parameter

Scheduler (sampler)

A scheduler (or sampler) is the diffusion-model algorithm that controls how noise is removed step-by-step from random noise into a final image — DPM++ 2M Karras, Euler, DDIM, FLOW-Match are 2026 popular choices.

Diffusion models start from random noise and remove noise step-by-step over N inference steps; the scheduler decides how much noise to remove per step + how to sample. Different schedulers trade speed vs quality: Euler (simple, fast), DDIM (deterministic, good for img2img), DPM++ 2M Karras (high quality, default in many UIs), UniPC (fast + quality), FLOW-Match (Flux models). Step counts: 20-30 typical for quality, < 10 for speed (Flux Schnell uses 4 steps). For non-SD models (Flux, Stable Diffusion 3, Pixart) the scheduler may be baked in; for ComfyUI / A1111 users it's a knob. Picking the right scheduler is the difference between 'fast' and 'good' inference.

When to use scheduler (sampler)

Common mistakes

FAQ

What is scheduler (sampler)?

A scheduler (or sampler) is the diffusion-model algorithm that controls how noise is removed step-by-step from random noise into a final image — DPM++ 2M Karras, Euler, DDIM, FLOW-Match are 2026 popular choices.

When should I use scheduler (sampler)?

Tuning quality / speed trade-off in SD-family models.

What are the most common mistakes with scheduler (sampler)?

Sticking with the default — workload-tuned schedulers can be 2× faster at same quality. Using 50+ steps when 20 is identical — wasted compute.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/scheduler.md.