ComfyUI Node

KSampler + Config Output

A boring KSampler clone that exists so your metadata stops lying

By jplenio·Created about 16 hours ago·Updated about 15 hours ago· 0
KSampler + Config Output
  • model
  • positive
  • negative
  • latent_image
  • LATENT
  • sampler_name
  • scheduler
seed0
steps40
cfg1.7
sampler_nameeuler
schedulersimple
denoise1.00

This is the least exciting node in the pack and also one of the most quietly useful. KSampler + Config Output is a drop-in replacement for ComfyUI's core KSampler - same inputs, same behavior, no model modifications - except it also returns the sampler and scheduler names as plain strings alongside the LATENT. Which sounds trivial until you're trying to reproduce a song six months later and your sidecar JSON says "sampler: euler" while the actual workflow was running euler_ancestral on karras because a workflow update silently swapped the defaults.

That's the entire point of this pack, honestly: reproducibility metadata that's honest. The core KSampler gives you a latent and forgets what settings produced it. This wrapper forwards every setting to ComfyUI's common_ksampler unchanged, then hands the effective sampler_name and scheduler back out as STRING outputs you can pipe straight into MiniMax Song Metadata's sidecar.

Inputs

Identical to core KSampler: model, positive, negative, latent_image, then seed, steps, cfg, sampler_name (44 samplers), scheduler (9 schedulers), and denoise. The default cfg is 1.7, which matches the conservative guidance this pack recommends for MiniMax audio - but in the example workflow this node actually drives the FLUX.2 Klein cover-artwork branch, not the audio diffusion. If you're sampling audio with it, use whatever cfg your model family wants; the wrapper doesn't second-guess you.

One honest caveat: sampler/scheduler names are not the whole reproducibility story. The same name + seed still depends on the ComfyUI version, the backend and the device. The node reports what it was asked to do; it can't guarantee identical trajectories across machines.

Outputs

LATENT (wire it to whatever you'd wire a KSampler latent into), plus the two metadata strings. That's it. Nothing to tune, no hidden behavior.

Installing it

Standard pack install via ComfyUI Manager (search "MiniMax Music Production Toolkit") or:

cd ComfyUI/custom_nodes
git clone https://github.com/jplenio/ComfyUI-MiniMax-Music-Production-Toolkit.git
cd ComfyUI-MiniMax-Music-Production-Toolkit
python -m pip install -r requirements.txt

Dependencies: scipy, soundfile, imageio-ffmpeg, mutagen, Pillow. Restart ComfyUI and hard-refresh once.

Gotchas

Not many, because it doesn't do much. The useful habit: if you've already got a hand-built metadata system that reads KSampler widgets, this node adds nothing for you. But if you want the effective sampler/scheduler in your JSON without trusting your memory, swap your core KSampler for this one and wire the two STRING outputs into the metadata node. The one thing to know: it's KSamplerWithConfig - the old workflow name - so don't go hunting for a display name that doesn't exist.

CategoryMiniMax Music Production Toolkit/utilities

Inputs (10)

NameTypeDefaultDescription
modelMODELComfyUI model object to sample. This wrapper does not modify the model; it forwards it to the core KSampler while also returning sampler/scheduler names.
positiveCONDITIONINGPositive conditioning supplied to the KSampler. It guides sampling toward the requested content.
negativeCONDITIONINGNegative conditioning supplied to the KSampler. It guides sampling away from unwanted content; some model families use zeroed/empty negative conditioning instead.
latent_imageLATENTInitial latent tensor to denoise/sample. Its dimensions and batch size determine the generated latent output shape.
seedINT00–18446744073709550000Random seed for the KSampler. The same model, inputs, settings and seed are intended to reproduce the same sampling trajectory, subject to backend/device determinism.
stepsINT401–10000Number of KSampler denoising steps. More steps increase computation and are not always better; use the range recommended for the model/workflow.
cfgFLOAT1.70–100Classifier-free guidance scale for the KSampler. Higher values force conditioning more strongly; too high can create harsh or unstable results.
sampler_nameCOMBOeulerSampling algorithm used by ComfyUI. Changing it alters the numerical denoising trajectory and can change detail, texture and reproducibility even with the same seed.
schedulerCOMBOsimpleNoise/sigma schedule paired with the sampler. It controls how sampling effort is distributed across the denoising trajectory and can affect character and convergence.
denoiseFLOAT1.000–1Sampling denoise strength. 1.0 performs the full denoising process; lower values retain more of an existing latent/input state where applicable.

Outputs (3)

NameTypeDescription
LATENTLATENT
sampler_nameSTRING
schedulerSTRING