional requirements.
• The text is processed by a language model that encodes the meaning and breaks it down into key elements.
• The generative part receives a text vector and begins to construct an image in latent or pixel space, gradually refining the details.
• At each step, the model takes into account which words are important for local areas of the image and adjusts the shape, color, and lighting.
• The output is an image of a given size, which the user can refine, regenerate, or modify using additional prompts.

This step-by-step process allows the neural network to adapt to requests of varying levels of detail: from short descriptions to complex prompts specifying artistic style, lens type, lighting settings, and depth of field.

Modern neural network image generation algorithms are based on a combination of powerful language models, generative architectures, and training on massive data sets. The user sees only an interface with a text field, but behind it lies a complex, multi-stage process in which statistics, linear algebra, and optimization are transformed into visual images. Understanding the operating principles of such systems helps formulate queries more consciously, assess the limitations of the technology, and utilize neural network image generation as a fully-fledged tool for creativity, design, and visual communications.