There are three groups of samplers:
1. Samplers that converge to a stable image with increasing number of steps
2. Samplers that do not converge but keep changing the image with increasing number of steps. Tthey have an 'a', from ancestral, in their name.
3. Samplers that may converge but do so to a different image from group 1. (They often have SDE in their name, Stochastic Differential Equation)

In general the workflow is:
1. Generate a large number of images in the shortest possible time to see if there is one that you like. For this phase we need a converging sampler that is fast and needs little steps to show the image it will converge to. Seven samplers are available in this group that converges, need only 9~12 steps, and are fast
2. Now select the image you like and work further on it to improve quality and upscale it. For this phase we need the sampler that gives good image quality in a reasonable amount of steps. My personal favourites are Euler and DPM++ 2M Karras,

Most samplers do not need many more steps 30. Increasing step count further may sometime render some more details but in general with the converging samplers the image won't improve much anymore above step 30.

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