Diffusion Model

Diffusion Models are a class of generative models that iteratively refine noise to generate data samples.

Diffusion Models are used in various applications such as image generation, speech synthesis, and other areas where high-quality data generation is required. They work by gradually adding noise to the data in a forward process and then learning to reverse this process to recover the original data. This iterative refinement allows the model to generate realistic samples from random noise.

For example, in image generation, a Diffusion Model starts with a noisy image and iteratively denoises it to produce a high-quality image. This process involves a series of steps where the model learns to predict and remove the noise at each step, resulting in a clear and realistic image.

In summary, Diffusion Models are important because they provide a robust framework for generating high-quality data samples. They are known for their stability and ability to produce detailed and realistic outputs, making them a valuable tool in the field of generative modeling.