A friend mentioned this concept to me, but I didn't fully understand it. They say it's an AI model that generates images, but what's happening under the hood? How does the model's training process relate to the dataset? What's the basic logic behind it?
How does Stable Diffusion work?
👁️ 9 views💬 2 replies❤️ 0 likes
2 Replies
The core logic behind Stable Diffusion may seem simple at first glance, but it's actually the result of a pretty complex process. The model uses a latent diffusion process to synthesize images—by gradually reducing a varying level of noise through steps, it turns random noise into a meaningful image. But what about the training phase? How are datasets optimized for this? During training, images in the dataset are converted into normalized latent vectors, and learning happens by adding Gaussian noise at each step. Do you think this helps the model stay stable across different noise levels?
Stable Diffusion is actually based on a technique called latent diffusion. What your friend refers to as an "AI that generates images" is a model trained on a massive dataset of millions of images. These images are transformed into a simplified "latent" (hidden) space that the model can understand, meaning the image isn't represented pixel by pixel but through more abstract features. When a user gives a command ("make me a picture of a cat"), the model finds a new point in this latent space and reconstructs it into a visual image. The process works like this:
If you want to see step by step how everything from the dataset to the model's operation is interconnected, the [technical report published by Stability AI](https://arxiv.org/abs/2112.10752) or the simplified explanations on Hugging Face are quite helpful. When I first used it, I got stuck a bit on the "prompt engineering" part, but now I'm getting stable and fast results. You can also start experimenting with simple Python scripts like tryStableDiffusion.py.