Creating AI-Generated Images with Diffusion Models

In the rapidly evolving world of generative AI, where models like large language models (LLMs) excel at generating human-like text, diffusion models are spotlighted for creating high-quality images. These models have emerged as powerful tools, particularly in multimedia content creation tasks.

In this lesson, we’ll break down how diffusion models work, how they relate to LLMs, and how they’re transforming industries, with real-world examples from companies like OpenAI, Google, and NVIDIA.

What are diffusion models?

Imagine you’re an artist trying to create a masterpiece, but instead of starting with a blank canvas, you begin with a messy canvas full of random scribbles. Little by little, you erase the scribbles and refine the image until a clear, detailed picture emerges. That’s how diffusion models work—they start with random noise and progressively transform it into meaningful data, like an image or even a sound.

How do diffusion models work?

At the core of diffusion models lies a fascinating process that revolves around transforming random noise into meaningful data. Diffusion models follow a two-phase process that ensures high-quality, coherent results.

Forward diffusion: In this phase, the model gradually adds noise to data, like turning a clear image into random static. Each step introduces more distortion, helping the model understand various noise levels.

Backward diffusion: After the data is fully degraded, the model reverses the process, slowly removing the noise to recover the original data. This teaches the model how to transform random noise into a clean, structured output, such as an image or sound.

Are diffusion models connected to LLMs?

LLMs like OpenAI’s GPT are primarily designed for text generation, though recent multimodal models (like GPT-4 or LLaMA 3.2) have started to handle images and other data types to a limited extent. However, traditionally, LLMs aren't inherently equipped to generate or process non-text data, such as images, videos, or sounds.

Diffusion models, on the other hand, are specialized in generating high-quality non-text data like images, making them complementary to LLMs. These models are often used in tasks like image generation (e.g., DALL•E) and other forms of content synthesis beyond text, which makes them ideal partners for LLMs in creating multimodal systems.

Imagine you’re creating an AI that not only writes a movie script but also generates the entire animated movie! The script would be handled by an LLM, while the diffusion model would create the visuals, sound effects, and maybe even the background score. This blend of text and media generation allows AI to bring imaginative stories to life in ways never seen before.

Why diffusion models matter for Generative AI engineers?

For AI engineers, diffusion models offer a powerful toolset for handling multimedia tasks. As mentioned, LLMs dominate text-based tasks, however, the multimodal capabilities of diffusion models open up possibilities for creating not just words, but images, videos, and sounds.

If you’re developing AI for entertainment, healthcare, or industrial automation, diffusion models let you expand beyond text into richer, more immersive media. And because they are more stable and easier to train than GANs, engineers can rapidly iterate on new ideas without having to spend tons of time troubleshooting.

As AI continues to evolve, mastering diffusion models will empower engineers and creators alike to build AI systems that don’t just think and speak—but can also see, hear, and create in ways that rival human creativity.

Quiz

Let’s test your understanding of the diffusion process with a short quiz.

Want to explore more?

To learn more about diffusion models, you can visit the following exciting course:

• Introduction to Diffusion Models