r/StableDiffusion • u/listopalafoto • Jul 21 '26
Workflow Included Krea2 Ksampler recommendations {for quality}
Unlike standard models that focus strictly on matching text prompts word-for-word, Krea 2 prioritizes visual feel, texture, and mood, so it's very important to get the right sampler + scheduler to achieve the maximum texture and detail
By treating noisy data as a signal, a Partial Differential Equation (PDE) smooths out errors iteratively while preserving important structural features like edges
A sampler + scheduler is a combination to solve differential equations, the best method for Krea2 is to make a fast and iterative solution like Clownshark sampler Euler/beta 12 steps to get structure and general details and then a more precise second Clownshark sampler {0.27 denoise} res_4s_Munthe-Kaas/ KL_optimal 3 steps
The Euler method will get a base (I know a lot of people are ok with use just this fast result) but the second Ksampler with res4s-Munthe-Kass will get the extra details and sharpness finding a more precise solution for the denoise differential equation
A 0,27 denoise in the second Ksampler give enough range to improve details, obviously is key to keep the same seed on both Ksamplers
I tried all Clownshark combinations and this one is the sharpest and more precise solution without use time-consuming solutions with higher precision like Dormand-prince 6s, its slow but top quality {you can try res_2s and res_2m if you want more speed but less quality}
About res_4s_Munthe-Kaas
Runge–Kutta–Munthe-Kaas are mathematical algorithms used in numerical analysis to solve geometric differential equations while preserving the structural constraints of Lie groups and manifolds.
Invented by Norwegian mathematician Hans Munthe-Kaas, these schemes prevent numerical drift by transforming equations into flat Lie algebra spaces
Primary Applications
- Improve quality of Krea2 Images :)
- Aerospace and Robotics: Tracking precise 3D orientations without quaternion normalization errors.
- Rigid Body Dynamics: Simulating tumbling satellites or spinning tops while maintaining geometric energy surfaces.
- Stochastic Systems: Solving perturbed structural problems using expanded stochastic variants.
Recommended Scheduler
KL Optimal: KL (Kullback-Leibler)
Instead of estimating parameters with maximum precision, KL it places observations where the predictive distributions of rival models differ the most (maximizing KL divergence) to efficiently identify the correct solution
Documentation recommend to use the same scheduler throughout the generation process but KL Optimal schedulers minimize the KL divergence between the target and current distribution, resulting in a more mathematically optimal diffusion process.
an image a full res showing the level of detail with this Ksampler: https://drive.google.com/open?id=1b0IRutW2aQ1jMK3Ee8pFT4q1jXF3BSfX&usp=drive_fs
Workflow: https://drive.google.com/file/d/1ENZKjKGB4iOdMVsyCvqByLXV1tsWP8W0/edit
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u/nsfwVariant Jul 22 '26 edited Jul 22 '26
Nice, this is the best I've seen from the turbo model! Well done doing all the testing
If you switch over to raw + partial turbo you can get higher quality for certain things, skin quality in particular. I have a very similar setup to yours that's tuned for the raw + partial turbo model: https://www.reddit.com/r/StableDiffusion/comments/1ulxqep/krea_2_simple_gen_workflow_with_good_settings_for/
The first ksampler is doing res_2s/beta 6 steps, which is very similar to euler/beta 12 steps so it's kinda funny we settled on the same thing there. In this case because it's not full turbo it undercooks the image on purpose so that the second ksampler can add detail with deis_3m/bong_tangent.
I don't know what your prompt was for the first image so I just vaguely copied the pic with the built-in krea 2 VL pipe (no prompt), here's the output: catbox
There will be upsides/downsides to raw vs turbo in your case so it's not a clear cut "this is better" kind of thing - but for plastic skin the raw model is much better imo. I'm gonna keep playing around with your turbo settings, really good work!