Diffusion models are a class of generative models that learn to create data by reversing a process of noise addition. Conceptually, the process begins with a clean dataset, which is progressively perturbed by adding Gaussian noise over multiple steps. This noise makes the data increasingly unrecognizable, and by the end of this process, you are left with a sample that is essentially random noise. The goal of the diffusion model is to reverse this diffusion process, effectively generating new data samples by iteratively removing noise.
To achieve this, diffusion models use a neural network that learns to predict the data distribution at various noise levels. During training, the model takes a noisy version of a data sample and learns to recover the original sample, guided by the noise level. This is typically done by using a loss function that compares the model’s output with the original sample. By training on many different noise levels, the model learns a series of steps or transitions that take a noisy input to a cleaner output. This is key to understanding how the model generates new data: it starts with random noise and then applies these learned transformations iteratively to produce a coherent final output that resembles the original data distribution.
An important aspect of diffusion models is their flexibility and stability. Compared to traditional generative models like GANs, which can suffer from mode collapse (where the generator produces only a few types of outputs), diffusion models tend to generate a wider variety of samples and are generally more stable during training. A well-known example of a diffusion model is DALL-E 2 by OpenAI, which generates images based on text prompts. The model starts from random noise and refines it step-by-step, guided by the associations learned from training data, producing high-quality images that are coherent with the input prompts. This demonstrates how diffusion models can be effectively utilized in creative and practical applications.