BasicScheduler - ComfyUI Built-in Node Documentation

The BasicScheduler node is designed to compute a sequence of sigma values for diffusion models based on the provided scheduler, model, and denoising parameters. It dynamically adjusts the total number of steps based on the denoise factor to fine-tune the diffusion process, providing precise “recipes” for different stages in advanced sampling processes that require fine control (such as multi-stage sampling).

Inputs

Parameter	Metaphor Description	Data Type	Input Type	Default	Range	Technical Purpose
`model`	Canvas Type: Different canvas materials need different paint formulas	MODEL	Input	-	-	Diffusion model object, determines sigma calculation basis
`scheduler`	Mixing Technique: Choose how paint concentration changes	COMBO[STRING]	Widget	-	9 options	Scheduling algorithm, controls noise decay mode
`steps`	Mixing Count: 20 mixes vs 50 mixes precision difference	INT	Widget	20	1-10000	Sampling steps, affects generation quality and speed
`denoise`	Creation Intensity: Control level from fine-tuning to repainting	FLOAT	Widget	1.0	0.0-1.0	Denoising strength, supports partial repainting scenarios

Scheduler Types

Based on source code comfy.samplers.SCHEDULER_NAMES, supports the following 9 schedulers:

Scheduler Name	Characteristics	Use Cases	Noise Decay Pattern
normal	Standard linear	General scenarios, balanced	Uniform decay
karras	Smooth transition	High quality, detail-rich	Smooth non-linear decay
exponential	Exponential decay	Fast generation, efficiency	Exponential rapid decay
sgm_uniform	SGM uniform	Specific model optimization	SGM optimized decay
simple	Simple scheduling	Quick testing, basic use	Simplified decay
ddim_uniform	DDIM uniform	DDIM sampling optimization	DDIM specific decay
beta	Beta distribution	Special distribution needs	Beta function decay
linear_quadratic	Linear quadratic	Complex scenario optimization	Quadratic function decay
kl_optimal	KL optimal	Theoretical optimization	KL divergence optimized decay

Outputs

Parameter	Metaphor Description	Data Type	Output Type	Technical Meaning
`sigmas`	Paint Recipe Chart: Detailed paint concentration list for step-by-step use	SIGMAS	Output	Noise level sequence, guides diffusion model denoising process

Node Role: Artist’s Color Mixing Assistant

Imagine you are an artist creating a clear image from a chaotic mixture of paint (noise). BasicScheduler acts like your professional color mixing assistant, whose job is to prepare a series of precise paint concentration recipes:

Workflow

Step 1: Use 90% concentration paint (high noise level)
Step 2: Use 80% concentration paint
Step 3: Use 70% concentration paint
…
Final Step: Use 0% concentration (clean canvas, no noise)

Color Assistant’s Special Skills

Different mixing methods (scheduler):

“karras” mixing method: Paint concentration changes very smoothly, like professional artist’s gradient technique
“exponential” mixing method: Paint concentration decreases rapidly, suitable for quick creation
“linear” mixing method: Paint concentration decreases uniformly, stable and controllable

Fine control (steps):

20 mixes: Quick painting, efficiency priority
50 mixes: Fine painting, quality priority

Creation intensity (denoise):

1.0 = Complete new creation: Start completely from blank canvas
0.5 = Half transformation: Keep half of original painting, transform half
0.2 = Fine adjustment: Only make subtle adjustments to original painting

Collaboration with Other Nodes

BasicScheduler (Color Assistant) → Prepare Recipe → SamplerCustom (Artist) → Actual Painting → Completed Work

This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub

​Inputs

​Scheduler Types

​Outputs

​Node Role: Artist’s Color Mixing Assistant

​Workflow

​Color Assistant’s Special Skills

​Collaboration with Other Nodes