How does a Diffusion Model generate images?

A Diffusion Model generates images by refining a noise signal into a realistic image. This process involves starting with a random noise image and progressively transforming it through a series of steps. Each step reduces the noise and moves the image closer to a coherent, aesthetically pleasing result.

This iterative process is guided by learned representations from training data, which allows the model to understand the patterns and structures that make up realistic images. The model effectively "denoises" the image, gradually steering it towards the desired output by applying transformations that are informed by its training.

In contrast, analyzing existing image data or combining multiple datasets does not directly describe the fundamental mechanism of image synthesis as performed by Diffusion Models. Utilizing rule-based systems is also distinct from how these models operate, as rule-based systems rely on predefined logic and algorithms rather than learning from data patterns through iterative denoising processes.