Understanding Diffusion Models: An Overview
Diffusion models are taking the world of AI generation by storm. In this in-depth post, we'll unpack how these models work, their key innovations, and why they are outperforming other generative models like GANs.
What is Diffusion?
In physics, diffusion is the spread of particles from high to low concentration until equilibrium is reached. Spraying perfume is a simple example - the scent spreads evenly through the room over time.
In machine learning, diffusion models aim to mimic this process of adding noise to data to make it more complex and disordered. The models learn to reverse this process, starting from noise and gradually transforming it into realistic data samples like images.
Core Components
Diffusion models have three key phases:
- Forward Diffusion Phase
This starts with clean data, like images from a dataset. The model adds small amounts of noise to make the images gradually more noisy and complex over multiple steps. Mathematically, this is done by sampling from Gaussian distributions and scaling the variance over time.
- Reverse Diffusion Phase
Now the model goes in reverse. Starting with completely noisy data, it removes a little noise in each step to recover the original clean data. This process of denoising through multiple small steps is key to generating new samples.
- Training Phase
The model is trained to optimize parameters for the reverse diffusion process. The aim is to make this reverse process convert simple noise into data samples that closely match the original data distribution.
How Are Diffusion Models Trained?
Diffusion models are trained using a technique called score matching. The model estimates the gradient of the log probability of the data at each point, known as the score.
Using stochastic differential equations, the model learns how to move along the score in the direction that most rapidly increases probability. This enables converting noise into diverse, realistic data points.
Specifically, the loss function during training measures how well the model's predicted score matches the true score at each diffusion step. Minimizing this loss trains the model to estimate scores that accurately reflect the data distribution.
Generating New Data Samples
Once trained, diffusion models can create new data points through the reverse diffusion process. Starting with Gaussian noise, the model progresses step-by-step while removing noise to transform the random noise into creative samples matching the training data distribution.
Why Are Diffusion Models Better Than GANs?
Diffusion models currently produce state-of-the-art sample quality compared to other generative models like GANs and VAEs. Some key advantages:
- Stable training: Diffusion models avoid problematic GAN training issues like mode collapse.
- Sample quality: Images have fewer artifacts and more realistic textures.
- Flexibility: Effective with low data and missing data. Latent space enables controlled generation.
- Interpretability: Score matching provides useful insights into the data distribution.
By mimicking physical diffusion processes, these models are opening new frontiers in AI generation and creativity!