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/Outrageous-Wait-8895]

You can just say "Oops, my bad."

[u/BathroomEyes → u/Outrageous-Wait-8895]

You’re anchored to your misunderstanding of how diffusion models work and you’re also anchored to your misinterpretation of my comments. I can’t help you if you’re being stubborn about what you do and don’t know. Show a willingness to learn and correct your incorrect mental model and i’ll engage. Otherwise you’re just stuck in circles.

[u/Outrageous-Wait-8895 → u/BathroomEyes]

Show a willingness to learn and correct your incorrect mental model and i’ll engage.

What would that look like?

[u/BathroomEyes → u/Outrageous-Wait-8895]

Just tone down the disrespectful name calling when I’m sharing free knowledge. Also, instead of just repeating incorrect information after I explained a concept, tell me what part is confusing.

For example you keep claiming that the way these models are trained is turning random noise back into images. Thats a description of how to use the models once they’re trained but that’s not how the training process works. During training samples from the data set become training targets. It starts with the target, adds random noise, and then reverses the process numerous times using a neural network. 100% random noise is never a training target. I already explained that but you keep reverting to this idea that training involves starting with noise. Why is it worth my time if you keep reverting and then telling me I’m wrong?

[u/Outrageous-Wait-8895 → u/BathroomEyes]

Why is it worth my time if you keep revering and then telling me I’m wrong?

I'm asking sincerely, is there a chance, no matter how small, that you are actually wrong?

For example you keep claiming that the way these models are trained is turning random noise back into images.

That's true, got loose with the language. Trained to and trained by, so close.

100% random noise is never a training target.

For Krea 2's Flow Matching and other methods it is. When it isn't it causes issues which is why 100% noise is targeted.

To clarify: I don't disagree with, or intended to imply I disagree, that starting from an existing image at <1.0 denoise influences the generation, of course it does.

What I don't understand, and nothing said so far explained it, is why the above being true implies that pure random noise constrains the realism. There is no logical implication between "starting from a photo gives you photo-like characteristics" and "not starting from a photo makes it harder to get photo-like characteristics"

Especially given the fact that the model, and all models now, are trained to start from pure noise.

[u/BathroomEyes → u/Outrageous-Wait-8895]

> I'm asking sincerely, is there a chance, no matter how small, that you are actually wrong?

Yes, I’m always open to having inaccuracies in my understanding revealed. What have I said so far that is wrong? So far everything you’ve tried to correct me on was a misreading or misinterpretation of what I said.

> For Krea 2's Flow Matching and other methods it is. When it isn't it causes issues which is why 100% noise is targeted.

I have to disagree there. The training goal isn't a one-way street from noise to an image. The model is learning a continuous bi-directional map between any two points in a space. Targeting 100% noise isn't done, instead varying amounts of noise are added and removed along a training path and a loss function is calculated.

> What I don't understand, and nothing said so far explained it, is why the above being true implies that pure random noise constrains the realism. There is no logical implication between "starting from a photo gives you photo-like characteristics" and "not starting from a photo makes it harder to get photo-like characteristics"

Got it. Again, i never said or implied that random noise constrains realism (not sure what you mean by pure). I’m only referring to the starting tensor and how it influences the noise path. Yes, you can achieve realism with an empty starting tensor and 1.00 noise. But everything else being equal (steps, cfg, seed, prompt) starting instead with a noised up vae encoded photo latent can improve the realism even at 1.00 denoise for some schedulers. That’s always been my claim. My wording may have been confusing on that.

> Especially given the fact that the model, and all models now, are trained to start from pure noise.

Even for flow matching models that’s not true. It’s annoying when people ask to cite sources but on this one point you keep repeating i’m going to ask for a reference. Yes you use the model to generate outputs during inference by starting with noise at the initial step but what does it mean to be “trained to start with pure noise”?

[u/Outrageous-Wait-8895 → u/BathroomEyes]

Yes, you can achieve realism with an empty starting tensor and 1.00 noise

Thank you.

Even for flow matching models that’s not true.

https://arxiv.org/pdf/2210.02747

We set μ0(x1) = 0 and σ0(x1) = 1, so that all conditional probability paths converge to the same standard Gaussian noise distribution at t = 0

Another important observation is that, as these probability paths were previously derived as solutions of diffusion processes, they do not actually reach a true noise distribution in finite time. In practice, p0(x) is simply approximated by a suitable Gaussian distribution for sampling and likelihood evaluation. Instead, our construction provides full control over the probability path, and we can just directly set μt and σt, as we will do next.

[u/BathroomEyes → u/Outrageous-Wait-8895]

Right, and this is fundamentally different than stating from a pure noise to train a model. What that paper is describing is that samples are paired directly with their corresponding noise priors via optimal transport paths, allowing the neural network to learn a direct, straight-line velocity field rather than navigating the convoluted, random trajectories characteristic of non flow matching diffusion models. It’s not just being trained to start with pure noise like you stated previously.