What is the main objective of using Gaussian noise in diffusion models?

The main objective of using Gaussian noise in diffusion models is to generate unique data from a text prompt. Diffusion models are designed to transform random noise into data samples by gradually introducing structure through a process that denoises the data. By starting with Gaussian noise, the models can learn to modulate this randomness based on specific input data, such as a text prompt, thereby creating diverse outputs that reflect the information embedded in the prompt.

In essence, the addition of Gaussian noise is crucial for the generative process, enabling the model to explore a wide range of possibilities while still anchoring the output to the desired characteristics defined by the input. This exploration leads to the synthesis of new and unique data, which is a fundamental aspect of how diffusion models function.