How AI Image Generators Work?

MeghaMegha
7 min read

We’ve all witnessed a surge in “Ghibli-style” AI images across social platforms- ordinary photos transformed into these soft, animated pictures. This visual overhaul is the handiwork of modern AI image generators.

Under the hood are some highly trained models that learn from enormous libraries of pictorial data. Through this blog, gain a better idea of what makes AI images possible- and why they appear the way they do.

The Basics of AI Image Generation:

These are machine-learning models trained on huge datasets containing images and their descriptions. Over time, they learn the relationships between an image and its elements. This lets them create realistic or imaginary images from minimal text inputs.

At its core lies the fundamentals of statistics that helps models know how text and visuals relate. There are various models that form the basis of these tools, so let’s find how they work.

Types of Models Used in Image Generation:

1. Generative Adversarial Networks (GANs)-

It implements a deep learning configuration made of two neural networks: a Generator and a Discriminator. These networks learn simultaneously but with complementary goals:

  • Generator creates new images from the original, e.g., facial features, by manipulating input samples.

  • Discriminator verifies if an image is real (from the source dataset) or not (from the generator).

As training progresses, both networks self-improve- generator gets better at making realistic images, and discriminator at predicting fakes. GANsadversarial” aspect originates from this reciprocal competition. It continues till the generator turns so skilled at producing realistic images that the discriminator can no longer distinguish them. This combination of learning with fine-tuning via competition lets GANs have the potential to create lifelike images, from human portraits to contemporary art.

2. Diffusion Models-

These models work by reversing the process of adding noise to data.

  • Initially, they use original images and deliberately add random noise until the image is completely static.

  • Next, it reverses this process by filtering out the noise until original image is restored.

  • When given a text prompt, the model begins from noise and uses what it’s been trained on- basically “denoising” its way towards a new image.

This stepwise transition makes diffusion models apt to create highly specific, realistic visuals. It is also found to be more stable to train and control as compared to other models. That’s why models such as Stable Diffusion have become popular- they can generate clean, coherent images from scratch via text-to-image synthesis.

3. Variational Autoencoders (VAEs)-

These models generate images by learning to compress and rebuild them in a systematic encoding process.

  • Encoding- initiates models to use real images and compress them into a simplified, low-dimensional form called a latent space (or latent variables), while following principles of probabilistic distribution.

  • Sampling- the model samples from latent space- introducing minor, controlled changes in order to create something new.

  • Decoding- sampled data is modified and model rebuilds it as a new image.

This format makes VAEs suitable for manipulating image features and induce variations. Although their outputs have been found blurrier than those of GANs or diffusion models, their handling and managing latent features is well-utilized in hybrid image generation frameworks such as Stable Diffusion.

4. Transformers-

These are widely used to process sequential data, so they are well-suited for both visual and language tasks.

  • This model begins by breaking the input text prompt into parts and turning it into a format that captures its meaning and context.

  • Then it uses a self-attention mechanism to prioritize important aspects of the input, enabling the model to understand connections between disparate words or image areas.

  • These contextual embeddings are then passed along to guide other generative models- such as diffusion or GANs- in creating the resultant image.

Such a stacked process enables transformers to serve as middlemen between human language and machine vision. Their capacity to handle long-range dependencies and multimodal inputs (such as text + image) is what positions them at the core of models such as DALL·E.

5. Autoregressive Models-

These models produce images by predicting one element at a time in an ordered sequence.

  • It starts with a blank canvas and fills pixels or patches in images sequentially, similar to how puzzles are solved.

  • They utilize probability distributions from training datasets to ensure each prediction makes sense in the ongoing context of the picture so far.

  • This linear process repeats over and over until the whole image takes shape.

This technique is slower and more computationally expensive compared to others. Yet, their accuracy is valuable in applications where precision and structure are important, such as text-to-image tasks and high-resolution synthesis.

Process of Image Generation:

AI image generation operates through several distinct modalities, determined by the nature of the input and the expected output. Here are 3 ways it proceeds:

a. Text-to-Image Generation-

It’s the most basic way wherein you enter a prompt- say, “a blue cat fighting with John Cena”- and the model generates its visual interpretation.

  • The model uses natural language processing (NLP) techniques such as transformers to scan your prompt and interpret its meaning.

  • It converts the semantic content into a latent representation, resembling a digital blueprint. This guides a generative model- typically a GAN or a diffusion- toward constructing an image aligned with the prompt.

  • Through iterations, the system constructs an image that reflects semantics and stylistic cues embedded in your prompt.

This is a flexible process that handles both stringent and abstract ideas. Thus, your prompt(s) directly influence picture parameters like palette, composition, context, and visual style.

b. Image-to-Image Generation-

Think of it like remixing a song. Instead of starting with a text prompt, this method works with an existing image, such as a rough scribble that you want to see as a painting. It is then modified as per user specifications. Use cases include sketch-to-photo translation, style transfer, and targeted feature modification (e.g., adding/removing objects).

Technically, this process often leverages VAEs or transformers augmented with image embeddings, allowing the retention of structural integrity with creative alterations.

c. Multimodal Generation-

This advanced approach builds output from multiple input types- combining text descriptions with images, &/or audio data.

  • Every input type is fed into each encoder- e.g., a text prompt into a language model, an image into a transformer.

  • The system then combines these into a unified latent space, blending data streams to form a holistic context.

  • A generative model interprets this blended representation, outputting a new image that reflects all input channels.

Multimodal models are best used where both visual and narrative cues matter, like AI video tools, interactive storytelling, advanced representations, combining speech and visuals in virtual assistants or education tools.

Study Spotlight: DALL·E Image Generator

Based on a transformer-based autoregressive model, DALL·E (by OpenAI) transforms text prompts into meaningful images by learning cross-modal correspondences.

Main Features:

  • CLIP & GPT-like Architecture: DALL·E stacks a text encoder and an image decoder. It reads prompts just like GPT and uses that for processing visual tasks.

  • Zero-shot Generation: It can generate images for ideas it hasn’t directly viewed, such as “a cat wearing an avocado-shaped jumpsuit”

  • Detail Preservation: With added diffusion refinement, it makes higher resolution and more naturalistic images.

  • Enhanced Deep language learning: DALL-E3 is now integrated with ChatGPT, allowing better understanding of the contexts of prompts.

  • Inpainting capabilities: It also supports user needs by accepting specific edits within the same output. Thus, knowing photo editing skills is not mandatory.

DALL·E is an impressive tool, but it has clear limitations. It can produce fictional features, misinterpret ambiguous prompts, and reflect biases present in its training data. Despite its ability to effectively convert text into visuals and its practical applications, one must acknowledge these significant drawbacks.

Conclusion

It’s already in picture that AI image generators are transforming the landscape of visual storytelling. Anyone- from experienced designers to total newcomers- can produce complex illustrations with minimum effort. This ease comes from years of research and relentless iterations.

Understanding how these systems work helps in using them wisely. It’s not just about avoiding copyright or ethical issues, but making the most of a tool that thrives on smart input(s). The bottom line: a little technical literacy goes a long way while working with such tech.

Suggestions:

  1. The Creativity of Text-to-Image Generation (by Jonas Oppenlaender, University of Jyväskylä, Finland)

  2. What is Deep Learning?

  3. 5 AI/ML Research Papers on Image Generation You Must Read

Disclaimer:

Backlinks provided within this blog are intended for the reader’s further understanding only. My personal experiences and research serve as the base for the content of this blog. Despite my best efforts to keep the content current and correct, not every situation may necessarily benefit from it. Images utilised in this blog are self-designed using Canva*.* While making any crucial life decisions, please consult professional advice or conduct independent research. This blog does not intend to be a substitute for expert guidance; it is solely meant to be informative.

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Megha
Megha