Sampling Methods for Stable Diffusion: Samplers Comparison

What are samplers and what is the difference between them? How to use them correctly to get the desired result? Answers of Sampling methods for Stable Diffusion are observed in this article. Learn about the different sampling methods for stable diffusions with our comprehensive comparison guide.

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What is Sampling

Sampling refers to the process of selecting a subset of data points from a larger dataset. The goal is to construct a smaller dataset that is representative of the larger set, while reducing the computational complexity and memory requirements of the analysis.

Let’s compare sampling methods for Stable Diffusions and give them a scores. They are available in Stable Diffusion AUTOMATIC1111.

Initial data

  • Sampling steps: 50
  • Seed: 2193120663
  • CFG Scale: 7
  • Model: deliberate v2 safetensors
  • Prompt: cute woman

Graphical Comparison of Sampling Methods

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Numerical Comparison of Sampling Methods for Stable Diffusion

Below is a table with a numerical comparison of sampling methods for Stable Diffusion.

Sampling methodSpeedStabilityQuality
Euler a6.61010
Euler6.7109.7
LMS78.58
Heun4108.5
DPM2476.7
DPM2 a47.57
DPM++ 2S a48.59
DPM++ 2M6.89.58.5
DPM++ SDE41010
DPM fast6.673
DPM adaptive1.76.57.5
LMS Karras7109.2
DPM2 Karras41010
DPM2 a Karras47.58
DPM++ 2S a Karras478
DPM++ 2M Karras79.710
DPM++ SDE Karras3.9109.6
DDIM1066.2
PLMS9.77.56.3

If you want to get realistic pictures of people at the output, choose Euler a and Euler samplers. If you want to get art or anime styles, choose DPM++ 2M and DPM++ 2M Karras samplers. I often use sampling methods for Stable Diffusion are Euler a and add prompts to clarify the desired style.

Alex Patel

An accomplished editor with a specialization in AI articles. Born and raised in NYC, USA. He graduated the Columbia University, and worked in tech companies of USA and Canada.

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