Deterministic sampling methods, such as Denoising Diffusion Implicit Models (DDIM), aim to produce a specific output from a given input, creating consistent and predictable results. In these methods, the randomness is controlled, enabling the same input to always yield the same output. For example, if you use a deterministic sampling technique to generate images, inputting the same parameters will consistently generate the same image. This predictability makes deterministic methods useful for applications requiring reproducibility, such as scientific experiments or quality assurance in software.
In contrast, stochastic sampling methods introduce randomness into the output, meaning the same input can lead to different results each time the process is run. This randomness can be beneficial in generative tasks where a variety of results can help in exploration or creativity. For instance, if a stochastic method is used to generate images, you might get a different result with each run, even with identical input parameters. This feature is often desired in applications like art generation or when designing systems that benefit from diverse outputs.
The key difference between the two approaches lies in their intent and application. Deterministic methods provide a stable, repeatable process essential for tasks that require consistent outcomes, while stochastic methods allow for variability, which can enhance creativity or exploration. Developers often choose between these two based on the needs of their projects, balancing the need for consistency versus the benefit of diversity in their outputs.