The BasicScheduler node is used to compute a sequence of sigma values for diffusion models based on the provided scheduler, model, and denoising parameters.
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).
Parameter | Data Type | Input Type | Default | Range | Metaphor Description | Technical Purpose |
---|---|---|---|---|---|---|
model | MODEL | Input | - | - | Canvas Type: Different canvas materials need different paint formulas | Diffusion model object, determines sigma calculation basis |
scheduler | COMBO[STRING] | Widget | - | 9 options | Mixing Technique: Choose how paint concentration changes | Scheduling algorithm, controls noise decay mode |
steps | INT | Widget | 20 | 1-10000 | Mixing Count: 20 mixes vs 50 mixes precision difference | Sampling steps, affects generation quality and speed |
denoise | FLOAT | Widget | 1.0 | 0.0-1.0 | Creation Intensity: Control level from fine-tuning to repainting | Denoising strength, supports partial repainting scenarios |
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 |
Parameter | Data Type | Output Type | Metaphor Description | Technical Meaning |
---|---|---|---|---|
sigmas | SIGMAS | Output | Paint Recipe Chart: Detailed paint concentration list for step-by-step use | Noise level sequence, guides diffusion model denoising process |
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:
Different mixing methods (scheduler):
Fine control (steps):
Creation intensity (denoise):
BasicScheduler
(Color Assistant) → Prepare Recipe → SamplerCustom
(Artist) → Actual Painting → Completed Work