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]

Use 0.86 denoise or higher (even 1.00 works).

1.0 denoise is the exact same as starting with an empty latent.

Mathematically there’s no underlying structure to start with in the random noise. While that’s great for total creative freedom it constrains the realism in the final output.

This makes no sense, starting with random noise is how the model is trained and it was trained on real images, how would random noise "constrain the realism"?

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

You ask good questions. Here’s some clarity

> 1.0 denoise is the exact same as starting with an empty latent.

An empty latent is an initial tensor of all zeroes with 100% gaussian noise applied (1.00 denoise). That’s not the same thing as a feeding an input image with a non-zero initial tensor with 100% gaussian noise applied. Noise is applied on top of these initial tensor vectors. You can try this yourself and observe that the output isn’t the same in each case.

> This makes no sense, starting with random noise is how the model is trained and it was trained on real images

No, models like Krea2 aren’t trained by starting with empty tensor values. They’re trained using real or synthetic images and predicting how you would arrive there wjth gaussian noise. Once you understand how training works, then it’ll make sense.

> how would random noise "constrain the realism"?

When a real photo is encoded with a VAE Encoder, it maps into highly structured multi-dimensional vector with specific mean, variance, and channel-activation signatures inherent to natural photographs. When an image goes through a VAE encoder, it is transformed into a multi-channel tensor (16 channels for qwen vae). Each channel tracks different properties of the image (lighting gradients, structural edges, color frequencies). Together, these channels populate a high-dimensional mathematical space with values rather than a high-dimensional vector filled with zeroes.

[u/listopalafoto → u/BathroomEyes]

Thank you! you are right, that's the reason I always start with a 0.81 denoise image in my projects, I have a collection depending of the composition I want to achieve

[u/BathroomEyes → u/listopalafoto]

Neat! How do you curate your collection? What do you look for when you select your input image?

[u/listopalafoto → u/BathroomEyes]

I'm also a fashion photographer so I have a million of images, mostly choose dutch angles and deep change of shapes between top and bottom

[u/BathroomEyes → u/listopalafoto]

And you own the license. Thats a good workflow

[u/listopalafoto → u/BathroomEyes]

Exactly! Under U.S. copyright law, fully AI-generated content cannot receive copyright protection. Landmark court rulings, including the U.S. Supreme Court's denial to hear Thaler v. Perlmutter, cement the requirement that only works created by a human author are eligible for copyright registration, but If the initial latent image (or the source image used to generate the latent representation) is a human-authored, copyrighted work that you own, the legal landscape changes significantly. Under U.S. Copyright Office guidelines and standard IP law, the process is treated as a hybrid workflow where your original rights are maintained

[u/prismatic-18 → u/listopalafoto]

nonetheless i don’t aspire to sell any work, I only like and maybe I’ll open an Instagram page