Guide to Stable Diffusion Samplers

In this brief guide, you will learn what samplers are and how they work. You will also find a comparison of images generated with each sampler available at getimg.ai.

What is sampling

Stable Diffusion uses a specific technique called sampling to create an image. Initially, a random image is generated in the latent space. Subsequently, the noise predictor assesses the image's noise, which is then subtracted from the image. This iterative process is repeated multiple times, resulting in a refined, noise-free image.

The denoising procedure, known as sampling, refers to generating a fresh sample image in each step using Stable Diffusion. The approach employed in this sampling technique is called the sampler or sampling method.

Although the framework remains constant, numerous approaches exist for implementing this denoising process. Frequently, there is a compromise to be made between speed and accuracy.

Samplers comparison

Now, let's observe the images produced by each sampler. We kept all generation parameters constant except for the sampler. KDPM Karras Ancestral, DPM++ 2M SDE, and PLMS need more steps than other samplers to generate a high-quality image, so we used 64 steps to generate these images. For the rest of the samplers, we used 27 steps.

Euler Ancestral

Euler

LMS

DPM-Solver++

PLMS (64 steps)

DDIM

KDPM Karras

KDPM Karras Ancestral (64 steps)

DEIS

UniPC

Heun

KDPM Ancestral

KDPM

DPM-Solver SDE

DPM++ 2M SDE Karras

DPM++ 2M SDE (64 steps)

DPM-Solver++ Karras

LMS Karras

We hope this concise guide has shed light on the intricacies of Stable Diffusion samplers and their comparative capabilities. Armed with this knowledge, we invite you to delve into the world of AI-generated imagery by experimenting with various samplers available in the advanced tab of our AI Generator. Your creative journey is just beginning, and the possibilities are as limitless as your imagination. Happy creating!