Interactive guide to Stable Diffusion steps parameter
How to adjust inference/sampling steps in Stable Diffusion
Generally speaking, the more steps you use, the better quality you'll achieve. But you shouldn't set steps as high as possible. It's all about the results you are trying to achieve.
First, let's briefly introduce the steps parameter in Stable Diffusion and diffusion models in general. Diffusion models are iterative processes – a repeated cycle that starts with a random noise generated from text input. Some noise is removed with each step, resulting in a higher-quality image over time. The repetition stops when the desired number of steps completes.
Around 25 sampling steps are usually enough to achieve high-quality images. Using more may produce a slightly different picture, but not necessarily better quality. In addition, the iterative nature of the process makes generation slow; the more steps you'll use, the more time it will take to generate an image. In most cases, it's not worth the additional wait time.
Use 25 steps set by default in getimg.ai tools. Increase when you believe the quality is low.
Below you can see single steps (1-100) used to generate a picture of the 'puppy in space'. Drag the slider to move between steps, starting with noise at step 1.
Note how the magic happens around steps 4-7, and the dog emerges from the blob. Then it reaches high quality around 20-25 steps into the generation. Steps above 25 do not create a significant difference in quality; the dog's form repeatedly changes without producing more details.
Here's another example showcasing 'a bear astronauts' at different steps.
Notice that with higher steps, the main form of a 'bear astronaut' stays the same. But in this case, some minor details are improving with more steps — for example, the hair quality, colors, and space suite details.
Of course, it's all subjective, and we leave the final decision on how many steps to use to you - the creator. We hope our guide helped you understand the steps parameter in the Stable Diffusion generation process, and you'll use what you've learned to create something unique.