The DDPM sampler, or Denoising Diffusion Probabilistic Model sampler, is a simple yet effective sampler for text-to-image diffusion models. It works by randomly sampling noise from the diffusion process and then subtracting it from the image. This process is repeated until the desired image quality is achieved.

The DDPM sampler is a good choice for applications where speed and efficiency are important. It is also a good choice for applications where diversity is important, as the DDPM sampler can produce a variety of different outputs from the same text prompt.

However, it is important to note that the DDPM sampler can be more susceptible to artifacts than other samplers, such as the DDIM sampler. This is because the DDPM sampler does not take into account the gradient of the diffusion process at the current image.