Diffusion Models Explained: The Technology Behind AI Image and Video Generation

If you’ve used Midjourney, DALL-E, or Adobe Firefly in the last two years, you’ve already met diffusion models — you just didn’t know it. These algorithms sit behind nearly every AI image generator that’s taken the internet by storm. And now they’re powering video tools too. Understanding how they work changes the way you think about AI creativity, intellectual property, and what comes next.

What Is a Diffusion Model?

A diffusion model is a type of neural network trained to generate images, audio, or video by learning to reverse a specific kind of chaos. The idea sounds bizarre at first. Start with a real image. Add random noise — pixel static, like a TV with no signal — gradually, in steps, until the image is pure noise. Then train the AI to work backwards: given noisy pixels, predict what the clean version looked like.

Do that billions of times across millions of images, and the model learns something remarkable. It learns the underlying patterns of the visual world — fur, fabric, faces, fire. When you ask it to generate a new image, it starts with pure noise and denoises step by step until something coherent emerges. The 2020 paper by Ho et al. that introduced DDPM (Denoising Diffusion Probabilistic Models) is what kicked the whole thing off. Around 47,000 researchers have cited it since.

The noise isn’t random luck. It’s a controlled mathematical process called the forward process, defined by a Markov chain of Gaussian noise additions. The reverse — what the model actually learns — is called the reverse process. That’s the bit that costs the serious GPU time.

How Text Gets Turned Into Images

Raw diffusion models generate images from noise, but they have no concept of words. That’s where CLIP comes in — a separate model from OpenAI trained on 400 million image-text pairs scraped from the internet. CLIP learned to connect language and vision: it knows what “a red apple on a wooden table” should look like.

When you type a prompt into DALL-E 3 or Stable Diffusion, your words get encoded into a numerical representation using a language model. That representation then guides the denoising process. Each denoising step is steered away from noise and towards images that match your description. The process runs anywhere from 20 to 100 steps depending on settings. More steps means sharper results and more computing cost.

When I first tried Stable Diffusion locally on my machine in late 2022, it took around 30 seconds per image on a mid-range GPU. The same quality now takes under 5 seconds with SDXL Turbo. That’s a 6× speed improvement in under two years.

Stable Diffusion vs DALL-E vs Midjourney

All three use diffusion at their core, but they’re built differently. Stable Diffusion from Stability AI is open-source — you can download the weights and run it on your own machine for free, no API limits. That’s why it has the biggest developer community. Midjourney runs on Discord and is subscription-only, but produces some of the most aesthetically polished output. DALL-E 3, integrated into ChatGPT Plus, is the most instruction-following — it’s harder to confuse but also harder to push into unusual creative territory.

There’s also Adobe Firefly, trained exclusively on licensed stock images and out-of-copyright works. Adobe designed it specifically for commercial use — no copyright ambiguity, which matters if you’re using AI art in professional projects. UK businesses adopting AI image tools should pay close attention to that distinction.

Google’s Imagen and Imagen 2 are Google DeepMind’s take, focused on photorealism. They’re not publicly available to run yourself but power some Workspace features. Black Forest Labs’ FLUX models, released in mid-2024, have become increasingly popular for fine detail and prompt adherence.

Latent Diffusion: Why Images Don’t Require a Supercomputer

Running diffusion directly on pixel values would be absurdly slow. A 1024×1024 image has over a million pixels. Doing 50 denoising steps across all of them, for every image, would require data centres just to handle casual users.

The breakthrough was latent diffusion. Instead of working in pixel space, the model first compresses the image into a lower-dimensional latent space using an encoder. It then runs the diffusion process in that compressed representation — 8× to 16× smaller than the original. After denoising, a decoder expands it back to full resolution. You lose some fine detail but gain enormous speed. Stable Diffusion’s full name is actually “Latent Diffusion Model.” That’s why it can run on consumer GPUs at all.

The encoder-decoder pair is a Variational Autoencoder (VAE). Training it is a separate research challenge, and the quality of the VAE significantly affects the sharpness of final images — especially faces and text.

Diffusion Models and Video

Generating consistent video requires every frame to fit together. Early video AI tools produced flickering disasters because each frame was generated independently. Diffusion models changed this by adding a temporal dimension — they learn patterns across time, not just space.

OpenAI’s Sora (announced early 2024) was the headline story: full-minute videos with consistent characters and physics-aware motion. Google’s Lumiere, Runway Gen-3, and Stability AI’s Stable Video Diffusion followed. The core idea in all of them is a 3D diffusion model — instead of denoising a 2D image, it denoises a sequence of frames simultaneously, so the motion stays coherent.

UK creatives and production studios are watching this space closely. Three major advertising agencies in London publicly trialled Sora for storyboarding in Q1 2025. The consensus: useful for pre-vis, not reliable enough for final assets. Yet.

The Copyright Problem No One Has Solved

Here’s where things get uncomfortable. Every major diffusion model was trained on images scraped from the internet — including copyrighted photographs, artworks, and illustrations — without the creators’ permission. Getty Images sued Stability AI in 2023. Multiple class action lawsuits are ongoing in both the US and UK. The UK Intellectual Property Office ran a consultation in 2023 on whether training AI on copyrighted works constitutes infringement.

As of 2026, there’s no settled law. UK courts have not yet ruled on a case that directly addresses AI training data and copyright. The EU AI Act introduces some transparency requirements around training data for high-risk models, but the UK, having left the EU, is developing its own framework separately. This matters if you use AI-generated images commercially — the legal ground is still shifting.

Adobe Firefly’s “trained on licensed data” approach is a direct response to this problem. So is Shutterstock’s AI generator, backed by licensing deals with contributing photographers. Expect more providers to follow that model as litigation pressure grows.

How Diffusion Models Are Being Used in the UK Right Now

Beyond art generation, diffusion models are turning up in unexpected places. The NHS Research Authority trialled synthetic patient scan generation in 2024 — real MRI and CT data is hard to share due to patient privacy, so synthetic diffusion-generated scans train diagnostic AI without the compliance headache. Early results showed synthetic data quality that matched real data in downstream model performance tests.

UK fashion retailers including ASOS have piloted AI-generated product images, reducing photoshoot costs for seasonal catalogue items. Architects use diffusion models to visualise planning proposals — a rough sketch becomes a photorealistic render in seconds. Game studios use inpainting tools (fill a masked region of an image to match the surroundings) to speed up environment art production.

Audio diffusion is growing too. Models like AudioCraft from Meta and Stable Audio generate music from text descriptions. BBC Research published a paper in late 2024 on using audio diffusion for sound effect generation in radio production. The technology is still rough but moving fast.

What Diffusion Models Can’t Do (Yet)

Consistency is the main weakness. Ask a diffusion model to generate a character who appears in ten scenes. You’ll get ten different people. Maintaining identity across multiple images requires additional techniques — LoRA fine-tuning, reference images, IP-Adapter embeddings — that add complexity and cost.

Text rendering is still broken. Diffusion models treat letters as shapes, not language. Asking for an image with the text “OPEN UNTIL 9PM” on a shop sign usually produces garbled letterforms that look plausible from a distance but make no sense up close. Text-to-image models trained with dedicated text rendering pipelines (like ideogram.ai) handle this better but it remains an active research problem.

Hands are notoriously bad. Six-fingered hands became a meme for a reason — the training data contains enough unusual hand positions that the model has broad uncertainty about how many fingers to include. This has improved significantly with newer models, but watch for it if realism matters.

What This Means for You

If you work in any creative field — design, marketing, photography, video — diffusion models are not going away. The question isn’t whether to engage with this technology but how. For UK businesses: be careful about commercial use of outputs from models with unclear training data provenance. Keep an eye on the UK IPO’s evolving guidance. Adobe Firefly and stock-licensed alternatives are the safer commercial bet right now.

For developers and enthusiasts: Stable Diffusion and its descendants are free to run locally and experiment with. The community around it — Civitai, Hugging Face, A1111/ComfyUI — is enormous. If you want to understand where AI is heading, getting hands-on with diffusion tools is one of the best ways in.

This article is for educational purposes only and does not constitute financial advice. Cryptocurrency investments involve significant risk. Always do your own research.