Sampler Convergence#
As features keep increasing, making the right choices for your needs can become increasingly difficult. What sampler to use? And for how many steps? Do you change the CFG value? Do you use prompt weighting? Do you allow variations?
Even once you have a result, do you blend it with other images? Pass it through img2img
? With what strength? Do you use inpainting to correct small details? Outpainting to extend cropped sections?
The purpose of this series of documents is to help you better understand these tools, so you can make the best out of them. Feel free to contribute with your own findings!
In this document, we will talk about sampler convergence.
Looking for a short version? Here's a TL;DR in 3 tables.
Remember
 Results converge as steps (
s
) are increased (except forK_DPM_2_A
andK_EULER_A
). Often at ≥s100
, but may require ≥s700
).  Producing a batch of candidate images at low (
s8
tos30
) step counts can save you hours of computation. K_HEUN
andK_DPM_2
converge in less steps (but are slower).K_DPM_2_A
andK_EULER_A
incorporate a lot of creativity/variability.
Sampler  (3 sample avg) it/s (M1 Max 64GB, 512x512) 

DDIM 
1.89 
PLMS 
1.86 
K_EULER 
1.86 
K_LMS 
1.91 
K_HEUN 
0.95 (slower) 
K_DPM_2 
0.95 (slower) 
K_DPM_2_A 
0.95 (slower) 
K_EULER_A 
1.86 
suggestions
For most use cases, K_LMS
, K_HEUN
and K_DPM_2
are the best choices (the latter 2 run 0.5x as quick, but tend to converge 2x as quick as K_LMS
). At very low steps (≤ s8
), K_HEUN
and K_DPM_2
are not recommended. Use K_LMS
instead.
For variability, use K_EULER_A
(runs 2x as quick as K_DPM_2_A
).
Sampler results#
Let's start by choosing a prompt and using it with each of our 8 samplers, running it for 10, 20, 30, 40, 50 and 100 steps.
Anime. "an anime girl" W512 H512 C7.5 S3031912972
Sampler convergence#
Immediately, you can notice results tend to converge that is, as s
(step) values increase, images look more and more similar until there comes a point where the image no longer changes.
You can also notice how DDIM
and PLMS
eventually tend to converge to Ksampler results as steps are increased.
Among Ksamplers, K_HEUN
and K_DPM_2
seem to require the fewest steps to converge, and even at low step counts they are good indicators of the final result. And finally, K_DPM_2_A
and K_EULER_A
seem to do a bit of their own thing and don't keep much similarity with the rest of the samplers.
Batch generation speedup#
This realization is very useful because it means you don't need to create a batch of 100 images (n100
) at s100
to choose your favorite 2 or 3 images.
You can produce the same 100 images at s10
to s30
using a Ksampler (since they converge faster), get a rough idea of the final result, choose your 2 or 3 favorite ones, and then run s100
on those images to polish some details.
The latter technique is 38x as quick.
Example
At 60s per 100 steps.
A) 60s * 100 images = 6000s (100 images at s100
, manually picking 3 favorites)
B) 6s 100 images + 60s 3 images = 780s (100 images at s10
, manually picking 3 favorites, and running those 3 at s100
to polish details)
The result is 1 hour and 40 minutes for Variant A, vs 13 minutes for Variant B.
Topic convergance#
Now, these results seem interesting, but do they hold for other topics? How about nature? Food? People? Animals? Let's try!
Nature. "valley landscape wallpaper, d&d art, fantasy, painted, 4k, high detail, sharp focus, washed colors, elaborate excellent painted illustration" W512 H512 C7.5 S1458228930
With nature, you can see how initial results are even more indicative of final result more so than with characters/people. K_HEUN
and K_DPM_2
are again the quickest indicators, almost right from the start. Results also converge faster (e.g. K_HEUN
converged at s21
).
Food. "a hamburger with a bowl of french fries" W512 H512 C7.5 S4053222918
Again, K_HEUN
and K_DPM_2
take the fewest number of steps to be good indicators of the final result. K_DPM_2_A
and K_EULER_A
seem to incorporate a lot of creativity/variability, capable of producing rotten hamburgers, but also of adding lettuce to the mix. And they're the only samplers that produced an actual 'bowl of fries'!
Animals. "grown tiger, full body" W512 H512 C7.5 S3721629802
K_HEUN
and K_DPM_2
once again require the least number of steps to be indicative of the final result (around s30
), while other samplers are still struggling with several tails or malformed back legs.
It also takes longer to converge (for comparison, K_HEUN
required around 150 steps to converge). This is normal, as producing human/animal faces/bodies is one of the things the model struggles the most with. For these topics, running for more steps will often increase coherence within the composition.
People. "Ultra realistic photo, (Miranda BloomKerr), young, stunning model, blue eyes, blond hair, beautiful face, intricate, highly detailed, smooth, art by artgerm and greg rutkowski and alphonse mucha, stained glass" W512 H512 C7.5 S2131956332
. This time, we will go up to 300 steps.
Observing the results, it again takes longer for all samplers to converge (K_HEUN
took around 150 steps), but we can observe good indicative results much earlier (see: K_HEUN
). Conversely, DDIM
and PLMS
are still undergoing moderate changes (see: lace around her neck), even at s300
.
In fact, as we can see in this other experiment, some samplers can take 700+ steps to converge when generating people.
Note also the point of convergence may not be the most desirable state (e.g. I prefer an earlier version of the face, more rounded), but it will probably be the most coherent arms/hands/face attributeswise. You can always merge different images with a photo editing tool and pass it through img2img
to smoothen the composition.
Sampler generation times#
Once we understand the concept of sampler convergence, we must look into the performance of each sampler in terms of steps (iterations) per second, as not all samplers run at the same speed.
On my M1 Max with 64GB of RAM, for a 512x512 image
Sampler  (3 sample average) it/s 

DDIM 
1.89 
PLMS 
1.86 
K_EULER 
1.86 
K_LMS 
1.91 
K_HEUN 
0.95 (slower) 
K_DPM_2 
0.95 (slower) 
K_DPM_2_A 
0.95 (slower) 
K_EULER_A 
1.86 
Combining our results with the steps per second of each sampler, three choices come out on top: K_LMS
, K_HEUN
and K_DPM_2
(where the latter two run 0.5x as quick but tend to converge 2x as quick as K_LMS
). For creativity and a lot of variation between iterations, K_EULER_A
can be a good choice (which runs 2x as quick as K_DPM_2_A
).
Additionally, image generation at very low steps (≤ s8
) is not recommended for K_HEUN
and K_DPM_2
. Use K_LMS
instead.
Three key points#
Finally, it is relevant to mention that, in general, there are 3 important moments in the process of image formation as steps increase:

The (earliest) point at which an image becomes a good indicator of the final result (useful for batch generation at low step values, to then improve the quality/coherence of the chosen images via running the same prompt and seed for more steps).

The (earliest) point at which an image becomes coherent, even if different from the result if steps are increased (useful for batch generation at low step values, where quality/coherence is improved via techniques other than increasing the steps e.g. via inpainting).

The point at which an image fully converges.
Hence, remember that your workflow/strategy should define your optimal number of steps, even for the same prompt and seed (for example, if you seek full convergence, you may run K_LMS
for s200
in the case of the redhaired girl, but K_LMS
and s20
taking one tenth the time may do as well if your workflow includes adding small details, such as the missing shoulder strap, via img2img
).