DOGMA Sampler Select

If you run FLUX.2 Klein 9B, you already know the drill: the stock sampler dropdown was written for other people's models, so you end up on generic Euler or whatever a shared workflow told you to use. DOGMA Sampler Select is one filmmaker's attempt to fix exactly that. It hands you six custom ODE samplers actually tuned - by step count, sigma curve, and use case - for the two Klein 9B checkpoints.

The node itself is almost insultingly simple. One dropdown, one output. But it's the only place you'll meet these samplers outside of a restart, and it's how you pair them with a hand-drawn sigma curve, which is where they shine.

The six samplers, decoded

The names look like version-control accidents until you learn the pattern: the first half says which Klein checkpoint the sampler was designed for, the second half is the job it's built to do.

| Sampler | For | Model calls per step | |---|---|---| | DOGMA_klein_distilled_REBUILD | T2I, strong edits, damaged upscale tiles | 2 × non-final steps | | DOGMA_klein_distilled_BALANCED | general T2I / i2i / edit | 2 × non-final steps | | DOGMA_klein_distilled_DETAIL | soft edits, good upscale tiles | 3 × non-final steps | | DOGMA_klein_basemodel_REBUILD | fast strong reconstruction | 1 × steps | | DOGMA_klein_basemodel_BALANCED | general base-model work | ~1.2–1.35 × steps | | DOGMA_klein_basemodel_DETAIL | soft edit and upscale refinement | ~1.4–1.5 × steps |

The distilled trio is for the 4–6 step Klein distilled checkpoint; the basemodel trio is for the 20–50 step base. Don't mix them up - a distilled model was trained to denoise in a few big jumps, so feeding it a base-model sampler at 30 steps is how you get oversaturated nonsense. That rule isn't DOGMA-specific, it's just how distillation works.

How it actually works

Reading the source, these are honest-to-goodness ODE solvers, not marketing. The distilled variants use second-order Ralston steps (with an over-relaxed version for REBUILD) and a third-order Bogacki–Shampine for DETAIL; the base variants run a variable-step Adams–Bashforth 2 predictor with Heun corrections where drift shows up, and DETAIL switches to Ralston for the final stretch where fine detail settles. The "REBUILD" naming comes from a real trait: those samplers push early over-relaxation to drag the image away from bad source anatomy or heavily damaged upscale tiles, then fade to a normal solve.

Two details tell you the author actually ran this stuff. First, the terminal step snaps to the model's denoised prediction instead of doing one more integration - a common source of residual noise on custom curves. Second, there's per-batch guarding that clamps derivative extrapolation on uneven intervals, so when you hand it a weird hand-drawn sigma curve it degrades gracefully instead of exploding. That's the whole reason it advertises compatibility with any SIGMAS source, including NKD Sigmas Curve.

What you set, and where it goes

This is a one-input node. Set sampler_name to one of the six, and you get a SAMPLER output. Wire that into the sampler input of SamplerCustomAdvanced, feed it a SIGMAS source, and add your Klein model - that's the whole pattern. Because these are converging solvers, same seed reproduces the same image, which makes them genuinely useful for A/B testing instead of just vibing.

One thing worth knowing: the same samplers are also registered into ComfyUI's normal KSampler / KSamplerSelect dropdowns after a restart. If you never touch custom sampling, you can skip this node entirely and just pick DOGMA_klein_distilled_BALANCED from a plain KSampler. The node exists for the custom-sampling path.

Install

Grab it through ComfyUI Manager (search DOGMA Nodes) or:

comfy node install comfyui-dogma-nodes
# or
cd ComfyUI/custom_nodes
git clone https://github.com/axior/ComfyUI-DOGMA-Nodes

Then restart ComfyUI - restart is mandatory if you want the names in the standard dropdowns. There are no extra Python dependencies (the pack uses only PyTorch and ComfyUI's built-in sampler API), no model files to download, and no LoRA required. Refreshing.

Gotchas

The honest one: this pack is days old and has no community track record. The author, axior, is a professional filmmaker on the "Dogma team" who's talked openly on r/comfyui about running thousands of Wan VACE passes for a broadcast ad - so these come from someone who actually generates for a living, not a weekend experiment. But "experimental ODE samplers" is the author's own label, and they're tuned for Klein 9B specifically. If you point them at Z-Image or Anima, you're on your own.

Also watch the step cost. The DETAIL variants are the ones you'd want for upscaling, but at three model calls per step on a 30-step base run, that's a real wall-clock tax. Start with BALANCED, escalate to DETAIL only where you can see the difference.