Adept Scheduler (Entropic)

Most schedulers in this pack are fixed curves with a name and a purpose. Entropic is the flexible one - a single power parameter stretches a family of schedules between two extremes, which makes it the node you reach for when you want to understand how your model responds to different step distributions without installing a whole menu of options.

The mechanism is an average of two ramps. One is linear - even noise removal across the whole run. The other is a power curve that starts fast and dies off slowly (1 − t^power), which piles the denoising work at the beginning of the trajectory. The node averages them, then maps through the standard Karras rho formula using your checkpoint's sigma bounds. Turning power up doesn't make anything "more powerful" - it shifts the average toward the power curve, pulling more step density toward the noisy, structural end of the run. At power=1 the two ramps nearly coincide and you get something close to linear; at power=10 you get a heavily front-loaded schedule.

There's a genuinely useful hidden detail in the code: when sigma_max is 1.0 - which is what flow-matching-style models report - the Karras rho=7 formula collapses and produces a garbage first step. Entropic detects that case and applies its ramp directly in sigma space instead. So this is the one scheduler in the pack that explicitly handles flow-matching sigma ranges without white-image bugs. That's a real edge case the author thought about.

Inputs: model, steps (default 20), and power (1–10, default 6). Output: SIGMAS into SamplerCustom. Note the same power value appears as the optional entropic_power on the pack's Advanced scheduler dropdown - they're the same knob.

Install is the pack-wide one:

cd ComfyUI/custom_nodes
git clone https://github.com/nawka12/ComfyUI-Adept-Sampler

Restart ComfyUI, or install via ComfyUI Manager by searching "ComfyUI-Adept-Sampler". No dependencies beyond torch, no model downloads.

The honest take: Entropic is a tuning tool more than a destination. You won't usually park on it as your forever scheduler - the AOS and AYS nodes are more opinionated and generally better tuned for their targets. But its value is real: if your images come out consistently under- or over-structured, sweeping power is a fast way to learn which way your model leans, and the flow-matching handling makes it a safe experimental choice. Think of it as the schedule you use to gather data, not the one you ship.