Diffusion Workflow Intel

Diffusion Sampler Comparison Table

Pick the right sampler for your video generation without digging through Discord threads. This cheat sheet summarizes how each solver behaves so you can prioritize speed, stability, temporal consistency, or detail.

12

Samplers explained

Use cases

Motion & stills

πŸ“Œ Tip: Use this table alongside the AI video glossary.

SamplerSpeedAccuracySteps NeededBest Use CasesNotes / Behavior
DDPMSlowestStable200–1000Reference, researchOriginal diffusion sampler; rarely used now
DDIMFastMedium20–100General-purpose, quick testsDeterministic shortcut; good default
EulerFastMedium-High20–40High-contrast images, consistent detailOne of the simplest and cleanest samplers
Euler a (Ancestral)FastStylized / soft20–40Artistic styles, more randomnessAdds controlled noise for more creativity
HeunMediumMedium-High20–50Balanced structure and detailMore stable than Euler in some cases
DPM++FastHigh15–30High-detail still imagesCan flicker in video unless tuned
DDPMSolverVery FastVery High5–30Images + video, stable consistencyExcellent at low steps; not the same as DPM++
DEISMedium-FastHigh10–50Smooth video transitionsGreat for temporal consistency
UniPCFastHigh8–30Stable image or video samplingPredictor-corrector; excels at low steps
CausVidMediumHigh (temporal)15–40Video generation with motion coherenceCausal progression reduces flicker
ODE (Euler/Heun/RK)Medium-SlowVery High20–60Scientific, precise samplingDeterministic and mathematically stable
SDE samplersMediumHigh20–60Complex motion patternsIntroduce stochastic noise for variety

Quick Takeaways

  • β€’Simplest and fastest: Euler, Euler a
  • β€’Best balance of speed and detail: Heun, UniPC
  • β€’Highest fidelity for stills: DPM++
  • β€’Best temporal consistency for video: CausVid, DEIS, DDPMSolver
  • β€’Legacy / research reference: DDPM

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