u/Capitan01R-

dropping some news real quick

I'm releasing a proper Ksampler for flux2klein because I figured out that using the raw formula produces way more accurate colors and I genuinely think THIS is the main reason we keep getting that color shift and washed out results.

and before anyone asks, yes I benchmarked it against ModelSamplingFlux using the exact same shift settings and the ksampler I built wins every time. accurate colors, zero washout, no exceptions.

the difference comes down to the ODE formula. what's inside comfy right now is:

x_new = x + dt * (x + v)

that extra x getting thrown in is what's drifting your colors every single step. my ksampler uses the raw formula the way it's actually supposed to be:

x_new = x + dt * v

that's it. clean velocity, straight line, no gray fog creeping into your renders.

proper release coming soon!!!

will post results in the comments

I successfully found a way to transfer the character from the reference latent into the generation process without losing features; meaning I give full freedom to flux2klein to generate whatever it wants. My previous approach was a bit rigid as I scaled the k/v layers, which worked but was tough to move at times. Instead, this new approach uses attention output steering. The reference latent stays in the image stream, but after every attention layer, the model finds where the generation's features are similar to the reference and pulls them closer. Because it is similarity-gated, features that are completely different like new backgrounds or different poses are left entirely alone. This lets us lock in the identity of the full character deep in the blocks while allowing the model to change poses and follow the prompt without restraints. I am preparing the documentation and preparing the release!

Examples are in order, first vanilla and second is with node

Flux2Klein Ksampler Soon!

Coming up Tomorrow! Flux2Klein Identity transfer