Euler Ancestral

Euler Ancestral is a sampler in text-to-image diffusion models that is based on the Euler method for solving differential equations. It is a fast and efficient sampler that can often generate good outputs in 20-30 steps.

Euler Ancestral works by sampling noise from the diffusion process and then subtracting it from the image. However, unlike other samplers, Euler Ancestral also adds some random noise back to the image at each step. This helps to prevent the image from becoming too blurry and to generate more diverse images.

Euler Ancestral is a good choice for applications where speed and efficiency are important. It is also a good choice for applications where diversity is important, such as generating creative images or generating images that are different from each other.

Here are some examples of how Euler Ancestral can be used to generate different types of images:

• A simple image, such as a black cat sitting on a red couch: Euler Ancestral can generate this type of image in a few dozen steps.
• A more complex image, such as a realistic portrait of Albert Einstein: Euler Ancestral may require more steps to generate this type of image, but it can still generate good results in a reasonable amount of time.
• A creative image, such as a painting of a cat in the style of Pablo Picasso: Euler Ancestral is a good choice for generating creative images, as it can produce a variety of different outputs from the same text prompt.

Overall, Euler Ancestral is a versatile and powerful sampler for text-to-image diffusion models. It is a good choice for applications where speed, efficiency, and diversity are important.