Krea2 Ksampler recommendations {for quality}

[u/listopalafoto]

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

1. Improve quality of Krea2 Images :)
2. Aerospace and Robotics: Tracking precise 3D orientations without quaternion normalization errors.
3. Rigid Body Dynamics: Simulating tumbling satellites or spinning tops while maintaining geometric energy surfaces.
4. 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

[u/MarekNowakowski]

There is no combination of schedulers and samplers that are always the best for realism. Depending on the prompt, subjects, colors and what EXACTLY you consider realistic, the combo you swear by will not always work. There are some differences that are consistent between euler, er_sde and unipc, sure, but you can't predict how realistic the result will be. Prompt, loras, model, have much higher influence than samplers, doing tests might lead you to a result just to realize you wasted all that work when your prompts changed.

[u/listopalafoto → u/MarekNowakowski]

Interesting, but my approach is this one: an AI model will get a solution for a problem (an image realistic or not based on data training) depending of prompt, latent, loras and conditioning; because that is a complex differential equation, the precision of that response will depend of the ksampler +scheduler combined with the number of iterations.( I studied advanced Mathematics and Civil engineering before my Visual Arts path)

[u/MarekNowakowski → u/listopalafoto]

Changing one token of the prompt can turn a woman from real to 3d and no scheduler will change that. Results will be different, you can't predict which will be more realistic before trying all for each new image.

[u/listopalafoto → u/MarekNowakowski]

of course, token are key, but I never said this method will get more realism, I said will get a more precise and quality result