(With examples: Stable Diffusion, Midjourney, DALL·E)
1. Introduction
Diffusion models are a type of generative AI used for creating images, art, and other media from text prompts.
They work by starting with random noise and gradually turning it into a coherent image through a step-by-step denoising process.
Examples:
- Stable Diffusion → Open-source, runs locally, customizable.
- Midjourney → Proprietary, art-focused, Discord-based.
- DALL·E → OpenAI’s text-to-image system, integrated with ChatGPT.
2. The Core Idea
The process involves two main phases:
A. Forward Diffusion (Noise Addition)
- Start with a real image.
- Gradually add random noise over many steps until the image becomes pure noise.
- This teaches the model what noise looks like at different levels.
B. Reverse Diffusion (Image Generation)
- Start from pure noise.
- The model learns to reverse the noise process step by step.
- Guided by a text prompt (via a language-image encoder like CLIP).
- Result: A brand-new image matching the prompt.
3. Step-by-Step Example
Prompt: “A cat wearing sunglasses on a beach, digital art style.”
- Start with random noise.
- Model predicts what the image should look like, given the prompt.
- Removes a little noise → partial image appears.
- Repeats this for 50–1000 steps until the final image is clear.
4. Key Components
- Text Encoder → Converts your prompt into a numerical representation.
- UNet Model → Core denoising neural network.
- Scheduler → Controls how much noise to remove per step.
- VAE (Variational Autoencoder) → Compresses and reconstructs images.
5. Popular Diffusion Models
| Model | Type | Strengths | Weaknesses |
|---|---|---|---|
| Stable Diffusion | Open-source | Free, customizable, can run locally | Requires good GPU |
| Midjourney | Closed-source | Stunning artistic styles | Less control over exact details |
| DALL·E | Proprietary | Integrated with ChatGPT, easy to use | Limited customization compared to open-source |
6. Advantages
✅ High-quality, realistic images.
✅ Creative flexibility.
✅ Works with detailed prompts.
7. Limitations
❌ Can produce biased or inaccurate outputs.
❌ Requires significant computing power for training.
❌ Sometimes ignores small prompt details.