Diffusion Models

Diffusion models are a class of generative models that learn to generate data by gradually transforming a simple distribution into a complex one. They operate by simulating a diffusion process, where noise is progressively added to the data and then reversed to recover the original input. This approach allows for high-quality image generation and has gained popularity in various applications, including art creation and data augmentation. Diffusion models are characterized by their ability to produce diverse outputs and their robustness against mode collapse, a common issue in other generative models. They are particularly effective in scenarios where high fidelity and diversity in generated samples are required.